We investigated the shape of the relationship between longitudinal uric acid (UA) and the hazard of kidney failure and death in chronic kidney disease (CKD) patients, and attempted to identify thresholds associated with increased hazards. We included CKD stage 3–5 patients from the CKD-REIN cohort with one serum UA measurement at cohort entry. We used cause-specific multivariate Cox models including a spline function of current values of UA (cUA), estimated from a separate linear mixed model. We followed 2781 patients  for a median of 3.2 years with a median of five longitudinal UA measures per patient. The hazard of kidney failure increased with increasing cUA, with a plateau between 6 and 10 mg/dl and a sharp increase above 11 mg/dl. The hazard of death had a U-shape relationship with cUA, with a hazard twice higher for 3 or 11 mg/dl, compared to 5 mg/dl. In CKD patients, our results indicate that UA above 10 mg/dl is a strong risk marker for kidney failure and death and that low UA levels below 5 mg/dl are associated with death before kidney failure. In the past two decades, uric acid (UA) has drawn attention in the nephrology community.Chronic kidney disease (CKD) is recognized as a major public health problem and identification of modifiable determinants of CKD progression, other than hypertension or proteinuria11, and did not well characterize the shape of this longitudinal relationship12. The discrepancy between results may be due to differences in CKD stages at baseline, duration of follow-up, definitions of CKD progression, the choice of adjustment factors13, or to the use of a single measure of UA assessed at baseline, i.e. entry into the cohort10. Indeed, entry into a cohort does not usually correspond to any relevant time point in patient’s course of CKD and varies across studies and from patient to patient. To estimate the association between UA and CKD progression, most previous studies thus compared the baseline uric acid values between two patients who potentially did not enter the cohort at the same moment of their CKD history, and fully ignored their respective subsequent evolution of uric acid between baseline and the current time when the risk was assessed. Yet, UA may change over time, and from a clinical point of view, it might be more relevant to compare the level of UA reached at the time when the risk is assessed. To our knowledge, only one single-center cohort study in Taiwan used longitudinal measures of UA14. They found that elevated UA trajectories had an increased hazard of dialysis initiation and death. However, the analysis based on classes of trajectories did not allow the examination of the shape of the relationship between the level of UA reached at a given time and the hazard of kidney failure or death at the same time, and thus the identification of potential critical thresholds of UA, which yet might be useful in monitoring patients with CKD.Previous cohort studies found conflicting results on the relationship between UA and CKD progression15, the shape of the relationship between longitudinal UA and the hazard of kidney failure and death in CKD stage 3–5 patients, and identify potential thresholds of longitudinal UA associated with increased hazards.The objective of the present study was to investigate, in the French Chronic Kidney Disease-Renal Epidemiology and Information Network (CKD-REIN) cohort15. The study included 3033 patients over 18 years of age. They were first selected and recruited in an enrolment phase in 40 nephrology centers located over all metropolitan France and representative of all centers with respect to legal status , and then actively included into the cohort at a first visit (baseline) between 2013 and 2016, in the same centers, after obtaining inform consent15. The study protocol was conducted with adherence to the Declaration of Helsinki and approved by the institutional review board at the French National Institute of Health and Medical Research . The study was registered at ClinicalTrials.gov (NCT03381950). In the present study, we included patients who had at least one serum UA and one creatinine measurement within 6 months of their inclusion into the cohort.CKD-REIN is an ongoing prospective cohort of CKD Stage 3–5 patients receiving nephrologist-led care, without prior chronic dialysis or kidney transplantationThe exposure was serum UA concentration, assessed in each center along with routine laboratory investigations, at baseline, annually per protocol and more frequently if considered necessary by the nephrologist.The outcomes of interest were (1) kidney failure assessed by initiation of chronic dialysis or pre-emptive transplantation, and (2) death before kidney failure. To ensure complete collection of kidney failure, a record linkage was performed with the national REIN registry. Administrative censoring was performed on July 30, 2018.16), salt intake (estimated by 24-h natriuresis) and protein intake (estimated by 24-h urinary urea)17, medication adherence according to the Girerd score in categories 18, health literacy according to their need for help reading medical documents 19 and type of center .Baseline characteristics were recorded by clinical research associates from medical files or by interview. Data included age, sex, body mass index (BMI), hypertension , cardiovascular history , diabetes (diabetes history or antidiabetic treatment or glycated hemoglobin ≥ 6.5% or fasting glycemia ≥ 7 mmol/l or non-fasting glycemia ≥ 11), gout history, dyslipidemia, primary kidney disease, time since CKD diagnosis , number of consultation in the previous year with nephrologist and dietician, treatment (urate-lowering therapy (ULT), diuretics, antiplatelet agents, renin-angiotensin system inhibitors (RASi)), laboratory data  included a 3-knot natural cubic spline function of time with random effects on each coefficient, and some selected baseline factors  to make the estimation of all individual UA trajectories more accurate. The cause-specific Cox models (at Stage 2) included a 2-knot natural cubic spline function of cUA (derived from Stage 1) to allow the estimation of nonlinear association and thus the detection of potential thresholds. The number of knots were selected using the Akaike Information Criterion (AIC)23. The Cox models were adjusted for a set of confounders which were selected from a directed acyclic graph (DAG), i.e. a diagram of causal pathways summarizing a priori hypothetical causal relationship between variables , sex, primary kidney disease , hypertension (yes/no), diabetes (yes/no), cardiovascular disease (yes/no), dyslipidemia (yes/no), BMI , albuminuria , CKD stages , medication adherence , RASi (yes/no), and ULT (yes/no) , and the hazard of kidney failure or death at the same time, we used a two-stage statistical approach which have been evaluated previouslyes Figs.  and S2. To investigate if the association between cUA and the hazard of kidney failure or death differed according to sex, we included interaction terms with the spline function of cUA, and test the interaction using the likelihood ratio test.26 and CKD progression28. We perform this further adjustment in the subsample of patients having 24-h natriuresis and urinary urea at baseline. In a second sensitivity analysis, for comparison with previous studies, we estimated the association between baseline value of UA and outcomes using the same set of adjustment factors as Model 1 (Model 3).In a first sensitivity analysis, we further adjusted Model 1 for salt intake  and protein intake (in mmol/day) at baseline  (Model 2) because patients with high salt or high protein intake may be at higher risk of hyperuricemia29. Proportional hazards assumption was checked using Schoenfeld residuals. Linearity of the effect of all adjusting quantitative variables was checked using 4-df penalized spline functions30, which was kept in the model if the effect was nonlinear31. All analyses were performed using R version 3.6.032.In all Cox models, we accounted for correlation between patients of the same type of center using robust standard errors based on the sandwich estimator2 (IQR: 23–41), with 94% of CKD stage 3 or 4 . Median salt intake was 6 g/day (natriuresis of 128 mmol/day) and estimated median protein intake was 61.1 g/day (urinary urea of 305 mmol/day). A total of 938 patients (33.7%) were prescribed ULT , the 2781 included patients had a median of 5 UA measures, with a median of 120 days between two consecutive measures Table . This leCompared to the 3033 patients participating in the CKD-REIN cohort, the 2781 included patients (with UA measurement within six months of inclusion into the cohort) had similar baseline characteristics  in a sensitivity analysis only because it was available for only half of patients. The estimated effect of cUA was weaker after adjustment, but this was more likely due to a selection bias in the subsample of half patients, than to a strong confounding bias. Indeed, adjusting or not for salt and protein intake in the subsample produced very similar results. However, it would be important to further investigate the role of diet in general in the association. It would also be of interest to investigate if the association is similar in patients with and without mutations or common variants in To conclude, our study investigating longitudinal UA rather than baseline UA only, indicates a strong nonlinear monotonic association between the reached level of UA and the hazard of kidney failure, and confirms the U-shape relationship with mortality. Although the use of ULT to slow progression of CKD is not recommended, we believe that a UA above 10 mg/dl may be considered as a strong risk marker for kidney failure and death, and thus should encourage nephrologists to be stricter in controlling cardiovascular and nephroprotective factors.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.


Upper gastrointestinal bleeding (UGIB) is a common complication in renal transplant recipients. However, the risk stratification value of bleeding scoring systems in these patients is unclear, and data regarding risk factors are limited.Clinical data of renal transplant recipients in The Third Xiangya hospital were collected. The predictive ability of Glasgow Blatchford score (GBS), pre-endoscopy Rockall score (pRS), and AIMS65 score were assessed by the area under the receiver operating characteristic curve (AUROC). Risk factors of UGIB were analyzed using binary logistic regression analysis.p = 0.30) and pRS score (p = 0.42). Viral hepatitis, intravenous hormone usage, low platelet count, and low albumin level were significant factors associated with UGIB.A total of 220 patients were enrolled, of which 55 with UGIB. Endoscopy improved the overall survival rate of patients. Glasgow Blatchford score (AUROC 0.868) performed best at predicting UGIB patients who need intervention or death, with a threshold of 10, sensitivity and specificity were 82.4% and 70%, respectively. In terms of predicting mortality, the GBS score was comparable with AIMS65 score (The Glasgow Blatchford score (AUROC 0.868) was best at predicting the need for intervention or death. However, their ability to predict mortality was limited, with AUROC less than 0.8. Our study also identified four independent risk factors for renal transplant recipients with UGIB.The online version contains supplementary material available at 10.1186/s12876-022-02426-3. Renal transplantation is the best treatment for patients with end-stage kidney disease. However, various postoperative complications lead to frequent readmission and high treatment costs. With increasing numbers of renal transplant recipients, more attention needs to be paid to the risk of post-transplant complications to optimize management. Upper gastrointestinal bleeding (UGIB) is a common complication in renal transplant recipients, with an incidence of 0.3% to 21.5% . PossiblEarly identification and active intervention of critical patients with UGIB play an essential role in improving the prognosis of patients. The international guideline recommends bleeding scoring systems for risk stratification . The fulThe scoring system like GBS consists of a patient's medical history, vital signs, and laboratory indicators at admission, in which Different levels of hemoglobin, blood urea nitrogen (BUN), and blood pressure correspond to different scores. The reference values are formulated for the general population. Renal transplant recipients are usually complicated with anemia, azotemia, and hypertension, leading to higher bleeding scores, and being classified as high-risk population. Whether the pre-endoscopy scoring system can be used for these patients is unclear.This study aims to identify the risk factors of UGIB in renal transplant recipients without renal failure, and to compare the predictive value of the three pre-endoscopy scoring systems for risk stratification. This might facilitate a low-risk group for outpatient management, identify the high-risk group and formulate intervention strategies as soon as possible. Therefore, improving the overall prognosis in two aspects of prevention and intervention.1. Patient selection and data collection: the clinical data of renal transplant recipients in The Third Xiangya Hospital of Central South University from January 2015 to December 2019 were collected for retrospective analysis. The exclusion criteria include patients with lower gastrointestinal bleeding, patients with renal graft failure and dialysis, and patients with gastrointestinal bleeding due to other diseases during hospitalization. A total of 220 patients were enrolled, of which 165 patients were without bleeding and 55 patients with UGIB. The data included gender, age, height, weight, complications , drug use , clinical manifestations, vital signs, and laboratory results of the bleeding , blood transfusion, endoscopy and intervention, surgery or interventional therapy, and patients’ outcomes.The scores of all patients before endoscopy on the first day of admission were calculated, and risk stratification was carried out by using pRS, GBS, and AIMS65 score. It is worth noting that risk stratification was carried out before endoscopy, therefore, standards related to endoscopy in the pRS were not included, leading to the total score of the pRS between zero to seven.UGIB is defined as the clinical manifestation of hematemesis and/or black stool. Endoscopic reports describing active bleeding  or recent bleeding (i.e. adhesive clots), are defined as diagnostic endoscopy. An endoscopic report not identifying bleeding sources is defined as non-diagnostic endoscopy. Patients who need urgent hemostatic intervention are defined as having a blood transfusion, or surgery, or non-surgery treatment  to stabilize the vital sign.The endpoints were composite outcomes (the need for urgent hemostatic intervention or death), the need for endoscopic intervention, and 90-day mortality. Death was determined through medical records in hospital healthcare systems and confirmed with patients’ relatives if necessary.p < 0.05). The area under the receiver operating characteristic curve (AUROC) was drawn to evaluate the predictive ability of each scoring system. For each scoring system, the effective cut-off value was obtained by calculating the Youden index. De Long's index was used to compare AUROC values. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for study outcomes were calculated.2. Statistical analysis: SPSS 22.0 software was used for data analysis. The measurement data were expressed as mean ± standard deviation or median ± interquartile range, and percentages were calculated for categorical variables. The comparison between the two groups was conducted by independent sample t-test. The counting data were expressed by the number of cases and percentage, and the chi-square test was used for the comparison between the two groups. Binary logistic regression analysis (forward: likelihood ratio (LR)) was used to analyze the independent risk factors of UGIB, including variables with a p-value less than 0.05 at group comparison in the single factor analysis. The survival curve was drawn by the Kaplan Meier method and compared between the two groups , 15 patients with hematemesis and black stool (27.3%). Endoscopy has revealed the bleeding source in 22 patients, accounting for 66.7% of all 33 patients who underwent endoscopy (Table 2) group and eGFR < 60 ml / (min * 1.73m2) group decreased after UGIB. There was no difference in survival rate between the two groups (p = 0.48). There was no significant difference in renal function between patients with or without endoscopy. However, the overall survival rate of those with endoscopy was significantly improved   in both eGFR ≥ 60 ml / min * 1.73m.73m2 groWe have compared the power of different scoring systems for risk stratification in renal recipients with UGIB. Overall, the average score of GBS was 10.2 ± 4.2 points, AIMS65 was 0.3 ± 0.6 points, and pRS was 1.2 ± 1.3 points, as shown in Table p < 0.001) and pRS . There was no significant difference between AIMS65 and pRS (p = 0.95)  required for urgent hemostatic intervention or died, of which 29 patients received a blood transfusion, 5 patients with ulcer bleeding were treated under gastroscopy, and 1 patient with gastric variceal bleeding underwent surgery. In predicting need for urgent intervention or death, the ability of GBS (AUROC 0.868) was significantly higher than AIMS65  and pRS (AUROC 0.552) failed at predicting it (Table 2 and pRSp = 0.30) and pRS , the same with AIMS65 and pRS (p = 0.85)  patients died within 90 days after UGIB. In predicting mortality, the ability of GBS (AUROC 0.756) was comparable to AIMS65  performed best in predicting urgent hemostatic intervention or death. A GBS threshold of 10 or more was best at predicting the high-risk patients. Whereas all three had limited value in predicting the need for urgent endoscopy or 90-day mortality. This study provides data on the early identification of patients with bleeding tendency and risk stratification of bleeding severity.Early identification of high-risk patients needing urgent intervention or at high risk of death can help to improve the prognosis. So far there is no unified scoring system to predict all these endpoints accurately. This study compared the predictive value of three widely used pre-endoscopy scoring systems, 19 in rIdentification of a low-risk population was helpful for outpatient management. StanleyEndoscopy is an essential method for UGIB. To the best of our knowledge, there is no study on the role of endoscopy in renal transplant recipients with UGIB. Endoscopy has limited diagnosis and treatment effects in GIB patients with continuous flow-left ventricular assist devices (CF-LVADs), since it rarely identifies single treatable bleeding source and cannot reduce the recurrence rate of GIB . Our resHowever, none of those three scoring systems can effectively predict needing for endoscopic intervention, with AUROC all lower than 0.8, which is consistent with several other studies, 25.The risk of UGIB in renal transplantation would exist for a long time. Our results showed a mortality of 29.3% and graft loss of 32.6% in renal transplant recipients with UGIB, with an obvious drop in recent years. This may be related to a better understanding of UGIB in recent years, more standardized clinical management, and a close follow-up of experienced teams. Several studies, includi2) in both groups, the renal function staging was both in chronic kidney disease stage 3. Therefore, the bleeding tendency might be at the same level, as shown in a study by Ishigami and his colleagues[So far, there are limited data on the risk factors of UGIB in these patients. We identified four independent risk factors. A study by Ardalan and his colleagues suggesteolleagues. SecondlThe main strengths of this study lie in two points. First, some new independent risk factors of UGIB in renal transplant recipients was identified. Second, it compared the predictive ability of three pre-endoscopy bleeding scoring systems in these patients for the first time. However, this study also has some limitations. First, this is a single centred retrospective study. Data collection and risk assessment are determined through existing clinical records, and the sample size is relatively small. Second, although we follow the established clinical treatment guidelines for the choice of treatment methods, the results may vary according to experienced endoscopists and intensive care ability. Third, the period of data collection in this study is extensive. The detailed past histories such as Helicobacter pylori infection, previous peptic ulcer disease and CMV infection were missing and weren’t included in the study.In conclusion, we have identified four independent risk factors of UGIB after renal transplantation, including intravenous hormone usage, low platelet count, low albumin level and viral hepatitis. Glasgow Blatchford score is the best to predict intervention or death. The three pre-endoscopy scoring systems have limited value in predicting mortality and the need for endoscopic intervention. However, endoscopy can improve patients' overall survival rate. We recommend endoscopy for UGIB patients without contraindications. Overall, this study provides some new information to improve the prognosis of renal transplant recipients with UGIB from three aspects, prevention, risk stratification and treatment.Additional file 1: Supplementary Table 1. Clinical characteristics of renal transplant recipients with upper gastrointestinal bleeding between endoscopic and non-endoscopic groups. Supplementary Table 2. Optimal cut-off values of each scoring system in predicting patients who might not need intervention. Supplementary Table 3. Optimal thresholds to predict 90-day mortality in renal transplant recipients with upper gastrointestinal bleeding. Supplementary figure 1. The survival rate and renal function of kidney transplant recipients with upper gastrointestinal bleeding. Supplementary figure 2. Comparison of three pre-endoscopy scoring systems on predicting need for urgent endoscopic intervention. AUROC: area under the receiver operating characteristic curve.


Studies have demonstrated that individuals of low socioeconomic status have higher blood pressure. Yet, whether socioeconomic inequality would influence blood pressure control and the underlying mechanisms associated with socioeconomic inequality in blood pressure control are unknown. Central to socioeconomic inequality is relative deprivation. We aim to examine the association between relative deprivation and blood pressure control and to investigate the pathways of the association among middle-aged and older adults with hypertension.Data were collected from the 2020 Household Health Interview Survey in Taian City, Shandong province. This study included 2382 eligible respondents aged 45 years and older with a diagnosis of hypertension. Our primary outcome was dichotomous blood pressure control. Relative deprivation was calculated with the Deaton Index. Depressive symptoms and medication adherence were considered as mediators. Multivariable binary logistic regression models were used to estimate the effect of relative deprivation on blood pressure control. The “KHB-method” was used to perform mediation analysis.Among 2382 middle-aged and older adults with hypertension, the mean age was 64.9 years (SD 9.1), with 61.3% females. The overall proportion of participants with uncontrolled blood pressure was 65.1%. Increased relative deprivation was likely to have higher odds of uncontrolled blood pressure . Furthermore, depressive symptoms and medication adherence partially mediated the overall association between relative deprivation and blood pressure control, with depressive symptoms and medication adherence explaining 5.91% and 37.76%, respectively, of the total effect of relative deprivation on blood pressure control.Individual relative deprivation could threaten blood pressure control among middle-aged and older hypertension patients through the mechanisms of depression and medication adherence. Hence, improving blood pressure control may require more than just health management and education but fundamental reform of the income distribution and social security system to narrow the income gap, reducing relative economic deprivation. Additionally, interventions tailoring psychological services and medication adherence could be designed to reduce the harmful effect of relative deprivation on blood pressure control among disadvantaged individuals. Hypertension is the most critical risk factor for cardiovascular disease and premature death worldwide , 2. The Several factors contribute to blood pressure control, and socioeconomic status is the repeatedly mentioned one. Prior observational studies have shown that hypertension is more common and poorly controlled among lower versus higher socioeconomic status groups, with the lack of access to appropriate treatment , 10. HowWithin the field of population health, relative deprivation resonated strongly among researchers investigating the impact of income disparity on health, and has been proposed as one mechanism explaining the observed associations between income inequality and adverse health outcomes . RelativAccording to the relative deprivation theory, there may be several potential explanations for the association between relative deprivation and health: material pathway, psychosocial pathway, and behavioral pathway . In the China has experienced rapidly increased income inequality since promulgating the economic reform and open-door policy in the late 1970s. China was found to have the joint highest inequality in Asia, and the Gini coefficient for per capita household income was 0.46 in 2015 , 33. AftAccordingly, this present study sought to (1) examine the association between relative deprivation and blood pressure control in middle-aged and older adults with hypertension; (2) investigate the mediating roles of depressive symptoms and medication adherence in the association between relative deprivation and blood pressure control. We hypothesized that a higher level of relative deprivation was likely to associate with higher odds of uncontrolled blood pressure; emotional discomfort and unhealthy behavior caused by relative deprivation manifest through depressive symptoms and poor medication adherence, leading to a detrimental impact on blood pressure control.The 2020 Household Health Interview Survey is a cross-sectional, community-based, and observational investigation designed to explore the health status and health service demand and utilization of Chinese adults. This survey was conducted in Taian City, Shandong Province, China. Details on the survey design can be found in our previous study . SamplesIn the present study, our analyses were restricted to 2382 eligible respondents aged 45 years and older with hypertension diagnosed by doctors  and who have been advised to take antihypertensive drugs. Each participant provided informed consent. The study protocol was approved by the Ethical Committee of the Centre for Health Management and Policy Research, Shandong University .Blood pressure levels were measured in the participants' homes by primary care providers trained in the study procedures using automated sphygmomanometers . The blood pressure before taking medicine is close to the actual value. Therefore, the blood pressure levels were measured in the participants' homes at 7–8 a.m., and try to ensure that the patient's blood pressure was measured before taking the antihypertensive drugs. Readings were obtained after 5 min of seated rest. Three blood pressure measurements were obtained at 30-s intervals . The meaThe study subjects were categorized into two groups according to blood pressure levels: controlled and uncontrolled blood pressure. The latter was defined as systolic blood pressure ≥ 130 mm Hg or diastolic blood pressure ≥ 80 mm Hg for patients with diabetes mellitus, coronary heart disease, or renal disease, and systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg for all others , 39.From the concept of relative deprivation in this study, we shall focus on relative income deprivation. Income should be considered as an indicator of a person’s ability to consume commodities; each unit of income reflects a different bundle of commodities that he can consume . Many stN is the reference group size of individual i. i’s income, and i within that individual’s reference group. Deaton values range from 0 to 1, with a higher score representing a higher relative deprivation.This study uses Deaton formulation to measure objective relative deprivation . Deaton The income used to calculated Deaton value was the individual’s average annual income in the past year. However, in China’s current cultural background, especially in rural areas and some self-employed workers, it is hard to quantify how much each family member contributes to family finance. Therefore, data on gross annual household income  from all household members were collected in our survey. To determine individual income, equivalized income was calculated for each participant by dividing their gross annual household income by the square root of their household size .Also very important is the choice of the reference group when defining relative deprivation. During the investigation, we did not inquire about the participant’s reference group . Previous studies suggested that income-based social comparisons between individuals were based on similar education level, same age group, and same location . The preDepressive symptoms during the past two weeks were assessed using the Patient Health Questionnaire-9 (PHQ-9) . The PHQBecause the optimal cut-off score for diagnosing major depressive disorder with the PHQ-9 remains unclear , we use Medication adherence was identified using an unstructured self-report question, “Do you sometimes forget to take your antihypertensive pills?” with responses of “yes” or “no.” This item has been frequently used to detect treatment adherence in older patients with hypertension in China, where forgetting to take pills is a leading contributor to poor adherence , 52.According to previous research , 53–55, First, we examined the descriptive statistics for all the study variables. The summary statistics for all participants were reported separately by blood pressure control status using proportions for categorical variables and means, and standard deviation for continuous variables conformed to normal distribution. T-tests and chi-square tests were used to assess differences between blood pressure control groups. For variables that do not conform to a normal distribution, we applied median to conduct descriptive statistics and Mann–Whitney-U test to assess differences between different blood pressure control groups.Then, a series of multivariable regression models were conducted to estimate the total, direct and indirect effects of relative deprivation on blood pressure control. First, we conducted a binary logistic regression model to examine the total effect of relative deprivation on blood pressure control. Second, the depressive symptoms and medication adherence were subsequently added as covariates to the logistic regression model to examine the attenuation of the association between relative deprivation and blood pressure control. Third, we conducted one linear regression model and one logistic regression, respectively, with depressive symptoms and medication adherence as dependent variables to examine whether relative deprivation affected depression and medication adherence.Further, a mediation analysis was conducted to assess if depressive symptoms and medication adherence explained the association between relative deprivation and blood pressure control. Given the dichotomous nature of blood pressure control used in this study, we employed a binary logistic model based on the KHB mediation analysis proposed by Karlson, Hom, and Breen . This meP = 0.05.All models were adjusted for ten control variables prescribed. Sampling weights, estimated as the inverse probability of being selected for the survey, were used in all calculations to obtain representative estimates. All data were analyzed using Stata software, version 15 (StataCorp LLC). The significance threshold was P < 0.05).Table P = 0.001), more severe depressive symptoms (mean (SD), 4.13(4.5) vs 3.13(3.9), P < 0.001), and poorer medication adherence .Significant differences were also observed in relative deprivation, depressive symptoms, and medication adherence. Compared with participants with controlled blood pressure, those with uncontrolled blood pressure had higher relative deprivation score , 0.47 vs 0.41, Table P < 0.05). In Model 4, after controlling the covariates, relative deprivation was associated with high risks of having poor medication adherence . Moreover, depressive symptoms increased the odds of poor medication adherence. The multicollinearity test was conducted, and there was no multicollinearity in these four models.Model 3 and Model 4 show that relative deprivation was significantly associated with depressive symptoms and poor medication adherence. Specifically, after controlling the covariates in Model 3 (linear regression model), relative deprivation is positively related to depressive symptoms (β: 2.13, Table The KHB method summarizes the mediation effect due to depressive symptoms and medication adherence at the end of Table This study examined the role of relative deprivation on blood pressure control in middle-aged and older Chinese adults with hypertension. Specifically, we examined whether relative deprivation increased the odds of uncontrolled blood pressure, and whether depressive symptoms and medication adherence could partly explain this. Our findings indicated that higher relative deprivation coincides with higher odds of uncontrolled blood pressure, supporting our first hypothesis. Furthermore, depressive symptoms and medication adherence explained nearly half of the effect of relative deprivation on blood pressure control, supporting our second hypothesis. This was mostly driven by medication adherence, which accounted for 37.76% of the association between relative deprivation and blood pressure control. Depressive symptoms were also a mediator, explaining 6% of the association between relative deprivation and blood pressure control.The current study confirmed that the association between relative deprivation and blood pressure control would be partially mediated by depression and medication adherence, possibly revealing the psychosocial and behavioral mechanisms concerning how relative deprivation might indirectly influence blood pressure control. Since relative deprivation is closely connected to income distribution, higher income inequality may trigger emotional discomfort through depressive symptoms . Study eMoreover, participants with high relative deprivation are more likely to have poor medication adherence, which leads to a higher risk of uncontrolled blood pressure. The relationships between income inequality and unhealthy behaviors such as tobacco use, physical inactivity, obesity have been well demonstrated empirically, as well as poor medication adherence –66. The In addition to the psychosocial pathway, and behavioral pathway analyzed in our study, the unique cultural background of China also plays a potential role in the positive association between higher relative deprivation and higher odds of uncontrolled blood pressure. Ceremonies, such as funerals and weddings, are important traditions in Chinese society. People who skip these events or spend less than their more affluent counterparts lose face and risk becoming social isolation . SpecifiOur study indicate the importance of relative deprivation to the blood pressure control among middle-aged and older hypertension patients. These findings are consistent with and extend previous studies reporting associations between socioeconomic context and hypertension outcomes , 74, 75.This study also has several limitations that provide directions for future research. First, the cross-sectional design of the survey is difficult to determine temporal ordering between relative deprivation and uncontrolled blood pressure, which prevents us from making causal relationships between them. Future studies may need to define more pathways of associations between relative deprivation and health outcomes using long-term follow-up data. Second, it is impossible to explain how much relative deprivation is here. The measurement of subjective relative deprivation was absent, so it is unknown whether those with a higher Kakwani Index in fact perceived themselves as relatively deprived. However, using objective relative deprivation can simultaneously prevent common method bias caused by subjective predictors and self-reported outcome measures. Future research relating to the health effects of subjective relative deprivation would be conducted to compare to objective relative deprivation. Third, we did not have data regarding current pharmacological treatment for depression. A side effect of antidepressants was elevated blood pressure, which may be associated with blood pressure control. Forth, using a single item of medication adherence is not accurate enough, although it is simple and general. Meanwhile, the antihypertensive drug type and quantity were also not collected, which may be the critical covariates of medication adherence. In addition, this survey uses a structured self-reported questionnaire, and the response cannot completely rule out the possibility of recall bias. Last, our study was conducted in a single city, potentially limiting the findings' generalizability.In conclusion, individual relative deprivation could threaten blood pressure control among middle-aged and older hypertension patients through the mechanisms of depression and medication adherence. Hence, improving blood pressure control may require more than just health management and education but fundamental reform of the income distribution and social security system to narrow the income gap, reducing relative economic deprivation. Additionally, interventions tailoring psychological services and medication adherence could be designed to reduce the harmful effect of relative deprivation on blood pressure control among disadvantaged individuals. Finally, as government implements strategies to improve blood pressure control, it is essential to account for the potential health risks associated with relative deprivation to ensure that the care we deliver improves outcomes for all populations.


Frequency distribution analysis stratified by CKD stage and UP for hypothyroidism revealed that the prevalence of hypothyroidism was higher among participants with higher CKD stage and nephrotic range proteinuria. Multivariate logistic regression analysis revealed that 24hrCcr and UP were significantly correlated with hypothyroidism . In addition, nephrotic range proteinuria, but not moderate UP (UP: 1.5–3.49 g/day), was significantly correlated with hypothyroidism compared to UP < 0.5 g/day. In summary, decreased kidney function and nephrotic range proteinuria, not non-nephrotic proteinuria, are independently associated with the hypothyroidism.Hypothyroidism is known to be correlated with kidney function and nephrotic range proteinuria. However, it is uncertain whether non-nephrotic proteinuria is associated with hypothyroidism. This study aimed to evaluate the association of proteinuria and hypothyroidism in chronic kidney disease (CKD) patients. We conducted a cross-sectional study composed of 421 CKD patients in a single hospital with measurements of 24-h urine protein excretion (UP) and thyroid function tests. Spearman correlation analysis revealed that 24-h Cr clearance (24hrCcr) was positively (r = 0.273,  As the side of the effect of renal function on the thyroid disorder, there are several reports indicating the high prevalence of hypothyroidism in chronic kidney disease (CKD) patients13. For example, using the data from the Third National Health and Nutrition Examination Survey in US, it is reported that a prevalence of hypothyroidism in persons with eGFR < 60 ml/min/1.73 m2 was more than 20% compared to that of 5.4% for persons with eGFR ≥ 90 ml/min/1.73 m29, indicating a higher prevalence of hypothyroidism in CKD patients. Nevertheless, most of them collected the medical checkup data whose participants were mostly healthy without CKD and there are still few reports analyzing the thyroid function among CKD population14. It is reported that kidney dysfunction may cause hypothyroidism by several mechanisms: high exposure to iodine by the reduction in iodine clearance related to renal dysfunction16, decrease in thyrotropin-releasing hormone (TRH) by uremia18, and the reduction in triiodothyronine (T3) concentrations by chronic metabolic acidosis19.It is known that thyroid and kidney function have a strong influence on each other20, which result in the reduction of the serum thyroid hormone levels21. Nevertheless, there are still few reports analyzing the urine protein and hypothyroidism. While it is reported that a semi-quantitative proteinuria by urine dipstick is not associated with hypothyroidism4, the effect of degree of proteinuria on hypothyroidism is still unclear. Although 24-h urinary protein excretion (UP) is desirable for accurate measurement of proteinuria because of fluctuation of urinary protein excretion by measurement time, position, exercise, and blood pressure, it is inconvenient, and spot urinary protein / creatinine ratio is a convenient alternative and is in widespread use22. Nevertheless, it is suggested that thyroid function may affect urine creatinine excretion23, suggesting that spot urinary protein / creatinine ratio may not reflect accurate proteinuria in the case with hypothyroidism. Therefore, in the present study, we applied UP to analyze the association between urinary protein levels and the presence of hypothyroidism in CKD patients.In addition to the kidney function, the severity of CKD is also defined by urine protein excretion. It is reported that proteinuria may cause urinary loss of thyroid hormones bound to the various binding proteins such as thyroxine-binding globulin (TBG), albumin, prealbumin, and transthyretinWe retrospectively reviewed patients with measurement of thyroid function and renal function during the hospitalization in the division of kidney, diabetes, and endocrine diseases at Okayama University Hospital from 2006 to 2019. Data were collected from electronic-based records in 2020–2021. Among 18,068 patients from our database, we collected 515 CKD patients with all the following data of the measurements; eGFRcre, eGFRcys, 24hrCcr, thyroid-stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), UP, total cholesterol, serum albumin, and glycated hemoglobin (HbA1c). We extracted 463 hypothyroid and euthyroid patients after excluding patients with hyperthyroidism or central hypothyroidism, followed by the exclusion according to the following criteria: (1) age < 18 years, (2) on dialysis, (3) post kidney transplantation, (4) on steroid treatment, and (5) type 1 diabetes mellitus (DM). Consequently, 421 patients were enrolled in this study . The presence of hypertension was defined with prior diagnosis or use of anti-hypertensive medications. eGFRcre was calculated by using Modified for Japanese subjects: eGFRcre (mL/min/1.73 m2) = 194 × s-Cr (mg/dL)−1.094 × Age−0.287 25. CKD was defined as eGFRcre < 60 mL/min/1.73 m2 and/or urinary protein ≥ 0.15 g/day26 and CKD stages were defined by a modification of the National Kidney Foundation CKD staging based on estimated GFR categories of ≥ 90, 60 to 89, 45 to 59, 30 to 44, and < 30 mL/min/1.73 m227. Nephrotic syndrome (NS) was defined by both substantial proteinuria (> 3.5 g/24 h) and hypoalbuminemia (< 3.0 g/dL). eGFRcys was calculated by: eGFRcys (mL/min/1.73 m2) = (104 × serum cystatin C (mg/L)−1.019 × 0.996 age)—8 , (104 × serum cystatin C (mg/L)−1.019 × 0.996 age × 0.929)—8 28. 24hrCcr (mL/min) was calculated as follows: 24-h urinary creatinine (mg/dL) × 24-h collected urine (mL/day)/24 (hour)/60 (min)/serum creatinine (mg/dL) × 1.73/body surface area (BSA)29. BSA was calculated by: BSA (m2) = body weight0.425 × height0.725 × 0.007184 30. Thyroid function was analyzed by an electrochemiluminescence assay . The normal reference range in our institute for TSH was 0.27–4.2 µIU/mL, FT4 was 0.93–1.7 ng/dL, and FT3 was 2.3–4.0 pg/mL. Participants were classified into 3 groups according to the results of thyroid function test and the use of THRT: subclinical hypothyroidism (0.93 ≤ FT4 ≤ 1.7 ng/dL and TSH > 4.2 µIU/mL), overt hypothyroidism , and euthyroidism (0.93 ≤ FT4 ≤ 1.7 ng/dL and 0.27 ≤ TSH ≤ 4.2 µIU/mL). Total hypothyroidism was defined as combined overt and subclinical hypothyroidism. The etiology for CKD was diagnosed by experienced nephrologists based on patients’ medical history, physical information, clinical examination, and pathological findings by renal biopsy, if applicable.The following clinical characteristics were collected at the time of the hospitalization: age, sex, body mass index (BMI), and the use of angiotensin-converting-enzyme inhibitor (ACE-i), angiotensin II receptor blocker (ARB), insulin, dipeptidyl peptidase-4 (DPP-4) inhibitor, glucagon-like peptide-1 (GLP-1) receptor agonist, biguanide, sodium-glucose cotransporter 2 (SGLT-2) inhibitor, sulfonylurea (SU), thiazolidinedione (TZD), glinide, and thyroid hormone replacement therapy (THRT). HbA1c data are presented as National Glycohemoglobin Standardization Program values according to the recommendations of the Japanese Diabetes Society and International Federation of Clinical ChemistryP values were calculated as two-sided. The association was considered significant with P values less than 0.05.Data were expressed as n (%) for categorical variables and mean ± standard deviation (SD) for continuous variables. Categorical variables were analyzed with the chi-square test, while continuous variables were compared by using the student's t-test or Mann–Whitney U test as appropriate. P for trend was calculated by Cochran-Armitage trend test or Cuzick test. The estimated standard error of the confidence estimate was used to establish confidence intervals (CI) of the estimated odds ratio (OR). Correlations of renal function and UP with thyroid function  were evaluated by Spearman correlation analysis. The statistical analyses were performed by JMP version 14.0.0 , and Stata/SE version 16.1 . All 2. The average level of UP was 2.09 ± 3.72 g/day, and 11% subjects were diagnosed with NS. 11% subjects took THRT and the average TSH was 5.6 ± 18.1 µU/mL. The overall prevalence of overt hypothyroidism, subclinical hypothyroidism and total hypothyroidism was 12%, 16% and 28%, respectively. The average HbA1c levels were 6.8% and 48% of subjects had a history of DM.Among 515 patients with all the data set, 421 patients were included in the eventual analysis after the exclusion. A flow chart of the screening and registration of the study participants is shown in Fig. P value for trends < 0.001, 0.003, and < 0.001, respectively)  and higher TSH levels  with higher CKD stage   Fig. A, which age Fig. B. We alsNext, the explanatory variables for hypothyroidism were evaluated by univariate and multivariate logistic regression analysis Table . In the To further analyze the association of proteinuria with hypothyroidism, we divided the participants into four categories based on the UP: < 0.5, 0.5–1.49, 1.5–3.49, and 3.5 ≥ g/day. Clinical variables stratified by these categories are shown in Table To the best of my knowledge, this is the first study to investigate the association between 24-h urine protein and hypothyroidism in CKD population. Our study indicated that kidney function, nephrotic range proteinuria, and UP, but not non-nephrotic proteinuria, are significantly correlated with hypothyroidism. In addition, frequency distribution analysis stratified by CKD stage and UP for hypothyroidism clarified the possibility of hypothyroidism in CKD population. It may help for the decision of routine analysis of thyroid function in CKD patients.32. The levels of FT3, not FT4 or TSH, were correlated with kidney function and UP. It is important because FT3 is the most metabolically active hormone. T3 and thyroxine (T4) are available in circulation as free hormones, which are biologically active33. Thyroid hormone molecules can also be activated by conversion from T4 to T3 34. Various factors related to CKD may affect these processes. Considering the higher prevalence of hypothyroidism especially in CKD stage 5, uremic toxin and/or metabolic acidosis due to end-stage renal disease, may be the important cause of hypothyroidism as previously reported19.There was a significantly increased proportion of hypothyroidism with the progression of CKD, that is in agreement with previous reports4. More recently, it is reported that UP is an independent factor associated with hypothyroidism in CKD population14. However, most of the participants in the study were end-stage renal disease (46% were CKD stage 5), thus it may not represent the whole CKD population. In addition, urinary protein / creatinine ratio was used in the study. Since it is reported that thyroid function may affect urine creatinine excretion23 and urine creatinine excretion decreases as kidney function declines35, urinary protein / creatinine ratio might be unreliable in the analysis for the thyroid disorder in CKD population. In this point, our study has the strong point since 24-h urine protein excretion was applied. In addition, participants enrolled in the study are widely distributed into CKD stage 1–5, which may represent the broad CKD population.In the point of the association between UP and hypothyroidism, it has been reported that a semi-quantitative measure of proteinuria by urine dipstick is not associated with hypothyroidism20, which result in the reduction of the serum thyroid hormone levels21. Nephrotic range proteinuria, but not non-nephrotic range proteinuria, can be a risk factor for hypothyroidism because the heavier proteinuria may lead more loss of the serum thyroid hormone levels. Our analysis also clarified that nephrotic range proteinuria with DM may not be a risk factor for hypothyroidism, suggesting that the effect of proteinuria on thyroid function depends on the etiologies of CKD. In addition to the effect of heavy proteinuria on hypothyroidism, some causes of NS, such as amyloidosis, systemic lupus erythematosus and mixed connective tissue disease, may lead to hypothyroidism as their possible complications39. Therefore, hypothyroidism under NS status needs to be differentiated carefully.The participants with NS showed significantly lower FT4 and FT3 levels as well as higher TSH levels, compared to the participants without NS, indicating that heavy proteinuria, but not mild-moderate proteinuria, may be associated with hypothyroidism. It is reported that proteinuria may cause urinary loss of thyroid hormones bound to the various binding proteins such as thyroxine-binding globulin (TBG), albumin, prealbumin, and transthyretin43, the appropriate diagnosis of hypothyroidism, followed by the appropriate treatment may delay CKD progression. Our study highlighted the importance of the routine evaluation of thyroid function in CKD patients, especially for the patients with CKD stage 3–5 and/or with nephrotic range of proteinuria.Since it has been reported that THRT improved kidney function and slower the decline in kidney function in CKD patients with hypothyroidism14. Considering high positivity of these antibodies in CKD patients with hypothyroidism, it might be reasonable to include patients with these autoantibodies in the present study.There are several limitations in this study. First, the sample size is relatively small in a single university hospital and the participants are mainly hospitalized for education or examination, thus there may be a selection bias. Therefore, it may not represent the CKD population. Nevertheless, considering the patient population enrolled (the highest population is CKD stage 3), it may represent the whole CKD population. Second, this is a retrospective and cross-sectional study, thus the causal relationship between hypothyroidism and CKD cannot be established. Further prospective study is required to clarify the causal relationship. Lastly, thyroid autoantibody or other autoimmune antibodies were not evaluated in this study. Therefore, the possibility of a common underlying autoimmune process related to hypothyroidism cannot be excluded. Nevertheless, it is reported that CKD patients with overt hypothyroidism had 37.5% positive in anti-thyroglobulin antibody (Tg-Ab) and 40% in anti-thyroid peroxidase antibody (TPO-Ab)In conclusion, we re-emphasized the important association between hypothyroidism and CKD. Our study revealed that decreased kidney function and nephrotic range proteinuria, not mild-to-moderate levels of proteinuria, are the independent risk factor of hypothyroidism. Appropriate analysis of thyroid function and appropriate treatment for hypothyroidism in CKD patients are recommended to slow the progression of CKD, and at least a routine analysis of thyroid function in CKD patients with decreased kidney function  or nephrotic range proteinuria would be recommended.Supplementary Information.


Enhanced Recovery After Surgery (ERAS) protocols are increasingly used in the perioperative period around the world. The concept of goal-directed fluid therapy (GDT) is a key element of the ERAS protocols. Inadequate perioperative infusion therapy can lead to a number of complications, including the development of an infectious process, namely surgical site infections, pneumonia, urinary tract infections. Optimal infusion therapy is difficult to achieve with standard parameters , so there are various methods of monitoring central hemodynamics – from invasive, minimally invasive to non-invasive. The latter are increasingly used in clinical practice. The current evidence base shows that perioperative management, specifically the use of GDT guided by real-time, continuous hemodynamic monitoring, helps clinicians maintain a patient’s optimal fluid balance. The manuscript presents the analytical data, which describe the benefits and basic principles of perioperative targeted infusion therapy based on central hemodynamic parameters to reduce the risk of complications. Enhanced Recovery After Surgery (ERAS) protocols are increasingly used in the perioperative period around the world , 2. The Monitoring of hemodynamics, volemia, blood loss, hemocoagulation and metabolism is the basis for selecting adequate methods to restore and maintain proper tissue perfusion. The value of monitoring lies in the use of the obtained data to determine the goals of therapeutic effects .2) and oxygen consumption (VO2). Indications may be due to the patient’s condition and the cause of circulatory failure ;–severe sepsis;–shock or severe hypovolemia of any origin;–2 < 60 mm Hg, SaO2 < 90% in a patient on spontaneous breathing or PaO2/FiO2 < 300 mm Hg in a patient on mechanical ventilation);respiratory failure ;–acute renal failure .In addition, there are indications associated with surgery:–major non-cardiac surgery ;–major (combined) interventions on the heart and blood vessels ;–long-term surgical interventions ;–urgent cavitary surgery .Until recently, only invasive hemodynamic monitoring was possible to assess key indicators used in GDT protocols. However, in recent decades there have been restrictions on the use of pulmonary artery catheters (PAC) in the perioperative period. Routine use of a PAC is not recommended in patients with surgical pathology, except for heart surgery –10.The need for monitoring and its volume change over time, taking into account the patient’s condition, risk of complications, stage of the disease and intensive care  11)..11).Perioperative infusion therapy plays an important role in reducing the risk of surgical infections. Both fluid overload and hypovolemia can impair tissue oxygenation, which adversely affects wound healing as well as the development of surgical infections , 13. OptIn recent years, various non-invasive hemodynamic monitoring technologies have been proposed . InnovatAmong modern technologies of non-invasive monitoring, the pulse wave transit time (PWTT) is one of the newest ways to determine the main indicators of hemodynamics. The wide possibilities of this method is little studied. The strong correlation between stroke volume (SV) and PWTT discovered by Japanese scientists at Nihon Kohden was the basis of a formula that allows continuous monitoring of the most important volumetric parameters of the heart (stroke and heart indices). The results of a limited number of clinical trials of this non-invasive and convenient technique are available in the literature. An unequivocal opinion on this method has not yet been formed, but its accuracy and reliability for trend monitoring are considered quite satisfactory .One critical element of all ERAS programs is a protocol known as perioperative goal-directed therapy (PGDT), which helps ensure adequate hydration and maintain euvolemia while avoiding hypervolemia or hypovolemia that can contribute to postoperative complications  21, 22), 2221, 2Fluid management within ERAS protocols should be viewed as a continuum through the preoperative, intraoperative, and postoperative period . The goaTo achieve optimal fluid balance for the surgical patient, PGDT relies on continuous monitoring of a variety of hemodynamic targets, including cardiac output (CO), cardiac index (CI), stroke volume (SV), stroke volume variation (SVV), and pulse pressure variation (PPV).Routine hemodynamic measurements, such as HR and mean arterial pressure (MAP), remain relatively unchanged despite reduced blood flow and are considered insensitive indicators of hypovolemia or changes in CI , 25. As Modern monitoring of perioperative infusion therapy is a personalized correction of hemodynamics, rather than on the basis of generally accepted schemes and rules. Some of the important requirements for monitoring methods are given in Cardiac output monitoring is considered the gold standard for assessing central hemodynamic parameters and infusion response. There are many ways to measure CO, which differ in the degree of invasiveness and continuous or periodic research method –30. The 2), CO, SV, systemic vascular resistance (SVR), right ventricular ejection fraction (RVEF), right ventricular end diastolic volume (RVEDV), pulmonary artery pressure (PAP), pulmonary artery occlusion pressure (PAOP) . Swan-Ganz advanced technology pulmonary artery catheters enable continuous assessment of flow, pressure, and oxygen delivery and consumption. Pulmonary artery catheterization or transpulmonary thermodilution is indicated for the most difficult patients to determine the type of shock. Swan-Ganz catheter allows continuous monitoring of the balance of oxygen delivery and consumption with the following advanced hemodynamic parameters: mixed venous oxygen saturation (SvOe (PAOP) .This technique requires technical skills. Risks of a PAC procedure include arrhythmias, pneumothorax, heart block, lung infarction, perforation of the balloon, thrombosis, air embolism, knotting of the catheter, valvular damage, or infection . In lessTo date, routine use of these methods is not recommended . Specifi2, LIDCOplus) and analysis of arterial pulse wave .Minimally invasive methods based on usage transpulmonary dilution of the indicator  is implemented as follows. The ProAQT sensor is an instantaneous pressure sensor and is inserted into a standard arterial catheter. A continuous blood pressure signal is supplied from the ProAQT sensor to a special monitor. Based on this signal, a number of hemodynamic parameters are determined. The most important thing in shock is the trend of minute blood volume (MBV). Initial calibration, which is calculated on the basis of basic anthropometric indicators , is requ®  is a method developed for continuous CO monitoring based on BP parameters. This technique does not require initial calibration and does not require central venous access. MostCare® requires access to one of the peripheral arteries. The technology is based on the principle that a change in pressure changes the diameter of the vessel and, as a result, changes the volume of blood passing through its cross section. Variables such as LV contractility, pulse wave parameters, arterial wall elasticity, flexibility and peripheral resistance of small arterioles are closely interrelated and evaluated by the system. Thus, the flow gives the area under the curve, and then the formula is calculated by CO  and a processing/display unit . The sensor is a transducer that preprocesses and sends a signal to both a cardiovascular monitor  and the Vigileo monitor. The processing unit applies a proprietary algorithm to the digitized wave, and reports CO, CI, SV, stroke volume index (SVI) and SVV. If a central venous pressure catheter has been placed, its signal can be interfaced with the Vigileo, allowing for the calculation of SVR and SVR index (SVRI). When used with a central venous oximetry catheter, the Vigileo also provides continuous central venous oxygen saturation (ScvO2).Potential weaknesses of the system include possible inaccuracy in the presence of arterial wave artifact, compromise of the arterial catheter, aortic regurgitation, intense peripheral vasoconstriction and irregular pulse.The Acumen IQ Sensor with Acumen hypotension prediction index (HPI) software (Edwards Lifesciences Ltd) is designed to predict the chance of an individual having a hypotensive event in surgical and non-surgical settings. A hypotensive event is defined as MAP of less than 65 mmHg, that exceeds a cumulative length of 15 min. The sensor attaches to any existing radial arterial line and is used to automatically calculate key parameters every 20 s. The parameters include HPI, contractility , afterload , CO, pulse rate, SV, SVV, MAP, CI, PPV and SVR. HPI requires a good-quality arterial line waveform. To the extent that the waveform is damped or varies consequent to changes in patient position or other mechanical issues, predictions may be compromised.Non-invasive monitoring is increasingly used in routine practice compared to invasive techniques, due to the lack of complications and the need for special technical skills, technical support .Pleth variability index  – variations in the perfusion index during the respiratory cycle (Masimo Rainbow Pulse CO-Oximetry technology) . A numbe2 rebreathing technique technology (NICO) introduced by Novametrix allows the measurement of CO by analyzing the CO2 content at the end of exhalation. The accuracy of NICO technology is lower compared to invasive techniques, also there is a dependence on ventilation and gas exchange. Partially reversible breathing is used to implement this technology. The monitor processor analyzes 4 parameters: the amount of carbon dioxide released during normal and reversible breathing and the content of carbon dioxide in the arterial blood during normal and reversible breathing  is a non-invasive device that is fixed on the patient’s finger. It allows continuous measurement of blood pressure. In addition, the device is able to calculate such parameters as CO, SVR, stroke volume, MAP.The device has a so-called Heart Reference Sensor (HRS), which automatically compensates for changes in hydrostatic pressure due to differences in height between the finger and the heart. HRS compensates for changes in the patient’s arm position during any procedure or during patient movement .Impedance cardiography (ICG) is a non-invasive method that uses changes in impedance of the chest to assess hemodynamic parameters, including CO. Studies of ICG have reported conflicting results and are difficult to compare, since they have been performed using devices of different generations in patients with different characteristics, while also using different equations . The tecElectrical Cardiometry (EC) is a monitor for non-invasive method of measuring continuous CO monitoring based on measurement of thoracic electrical bioimpedance. Bioimpedance CO is based on the principle that cyclical increases in blood volume in the great vessels, as well as alignment of red blood cells (RBCs) in the thoracic aorta resulting from increased velocity, cause concomitant decreases in the electrical impedance in the chest. An alternating current of low amplitude is introduced and simultaneously sensed by electrodes placed around the neck, and laterally on the thorax to measure thoracic electrical bioimpedance. Changes in thoracic bioimpedance are induced by ventilation and pulsatile blood flow. EC is often confused with the traditional bioimpedance technology most commonly known as ICG. Though both methods use sensors placed on the thorax, traditional bioimpedance or ICG methods rely on the assumption of periodical volumetric changes in the aorta to determine SV and CO. ICG attributes the steep increase in the conductivity waveform to a volumetric expansion of the aorta during systole, while EC contributes the increase in conductivity to the orientation change of the RBCs to determine the velocity of the blood flow. EC method may be used for measuring cardiac output in a wide spectrum of diseases and patient populations including neonates and children, while ICG is limited to relatively healthy adults. The disadvantage associated with electric cardiometry is that the parameters are not available during electrical interference (electric cautery) .The EC technology is utilized on the Aesculon and ICON device .Echocardiography, as a non-invasive or semi-invasive method for the assessment of cardiac anatomy and function, is favored in clinical practice. The methods commonly used for echocardiography include transthoracic echocardiography (TTE), transesophageal echocardiography (TEE) and ultrasonic CO monitor (USCOM). TEE or TTE echocardiography can provide immediate point-of-care assessment of acute hemodynamic changes in selected patients. Indications for TEE include the evaluation of cardiac and aortic structure and function in situations in which the findings will alter management and the results of TTE are non-diagnostic or TTE is deferred because there is a high probability that results will be non-diagnostic. Situations in which TTE may be non-diagnostic include, but are not limited to, detailed evaluation of the abnormalities in structures that are typically in the far field, such as the aorta and the left atrial (LA) appendage; evaluation of prosthetic heart valves; evaluation of native valve masses; evaluation of paravalvular abscesses ; and various uses in critically ill patients. Transthoracic echocardiographic image quality may be compromised in patients on ventilators, those with chest wall injuries, obese patients, and those unable to move into the left lateral decubitus position.Contraindications include esophageal disease with known stricture, diverticuli, varices or tumor, prior esophageal or stomach surgery, perforated viscus, or an uncooperative patient. Relative contraindications include cervical spine disease, hiatal hernia, coagulopathy, prior chest radiation, or facial or airway trauma. In addition to the estimation of CO , Doppler echocardiographic examination can provide an indication of cardiac function because it allows visualization of the cardiac chambers, valves, and pericardium . DopplerThe USCOM ultrasonic cardiac output monitor  provides non-invasive transcutaneous measurement of CO. The flow profile is obtained by using a transducer (2.0 or 3.3 MHz) placed on the chest in either the left parasternal position to measure transpulmonary blood flow or the suprasternal position to measure transaortic blood flow. This flow profile is presented as a time–velocity spectral display showing variations of blood flow velocity with time. The CO is then calculated from the equation :CO = heart rate × stroke volumewhere the SV is the product of the velocity time integral (VTI) and the cross-sectional area (CSA) of the chosen valve.The USCOM monitor is limited to cardiac output measurement and gives no indication of other hemodynamic variables, such as pressure measurements, vascular resistance or stroke work calculations. In addition, it does not provide the means to measure mixed venous oxygenation.Ultrasonic transesophageal Doppler  provide continuous assessment of CO by measuring the linear velocity of blood flow in the aorta. Doppler techniques are non-invasive and relative simple in application, but the results are approximate and depend on the position of the esophageal sensor and dysphagia may occur. Unstable hemodynamics and a narrow ultrasound window increase the measurement error. In addition, ultrasound techniques require a specially trained specialist .CardioQ and CardioQ-ODM use Doppler ultrasound to monitor HR and intravascular fluid volume. CardioQ and CardioQ-ODM monitors are designed for use with a number of Doppler esophageal probes for Deltex Medical.Recently, a non-invasive method of CO monitoring – estimated continuous cardiac output (esCCO), based on the assessment of pulse wave transit time (PWTT), has become available in clinical practice. The esCCO system defines PWTT as the time interval between the moment of appearance of the R wave on the electrocardiogram (ECG) and the beginning of the pulse wave on the plethysmogram of the pulse oximeter. This interval includes three points of measurement of time intervals : PEP – fMeasured PWTT, BP and HR are used to calculate CO according to the following formula :CO = K × (α × PWTT + β) × HR,where CO – cardiac output; α is a constant that has been determined in previous clinical trials of esCCO technology; β is a variable that is a derivative of pulse pressure; K – calibration factor based on the biometric characteristics of the patient ; PWTT – pulse wave transit time; HR – heart rate.Estimated continuous cardiac output technology is effective because it does not require to use of any additional sensors or specially trained personnel . EsCCO tHowever, the expressed disturbances of peripheral microcirculation, the presence of clinically significant cardiac arrhythmias, significant damage to peripheral arteries, severe heart valve dysfunction are the limitations of the method .All monitoring systems have unique characteristics in terms of accuracy, reliability, measurement accuracy, stability and reliability . PhysiciIndications for the different methods of CO assessment during the perioperative hemodynamic management of surgical patients are shown in To assess the susceptibility to infusion, the simplest method is considered to be the test with passive raising of the legs by 30–45° to assess the response of CO and BP . VentilaThe response to infusion therapy using esCCO method can be assessed as follows. If infusion bolus of 500–1,000 ml caused a significant increase in cardiac output (4–6 l/min) and stroke volume (60–100 ml), the patient was considered susceptible to volemic therapy and continued to fill the circulating volume . In the 2 in damaged tissues, which is associated with an increased incidence of perioperative complications  while allowing patients to take solid food earlier, become more alert, and start walking sooner after surgery, ultimately reducing hospital LOS , 22.The goal of targeted perioperative infusion therapy is to optimize the balance between delivery and oxygen consumption. Tissue hypoxia may occur with normal hemodynamic values such as MAP, CVP, and HR. Tissue hypoperfusion contributes to postoperative complications and increases the incidence of mortality, which requires adequate methods of enhanced hemodynamic monitoring and implementation of algorithms for targeted infusion therapy.It is known that surgical patients following the principles of targeted infusion therapy during the perioperative period had a low risk of developing infectious complications, namely surgical site infections (SSIs), pneumonia and urinary tract infections (UTIs). There were no convincing data on catheter-related bloodstream infections .Surgical patients carry a high overall risk of hospital-acquired infections (HAIs), mainly because SSIs occur in addition to non-surgery-specific infections . Despite2 plays an important role. It is known that a sufficient level of oxygen in the tissues promotes wound healing and increases resistance to infections , the patient (immune status) and the nature of the surgery (duration of the operation and the volume of damaged tissue). The infectious process occurs due to an unbalanced relationship between bacterial load and patient resistance. During the operation, the patient’s susceptibility to infection increases, the risk of infection increases due to damage to the integrity of the skin and mucous membranes, impaired microbicidal activity of immune cells . In thisfections , 88–90. fections  depend ofections . It is pfections . Therefofections .2 can be improved with GDT, which reduces the incidence of postoperative infectious complications. In the perioperative period, hypovolemia and decreased CO cause musculoskeletal and splanchnic vasoconstriction, which causes hypoperfusion and tissue hypoxia (Oxygen delivery DO hypoxia –97. This hypoxia . In addi hypoxia , 99.2 by maintaining or increasing cardiac output. This preserves the microbicidal function of immune cells and the protection of organs that are particularly sensitive to perioperative hypoperfusion (Goal-directed fluid therapy aims to optimize DOerfusion , avoidinerfusion .2 have a potential pathogenetic role (Pathophysiological mechanisms of postoperative pneumonia are complex –103. Inttic role –107.2 and prevents HAIs. Therefore, hemodynamically controlled principles of infusion therapy should be followed, especially in high-risk surgical patients (In surgical patients, GDT optimizes DOpatients –110 withThe current evidence base shows that perioperative management, specifically the use of PGDT guided by real-time, continuous hemodynamic monitoring, helps clinicians maintain a patient’s optimal fluid balance. Meta-analyses of published studies focused on major abdominal surgery show that applying ERAS practice guidelines reduces postoperative complications by up to 50% and hospital LOS by 2.5 days , 112.Goal-directed fluid therapy is a key element of the ERAS protocols, which can only be achieved through high-quality monitoring. Among many techniques non-invasive hemodynamic monitoring is now evolving rapidly and is a highly accurate tool for clinical use. The use and further study of this method of hemodynamic monitoring can improve the understanding of the mechanisms underlying the systemic capillary leakage syndrome in critical conditions, and serve as a basis for developing new algorithms for GDT. Modern non-invasive technologies, like esCCO, allow to assess the heart failure as a component of circulatory failure and its targeted correction by optimizing the preload, postload and inotropic function of the heart, which meets most of the requirements for adequate continuous hemodynamic monitoring , 113.Infusion therapy improves hemodynamic status by increasing SV and CO. CO changes of at least 10–15% are used to determine a positive response to fluid resuscitation after 250 to 500 ml of fluid infusion , 115. IfGoal-directed fluid therapy provides adequate systemic oxygenation, protects against ischemic-reperfusion injury and reduces the incidence of SSIs, respiratory infections and UTIs after surgery. Hemodynamically based perioperative optimization of infusion therapy reduces postoperative mortality and morbidity in surgical patients at high risk of infectious complications , 69, 70.Therefore, it is necessary to continue research on aspects of perioperative GDT based on methods of monitoring central hemodynamics to reduce the risk of complications and improve the treatment outcomes of surgical patients.DD, ON, MM, and BL: investigation and resources. DD and MM: writing – original draft preparation. DD, ON, and MM: writing – review and editing. DD and ON: supervision. All authors have read and agreed to the published version of the manuscript.


Obstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.We retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.A total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability , and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaOWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society. Obstructive sleep apnea (OSA) is a sleep disorder characterized by intermittent hypoxemia, autonomic fluctuation, and sleep fragmentation. As of 2019, the prevalence of OSA aged 30–69 years (men and women) in China has reached 24.2%, ranking first in the world , 6, and Hypertension that is primarily caused or exacerbated by OSA is called OSA-related hypertension after excluding other definite secondary etiologies  . In addiAs for the general population, the reference method for blood pressure testing is primarily an in-office measurement. However, this diagnostic method is unreliable in the OSA population because of the specific characteristics of OSA-related hypertension. Previous studies have shown that among OSA patients, masked hypertension was found in 30% of patients, and white-coat hypertension was found in approximately 33% of patients –13. It mMachine learning (ML) has been widely developed and used in the medical field because of its remarkable performance in recent years. It can extract information from complex and non-linear data, establish models through science, reveal hidden dependencies between factors and diseases in the big data environment, and help clinicians better understand the diseases . EspeciaTimely blood pressure screening and early accurate identification of OSA-related hypertension are crucial in minimizing the associated negative health effects. Regrettably, no ML models are available to predict the risk of OSA-related hypertension. In this study, we aimed to develop ML-based prediction models for OSA-related hypertension based on available clinical data from patients to identify high-risk patients. In addition, we used Shapley additive explanations (SHAP) , a methoThis is a retrospective observational study. It retrospectively included the OSA patients admitted to the Department of Otorhinolaryngology—Head and Neck Surgery of the Second Affiliated Hospital of Xi’an Jiaotong University between October 2019 and December 2021. All study subjects underwent nighttime polysomnography or home sleep apnea testing and blood pressure monitoring, additionally, cardiologists assessed their blood pressure. OSA was diagnosed on the basis of apnea–hypopnea index (AHI) ≥ 5 events per hour through polysomnography (The inclusion criteria were as follows: (1) patients with age ≥ 18 years, (2) patients with AHI ≥ 5 events per hour, and (3) patients who have not received OSA-related treatment in the past. The exclusion criteria were as follows: (1) patients with incomplete baseline data; (2) patients with disease potentially affecting blood pressure regulation, such as multiple organ dysfunction syndrome, uremia, severe cardiac heart failure, renal, or cardiac transplantation; (3) patients with the most common causes of secondary hypertension, namely, renal parenchymal disease, renovascular diseases, coarctation of the aorta, Cushing’s syndrome, primary hyperaldosteronism, pheochromocytoma, hyperthyroidism, and hyperparathyroidism; (4) pregnant women; (5) patients with history of snoring shorter than the duration of hypertension; and (6) patients who were diagnosed with central sleep apnea .This study was approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University . In addition, all patients who participated in the research provided informed consent. The inspection items and processes involved in this study are in line with the Declaration of Helsinki.2/10), and percentage of time of SaO2 < 90%/10 (CT90/10).Twenty-seven relevant clinical indicators were collected, and overall, the 27 candidate variables included were as follows: (1) demographic characteristics, namely, gender, heart disease family history of hypertension, diabetes, hypothyroidism, body mass index (BMI), waist circumference, neck circumference, and age/10; (2) lifestyle behaviors, namely, drinking, smoking, high-salt diet, high-fat diet, poor sleep quality, sedentariness, emotionally stable, mental stress, and smoking amount; and (3) OSA-related medical history and indicators, namely, memory decline, inattention, Epworth Sleepiness Scale , course By comparing the clinical characteristics of the hypertension and non-hypertension groups, the risk factors for predicting OSA-related hypertension were analyzed using the univariate analysis, and they were incorporated into machine learning as characteristic variables. Additionally, they were also used in the multivariate logistic regression analysis to obtain independent predictors associated with OSA-related hypertension.All patients were randomly divided into a training set for constructing the predictive model and a test set for the model validation at a ratio of 7:3. The following six representative supervised ML algorithms were used for model construction in the training dataset: adaptive boosting, GBM, multilayer perceptron, bootstrapped aggregating, logistic regression, and extreme gradient boost –24. DuriShapley additive explanations (SHAP) is a model-agnostic explanation technique based on cooperative game theory that helps interpret the results from a predictive model. The interpretation is based on quantifying the SHAP value for each feature, representing the contribution of a feature to the predicted risk of OSA-related hypertension , 26. ForP < 0.05 was considered statistically significant.All analyses and random division of training and validation sets were performed with R software (version 3.6.0). Continuous variables were represented as the median , whereas categorical variables were represented as numbers (n) and proportions (%). The Wilcoxon rank-sum test compared the two groups’ differences for continuous variables, and categorical variables were evaluated using the chi-squared test. Logistic regression analysis was used to analyze the relationship between various predictor variables  and an outcome that is binary (dichotomous). The Python programming language (version 3.8) was also used to develop and evaluate ML models and design network calculators. For model interpretation, the SHAP was implemented using the Python Shap package. P < 0.05). For the lifestyle behavior variables, high-salt diet, poor sleep quality, and smoking amount were significant variables . For OSA-related medical history and indicators, memory decline, Epworth Sleepiness Scale, course of snoring, course of choking, AHI, OAI, minimum SaO2/10, and CT90/10 were all significantly different between the two groups .After the screening process, a total of 1,493 OSA patients were eligible for the study . The basP < 0.05 in the univariate analysis were selected for multivariate logistic regression analysis to identify the independent risk factors of OSA-related hypertension patients .Variables with a patients , and allhttps://shimunana-true-ml-vmz425.streamlitapp.com/  values not only could show the contribution of each feature to the final prediction but also could effectively clarify and explain model predictions for individual patients. We provided two living examples to illustrate the role of the SHAP method in describing the machine learning model: a 46-year-old female patient who was diagnosed with OSA but with normal blood pressure and a 54-year-old male patient who was diagnosed with OSA-related hypertension . The conThe present study is the first study to assess the predictive performance of several machine learning algorithms for OSA-related hypertension, obtain a GBM model that can be used to predict OSA-related hypertension clinically, and explain the model. GBM is a commonly used ML algorithm with satisfactory performance in managing large and complex non-linear datasets and avoiding overfitting . Subsequ2/10 were significant independent risk factors for OSA-related hypertension, which converges with previous research. Pan et al. found that among police officers in southern China, the prevalence of OSA-related hypertension was associated with the age of the patients. However, their study population was small and occupation-specific  might be better predictors of OSA-related hypertension than AHI.Interestingly, in addition to identifying several known risk factors, multivariate logistic regression and SHAP analysis also found that CT90/10, a variable that had been overlooked in previous cardiovascular studies, also plays an important role in OSA-related hypertension. Previous studies have shown a significant association between CT90 and Coronary Artery Calcium, cerebral small vessel disease and diabetic nephropathy –32, but 2/10 has the highest predictive value for OSA-related hypertension. The discrepancy between multivariate logistic regression and SHAP values can be explained by the prevalence of a variable. Odds ratios were calculated only for patients associated with this variable, but the mean SHAP value for all patients was calculated. In addition, the average SHAP value was further used to evaluate the importance of features and rank them. Hence, variables with low impact and high prevalence will have low odds ratios but high SHAP values.Notably, the risk for OSA-related hypertension is increased most by family history of hypertension in the multivariate logistic regression, followed by age/10. However, the SHAP analysis showed that minimum SaOIn our study, full integration of the standard clinical variables with Polysomnography parameters was performed during the construction of the ML model. The model can thus predict OSA-related hypertension risk stratification for the patient, using all relevant covariates rather than individual measures. Our approach was also validated with repeated 10-fold cross-validation to provide a robust estimation of prediction accuracy with minimal bias. The six models performed well, with AUC ranging from 0.698 to 0.873 and sensitivity from 0.353 to 0.713 in the test dataset. And the GBM prediction model with the highest AUC, accuracy, and sensitivity was identified as the final model for this study and clinical use. The GBM model with 0.873 AUC and 0.713 sensitivity proves good discrimination and stability. What’s more, we introduce the Shapley value to explain the GBM model. SHAP is a model-independent interpretation technique that interprets black box models globally and locally, and can provide a rational explanation for the prediction, which can significantly enhance the confidence of clinicians in the model.However, despite our best efforts to improve it, this study still has some limitations. First, this is a single-center retrospective study, and the performance of machine learning algorithms may vary for datasets with different distributions of patient characteristics and various institutions. Therefore, more patients from multiple sources are required to validate our model’s robustness and repeatability in the future. Second, the undesirable sensitivity may be that the ML algorithm learns from input features, and some discreet relationships may have been lost because of unknown or disregarded features not registered by doctors. In the future, we will conduct prospective validation based on this model, continue to explore crucial risk factors for OSA-related hypertension, and modify the model further to improve the accuracy and reliability of the GBM prediction model.We established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that the GBM model performs best among the six ML models. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and reduce the burden on society.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of The Second Affiliated Hospital of Xi’an Jiaotong University . Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.YS, SW, and XR designed the research. XC and LM wrote the manuscript. YF, YY, HL, and LY collected the data. YX, ZC, and CZ performed data curation. YZ, LM, and WL analyzed and processed the data. XR directed the research and the guarantor of the manuscript and takes full responsibility for the integrity of the work, from its inception to the published manuscript. All authors reviewed the results and approved the final version of the manuscript.