Derivation and Validation of a Machine Learning Risk Score Using Biomarker and Electronic Patient Data to Predict Progression of Diabetic Kidney Disease

Details

• Personal Author:
  Chan L ; Coca SG ; Connolly P ; Damrauer SM ; Donovan MJ ; El Salem F ; Fleming F ; Kattan MW ; McCullough JR ; Mosoyan G ; Murphy B ; Nadkarni GN ; Vassalotti JA
• Description:
  Aim: Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX (TM)) combining electronic health records (EHR) and biomarkers. Methods: This is an observational cohort study of patients with prevalent DKD/banked plasma from two EHR-linked biobanks. A random forest model was trained, and performance (AUC, positive and negative predictive values [PPV/NPV], and net reclassification index [NRI]) was compared with that of a clinical model and Kidney Disease: Improving Global Outcomes (KDIGO) categories for predicting a composite outcome of eGFR decline of >=5 ml/min per year, >=40% sustained decline, or kidney failure within 5 years. Results: In 1146 patients, the median age was 63 years, 51% were female, the baseline eGFR was 54 ml min-1 [1.73 m]-2, the urine albumin to creatinine ratio (uACR) was 6.9 mg/mmol, follow-up was 4.3 years and 21% had the composite endpoint. On cross-validation in derivation (n=686), KidneyIntelX had an AUC of 0.77 (95% CI 0.74, 0.79). In validation (n=460), the AUC was 0.77 (95% CI 0.76, 0.79). By comparison, the AUC for the clinical model was 0.62 (95% CI 0.61, 0.63) in derivation and 0.61 (95% CI 0.60, 0.63) in validation. Using derivation cut-offs, KidneyIntelX stratified 46%, 37% and 17% of the validation cohort into low-, intermediate- and high-risk groups for the composite kidney endpoint, respectively. The PPV for progressive decline in kidney function in the high-risk group was 61% for KidneyIntelX vs 40% for the highest risk strata by KDIGO categorisation (p<0.001). Only 10% of those scored as low risk by KidneyIntelX experienced progression (i.e., NPV of 90%). The NRIevent for the high-risk group was 41% (p<0.05). Conclusions: KidneyIntelX improved prediction of kidney outcomes over KDIGO and clinical models in individuals with early stages of DKD. [Description provided by NIOSH]
• Subjects:
  Biomarkers Blood Hematologic Diseases Occupational Health Safety Software
• Keywords:
  Author Keywords: Biomarkers Blood Sugar Disorders Computer Applications Diabetic Kidney Disease Electronic Data Electronic Health Records Kidney Disorders Kidneys Machine Learning Nephrological Disorders Prediction

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• ISSN:
  0012-186X
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  New York ; Ohio ; OSHA Region 2 ; OSHA Region 3 ; OSHA Region 5 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  64
• Issue:
  7
• NIOSHTIC Number:
  nn:20063294
• Citation:
  Diabetologia 2021 Jul; 64(7):1504-1515
• Contact Point Address:
  Lili Chan, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
• Email:
  lili.chan@mountsinai.org
• Federal Fiscal Year:
  2021
• Performing Organization:
  Icahn School of Medicine at Mount Sinai, New York
• Peer Reviewed:
  True
• Start Date:
  20170701
• Source Full Name:
  Diabetologia
• End Date:
  20200630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:9e6c762436b89c1d7eb5980a1b4bb3a49a71aed3c302982aa319c01a61a4658c1019c67694fc7a88431a8ca0a8f0010d5749d39a6bb838b3cdd3c8aada942ad7
• Download URL:
  https://stacks.cdc.gov/view/cdc/226382/cdc_226382_DS1.pdf
• File Type:
  [PDF - 1.31 MB ]