A semantic-based carry-forward approach: Uncovering chronic disease burden in real-world data analysis.
TL;DR
The algorithm developed provides a better representation of comorbidities, enabling a more reliable identification, in databases, of patients with established comorbidity burden and strengthening the interpretation of patient pathways, with downstream benefits for risk stratification, cohort selection, and care planning.
Abstract
Objective
By carefully accounting for the semantic structure of the International Classification of Diseases (ICD), this study aims to mitigate impact arising from incomplete recording of chronic diseases in ICD-coded data.
Materials And Methods
We redefined four chronicity statuses (Groups). We then assigned chronicity status to ICD codes (from different versions: ICD-10-CM, ICD-10-CA, and ICD-11) using the semantic structures of ICD and SNOMED CT. Based on this status, an algorithm was developed to carry forward relevant codes across a patient's multiple encounters. Codes classified as Group 2 (incurable diseases) were systematically assigned to all subsequent encounters for that patient, whereas Group 1 (chronic diseases) codes were assigned only to encounters occurring within one year of the first identified coding. We then tested the impact of this chronicity adjustment on current and adaptations of comorbidity/frailty indices (temporal stability and predictive performance) using different versions of ICD and two data sources: critically ill, heterogeneous Intensive Care Unit patients (MIMIC database) and a more homogeneous, procedure-specific hip-related cohort (SI-CPSS).
Results
Across each dataset and indices, we found that chronicity adjustment improved the temporal stability and precision of scores as evidenced by intra-class correlation. Adjusted indices consistently outperformed their unadjusted counterparts for hospital length-of-stay (LOS), 30-day readmission, and all-cause mortality prediction with improvement ranged, respectively in the SI-CPSS and MIMIC datasets, from 0.108 to 0.168 and 0.006 to 0.014 for readmission AUC, from 0.115 to 0.135 and 0.059 to 0.064 for mortality C-statistic and also from 0.081 to 0.330 and 0.003 to 0.010 for R2 LOS. DISCUSSION-
Conclusion
Our algorithm provides a better representation of comorbidities, enabling a more reliable identification, in databases, of patients with established comorbidity burden and strengthening the interpretation of patient pathways, with downstream benefits for risk stratification, cohort selection, and care planning.