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Prediction models hold tremendous promise as a way to improve patient outcomes and healthcare quality more generally by efficiently targeting interventions to those patients most likely to benefit. Most aspects of healthcare (ie, tests, medications and procedures) have associated risks and burdens. Accurate prediction of patients at higher risk for adverse outcomes from their underlying conditions allows for better targeting of interventions, so that only patients for whom the benefits of the intervention outweigh the risks of treatment are provided the intervention. Conversely, accurate prediction of patients at low risk for adverse outcomes (for whom the risks of the intervention outweigh the benefits) can help those patients avoid unnecessary and potentially harmful interventions. Thus, accurate prediction models play a pivotal role in the vision of personalised medicine, where all clinical decisions are tailored to each individual’s unique risk profile.1
In this issue of BMJ Quality & Safety, McAlister and van Walraven2 expand our knowledge of prediction for common, adverse hospitalisation outcomes (prolonged hospitalisation, 30-day mortality and readmission) in older adults. They use province-wide data from Ontario, Canada in 2004–2010 to compare how two previously published prediction models (Hospital Frailty Risk Score or HFRS3 and Hospital-patient One-year Mortality Risk or HOMR4) predict these outcomes in historical Ontario data. They found that the HFRS more accurately predicted prolonged hospitalisation, while the HOMR more accurately predicted 30-day mortality. Both HFRS and HOMR poorly predicted 30-day readmissions to hospital.
The authors should be commended for conducting a methodologically rigorous external validation of previously developed prediction models. Previous …
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