The association of the COVID-19 pandemic with AUD and AW has been much debated. 5 This cohort study found an overall increase in AW rates in 2020, with a peak at the end of the stay-at-home order. Moreover, increased AW rates continued during the reopening phases. It is not clear why IRRs were higher in 2020 before the stay-at-home orders. Although the use of the revised Clinical Institute Withdrawal Assessment for Alcohol to identify AW limits the false-positive rate, it may underestimate the true AW rate and may be a limitation of the study. These findings suggest negative associations of the pandemic with AW. Stress, anxiety, disrupted treatment plans, and increased alcohol use might be factors associated with higher rates of AW, because higher rates persisted during the reopening phases. With the recent surge in COVID-19 cases, many states might revert to stay-at-home orders and this trend may worsen. Increased vigilance to identify AW among hospitalized patients and to use systematic screening will be pivotal in the management of AW.

Background: Opioid-related inpatient hospital stays are increasing at alarming rates. Unidentified and poorly treated opioid withdrawal may be associated with inpatients leaving against medical advice and increased health care utilization. To address these concerns, we developed and implemented a clinical pathway to screen and treat medical service inpatients for opioid withdrawal. Methods: The pathway process included a two-item universal screening instrument to identify opioid withdrawal risk (Opioid Withdrawal Risk Assessment [OWRA]), use of the validated Clinical Opiate Withdrawal Scale (COWS) to monitor opioid withdrawal symptoms and severity, and a 72-h buprenorphine/naloxone-based treatment protocol. Implementation outcomes including adoption, fidelity, and sustainability of this new pathway model were measured. To assess if there were changes in nursing staff acceptability, appropriateness, and adoption of the new pathway process, a cross-sectional survey was administered to pilot four hospital medical units before and after pathway implementation. Results: Between 2016 and 2018, 72.4% (77,483/107,071) of admitted patients received the OWRA screening tool. Of those, 3.0% (2,347/77,483) were identified at risk for opioid withdrawal. Of those 2,347 patients, 2,178 (92.8%) were assessed with the COWS and 29.6% (645/2,178) were found to be in active withdrawal. A total of 49.5% (319/645) patients were treated with buprenorphine/naloxone. Fifty-seven percent (83/145) of nurses completed both the pre- and post-pathway implementation surveys. Analysis of the pre/post survey data revealed that nurse respondents were more confident in their ability to determine which patients were at risk for withdrawal ( p  =  .01) and identify patients currently experiencing withdrawal ( p < .01). However, they cited difficulty working with the patient population and coordinating care with physicians. Conclusions: Our study demonstrates a process for successfully implementing and sustaining a clinical pathway to screen and treat medical service inpatients for opioid withdrawal. Standardizing care delivery for patients in opioid withdrawal can also improve nursing confidence when working with this complex population. Plain Language Summary: Opioid-related hospital stays are increasing at alarming rates. Unidentified and poorly treated opioid withdrawal may be associated with patients leaving the hospital against medical advice and increased health care utilization. To address the concerns surrounding an increase in admissions associated with unidentified or poorly treated opioid withdrawal, we developed and implemented a clinical pathway process to consistently screen and treat hospitalized patients for opioid withdrawal. We found that opioid withdrawal screening was successfully implemented and sustained over a 24-month evaluation period. We also found that standardizing care delivery for patients in opioid withdrawal improved nursing confidence when working with this patient population. A robust and ongoing education and training process is important for current staff to ensure knowledge does not erode over time and that training for new staff is embedded in the pathway process to maintain training consistency.

ObjectiveTo measure trends for the emergence of opioid withdrawal (OW) and leaving against medical advice (AMA) among hospitalized patients.MethodRetrospective time-series of hospitalized patients with OW, defined by a Clinical Opioid Withdrawal score >8, using electronic health record data at a tertiary health system and of patients with a discharge status of AMA from January 1, 2017 to December 31, 2020.ResultsThe average number of monthly hospitalizations with OW showed a year-to-year increment of 15% in 2018, 21% in 2019, and 34% from 2019 to 2020, whereas the total monthly hospitalizations remained stable. The segmented regression analysis showed that the upward trend in hospitalizations with OW became significant after January 2019 (slope: 1.14, 95% confidence interval [CI]: 0.70, 1.57). After August 2019, Fentanyl was added to the hospital urine drug testing panel and was identified in most OW patients. The monthly proportion of patients who left AMA was significantly higher among the OW patients than among all other admitted patients. There was a significant increase of 0.39 (95% CI: 0.29–0.49, P < 0.001) per month in %AMA among patients with OW. The estimated difference in %AMA among OW patients versus all other patients was 7.25 (95% CI: 5.12–9.38) in January 2017, and 16.92 (95% CI: 14.60–19.24) in December 2020.ConclusionsThe number of hospitalized patients either presenting with or developing OW increased between 2017 and 2020 with a significant rise occurring after January 2019. The percentage of patients who left AMA among those who developed OW steadily worsened during the entire study period.