Smoking Status, Nicotine Medication, Vaccination, and COVID-19 Hospital Outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) Study.

INTRODUCTION: Available 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. METHODS: Electronic 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. RESULTS: Current (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). CONCLUSIONS: Former 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. IMPLICATIONS: Prior 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.

A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19.

It is vital to determine how patient characteristics that precede COVID-19 illness relate to COVID-19 mortality. This is a retrospective cohort study of patients hospitalized with COVID-19 across 21 healthcare systems in the US. All patients (N = 145,944) had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stays from February 1, 2020 through January 31, 2022. Machine learning analyses revealed that age, hypertension, insurance status, and healthcare system (hospital site) were especially predictive of mortality across the full sample. However, multiple variables were especially predictive in subgroups of patients. The nested effects of risk factors such as age, hypertension, vaccination, site, and race accounted for large differences in mortality likelihood with rates ranging from about 2-30%. Subgroups of patients are at heightened risk of COVID-19 mortality due to combinations of preadmission risk factors; a finding of potential relevance to outreach and preventive actions.

Relations of Current and Past Cancer with Severe Outcomes among 104,590 Hospitalized COVID-19 Patients: The COVID EHR Cohort at the University of Wisconsin.

BACKGROUND: There 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. METHODS: Electronic 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. RESULTS: 6.8% 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 (aORs) for mortality were 1.58 (95% 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 amongst those with current cancer (aOR = 0.69, 95% CI = 0.53 to 0.90). CONCLUSIONS: Current cancer, especially amongst younger patients, posed a substantially increased risk for death and ICU admission among COVID-19 patients; 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. IMPACT: This 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.

The Relationship of COVID-19 Vaccination with Mortality Among 86,732 Hospitalized Patients: Subpopulations, Patient Factors, and Changes over Time.

BACKGROUND: Information on COVID-19 vaccination effects on mortality among patients hospitalized with COVID-19 could inform vaccination outreach efforts and increase understanding of patient risk. OBJECTIVE: Determine the associations of vaccination status with mortality in adult patients hospitalized with COVID-19. DESIGN: This retrospective cohort study assessed the characteristics and mortality rates of adult patients hospitalized with COVID-19 across 21 healthcare systems in the USA from January 1, 2021, to January 31, 2022. PARTICIPANTS: Adult patients admitted to participating hospitals who had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stay via discharge or death. MAIN MEASURE: In-hospital mortality vs. discharge (outcome) and patient age, sex, race, ethnicity, BMI, insurance status, comorbidities, and vaccination status extracted from the electronic health record (EHR). KEY RESULTS: Of 86,732 adult patients hospitalized with COVID-19, 45,082 (52%) were female, mean age was 60 years, 20,800 (24%) were Black, and 22,792 (26.3%) had one or more COVID-19 vaccinations. Statistically adjusted mortality rates for unvaccinated and vaccinated patients were 8.3% (95% CI, 8.1-8.5) and 5.1% (95% CI, 4.8-5.4) respectively (7.9% vs. 4.5% with no immune compromise). Vaccination was associated with especially large reductions in mortality for obese (OR = 0.67; 95% CI 0.56-0.80) and severely obese (OR = 0.52; 95% CI, 0.41-0.67) patients and for older patients (OR = 0.99; 95% CI, 0.98-0.99). Mortality likelihood was higher later in the study period (August 2021-January 31, 2022) than earlier (January 1, 2021-July 30, 2021) (OR = 1.10; 95% CI = 1.04-1.17) and increased significantly for vaccinated patients from 4.6% (95% CI, 3.9-5.2%) to 6.5% (95% CI, 6.2-6.9%). CONCLUSIONS: Patients vaccinated for COVID-19 had reduced mortality, especially for obese/severely obese and older individuals. Vaccination's protective effect against mortality declined over time and hospitalized obese and older individuals may derive especially great benefit from prior vaccination against SARS-CoV-2.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems.

MAIN OBJECTIVE: There 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 METHODS: University 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 SIGNIFICANCE: The 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. CONCLUSIONS: Mortality, 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 REGISTRATION: ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

Point of care tobacco treatment sustains during COVID-19, a global pandemic.

BACKGROUND: Tobacco cessation treatment for cancer patients is essential to providing comprehensive oncologic care. We have implemented a point of care tobacco treatment care model enabled by electronic health record (EHR) modifications in a comprehensive cancer center. Data are needed on the sustainability of both reach of treatment and effectiveness over time, including the COVID-19 pandemic. METHODS: Using EHR data from the pre-implementation (P: 5 months) and post-implementation periods (6 month-blocks, T1-T5 for a total of 30 months), we compared two primary outcomes: 1) reach of treatment among those smoking and 2) effectiveness assessed by smoking cessation among those smoking in the subsequent 6 month period. We analyzed the data using generalized estimation equation regression models. RESULTS: With the point of care tobacco treatment care model, reach of treatment increased from pre to post T5 (3.2 % vs. 48.4 %, RR 15.50, 95 % CI 10.56-22.74, p < 0.0001). Reach of treatment in all post periods (T1-T5 including the COVID-19 pandemic time) remained significantly higher than the pre period. Effectiveness, defined by smoking cessation among those smoking, increased from pre to post T2 before the pandemic (12.4 % vs. 21.4 %, RR 1.57, 95 % CI 1.31-1.87, p < 0.0001). However, effectiveness, while higher in later post periods (T3, T4), was no longer significantly increased compared with the pre period. CONCLUSION: A point of care EHR-enabled tobacco treatment care model demonstrates sustained reach up to 30 months following implementation, even during the COVID-19 pandemic and changes in healthcare prioritization. Effectiveness was sustained for 12 months, but did not sustain through the subsequent 12 months.

Precision health diagnostic and surveillance network uses S gene target failure (SGTF) combined with sequencing technologies to track emerging SARS‐CoV‐2 variants

Introduction The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic revealed a worldwide lack of effective molecular surveillance networks at local, state, and national levels, which are essential to identify, monitor, and limit viral community spread. SARS‐CoV‐2 variants of concern (VOCs) such as Alpha and Omicron, which show increased transmissibility and immune evasion, rapidly became dominant VOCs worldwide. Our objective was to develop an evidenced‐based genomic surveillance algorithm, combining reverse transcription polymerase chain reaction (RT‐PCR) and sequencing technologies to quickly identify highly contagious VOCs, before cases accumulate exponentially. Methods Deidentified data were obtained from 508,969 patients tested for coronavirus disease 2019 (COVID‐19) with the TaqPath COVID‐19 RT‐PCR Combo Kit (ThermoFisher) in four CLIA‐certified clinical laboratories in Puerto Rico (n = 86,639) and in three CLIA‐certified clinical laboratories in the United States (n = 422,330). Results TaqPath data revealed a frequency of S Gene Target Failure (SGTF) > 47% for the last week of March 2021 in both, Puerto Rico and US laboratories. The monthly frequency of SGTF in Puerto Rico steadily increased exponentially from 4% in November 2020 to 47% in March 2021. The weekly SGTF rate in US samples was high (>8%) from late December to early January and then also increased exponentially through April (48%). The exponential increase in SGFT prevalence in Puerto Rico was concurrent with a sharp increase in VOCs among all SARS‐CoV‐2 sequences from Puerto Rico uploaded to Global Influenza Surveillance and Response System (GISAID) (n = 461). Alpha variant frequency increased from <1% in the last week of January 2021 to 51.5% of viral sequences from Puerto Rico collected in the last week of March 2021. Conclusions According to the proposed evidence‐based algorithm, approximately 50% of all SGTF patients should be managed with VOCs self‐quarantine and contact tracing protocols, while WGS confirms their lineage in genomic surveillance laboratories. Our results suggest this workflow is useful for tracking VOCs with SGTF. The evidence‐based Molecular Epidemiology and Genomic Surveillance algorithm, developed in this study to quickly identify emerging Variants of Concern (VOCs), is a valuable tool for identifying individual carriers of highly infectious variants with the S Gene Target Failure (SGTF) feature, such as Alpha and Omicron, who can then be effectively triaged for isolation, contact tracing, and treatment purposes.

Precision health diagnostic and surveillance network uses S gene target failure (SGTF) combined with sequencing technologies to track emerging SARS-CoV-2 variants.

INTRODUCTION: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic revealed a worldwide lack of effective molecular surveillance networks at local, state, and national levels, which are essential to identify, monitor, and limit viral community spread. SARS-CoV-2 variants of concern (VOCs) such as Alpha and Omicron, which show increased transmissibility and immune evasion, rapidly became dominant VOCs worldwide. Our objective was to develop an evidenced-based genomic surveillance algorithm, combining reverse transcription polymerase chain reaction (RT-PCR) and sequencing technologies to quickly identify highly contagious VOCs, before cases accumulate exponentially. METHODS: Deidentified data were obtained from 508,969 patients tested for coronavirus disease 2019 (COVID-19) with the TaqPath COVID-19 RT-PCR Combo Kit (ThermoFisher) in four CLIA-certified clinical laboratories in Puerto Rico (n = 86,639) and in three CLIA-certified clinical laboratories in the United States (n = 422,330). RESULTS: TaqPath data revealed a frequency of S Gene Target Failure (SGTF) > 47% for the last week of March 2021 in both, Puerto Rico and US laboratories. The monthly frequency of SGTF in Puerto Rico steadily increased exponentially from 4% in November 2020 to 47% in March 2021. The weekly SGTF rate in US samples was high (>8%) from late December to early January and then also increased exponentially through April (48%). The exponential increase in SGFT prevalence in Puerto Rico was concurrent with a sharp increase in VOCs among all SARS-CoV-2 sequences from Puerto Rico uploaded to Global Influenza Surveillance and Response System (GISAID) (n = 461). Alpha variant frequency increased from <1% in the last week of January 2021 to 51.5% of viral sequences from Puerto Rico collected in the last week of March 2021. CONCLUSIONS: According to the proposed evidence-based algorithm, approximately 50% of all SGTF patients should be managed with VOCs self-quarantine and contact tracing protocols, while WGS confirms their lineage in genomic surveillance laboratories. Our results suggest this workflow is useful for tracking VOCs with SGTF.

Can inpatient pharmacists move the needle on smoking cessation? Evaluating reach and representativeness of a pharmacist-led opt-out smoking cessation intervention protocol for hospital settings.

PURPOSE: Hospitalization affords an opportunity to reduce smoking, but fewer than half of patients who smoke receive evidence-based cessation treatment during inpatient stays. This study evaluated a pharmacist-led, electronic health record (EHR)-facilitated opt-out smoking cessation intervention designed to address this need. METHODS: Analyses of EHR records for adult patients who smoked in the past 30 days admitted to an academic medical center in the upper Midwest were conducted using the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework. The reach of a pharmacist-led, EHR-facilitated protocol for smoking cessation treatment was assessed by comparing patients' receipt of nicotine replacement therapy (NRT) and tobacco quitline referral before and after implementation. χ2 tests, t tests, and multiple logistic regression models were used to compare reach across patient demographic groups to assess treatment disparities and the representativeness of reach. Adoption of the program by hospital services was also assessed. RESULTS: Of the 70 hospital services invited to implement the program, 88.6% adopted it and 78.6% had eligible admissions. Treatment reach increased as rates of delivering NRT rose from 43.6% of eligible patients before implementation to 50.4% after implementation (P < 0.0001) and quitline referral rates rose from 0.9% to 11.9% (P < 0.0001). Representativeness of reach by sex and ethnicity improved after implementation, although disparities by race and age persisted after adjustment for demographics, insurance, and primary diagnosis. Pharmacists addressed tobacco use for eligible patients in 62.5% of cases after protocol implementation. CONCLUSION: Smoking cessation treatment reach and representativeness of reach improved after implementation of a proactive, pharmacist-led, EHR-facilitated opt-out smoking cessation treatment protocol in adult inpatient services.

The End of Step 2 CS Should Be the Beginning of a New Approach to Clinical Skills Assessment.

The discontinuation of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) in 2020 in response to the COVID-19 pandemic marked the end of a decades-long debate about the utility and value of the exam. For all its controversy, the implementation of Step 2 CS in 2004 brought about profound changes to the landscape of medical education, altering the curriculum and assessment practices of medical schools to ensure students were prepared to take and pass this licensing exam. Its elimination, while celebrated by some, is not without potential negative consequences. As the responsibility for assessing students' clinical skills shifts back to medical schools, educators must take care not to lose the ground they have gained in advancing clinical skills education. Instead, they need to innovate, collaborate, and share resources; hold themselves accountable; and ultimately rise to the challenge of ensuring that physicians have the necessary clinical skills to safely and effectively practice medicine.

Harnessing Empathy to Scale a Healthtech Startup During the COVID-19 Pandemic: A Case Study Of myICUvoice, a Communication Tool Designed for Critical Care

This case study explores the scaling experience of an early-stage healthtech startup company called myICUvoice. During the Covid-19 pandemic, myICUvoice rapidly scaled from a single intensive care environment to being widely used nationally (UK) as well as globally. We explore why and how so many volunteers were motivated to donate their time and expertise to help scale this early stage startup. Specifically, we examine the roles that empathy played throughout the scaling process. There are three distinct types of empathy that we have identified in our story: em-pathos, empathetic understanding, and mass-empathy. These each had a distinct role in driving the startup forward. Importantly, we note that human-centered design (which often focuses almost exclusively on achieving empathetic understanding) will immensely benefit from considering the multiple types, and multi-faceted powers, of empathy.