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Dynamic prediction of death risk given a renewal hospitalization process
Methodology · Submitted 7 Jun 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We find that more concentrated hospitalizations increase the risk of death.
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The abstract, sentence by sentence
Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history, which is generally available to physicians. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework accommodates various submodels for the hospitalization process. In particular, we study prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modelling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from prediction in the Poisson model, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 512 patients treated for COPD in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. We find that more concentrated hospitalizations increase the risk of death.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history, which is generally available to physicians. | contribution 0.99 | 0.87 | 0.05 | asserted 1.00 |
| s2 | Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. | method 0.93 | 0.61 | 0.05 | asserted 0.91 |
| s3 | The framework accommodates various submodels for the hospitalization process. | method 0.66 | 0.62 | 0.02 | asserted 0.99 |
| s4 | In particular, we study prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modelling. | method 0.55 | 0.72 | 0.04 | asserted 0.92 |
| s5 | In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. | result 0.47 | 0.59 | 0.11 | asserted 1.00 |
| s6 | This result differs from prediction in the Poisson model, previously studied, where only the number of hospitalizations matters. | result 0.46 | 0.44 | 0.08 | asserted 1.00 |
| s7 | We apply our methodology to a prospective, observational cohort study of 512 patients treated for COPD in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. | method 0.90 | 0.90 | 0.02 | asserted 0.82 |
| s8 | We find that more concentrated hospitalizations increase the risk of death. | result 1.00 | 0.96 | 0.78 | asserted 1.00 |
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Original abstract
Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history, which is generally available to physicians. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework accommodates various submodels for the hospitalization process. In particular, we study prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modelling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from prediction in the Poisson model, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 512 patients treated for COPD in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. We find that more concentrated hospitalizations increase the risk of death.
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