Background and objective: The impact of different comorbidity ascertainment lookback periods on modeling posthospitalization mortality and readmission was examined.
Methods: Index cases comprised medical (n = 326,456) and procedural (n = 349,686) patients with a hospital admission from 1990-1996. Administrative hospital data were extracted for 102 comorbidities, ascertained at index admission and for 1-, 2-, 3-, and 5-year lookback periods. Deaths and readmissions were identified within 12 months and 30 days of separation, respectively. Hierarchically nested and nonnested Cox regressions as well as Receiver Operator Characteristic Area Under the Curve (ROC-AUC) were used to determine model-fit and predictive ability of lookback period models.
Results: The 1-year lookback period provided the best model-fit for both patient groups when modeling mortality. A similar model-fit was seen at index admission for procedural but not medical patients. The superior readmission model employed 5 years of lookback for both patient groups. With one exception, all lookback period models were superior to those abstracting comorbidity from index admission only. Similar results were evident from ROC-AUC, although greater predictive ability was seen with modeling of mortality (0.847-0.923) compared with readmission (0.593-0.681).
Conclusion: The explanatory power of regression models, when adjusting for comorbidity, is influenced by length of lookback, outcome investigated and clinical subgroup. Shorter periods (approximately 1 year) appear appropriate for modeling posthospitalization mortality, whereas longer lookback periods are superior for readmission outcomes.