Higher concentrations of one non-esterified fatty acid were associated with higher diastolic blood pressure throughout pregnancy . Using penalized regression, we identified 12 individual early-pregnancy amino-acids, non-esterified fatty acids, diacyl-phosphatidylcholines and acyl-carnitines and the glutamine/glutamic acid ratio, that were jointly associated with larger changes in systolic and diastolic blood pressure from first to third trimester. These metabolites did not improve the prediction of gestational hypertensive disorders in addition to clinical markers. In conclusion, altered early pregnancy serum metabolite profiles mainly characterized by changes in non-esterified fatty acids and phospholipids metabolites are associated with higher gestational blood pressure throughout pregnancy within the physiological ranges. These findings are important from an etiological perspective and, after further replication, might improve the early identification of women at increased risk of gestational hypertensive disorders.Blood pressure development plays a major role in both the etiology and prediction of gestational hypertensive disorders. Metabolomics might serve as a tool to identify underlying metabolic mechanisms in the etiology of hypertension in pregnancy and lead to the identification of novel metabolites useful for the prediction of gestational hypertensive disorders. In a population-based, prospective cohort study among 803 pregnant women, liquid chromatography—mass spectrometry was used to determine serum concentrations of amino-acids, non-esterified fatty acids, phospholipids and carnitines in early pregnancy. Blood pressure was measured in each trimester of pregnancy. Information on gestational hypertensive disorders was obtained from medical records. Higher individual metabolite concentrations of the diacyl-phosphatidylcholines and acyl-lysophosphatidylcholines group were associated with higher systolic blood pressure throughout pregnancy -adjusted  Gestational hypertensive disorders are among the most common causes of maternal and fetal morbidity and mortality worldwide . GestatiTherefore, in a subsample of a population-based prospective cohort study, we first examined the associations of early-pregnancy serum metabolites with blood pressure development throughout pregnancy. We used a targeted metabolomics approach for amino acids, carnitines, fatty acids and phospholipids. These metabolites have been associated with cardiovascular diseases, play an important role in lipid metabolism, oxidative processes, inflammatory responses and immunity, and might therefore be involved in the pathophysiology of blood pressure development throughout pregnancy and gestational hypertensive disorders ,11,12,13This study was embedded in the Generation R Study, a population-based prospective cohort study from fetal life until adulthood in Rotterdam, The Netherlands. Study approval was obtained by the Medical Ethical Committee of the Erasmus MC, University Medical Center, Rotterdam (MEC 198.782/2001/31) . WrittenAt enrollment in the study, maternal non-fasting blood samples were obtained at a mean gestational age of 13.0 weeks (standard deviation ± 1.7 weeks) by research nurses . Blood sIndividual metabolites were clustered in the following metabolite groups, based on their chemical structure: AA, NEFA, PC.aa, PC.ae, Lyso.PC.a, Lyso.PC.e, SM, free Carn and Carn.a . As we eSystolic and diastolic blood pressure were measured in early pregnancy , mid pregnancy  and late pregnancy  using a validated Omron 907 automated digital oscillometric sphygmomanometer  . ParticiWe obtained information on maternal characteristics related to blood pressure. At enrollment, age was assessed, and weight and height were measured without shoes. We obtained information on parity (nulliparous or multiparous), educational level  and smoking status , folic acid supplementation use (yes/no), obstetric history of gestational hypertensive disorders (yes/no), preexisting hypertension (yes/no) and hypertensive disorders in first-degree family members (yes/no) through questionnaire. p-values were obtained to correct for multiple hypothesis testing, which is a widely used approach for multiple testing in omics studies [p-value of <0.05 statistically significant. First, we square root transformed metabolites to obtain normal distributions . We stan studies . We consglmnet) [Next, we aimed to identify metabolites for the prediction of gestational hypertensive disorders. We used the outcome blood pressure changes from first to third trimester, as larger increases are associated with higher risks of gestational hypertension and preeclampsia. We did not use the outcomes gestational hypertension or pre-eclampsia due to lack of power for model development in our population-based study sample. We used a penalized regression method (lasso regression) to select a combination of individual metabolites that were jointly associated with systolic and diastolic blood pressure change from first to third trimester, in addition to the selected confounders (R package glmnet) . Lasso rglmnet) . We perfglmnet) . We assep-values for all analysis were presented. The analyses were performed using the Statistical Package for the Social Sciences version 25.0  and R Statistical Software version 4.1.0 . Missing values were imputed using multiple imputation by the fully conditional specification method and pooled results from 5 imputed datasets were reported . The per2   4.1 years), and the median pre-pregnancy body mass index was 22.6 kg/m2 kg/m2) . Most wop-values < 0.05). The strongest associations were present for higher early pregnancy Lyso.PC.a.C14.0 and higher blood pressure in first and second trimester  and 8.75 mmHg  increase in systolic blood pressure per SDS increase in metabolite concentration, respectively). In total, higher concentrations of 28 early-pregnancy serum metabolites of the groups AA, Carn.a, LysoPC.a, NEFA, PC.aa and PC.ae groups and of the metabolite ratio PC.aa:PC.ae were associated with higher systolic blood pressure in first trimester. Higher concentrations of eight Lyso.PC.a and PC.aa metabolites were associated with higher systolic blood pressure in second trimester, and of eight NEFA, Lyso.PC.a, PC.aa and SM metabolites with higher systolic blood pressure in third trimester . We found no associations of early-pregnancy serum concentrations of free Carn, PC.ae or Lyso.PC.e metabolites with systolic blood pressure in pregnancy.p-value < 0.05) . We found no associations of early-pregnancy serum concentrations of Lyso.PC.e metabolites or free carnitine with diastolic blood pressure in pregnancy. Additionally adjusting for family history of hypertension in general yielded similar results to the main analysis  . In totaanalysis . Only thanalysis .p-value DeLong 0.516)  .In this prospective cohort study, we observed that altered early-pregnancy PC.aa and Lyso.PC.a metabolites were associated with higher systolic blood pressure development throughout pregnancy, whereas higher concentrations of NEFA.24.5 were associated with a higher diastolic blood pressure throughout pregnancy. To a lesser extent, changes in several AA and Carn.a metabolite concentrations were associated with higher blood pressure in pregnancy. We identified 12 individual early-pregnancy AA, NEFA, PC.aa and Carn.a metabolites and 1 metabolite ratio representing reduced Krebs cycle anaplerosis, that were jointly associated with larger increase in diastolic and systolic blood pressure from first to third trimester. These metabolites improved the prediction of variation in blood pressure throughout pregnancy, but did not improve the prediction of gestational hypertensive disorders in addition to clinical markers.Blood pressure development plays a major role in both the etiology and prediction of gestational hypertensive disorders. The identification of metabolic changes in early-pregnancy serum associated with higher blood pressure throughout pregnancy enables more insight into underlying metabolic changes and mechanisms of hypertension in pregnancy. Previous studies investigated the association of metabolite profiles and blood pressure in non-pregnant populations. A systematic review compared metabolite profiles of patients with essential hypertension with healthy controls . Of the No previous studies assessed the association of early-pregnancy metabolites with blood pressure development throughout pregnancy. We observed the most consistent associations of higher early-pregnancy serum concentrations of PC.aa.C38.3, PC.aa.C40.4, Lyso.PC.a.C14.0, Lyso.PC.a.C16.1, Lyso.PC.a.20.3 and NEFA.C24.5 with higher blood pressure development throughout pregnancy. Previous studies have reported associations of PC.aa, Lyso.PC.a and NEFA metabolites with cardiovascular risk factors and cardiovascular disease in adults ,46. PhosOur population-based study enabled the identification of changes in maternal early-pregnancy metabolome associated with higher blood pressure in pregnancy in the general population. Our results show that alterations in serum metabolites involved in lipid metabolism, energy processes, oxidation, and inflammatory processes are associated with gestational blood pressure changes, even within the physiological ranges. Our results should be considered hypothesis generating and are important from an etiological perspective. They provide novel insight into underlying biological mechanisms, potential future markers and intervention targets for gestational hypertensive disorders. Future large studies among multi-ethnic populations are needed, to confirm the associations of the metabolites with higher blood pressure in pregnancy. Hereafter, the identified metabolites should be taken forward to further investigate their diagnostic and prognostic value for gestational hypertensive disorders, in addition to common clinical characteristics. This study benefitted from the prospective data collection from early pregnancy onwards, allowing repeated blood pressure measurements throughout the entire pregnancy. The low prevalence of gestational hypertensive disorders in our study sample might have reduced the ability to detect associations. Furthermore, due to the low prevalence in preeclampsia, we could not distinguish between early- and late-onset. Therefore, further studies including a larger number of women with gestational hypertensive disorders are needed. The metabolomics analyses were only available for a subsample of the Generation R cohort, which consisted of Dutch, relatively high educated and healthy participants. This might have affected the generalizability of our findings. We used a targeted metabolomics approach, allowing us to optimize the quantification of the metabolites of interest. However, relevant biological pathways might have been missed. Although we adjusted for many potential confounders, residual confounding cannot be excluded due to the observational nature of the study. Maternal serum metabolites concentrations in our study were all measured in early pregnancy, at a mean gestational age of 13 weeks. We had no information on metabolic profiles of mothers in the preconception period available. Therefore, in our study, we were unable to distinguish which metabolic alterations associated with higher blood pressure in pregnancy were specifically pregnancy-related and which were not. However, metabolic adaptations, such as changes in insulin resistance and glucose levels, already originate in first trimester of pregnancy, and so might gestational hypertensive disorders . By exclAltered early pregnancy serum metabolite profiles mainly characterized by higher concentrations of metabolites from the PC.aa, Lyso.PC.a and NEFA groups, and to a lesser extent the AA and Carn.a groups, are associated with higher gestational blood pressure development within the physiological ranges. Those metabolite groups are involved in lipid metabolism, energy processes, oxidation, and inflammatory processes. Our findings are important from an etiological perspective and, after further replication in large multi-ethnic populations including more women with gestational hypertension and preeclampsia, might improve the early identification of women at increased risk of gestational hypertensive disorders.


High blood pressure is the number one killer in the world. About 1.5 billion people suffered from hypertension in 2010, and these numbers are increasing year by year. The basics of the management of high blood pressure are described in the Canadian, American, International and European guidelines for hypertension. However, there are similarities and differences in the definition, measurement and management of blood pressure between these different guidelines. According to the Canadian guidelines, normal blood pressure is less than 140/90 mmHg (systolic blood pressure/diastolic blood pressure). The AHA and ESC estimate normal blood pressure to be less than 120/80 mmHg (systolic blood pressure/diastolic blood pressure). Regarding treatments, the AHA, ISH and ESC are also in agreement about dual therapy as the first-line therapy, while Canadian recommendations retain the idea of monotherapy as the initiation of treatment. When it comes to measuring blood pressure, the four entities agree on the stratification of intervention in absolute cardiovascular risk. High blood pressure (HBP) is the leading preventable cause of cardiovascular death in the world. Stroke is the second cause of death and third cause of disability in the world; however, HBP is the primary risk factor for stroke . In 2010Four international recommendations on high blood pressure were published in 2017, 2018 and 2020, and form the basis for hypertensive patient management: the Canadian and American recommendations published in 2017 ,5, the EWhile all four societies agree that high blood pressure is a major public health problem, it is not defined in the same way by each of them. The definition and objectives of treatment are different according to international recommendations. Lowering the definition of high blood pressure in the United States mechanically increased the number of hypertensive patients from 32% of the adult population to 46% , which cManagement also differs for diabetic patients. All guidelines agree that diabetes is a strong risk modifier, and that there is little evidence of the optimal level of BP being attained. Although high BP aggravates chronic kidney disease progression and represents an important target for treatment, at the time of writing of the guidelines, strict BP control was more harmful than protective, mainly based on the ACCORD trial . AccordiAn essential point concerns the measurement of BP. The measurement taken in the doctor’s office remains the reference for the four recommendations, against all scientific logic, but with pragmatism. Indeed, office BP measurement is universal, and most of the scientific evidence relies on such measures, despite the massive flaws related to white-coat hypertension, masked hypertension, a lack of standardization, and the demonstrated superiority of other modalities  of measurements. No guideline elaborates on the limits of BP monitors, despite alarming reports , with thThe four international recommendations converge on the need to integrate blood pressure into an overall assessment of the patient’s cardiovascular risk, which should help prioritize the timing of pharmacological intervention and its intensity, in line with the position of the Lancet Commission on Hypertension . InterveWeight reduction for overweight patients;Adaptation of the diet;Reduction in salt consumption;Practice of regular physical activity;Reduction in alcohol consumption;Smoking cessation.The management of a hypertensive patient is based on two main axes: non-pharmacological measures and pharmacological measures. The four international recommendations converge on the non-pharmacological management of the patient. The main recommendations are listed below:All of these measures should be recalled at each consultation. On average, the correction of each of these factors causes a drop of 4 to 5 mmHg in systolic pressure. The rules for the implementation of these recommendations are elusive; in particular, there is no incentive that these rules should be prescribed (as a full-fledged treatment). Although symbolic, and with low evidence for efficacy, this recommendation would have been very helpful.For pharmacological management, all of the therapeutic strategies have recently been modified. The main novelty is the first-line introduction of dual therapy in the recommendations of the ESH, ISH and AHA/ACC. This is justified because the combination of two drug classes is about five times more effective in lowering blood pressure levels, compared to doubling the dose of a monotherapy . This alThe choice of drug classes is left free and is based on the three main classes , with indications based on the elective properties of each of the classes and the possible presence of comorbidities or the notion of prior tolerance. Angiotensin-converting enzyme inhibitors (ACEi) and angiotensin 2 receptor antagonists (ARB) have practically identical effectiveness, whether on BP control, total mortality or on the occurrence of cardiovascular events. Both classes are very effective at delaying chronic kidney disease, notably in diabetics, as underlined by the four guidelines. Regarding clinical tolerance, ARBs seem to be better tolerated by patients than ACEis, and lead to fewer discontinuations of treatment due to adverse effects ; howeverThe different international guidelines use slightly different definitions of hypertension, but seem to agree on the therapeutic strategies to be used in the first line. However, they generate many difficulties for the patient. When should you be diagnosed with hypertension? The AHA/ACC recommendations, by lowering the threshold defining arterial hypertension, lead to a considerable increase in the number of affected patients and can put health systems in jeopardy. This might be acceptable in the US, where most of the financial effort is deferred to patients and private insurers, at the risk of further increasing social inequalities . If it iThe four guidelines are very conservative concerning the measurement of BP by not downgrading office BP measurement. This is a major problem, since BP measurement is now increasingly carried out outside the medical office, and out-of-office measurement reclassifies the patient , with major consequences for management. The multiplication of measures without appropriate pedagogy also leads to patient confusion, since they are not in a position to understand the figures delivered to them. The subject is addressed by the recommendations, but is not accompanied by a clear position, which is really a missed opportunity.The therapeutic objectives are also limited to the control of BP, and therefore come up against the methodological difficulties raised when discussing diagnosis. There is a lack of objective criteria for initiating treatment, regulating its uptitration, and judging its effectiveness. The challenge in the coming years will be to find substitution criteria that make it possible to decide when to initiate pharmacological treatment, by combining optimal measurement of BP with HMOD using those new criteria for the uptitration (or reduction) of antihypertensive treatment. HMOD such as left-ventricular hypertrophy, microalbuminuria, and arterial stiffness may be used for this purpose. For the sake of illustration, pulse wave velocity (PWV) could change the way in which we manage our patients. Indeed, PWV reflects all of the cardiovascular risk factors, among them high blood pressure ; it can High blood pressure affects more than 1.5 billion people worldwide and the number is on the rise, especially in LMIC, and is largely due to shifting of the definition of hypertension to lower values. International guidelines use different thresholds to define the hypertensive patient, and all four of them minimize the methodological issues related to BP measurement. The main novelty of the international recommendations is the introduction of fixed-dose association therapies using pharmacologically synergistic drugs during the initiation of treatment, at least for the AHA/ACC and ESH. The semantic shift from hypertension taken in isolation to hypertension fitting into the larger picture of cardiovascular prevention is underway ; however


The risk factors for residual venous thrombosis (RVT) in patients with post-trauma deep vein thrombosis (DVT) are unknown.We evaluated 127 patients with DVT after trauma, all of whom were treated with conventional anticoagulation and assessed for the presence of RVT with venous compression ultrasound (CUS), using an internal diameter of the venous lumen ≥ 4 mm after compression as the criterion.RVT was present in 59 (46%) patients, and complete thrombus dissolution was present in 68 (54%) patients. Among them, mixed proximal and distal thrombosis , diabetes , fibrinogen > 4.145 g/L , the time between detection of thrombus and initiation of antithrombotic therapy > 2.5 days  was an independent risk factor for RVT in patients with posttraumatic DVT.A mixed proximal and distal thrombosis, diabetes mellitus, late initiation of antithrombotic therapy, and high fibrinogen levels increase the risk of RVT in patients with posttraumatic DVT. Therefore, treatment regimens for patients with posttraumatic DVT can be adjusted according to the site of thrombosis, the presence of diabetes mellitus, and the level of fibrinogen, and antithrombotic therapy can be started as early as possible after the detection of thrombosis to prevent the development of RVT and its serious complications. Trauma is the third leading global cause of death . PosttraResidual venous thrombosis (RVT) is a thrombus that is not entirely lysed by the fibrinolytic system and is partially organized or fibrotic after 3 months of deep vein thrombosis (DVT) occurrence , 6. The It is a retrospective single-center study selected from trauma patients admitted to the Third Xiangya Hospital of Central South University from January 2019 to December 2021. Patients admitted for trauma with DVT  were included, which is the recommended minimum duration of anticoagulation . PatientPatients underwent venous compression ultrasound (CUS) within 48 h of admission to screen for DVT and received a sonographic review every 3 days during their hospitalization . EventuaThe following data were collected among all patients: (1) demographic information including age, gender, body mass index (BMI), hypertension, and diabetes mellitus; (2) trauma-related factors including injury severity score (ISS), trauma location, and mechanism of injury. The mechanism of injury is divided into low-energy injuries  and high-energy injuries . Injury severity score (ISS) ≤ 16 points to be defined as a minor injury, >16 points as a moderate injury, >25 points as a severe injury. (3) Clinical data including shock index (heart rate/systolic blood pressure), routine blood indicators , coagulation function , thrombus site , transfusion history, and time of starting antithrombotic therapy. Laboratory indicators were obtained within 24 h of admission.t-test was applied for comparison. The continuous variables that do not obey the normal distribution were presented as median (range) and compared by the Mann–Whitney U test. The continuous variables with statistical significance (P < 0.05) were analyzed with receiver operating characteristic (ROC) curves to calculate the optimal cut-off value and were transformed into categorical variables according to the optimal cut-off value. Pearson chi-square test or Fisher’s exact test was used to clarify the associations between each categorical variable and RVT. Logistic regression analysis was then performed, and multiple logistic regression included only those significant predictors (p < 0.05) in the univariate logistic regression analysis. The dominance ratio (OR) and 95% confidence interval (CI) were used to assess the degree of association between the variables and RVT.The statistical analyses were performed by SPSS version 25.0. The continuous variables that obeyed normal distribution were presented as mean ± standard deviation (mean ± SD), and the Student’s In this study, 176 patients with DVT after trauma were admitted, including 2 cases with a history of venous thromboembolism, 1 case aged <18 years, 21 cases with indefinite anticoagulation needs, and 25 cases lost to follow-up. P = 0.002), time from discovery of thrombus to antithrombotic treatment (P = 0.012). P = 0.001), diabetes mellitus (P = 0.001), hypertension (P = 0.046), thrombus site (P < 0.001), fibrinogen (P < 0.001), and time from detection of thrombosis to antithrombotic therapy (P = 0.001). Therefore, these six factors were analyzed by multiple logistic regression.The three main factors leading to venous thrombosis are endothelial lesions, venous stasis, and blood hypercoagulation. In trauma patients, endothelial damage and coagulation disorders caused by trauma and slow blood flow caused by braking or bed rest increased the risk of VTE . VTE is Fibrinogen, known as coagulation factor I, is an acute phase protein synthesized by the liver. It is activated into fibrin with the action of thrombin and is essential in the coagulation process of the body. Elevated fibrinogen increases blood viscosity, promotes platelet aggregation, and aggravates thrombus formation. The normal fibrinogen level in the blood is 2–4 g/L. This study revealed that acute phase fibrinogen > 4.145 g/L increases the incidence of RVT in patients with posttraumatic DVT . Several studies have shown that elevated fibrinogen is related to an increased risk of venous thrombosis in a concentration-dependent manner –23. FurtDeep vein thrombosis is classified into proximal thrombosis , distal thrombosis , and mixed proximal and distal thrombosis according to the location of the thrombosis. It has been shown that patients with extensive deep venous system involvement are more susceptible to RVT . Our stuAnticoagulation in patients with DVT has been a puzzling problem to clinicians, how to minimize the danger of bleeding while allowing the complete dissolution of the thrombus . Then, wDiabetes is a chronic metabolic disorder. Altered intravascular homeostasis resulting from endothelial and smooth muscle cell dysfunction promotes the progress of inflammation and thrombosis . Althougp < 0.001). ISS is based on the Abbreviated Injury Scale score (AIS), which divides the body into six areas, and is the squared sum of the AIS values of the body’s three most severe injury areas (D-dimer is a sensitive thrombotic marker commonly used for the exclusionary diagnosis of acute VTE . Howeverry areas , 40. In Cockett syndrome, or May-thurner syndrome or iliac vein compression syndrome, is a condition in which an iliac vein is compressed by the iliac artery or has an abnormal intraluminal adhesion structure, causing obstruction of venous return in the lower extremities and pelvis, predisposing the formation of deep vein thrombosis, and making it hard to recanalize the formed thrombosis . CockettIn summary, elevated fibrinogen levels, mixed proximal and distal thrombosis, more than 2.5 days to start anticoagulation therapy, and diabetes mellitus are associated with the risk of RVT in patients with posttraumatic DVT. Risk stratification of patients with posttraumatic DVT can be performed based on factors such as D-dimer and fibrinogen levels, thrombus site, and whether or not they have diabetes mellitus to guide the adjustment of clinical treatment plans. Meanwhile, starting the antithrombotic therapy at the earliest possible time after thrombosis is detected to reduce the occurrence of RVT and its severe complications and enhance the long-term prognosis of patients.The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.YL designed the experiment, collected the data, analyzed and interpreted the data, and wrote the manuscript. WZ and ZW were responsible for providing the experimental ideas, guiding the experiments, and playing a pivotal role. XD collected the clinical data. FZ was in charge of reviewing and revising the manuscript. All authors contributed to the article and approved the submitted version.


Changes from the baseline on estimated glomerular filtration rate (eGFR) categories  were used for evaluating the risk of decreased kidney function. Exposure groups for DAAs and relevant concomitant drugs were categorized into 10 patterns based on the PI. Among the 10 patterns, a significant increase in the incidence rate ratio (P < 0.01) was observed in the prescription patterns of concomitant use of telaprevir with peginterferon alpha and ribavirin, concomitant use of daclatasvir hydrochloride with asunaprevir, and ombitasvir hydrate combined with paritaprevir hydrate and ritonavir, which were concomitantly used with ribavirin; such an increase was not observed in the other prescription patterns. The effects of DAAs on kidney function may differ among drugs, suggesting the possibility that the risk of kidney disease is not a class effect of DAAs and should be evaluated individually for each DAA.An association between kidney disease and direct-acting antivirals against hepatitis C (DAAs) has been suggested, however the warning on the package insert (PI) of the drug varies among DAAs. In this study, the risk of decreased kidney function associated with DAAs marketed in Japan was investigated to determine whether the risk of kidney disease is a common adverse event and class effect of DAAs. Data for patients who were new users of DAAs marketed in Japan, with eGFR ≥ 45 mL/min/1.73 m Direct acting antivirals against hepatitis C (DAAs) have been widely used for the treatment of hepatitis C in Japan since telaprevir was first approved in 2011 , 399 domIn addition to the background described above, since the inhibitory action of telaprevir on renal drug transporters, on organic cation transporter 2 and multidrug and toxin extrusion-type transporter 1 have been postulated to be mechanisms for reducing the estimated glomerular filtration rate (eGFR) , 5, othe®, a reliable and valuable database operated by the PMDA with the support of the Ministry of Health, Labour and Welfare of Japan [® in the middle of July 2019.Data from MID-NETof Japan , 7, were® for this study was approved on September 18, 2018, by the expert committee of MID-NET® [The utilization of MID-NETMID-NET® , it was MID-NET® .®. All DAAs marketed in Japan during the study period were investigated in this study, except for the combination product of glecaprevir hydrate with pibrentasvir; this combination product was excluded because an insufficient number of patients for analysis was expected at the planning stage based on the following information: (1) expected patient number of this product was less than 30 in total according to the information available on the website of MID-NET®; (2) the end of the study period was only two months after the launch of this combination product. The target DAAs in this study were as follows: telaprevir, simeprevir sodium, asunaprevir, daclatasvir hydrochloride, vaniprevir, sofosbuvir, grazoprevir hydrate, and elbasvir, and the combination products of ledipasvir acetonate with sofosbuvir, ombitasvir hydrate with paritaprevir hydrate and ritonavir, and daclatasvir hydrochloride with asunaprevir and beclabuvir hydrochloride. It should be noted that telaprevir, simeprevir sodium, vaniprevir, the combination product of ombitasvir hydrate with paritaprevir hydrate and ritonavir and the combination product of daclatasvir hydrochloride with asunaprevir and beclabuvir hydrochloride are no longer marketed in Japan [t0 (the first prescription date of DAAs) were excluded to identify new users of each product.The flowchart for patient selection in this study is shown in Fig. in Japan –15. Pati2 and without specific risk factors relating to kidney disease: (1) patients with an eGFR within 90 days before t0 < 45 mL/min/1.73 m2; (2) patients with records of renal dialysis, renal transplantation, chronic kidney disease, or renal failure before t0; (3) patients diagnosed with diabetes, heart failure, or hypertension before t0; (4) patients who had no eGFR or serum creatinine (sCRE) results within 90 days before t0; (5) patients who were prescribed multiple DAAs at t0, except if coadministration of multiple DAAs was required in the PI  [t0.In addition, for analysis, the following exclusion criteria were applied to identify patients with eGFR ≥ 45 mL/min/1.73 maprevir) , 17; andt0 during the 90 days before t0. It should be noted that the exact time of onset of the events in a day was not available in MID-NET®. A value of eGFR at t0 might be observed at either before or after DAA prescription. Therefore, the eGFR value at t0 was not used for analysis to avoid misinterpretation of results due to a reverse causality on the timing between the onset of decreased kidney function and DAA prescriptions. For analysis, a decrease from the baseline eGFR by one or more eGFR categories . The treatment period consisted of the prescription date (starting date and duration of a prescription) with a 28-day gap and a 7-day grace period for each DAA, except for asunaprevir or grazoprevir hydrate. The treatment period of asunaprevir or grazoprevir hydrate was proportional to that of daclatasvir hydrochloride or elbasvir, because these drugs were only concomitantly used with daclatasvir hydrochloride or elbasvir, respectively. Additional analysis was also conducted by changing the definition to two or more decreases from the baseline in the categories.In the risk assessment of decreased kidney function, we examined changes from the baseline in eGFR categories established by the Kidney Disease Improving Global Outcomes (KDIGO), as shown in Table t0. No patients with the prescription of (10) DCV/ASV/BCV were identified in this study. Thus, further analysis was not conducted on this product. Pattern (7) LDV/SOF was set as the control for analysis, because no safety information relating to kidney disease was included in the serious adverse events section of the PI of this drug, and the greatest number of patients were identified among the patterns without concomitant use of peginterferon alpha or ribavirin, which included a warning for kidney disease in the PI.Based on the PIs of DAAs, exposure groups were categorized into the following 10 patterns: (1) concomitant use of telaprevir with peginterferon alpha and ribavirin (TVR + Peg-IFN + Rib); (2) concomitant use of simeprevir sodium with peginterferon alpha and ribavirin (SMV + Peg-IFN + Rib); (3) concomitant use of daclatasvir hydrochloride with asunaprevir (DCV + ASV); (4) concomitant use of vaniprevir with peginterferon alpha and ribavirin (VAN + Peg-IFN + Rib); (5) concomitant use of sofosbuvir with ribavirin (SOF + Rib); (6) concomitant use of elbasvir with grazoprevir hydrate (EBV + GZR); (7) ledipasvir acetonate combined with sofosbuvir (LDV/SOF); (8) ombitasvir hydrate combined with paritaprevir hydrate and ritonavir (OBV/PTV/r); (9) ombitasvir hydrate combined with paritaprevir hydrate and ritonavir, which was concomitantly used with ribavirin (OBV/PTV/r + Rib); and (10) daclatasvir hydrochloride combined with asunaprevir and beclabuvir hydrochloride (DCV/ASV/BCV). The concomitant drug was decided based on prescription records within 30 days before and after t0 were calculated for each prescription pattern and were compared among the patterns to understand patients’ background, and the incidence rate ratio of decreased kidney function for each prescription pattern to the control (LDV/SOF) was calculated. When the eGFR value was not available, eGFR value was calculated using the sCRE based on the following formulae which have been widely used in clinical practice in Japan [−1.094 × age−0.287 and female, 194 × sCRE−1.094 × age−0.287 × 0.739. The mean eGFR value was used if multiple eGFR values were recorded on the same day. For calculating patient-years, patient follow-up days were divided by 365.25. The patient-year was calculated as 0 if the follow-up period ended at t0. SAS version 9.4  was used for all analyses.For analysis, median age, sex proportion, and median eGFR at in Japan , 20: maln = 4103), or a lack of eGFR and sCRE results (n = 847). It should be noted that since a patient who met more than one exclusion criterion was independently counted for each item, these numbers include duplicate counts of a patient.As shown in Fig. P > 0.05, Wilcoxon rank sum test, vs. the control) or sex ratio  among the prescription patterns, although slight differences were observed in age. In the population for analysis, the median (interquartile range) of the follow-up period was 87 days (34–90 days) and the median number (interquartile range) of kidney function tests per patient was 4 (2–6) during the follow-up period.Baseline patient characteristics are shown in Table P < 0.01), whereas such an increase was not observed in the other prescription patterns.The incidence rate ratio of decreased kidney function, defined as one or more decreases from the baseline in eGFR categories (see methods), of each prescription pattern to the control (LDV/SOF) is shown in Fig. When the definition of decreased kidney function was changed to two or more decreases from the baseline in eGFR categories, a significantly increased risk was also observed for (1) TVR + Peg-IFN + Rib . This increased risk was not clear for the other prescription patterns because only a small number or no observed cases of decreased kidney function were identified, resulting from a lower frequency of the event (data not shown).The present study was conducted to evaluate the risk of decreased kidney function in patients prescribed DAAs marketed in Japan. A significantly increased risk in comparison with the control ((7) LDV/SOF) was observed for (1) TVR + Peg-IFN + Rib, (3) DCV + ASV, and (9) OBV/PTV/r + Rib. These results support previous findings regarding the risk of kidney disease associated with TVR, DCV + ASV, and OBV/PTV/r, such as a decline of eGFR and an occurrence of acute kidney injury during DAA treatment , 22. The®, which has been reported to be a reliable database [2 at baseline and excluded patients who had risk factors  related to kidney disease. Thus, we believe that no further adjustments are necessary to interpret the results of this study. Another limitation was that some of the patients who were prescribed DAAs were excluded due to a lack of results on kidney function tests within 90 days before t0. This may affect the results in a part, although such effects could be negligible or limited because the number of such patients was limited and patients with a higher risk of kidney disease would be carefully monitored in clinical practice, including monitoring of eGFR. Another study may also be necessary to examine a reversibility of decreased kidney function by DAAs, which was out of the scope in this study.The strength of this study was its utilization of eGFR as an objective variable to examine decreased kidney function based on data from MID-NETdatabase . As a liThe PMDA conducted a safety assessment regarding kidney disease associated with DAAs based on the results of this study, and concluded that no new signals were identified and that the study results support the current safety warnings in the PIs so that no additional safety measures are required at present. However, as discussed above, these different effects of DAAs on kidney function should be recognized by marketing authorization holders of DAAs and health care professionals for its proper use, although more research will be necessary for examining specific measures in clinical practice.The effects of DAAs on kidney function may differ among drugs, suggesting the possibility that the risk of kidney disease is not a class effect of DAAs and should be evaluated individually for each DAA.


The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML).Of 3382 patients with chest pain/discomfort or dyspnea in whom CAG was performed, 1893 were included. All clinical data were divided as follows (i): Demographics, history, and physical examination; (ii): (i) plus electrocardiography; and (iii): (ii) plus echocardiography, and analyzed by cLR and ML.In multivariable analysis via cLR, the AUC and accuracy of the model using the final 20 variables were 0.795 and 72.62%, respectively. In multivariable analysis via ML, the best AUCs in the internal validation were 0.8 with (i), 0.81 with (ii), 0.83 with (iii), and in external validation, the best AUCs were 0.71 with (i), 0.74 with (ii), and 0.79 with (iii). The best AUCs and accuracy of the fittest model including 21 importance variables by ML were 0.81 and 72.48% in internal validation; and 0.75 and 70.5% in external validation, respectively. The importance variables in ML and cLR were similar, but slightly different and the additional discriminators via ML were found.The assessment using the fittest importance variables can assist physicians in differentiating mimicking diseases in which coronary angiography may not be required in patients suspected of having acute coronary syndrome in emergency department. Seventy-one percent of patients visiting the emergency room (ER) have chest pain , and 34.Various diseases can mimic SCDs; furthermore, it takes a long time to perform the necessary examinations, such as coronary CT, stress cardiac MRI, treadmill test, stress echo, and stress radionuclide imaging, on potential SCD patients . By perfWhen patients are suspected of having ACS, management such as immediate angiography and early or delayed invasive strategies, is scheduled and performed according to the prognosis of patients using risk stratification . HoweverThere have been many studies on the diagnosis of CAD using machine learning (ML) and deep learning –15. HoweThe aim of this study was to determine the best model to discriminate between SCDs and mimics of SCD before the CAG strategy in patients who visited the ER complaining of chest pain/discomfort or dyspnea using all relevant clinical data. In addition, we explored how effectively clinical screening by the importance variables deduced from the conventional logistic regression (cLR) method and ML models, can be applied to real-world clinical scenarios.This multicenter retrospective observational cohort study using data collected in two tertiary teaching hospitals  was conducted from November 2013 to August 2020. This study was first approved by the Dong-A University Hospital institutional review board under entry code DAUHIRB-20-050 for internal validation and waived by the SamSung Changwon University Hospital institutional review board for external validation. Informed consent was not required as this study was conducted retrospectively and was waived by the two ethics committee. All methods were performed in accordance with the relevant guidelines and regulations.The factors that enabled us to suspect SCDs at an early stage were investigated . First, Patients with various diseases that mimic SCDs, including stress-induced cardiomyopathy, heart failure, hypertrophic cardiomyopathy, valvular heart disease, acute aortic syndrome, pulmonary embolism, perimyocarditis, infectious endocarditis, cardiac tamponade, arrhythmia, sepsis or septic shock, lung disease, stroke, muscle pain, herpes zoster, cancers, and neurogenic and gastric diseases, were included and assigned to the non-SCD group  to assess the performance of these models was used and plotted using the potential data with or without electrocardiographic and echocardiographic data that were found in the early phase of admission. ML and cLR for internal and external validation were compared. Precision-recall (PRC) curves were constructed to exclude the error of ROC curves that were impacted by the addition of patients without disease but with positive test results.Various patients were diagnosed with serious diseases, such as perimyocarditis, acute aortic syndrome or pulmonary embolism, and mild diseases, such as anxiety or gastroesophageal reflux, were included in the control group for comparison with the SCD group. In the univariable analysis, significant variables of all clinical data are shown in Tables In multivariable analysis via cLR, significant variables were associated with SCDs . The mosIn multivariable analysis via ML, the AUCs in the internal validation were 0.8 with demographic, history, and physical examination data; 0.81 with the preceding data plus electrographic data; and 0.83 with the preceding data plus echocardiographic data, and in external validation, the AUCs were 0.71 with demographic, history, and physical examination data; 0.74 with the preceding data plus electrographic data; and 0.79 with the preceding data plus echocardiographic data . The accElectrocardiographic variables had little ability to predict SCDs in all analytic methods. The LightGBM and XGBoost analyses including all significant variables  were the best methods to predict SCDs . The fittest model using 21 variables was a competitive discriminator, as was the model using 85 variables. The levels of predictive power of ML and cLR were not largely different, although the composition of the fittest variables showed small differences in each analysis.Non-SCDs was best distinguishable from SCDs when using ML with 85 variables, but it showed no major difference with the fittest model using 21 variables. Additionally, the predictive powers of cLR and ML were also not largely different. Common importance variables from cLR and ML were RWMA, exertional pain, tenderness, male sex, dyspnea (on exercise), prior myocardial infarction, squeezing pain, nausea or vomiting, hypertension, ejection fraction, left atrial diameter, and QTc. Moreover, pleuritic pain, emotional stress, recent infection, and diabetes mellitus were likely to deserve consideration as significant variables, because the importance of variables can vary according to the distribution of patients included in the non-SCD group. The models determined to be predictive by ML were proven to be an available tool to predict significant SCDs at an early clinical stage in patients suspected of having non-ST elevation ACS. To the best of our knowledge, there were few studies that incorporated patients with vasospasm differentiating acute coronary syndrome from severe mimicking diseases as predictive analytic modeling to consider a coronary angiography. Those importance variables would be used to distinguish acute coronary syndrome from various mimicking diseases at an early stage.In previous studies, stabbing, pleuritic chest pain, and reproducible chest wall pain on palpation were less associated with ACS (likelihood ratios 0.2–0.3), and radiating and exertional chest pain were most associated with ACS (likelihood ratios 2.3–4.7) . PressedDemographic findings, such as age and male sex, and past history, such as diabetes mellitus, current smoking, dyslipidemia, and previous MI other than hypertension were good predictors of CAD ,21, whilThe heart score as the TIMI or GRACE score is a reliable factor that predicts major adverse cardiac events , and it A total of 52.8% of patients with CAD among the NSTEMI patients visiting the ER had RWMA, but the RWMA was also shown in 43.7% of patients with non-CAD and RWMA failed to distinguish between CAD and non-CAD with no difference in peak troponin T in a previous study . RWMA caDistinguishing SCDs and non-SCDs using ML can reduce physician fatigue and the number of misdiagnosed cases. In cases using simple history taking and basic information, ML showed an AUC of 0.76–0.8 . Using mThis study has several limitations. First, this was a retrospective observational study conducted in two hospitals. There is a possibility of overfitting in this case, but we minimized this possibility statistically by using a random sampling method for cross-validation and out-of-bag error. Second, several diseases, such as diseases of unknown cause and suspected vasospasm without spasm tests were excluded. These obscure diseases must be classified as more specific diseases. Third, the quantity of mimics with a severe illness in the non-SCD group might change the importance of variables when predicting SCDs with our algorithm in patients with chest symptoms. Last, the predictive ability of the models was not much high. Which may be because a number of patients with vasospasm and severe mimicking diseases that may not be plainly distinguishable were included in our study. Previously, there have not been studies on prediction of SCDs including vasospasm and predicting SCDs excluding many vasospasm patients may be not available for consideration of CAG in various clinical scenario.SCDs were significantly predicted through ML models using all clinical data that were collected at an early stage of admission from the patients suspected of having non-ST elevation ACS. The analysis including 21 significant variables to predict SCDs was not largely different from the analysis using all 85 variables. The variables with high-ranking importance that were selected in the fittest model may be promising discriminators in various clinical scenarios in which ACS is suspected and should be screened to differentiate the mimicking diseases for which coronary angiography is not required. Screening these importance variables in patients suspected of having ACS at an early stage of admission can assist physicians in distinguishing SCDs from mimicking diseases.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


Diabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.A retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).We recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767, AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.A deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation. Diabetic kidney disease (DKD) has been one of the most serious diabetic microvascular complications, implicating up to 50% of patients with diabetes and becoming the major cause of the end-stage renal disease (ESRD) worldwide –3. DKD pThere are many risk factors associated with the high prevalence of CVD in DKD patients. Studies of risk factors and predictive tools for CVD are common among the general population, such as the Framingham, QRISK, and China-PAR models –12. But In recent years, artificial intelligence, particularly deep learning is developing rapidly and has been applied to a variety of medical fields, such as disease prediction , machineIn this study, we aimed to develop a predictive model based on the deep learning method to predict the progression of CVD in DKD patients. It also can help us to investigate the associated risk factors, to provide treatment recommendations for better cardiovascular outcomes and support personalized medicine. Patients were stratified into different risk subgroups using the output risk values from the model. We further evaluated the performance of the DeepSurv model with the classical Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), to prove that machine learning can effectively improve the predictive performance. Additionally, an easy-used online tool for calculating the incidence rate of CVD in DKD patients based on the predictive model was developed.n = 17); (2) age <18 years (n = 1); (3) patients had history of cardiovascular diseases or coronary revascularization ; (4) patients with surgery, infection or injured at baseline (n = 18); and (5) patients with autoimmune disease or malignant tumor (n = 45). The selection process of patients is shown in We retrospectively reviewed the patients who were diagnosed with DKD from the electronic medical records of the First Affiliated Hospital of Zhengzhou University from January 2013 to January 2020. DKD is defined as diabetic patients who performed persistent urinary albumin excretion or a reduction in estimated glomerular filtration rate (eGFR) for more than 3 months ; cerebrovascular disease (hemorrhagic stroke and ischaemic stroke); congestive heart failure and peripheral arterial disease  and the combination of cardiovascular events. Outcomes were defined by the International Classification of Diseases, Tenth Revision (ICD-10) codes. The ICD-10 codes are summarized in Z-score normalization method to normalize all variables to reduce the bias. The least absolute shrinkage and selection operator (LASSO) regression was performed to determine the significant clinical variables using 10-fold cross-validation   and compared by Mann–Whitney U-tests, depending on whether the data is normally distributed or not. Categorical variables are shown as frequency (percentage) and compared by the chi-squared test. The cumulative incidence rate of the two sets was plotted by the Kaplan-Meier method and then compared using the log-rank test. Deep Learning-based Survival Model (DeepSurv) was used to perform the task of predicting patient-individual cardiovascular risk using the preselected variables by LASSO regression. We applied Bayesian hyperparameter optimization including learning rate decay, dropout, and other hyperparameters, to prevent overfitting (https://github.com/jaredleekatzman/DeepSurv). After splitting the datasets into the training and validation sets, we trained the model performing a 5-fold cross-validation on the training set to assess for overfitting and determine the model validity. Then we estimated the model performance in the validation set.The database was divided into two mutually exclusive datasets with balanced data distribution, 70% as a training set and 30% as an internal validation set. Continuous variables are presented as mean ± standard deviation (SD) and compared between groups using the rfitting . The lisp-value < 0.05 was considered statistically significant.After obtaining the output predicted individual risks with the DeepSurv model, the DKD patients were then divided into high-risk and low-risk groups based on the cut-off value determined by ROC (receiver operating characteristic) curve. The cumulative incidence curves were plotted using the Kaplan-Meier method and compared using the log-rank test to visualize the difference in the predicted cumulative incidence of patients in two risk groups. The importance values of the selected variables were calculated by their component weights in the DeepSurv model, indicating the univariate contribution to the model. Finally, we compared the performance of the DeepSurv model with the CPH model and RSF model as conventional prediction methods to verify its performance. The discrimination performance of the proposed methods was assessed using the C-index, integrated Brier score (IBS) , and thep = 0.21).A total of 890 patients were included in the final analysis. During a medium follow-up of 10.4 (IQR: 3.8–23.4) months, 284(31.91%) patients required rehospitalization due to subsequent cardiovascular events . The median age for all patients was 52 years (IQR = 43–57), and that was 56 years (IQR = 48–65) for patients who sustained a subsequent CVD. Baseline demographic and clinical characteristics of the included patients, stratified by CVD outcomes are summarized in There are 91 baseline clinical variables with at least 70% data completeness as candidate predictors used for LASSO regression. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), were selected using the lambda with 1 SE of the minimum partial likelihood deviance. These variables are all easily available demographic, clinical characteristics, and laboratory results. Baseline characteristics of the selected variables were presented in After the feature selection process, all seven variables as independent predictors were used for the model development. We use the DeepSurv method to construct survival models to analyze individual CVD outcomes. A three-layer neural network with one input layer, one hidden layer, and one output layer is used to construct the predictive model. We used dropout, batch normalization, and L1 and L2 regularization during training and selected adaptive moment (Adam) as the optimizer and Tanh as the activation function to reduce overfitting. The optimized hyperparameters for DeepSurv are shown in Furthermore, after calculating the predicted individual risk score, patients were divided into high-risk and low-risk groups based on the risk cut-off values (the sensitivity of 76.7% and specificity of 72.4%). 410 patients were classified as high risk and 480 patients were classified as low risk group. We plotted the cumulative incidence curves for the two risk subgroups. http://model.51ehealth.com/). It can calculate the individual CVD risk and monitor the trend of the risk, providing a more intuitive and understandable way to interpret the predictive model. The DeepSurv model was able to plot the predicted Kaplan-Meier survival curves for each patient. Meanwhile, for understanding convenience, we transformed the predicted time-to-event curves output from the model into curves of event incidence rates. DKD patients can input their personal information to get their risk stratification and the incidence of CVD for 1, 3, and 5 year. The interface of this risk calculator is shown in The best performing model, the DeepSurv model, was used to construct an easy-used online tool to predict CVD risk in DKD patients , the General Program of the National Natural Science Foundation of China General Project (Grant No. 81970633), and the Major public welfare special projects in Henan Province (Grant No. 201300310600).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Results of ACL reconstruction are influenced by both patient and surgical variables. Until now a significant amount of studies have focused on the influence of surgical technique on primary outcome, often leaving patient variables untouched. This study investigates the combined influence of patient and surgical variables through multivariate analysis.p-value ≤ 0.05 were included in the multivariate model.Single-center retrospective cohort study. All patients who underwent primary ACL hamstring reconstruction within a 5-year period were included. Patient characteristics  and surgical variables  were collected. Patients were asked about Tegner Activity Scale (TAS), complications and revision surgery. Multivariate logistic regression was used to study risk factors. First graft failure and potential risk factors  were univariately assessed. Risk factors with a Six hundred forty-seven primary ACL hamstring reconstructions were included. There were 41 graft failures (failure rate 6.3%). Patient gender, age, height and preoperative TAS had a significant influence on the risk of failure in the univariate analysis. The multivariate analyses showed that age and sex remained significant independent risk factors. Patients with a failed ACL reconstruction were younger , with women at a lower risk for failure of their ACL reconstruction . ACL graft diameter and other surgical variables aren’t confounders for graft failure.This study shows that patient variables seem to have a larger influence on the failure rate of ACL hamstring reconstructive surgery than surgical variables. Identification of the right patient variables can help us make more informed decisions for our patients and create patient-specific treatment protocols. Young men’s higher risk of failure suggests that these patients may benefit from a different reconstruction technique, such as use of a patellar tendon or combined ligament augmentation.Retrospective cohort III.The online version contains supplementary material available at 10.1186/s12891-022-05547-8. Anterior cruciate ligament (ACL) surgery has evolved tremendously over the past 50 years , 2. DespThe risk of ACL failure with hamstring autografts is reported to be 3–12% . The majA minority of studies have identified patient-specific predictors of failure. Failure has been associated with younger age –11, 14. All patients who underwent primary ACL hamstring reconstruction within a 5-year period at a single-center teaching hospital were included. Patients had a minimum follow-up of two years. Patients with ACL reconstruction other than hamstring, multiligament reconstructions and open growth plate at the time of reconstruction were excluded. Patients aged 18 and older at the time of follow-up were contacted.After approval of the local Medical Ethics Committee (METC nr: 16.06105), all ACL reconstructions between 1 January 2010 and 31 December 2014 were included. Failure was defined as repeat ACL reconstruction, ACL graft failure objectified by MRI, or arthroscopic surgery. Baseline patient characteristics  and surgical variables  were collected from hospital records.Patients were contacted by one of the researchers (MK) by phone, between January 1, 2017 and July 1, 2017. After obtaining consent they were asked about preoperative activity level using the Tegner Activity Scale (TAS) [All ACL reconstructions were performed according to national guidelines, and a uniform postoperative rehabilitation protocol was prescribed for all participants .Patients underwent ACL reconstruction with a semitendinosus and gracilis tendon. Due to an institutional change in treatment protocol two surgical techniques were performed. First we used a transtibial reconstruction technique (TT), for non-anatomical ACL reconstruction. The graft is fixated using the transfix on the femoral side and an interference screw on the tibial side . The other technique was anteromedial portal (AMP) , for anaAll patients received a standardized protocol for rehabilitation with clinical physiotherapy starting on day 1 postoperatively. Standard follow-up was performed 2 weeks, 6 weeks and 3 months postoperatively. After this follow-up only those patients with persisting complaints or complications visited the outpatient clinic.p-value ≤ 0.05 were considered eligible for inclusion in the multivariate logistic regression analysis model (stepwise Backwards Likelihood Ratio model). As due to the limited number of ACL failures we were restricted to include a maximum of four variables in the multivariate logistic regression analysis, we opted for the four variables with the highest significance. Using a multivariate logistic regression analysis we were able to correct for missing data. We used the largest possible dataset for all variables. Additionally, we performed a sensitivity analysis between the entire ACL reconstruction group and those patients available for questionnaires. A p-value ≤ 0.05 was considered statistically significant.Statistical analyses were performed using IBM SPSS Statistics 24 . Descriptive statistics were used to describe demographic characteristics and failure rate. The Pearson chi-squared test and a Mann–Whitney U-test were conducted to determine the influence of patient and surgical characteristics on early and late failure. Logistic regression analysis was used to determine risk factors for graft failure. First graft failure and each potential risk factor  were univariately assessed. Risk factors with a A total of 748 ACL reconstructions were performed between 1 January 2010 and 31 December 2014. After exclusion of 101 ACL reconstructions, 647 primary ACL reconstructions (638 patients) were available for this study. Of these reconstructions 553 (85.5%) had full surgical data available, with an mean follow-up of 5.5 years, and 418 (75.6%) patients were available by phone to answer the research questionnaires . Table Six of the 41 failed ACL reconstructions were threated in other clinics. From these 6 we couldn’t accurately determine the time of failure, due to this we allocated them as missing. From the remaining failed ACL reconstructions 18 (43%) occurred within the first 12 months after surgery, 4 (10%) between 12 and 24 months and 13 (32%) after two years.To gain insight into the influence of the variables on the risk of failure, first an univariate analysis was conducted. Patient gender, age, height and preoperative TAS had a significant influence on the risk of failure Table , with a p < 0.01, OR 0.937) and gender   is in line with current literature. By comparison, the average range described for hamstring autograft ACL surgery is 4–14% , 16, 23.In this study the 0.123 OR indicates that women have an eightfold lower risk of failure than men. There is wide discrepancy in literature when it comes to gender. Wernicke et al. also showed a higher risk of failure in male patients , but sevThe risk of ACL graft failure at a younger age seems to be very limited in our study, with a 0.94 OR per year. Many other studies on ACL graft failure identify younger age as a predictor for graft failure , 18, 24.The surgical variables in this study did not have any influence on the risk of failure. Many studies have investigated the role of surgical variables on failure rate , 25, 26,Based on our results, pre-injury activity level is not a risk factor for failure after ACL surgery. This outcome is in line with the results of Yabroudi et al., evidencing higher risk of failure with participation in sports at a competitive level in a univariate analysis but no difference in a multivariate analysis . In otheGraft diameter was not of significant influence for failure. Our study complements multiple others showing no correlation between graft diameter and graft failure , 15, 18.Several limitations of our study should be mentioned. First of all, this is a retrospective analysis, and although we weren’t able to contact a quarter of the patients we did use their available data in the multivariate analysis. Patients were asked about instability and revision surgery, but this study is lacking a clinical score to objectify such instability − plus if there are no complaints or instability there is no need for revision surgery. Unfortunately we weren’t able asses time of return to sport and patients activity level at the last follow-up. Early return to sport or more aggressive rehabilitation may be a cause of early failure.Strength of the current study is that we performed a multivariate analysis that included both patient and surgical variables. Several recently published studies used multivariate analysis on ACL reconstructive surgery , 28. RahDrawback of multivariate analysis is that it needs at least 10 cases per variable. Most randomized trials lack the number of patients and data needed to draw conclusions based on multivariate analyses. Registry studies provide more consistent data and a larger number of patients. This will hopefully allow us to demonstrate more accurate correlations between patient characteristics, surgical variables and outcome. Currently there are only a few national registries. The implementation of more national registries could lead to more insights, and registries are upcoming in different countries.There are many risk factors for graft failure and factors as tibial slope, notch width, ongoing anterolateral rotational laxity are not included in this article. There is also evidence that patellar tendon reconstruction or reconstruction combined with lateral extra-articular tenodesis have a lower risk for graft failure than isolated ACL hamstring reconstruction , 29. TheWe hope our article adds to better understanding the risk factors in ACL reconstruction and identifying those patients at risk of graft failure. Identification of the right patient variables can help us make more informed decisions for our patients and create patient-specific treatment protocols.This study shows that patient variables seem to have a larger influence on the failure rate of ACL hamstring reconstructive surgery than surgical variables. Identification of the right patient variables can help us make more informed decisions for our patients and create patient-specific treatment protocols. The fact that young men are at higher risk of failure suggests that these patients may benefit from a different reconstruction technique such as use of a patellar tendon or combined ligament augmentation.Additional file 1: Multivariate analysis of the four significant univariate variables.


However, the systolic and diastolic BPV values were significantly higher in the severe volume overload group than in the other groups (p < 0.05). Further correlation and regression analysis showed volume overload severity and BPV existed positive association, and BPV was also significantly associated with PD volume related indexes, including diabetes mellitus, blood parathyroid hormone levels, Kt/V and subjective global assessment scores in the volume overloaded group (p < 0.05). All these results suggested BPV may be a useful indicator for fluid status in PD patients.Fluid overload is a common complication in patients who are on continuous ambulatory peritoneal dialysis (CAPD). Blood pressure is the traditional indicator of fluid status in these patients. However, it has poor sensitivity. Blood pressure variability (BPV) can detect fluctuations in blood pressure sooner and more accurately and be useful for the assessment of fluid volume in hemodialysis patient. However, there are limited relevant studies involving patients on CAPD. This retrospective study included 175 patients on CAPD who underwent regular assessment of the peritoneal dialysis adequacy at 2–3-month intervals at our center from January 2018 to December 2020. The overhydration (OH) value was measured using a body composition monitor. Blood pressure measurements obtained in each visit over one-year follow-up were used to determine each patient’s BPV under peritoneal dialysis. The patients were divided into the normal volume (OH ≤2 L), mild volume overload (OH 2–4 L), and severe volume overload groups (OH ≥4 L). There was no significant between-group difference in the mean blood pressure ( Continuous ambulatory peritoneal dialysis (CAPD) is widely used as a renal replacement therapy in end-stage renal disease. Volume overload is a common complication of CAPD with an incidence of more than 60%, which is much higher than that in patients on hemodialysis. Previous studies have shown that volume overload is an independent risk factor for cardiovascular disease and mortality in patients with CAPD. However, it is difficult to detect and evaluate retention of water and sodium in patients on peritoneal dialysis (PD) .Hypertension is highly prevalent in patients on PD, and volume overload is the main cause of uncontrolled hypertension in these patients. Therefore, the International Society for Peritoneal Dialysis guidelines recommend volume control as the first-line treatment for hypertension in this patient population. In clinical practice, blood pressure (BP) levels are routinely used to evaluate volume status in patients on CAPD. Other indicators, including edema, urine volume, ultrafiltration volume, and body weight changes, are ever used to guide early interventions but showed poor specificity and sensitivity .Blood pressure variability (BVP) reflects fluctuations in BP within a specific period of time. Previous studies have shown that BPV is a more sensitive indicator of volume overload than absolute BP in patients on hemodialysis and a better predictor of target organ damage and mortality ,8. HowevBody composition monitoring is an accurate and noninvasive way of measuring a patient’s intracellular fluid, extracellular fluid, and overall water content and calculates the overhydration (OH) value. The precision of this approach is close to that of the accepted isotope dilution method . PreviouOne hundred and seventy-five patients who underwent CAPD at Peritoneal Dialysis Center of Ruijin Hospital from January 2018 to December 2020 were retrospectively enrolled. All patients were over the age of 18 years and had received regular PD for >6 months. They accepted regular assessment of the adequacy of PD at 2–3-month intervals, as well as physical examinations and clinical assessments  to estimate hydration status. The exclusion criteria were as follows: (1) placement of coronary stents or a pacemaker; (2) a metal implant, such as an artificial joint; (3) contralateral or bilateral amputation; (4) severe heart failure (New York Heart Association Class IV); and (5) severe acute infectious disease or malignant tumor. All patients were treated with a lactate-based dialysate  with an exchange volume of 6–8 L/day ,12. The All patients had complete baseline demographic information, medical history, and regular follow-up data, including measurements for weight, height, systolic and diastolic BP, urine volume, ultrafiltration volume, body mass index and laboratory test results. Blood, urine, and PD fluid samples were obtained using consistent techniques. All laboratory values were measured using automated and standardized methods. Appropriate clinical treatment was provided in all cases.At the last visit of the year, BCM measurements were performed in the morning before the infusion of peritoneal dialysis fluid. The patients sit relaxed in the dialysis chair for at least 5 min. Bioimpedance spectroscopy was performed using the body composition monitor , strictly according to the manufacturer’s recommendations. Electrodes were attached to one hand and one foot at the ipsilateral side.The BCM uses alternating electric currents at 50 different frequencies  to measure fluid status.The OH value is calculated on the basis of the physiologic tissue model that comprises the individual’s normal ECW, normohydrated lean and adipose tissue. The OH value can be calculated from the difference between the normal expected ECW and the measured ECW . PatientAt each visit in our PD center, the blood pressures were routinely measured by renal clinicians or nurse specialists before the start of daily peritoneal dialysis. The mean values of the systolic and diastolic blood pressure were recorded, and the standard deviations (SD) were calculated. BPV was defined as the coefficient of variation (standard deviation/mean BP*100), which had been validated in previous studies of dialysis population.t-test for continuous variables and the chi-squared test for categorical variables. Linear regression analysis was conducted to determine the associations between OH values and BPV, including BPV of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Data for the continuous variables were analyzed in univariable and multivariable models, and hazard ratios and 95% confidence intervals per SD are presented for each parameter. Multiple linear regression was used to identify determinants of BPV. All statistical analyses were performed using SPSS software . P-value of <0.05 was considered statistically significant.The data for categorical variables are expressed as numerical and percentage values. Data for continuous variables are expressed as the mean ± SD. Between-group comparisons were performed using the One hundred and one (57.7%) of the 175 study participants were male (median age 52.6 years) and 74 (42.3%) were female (median age 53.6 years). There were 91 patients (52%) in the normal volume group, 51 (29%) in the mild volume overload group, and 33 (19%) in the severe volume overload group. The patient demographic characteristics were presented in p > 0.05). But respective systolic and diastolic BPV values in severe volume overload groups were significantly higher than other two groups. The results of the BP variability parameters were shown in The mean systolic and diastolic BP values were no significant between-group differences  and men (84.8%). Serum levels of phosphorus and parathyroid hormone were significantly higher in this group than in the other groups . The meaIn correlation analysis, clinical indexes including serum phosphorus, PTH, Kt/V, CCr, nPCR, SGA scores, diabetes status, transport status (high average%) correlated well with BPV in different volume groups . We furtPart of PD patients are prone to over-hydrated status which usually progress slowly and not easily to be detected when compared to hemodialysis patients. These fluid overloads can lead to a range of complications, including increasing cardiovascular events, which seriously affect the prognosis of PD patients. Therefore, early detection of fluid overload is important. Brain-type natriuretic peptide has been validated to correlate well with fluid overload in dialysis patients , but othFluid overload is an important factor in patients on CAPD who have uncontrolled hypertension, pulmonary and peripheral edema, heart failure, or other cardiovascular complications. However, traditional BP monitoring cannot detect a potential increase in volume status over time. BPV has recently been confirmed as a more sensitive method for reflecting fluctuation in homeostasis of BP, even in patients with “normal” BP. Several studies, particularly in the hemodialysis population, indicate that BPV is an independent risk factor for cardiovascular events and mortality. However, less studies have been performed in PD patients .p > 0.05), the long-term systolic and diastolic BPV values were significantly higher in the severe volume overload group than in the other two groups (p < 0.05) and correlated positively with the degree of volume overload.BPV consists of short-term, medium-term, and long-term BPV (egvisit-to-visit BPV). Long-term monitoring of BPV requires a lengthy period of time, but could avoid interruption of environments and temperatures . In clinWe also sought factors that correlated with BPV in the volume overload group and found a significant positive correlation of BPV with diabetes status, which is consistent with previous reports. Ploumis et al. found that diabetic patients on hemodialysis readily developed fluid retention that was particularly difficult to control. Moreover, research groups led by Ronco and Kwan also found that nearly 22% of diabetic patients on hemodialysis had fluid overload with OH values as high as 4.41 ± 3.72 L, whereas the mean value in their nondiabetic counterparts was only 1.59 ± 1.98 L. The reasons cited for the disorders are as follows: (1) increased fluid intake, stimulated by thirst, and reduced transperitoneal osmotic gradient; (2) ultrafiltration loss secondary to peritoneal membrane hyperpermeability, which may be linked to enhanced inflammation and neoangiogenesis; (3) cardiac insufficiency and atherosclerotic vascular disease, which are common in patients with diabetes independent of age and can cause arterial stiffness and abnormal permeability of blood vessels .We also observed that BPV had a close relationship with low adequacy of PD in the volume overload group. Univariate correlation analysis showed that Kt/V and CCr were negatively correlated with BPV while hyperphosphatemia and hyperparathyroidism were positively correlated. Multivariate analysis showed Kt/V and PTH were independently associated with BPV. Decreased adequacy of dialysis can directly lead to fluid overload and contribute to fluctuations in BP. Calcium, phosphate and PTH disturbance is common in dialysis patients. The reasons for the higher serum phosphorus and PTH in patients with severely volume-overloaded are complex, which may be related to factors such as insufficient dialysis due to noncompliance, poor dietary control, and higher blood pressure variation. Excessive serum calcium can cause vascular calcification and deposit in the heart muscle, resulting in cardiac hypertrophy, myocardial interstitial fibrosis and insufficiency. Meanwhile, PTH has a direct repressive effect on cardiac muscle. All these disorders result in structural and functional abnormalities of the cardiovascular system and BP regulation mechanisms ,25,26.We also found that BPV in the volume overload group was associated with the severity of protein-energy malnutrition, which exhibited a positive relationship with the normalized protein catabolic rate (nPCR) and an inverse relationship with the SGA score. There were no significant between-group differences in serum albumin, C-reactive protein, or lipid levels. Multivariate analysis showed SGA score was independently associated with BPV. The SGA is a widely used tool to assess nutritional status in peritoneal dialysis patients, which includes subjective assessments about patient’s history of weight loss, presence of anorexia, and vomiting, as well as the physician’s grading of muscle wasting, presence of edema, and loss of subcutaneous fat. These findings are in line with previous reports. Fan et al. studied volume overload in a cohort of nearly 600 patients on PD and found that the degree of volume overload was negatively related with nutritional status and was an independent predictor of quality of life . PersistThe limitations of our study need to be mentioned. First, due to the retrospective and observational design, the conclusions obtained from this study need further confirmation by prospective studies; Second, BCM was measured only one time for each patient at the end of 1-year follow-up period, multiple and regular measurements of BCM can better reflect the patient’s volume over time; Third, the number of participants included in our study was small, thus, our findings need to be confirmed in a larger study population; Fourth, we should pay more attention to improve patient compliance on diet and blood pressure control, even in patients with higher transport qualities, to ameliorate disorders of calcium and phosphate metabolism in PD patients .In conclusion, fluid overload is a frequent problem in patients on PD, and reliance on clinical parameters alone for assessment of fluid status might be misleading. The findings of this study indicate that BPV has value as a sensitive and convenient indicator of fluid status in patients on CAPD. Larger studies that include stricter outcome measurements and a longer follow-up period are needed in the future.China.


Uncontrolled hypertension in patients with type 2 diabetes: What are the correlates,” which determined that body‐mass index (BMI), pulse pressure, total cholesterol, and non‐HDL cholesterol are significant predictors of uncontrolled hypertension in patients with type 2 diabetes mellitus (T2DM).We read with pleasure the article by Rabizadeh et al., titled “First and foremost, this study provided a loose definition of uncontrolled hypertension. Researchers characterized uncontrolled hypertension as patients who were measured as having a systolic blood pressure (SBP) >140 mmHg or diastolic blood pressure (DBP) >90 mmHg in their first visit of the study.Additionally, the generalizability of this study to a broader population is limited given that it was performed at a single diabetes clinic in Iran. Although the study population was large, this study failed to demonstrate the impact of the physical, cultural, and socioeconomic factors that has been shown to impact high BP and T2DM.Moreover, the study excluded patients younger than the age of 30 years, which further reduces the generalizability of the results. Commonly, in studies examining T2DM, the inclusion criteria are age >18 years.Despite potential limitations mentioned above, we believe that the authors developed a strong case for the association of various factors, such as BMI, pulse pressure, and triglycerides, as predictors for the development of uncontrolled hypertension in those with T2DM. The study indicates the need for further research in this area.The authors declare that they have no conflicts of interests.