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Updated: Feb 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
The new measuring multimorbidity index predicted mortality better than Charlson and Elixhauser indices among the
James Stanley1, Diana Sarfati2
1Department of Public Health, University of Otago, Wellington, PO Box 7343, Wellington, New Zealand; Biostatistical Group, University of Otago, Wellington, PO Box 7343, Wellington, New Zealand.
Objectives:
The aim of the study was to develop and validate an updated morbidity index for short-term mortality risk, using chronic conditions identified from routine hospital admission ICD-10 data.
Study Design And Setting:
Retrospective cohort study of all adult New Zealand (NZ) residents at January 1, 2012. Adult NZ residents aged 18 years and over, defined by enrollment with a Primary Healthcare Organisation or accessing public health care in preceding year. Data were split into two data sets for index development (70%, n = 2,331,645) and validation (30%, n = 1,000,166).
Results:
The M3 index was constructed using log hazard ratios for 1-year mortality modeled from presence of 61 chronic conditions. Validation results were improved for the M3 index for predicting 1-year mortality compared to Charlson and Elixhauser on the c-statistic (M3: 0.931, Charlson: 0.921, Elixhauser: 0.922; difference M3 vs. Charlson = 0.010, 95% confidence interval [CI]: 0.008, 0.012; M3 vs. Elixhauser = 0.009, 95% CI: 0.007, 0.012) and integrated discriminative improvement (M3 vs. Charlson = 0.024, 95% CI: 0.021, 0.026; M3 vs. Elixhauser = 0.024, 95% CI: 0.022, 0.027).
Conclusion:
The M3 index had improved predictive performance for 1-year mortality risk over Charlson and Elixhauser indices, allowing better adjustment for mortality risk from chronic conditions. This provides an important tool for population-level analyses of health outcomes.
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