Despite evidence for the general effectiveness of psychological therapies, there exists substantial heterogeneity in patient outcomes. We aimed to identify factors associated with baseline severity of depression and anxiety symptoms, rate of symptomatic change over the course of therapy, and symptomatic recovery in a primary mental health care setting.Using data from a service evaluation involving 35 527 patients in England's psychological and wellbeing [Improving Access to Psychological Therapies (IAPT)] services, we applied latent growth models to explore which routinely-collected sociodemographic, clinical, and therapeutic variables were associated with baseline symptom severity and rate of symptomatic change. We used a multilevel logit model to determine variables associated with symptomatic recovery.Being female, younger, more functionally impaired, and more socioeconomically disadvantaged was associated with higher baseline severity of both depression and anxiety symptoms. Being older, less functionally impaired, and having more severe baseline symptomatology was associated with more rapid improvement of both depression and anxiety symptoms . Therapy intensity and appointment frequency seemed to have no correlation with rate of symptomatic improvement. Patients with lower baseline symptom severity, less functional impairment, and older age had a greater likelihood of achieving symptomatic recovery (as defined by IAPT criteria).We must continue to investigate how best to tailor psychotherapeutic interventions to fit patients’ needs. Patients who begin therapy with more severe depression and/or anxiety symptoms and poorer functioning merit special attention, as these characteristics may negatively impact recovery. Evidence abounds for the effectiveness of psychological therapies in treating a wide range of mental health problems  routinely-collected variables] and practical (i.e. relevant to outcomes of interest) variables to explain heterogeneity in treatment response trajectories and symptomatic recovery. In this exploratory analysis, we aimed to identify variables that are associated with (1) baseline symptom severity, (2) rate of symptomatic change, and (3) symptomatic recovery of patients receiving psychological therapy in England's IAPT primary mental health care setting.The IAPT programme in England began in 2008 with a direct objective to increase public access to National Institute for Health and Care Excellence (NICE)-approved psychological therapies for depression and anxiety. IAPT currently assesses over 1 300 000 people annually and delivers therapy to approximately 550 000. The programme offers low- (step 2) and high-intensity (step 3) treatments. Low-intensity approaches include guided self-help, psychoeducation, computerised cognitive–behavioural therapy (CBT), behavioural activation, and structured group activity programmes Clark, . In highAt each session, therapists assess depression and anxiety symptoms using the 9-item Patient Health Questionnaire (PHQ-9) . The sample analysed for determinants of baseline symptom severity and rate of symptomatic change consisted of 28 810 individuals .Table 1n = 27 835); (b) were considered at ‘caseness’ at initial assessment ; (c) had completed treatment . Over 67% (5438) achieved symptomatic recovery.To assess which variables were associated with symptomatic recovery, we analysed therapy outcomes for a subsample of 8114 individuals comprising those patients who (a) had non-missing values for both PHQ-9 and GAD-7 scores at first and last assessment , which is estimated using total scores on the corresponding scale across therapy appointments. Symptomatic recovery in IAPT is achieved when a patient who is at ‘caseness’ at pre-treatment has dropped below the clinical cut-off for both depression and anxiety post-treatment (i.e. PHQ-9 < 10 and GAD-7 < 8) , two clinical variables (baseline symptom severity and baseline level of functioning), and three therapeutic variables . All of these variables were directly obtained or derived from data routinely collected in IAPT services (see online Supplementary Table S1 for a detailed variable list).The set of variables tested for associations with our outcomes of interest included three sociodemographic variables  . IMD deciles are publicly available data for postcodes across the UK. Anonymised data provided by IAPT, however, included only the outward area postcode. The IMD for each individual was therefore estimated as a median IMD decile for the corresponding outward area, with lower values representing higher deprivation.We used growth models to estimate patients' treatment response trajectories of depression and anxiety symptoms. This modelling approach fits, for each patient, a non-linear trend for the total scores of PHQ-9 or GAD-7 over the course of therapy. We inferred values for baseline symptom severity and rate of symptomatic change from the intercept and slope of patients' treatment response trajectories, respectively. In all analyses we used full information maximum likelihood estimator to account for data missingness.The IAPT treatment model is based on 12–20 sessions as set out in the NICE guidelines. Very few individuals attended more than 20 appointments and thus we only used the first 20 appointments for each patient. We first estimated the basic nonlinear growth model  anchored at every attended appointment .At this stage, we investigated whether the individual treatment trajectories clustered into homogeneous classes using a growth mixture model. If few interpretable classes were found, then the variables associated with baseline symptom severity and rate of symptomatic change could be considered for these classes instead of individual trajectories. However, we did not find such classes (see online Supplementary Appendix 1) and thus carried out the analysis at an individual level.Next, we reparameterised the model so that the slope represented change over the first seven appointments rather than between the first and the second appointments as in the basic model . We chose to report the symptomatic change in the first seven appointments because it is the average number of appointments nationally and thus represents a typical length of therapeutic intervention within IAPT . Accounting for this analytically provides unbiased treatment response trajectories. In addition, it allowed us to assess variables associated with the average rate of symptomatic change for each therapist's patients. Additional details are provided in online Supplementary Appendix 3.v. high) as an important covariate accounting for different therapeutic approaches applied for low- and high-intensity IAPT patients. Therapy frequency was considered only in the context of the slope as its association with the intercept would be conceptually non-sensical. We also explored the association between baseline symptom severity and rate of symptomatic change. At the therapist level (the between-level part of model), we examined whether caseload was associated with (a) the average rate of symptomatic change and (b) recovery for each therapist's patients.We then added variables considered to be related to the intercepts (baseline symptom severity) and slopes (rate of symptomatic change) of treatment response trajectories to the reparameterised model. At the patient level (the within-level part of the model), we included gender, age, socioeconomic status, and baseline level of functioning (WSAS). We included therapy intensity (low r = 0.46), allowing inclusion of both variables in the same model. We bootstrapped the model (1000 iterations) to obtain bootstrapped confidence intervals (CIs) for odds ratios. We conducted all analyses in MPlus 8.4  we included baseline symptom severity of both depression and anxiety (as recovery in IAPT requires having scores below corresponding threshold on both measures) and (b) these baseline symptom severities were operationalised as total scores of PHQ-9 and GAD-7 at initial assessment (i.e. not as intercepts of treatment response trajectories). Total PHQ-9 and GAD-7 scores at baseline were moderately correlated . Patients' modelled baseline symptom severity (the within-level intercept of their treatment response trajectory) was significantly related to gender , age (younger patients have greater baseline symptom severity), baseline functioning , socioeconomic status (patients living in areas of higher deprivation have greater baseline symptom severity), and therapy intensity. The significant positive relationship between baseline symptom severity and therapy intensity confirms that patients with more severe depression symptoms tend to be assigned to high-intensity therapy. In terms of standardised coefficients, baseline symptom severity had the strongest relationship with baseline functioning scores.not significantly related to therapy frequency or intensity.Patients' rate of symptomatic change for depression (the within-level slope of patients' treatment response trajectories) was most strongly related (in terms of magnitude of impact) to their baseline depression severity. More specifically, the higher the baseline symptom severity, the faster the improvement. Additionally, the rate of symptomatic change was related to gender , age (older patients improve more rapidly), baseline level of functioning , and socioeconomic status (patients living in areas of higher deprivation improve more rapidly). The improvement rate was The average improvement of a particular therapist's patients (the between-level part of the model), was significantly related to that therapist's caseload, however, in an unexpected direction – a larger caseload was related to more rapid improvement.Results for the GAD-7 treatment response trajectories were similar to those of the PHQ-9. However, contrary to the PHQ-9 results, the relationship between baseline symptom severity and therapy intensity was at the borderline of statistical significance and was in the opposite direction than for depression. Furthermore, gender and socioeconomic status were not related to the rate of improvement for GAD-7 scores. Finally, considering the standardised coefficients, functioning had a larger effect size for anxiety than for depression (with lower impairment related to more rapid improvement), when controlling for all other variables.Therapy intensity and frequency were significantly related to symptomatic recovery; however, bootstrapped odds ratios were not and thus the results should be interpreted with caution. In addition, both variables have a relatively small effect on recovery. For example, an increase of a day in the median number of days between sessions reduced the probability of recovery by 0.7% and being assigned to high-intensity therapy lowered chances of recovery by approximately 11.2% when adjusting for all other variables .Socioeconomic status, gender, and therapist caseload were not significantly associated with symptomatic recovery.The results for the sample including those individuals who had not yet completed therapy or who had dropped out (whose symptomatic recovery was derived from the last recorded session) are presented in online Supplementary Appendix 4. They were very similar to those presented in In this study, we explored which sociodemographic, clinical, and therapeutic variables may be related to (1) baseline symptom severity, (2) rate of symptomatic change, and (3) symptomatic recovery (as defined by IAPT criteria) for patients with depression and/or anxiety engaging in psychological therapy in IAPT services. Importantly, our predictors were all variables that are routinely collected in IAPT services, including individual variables , clinical variables , and therapeutic variables [therapy intensity and median number of days between sessions (i.e. frequency)]. Our multilevel approach also allowed us to test the relationship between therapists' IAPT caseload and the average rate of symptomatic change and recovery of their corresponding patients.Although baseline severity of both depression and anxiety symptoms was significantly related to all included variables , baseline functioning had by far the largest effect sizes . Age, baseline functioning, baseline symptom severity, and therapist caseload were significantly related to the rate of change for both depression and anxiety symptoms, and gender and socioeconomic status were additionally related to rate of change for depression (but not anxiety) symptoms. Therapy characteristics such as intensity and frequency seemed to have no relation to the rate of symptomatic change. Baseline symptom severity was the most important variable associated with rate of change for both depression and anxiety symptoms, whereby patients with more severe symptomatology at the start of therapy improved more rapidly. On average, a 1-point difference in total score on the corresponding scale between two patients at baseline results in an expected difference of 0.748 (i.e. 1 − 0.252) points on the PHQ-9 and 0.738 (i.e. 1 − 0.262) points on the GAD-7 between them at seventh session. Baseline functioning was the only other variable to show a clinically-relevant effect size at the individual level , but only for anxiety symptoms.Our finding that greater baseline severity of depression and anxiety symptoms was associated with more rapid symptom improvement is unusual. In general, others have found either that baseline severity negatively impacts the rate of symptomatic improvement [e.g. Sunderland et al.'s  study ofWe located only one study that reflected our finding that baseline severity was positively related to the rate of symptomatic improvement [Elliott et al.'s  study ofOur finding about the negative impact of functional impairment on rate of symptomatic improvement is more consistent with the literature. For example, Lutz et al.'s  study ofIAPT patients seen by each therapist and is not weighted for the number of days worked in IAPT. It is possible that each therapist sees additional patients outside of IAPT, in which case the effect of total caseload would be unmeasured in our analyses. Second, some IAPT therapists focus on specific groups of patients , which could affect their caseload and potentially bias results; however, this specialisation applies to a relatively small group of therapists.At the therapist level, our finding that a larger therapist caseload was associated with more rapid improvement could indicate that more frequent application of IAPT techniques facilitates greater therapist competency. Alternatively, this could simply indicate that more competent therapists are assigned more patients. In either case, this result should not be interpreted causally. First, the caseload variable represents only the number of Higher chance of symptomatic recovery, as defined by IAPT criteria, was associated with lower baseline severity of depression and anxiety symptoms, lower functional impairment, and increased age, with baseline symptom severity having the greatest effect. These findings are not particularly surprising: while starting therapy with a higher score on the PHQ-9/GAD-7 enables more scope for improvement (hence the sensibility of its association with faster symptomatic improvement), it also implies a further distance to IAPT's recovery threshold. Several other studies have found similar results in terms of baseline symptom severity  is centred around absolute improvement (i.e. whether their symptoms were reduced beyond the recovery ‘threshold’) rather than relative improvement . Hence, in order to be most informative, results about symptomatic recovery should be contextualised within our discussion of treatment response trajectory.In interpreting our results, it must be acknowledged that people with ‘less favourable’ characteristics  do not necessarily benefit R2 values for our models.The main strength of this study is the large clinical sample. Furthermore, although this is not the first study that has explored variables related to symptomatic improvement and recovery in the context of psychological therapy, we used an analytical approach that is more appropriate for the complex data structure of longitudinal outcomes nested under therapists. A further strength is the applicability to clinical settings in general, and to the IAPT setting in particular, as we used routinely-collected, easily-obtained measures in our analyses. However, this may also be considered as a weakness, as many potentially relevant variables were not available for inclusion in our analyses, including medication use, treatment history, therapist competence, and therapeutic alliance, as well as other key risk and protective factors. The absence of such prognostic variables can be seen in the relatively low We acknowledge additional limitations in terms of sample selection and available variables. The selection of referrals meeting IAPT service criteria for treatment may have introduced Berkson's bias into our analyses, particularly in those regarding recovery. We were unable to quantify this bias because we have no recovery data for referrals not admitted to IAPT. Furthermore, the subsample of 8114 with the requisite data for evaluation of predictors of recovery may not be representative of the full sample as patients drop out for non-random reasons; again, this is inherent in many clinical samples where drop-outs may be due to recovery or worsening of symptoms. Therefore, inferences from these findings need to be made with caution. Finally, although our sample size was large, it represented only two mental health trusts, which may limit the generalisability of our results.within the IAPT setting. Yet, it is not uncommon for IAPT therapists to see additional patients outside of IAPT.In terms of limitations relating to individual variables, we could not investigate relationship between ethnicity and treatment response trajectories as we had very few Black, Asian, and Minority Ethnic (BAME) individuals in our sample . Although beyond the scope of this paper, the low proportion of BAME individuals accessing IAPT services merits careful consideration. Low participation may be due to a number of causes, including individual factors , service-level factors (e.g. inaccessibility or unacceptability), and wider cultural issues (e.g. discrimination and stigma). Furthermore, our calculation of socioeconomic status has limitations; as IAPT does not collect this information on an individual level, we derived this variable by using the outward code of each individual's postcode to calculate the median IMD decile for the area, which may not be representative of an individual's experience. Finally, in terms of caseload, we were only able to identify the number of patients the therapist sees expectations of therapy outcome have been consistently linked to better actual outcomes (Mondloch, Cole, & Frank, Therapist confidence and self-efficacy are important factors for determining therapy effectiveness (Green et al., Furthermore, our results regarding variables associated with symptomatic recovery are useful for highlighting groups of patients that may benefit from additional or more intensive intervention. One such group consists of patients who begin therapy with more severe depression and/or anxiety symptoms and poorer functioning, as these two characteristics have a significant negative impact on symptomatic recovery. In order to ensure that everyone has the potential to reach the IAPT symptomatic recovery threshold, we must continue to investigate how to best tailor interventions to fit individual patients' needs.


To explore the relationship between genomic DNA methylation and diabetic chronic complications.299 patients with type 2 diabetes mellitus (T2DM) hospitalized in the Second Affiliated Hospital of Soochow University were enrolled. We divided the patients into different complications groups and corresponding non-complication groups. Clinical and biochemical parameters were compared between the two groups. The level of genomic DNA methylation in leukocytes was determined by high-performance liquid chromatography-tandem mass spectrometry.(1) Age, duration of diabetes, creatinine (Cr), blood urea nitrogen (BUN), genomic DNA methylation, 24- hour urine total protein (24-hUTP), and intima-media thickness (IMT) were significantly higher in the carotid plaque (CP) group. Waist-to-hip ratio (WHR), body mass index (BMI), estimated glomerular- filtration rate (eGFR), and albumin (Alb) were significantly lower in the CP group. Gender, age and BMI were the influencing factors of CP. (2) Age, duration, Cr, BUN, urinary microalbumin creatinine ratio (UACR), systolic blood pressure (SBP), TCSS, and 24- hUTP were significantly higher in the diabetic retinopathy (DR) group. eGFR, 2h postprandial C- peptide, and Alb were lower in the DR group. Age, duration, Cr, Alb, SBP, and the presence of DN were the influencing factors of DR. (3) Age, duration, HbA1c, BUN, TCSS, SBP, and IMT(R) were significantly higher in the diabetic nephropathy (DN) group. 2h postprandial C-peptide, and Alb were lower in the DN group. HbA1c, BUN, DR, and HBP were the influencing factors of DN. (4) Age, duration, total cholesterol (TC), low-density lipoprotein (LDL-C), triglyceride (TG), Cr, BUN, uric acid (UA), and SBP were significantly higher in the diabetic peripheral neuropathy (DPN) group. The level of genomic DNA methylation and eGFR were significantly lower in the DPN group. Age, duration, LDL-C, UA, the presence of DR, and the genomic DNA methylation level were the influencing factors for DPN. Incorporating the level of genomic DNA methylation into the prediction model could improve the ability to predict DPN on the basis of conventional risk factors.Low level of genomic DNA methylation is a relatively specific risk factor for DPN in patients with T2DM and not a contributing factor to the other chronic complications. The prevalence of type 2 diabetes mellitus (T2DM) is supposed to increase year by year, with the number of people suffering from diabetes likely to reach 147.2 million by 2045  to cytosine on a CpG dinucleotide by DNA methyltransferase (DNMTs). DNA methylation is associated with the abnormal gene expression, DNA repair, genomic instability, and changes in genetic traits. It alters the structure of chromatin and gene expression. It is reported that the role of DNA methylation is very important in T2DM and its chronic complications  group and non-DN group, diabetic retinopathy (DR) group and non-DR group, diabetic peripheral neuropathy (DPN) group and non-DPN group, and carotid plaque (CP) group and non-carotid plaque (non-CP) group, according to the results of urinary albumin creatinine ratio (UACR), fundus photographs, Toronto Clinical Scoring System (TCSS) scores, and ultrasound scan of carotid artery, respectively.A total of 299 T2DM patients hospitalized in the endocrine department of the Second Affiliated Hospital of Soochow University were enrolled in this study. T2DM was diagnosed using WHO diagnostic criteria published in 1999. The following diseases or conditions were excluded: symptomatic cardiovascular disease, previous surgery to the carotid arteries, severe acute and chronic inflammation, chronic alcohol addiction, cancer, and other severe disorders. In addition, patients suffering from hyperosmotic, hyperthyroidism, acute complications of diabetes, including hyperglycemic hyperosmolar syndrome (HHS), diabetic ketoacidosis (DKA), and lactic acidosis, were also excluded. During interviews with the subjects, physicians or the coordinators completed case report forms, recording the disease profile of diabetes, and related treatment histories including the medicines they took. The protocol for this study was authorized by the Institutional Review Board of the Second Affiliated Hospital of Soochow University, and the authorized registration number is (2016) Ethics Review No.K11. Each patient was given complete information about this trial and was required to sign a written consent form. The sample data was saved electronically in a dedicated computer with a password only accessible by researchers, ensuring that the patients’ personal information was kept private. The samples were kept in a special biological sample storage refrigerator, and the key of refrigerator was held by the principal investigators. The blood samples could only be measured in the present study. The medical records of all the patients were retained in the hospital.Anthropometric measurements including body weight, height, measured with light clothes and bare feet, and blood pressure (BP), were performed on each patient. Hypertension refers to the measurement of blood pressure ≥140/90 mmHg in resting state. If the patient has previously been diagnosed with hypertension and is currently receiving antihypertensive medication, he or she will still be diagnosed with hypertension even if the measured blood pressure is less than 140/90mmHg. The formula: BMI (kg/m²) = weight(kg)/height(m)², was used to calculate body mass index (BMI). The waist-to-hip ratio (WHR) was obtained by dividing waist circumference (cm) by hip circumference (cm).The “gold standard” for diagnosing DPN is the electroneuromyography testing. However, it is not suitable for large-scale screening of diabetic patients in practice. TCSS is well applied in early screening and diagnosis of DPN, because when the TCSS scores ≥ 6, the sensitivity and specificity are more than 70% . In the The blood samples were collected after all patients fasted for at least 8 hours. The fully automated blood cell analyzer  was used to determine blood routines. Biochemical parameters, such as total cholesterol (TC), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), triglyceride (TG), creatinine (Cr), uric acid (UA), blood urea nitrogen (BUN), serum albumin (Alb), C-reactive protein (CRP), 24h urine total protein (24-hUTP) and urinary microalbumin creatinine ratio(UACR) were tested using an automated biochemical instrument . Roche Cobas e601  was used for fasting C-peptides and 2 hours postprandial C-peptides. The Bole D100 glycation hemoglobin meter  was used for glycosylated hemoglobin. eGFR was calculated with the modified MDRD equation. Intima-media thickness (IMT) of the carotid artery and carotid plaque were tested by the Korea GE Color Ultrasound Diagnostic Instrument (LOGIQ S7). Diagnosis of atherosclerosis was according to the 2009 Chinese Physicians Association Ultrasound Division Vascular Ultrasound Guidelines. The normal IMT of the carotid artery is less than 1.0mm. The diagnostic criteria for thickening of the carotid artery are 1.0mm≤ IMT ≤ 1.5mm. The diagnostic criteria for atherosclerotic plaque formation are that the carotid intimal thickening exceeds 50% of the peripheral IMT or protrudes into the vascular lumen, and IMT ≥ 1.5mm  were used to extract DNA from white blood cells. As previously described in details , LC-MS/Mt-test, Mann-Whitney U test, or χ2 test were used to compare the differences between the two groups. Spearman correlation analysis was used to analyze the associations among clinical and biochemical factors, the level of genomic DNA methylation, and TCSS scores. Multiple logistic regressions analysis was used to evaluate the influencing factors of different diabetic chronic complications. Multiple linear regression analysis was used to evaluate the influencing factors of genomic DNA methylation. A p value less than 0.05 was considered statistically significant.Patients’ characteristics were compared by presence of DPN or not, presence of DR or not, presence of DN or not and presence of CP or not. All data were shown as means ± standard error (SEM). We used Software SPSS 24.0 for data analysis. Normality was checked for all data prior to analysis. Two-sample 2.A total of 299 patients with T2DM were recruited in the present study. Of the 299 diabetic patients, average age was 54.43 years with 33.78% being female, and average BMI was 25.56 kg/mp < 0.01, *p < 0.05, compared with non-CP, two-sample t-test and Mann-Whitney U test). BMI, WHR, Alb and eGFR were significantly lower in the CP group . Male patients and smokers were more likely to develop CP . In the multiple logistic regression analysis, we set the presence or absence of CP as the dependent variable and the other fourteen variables as independent variables. The findings revealed that gender, age and BMI were still independently associated with the risk of CP, while duration, WHR, Cr, BUN, eGFR, the level of genomic DNA methylation, Alb, 24-hUTP and smoking were no longer related .As shown in p < 0.01, *p < 0.05, compared with non-DR, two-sample t-test and Mann-Whitney U test). Alb, 2h postprandial C-peptide and eGFR was significantly lower in the DR group . The presence of DR was associated with the presence of DN and DPN . In the multiple logistic regression analysis, we set the presence or absence of DR as the dependent variable and the other thirteen variables as the independent variables. The findings revealed that age, duration, Alb, Cr, SBP and the presence of DN were still independently associated with the risk of DR, while 2h postprandial C-peptide, BUN, eGFR, UACR, TCSS, 24-hUTP and DPN were no longer related .As shown in p < 0.01, *p < 0.05, compared with non-DN, two-sample t-test and Mann-Whitney U test). 2h postprandial C-peptide and Alb were much lower in DN group . DN was associated with the presence of DR, DPN and HBP . In the multiple logistic regression analysis, we set the presence or absence of DN as a dependent variable and the other twelve variables as independent variables. The findings revealed that HbA1c, BUN, the presence of DR and HBP were still independently associated with the risk of DN, while age, duration, TCSS, Alb, SBP, IMT(R), 2h postprandial C-peptide and the presence of DPN were no longer related .As shown in p < 0.01, *p < 0.05, compared with non-DPN, two-sample t-test and Mann-Whitney U test). The levels of genomic DNA methylation and eGFR were significantly lower in the DPN group . The presence of DPN was associated with the presence of DR and DN . In order to explore the predictive ability of genomic DNA methylation in DPN, we established two multiple logistic regression models and performed ROC analysis. In the first multiple logistic regression analysis, we set the presence or absence of DPN as a dependent variable, and the other twelve variables except for the level of genomic DNA methylation as independent variables. The findings revealed that age, duration, LDL-C, UA, and the presence of DR were still independently associated with the risk of DPN, while TC, Cr, BUN, eGFR, CRP, SBP, and the presence of DN were no longer related . The area under the ROC curve (AUC) for the five conventional risk factors was 0.872, as shown in p < 0.01, *p < 0.05). Introducing the genomic DNA methylation level into the model statistically increased the AUC to 0.883. The AUC for composite factors was greater than any single conventional risk factors, with a sensitivity of 81.9%, specificity of 85.7%, as shown in As shown in p<0.05, ∗∗p<0.01). In the multiple linear regression analysis, we set the genomic DNA methylation level as a dependent variable and the other 3 variables as independent variables. TCSS and BMI were the risk factors of genomic DNA methylation. Unstandardized coefficients for BMI was -0.069 and for TCSS was -0.303 , respectively . T2DM patients with higher BMI and TCSS scores had lower levels of genomic DNA methylation.To investigate the factors that influenced genomic DNA methylation, we performed further correlation analysis among the genomic DNA methylation, clinical characteristics, and TCSS scores, and the results were shown in Diabetes mellitus is estimated to affect 380 million people worldwide by 2025 . The disCardiovascular disease is considered to be the most common cause of disability and death in patients with diabetes. Pathophysiology is thrombus, plaque ulceration, luminal stenosis or bleeding from atherosclerosis. Cervical atherosclerosis is considered to be the most important risk factor for acute ischemic cerebrovascular disease. Therefore, in this study, we analyzed the related risk factors of carotid atherosclerosis in patients with diabetes. As shown in the results, gender, age and BMI were independently associated with the risk of CP, consistent with the most studies –10. HoweDiabetic retinopathy, diabetic nephropathy and diabetic peripheral neuropathy are all microvascular complications, which have common pathogenesis including hyperglycemia, polyol metabolic abnormalities, protein non-enzyme glycation, cytokines, free radical action, inflammatory media oxidative stress and genetic background . So, pat2S synthase, was involved in diabetic gastric hypersensitivity  and from the Jiangsu Youth Medical Talents Project (QNRC2016874 to H-HZ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This randomized clinical trial compares outcomes among patients who received a donated-after-cardiac-death kidney allograft and either dexmedetomidine or placebo during and after the surgical procedure. Does perioperative use of dexmedetomidine reduce delayed graft function following a donation-after-cardiac-death (DCD) kidney transplant?In this randomized clinical trial of 111 adults who underwent a DCD kidney transplant, delayed graft function occurred in 17.9% of patients who received the 24-hour perioperative dexmedetomidine infusion, which was significantly lower than the 34.5% occurrence in patients who received normal saline.The findings of this trial support the perioperative use of dexmedetomidine for reducing delayed graft function in DCD kidney transplants. Delayed graft function (DGF) is a risk factor for acute rejection and graft failure after kidney transplant. Previous studies have suggested that dexmedetomidine may be renoprotective, but whether the use of dexmedetomidine would improve kidney allograft function is unknown.To investigate the effects of perioperative dexmedetomidine on DGF following a donation-after-cardiac-death (DCD) kidney transplant.This single-center, double-blind, placebo-controlled randomized clinical trial was conducted at The First Affiliated Hospital of Soochow University in Suzhou, China. Adults (18 years or older) who were scheduled for DCD kidney transplant were enrolled between September 1, 2019, and January 28, 2021, and then randomized to receive either dexmedetomidine or normal saline (placebo). One-year postoperative outcomes were recorded. All analyses were based on the modified intention-to-treat population.Patients who were randomized to the dexmedetomidine group received a 24-hour perioperative dexmedetomidine intravenous infusion (0.4 μg/kg/h intraoperatively and 0.1 μg/kg/h postoperatively). Patients who were randomized to the normal saline group received an intravenous infusion of the placebo with the same dose regimen as the dexmedetomidine.The primary outcome was the incidence of DGF, defined as the need for dialysis in the first posttransplant week. The prespecified secondary outcomes were in-hospital repeated dialysis in the first posttransplant week, in-hospital acute rejection, and serum creatinine, serum cystatin C, estimated glomerular filtration rate, need for dialysis, and patient survival on posttransplant day 30.P = .04) and repeated dialysis , without significant effect on other secondary outcomes. Dexmedetomidine vs normal saline infusion led to a higher median (IQR) creatinine clearance rate on postoperative days 1 (9.9 [4.9-21.2] mL/min vs 7.9 [2.0-10.4] mL/min) and 2 (29.6 [9.7-67.4] mL/min vs 14.6 [3.8-45.1] mL/min) as well as increased median (IQR) urine output on postoperative days 2 (106.5 [66.3-175.6] mL/h vs 82.9 [27.1-141.9] mL/h) and 7 (126.1 [98.0-151.3] mL/h vs 107.0 [82.5-137.5] mL/h) and at hospital discharge discharge (110.4 [92.8-121.9] mL/h vs 97.1 [77.5-113.8] mL/h). Three patients (5.5%) from the normal saline group developed allograft failure by the post hoc 1-year follow-up visit.Of the 114 patients enrolled, 111 completed the study , of whom 56 were randomized to the dexmedetomidine group and 55 to the normal saline group. Dexmedetomidine infusion compared with normal saline reduced the incidence of DGF (17.9% vs 34.5%; odds ratio [OR], 0.41; 95% CI, 0.17-0.98; This randomized clinical trial found that 24-hour perioperative dexmedetomidine decreased the incidence of DGF after DCD kidney transplant. The findings support the use of dexmedetomidine in kidney transplants.ChiCTR1900025493Chinese Clinical Trial Registry Identifier:  We hypothesized that perioperative dexmedetomidine infusion reduces the incidence of DGF after DCD kidney transplant. We compared the incidence of DGF between patients with kidney allograft who received dexmedetomidine and patients who received normal saline (placebo) during and after surgery for a total of 24 hours.CONSORT) reporting guideline. The trial protocol and statistical plan are provided in This randomized clinical trial was conducted at The First Affiliated Hospital of Soochow University in Suzhou, China. The trial was approved by the ethics committee of The First Affiliated Hospital of Soochow University. Written informed consent was obtained from all patients. We followed the Consolidated Standards of Reporting Trials  were collected.19 Donor kidneys were assessed for the expanded-criteria donor20 subgroup and evaluated using the donor-only US Kidney Donor Risk Index (KDRI) and the Chinese-donor DGF risk prediction model ,Patients who were 18 years or older, diagnosed with end-stage kidney disease, undergoing kidney replacement therapy, and scheduled for DCD kidney transplant were eligible for inclusion. Patients who had sick sinus syndrome, an atrioventricular block, a left ventricular ejection fraction less than 30%, or a multiorgan transplant were excluded.All of the patients were of Han Chinese ethnicity. Race and ethnicity data were not collected because, we believe, they would have had no impact on the perioperative care and study outcomes.Eligible adults were enrolled from September 1, 2019, to January 28, 2021. Patients were randomized to either dexmedetomidine or normal saline  using a Intraoperative monitoring included noninvasive cuff blood pressure, electrocardiography, pulse oximetry, end-tidal carbon dioxide, radial artery blood pressure, central venous pressure, and bispectral index. Intravenous infusion of lactated Ringer solution was provided. After anesthesia induction, patients were endotracheally intubated and mechanically ventilated. Anesthesia was maintained using sevoflurane titrated to bispectral index values of 40 to 60. Hypotension  and bradycardia (heart rate <50 beats/min) were treated. A sufentanil-based patient-controlled analgesia was used for postoperative pain relief. Additional information on anesthetic care is available in the eMethods in 24 The perioperative dialysis treatment is presented in the eMethods in The perioperative care of patients who underwent kidney transplant was based on the Kidney Disease: Improving Global Outcomes guideline.25 and older patients who underwent noncardiac surgery.26 The control group received an intravenous infusion of normal saline administered in the same dose regimen as dexmedetomidine.The dexmedetomidine group received an intravenous infusion of dexmedetomidine, 0.4 μg/kg/h, immediately after anesthesia induction and continued throughout the procedure. After surgery, all patients were transferred to the designated transplant unit and received dexmedetomidine, 0.1 μg/kg/h. Dexmedetomidine was administered for a total of 24 hours. The dose regimen of dexmedetomidine was based on the dose used in previous studies in cardiac surgery5 The prespecified secondary outcomes were in-hospital repeated dialysis during the first posttransplant week; in-hospital acute rejection; and serum creatinine, serum cystatin C, estimated glomerular filtration rate (eGFR), need for dialysis, and patient survival on posttransplant day 30. Repeated dialysis was defined as 2 or more dialysis sessions during the first posttransplant week.27 Acute rejection was confirmed by biopsy of the kidney allograft. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation.28The primary outcome was the incidence of DGF, defined as the need for dialysis during the first posttransplant week.29; (2) proportion of creatinine reduction ratio on posttransplant day 2 that was less than 30%30; (3) number of dialysis treatments in the first posttransplant week; (4) timing of acute rejection diagnosis; (5) acute rejection in the first posttransplant week; (6) pneumonia; (7) deep vein thrombosis; (8) incidence of DGF in donor criteria subgroups and KDRI subgroups; and (9) serum creatinine, serum cystatin C, eGFR, acute rejection, allograft failure ,31 and patient survival at 1 posttransplant year.Several in-hospital and long-term outcomes were assessed post hoc: (1) creatinine reduction ratio on posttransplant day 233 on PODs 1 to 3.The perioperative data were (1) graft function–related parameters: baseline serum creatinine and serum cystatin C before surgery as well as serum creatinine, serum cystatin C, creatinine clearance rate, and urine output on postoperative days (PODs) 1, 2, 3, 5, and 7, and at the time of hospital discharge; (2) arterial blood gas and electrolytes at the end of surgery; (3) visual analog scale pain score  at 30 minutes, 24 hours, and 48 hours postoperatively; (4) sufentanil consumption over 24 hours and 48 hours postoperatively; (5) perioperative bradycardia and hypotension; (6) transplant induction therapy and immunosuppressive medications; (7) duration of surgery, anastomosis time, time to extubation, and length of hospital stay; (8) intraoperative infusion of lactated Ringer solution; (9) furosemide use in the first posttransplant week; (10) level of postoperative nursing care  on PODs 1 to 3; and (11) level of physical activity assessed using the Barthel index score The creatinine clearance rate, urine output, and immunosuppressive medications are presented in the eMethods in 31 The (unpublished) pilot study we conducted showed that 9 of 20 patients (45%) who did not receive dexmedetomidine experienced DGF, which was in line with the previous report. The therapeutic effect of dexmedetomidine on DGF is unknown. We hypothesized that dexmedetomidine would reduce the incidence of DGF by 50%. Therefore, this trial required 54 patients in each group with a power of 80% at a significance of α = .05. We decided to recruit 114 patients (with 57 in each group) with consideration of a possible dropout rate of 5%.According to the literature, the incidence of DGF after DCD kidney transplant was 45% to 55%.2 test, and the therapeutic effect was assessed with odds ratios (ORs) and 95% CIs. A 2-sided P < .05 indicated a statistically significant difference. As appropriate, the secondary outcomes were analyzed with the unpaired, 2-tailed t test; Mann-Whitney rank sum test; χ2 test; or Fisher exact test. The therapeutic effect was assessed using the OR (or difference) and 95% CI. Multiple testing was corrected using the Bonferroni method, with P < .007 regarded as statistically significant. Because multiple testing corrections were not planned for the nonoutcome perioperative data and post hoc analyses, these results should be considered exploratory.34 The effects of interventions on DGF and 1-year postoperative acute rejection, allograft failure, and patient survival were assessed using the Kaplan-Meier curve and the log-rank test, and the therapeutic effect was analyzed with the hazard ratio (HR) and 95% CI.Continuous variables were presented as means (SDs) or medians (IQRs), depending on data distribution. The categorical variables were presented as numbers (percentages). The between-group difference in the DGF incidence was analyzed using the χAll analyses were based on the modified intention-to-treat population principle, which included any randomized patient who had undergone kidney transplant and for whom the result of the primary outcome was available. Neither an interim analysis nor missing data imputation was planned a priori. Statistical analyses were performed using the SPSS software, version 23.0 (IBM SPSS).Of the 122 patients screened for eligibility, 8 were excluded and 114 were randomized . One patIn total, 47 (42.3%) female and 64 (57.7%) male individuals with a mean (SD) age of 43.4 (10.8) years participated in the trial . The donThe blood gas and electrolyte parameters at the end of surgery were within the normal ranges in both groups . The 2 gThe graft function–related results are shown in P = .04) (P = .04)  in the dexmedetomidine group and 19 of 55 patients (34.5%) in the normal saline group  . The schP = .02)  , withoutThe median (IQR) creatinine reduction ratio on posttransplant day 2 was 36.5% (1.4%-53.5%) in the dexmedetomidine group and 23.6% (−0.1% to 51.2%) in the normal saline group  . The proAt 1 year postoperatively, the mean (SD) serum creatinine level was 1.18 (0.35) mg/dL in the dexmedetomidine group and 1.57 (1.83) mg/dL in the normal saline group  ; to convTo our knowledge, this trial was the first to show that 24-hour perioperative dexmedetomidine administration reduced the incidence of DGF after DCD kidney transplant. Decreased need for repeated dialysis further suggested the favorable effects of dexmedetomidine on kidney allograft during the first posttransplant week, a higher creatinine clearance rate on PODs 1 and 2, and increased urine output on PODs 2 and 7 and at hospital discharge. The dexmedetomidine infusion neither led to concerning adverse events nor adversely affected postoperative recovery.35 Interventions, including eculizumab, dopamine, epoetin alfa, and hypothermic machine perfusion, had minimal or no effect on reducing DGF after kidney transplant.39 A retrospective cohort study found an association between dexmedetomidine and decreased incidence of DGF, overall complications, infection, acute rejection in the early posttransplant phase, and length of hospital stay.13 That study had several limitations, however, including its cohort design, the heterogenous donor sources, the mixture of kidney-only and combined kidney-pancreas transplants, lack of multiple testing corrections, and potentially inadequate confounder control.Delayed graft function is associated with an increased risk of acute rejection, inferior graft function, prolonged hospital stay, and reduced long-term graft survival and patient survival.40 Dexmedetomidine has a favorable safety profile and is currently used in perioperative and critical care. A recent study found an association between dexmedetomidine and improved 5-year survival after cardiac surgery.34 The infusion rate and duration of dexmedetomidine in this trial were in concordance with those in current practice. Dexmedetomidine infusion (0.4 μg/kg/h intraoperatively and 0.1 μg/kg/h postoperatively) has been used in the perioperative setting.40 The low infusion rate may enhance postoperative recovery via anxiolysis, analgesia, sleeping promotion, and delirium reduction.26 The pilot study and this trial found neither concerning adverse effects nor delayed postoperative recovery associated with dexmedetomidine. The 1-year postoperative outcomes suggested that the dexmedetomidine treatment may improve longer-term kidney allograft function. The dexmedetomidine group had lower serum creatinine and cystatin C levels, higher eGFR, and a lower allograft failure rate up to 1-year postoperatively, although these between-group differences were not statistically significant. The present trial was likely underpowered for these long-term outcomes. The overall risk-benefit profile supports the use of dexmedetomidine in kidney transplants.In contrast, the present trial was based on a randomized design and showed the DGF reduction effect exerted by dexmedetomidine in DCD kidney transplant. The finding was corroborated by previous studies reporting that dexmedetomidine decreased acute kidney injury in cardiac surgery.2-adrenoreceptor agonist, dexmedetomidine engages in the α2-adrenoreceptors that are widely populated in kidney tubules and peritubular vascular structures. The activation of the α2-adrenoreceptor pathway decreases sympathoadrenal hyperactivity. It induces vasodilatation via endothelial nitric oxide regulation, leading to enhanced glomerular filtration and increased urine output.42 Preclinical studies reported that dexmedetomidine attenuated inflammation, reduced kidney endothelial chemokines, inhibited reperfusion-induced cell death signaling, and enhanced cell survival signaling.44 A meta-analysis found that dexmedetomidine as an adjuvant during anesthesia attenuated surgical stress and inflammation,45 which may enhance postoperative recovery and improve overall outcomes.Several potential mechanisms may underlie the favorable effects of dexmedetomidine on kidney transplants. As an α13 we did not observe such an effect. The median length of hospital stay of 25 days in this trial was in line with the data originated in China47 but appeared much longer than that in the US and European countries.50 This difference may be attributed to the different health care systems in various countries. Fourth, as a single-center trial based on controlled donors after cardiac death and characterized by a relatively short warm ischemic time, the generalizability of its findings should be tested in future studies.This trial has several limitations. First, although the sample size agreed with the result of the power analysis, including more patients would have enhanced the power. Second, the long-term outcomes were based on post hoc analysis. Third, although dexmedetomidine may reduce the length of hospital stay after kidney transplant,In this randomized clinical trial, the 24-hour perioperative dexmedetomidine infusion reduced the incidence of DGF without incurring adverse effects after DCD kidney transplant. The findings of this trial support the use of dexmedetomidine in kidney transplants. Further trials are needed to determine the effects of dexmedetomidine on long-term outcomes and on different kidney transplant scenarios.


Conclusions: Among STEMI patients undergoing primary PCI, elevated NGAL levels are associated with adverse renal and cardiovascular outcomes, independent of traditional inflammatory markers. Further studies are needed to assess the potentially unique role of NGAL in cardio–renal interactions.Background: Elevated serum neutrophil gelatinase-associated lipocalin (NGAL) levels reflect both inflammatory reactions and renal tubular injury. Recently, associations with endothelial dysfunction and plaque instability were also proposed. We investigated the prognostic utility of elevated NGAL levels for renal and clinical outcomes among ST-segment elevation myocardial infarction (STEMI) patients treated with primary coronary intervention (PCI). Methods: We performed a prospective, observational, open-label trial. High NGAL was defined as values within the third tertile (>66 percentile). Results: A total of 267 patients were included . Short-term adverse outcomes were consistently increased in the high NGAL group with more acute kidney injury, lower mean left ventricular ejection fraction, higher 30-day mortality, and higher incidence for the composite outcome of major adverse cardiac events (MACE). In a multivariate logistic regression model, high NGAL emerged as a strong and independent predictor for MACE (OR 2.07, 95% CI 1.15–3.73,  Cardio–renal interactions in acute myocardial infarction are complex and predict worse outcomes ,2. NeutrInflammatory biomarkers are known to rise and have significant predictive value for adverse outcomes in patients with acute coronary ischemia ,9. Indeen = 19). No patients were lost to follow-up. The final study population included 267 STEMI patients. The diagnosis of STEMI was established by a typical history of chest pain, diagnostic electrocardiographic changes, and serial elevation of serum cardiac biomarkers [A prospective, observational, open-label trial was performed in the Tel-Aviv Sourasky Medical Center. Included were STEMI patients admitted to the cardiac intensive care unit (CICU) following successful primary PCI between December 2017 and August 2021. As plasma NGAL in these patients can reflect chronic inflammation, we excluded patients with chronic inflammatory syndromes and malignancies .2 [Serum creatinine (sCr) was determined upon hospital admission, prior to PCI, and at least once a day throughout hospitalization. AKI was determined using the KDIGO criteria and was defined as an increase in sCr ≥ 0.3 mg/dL within 48 h of admission or an increase in sCr ≥ 1.5 times baseline, which was known or presumed to have occurred within the prior 7 days . Chronic2 .Blood samples for C-reactive protein (CRP) levels and a complete blood count (CBC) were drawn following primary PCI. Quantitative CRP analysis was performed by the Bayer wide-range assay.t-test or the Mann–Whitney U test, depending on the distribution. The predictive value of NGAL for the risk of MACE was evaluated with multivariate binary logistic regression models adjusted for other inflammatory laboratory variables. Adjusted odds ratios (OR) with a 95% confidence interval (95% CI) were reported for all variables. To further investigate the predictive power of NGAL, receiver operating characteristic (ROC) curves were constructed. Area under the curves (AUC) with corresponding 95%CI were reported for relevant inflammatory markers and compared using the Delong and Delong method [p-value of <0.05 was considered significant for all analyses. Analyses were performed with the IBM SPSS software .Patients were stratified into two groups based on serum NGAL levels (ng/mL). Low NGAL levels were defined as values within the lower two tertiles (≤66 percentile), under an NGAL level of ≤109 ng/mL. High NGAL were defined as values within the highest tertile (>66 percentile), above an NGAL value of >109. Categorical variables were expressed as frequency and percentages. Normally distributed continuous variables were described using mean and standard deviation (SD) and non-normal ones with median (interquartile range 25–75%). Chi-square test was used to evaluate associations between categorical variables. Continuous variables were compared using the independent samples g method . A two-tp < 0.0001).A total of 267 patients were included ; of them, 89 (33%) were defined as the high NGAL group. Median total NGAL levels were 90 (65–124) ng/mL, 76 (59–90) ng/mL in the low NGAL group as compared to 141 (123–183) ng/mL for the high NGAL group (p = 0.004) but similar white blood cell counts (WBC) (10.5 (8.4–13.2) 109/L vs. 10.9 (8.5–12.9) 109/L, p = 0.58). Admission hemoglobin and sCR levels differed between both groups as well.Baseline characteristics are shown in p = 0.04), higher incidence of periprocedural bleeding (6 (3.4%) vs. 9 (10.1%), p = 0.02), and a longer length of hospitalization (4 (3–5) days vs. 5 (4–7) days, p < 0.001). Incidence of AKI (9 (5.1%) vs. 38 (43%), p < 0.001), 30-day mortality (1 (0.6%) vs. 4 (4.5%), p = 0.04), and the composite outcome of MACE (70 (39)% vs. 52 (58)%, p = 0.003) were all significantly increased in the high NGAL group (p = 0.014) and when defined as a high vs. low binary variable . Other independent predictors were baseline chronic kidney disease and occurrence of acute kidney injury during hospitalization.As presented in AL group . In multAL group . This wap < 0.001). The ROCs for the prediction of the combined outcome of AKI or MACE showed a higher AUC for NGAL (NGAL 0.67 vs. CRP 0.64 vs. WBC 0.60); however, these differences  did not reach statistical significance (Furthermore, as demonstrated by the ROC in ificance .The results of this study provide more evidence for the unique value of NGAL in the assessment of cardio–renal interactions. Our results demonstrate that NGAL has an independent value not only for renal outcomes but additionally may predict concomitant cardiovascular outcomes in acute myocardial infarction.NGAL, a 25-kDa protein covalently bound to gelatinase proteins in human neutrophils, was reported as an early marker of kidney tubular injury in various patient populations and hence was nicknamed “kidney troponin.” It is released by renal tubular cells in cases of acute tubular damage and may be detected within a few hours of the tubular insult and even in the absence of functional AKI, manifested by serum creatinine elevation . PreviouCardiorenal interactions are complex and often bidirectional in nature. The typical insult in STEMI is Type 1 cardiorenal syndrome, in which rapid worsening of cardiac function leads to acute kidney injury ,21. AddiBesides the well-described associations with inflammation and kidney function, NGAL is also produced by cardiomyocytes and increased gene expressions were observed in various pathological cardiac processes such as chronic heart failure , myocardHowever, the elevation of traditional markers of inflammation such as CRP and WBC levels are also associated with an increased risk of heart failure and mortality after acute myocardial infarction ,26,27. IIn a study by Li et al., NGAL levels had a better ability to discriminate patients with severe coronary stenosis as compared to other inflammatory markers such as CRP . In anotHowever, other investigators have argued that these associations are largely mediated by leukocytosis, stating the lack of data regarding leukocyte counts in some publications ,32. OtheOnly a few studies have assessed the value of NGAL in STEMI patients undergoing PCI. A small-scale study showed higher MACE and mortality with increasing NGAL levels . HelanovA study published by Lindberg et al.  demonstrOur results are consistent with the study by Lindberg et al. . HoweverOur study bears notable limitations. This was a single-center study, and the number of patients recruited represents a modest sample size. We used sCr and eGFR as surrogate markers of kidney function, knowing that these markers have limitations when used in acute hospitalized patients with STEMI. In addition, no information was present on contrast media volume. Further investigations are needed before firm conclusions about the clinical implications of NGAL measurements can be drawn.Among STEMI patients undergoing primary PCI, NGAL levels are associated with adverse renal and clinical outcomes, independent of traditional inflammatory markers such as WBC counts and CRP. Further investigations assessing the possible role of NGAL in cardio–renal interactions are warranted.


Ketamine is a versatile medication with an emerging role for the treatment of numerous psychiatric conditions, including treatment resistant depression. Current psychiatry guidelines for its intravenous administration to treat depression recommend regular blood pressure monitoring and an aggressive approach to potential transient hypertensive episodes induced by ketamine infusions. While this approach is aimed at ensuring patient safety, it should be updated to align with best practice guidelines in the management of hypertension. This review defines and summarizes the currently recommended approach to the hypertensive emergency, the asymptomatic hypertensive urgency, and discusses their relevance to intravenous ketamine therapy. With an updated protocol informed by these best practice guidelines, ketamine treatment for depression may be more accessible to facilitate psychiatric treatment. Ketamine is versatile as both an analgesic and sedating medication. At the lower dosing range, ketamine is an adjunctive pain medication to acetaminophen, non-steroidal anti-inflammatory drugs, and opioids. Ketamine also provides analgesia and sedation for prolonged procedures such as fracture reduction or chest tube insertion. In higher doses of 1–1.5 mg/kg ketamine is used for induction of general anesthesia in both the emergency department and operating room settings. In addition to these various applications, interest is growing in the use of ketamine for its utility in treating psychiatric disorders.Intravenous ketamine delivered in sub-anesthetic doses 0.5–1.0 mg/kg) has shown effectiveness in reducing depressive symptoms and suicidal ideation in both unipolar and bipolar depression .0 mg/kg . Consensvia intravenous administration at a sub-anesthetic dose (0.1–0.7 mg/kg) to healthy volunteers  task force statement on the use of ketamine for depression recommends that blood pressure should be monitored before and during ketamine infusions and for at least 1 h post infusion. In addition, it is recommended that patients with baseline blood pressures <140/90 mmHg should not proceed with treatment . FurtherBased on the recommendations, a practitioner hoping to administer intravenous ketamine for TRD must involve the availability of personnel trained in resuscitation, airway support, and advanced cardiac life support. In addition, uncontrolled hypertension and white coat hypertension in the general patient population is not uncommon. Based on above criteria these patients may not be eligible for ketamine therapy. Data from clinical trials shows that this threshold for pausing infusion is crossed not infrequently. This suggests that applying these parameters to a real-world population would mean that a number of patients would not receive a complete treatment session, or would receive antihypertensive treatment before resuming. Clearly, there are multiple barriers to the routine administration of intravenous ketamine therapy and it is unclear what level of concern is necessary regarding hypertension associated with treatment.Hypertension is a very common co-morbidity in the emergency department patient . The manThe approach to hypertension in the emergency room requires classifying hypertensive patients into those with asymptomatic elevated blood pressure and those in hypertensive emergency. The true hypertensive emergency warrants acute blood pressure reduction and specialist consultation for further workup and management.A hypertensive emergency is defined as moderate to severe hypertension (systolic blood pressure (SBP) ≥ 180 mmHg, diastolic blood pressure (DBP) ≥ 110 mmHg) with evidence of end organ dysfunction (EOD), most commonly involving the heart, brain, or kidneys. Specifically, emergency physicians look for signs and symptoms of acute stroke, cardiac ischemia, pulmonary edema, encephalopathy, and congestive heart failure . AlthougOutside of a true hypertensive emergency, patients may have elevated blood pressures with or without a history of pre-existing hypertension. In cases where patients present with asymptomatic elevated blood pressure, there is no indication for acute blood pressure lowering. This is supported by the American College of Emergency Physicians policy statement on asymptomatic elevated blood pressure, which recommends that routine emergency department investigations and medical intervention is not required for patients with asymptomatic markedly elevated blood pressure . TreatinThe adverse effects of intravenous ketamine infusions for treatment-resistant depression (TRD) are well-characterized, most commonly including dissociative effects and transient hypertension. Other common, also transient effects include anxiety, blurred vision, dizziness, headache, and nausea or vomiting , 20. MosIn randomized trials, typically involving administration of a single dose of intravenous ketamine, transient hemodynamic effects have been observed including increases in heart rate and blood pressure . This hyIncreases in SBP and DBP begin shortly after administration and peak at around 30–50 min with SBP and DBP increases from 10 to 50% above pre-dose values. Increases in blood pressure and heart rate are usually mild and transient, resolving at ~2–4 h after dose administration, with no serious or persistent cardiovascular events reported .A relatively large case series involving 66 participants with 684 total infusions reported that blood pressure peaked at 30 min after infusion starts and only 9% of infusions showed increases in blood pressure of systolic > 30 mmHg and diastolic > 15 mmHg, with none requiring intervention .However, a higher incidence of elevated blood pressures were observed in a retrospective study that reviewed 203 patients with treatment resistant depression who were treated with intravenous ketamine in a clinical setting. Specifically, 44.3% of all patients exhibited transient “treatment-emergent” hypertension, defined as SBP ≥ 165 mmHg or diastolic blood pressure ≥ 100 mmHg at any point during treatment . Twelve An earlier report on pooled data from 3 studies of 84 participants and 205 total infusions found mean peak blood pressures of systolic 141.9 mmHg and diastolic 86.4 mmHg with blood pressures returning to baseline at 60–70 min after infusion . In thisPre-infusion blood pressure measurement is prudent, but the presence of a hypertensive reading with no signs and symptoms of acute stroke, cardiac ischemia, pulmonary edema, encephalopathy, and congestive heart failure need not be considered an absolute contraindication for treatment. While generally patients receiving non-emergent ketamine treatment should have any pre-existing hypertension treated, patients may commonly have elevations in blood pressure pre-treatment that can even be related to anxiety or white coat hypertension. Recommendations to not proceed with treatment if blood pressure is elevated may be unnecessarily limiting and should be revisited.Current administration guidelines for ketamine infusions recommend frequent monitoring of vital signs before, during, and after infusion. This is accompanied by a recommendation and suggestions to pause or discontinue infusion, or treat with anti-hypertensives when blood pressures rise above systolic 160 mmHg and/or diastolic 100 mmHg  level of consciousness (that would be defined as a GCS <8). If these occur, blood pressure should be checked. If there is an accompanying hypertensive blood pressure (≥ 180/110 mmHg), anti-hypertensive treatment should be considered with immediate transport to an ED for further workup, management, and evaluation. Anxiety, blurred vision, dizziness, headache, and nausea or vomiting are common ketamine side effects, and not considered signs or symptoms of hypertensive emergencies.Intravenous ketamine administration is a shift outside the realm of traditional psychiatric treatment for depression. Initial guidelines for patient monitoring reflected the anesthetic nature of this medication and erred on the side of caution given the initial experimental and novel nature of the treatment. However, as efficacy data has accumulated, safety data and clinical experience has as well. The approach to blood pressure could be reconsidered, given the previously described best practices for asymptomatic hypertension management. The EM perspective suggests that for patients who are medically stable with no cardiovascular disease, routine continuous cardiac monitoring and/or frequent vital sign measurements are not necessary and may carry potential harms if blood pressure elevations are treated when asymptomatic. Instead, clinical monitoring for worrisome signs and symptoms  could alAlthough a hypertensive emergency has not been reported with IV ketamine, it must also be considered that common practice in psychiatric clinical trials and ketamine programs have followed previous recommendations. This includes intervening by pausing an infusion or treating with antihypertensives when blood pressure rises above a threshold. The practice of responding to an asymptomatic elevation is not consistent with the previously described EM best practice, but it must be considered that this approach has prevented any episodes of hypertensive emergency. Any change in practice to reflect EM best practices must be accompanied by vigilance for warning signs and symptoms that would signal a hypertensive emergency and immediate action must be taken if observed.Another key issue this perspective raises is eligibility for ketamine therapy. Previous exclusion criteria for IV ketamine cited examples as uncontrolled hypertension, unstable medical condition, central aneurysmal disease, significant valvular disease, recent myocardial infarction, or New York Heart Association Class III heart failure , 5. In rThe combined accumulation of experience with ketamine for TRD and the EM perspective provided by this paper should provoke a reconsideration of best practices within psychiatry for intravenous ketamine therapy. Although this paper focused on IV ketamine, addressing hypertension associated with similar treatments including intranasal esketamine or non-IV ketamine should be similarly reconsidered through this EM lens. Protocols must balance the minimal risk with healthy individuals, the variety of comorbidities in the depressed patient, the low rate of complications, and the potential harms of treating transient hypertension if it is not a hypertensive emergency. The goal is an appropriate safety monitoring regimen that will not overburden limited resources and will avoid unnecessary interventions which may carry their own risks. Continued interdisciplinary input, both in protocols and individual consultation for at-risk patients will be important to offer optimal care.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.RY wrote the manuscript draft and was supervised by KS. KS and JS contributed to writing of subsequent drafts. RY and KS provided the emergency medicine context for this article. JS, AK, and RM provided the psychiatry context and discussion. RY, JS, AK, RM, and KS reviewed the paper and provided edits. All authors contributed to the article and approved the submitted version.KS salary was supported by the Canadian Institute of Health Research through a Scientific Director's Grant (SOP 168483) to Dr. Brian Rowe. Funding for the publication costs of this manuscript was provided by the Department of Psychiatry at the University of Alberta.Author AK has received speaker/consultation fees from Lundbeck, Elvium, Pfizer, Otsuka, Takeda, Sunovion, Bausch Health, Novo Nordisk, Eisai, Jazz, Paladin, the Newly Institute and Abbvie. Author RM has received research grant support from CIHR/GACD/National Natural Science Foundation of China (NSFC) and the Milken Institute; speaker/consultation fees from Lundbeck, Janssen, Alkermes,Neumora Therapeutics, Boehringer Ingelheim, Sage, Biogen, Mitsubishi Tanabe, Purdue, Pfizer, Otsuka, Takeda, Neurocrine, Sunovion, Bausch Health, Axsome, Novo Nordisk, Kris, Sanofi, Eisai, Intra-Cellular, NewBridge Pharmaceuticals, Viatris, Abbvie, Atai Life Sciences and also he is a CEO of Braxia Scientific Corp. Author JS has received speaker/consultation fees from Abbvie, Eisai, Lundbeck, Bausch Health, Janssen, and serves as a medical advisor for the Newly Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Iron plays an important role in hemodynamics and the immunity, independent of anemia. Since dynamic changes occur in iron storage after kidney transplantation (KT), we investigated the association between iron status and kidney outcomes in KT patients. We analyzed data from the KoreaN cohort study for Outcome in patients With KT (KNOW-KT). The iron status was classified into three groups based on ferritin or transferrin saturation (TSAT) levels one year after KT, with reference ranges of 20‒35% and 100‒300 ng/mL for TSAT and ferritin, respectively. The primary outcome was the composite outcome, which consisted of death, graft failure, and an estimated glomerular filtration rate decline ≥ 50%. In total, 895 patients were included in the final analysis. During a median follow-up of 5.8 years, the primary outcome occurred in 94 patients (19.8/1000 person-years). TSAT levels decreased one year after KT and thereafter gradually increased, whereas ferritin levels were maintained at decreased levels. The adjusted hazard ratios  for the composite outcome were 1.67 (1.00–2.77) and 1.20 (0.60–2.40) in the TSAT > 35% and ferritin > 300 ng/mL groups, respectively. High iron status with high TSAT levels increases the risk of graft failure or kidney functional deterioration after KT. Kidney transplantation (KT) is the preferred treatment over dialysis for ESKD since KT shows lower mortality and morbidity with better quality of life than dialysis2. According to the United States Renal Data System annual data report, the cumulative number of KT recipients reached 22,393, which is an increase of 6.5% since 20171. The development of surgical techniques and immunosuppressive drugs has markedly improved short-term graft survival in KT recipients3. However, there has been little improvement in the long-term outcomes5. Therefore, further efforts other than optimizing immunosuppression are needed to improve KT outcomes.The prevalence of end-stage kidney disease (ESKD) is increasing worldwide6. According to recent studies, post-KT anemia shows a biphasic pattern. The prevalence of anemia was found to be 76% at the time of KT and 21% and 36% after one and four years of transplantation, respectively7. Iron deficiency is the most important cause of anemia during the first year after KT, while impaired renal function plays an important role in causing anemia thereafter8. Iron storage undergoes dynamic changes after KT. Transferrin saturation (TSAT) and ferritin levels, the parameters reflecting iron status, showed a decreasing pattern during the recovery from anemia after KT9. Several factors such as surgical blood loss, frequent blood sampling, inadequate nutrition, and increased erythropoietin production by kidney graft can decrease iron storage10. Medications such as immunosuppressants also influence iron metabolism by increasing hepcidin expression and ferroportin degradation, reducing iron absorption11. Moreover, infection and systemic inflammation are also known to affect iron metabolism. Interestingly, almost 10% of KT recipients continued to have iron overload status even after 3 years of KT, although overall iron storage showed decreasing patterns after KT12.Anemia is a common complication in patients with chronic kidney disease (CKD) and also has a high prevalence in KT recipients14. Moreover, iron induces oxidative stress and inflammation in tissues, increasing the risk of CKD progression15. Iron accumulation activates the renal renin-angiotensin system, increasing inflammation and fibrosis, ultimately accelerating diabetic nephropathy16. Although iron homeostasis also plays an essential role in kidney outcomes in those who have not undergone KT, the association between iron status and graft outcomes in KT recipients is controversial20. Herein, we investigated the iron status based on TSAT and ferritin levels one year after KT and its association with kidney outcomes using a Korean multicenter KT cohort.An increasing body of evidence suggests an association between iron dysregulation and adverse kidney outcomes. Recent studies have shown an association between iron deficiency and ESKD risk in patients with CKD regardless of anemiaThe clinical characteristics of the 895 patients stratified by TSAT level one year after KT are presented in Table The baseline characteristics according to ferritin levels at one year after KT are shown in Supplementary Table The iron status from KT to six years after KT is shown in Fig. The outcome event rates according to the iron statuses are presented in Table Recipient and donor age, recipient and donor sex, type of transplantation (living or deceased donor), history of diabetes mellitus, smoking history, and laboratory tests including hemoglobin, albumin, and eGFR one year after KT were used as covariates in the multivariate Cox regression analysis. The high TSAT group was associated with a higher risk of composite outcomes Table . In patiIn the secondary outcome analyses, there was no statistically significant association with iron parameters . All study procedures were performed in accordance with the Declaration of Helsinki guidelines and the study was approved by the institutional review boards of the participating clinical centers (IRB 2021-4265-001). Informed consent was obtained from all study participants.The KoreaN cohort study for Outcome in patients With Kidney Transplantation (KNOW-KT) is a prospective, observational, cohort study conducted by eight transplant centers in South KoreaIn total, 1,080 KT recipients were enrolled. We excluded 99 patients who had no follow-up six months after KT and 12 who underwent graft loss within one year after KT. Moreover, 46 patients with missing baseline demographic data and 28 patients without baseline laboratory data were excluded. Finally, 895 patients were included in the study Fig. .Figure 355, and the eGFR was calculated using the four-variable Modification of Diet in Renal Disease formula56.The baseline demographics, anthropometric measurements, and comorbid status of both recipients and donors were collected during the pre-transplant screening. Baseline laboratory tests, including hemoglobin, creatinine, albumin, iron profiles , total cholesterol, and C-reactive protein (CRP), were measured at each visit. Serum creatinine level was measured using an isotope-dilution mass spectrometry-traceable methodThe main predictors of this study were iron status, defined using TSAT, and ferritin levels measured one year after KT. Since the iron status as well as renal functions stabilize one year after KT, we selected it as a baseline in our study. TSAT was calculated as the percentage of the ratio of serum iron levels divided by the total iron binding capacity. Ferritin and TSAT levels were classified into three groups, where 20%, and 35% were the reference points for the TSAT, and 100 ng/ml and 300 ng/ml were the reference points for ferritin.The primary outcome was a composite outcome of all-cause mortality, graft loss, and a ≥ 50% decline in eGFR. Degree of eGFR decline was calculated as eGFR decline from the eGFR value at 1 year after KT as the baseline. Graft loss was defined as the development of kidney failure that required dialysis or KT. The secondary outcome included separate outcomes from the primary outcome, which were all-cause mortality, graft loss, and a ≥ 50% decline in eGFR. Rejection was also included in our secondary outcome, which was defined as biopsy-confirmed acute or chronic rejection. Survival time was defined as the time from one year after transplantation to the date of composite event occurrence. Patients who failed to follow-up were censored at the time of their last visit.P < 0.05. All statistical analyses were performed using Stata version 15 .Continuous variables are presented as mean ± standard deviation, while categorical variables are presented as numbers and proportions. Categorical variables were compared using the chi-squared test or Fisher’s exact test, as appropriate. The Kolmogorov–Smirnov test was used to confirm the normality of the distribution. Continuous variables with a normal distribution were compared using the analysis of variance test. Variables with skewed distributions are presented as medians with interquartile ranges (IQR) and compared using the Kruskal–Wallis test. Furthermore, Cox proportional hazard regression models were used to investigate the association between iron status and composite outcomes. Univariate models were first used in the primary analysis. Subsequently, multivariate models were used with covariates having a significance level of < 0.1 in the univariate analysis. The results were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Statistical significance was defined as Supplementary Tables.


Heart failure (HF) is a major health problem that affects patients and healthcare systems worldwide. It is the leading cause of morbidity and death and negatively impacts the quality of life, healthcare costs, and longevity. However, the causes of death were not well defined. This study aimed to identify the determinants of death among patients with HF in the Amhara Region, Northwest Ethiopia.A multicenter retrospective cohort study was conducted on 285 patients in the age group 15 years or older under follow-up from 1 January 2015 to 31 December 2019. Descriptive analyses were summarized using the Kaplan–Meier survival curve and the log-rank test. Then, the Cox-proportional hazard regression model was employed to estimate the hazard of death up to 5 years after they were admitted to the HF department to follow up on their treatment.Out of 285 patients with HF, 93(32.6%) of the respondents were dying within 5 years of follow-up. Anemia was the common comorbid disease (30.5%), and valvular heart disease was the most common etiology (33.7%) of chronic heart failure in this study. This study showed a significant mortality difference between hospitals. HF patients with hypertension , anemia , pneumonia , chronic kidney disease , and diabetes mellitus  were at a higher risk of death. Moreover, patients with symptoms listed in the New York Heart Association Class (III and IV), Ischemic Heart Disease and unknown etiologies, men , and those with a high pulse rate  were at a higher risk of death.There was a mortality difference between hospitals. This study has revealed that HF patients with anemia, diabetes mellitus, pneumonia, hypertension, chronic kidney disease, HF etiologies, severe New York Heart Association Class (III and IV), men, and high pulse rate were the main factors associated with death. Health professionals could give more attention to patients whose pulse rate is high, men, and a patient who had comorbidities in the ward. Heart failure (HF) is a major health problem that affects patients and healthcare systems worldwide . It is tChronic heart failure (CHF) is the final common pathway of various cardiac diseases and is characterized by high morbidity and mortality . While mPatients with CHF often have multiple factors that accelerate disease progression to a greater or a lesser extent and worsen the response to treatment , 11. DurMortality in patients with CHF remains high, but the causes of death were not well defined. Despite the high death rate among patients with CHF, most kinds of HF can be prevented with a healthy lifestyle. Even once HF has been established, premature fatalities can be avoided by seeking medical help as soon as possible. Most patients with HF have other conditions that dominate their health experience . To imprOur study area was purposely selected from three Referral Hospitals in the Amhara Region, namely, Debre Tabor Referral Hospital (DTRH), Felege Hiwot Specialized and Comprehensive and Specialized Hospital (FHCSH), and the University of Gondar Comprehensive and Specialized Hospital (UoGCSH), with the respective locations at 666 km, 578 km, and 725 km from Addis Ababa, the capital city of Ethiopia. DTRH is the only referral hospital in the zone and surrounding regions, serving 2.3 million people with curative and preventive health treatment. There are 91 beds available for inpatient services and 12 outpatient departments (OPDs). Patients with specific chronic conditions are referred to the hospital's specialty chronic illness clinics for follow-up . FHCSH iThe duration of the study was 5 years. The investigator reviewed the medical profile of each patient from his or her charts. The study period was between 1 January 2015 and 31 December 2019.In this study, patients with CHF in age groups 15 years or older were selected. All patients with CHF follow-up in all Amhara Region Referral Hospitals were our target population. All randomly selected patients with CHF who took CHF treatment for a minimum of 1 month during the follow-up period in the study hospitals were included. Patients with incomplete baseline variables and patients with acute heart failure were not considered for this study.In this study, we used a secondary source of data. The data were obtained from three selected referral hospitals in the Amhara region. The variables that we used in this study were extracted from patients' chart which contains epidemiological, laboratory, and clinical information of all patients with CHF under follow-up including a detailed HF history and socio-demographic variables. The data were collected by healthcare service providers of the CHF clinic after we had given adequate orientation for them about the way of data collection and the variables that were included in this study.The sample size estimation was determined by considering financial constraints, time constraints, and data analysis techniques. Before the actual data were collected, emphasis was made on the determination of the sample size, which mainly depends on the purpose of the study, the available resources, and the precision required. By taking the proper sample size, the degree of precision required for generalization was increased. Thus, the sample size determination formula  adopted no is the sample size needed;where N is the total population size of the patients with CHF in three selected referral hospitals ;Z is the upper α/2 values of standard normal distribution, and, for this study, we used a value of α = 0.05 as the significance level, which is Z = 1.96;P is the proportion (death of patients with CHF); andd is the level of precision .d must be small to have good precision (d = 0.05). We used the probability of the event, that is, 31.3% of the total patients, and this was obtained from a previous study in Ethiopia based on the University of Gondar referral hospital (P = 0.313 as the probability (proportion) of death. Therefore, no = 306, and if n = 285.The specification of recision . We usedhospital . TherefoNDTRH= 1,140, NUoGCSH = 1,354, and NFHCSH = 1,570. We used proportional allocation to select the sample from each hospital. The proportion was calculated as follows: the total number of CHF patient follow-ups on a CHF clinic at a given hospital between 1 January 2015, and 31 December 2019, multiplied by our calculated sample size (285), and then divided by the total number of patients with CHF who started CHF follow-up in the three hospitals in the study period . The total sample size in each hospital was nDTRH= 80, nUoGCSH = 95, and nFHCSH = 110. A simple random sampling technique was employed to select a representative sample from each hospital.The total number of patients with CHF in the study period in each hospital was The response variable (outcome variable) for this study is the time to death of patients with CHF. In this study, the patients who had experienced death during the observation period were the event of interest. The patients who had not experienced death during the follow-up period were censored. These included patients with CHF lost to follow-up, those who were referred to other health institutions, those who were discharged with improvement, or those who stayed with admission beyond the study period.The predictors associated with the time to death of patients with CHF are either socio-demographic variables or clinical variables. These variables are gender, age, CHF type , hospitals , etiology of HF , NYHA class , LVEF, residence , pulse rate, respiratory rate, systolic blood pressure, diastolic blood pressure, weight, and presence of atrial fibrillation, diabetes mellitus, hypertension, pneumonia, chronic kidney disease (CKD), and anemia as comorbidities.P ≤ 0.25 in the bivariable analysis were included in a multivariable analysis. The Cox-proportional hazard model assumption was checked using a formal statistical test, the GLOBAL test. In the final model, hazard ratios with 95% confidence intervals (CIs) and P-values (< 0.05) were used to identify statistically significant predictors and to measure the strength of association.SPSS version 23.0 was used for data entering. R version 4.0.3 statistical software was used for statistical analysis. Data are described by numbers and percentages or by means and standard deviations depending on the scale of measurements. Descriptive statistics for continuous variables were summarized using mean and standard deviation. For comparison between groups, exclusively nonparametric tests were used. The survival probability among patients with CHF from the starting date to the follow-up to the event was estimated using the Kaplan–Meier survival curve. After we estimated the survival probability, the Cox proportional hazard regression model was fitted. All variables with a Since the data collection was conducted retrospectively from patient medical record charts and patients were not directly involved in data collection, informed consent from patients was not applicable. To access patients' medical record charts, ethical clearance was obtained from the Institutional Review Board, Faculty of Natural and Computational Science, Debre Tabor University, Ethiopia, with reference number: RCS/181/2019. This study was conducted in accordance with the guidelines of Good Clinical Practice and the Principles of the Declaration of Helsinki.The data consist of 285 congestive patients with HF who were treated under CHF follow-up in three selected hospitals, Amhara, North Western Ethiopia. We have a 100% response rate. The survival endpoint of interest is the time to death of patients with CHF. Thus, 93(32.6) patients were dying at the time of the study while the remaining 192 (67.4%) patients were censored.The Kaplan–Meier estimator was applied to estimate the survival curves for categorical predictors. To check for significant differences among categories of factors, the log-rank tests were applied to all categorical variables. The null hypothesis is that there is no significant difference between the survival experiences of different groups of categorical variables.In The univariable Cox proportional hazard regression models were fitted for every covariate to check covariates that affected the survival of patients with CHF before proceeding to higher models. Consequently, the candidate variables for building a multivariable Cox model are the sex of patients, the place of residence, hospital, PR, RR, SBP, DBP, NYHA class, CHF-type, etiology, and the presence of hypertension, CKD, anemia, pneumonia, and AF as comorbidity.The proportional hazard model assumption asserts that the hazard ratios are constant over time. This means the risk of failure must be the same, no matter how long subjects have been followed. Cox proportional hazard assumptions were checked using formal statistical tests and graphical methods. To test this assumption, GLOBAL tests were used.P-values of all covariates are >5%, indicating that the correlation between Schoenfeld residuals and survival time is not significant, which implies that all the covariates satisfy the proportionality assumption at 0.05 levels of significance and that the P-value of the GLOBAL test (0.0679) is not significant. This indicates that the proportional hazard (PH) assumption for the Cox model was not violated . In contrast, the 95% CIs for the nonsignificant risk factors include the null value. The results of the Cox proportional hazard model are presented in P = 0.001)] higher than that among female patients. The hazard of death among patients with CHF followed up by FHCSH was 3.6-fold  higher and among patients with CHF followed up by UoGCSH was 2.6-fold  higher than in patients who did not have pneumonia as comorbidity. The hazard of death among anemic patients with CHF was 2.8-fold  higher compared with nonanemic patients. The hazard of death among CHF patients with diabetes was 2.4-fold  higher compared with nondiabetic patients. The hazard of death among NYHA Class IV patients with CHF was 8-fold  higher and among NYHA Class III patients was 7-fold  higher compared with NYHA Class II patients. This means that there was an increasing risk of death for NYHA Class IV patients and NYHA Class III when compared with NYHA Class II patients.Based on P = 0.024) higher and those caused by other etiologies were 0.3-fold  higher compared with patients caused by VHD. Lastly, a higher baseline heart rate was a significant predictor of mortality . When PR is increased by one unit, the expected hazards of death of the patient are increased by 2%  in this study. The risk of death among anemic patients with CHF was 2.8-fold higher as compared with their nonanemic counterparts. Similar to previous studies , 28–31, Another finding of this study revealed that the death rate among patients with CHF with advanced class [NYHA class (IV) and NYHA class (III)] was higher compared with patients with NYHA class (II). This finding was highly supported by previous studies , 38, whiThis study reveals that patients with CHF presenting with hypertension were at higher risk of mortality. This finding was in line with the previous findings , 39, 40,Further findings of this study demonstrated that VHD was the most common etiology (33.7%) in this study. However, patients with CHF caused by IHD were at a higher risk of death compared with patients with CHF caused by VHD. This finding is in agreement with the previous Ethiopian studies . A signiA higher baseline heart rate was known to be a significant predictor of death. This study was in line with the studies, 45, shoThis study was performed in a multicenter setting, which can enhance the generalizability of the data to the entire population. In addition, this study has provided a real insight into the current clinical pattern among hospitalized patients with CHF in Northwest Ethiopia. However, at the same time, there were certain limitations in this study. First, due to the retrospective nature, the data obtained might be affected by the documentation culture of the hospital and the healthcare providers. Second, potentially relevant variables such as body mass index, alcoholism, marital status, education level, and smoking status were not included.In this study, we aimed to examine the time to death and its determinant factors among patients with CHF in Northwest Ethiopia. According to the finding, men, hypertension, CKD, pneumonia, diabetes mellitus, and anemia were positive significant factors of death compared with their counter groups. There is a mortality difference between hospitals. The highest heart rate was associated with a high risk of mortality among patients with CHF. Health professionals could provide more attention to patients with CHF whose pulse rate is high and to patients with comorbidities of hypertension, coronary kidney disease, pneumonia, diabetes mellitus, and anemia in the ward. Finally, to show the association factor between longitudinal and survival analyses, a future extension of this study—a joint model of longitudinal measure pulse rate and time to death—is recommended.The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by to access patient's medical record charts, Ethical Clearance was obtained from Debre Tabor University Institutional Review Board of Faculty of Natural and Computational Science, Ethiopia, with reference number: RCS/181/2019. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.YM contributed to the write-up, development of the proposal, data collection format, data entry, data analysis, and write-up of the manuscript. MM, AB, and SF participated in the design and data analysis, critically read the manuscript, and edited the manuscript. All authors have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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