According to a report in the October 19 issue of JAMA, researchers reviewed and examined 26 validated hospital readmission risk prediction models and found that, regardless of whether they were used for clinical purposes or hospital comparison their predictive ability was poor.
Background information in the article suggests:
“An increasing body of literature attempts to describe and validate hospital readmission risk prediction tools. Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-adjust readmission rates for the purposes of hospital comparison.”
In order to analyze the performance of these models and evaluate their suitability for clinical or administrative use, Devan Kansagara, M.D., M.C.R., of Portland Veterans Affairs Medical Center and Oregon Health and Science University, Portland, and his team carried out a systematic review of investigations on validated readmission risk prediction models. Out of the 7,843 studies examined, 30 investigations of 26 unique models over a wide range of settings and patient populations met inclusion criteria. Total sample size varied from 173 patients to over 2.7 million patients.
Even though some models used follow-up intervals that ranged from two weeks to four years, the outcome of 30-day readmission was most commonly reported. 14 models were based on retrospective administrative data, these models could possibly be used for the purpose of hospital comparison. The majority of these models contained variables for medical comorbidity as well as use of previous medical services. 9 of these models were tested in large populations in the U.S. and had poor discriminative ability.
The researchers explain:
“Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization, and 5 could be used at hospital discharge. Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health.
… the poor discriminative ability of most of the administrative models we examined raises concerns about the ability to standardize risk across hospitals to fairly compare hospital performance. Until risk prediction and risk adjustment become more accurate, it seems inappropriate to compare hospitals in this way and reimburse (or penalize) them on the basis of risk-standardized readmission rates.”
They add that in order to evaluate the true preventability of readmissions in the U.S. further investigations are needed. They say:
“Given the broad variety of factors that may contribute to preventable readmission risk, models that include factors obtained through medical record review or patient report may be valuable. Innovations to collect broader variable types for inclusion in administrative data sets should be considered. Future studies should assess the relative contributions of different types of patient data (e.g., psychosocial factors) to readmission risk prediction by comparing the performance of models with and without these variables in a given population. These models should ideally be based on population-specific conceptual frameworks of risk.”
They conclude that so far the majority of the models created have poor predictive ability, whether for clinical purposes or hospital comparison. “Although in certain settings such models may prove useful, better approaches are needed to assess hospital performance in discharging patients, as well as to identify patients at greater risk of avoidable readmission.”
Written by Grace Rattue