Machine Learning to Identify Dialysis Patients at High Death Risk.

IntroductionGiven the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality.MethodsThis study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation.ResultsThe mean (±SD) age of our cohort was 68.7 ± 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994-0.7377), 0.7446 (0.7346-0.7546), 0.7504 (0.7425-0.7583), and 0.7488 (0.7421-0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors.ConclusionWe implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD

Pre-End-Stage Renal Disease Hemoglobin Variability Predicts Post-End-Stage Renal Disease Mortality in Patients Transitioning to Dialysis.

BackgroundHemoglobin variability (Hb-var) has been associated with increased mortality both in non-dialysis dependent chronic kidney disease (NDD-CKD) and end-stage renal disease (ESRD) patients. However, the impact of Hb-var in advanced NDD-CKD on outcomes after dialysis initiation remains unknown.MethodsAmong 11,872 US veterans with advanced NDD-CKD transitioning to dialysis between October 2007 through September 2011, we assessed Hb-var calculated from the residual SD of at least 3 Hb values during the last 6 months before dialysis initiation (prelude period) using within-subject linear regression models, and stratified into quartiles. Outcomes included post-transition all-cause, cardiovascular, and infection-related mortality, assessed in Cox proportional hazards models and adjusted for demographics, comorbidities, length of hospitalization, medications, estimated glomerular filtration rate (eGFR), type of vascular access, Hb parameters (baseline Hb [i.e., intercept] and change in Hb [i.e., slope]), and number of Hb measurements.ResultsHigher prelude Hb-var was associated with use of iron and antiplatelet agents, tunneled dialysis catheter use, higher levels of baseline Hb, change in Hb, eGFR, and serum ferritin. After multivariable adjustment, higher prelude Hb-var was associated with higher post-ESRD all-cause and infection-related mortality, but not cardiovascular mortality (adjusted hazard ratios [95% CI] for the highest [vs. lowest] quartile of Hb-var, 1.10 [1.02-1.19], 1.28 [0.93-1.75], and 0.93 [0.79-1.10], respectively).ConclusionsHigh pre-ESRD Hb-var is associated with higher mortality, particularly from infectious causes rather than cardiovascular causes. Further research is required to clarify the underlying mechanisms and true causal nature of the observed association

Pre-ESRD Depression and Post-ESRD Mortality in Patients with Advanced CKD Transitioning to Dialysis.

Background and objectivesDepression in patients with nondialysis-dependent CKD is often undiagnosed, empirically overlooked, and associated with higher risk of death, progression to ESRD, and hospitalization. However, there is a paucity of evidence on the association between the presence of depression in patients with advanced nondialysis-dependent CKD and post-ESRD mortality, particularly among those in the transition period from late-stage nondialysis-dependent CKD to maintenance dialysis.Design, setting, participants, & measurementsFrom a nation-wide cohort of 45,076 United States veterans who transitioned to ESRD over 4 contemporary years (November of 2007 to September of 2011), we identified 10,454 (23%) patients with a depression diagnosis during the predialysis period. We examined the association of pre-ESRD depression with all-cause mortality after transition to dialysis using Cox proportional hazards models adjusted for sociodemographics, comorbidities, and medications.ResultsPatients were 72±11 years old (mean±SD) and included 95% men, 66% patients with diabetes, and 23% blacks. The crude mortality rate was similar in patients with depression (289/1000 patient-years; 95% confidence interval, 282 to 297) versus patients without depression (286/1000 patient-years; 95% confidence interval, 282 to 290). Compared with patients without depression, patients with depression had a 6% higher all-cause mortality risk in the adjusted model (hazard ratio, 1.06; 95% confidence interval, 1.03 to 1.09). Similar results were found across all selected subgroups as well as in sensitivity analyses using alternate definitions of depression.ConclusionPre-ESRD depression has a weak association with post-ESRD mortality in veterans transitioning to dialysis

Machine Learning to Identify Dialysis Patients at High Death Risk

Introduction: Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality. Methods: This study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation. Results: The mean (+/- SD) age of our cohort was 68.7 +/- 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994-0.7377), 0.7446 (0.7346-0.7546), 0.7504 (0.7425-0.7583), and 0.7488 (0.7421-0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors. Conclusion: We implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD.National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [U01-DK102163]; VA Information Resource Center [SDR 02-237, SDR 98-004]; NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASESUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [U01DK102163] Funding Source: NIH RePORTERThis study is supported by grant U01-DK102163 from the National Institutes of Health to KKZ and CK and by resources from the US Department of Veterans Affairs (VA). The data reported here have been supplied in part by the US Renal Data System (USRDS). Support for VA/Centers for Medicare and Medicaid Services (CMS) data is provided by the Veterans Health Administration, Office of Research and Development, Health Services Research and Development, and VA Information Resource Center (project numbers SDR 02-237 and 98-004).WOS:0004843840000042-s2.0-85070215161PubMed: 3151714

Treatment of rheumatoid arthritis with biologic agents lowers the risk of incident chronic kidney disease.

Rheumatoid arthritis is associated with reduced kidney function, possibly due to chronic inflammation or the use of nephrotoxic therapies. However, little is known about the effects of using the newer novel non-nephrotoxic biologic agents on the risk of incident chronic kidney disease (CKD). To study this we used a cohort of 20,757 United States veterans diagnosed with rheumatoid arthritis with an estimated glomerular filtration rate (eGFR) of 60 mL/min/1.73m2 or more, recruited between October 2004 and September 2006, and followed through 2013. The associations of biologic use with incident CKD (eGFR under 60 with a decrease of at least 25% from baseline, and eGFR under 45 mL/min/1.73m2) and change in eGFR (<-3, -3 to <0 [reference], and ≥0 mL/min/1.73m2/year) were examined in propensity-matched patients based on their likelihood to initiate biologic treatment, using Cox models and multinomial logistic regression models, respectively. Among 20,757 patients, 4,617 started biologic therapy. In the propensity-matched cohort, patients treated (versus not treated) with biologic agents had a lower risk of incident CKD (hazard ratios 0.95, 95% confidence interval [0.82-1.10] and 0.71 [0.53-0.94] for decrease in eGFR under 60 and under 45 mL/min/1.73m2, respectively) and progressive eGFR decline (multinomial odds ratios [95% CI] for eGFR slopes <-3 and ≥0 [versus -3 to <0] mL/min/1.73m2/year, 0.67 [0.58-0.79] and 0.76 [0.69-0.83], respectively). A significant deceleration of eGFR decline was also observed after biologic administration in patients treated with biologics (-1.0 versus -0.4 [mL/min/1.73m2/year] before and after biologic use). Thus, biologic agent administration was independently associated with lower risk of incident CKD and progressive eGFR decline