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IntroductionAvailable evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown.MethodsElectronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission.ResultsCurrent (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06-1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04-1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50-0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16-0.66) and former smokers (aOR, 0.47; 95% CI, 0.39-0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66-0.83) than never smokers (aOR, 0.87; 95% CI, 0.79-0.97).ConclusionsFormer but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers.ImplicationsPrior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

BackgroundThere is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics.MethodsElectronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined.Results6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46-1.70] and 1.04 (95% CI, 0.96-1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53-0.90).ConclusionsCurrent cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types.ImpactThis study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3.

Main objectiveThere is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic.Study design and methodsUniversity of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission.Results and significanceThe 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months.ConclusionsMortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes.Trial registrationClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Existing outpatient parenteral antibiotic therapy (OPAT) registries are resource intensive, and OPAT programs struggle to produce objective data to show the value of their work. We describe the building and validation of an automated OPAT registry within our electronic medical record and provide objective data on the value of the program. Variables and outcomes include age, sex, race, ethnicity, primary insurance payor, antibiotic names, infection syndromes treated, discharge disposition, 30-day all-cause readmission and death rates, complications, and an estimate of the hospital days saved. Records for 146 OPAT episodes were reviewed manually to validate the registry. Data were displayed in a dashboard within the electronic medical record. Over the 4-year time frame, our registry collected 3956 unique patients who completed 4710 episodes (approximately 1200 episodes per year). A total of 400 complications during OPAT were identified. All variables had an accuracy of >90% on validation. The OPAT program resulted in a reduction in hospital length of stay by 88 820 days, or roughly 22 000 days per year. We intend our registry to serve as a blueprint for similar OPAT programs with limited administrative resources. Wider application of our system would allow for easier aggregation and comparisons of OPAT practice and address the lack interinstitutional standardization of OPAT data and outcomes. [ABSTRACT FROM AUTHOR]

Background: Treatment of alcohol withdrawal syndrome (AWS) in hospitals is inconsistent. Electronic health record (EHR) order sets protocolize care., Objective: We examined variation in AWS order sets across hospital organizations and their concordance with AWS guidelines., Methods: We conducted a cross-sectional study of hospital organization user-created EHR order sets for AWS extracted from the December 2021 Epic® userweb community library. Hospital organizations with an acute care hospital and ≥ $\ge $ 1 AWS order set were included. We measured the proportion of guideline-concordant care practices within four categories: (1) laboratory assessment, (2) risk assessment for severe AWS and associated management changes, (3) symptom assessment and treatment of AWS, and identification and management of complications and (4) screening, diagnosis, and treatment of unhealthy alcohol use and AUD including medications for alcohol use disorder (MAUD)., Results: Ninety-five organizations with 289 order sets were included. The proportion of organizations with guideline-concordant laboratory assessments included testing of electrolytes (83%), hepatic function (75%), substance use (83%), and screening for infections (33%). Guidance for assessing risk of severe AWS (34%) and indications for care escalation (63%) used inconsistent definitions. Use of guideline-concordant medications for AWS (99%) and AWS symptom scores (91%) were nearly universal. MAUD was included by two organizations (2%). A common templated order set was used by 26% of organizations in EHR order sets., Conclusions: We observed frequent organizational inclusion of guideline-concordant medications and symptom scores but rare and/or poorly defined guidance for evaluating risk of severe AWS, escalation of care, and MAUD., (© 2024 Society of Hospital Medicine.)

Competing Interests: None declared.

Accurate medication lists are essential data required to make clinically informed decisions. Obtaining a comprehensive, up-to-date medication list is difficult for clinicians. Patients have limited input into reviewing and reconciling their own medication data. Ideally, a medication list would comprise a 360-degree view of all prescribed, dispensed, purchased medications and would seamlessly connect patients and providers to medication data from multiple sources. While an ideal medication list would capture every aspect of medication management, in reality a Best-Possible Medication History (BPMH) is a more achievable goal. In an effort to realize a BPMH and to facilitate the goals of the State of Connecticut's Office of Health Strategy's Medication Reconciliation and Polypharmacy Committee (MRPC), we engaged stakeholders (patients, clinicians, advocates) in focus-groups and interviews to solicit feedback on the user interface requirements for a BPMH. Feedback was obtained via facilitated discussions that occurred in-person, via virtual meetings, and through online surveys., (©2022 AMIA - All rights reserved.)

The Guideline Elements Model (GEM) was developed in 2000 to organize the information contained in clinical practice guidelines using XML and to represent guideline content in a form that can be understood by human readers and processed by computers. In this work, we systematically reviewed the literature to better understand how GEM was being used, potential barriers to its use, and suggestions for improvement. Fifty external and twelve internally produced publications were identified and analyzed. GEM was used most commonly for modeling and ontology creation. Other investigators applied GEM for knowledge extraction and data mining, for clinical decision support for guideline generation. The GEM Cutter software-used to markup guidelines for translation into XML- has been downloaded 563 times since 2000. Although many investigators found GEM to be valuable, others critiqued its failure to clarify guideline semantics, difficulties in markup, and the fact that GEM files are not usually executable.