Estimating the Charlson Comorbidity Index From Privacy-Truncated Diagnosis Codes: Methodological Quantitative Study Evaluating Computational Strategies in 507,949 Inpatient Encounters
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Published on: January 8, 2020
Gaetan Kamdje Wabo1, Piotr Pawel Sokolowski1, Mahboubeh Jannesari Ladani1
1Department of Biomedical Informatics at the Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty of Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, Building 3, Level 4, Mannheim, DE.
Background:
The Charlson Comorbidity Index (CCI) is a standard instrument for risk adjustment in the secondary use of clinical data. Its coding algorithms require ICD-10 codes with at least four characters. Hospital data integration centers may truncate codes to three characters for privacy. Truncation makes subcodes ambiguous across Charlson groups and can render the CCI non-computable.
Objective:
The primary objective was to develop, openly release, and evaluate computational strategies that estimate the CCI from three-character ICD-10-GM codes using national four-character subcode frequencies from the German Federal Statistical Office (Destatis). The secondary objective was to identify and quantify potential error caused by truncation and error induced by the national prior.
Methods:
This single-center retrospective cohort study analyzed 507,949 inpatient encounters of 243,677 patients from University Hospital Mannheim (2010-2024). The open-source R package miCCI implements a deterministic interval estimator (S1), an exact expected-value estimator (S2), multiple imputation (S3), a Dirichlet-smoothed predictive median (S4), and a cross-validated Meta Learner. Age-conditioned priors came from Destatis table 23131-01. The reference standard was the Quan-weighted CCI computed from full-length codes. We report mean absolute error (MAE), root mean square error (RMSE), Bland-Altman limits, Kolmogorov-Smirnov (KS) distance, and exact agreement with patient-level cluster bootstrap 95% CIs. Robustness analyses covered hyperparameters, a local oracle prior, prefixes without Destatis subcodes, Bonferroni-corrected subgroups, and nested cross-validation.
Results:
S2, S3, and S4 achieved nearly identical MAE. S2 reached 0.1394 (95% CI 0.1379-0.1408), S3 0.1395 (95% CI 0.1380-0.1408), and S4 0.1390 (95% CI 0.1374-0.1405). All three were more accurate than S1 (0.1678) and the Meta Learner (0.1451). S2 computes exactly the expected score that S3 approximates by sampling, which explains their near-identical errors. S4 preserved the reference distribution best (KS distance 0.023, exact agreement 87.0%). The Meta Learner attained the lowest squared error (RMSE 0.318), also under nested cross-validation. In paired testing within a random subsample of 10,000 patients, S4 at its default setting did not differ from the best S3 setting. Under an in-sample oracle prior, 73% to 90% of the MAE of S2 to S4 remained, and prior mismatch accounted for the rest. After Bonferroni correction, 10 of 20 ICD-10 chapters retained a decisive best strategy.
Conclusions:
In this single-center cohort, frequency-informed estimation reconstructed the CCI from three-character codes with an MAE of about 0.14 points. Expected-value estimation (S2) is the most parsimonious choice for encounter-level accuracy. The integer-valued median (S4) can suit analyses that depend on the score distribution. Use in clinical research remains a potential application that requires multisite validation.
