Key result
Combining disease-specific models with time-varying estimates of hospitalization risk yielded an AUC of 0.923 for predicting death up to 7 days post-discharge.
Why the study?
Does a combined risk model incorporating time-varying risks of hospitalization (day of week and time of day) improve the prediction of death up to 7-days post-discharge compared to standard disease-specific models?
Observational (n=11,732,260)
Yes
Does a combined risk model incorporating time-varying risks of hospitalization (day of week and time of day) improve the prediction of death up to 7-days post-discharge compared to standard disease-specific models?
Absolute Event Rate: 0.923% vs 0.786%
Incorporating the cumulative, time-varying risks of hospitalization—such as the day of the week and time of day of admission—into prognostic models substantially improves the prediction of short-term mortality compared to traditional disease-specific models alone.
May enhance post-discharge mortality prediction; hypothesis-generating and requires prospective validation before clinical adoption.
BACKGROUND: Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. METHOD: Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. RESULTS: There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). CONCLUSIONS: Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to result in robust predictive performance. Such risk exposure models should find utility both in enhancing standard prognostic models as well as estimating the risk of continuation of hospitalization.
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Coiera et al. (2014) conducted an observational in Hospitalized patients (n=11,732,260). Combined risk exposure model (background, time-varying, and disease-specific risks) vs. Disease-specific model alone (Charlson comorbidity index) was evaluated on Prediction of death up to 7 days post-discharge (AUC) (95% CI 0.921-0.926). Combining disease-specific models with time-varying estimates of hospitalization risk yielded an AUC of 0.923 for predicting death up to 7 days post-discharge.
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