scholarly journals Derivation of an electronic frailty index for predicting short‐term mortality in heart failure: a machine learning approach

2021 ◽  
Author(s):  
Chengsheng Ju ◽  
Jiandong Zhou ◽  
Sharen Lee ◽  
Martin Sebastian Tan ◽  
Tong Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Frailty Index ◽  
Learning Approach ◽  
Short Term ◽  
Machine Learning Approach ◽  
Short Term Mortality

scholarly journals Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

2021 ◽  
Author(s):  
Chengsheng Ju ◽  
Jiandong Zhou ◽  
Sharen Lee ◽  
Martin Sebastian Tan ◽  
Ying Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Logistic Regression ◽  
Decision Tree ◽  
Frailty Index ◽  
Gradient Boosting ◽  
Importance Score ◽  
Multivariate Logistic Regression ◽  
Short Term ◽  
Short Term Mortality

AbstractObjectiveFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.MethodsThis was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo’s Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Comparisons were made with decision tree and multivariate logistic regression.ResultsA total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality).ConclusionsThe electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

scholarly journals A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

2017 ◽  
Author(s):  
Aymen A. Elfiky ◽  
Maximilian J. Pany ◽  
Ravi B. Parikh ◽  
Ziad Obermeyer
Keyword(s):  
Machine Learning ◽  
Mortality Risk ◽  
Palliative Chemotherapy ◽  
Learning Algorithm ◽  
Cancer Center ◽  
Short Term ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Short Term Mortality ◽  
And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

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