Stress Resilience Program for Health Care Professionals During a Pandemic: A Pilot Program.

BACKGROUND: The COVID-19 pandemic has led to increased burnout and staff turnover for health care providers (HCPs). The purpose of this pilot study was to evaluate the safety and acceptability of a Stress Resilience Program (SRP) for reducing perceived stress and improving resilience among HCPs during a pandemic. METHOD: Of the 12 HCPs expressing interest in the study, 10 were enrolled in this study. Participants attended three in-person visits (consent/screen, baseline, and end-of-study). The SRP consisted of education related to resilience enhancement and a breathing device (BreatherFit®) for combined respiratory muscle training (cRMT). Participants completed 4 weeks of cRMT and applied situational breathing strategies as needed. Outcomes measured were changes in stress (PSS-10), resilience (BRS), depression (PRIME-MD), and sleep (PSQI and Oura Ring®). FINDINGS: The majority of participants were male (60%) and White (60%) with an average age of 39.7 years. Changes from baseline to end-of-treatment indicated a positive trend with significant stress reduction (-3.2 ± 3.9, p = .028) and nonsignificant depression reduction (-0.5 ± 0.7, p = .05). Resilience was high at baseline and continued to stay high during the study with a nonsignificant increase at end-of-study (+0.07 ± 0.7, p = .77). No changes in overall sleep scores were noted. All participants agreed the study was worthwhile, 80% indicated they would repeat the experience, while 90% indicated they would recommend the study to others. CONCLUSION/APPLICATION TO PRACTICE: Because of its size and portability, SRP is an easily applicable and promising option for reducing stress among HCPs during a high-stress period, such as a pandemic. Larger studies are needed.

Post-discharge early assessment with remote video link (PEARL) initiative for patients discharged from hospital medicine services.

OBJECTIVES: The COVID-19 pandemic impacted the availability and accessibility of outpatient care following hospital discharge. Hospitalists (physicians) and hospital medicine advanced practice providers (HM-APPs) coordinate discharge care of hospitalized patients; however, it is unknown if they can deliver post-discharge virtual care and overcome barriers to outpatient care. The objective was to develop and provide post-discharge virtual care for patients discharged from hospital medicine services. METHODS: We developed the Post-discharge Early Assessment with Remote video Link (PEARL) initiative for HM-APPs to conduct a post-discharge video visit (to review recommendations) and telephone follow-up (to evaluate adherence) with patients 2-6 days following hospital discharge. Participants included patients discharged from hospital medicine services at an institution's hospitals in Rochester (May 2020-August 2020) and Austin (November 2020-February 2021) in Minnesota, US. HM-APPs also interviewed patients about their experience with the video visit and completed a survey on their experience with PEARL. RESULTS: Of 386 eligible patients, 61.4% were enrolled (n = 237/386) including 48.1% women (n = 114/237). In patients with complete video visit and telephone follow-up (n = 141/237), most were prescribed new medications (83.7%) and took them as prescribed (93.2%). Among five classes of chronic medications, patient-reported adherence ranged from 59.2% (narcotics) to 91.5% (anti-hypertensives). Patient-reported self-management of 12 discharge recommendations ranged from 40% (smoking cessation) to 100% (checking rashes). Patients reported benefit from the video visit (agree: 77.3%) with an equivocal preference for video visits over clinic visits. Among HM-APPs who responded to the survey (88.2%; n = 15/17), 73.3% reported benefit from visual contact with patients but were uncertain if video visits would reduce emergency department visits. CONCLUSION: In this novel initiative, HM-APPs used video visits to provide care beyond their hospital role, reinforce discharge recommendations for patients, and reduce barriers to outpatient care. The effect of this initiative is under evaluation in a randomized controlled trial.

Wellness of hospitalists and hospital medicine advanced practice providers during the COVID-19 pandemic, 2020-2021.

BACKGROUND: The early phase of the coronavirus disease 2019 (COVID-19) pandemic had a negative impact on the wellness of hospitalists and hospital medicine advanced practice providers (APPs). However, the burden of the pandemic has evolved and the change in hospitalist and hospital medicine APP wellness is unknown. OBJECTIVE: To evaluate the longitudinal trend in wellness of hospitalists and hospital medicine APPs during the COVID-19 pandemic and guide wellness interventions. DESIGN, SETTING AND PARTICIPANTS: Between May 4, 2020, and June 6, 2021, we administered three surveys to Internal Medicine hospitalists (physicians) and hospital medicine APPs (nurse practitioners and physician assistants) at 16 Mayo Clinic hospitals in four U.S. states. MEASUREMENTS: We evaluated the association of hospitalist and hospital medicine APP characteristics with PROMIS® measures of global wellbeing-mental health, global wellbeing-social activities and relationships, anxiety, social isolation, and emotional support, using logistic and linear regression models. RESULTS: The response rates were 52.2% (n=154/295; May 2020), 37.1% (n=111/299; October 2020) and 35.5% (n=114/321; May 2021). In mixed models that included hospitalist and hospital medicine APP characteristics and survey period, APPs, compared with physicians, had lower odds of top global wellbeing-social activities and relationships (adjusted odds ratio 0.42 [0.22-0.82]; p = .01), whereas survey period showed no association. The survey period showed an independent association with higher anxiety (May 2020 vs. others) and higher social isolation (October 2020 vs. others), whereas profession showed no association. Concern about contracting COVID-19 at work was significantly associated with lower odds of top global wellbeing-mental health and global wellbeing-social activities and relationships, and with higher anxiety and social isolation. Hospitalist and hospital medicine APP characteristics showed no association with levels of emotional support. CONCLUSIONS: In this longitudinal assessment of hospitalists and hospital medicine APPs, concern about contracting COVID-19 at work remained a determinant of wellness. The trend for global wellbeing, anxiety, and social isolation may guide wellness interventions.

Utilization of an Electronic Health Record Integrated Risk Score to Predict Hospitalization Among COVID-19 Patients.

OBJECTIVE: To evaluate the performance of an Electronic Health Record (EHR) integrated risk score for COVID-19 positive outpatients to predict 30-day risk of hospitalization. PATIENTS AND METHODS: A retrospective observational study of 67 470 patients with COVID-19 confirmed by polymerase chain reaction (PCR) test between March 12, 2020 and February 8, 2021. Risk scores were calculated based on data in the chart at the time of the incident infection. RESULTS: The Mayo Clinic COVID-19 risk score consisted of 13 components included age, sex, chronic lung disease, congenital heart disease, congestive heart failure, coronary artery disease, diabetes mellitus, end stage liver disease, end stage renal disease, hypertension, immune compromised, nursing home resident, and pregnant. Univariate analysis showed all components, except pregnancy, have significant (P < .001) association with admission. The Mayo Clinic COVID-19 risk score showed a Receiver Operating Characteristic Area Under Curve (AUC) of 0.837 for the prediction of admission for this large cohort of COVID-19 positive patients. CONCLUSION: The Mayo Clinic COVID-19 risk score is a simple score that is easily integrated into the EHR with excellent predictive performance for severe COVID-19. It can be leveraged to stratify risk for severe COVID-19 at initial contact, when considering therapeutics or in the allocation of vaccine supply.

Symptoms COVID 19 Positive Vapers Compared to COVID 19 Positive Non-vapers.

OBJECTIVES: The purpose of the present study was to assess and describe the severity of symptoms reported by Covid-19 positive patients who vaped (smoked e-cigarettes) when compared to those who did not vape or smoke at the time of the diagnosis of Covid-19. METHODS: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between March 1, 2020 and February 28, 2021; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Among the 1734 eligible patients, 289 patients reported current vaping. The cohort of vapers (N = 289) was age and gender matched to 1445 covid-19 positive patients who did not vape. The data analyzed included: date of birth, gender, ethnicity, race, marital status, as well as lifestyle history such as vaping and smoking and reported covid-19 symptoms experienced. RESULTS: A logistic regression analysis was performed separately for each symptom using generalized estimating equations (GEE) with robust variance estimates in order to account for the 1:5 age, sex, and race matched set study design. Patients who vaped and developed Covid-19 infection were more likely to have chest pain or tightness (16% vs 10%, vapers vs non vapers, P = .005), chills (25% vs 19%, vapers vs non vapers, P = .0016), myalgia (39% vs 32%, vapers vs non vapers, P = .004), headaches (49% vs 41% vapers vs non vapers, P = .026), anosmia/dysgeusia (37% vs 30%, vapers vs non vapers, P = .009), nausea/vomiting/abdominal pain (16% vs 10%, vapers vs non vapers, P = .003), diarrhea (16% vs 10%, vapers vs non vapers, P = .004), and non-severe light-headedness (16% vs 9%, vapers vs non vapers, P < .001). CONCLUSION: Vapers experience higher frequency of covid-19 related symptoms when compared with age and gender matched non-vapers. Further work should examine the impact vaping has on post-covid symptom experience.

COVID-19 Care Clinic in a Medical Center: Lessons Learned.

OBJECTIVE: The purpose of this report is to describe the elements of a Covid-19 Care Clinic (CCC), patient demographics, and outcomes. METHODS: Descriptive statistics were used to describe demographics, clinical characteristics, and outcomes. This report is based on 4934 unique patients seen in the CCC who provided research authorization within a 10-month period of time (April 1, 2020-January 31, 2021). The CCC infection control processes consisted of a rooming process that mitigated SARS-COV-2 transmission, preparing examination rooms, using PPE by staff, in room lab drawing, and escorting services to minimize the time in clinic. RESULTS: Of the 4934 unique patients seen (age range newborn-102 years), 76.8% were tested for COVID-19. Of those tested, 11.8% were positive for SARS-CoV-2. Ninety-two percent of the patients with the reason for the visit documented had COVID-19 type symptoms. Cough, shortness of breath, and chest pain were the most common presenting symptom in those with COVID-19. At the time of the visit in the CCC, 5.8% of the patients were actively contagious. Thirty days after being seen in the CCC, 9.1% of the patients were seen in the emergency department (ED) and 0.2% died. During the 10-month period there were no known occupationally related COVID-19 infections. CONCLUSION: The COVID-19 Care Clinic provided face-to-face access for all ages with COVID-19 type symptoms. A minority of patients had COVID-19 who were seen in the clinic. The clinic provided an additional venue of care outside of the ED. The infectious control measures employed were highly effective in protecting the staff. Lessons learned allow for decentralization of COVID-19 symptom care to the primary care practices employing the infection control measures.

Electronic Cigarette Use Is Not Associated with COVID-19 Diagnosis.

This analysis tested the hypothesis that current e-cigarette use was associated with an increased risk of SARS-CoV-2 infection in patients seeking medical care. E-cigarette and conventional cigarette use were ascertained using a novel electronic health record tool, and COVID-19 diagnosis was ascertained by a validated institutional registry. Logistic regression models were fit to assess whether current e-cigarette use was associated with an increased risk of COVID-19 diagnosis. A total of 69,264 patients who were over the age of 12 years, smoked cigarettes or vaped, and were sought medical care at Mayo Clinic between September 15, 2019 and November 30, 2020 were included. The average age was 51.5 years, 62.1% were females and 86.3% were white; 11.1% were currently smoking cigarettes or using e-cigarettes and 5.1% tested positive for SARS-CoV-2. Patients who used only e-cigarettes were not more likely to have a COVID-19 diagnosis (OR 0.93 [0.69-1.25], P = .628), whereas those who used only cigarettes had a decreased risk (OR 0.43 [0.35-0.53], P < .001). The OR for dual users fell between these 2 values (OR 0.67 [0.49-0.92], P = .013). Although e-cigarettes have the well-documented potential for harm, they do not appear to increase susceptibility to SARS-CoV-2 infection. This result suggests the hypothesis that any beneficial effects of conventional cigarette smoking on susceptibility are not mediated by nicotine.

Body Mass Index, Multi-Morbidity, and COVID-19 Risk Factors as Predictors of Severe COVID-19 Outcomes.

PURPOSE: The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. PATIENTS: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. MEASURES: Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. RESULTS: Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. CONCLUSION: Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

A Midwest COVID-19 Cohort for the Evaluation of Multimorbidity and Adverse Outcomes from COVID-19.

OBJECTIVE: To describe the process and outcome of creating a patient cohort in the early stages of the COVID-19 pandemic in order to better understand the process of and predict the outcomes of COVID-19. PATIENTS AND METHODS: A total of 1169 adults aged 18 years of age or older who tested positive in Mayo Clinic Rochester or the Mayo Clinic Midwest Health System between January 1 and May 23 of 2020. RESULTS: Patients were on average 43.9 years of age and 50.7% were female. Most patients were white (69.0%), and Blacks (23.4%) and Asians (5.8%) were also represented in larger numbers. Hispanics represented 16.3% of the sample. Just under half of patients were married (48.4%). Common comorbid conditions included: cardiovascular diseases (25.1%), dyslipidemia (16.0%), diabetes mellitus (11.2%), chronic obstructive pulmonary disease (6.6%), asthma (7.5%), and cancer (5.1%). All other comorbid conditions were less the 5% in prevalence. Data on 3 comorbidity indices are also available including the: DHHS multi-morbidity score, Charlson Comorbidity Index, and Mayo Clinic COVID-19 Risk Factor Score. CONCLUSION: In addition to managing the ever raging pandemic and growing death rates, it is equally important that we develop adequate resources for the investigation and understanding of COVID-19-related predictors and outcomes.

Stress, Resilience, and Coping of Healthcare Workers during the COVID-19 Pandemic.

OBJECTIVE: To estimate the health care workers (HCWs) self-reported stress, resilience, and coping during the COVID-19 pandemic, and to determine inter-professional differences. PARTICIPANTS AND METHODS: An email survey was sent to 474 HCW at a Midwestern HealthCare facility between April 9, 2020 and April 30, 2020. A total of 311 (65.6%) responses were received by May 31, 2020. The survey utilized 3 validated instruments: Perceived Stress Scale (PSS), Brief Resilience Scale (BRS), Brief Resilience Coping Scale (BRCS). RESULTS: Of the 311 responses, 302 were evaluated: 97 from nonmedical staff with patient contact (NMPC); 86 from nonmedical staff with no patient contact (NMNPC); 62 from medical doctors (MD), physician assistants (PA) and nurse practitioners (NP); and 57 from nurses. Significant differences were noted across job categories for stress and resilience, with nurses reporting highest PSS scores (effect estimates: -2.72, P = .009 for NMNPC; -2.50, P = .015 for NMPC; -3.21, P = .006 for MD/NP/PA respectively), and MD/NP/PA group with highest BRS scores: nurses (-0.31, P = .02); NMPC (-0.3333, P = .01); and NMNPC (-0.2828, P = .02). Younger personnel had higher stress (-1.59 per decade of age, P < .01) and more resilience (0.11 per decade of age, P = .002). CONCLUSION: These self-reported data indicate that MD/NP/PA had the highest resilience scores and the nurses had highest stress levels. Efforts are warranted to include all HCWs in systematic stress mitigating interventions with particular attention to understand specific factors contributing to stress for the nursing team.