Prescription of Nicotine Replacement Therapy for Hospitalized Tobacco Users.

OBJECTIVES: In hospitalized patients, cigarette smoking is linked to increased readmission rates, emergency department visits, and overall mortality. Smoking cessation reduces these risks, but many patients who smoke are unsuccessful in quitting. Nicotine replacement therapy (NRT) is an effective tool that assists patients who smoke with quitting. This study evaluates NRT prescriptions during and after hospitalization at a large health system for patients who smoke. METHODS: A retrospective cohort study was conducted to determine the number of patients who were prescribed NRT during an inpatient admission and at time of discharge from a network of nine hospitals across South Carolina between January 1, 2019 and January 1, 2023. RESULTS: This study included 20,757 patients identified as actively smoking with at least one hospitalization during the study period. Of the cohort, 34.9% were prescribed at least one prescription for NRT during their admission to the hospital. Of the patients identified, 12.6% were prescribed NRT upon discharge from the hospital. CONCLUSIONS: This study identified significantly low rates of NRT prescribed to smokers during hospitalization and at discharge. Although the management of chronic conditions is typically addressed in the outpatient setting, hospitalization may provide an opportunity for patients to initiate health behavior changes. The low rates of prescriptions for NRT present an opportunity to improve tobacco treatment during hospitalization and beyond.

Hospitalization as an opportunity to improve lung cancer screening in high-risk patients.

BACKGROUND: Lung cancer screening with annual low-dose computed tomography (LDCT) in high-risk patients with exposure to smoking reduces lung cancer-related mortality, yet the screening rate of eligible adults is low. As hospitalization is an opportune moment to engage patients in their overall health, it may be an opportunity to improve rates of lung cancer screening. Prior to implementing a hospital-based lung cancer screening referral program, this study assesses the association between hospitalization and completion of lung cancer screening. METHODS: A retrospective cohort study of evaluated completion of at least one LDCT from 2014 to 2021 using electronic health record data using hospitalization as the primary exposure. Patients aged 55-80 who received care from a university-based internal medicine clinic and reported cigarette use were included. Univariate analysis and logistic regression evaluated the association of hospitalization and completion of LDCT. Cox proportional hazard model examined the time relationship between hospitalization and LDCT. RESULTS: Of the 1935 current smokers identified, 47% had at least one hospitalization, and 21% completed a LDCT during the study period. While a higher proportion of patients with a hospitalization had a LDCT (24%) compared to patients without a hospitalization (18%, p<0.001), there was no association between hospitalization and completion of a LDCT after adjusting for potentially confounding covariates (95%CI 0.680 - 1.149). There was an association between hospitalization time to event and LDCT completion, with hospitalized patients having a lower probability of competing LDCT compared to non-hospitalized patients (HR 0.747; 95% CI 0.611 - 0.914). CONCLUSIONS: In a cohort of patients at risk for lung cancer and established within a primary care clinic, only 1 in 4 patients who had been hospitalized completed lung cancer screening with LDCT. Hospitalization events were associated with a lower probability of LDCT completion. Hospitalization is a missed opportunity to refer at-risk patients to lung cancer screening.

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.

Background: Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods: We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results: A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions: Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems.