Treatment with stimulants and the risk of COVID-19 complications in adults with ADHD.

Adults with attention deficit hyperactivity disorder (ADHD) have shown higher infection rates and worse outcomes from COVID-19. Stimulant medications are prescribed as the first-line treatment for ADHD in adults and mitigate risk of negative ADHD-related health outcomes, but little is known about the association between stimulant medications and COVID-19 outcomes. The objective of this study was to assess the risks of severe COVID-19 outcomes among people with ADHD who were prescribed stimulant medications versus those who were not. This retrospective cohort study used electronic health records in the TriNetX research database. We assessed records of adults with ADHD diagnosed with COVID-19 between January 1, 2020 and June 30, 2021. The stimulant cohort consisted of 28,011 people with at least one stimulant prescription; the unmedicated cohort comprised 42,258 people without prescribed stimulants within 12 months prior to their COVID infection. Multiple logistic regression modeling was utilized to assess the presence of critical care services or death within 30 days after the onset of COVID diagnoses, controlling for patient demographics, and comorbid medical and mental health conditions. The stimulant cohort was less likely to utilize emergency department, hospital, and intensive care services than the unmedicated cohort, and had significantly lower 30-day mortality. Further research, including prospective studies, is needed to confirm and refine these findings.

External Validation of the COVID-NoLab and COVID-SimpleLab Prognostic Tools.

Our objective was to externally validate 2 simple risk scores for mortality among a mostly inpatient population with COVID-19 in Canada (588 patients for COVID-NoLab and 479 patients for COVID-SimpleLab). The mortality rates in the low-, moderate-, and high-risk groups for COVID-NoLab were 1.1%, 9.6%, and 21.2%, respectively. The mortality rates for COVID-SimpleLab were 0.0%, 9.8%, and 20.0%, respectively. These values were similar to those in the original derivation cohort. The 2 simple risk scores, now successfully externally validated, offer clinicians a reliable way to quickly identify low-risk inpatients who could potentially be managed as outpatients in the event of a bed shortage. Both are available online (https://ebell-projects.shinyapps.io/covid_nolab/ and https://ebell-projects.shinyapps.io/COVID-SimpleLab/).

Patients' View of Their Primary Care Telemedicine During the COVID-19 Pandemic and Implications for Future Integration: A Multimethod Study.

INTRODUCTION: Telemedicine has become an integral part of primary care since the COVID-19 pandemic. This paper reports patients' assessments of their early telemedicine visits. METHODS: Adult primary care patients who had a telemedicine visit were identified from electronic medical records of a large Midwestern health system and randomly invited to participate in semistructured interviews. Participants compared telemedicine visits (audio and video) to face-to-face visits on measures of satisfaction and answered open-ended questions about the technology, primary care relationships, and ongoing use of telemedicine. Interviews were recorded and responses transcribed for qualitative analysis. RESULTS: The quantitative results revealed participants valued convenience and judged telemedicine visits "about the same" as office visits on satisfaction measures. Participants were largely willing to have another telemedicine visit but were concerned with the technological challenges and lack of physical examination. The qualitative analysis found most participants reported that telemedicine care was best with a known clinician. Further, they judged telemedicine to be best for follow-ups and simple or single problems and believed it should be balanced with face-to-face visits. CONCLUSIONS: Participants expect telemedicine will continue and have clearly articulated their telemedicine preferences. These preferences include telemedicine with a known clinician, the visits that they judged most appropriate for telemedicine, the need to balance telemedicine with face-to-face visits, and assured technologic access. The need for quality measures beyond patient satisfaction and the role of team-based telemedicine care emerged as areas for further research.

Risks of Severe COVID-19 Outcomes Among Patients With Diabetic Polyneuropathy in the United States.

CONTEXT: Diabetic neuropathy (DN) affects more than 50% of diabetic patients who are also likely to have compromised immune system and respiratory function, both of which can make them susceptible to the SARS-CoV-2 virus. OBJECTIVE: To assess the risk of severe COVID-19 illness among adults with DN, compared with those with no DN and those with no diabetes. SETTING: The analysis utilized electronic health records from 55 US health care organizations in the TriNetX research database. DESIGN: A retrospective cohort study. PARTICIPANTS: The analysis included 882 650 adults diagnosed with COVID-19 in January 2020 to June 2021, including 16 641 with DN, 81 329 with diabetes with no neuropathy, and 784 680 with no diabetes. OUTCOME MEASURES: The presence of health care utilization (admissions to emergency department, hospital, intensive care unit), 30-day mortality, clinical presentation (cough, fever, hypoxemia, dyspnea, or acute respiratory distress syndrome), and diagnostic test results after being infected affected by COVID-19. RESULTS: The DN cohort was 1.19 to 2.47 times more likely than the non-DN cohorts to utilize care resources, receive critical care, and have higher 30-day mortality rates. Patients with DN also showed increased risk (1.13-2.18 times) of severe symptoms, such as hypoxemia, dyspnea, and acute respiratory distress syndrome. CONCLUSIONS: Patients with DN had a significantly greater risk of developing severe COVID-19-related complications than those with no DN. It is critical for the public health community to continue preventive measures, such as social distancing, wearing masks, and vaccination, to reduce infection rates, particularly in higher risk groups, such as those with DN.

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study.

Objectives: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. Design: Multicenter retrospective observational cohort study. Setting: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. Patients: Coronavirus Disease 2019-confirmed and hospitalized adult patients. Measurements and Main Results: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score. Conclusions: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

COVID-19 outcomes among adult patients treated with long-term opioid therapy for chronic non-cancer pain in the USA: a retrospective cohort study.

OBJECTIVE: Patients treated with long-term opioid therapy (LTOT) are known to have compromised immune systems and respiratory function, both of which make them particularly susceptible to the SARS-CoV-2 virus. The objective of this study was to assess the risk of developing severe clinical outcomes among COVID-19 non-cancer patients on LTOT, compared with those without LTOT. DESIGN AND DATA SOURCES: A retrospective cohort design using electronic health records in the TriNetX research database. PARTICIPANTS AND SETTING: 418 216 adults diagnosed with COVID-19 in January-December 2020 from 51 US healthcare organisations: 9558 in the LTOT and 408 658 in the control cohort. They did not have cancer diagnoses; only a small proportion might have been treated with opioid maintenance for opioid use disorder. RESULTS: Patient on LTOT had a higher risk ratio (RR) than control patients to visit an emergency department (RR 2.04, 95% CI 1.93 to 2.16) and be hospitalised (RR 2.91, 95% CI 2.69 to 3.15). Once admitted, LTOT patients were more likely to require intensive care (RR 3.65, 95% CI 3.10 to 4.29), mechanical ventilation (RR 3.47, 95% CI 2.89 to 4.15) and vasopressor support (RR 5.28, 95% CI 3.70 to 7.53) and die within 30 days (RR 1.96, 95% CI 1.67 to 2.30). The LTOT group also showed increased risk (RRs from 2.06 to 3.98, all significant to 95% CI) of more-severe infection (eg, cough, dyspnoea, fever, hypoxaemia, thrombocytopaenia and acute respiratory distress syndrome). Statistically significant differences in several laboratory results and other vital signs appeared clinically negligible. CONCLUSION: COVID-19 patients on LTOT were at higher risk of increased morbidity, mortality and healthcare utilisation. Interventions to reduce the need for LTOT and to increase compliance with COVID-19 protective measures may improve outcomes and reduce healthcare cost in this population. Prospective studies need to confirm and refine these findings.

Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems.

PURPOSE: Develop and validate simple risk scores based on initial clinical data and no or minimal laboratory testing to predict mortality in hospitalized adults with COVID-19. METHODS: We gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. RESULTS: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive protein (CRP), and serum creatinine. The COVID-NoLab risk score includes age, respiratory rate, and oxygen saturation and identified risk groups with 0.8%, 11.4%, and 40.4% mortality in the validation group (AUROCC = 0.803). The COVID-SimpleLab score includes age, respiratory rate, oxygen saturation, WBC, CRP, serum creatinine, and comorbid asthma and identified risk groups with 1.0%, 9.1%, and 29.3% mortality in the validation group (AUROCC = 0.833). CONCLUSIONS: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.

Managing Close Contacts of COVID-19 Confirmed Cases in Metropolitan Areas in China.

The novel coronavirus (COVID-19) outbreak has rapidly spread across the world. As medical systems continue to develop vaccines and treatments, it is crucial for the public health community to establish nonpharmaceutical interventions (NPIs) that can effectively mitigate the rate of SARS-Coronavirus-2 (SARS-CoV-2) spread across highly populated residential areas, especially among individuals who have close contact with confirmed cases. A community-driven preparedness strategy has been implemented in metropolitan areas in China. The Chinese Center for Disease Control and Prevention (CCDC) has required that all COVID-19 confirmed cases be recorded and documented in a national notifiable disease surveillance system (NDSS). After receiving reports of newly confirmed cases, an epidemiological services team at the CCDC or trained medical professionals at local clinical facilities start a case-contact investigation. A task force performs home visits to infected individuals. Persons under investigation (PUIs) can stay in designated quarantine facilities for 14 days or in special circumstances can be quarantined at home. This community-based approach involved all stakeholders including local public health departments, public safety authorities, neighborhood councils, and community health centers.