Emergency Department Take-Home Naloxone Improves Access Compared with Pharmacy-Dispensed Naloxone.

BACKGROUND: Opioid overdose is a major cause of mortality in the United States. In spite of efforts to increase naloxone availability, distribution to high-risk populations remains a challenge. OBJECTIVE: To assess the effects of multiple different naloxone distribution methods on patient obtainment of naloxone in the emergency department (ED) setting. METHODS: Naloxone was provided to patients in three 12-month phases between February 2020 and February 2023. In Phase 1, physicians could offer patients electronic prescriptions, which were filled in a nearby in-hospital discharge pharmacy. In Phase 2, physicians directly provided patients with take-home naloxone at discharge. In Phase 3, distribution was expanded to allow ED staff to hand patients take-home naloxone at time of discharge. The total number of prescriptions, rate of prescription filling, and amount of take-home naloxone kits provided to patients were then statistically analyzed using 95% confidence intervals (CI) and chi-squared testing. RESULTS: In Phase 1, 348 naloxone prescriptions were written, with 133 (95% CI 112.5-153.5) filled. In Phase 2, 327 (95% CI 245.5-408.5) take-home naloxone kits were given to patients by physicians. In Phase 3, 677 (95% CI 509.5-844.5) take-home naloxone kits were provided to patients by ED staff. There were statistically significant increases in naloxone distribution from Phase 1 to Phase 2, and Phase 2 to Phase 3. CONCLUSIONS: Take-home naloxone increases access when compared with naloxone prescriptions in the ED setting. A multidisciplinary approach combined with the removal of regulatory and administrative barriers allowed for further increased distribution of no-cost naloxone to patients.

Ransomware Cyberattack Associated With Cardiac Arrest Incidence and Outcomes at Untargeted, Adjacent Hospitals.

OBJECTIVES: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO). DESIGN SETTING AND PATIENTS: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Emergency department and hospital mean daily census, number of CAs, mean daily CA incidence per 1,000 admissions, return of spontaneous circulation, survival to discharge, and survival with favorable neurologic outcome were measured. The study evaluated 78 total CAs: 44 out-of-hospital CAs (OHCAs) and 34 in-hospital CAs. The number of total CAs increased from the pre-attack to attack phase (21 vs. 38; p = 0.03), followed by a decrease in the post-attack phase (38 vs. 19; p = 0.01). The number of total CAs exceeded the cyberattack month forecast (May 2021: 41 observed vs. 27 forecasted cases; 95% CI, 17.0-37.4). OHCA cases also exceeded the forecast (May 2021: 24 observed vs. 12 forecasted cases; 95% CI, 6.0-18.8). Survival with favorable neurologic outcome rates for all CAs decreased, driven by increases in OHCA mortality: survival with favorable neurologic rates for OHCAs decreased from the pre-attack phase to attack phase (40.0% vs. 4.5%; p = 0.02) followed by an increase in the post-attack phase (4.5% vs. 41.2%; p = 0.01). CONCLUSIONS: Untargeted hospitals adjacent to ransomware-infected HDOs may see worse outcomes for patients suffering from OHCA. These findings highlight the critical need for cybersecurity disaster planning and resiliency.

Temporal Trends in Methamphetamine Use in Patients Admitted to the Hospital: A Retrospective Cohort Study.

OBJECTIVES: Although methamphetamine use is common, the scope of methamphetamine use and outcomes for patients admitted to the hospital is unclear. This study aims to identify the prevalence of methamphetamine use from January 2012 to January 2022, coingestions, hospital course, and readmission rate of admitted patients. METHODS: This was a retrospective cohort study conducted on patients admitted to our center with the following inclusions: age older than 18 years, positive/"pending confirm" value for methamphetamine on urine drug screen, and/or an International Classification of Diseases , Tenth Revision , code related to stimulant use disorder as an active issue. Urine drug screen data are reported as methamphetamine +/- and polysubstance (PS) +/-. Patient demographics, admission diagnosis, and hospital course were extracted. Statistical tests used included t tests and Mann-Whitney U tests. RESULTS: A total of 19,159 encounters were included, representing 12,057 unique patients. The median (interquartile range) age was 43 (33-54) years. Of all encounters, 35.3% were methamphetamine + and PS -, and 46.3% were methamphetamine + and PS +. Hospitalizations increased from 883 in 2012 to 2532 in 2021. The median (IQR) hospital stay was 48 (48-120) hours. Of all encounters, 16.8% included an intensive care unit (ICU) admission, and the median ICU stay was 42 (21-87) hours. A total of 2988 patients (24.7%) were readmitted within the study period, and 4988 (71.5%) returned within 1 year of the previous encounter. In context of all emergency department admissions from 2013 to 2022, 13.1% had a urine drug screen + for methamphetamine. CONCLUSIONS: Hospitalizations with recent methamphetamine use doubled at our institution from 2012 to 2022. In addition, 1 in 4 is readmitted (typically within 1 year), and a minority requires ICU care.

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

OBJECTIVES: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. DESIGN: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. SETTINGS: Thirty-five hospitals across the United States from 2017 to 2021. PATIENTS: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. CONCLUSIONS: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Impact of a deep learning sepsis prediction model on quality of care and survival.

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.