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Lakin JR, Robinson MG, Bernacki RE, et al. Estimating 1-Year Mortality for High-Risk Primary Care Patients Using the “Surprise” Question. JAMA Intern Med. 2016;176(12):1863–1865. doi:https://doi.org/10.1001/jamainternmed.2016.5928
Palliative care improves the value of care for seriously ill patients, but resource constraints necessitate targeting palliative care interventions to patients who need them most.1 The “surprise” question (SQ)—“Would you be surprised if this patient died in the next 12 months?”—has emerged as an attractive, simple solution for identifying patients who might benefit from palliative care.2,3 Despite optimism about the potential of the SQ to identify primary care patients who would benefit from palliative care,4 there is no evidence on its performance in this setting.
We identified patients screened for a high-risk care management program at a large academic primary care practice for whom the primary care physicians answered the SQ between August 30, 2012, and February 27, 2014. We assumed a no answer represented physician prediction of high 1-year mortality risk. Our primary outcome was mortality 1 year after SQ response, determined by linkage to Social Security Administration data. We obtained demographics and comorbidities5 from electronic health records. We assessed SQ performance for estimating 1-year mortality using area under the receiver operating characteristic curve, sensitivity, positive predictive value, and odds ratio of a no response for 1-year mortality using univariate logistic regression. To quantify incremental benefit of the SQ for predicting 1-year mortality over and above routinely collected administrative data, we calculated the integrated discrimination improvement6 of adding the SQ response to multivariate logistic regression of mortality on age, sex, and comorbidity score.5 The institutional review board of Partners HealthCare approved this study with a waiver of informed consent. Analysis is based on patient data from medical records and Social Security Administration data (identified records); tabulations, test characteristics, and regression analyses are deidentified data.
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