Machine learning algorithm for early detection of end-stage renal disease

• Published in: BMC nephrology Vol. 21; no. 1; p. 518
• Main Authors: Segal, Zvi, Kalifa, Dan, Radinsky, Kira, Ehrenberg, Bar, Elad, Guy, Maor, Gal, Lewis, Maor, Tibi, Muhammad, Korn, Liat, Koren, Gideon
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.11.2020; BioMed Central; BMC
• Subjects: Algorithm; Algorithms; Asymptomatic; Beneficiaries; Chronic illnesses; Chronic kidney failure; Codes; Computer-aided medical diagnosis; Coronary artery disease; Data warehouses; Demography; Diagnosis; Dialysis; Embedding; End stage renal disease; Heart diseases; Insurance claims; Ischemia; Kidney diseases; Kidney transplantation; Learning algorithms; Machine learning; Medicaid; Medical coding; Medicare; Methods; Nephrology; Patients; Pharmacy; Prediction model; Prediction models; Risk factors; Statistical analysis; United States; End stage renal disease; Algorithm; Machine learning; Prediction model
• ISSN: 1471-2369; 1471-2369
• DOI: 10.1186/s12882-020-02093-0

End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database. This study analyzed 10,000,000 medical insurance claims from 550,000 patient records using a commercial health insurance database. Inclusion criteria were patients over the age of 18 diagnosed with CKD Stages 1-4. We compiled 240 predictor candidates, divided into six feature groups: demographics, chronic conditions, diagnosis and procedure features, medication features, medical costs, and episode counts. We used a feature embedding method based on implementation of the Word2Vec algorithm to further capture temporal information for the three main components of the data: diagnosis, procedures, and medications. For the analysis, we used the gradient boosting tree algorithm (XGBoost implementation). The C-statistic for the model was 0.93 [(0.916-0.943) 95% confidence interval], with a sensitivity of 0.715 and specificity of 0.958. Positive Predictive Value (PPV) was 0.517, and Negative Predictive Value (NPV) was 0.981. For the top 1 percentile of patients identified by our model, the PPV was 1.0. In addition, for the top 5 percentile of patients identified by our model, the PPV was 0.71. All the results above were tested on the test data only, and the threshold used to obtain these results was 0.1. Notable features contributing to the model were chronic heart and ischemic heart disease as a comorbidity, patient age, and number of hypertensive crisis events. When a patient is approaching the threshold of ESRD risk, a warning message can be sent electronically to the physician, who will initiate a referral for a nephrology consultation to ensure an investigation to hasten the establishment of a diagnosis and initiate management and therapy when appropriate.