Validation of algorithms to identify elective percutaneous coronary interventions in administrative databases.

BACKGROUND: Elective percutaneous coronary interventions (PCI) are difficult to discriminate from non-elective PCI in administrative data due to non-specific encounter codes, limiting the ability to track outcomes, ensure appropriate medical management, and/or perform research on patients who undergo elective PCI. The objective of this study was to assess the abilities of several algorithms to identify elective PCI procedures using administrative data containing diagnostic, utilization, and/or procedural codes. METHODS AND RESULTS: For this retrospective study, administrative databases in an integrated healthcare delivery system were queried between 1/1/2015 and 6/31/2016 to identify patients who had an encounter for a PCI. Using clinical criteria, each encounter was classified via chart review as a valid PCI, then as elective or non-elective. Cases were tested against nine pre-determined algorithms. Performance statistics (sensitivity, specificity, positive predictive value, and negative predictive value) and associated 95% confidence intervals (CI) were calculated. Of 521 PCI encounters reviewed, 497 were valid PCI, 93 of which were elective. An algorithm that excluded emergency room visit events had the highest sensitivity (97.9%, 95%CI 92.5%-99.7%) while an algorithm that included events occurring within 90 days of a cardiologist visit and coronary angiogram or stress test had the highest positive predictive value (62.2%, 95%CI 50.8%-72.7%). CONCLUSIONS: Without an encounter code specific for elective PCI, an algorithm excluding procedures associated with an emergency room visit had the highest sensitivity to identify elective PCI. This offers a reasonable approach to identify elective PCI from administrative data.

Universal hepatitis B screening and management in patients with cancer who received immunosuppressive chemotherapy.

BACKGROUND: Clinical data to guide management of patients with cancer and hepatitis B virus (HBV) infection who are treated with immunosuppressive chemotherapy are lacking. The purpose of this study was to describe HBV+ rates in a population of patients with cancer and evaluate a risk-stratified management protocol for the prevention of HBV reactivation (HBVr). METHODS: This was a descriptive study conducted in an integrated healthcare delivery system. Patients with cancer and hepatitis B virus infection who received immunosuppressive chemotherapy between 1 January 2014 and 31 January 2016 were included. A risk-stratified management protocol that continued for six months after chemotherapy completion or 12 months after completion of B-cell targeted chemotherapy was assessed. Outcomes included the proportion of patients who were HBV+ and amongst patients who initiated immunosuppressive therapy, proportions who received hepatitis B virus monitoring or anti-hepatitis B virus prophylaxis, or experienced HBVr or hepatitis B virus-related complications. RESULTS: There were 2463 patients with cancer screened for hepatitis B virus with 114 (4.6%) HBV+ of whom 59 (51.8%) initiated chemotherapy. Included patients were primarily older, male, and white with gastrointestinal or hematologic cancers and initiated intermediate/low-risk cytotoxic chemotherapy. During follow-up, 41 (69.5%) received hepatitis B virus DNA monitoring and 17 (28.8%) initiated anti-hepatitis B virus prophylaxis. No HBVr was observed. ALT and AST abnormalities were common but mostly Grade 1 and primarily related to the patient's malignancy or medications. CONCLUSIONS: Universal hepatitis B virus screening coupled with a risk-stratified management strategy utilizing HBVr monitoring and anti-hepatitis B virus prophylaxis in HBV+ patients receiving immunosuppressive chemotherapy for cancer may prevent HBVr.

A Predictive Model to Identify Skilled Nursing Facility Residents for Pharmacist Intervention.

Objective Develop a predictive model to identify patients in a skilled nursing facility (SNF) who require a clinical pharmacist intervention. Design Retrospective, cross-sectional. Setting Nine freestanding SNFs within an integrated health care delivery system. Patients Patients who received a clinical pharmacist medication review between January 1, 2016, and April 30, 2017. Identified patients (n = 2,594) were randomly assigned to derivation and validation cohorts. Interventions Multivariable logistic regression modeling was performed to identify factors predictive of patients who required an intervention (i.e., medication dose adjustment, initiation, or discontinuation). Patient-specific factors (e.g., demographics, medication dispensings, diagnoses) were collected from administrative databases. A parsimonious model based on clinical judgment and statistical assessment was developed in the derivation cohort and assessed for fit in the validation cohort. Main Outcome Measures Model to predict patients requiring clinical pharmacist intervention. Secondary outcome was a comparison of factors between patients who did and did not receive a clinical pharmacist intervention. Results Ninety-five factors were assessed. The derivation (n = 1,299) model comprised 22 factors (area under the curve [AUC] = 0.79, 95% confidence interval [CI] 0.74-0.84). A clopidogrel dispensing (odds ratio [OR] = 2.42, 95% CI 1.19-4.91), fall (OR = 2.47, 95% CI 1.59-3.83), or diagnosis for vertebral fracture (OR = 2.33, 95% CI 1.34-4.05) in the 180 days prior to clinical pharmacist medication review were predictive of requiring an intervention. The model fit the validation cohort (n = 1,295) well, AUC = 0.79 (95% CI 0.74-0.84). Conclusion Administrative data predicted patients in a SNF who required clinical pharmacist intervention. Application of this model in real-time could result in clinical pharmacist time-savings and improved pharmacy services through more directed patient care.