Precision-Recall Curves for Gradient Boosted Models Predicting Numerous Outcomes in the Testing Data eFigure 12. eFigure 12. Decision Curves for the Predicted Probabilities From Gradient Boosted Models for Various Outcomes in the Screening Data jamanetwopen-3-e1918962-s001.pdf (1.1M) GUID:?AFCF4254-3460-44FB-A042-B5819C0D936C Mouse monoclonal to CD16.COC16 reacts with human CD16, a 50-65 kDa Fcg receptor IIIa (FcgRIII), expressed on NK cells, monocytes/macrophages and granulocytes. It is a human NK cell associated antigen. CD16 is a low affinity receptor for IgG which functions in phagocytosis and ADCC, as well as in signal transduction and NK cell activation. The CD16 blocks the binding of soluble immune complexes to granulocytes.This clone is cross reactive with non-human primate Key Points Question Can prediction of individual outcomes in heart failure based on routinely collected claims data be improved with machine learning methods and incorporating linked electronic medical records? Findings In this prognostic study including records on 9502 patients, machine learning methods offered only limited improvement over logistic regression in predicting key outcomes in heart failure based on administrative claims. Inclusion of additional predictors from electronic medical records improved prediction for mortality, heart failure hospitalization, and loss in home days but not for high cost. Meaning Models based on claims-only predictors may accomplish modest discrimination and accuracy in prediction of key patient outcomes in heart failure, and machine learning methods and incorporation of additional predictors from electronic medical records may offer some improvement in risk prediction of select outcomes. Abstract Importance Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients quality of life and outcomes. Objectives To compare machine learning methods with traditional logistic regression in predicting important outcomes in patients with HF and evaluate the added value of augmenting claims-based predictive models with CBB1007 electronic medical record (EMR)Cderived information. Design, Setting, and Participants A prognostic study with a 1-12 months follow-up period was conducted including 9502 Medicare-enrolled patients with HF from 2 health care provider networks in Boston, Massachusetts (providers includes physicians, clinicians, other health care professionals, and their institutions that comprise the networks). The study was performed from January 1, 2007, to December 31, 2014; data were analyzed from January 1 to December 31, 2018. Main Outcomes and Steps All-cause mortality, HF hospitalization, top cost decile, and home days loss greater than 25% were modeled using logistic regression, least complete shrinkage and selection operation regression, classification and regression trees, random forests, and gradient-boosted modeling (GBM). All models were trained using data from network 1 and tested in network 2. After selecting the most efficient modeling approach based on discrimination, Brier score, and calibration, area under precision-recall curves (AUPRCs) and net benefit estimates from decision curves were calculated to focus on the differences when using claims-only vs claims?+?EMR predictors. Results A total of 9502 patients with HF with a imply (SD) age of 78 (8) years were included: 6113 from network 1 (training set) and 3389 from network 2 (screening set). Gradient-boosted modeling consistently provided the highest discrimination, lowest Brier scores, and good calibration across all 4 outcomes; however, logistic regression experienced generally similar overall performance (C statistics for logistic regression based on claims-only predictors: mortality, 0.724; 95% CI, 0.705-0.744; HF hospitalization, 0.707; 95% CI, 0.676-0.737; high cost, 0.734; 95% CI, 0.703-0.764; and home days loss claims only, 0.781; 95% CI, 0.764-0.798; C statistics for GBM: mortality, 0.727; 95% CI, 0.708-0.747; HF hospitalization, 0.745; 95% CI, 0.718-0.772; high cost, 0.733; 95% CI, 0.703-0.763; and home days loss, 0.790; 95% CI, 0.773-0.807). Higher AUPRCs were obtained for claims?+?EMR vs claims-only GBMs predicting mortality (0.484 vs 0.423), HF hospitalization (0.413 vs 0.403), and home time loss (0.575 vs 0.521) but not cost (0.249 vs 0.252). The net benefit for claims?+?EMR vs claims-only GBMs was higher at various threshold probabilities for mortality and home time loss outcomes but comparable for the other 2 outcomes. Conclusions and Relevance Machine learning methods offered only.Operational Definitions for the Claims-Based Predictors Recognized Using a 6-Month Covariate Assessment Period Prior to the Index Date (Including the Index Date) eFigure 1. Gradient Boosted Models Predicting Various Outcomes in the Screening Data eFigure 12. Decision Curves for the Predicted Probabilities From Gradient Boosted Models for Various Outcomes in the Screening Data jamanetwopen-3-e1918962-s001.pdf (1.1M) GUID:?AFCF4254-3460-44FB-A042-B5819C0D936C Key Points Question Can prediction of individual outcomes in heart failure based on routinely collected claims data be improved with machine learning methods and incorporating linked electronic medical records? Findings In this prognostic study including records on 9502 patients, machine learning methods offered only limited improvement over logistic regression in predicting key outcomes in heart failure based on administrative claims. Inclusion of additional predictors from electronic medical records improved prediction for mortality, heart failure hospitalization, and loss in home days but not for high cost. Meaning Models based on claims-only predictors may accomplish modest discrimination and accuracy in prediction of key patient outcomes in heart failure, and machine learning methods and incorporation of additional predictors from electronic medical records may offer some improvement in risk prediction of select outcomes. Abstract Importance Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients quality of life and outcomes. Objectives To compare machine learning methods with traditional logistic regression in predicting important outcomes in patients with HF and evaluate the added value of augmenting claims-based predictive models with electronic medical record (EMR)Cderived information. Design, Setting, and Participants A prognostic study with a 1-12 months follow-up period was conducted including 9502 Medicare-enrolled CBB1007 patients with HF from 2 health care provider networks in Boston, Massachusetts (providers includes doctors, clinicians, other healthcare experts, and their organizations that comprise the systems). The analysis was performed from January 1, 2007, to Dec 31, 2014; data had been examined from January 1 to Dec 31, 2018. Primary Outcomes and Procedures All-cause mortality, HF hospitalization, best price decile, and house days loss higher than 25% had been modeled using logistic regression, least total shrinkage and selection procedure regression, classification and regression trees and shrubs, arbitrary forests, and gradient-boosted modeling (GBM). All versions had been qualified using data from network 1 and examined in network 2. After choosing the most effective modeling approach predicated on discrimination, Brier rating, and calibration, region under precision-recall curves (AUPRCs) and online benefit estimations from decision curves had been calculated to spotlight the differences when working with claims-only vs statements?+?EMR predictors. Outcomes A complete of 9502 individuals with CBB1007 HF having a suggest (SD) age group of 78 (8) years had been included: 6113 from network 1 (teaching arranged) and 3389 from network 2 (tests arranged). Gradient-boosted modeling regularly provided the best discrimination, most affordable Brier ratings, and great calibration across all 4 results; nevertheless, logistic regression got generally similar efficiency (C figures for logistic regression predicated on claims-only predictors: mortality, 0.724; 95% CI, 0.705-0.744; HF hospitalization, 0.707; 95% CI, 0.676-0.737; high price, 0.734; 95% CI, 0.703-0.764; and house days loss statements just, 0.781; 95% CI, 0.764-0.798; C figures for GBM: mortality, 0.727; 95% CI, 0.708-0.747; HF hospitalization, 0.745; 95% CI, 0.718-0.772; high price, 0.733; 95% CI, 0.703-0.763; and house days reduction, 0.790; 95% CI, 0.773-0.807). Higher AUPRCs had been obtained for statements?+?EMR vs claims-only GBMs predicting mortality (0.484 vs 0.423), HF hospitalization (0.413 vs 0.403), and house time reduction (0.575 vs 0.521) however, not price (0.249 vs 0.252). The web benefit for statements?+?EMR vs claims-only GBMs was higher in various threshold probabilities for mortality and house time loss results but identical for the additional 2 results. Conclusions and Relevance Machine learning strategies offered just limited improvement over traditional logistic regression in predicting crucial HF results. Inclusion of extra predictors from EMRs to claims-based versions seemed to improve prediction for a few, however, not all, results. Introduction With ageing from the global inhabitants, heart failing (HF) has been recognized as a growing clinical and general public health problem connected with significant mortality, morbidity, and healthcare expenditures, among particularly.
