Characteristics of study population.

<p>Abbreviations: CVD, cardiovascular disease; BP, blood pressure; iPTH, intact parathyroid hormone; Ca, calcium; P, phosphate; ABI, ankle-brachial index; RAS, renin-angiotensin system.</p><p>Characteristics of study population.</p

Kaplan—Meier survival curves.

<p>Probabilities of (A) overall survival with log-rank test: χ² = 60.89; <i>P</i> ≤ 0.001 in the four groups and χ² = 2.02; <i>P</i> = 0.364 in the three PAOD groups. (B) Cardiovascular survival with log-rank test: χ² = 45.24; <i>P</i> ≤ 0.001 in the four groups and χ² = 0.69; <i>P</i> = 0.708 in the three PAOD groups.</p

DataSheet1_The Protective Effects of Lipid-Lowering Agents on Cardiovascular Disease and Mortality in Maintenance Dialysis Patients: Propensity Score Analysis of a Population-Based Cohort Study.PDF

Lipid-lowering agents display limited benefits on cardiovascular diseases and mortality in patients undergoing dialysis. Therefore, they are not routinely recommended for dialysis patients. The aim of this study was to assess the effects of lipid-lowering agents on clinical outcomes in dialysis patients on the basis of real-world evidence. This research used Taiwan’s National Health Insurance Research Database to identify dialysis patients from January 2009 to December 2015; patients were then categorized into a case group treated with lipid-lowering agents (n = 3,933) and a control group without lipid-lowering agents (n = 24,267). Patients were matched by age, sex, and comorbidities in a 1:1 ratio. This study used the Cox regression model to estimate the hazard ratios (HRs) for mortality and major adverse cardiovascular events (MACEs) for events recorded until December 2017. During a mean follow-up period of approximately 3.1 years, 1726 [43.9%, incidence 0.123/person-year (PY)] deaths and 598 (15.2%, incidence 0.047/PY) MACEs occurred in the case group and 2031 (51.6%, incidence 0.153/PY) deaths and 649 (16.5% incidence 0.055/PY) MACEs occurred in the control group. In the multivariable analysis of the Cox regression model, lipid-lowering agent users showed a significantly lower risk of death [HR: 0.75; 95% confidence interval (CI): 0.70–0.80] and MACEs (HR: 0.88; 95% CI: 0.78–0.98) than lipid-lowering agent non-users. Moreover, the survival benefit of lipid-lowering agents was significant across most subgroups. Dialysis patients treated with lipid-lowering agents display a 25 and 12% reduction in their risk of mortality and MACEs, respectively. Therefore, lipid-lowering agents might be considered when treating dialysis patients with hyperlipidemia.</p

Cox proportional hazards regression analysis for all-cause mortality.

<p>Abbreviation: CVD, cardiovascular disease; BP, blood pressure; RAS, renin-angiotensin system; iPTH, intact parathyroid hormone; Ca, calcium; P, phosphate; PAOD, peripheral arterial occlusion disease.</p><p>Cox proportional hazards regression analysis for all-cause mortality.</p

DataSheet2_The Protective Effects of Lipid-Lowering Agents on Cardiovascular Disease and Mortality in Maintenance Dialysis Patients: Propensity Score Analysis of a Population-Based Cohort Study.PDF

Lipid-lowering agents display limited benefits on cardiovascular diseases and mortality in patients undergoing dialysis. Therefore, they are not routinely recommended for dialysis patients. The aim of this study was to assess the effects of lipid-lowering agents on clinical outcomes in dialysis patients on the basis of real-world evidence. This research used Taiwan’s National Health Insurance Research Database to identify dialysis patients from January 2009 to December 2015; patients were then categorized into a case group treated with lipid-lowering agents (n = 3,933) and a control group without lipid-lowering agents (n = 24,267). Patients were matched by age, sex, and comorbidities in a 1:1 ratio. This study used the Cox regression model to estimate the hazard ratios (HRs) for mortality and major adverse cardiovascular events (MACEs) for events recorded until December 2017. During a mean follow-up period of approximately 3.1 years, 1726 [43.9%, incidence 0.123/person-year (PY)] deaths and 598 (15.2%, incidence 0.047/PY) MACEs occurred in the case group and 2031 (51.6%, incidence 0.153/PY) deaths and 649 (16.5% incidence 0.055/PY) MACEs occurred in the control group. In the multivariable analysis of the Cox regression model, lipid-lowering agent users showed a significantly lower risk of death [HR: 0.75; 95% confidence interval (CI): 0.70–0.80] and MACEs (HR: 0.88; 95% CI: 0.78–0.98) than lipid-lowering agent non-users. Moreover, the survival benefit of lipid-lowering agents was significant across most subgroups. Dialysis patients treated with lipid-lowering agents display a 25 and 12% reduction in their risk of mortality and MACEs, respectively. Therefore, lipid-lowering agents might be considered when treating dialysis patients with hyperlipidemia.</p

Cox proportional hazards regression analysis for cardiovascular mortality.

<p>Abbreviation: CVD, cardiovascular disease; BP, blood pressure; RAS, renin-angiotensin system; PAOD, iPTH, intact parathyroid hormone; Ca, calcium; P, phosphate; peripheral arterial occlusion disease.</p><p>Cox proportional hazards regression analysis for cardiovascular mortality.</p

Table_1_Outcome Analysis of Transition From Peritoneal Dialysis to Hemodialysis: A Population-Based Study.DOCX

If a technical failure occurs during peritoneal dialysis (PD), the patients undergoing PD may be transitioned to hemodialysis (HD). However, the clinical outcomes of patients who have undergone such a transition are under studied. This study assessed whether patients undergoing HD who have transitioned from PD have the same clinical outcomes as HD-only patients. This research was a retrospective cohort study by searching a National Health Insurance research database for data on patients in Taiwan who had undergone HD between January 2006 and December 2013. The patients were divided into two groups, namely a case group in which the patients were transitioned from PD to HD and a HD-only control group, through propensity score matching at a ratio of 1:4 (n = 1,100 vs. 4,400, respectively). We used the Cox regression model to estimate the hazard ratios (HRs) for all-cause death, all-cause hospitalization, infection-related admission, and major adverse cardiac events (MACE). Those selected patients will be followed until death or the end of the study period (December, 2017), whichever occurs first. Over a mean follow-up of 3.2 years, 1,695 patients (30.8%) died, 3,825 (69.5%) required hospitalization, and 1,142 (20.8%) experienced MACE. Patients transitioning from PD had a higher risk of all-cause death (HR: 1.36; 95% CI: 1.21–1.53) than HD-only patients. However, no significant difference was noted in terms of MACE (HR: 0.91; 95% CI: 0.73–1.12), all-cause hospitalization (HR: 1.07; 95% CI: 0.96–1.18), or infection-related admission (HR: 0.97, 95% CI: 0.80–1.18) between groups. Because of the violation of the proportional hazard assumption, the piecewise-HRs showed that the risk of mortality in the case group was significant within 5 months of the transition (HR: 2.61; 95% CI: 2.04–3.35) not in other partitions of the time axis. In conclusion, patients undergoing HD who transitioned from PD had a higher risk of death than the HD-only patients, especially in the first 5 months after transition (a 161% higher risk). Therefore, more caution and monitoring may be required for patients undergoing HD who transitioned from PD.</p

Probabilities of overall survival according to dominance side of ABI value.

<p>(A) in all patients with log-rank test: χ² = 1.32; <i>P</i> = 0.249; (B) in patients without PAOD with log-rank test: χ² = 3.47; <i>P</i> = 0.062; (C) in patients with PAOD with log-rank test: χ² = 0.20; <i>P</i> = 0.651.</p

Characteristics of patients at inclusion according to the location of PAOD.

<p>Abbreviations: CVD, cardiovascular disease; BP, blood pressure; iPTH, intact parathyroid hormone; Ca, calcium; P, phosphate; RAS, renin-angiotensin system</p><p>*Comparison between all four groups</p><p>^# Comparison between all groups except non-PAOD group.</p><p>Characteristics of patients at inclusion according to the location of PAOD.</p

Data_Sheet_1_An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.docx

Background and objectivesChronic kidney disease (CKD) is a global health concern. This study aims to identify key factors associated with renal function changes using the proposed machine learning and important variable selection (ML&IVS) scheme on longitudinal laboratory data. The goal is to predict changes in the estimated glomerular filtration rate (eGFR) in a cohort of patients with CKD stages 3–5.DesignA retrospective cohort study.Setting and participantsA total of 710 outpatients who presented with stable nondialysis-dependent CKD stages 3–5 at the Shin-Kong Wu Ho-Su Memorial Hospital Medical Center from 2016 to 2021.MethodsThis study analyzed trimonthly laboratory data including 47 indicators. The proposed scheme used stochastic gradient boosting, multivariate adaptive regression splines, random forest, eXtreme gradient boosting, and light gradient boosting machine algorithms to evaluate the important factors for predicting the results of the fourth eGFR examination, especially in patients with CKD stage 3 and those with CKD stages 4–5, with or without diabetes mellitus (DM).Main outcome measurementSubsequent eGFR level after three consecutive laboratory data assessments.ResultsOur ML&IVS scheme demonstrated superior predictive capabilities and identified significant factors contributing to renal function changes in various CKD groups. The latest levels of eGFR, blood urea nitrogen (BUN), proteinuria, sodium, and systolic blood pressure as well as mean levels of eGFR, BUN, proteinuria, and triglyceride were the top 10 significantly important factors for predicting the subsequent eGFR level in patients with CKD stages 3–5. In individuals with DM, the latest levels of BUN and proteinuria, mean levels of phosphate and proteinuria, and variations in diastolic blood pressure levels emerged as important factors for predicting the decline of renal function. In individuals without DM, all phosphate patterns and latest albumin levels were found to be key factors in the advanced CKD group. Moreover, proteinuria was identified as an important factor in the CKD stage 3 group without DM and CKD stages 4–5 group with DM.ConclusionThe proposed scheme highlighted factors associated with renal function changes in different CKD conditions, offering valuable insights to physicians for raising awareness about renal function changes.</p