Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy.

BACKGROUND: Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking. OBJECTIVE: To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone. DESIGN: Retrospective cohort. SETTING: Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado. PARTICIPANTS: We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients. MAIN MEASURES: Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed. KEY RESULTS: A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively. CONCLUSIONS: Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.

Integration of data from a safety net health care system into the Vaccine Safety Datalink.

BACKGROUND: In 2013 the Institute of Medicine suggested that the Vaccine Safety DataLink (VSD) should broaden its population by including data of more patients from low income and racially and ethnically diverse backgrounds. In response, Kaiser Permanente Colorado (KPCO) partnered with Denver Health (DH), an integrated safety net health care system, to explore the integration of DH data. METHODS: We compared three different methods (reference date of September 1, 2013): "Empanelment" (any patient who has had a primary care visit in the past 18months), "Proxy-enrollment" (two health care visits in 3years separated by 90days), and "Enrollment" in a managed care plan. For each of these methods, we compared cohort size, vaccination rates, socio-demographic characteristics, and health care utilization. RESULTS: The empaneled population at DH provided the best comparison to KPCO. DH's empaneled population was 111,330 (57,173 adults; 54,157 children), while KPCO had 436,290 empaneled patients (336,462 adults; 99,828 children). Vaccination rates in both health care systems for empaneled patients were comparable. Two year-old up-to-date coverage rates were 83.2% (KPCO) and 86.9% (DH); rates for adolescent Tdap and MCV4 were 85.5% (KPCO) and 90.6% (DH). There were significant differences in the two populations in age, gender, race, preferred language, and % Federal Poverty Level (FPL) (DH 70.7%<100% FPL; KPCO 17.4%), as well as in healthcare utilization - for example pediatric emergency department utilization was twice as high at DH. CONCLUSIONS: Using a cohort of "empaneled" patients, it is possible to integrate data from a safety net health care system that does not have a uniform managed care population into the VSD, and to compare vaccination rates, socio-demographic characteristics, and health care utilization across the two systems. The KPCO-DH collaboration may serve as a model for incorporating data from a safety net healthcare system into the VSD.