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ObjectiveExtracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation.Material and MethodsWe included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics.ResultsECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO, had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO.Discussion and ConclusionsWe developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

Raw audit logs provide a comprehensive record of clinicians’ activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

Background Accurate estimation of surgical transfusion risk is essential for efficient allocation of blood bank resources and for other aspects of anesthetic planning. This study hypothesized that a machine learning model incorporating both surgery- and patient-specific variables would outperform the traditional approach that uses only procedure-specific information, allowing for more efficient allocation of preoperative type and screen orders. Methods The American College of Surgeons National Surgical Quality Improvement Program Participant Use File was used to train four machine learning models to predict the likelihood of red cell transfusion using surgery-specific and patient-specific variables. A baseline model using only procedure-specific information was created for comparison. The models were trained on surgical encounters that occurred at 722 hospitals in 2016 through 2018. The models were internally validated on surgical cases that occurred at 719 hospitals in 2019. Generalizability of the best-performing model was assessed by external validation on surgical cases occurring at a single institution in 2020. Results Transfusion prevalence was 2.4% (73,313 of 3,049,617), 2.2% (23,205 of 1,076,441), and 6.7% (1,104 of 16,053) across the training, internal validation, and external validation cohorts, respectively. The gradient boosting machine outperformed the baseline model and was the best- performing model. At a fixed 96% sensitivity, this model had a positive predictive value of 0.06 and 0.21 and recommended type and screens for 36% and 30% of the patients in internal and external validation, respectively. By comparison, the baseline model at the same sensitivity had a positive predictive value of 0.04 and 0.144 and recommended type and screens for 57% and 45% of the patients in internal and external validation, respectively. The most important predictor variables were overall procedure-specific transfusion rate and preoperative hematocrit. Conclusions A personalized transfusion risk prediction model was created using both surgery- and patient-specific variables to guide preoperative type and screen orders and showed better performance compared to the traditional procedure-centric approach. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

A randomized controlled trial (RCT) is used to study the safety and efficacy of new treatments, by comparing patient outcomes of an intervention group with a control group. Traditionally, RCTs rely on statistical analyses to assess the differences between the treatment and control groups. However, such statistical analyses are generally not designed to assess the impact of the intervention at an individual level. In this paper, we explore machine learning models in conjunction with an RCT for personalized predictions of a depression treatment intervention, where patients were longitudinally monitored with wearable devices. We formulate individual-level predictions in the intervention and control groups from an RCT as a multi-task learning (MTL) problem, and propose a novel MTL model specifically designed for RCTs. Instead of training separate models for the intervention and control groups, the proposed MTL model is trained on both groups, effectively enlarging the training dataset. We develop a hierarchical model architecture to aggregate data from different sources and different longitudinal stages of the trial, which allows the MTL model to exploit the commonalities and capture the differences between the two groups. We evaluated the MTL approach in an RCT involving 106 patients with depression, who were randomized to receive an integrated intervention treatment. Our proposed MTL model outperforms both single-task models and the traditional multi-task model in predictive performance, representing a promising step in utilizing data collected in RCTs to develop predictive models for precision medicine.

Psychotherapy is a standard depression treatment; however, determining a patient's prognosis with therapy relies on clinical judgment that is subject to trial-and-error and provider variability.To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST).Using data from the treatment arm of 2 randomized trials, ML models were trained and validated on ENGAGE-2 (ClinicalTrials.gov, #NCT03841682) and tested on RAINBOW (ClinicalTrials.gov, #NCT02246413) for predictions at baseline and at 2-months. Primary outcome was depression remission using the Depression Symptom Checklist (SCL-20) score 0.5 at 6 months. Predictor variables included baseline characteristics (sociodemographic, behavioral, clinical, psychosocial) and intervention engagement through 2-months.Of the 26 candidate variables, 8 for baseline and 11 for 2-months were predictive of depression remission, and used to train the models. The best-performing model predicted remission with an accuracy significantly greater than chance in internal validation using the ENGAGE-2 cohort, at baseline [72.6% (SD = 3.6%), p 0.0001] and at 2-months [72.3% (5.1%), p 0.0001], and in external validation with the RAINBOW cohort at baseline [58.3% (0%), p 0.0001] and at 2-months [62.3% (0%), p 0.0001]. Model-agnostic explanations highlighted key predictors of depression remission at the cohort and patient levels, including female sex, lower self-reported sleep disturbance, lower sleep-related impairment, and lower negative problem orientation.ML models using clinical and patient-reported data can predict depression remission for patients undergoing PST, affording opportunities for prospective identification of likely responders, and for developing personalized early treatment optimization along the patient care trajectory.

BACKGROUND: The temporal progression and workload-related causal contributors to physician burnout are not well-understood. OBJECTIVE: To characterize burnout’s time course and evaluate the effect of time-varying workload on burnout and medical errors. DESIGN: Six-month longitudinal cohort study with measurements of burnout, workload, and wrong-patient orders every 4 weeks. PARTICIPANTS: Seventy-five intern physicians in internal medicine, pediatrics, and anesthesiology at a large academic medical center. MAIN MEASURES: Burnout was measured using the Professional Fulfillment Index survey. Workload was collected from electronic health record (EHR) audit logs and summarized as follows: total time spent on the EHR, after-hours EHR time, patient load, inbox time, chart review time, note-writing time, and number of orders. Wrong-patient orders were assessed using retract-and-reorder events. KEY RESULTS: Seventy-five of 104 interns enrolled (72.1%) in the study. A total of 337 surveys and 8,863,318 EHR-based actions were analyzed. Median burnout score across the cohort across all time points was 1.2 (IQR 0.7–1.7). Individual-level burnout was variable (median monthly change 0.3, IQR 0.1–0.6). In multivariable analysis, increased total EHR time (β=0.121 for an increase from 54.5 h per month (25th percentile) to 123.0 h per month (75th percentile), 95%CI=0.016–0.226), increased patient load (β=0.130 for an increase from 4.9 (25th percentile) to 7.1 (75th percentile) patients per day, 95%CI=0.053–0.207), and increased chart review time (β=0.096 for an increase from 0.39 (25th percentile) to 0.59 (75th percentile) hours per patient per day, 95%CI=0.015–0.177) were associated with an increased burnout score. After adjusting for the total number of ordering sessions, burnout was not statistically associated with an increased rate of wrong-patient orders (rate ratio=1.20, 95%CI=0.76–1.89). CONCLUSIONS: Burnout and recovery were associated with recent clinical workload for a cohort of physician trainees, highlighting the elastic nature of burnout. Wellness interventions should focus on strategies to mitigate sustained elevations of work responsibilities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-022-07620-3.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause neurological sequelae after the resolution of symptomatic COVID-19 illness, but the occurrence of peripheral neuropathy symptoms and cranial nerve dysfunction is unknown. This study aimed to characterize the occurrence and severity of pain and peripheral neuropathy symptoms in patients with SARS-CoV-2 infection. An observational cohort study included adults tested for a SARS-CoV-2 infection at an academic medical center (assigned as CV+ or control, based on test results). Thirty to 90 days after the index SARS-CoV-2 test, patients completed a web-based questionnaire assessing pain, peripheral neuropathy-related sensory symptoms, and symptoms in the distribution of cranial nerves (current symptoms, symptoms at testing and 2 weeks thereafter). Univariate analyses compared the outcomes between the groups. Multivariable analysis was used to determine the odds for neuropathy symptoms after adjusting for key baseline variables. A total of 1556 participants were included: 542 CV+ patients and 1014 control subjects. CV+ patients reported a higher occurrence of peripheral neuropathy symptoms in the extremities anytime within 90 days postinfection (28.8% vs 12.9%, odds ratio [OR] [95% confidence interval] = 2.72 [2.10-3.54]), as well as such symptoms persisting up to 90 days after infection (6.1% vs 1.9%, OR = 3.39 [1.91-6.03]). The occurrence of pain in the extremities was higher in the CV+ group (24.2% vs 9.8%, OR = 2.95 [2.21-3.91]). SARS-CoV-2 infection was also associated with higher occurrence of peripheral neuropathy symptoms, after adjusting for the history of chronic pain and neuropathy (OR = 3.19 [2.37-4.29]). The results suggest that SARS-CoV-2 infection was independently associated with an increased risk of pain and peripheral neuropathy symptoms.

Electronic health record audit logs capture a time-sequenced record of clinician activities while using the system. Audit log data therefore facilitate unobtrusive measurement at scale of clinical work activities and workflow as well as derivative, behavioral proxies (eg, teamwork). Given its considerable research potential, studies leveraging these data have burgeoned. As the field has matured, the challenges of using the data to answer significant research questions have come into focus. In this Perspective, we draw on our research experiences and insights from the broader audit log literature to advance audit log research. Specifically, we make 2 complementary recommendations that would facilitate substantial progress toward audit log-based measures that are: (1) transparent and validated, (2) standardized to allow for multisite studies, (3) sensitive to meaningful variability, (4) broader in scope to capture key aspects of clinical work including teamwork and coordination, and (5) linked to patient and clinical outcomes.

ImportancePrevalence of post-viral olfactory loss has increased dramatically due to the frequency and severity of olfactory dysfunction associated with infection by the SARS-CoV-2 virus.ObjectiveTo determine the trajectory of COVID-19 olfactory loss over a six-month period. A key secondary objective is to assess predictive factors associated with the recovery of olfaction.DesignLongitudinal repeated-measures study that enrolled from May 5, 2020 to February 2, 2021, with the last date of data collection on June 15, 2021.SettingBarnes-Jewish HealthCare/Washington University School of Medicine facilities (Saint Louis, Missouri, USA).ParticipantsIndividuals who tested positive for SARS-CoV-2 by real-time polymerase chain reaction on nasopharyngeal swab and indicated olfactory loss on COVID-19 screening questionnaire. Individuals were excluded if they had previously diagnosed history of olfactory loss, neurodegenerative disorders, less than 18 years of age, admitted to hospital service, unable to read, write, and understand English, or lacked computer or internet access.Interventions/ExposuresWatch and wait for spontaneous recovery.Main Outcome(s) and Measure(s)Participants completed olfactory assessments every 30 days for six months. Each assessment consisted of the University of Pennsylvania Smell Identification Test (UPSIT), an objective “scratch-and-sniff” test, and Clinical Global Impressions (CGI), a subjective Likert rating scale.ResultsThe mean age was 41 years old (SD = 16). 39 (80%) were female and 42 (86%) white. At baseline assessment of objective olfaction, 18 (36%) participants had anosmia or severe hyposmia. Subjective, complete recovery at six months was 81% (95% CI 74% to 88%). Likelihood of recovery was associated with age less than 50 years (aHR = 8.1 (95% CI 1.1 to 64.1)) and mild olfactory loss at baseline (UPSIT = 30-33 for males and 31-34 for females) (aHR 6.2 (95% CI 1.2 to 33.0)).Conclusions and RelevanceThe trajectory of olfactory recovery among adults with COVID-19 olfactory loss illustrated rapid recovery within 2-3 weeks of infection, and by six months 81% had recovered based on self-report. Age less than 50 years old and mild severity of olfactory loss at baseline were associated with increased likelihood of recovery of olfaction. These findings can be used to inform shared decision-making with patients.

Objective The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)—risk-inducing or risk-mitigating workflow deviations—and ascertain ACT’s impact on clinical workflow and patient safety. Materials and Methods We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT’s role and impact on safety, conducting content analysis to assess NREs. Results Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT’s role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning. Discussion ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT’s sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks. Conclusion Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

Objective To systematically synthesize and appraise the evidence on the effectiveness of health information technology (HIT)-based discharge care transition interventions (CTIs) on readmissions and emergency room visits. Materials and Methods We conducted a systematic search on multiple databases (MEDLINE, CINAHL, EMBASE, and CENTRAL) on June 29, 2020, targeting readmissions and emergency room visits. Prospective studies evaluating HIT-based CTIs published as original research articles in English language peer-reviewed journals were eligible for inclusion. Outcomes were pooled for narrative analysis. Results Eleven studies were included for review. Most studies (n = 6) were non-RCTs. Several studies (n = 9) assessed bridging interventions comprised of at least 1 pre- and 1 post-discharge component. The narrative analysis found improvements in patient experience and perceptions of discharge care. Discussion Given the statistical and clinical heterogeneity among studies, we could not ascertain the cumulative effect of CTIs on clinical outcomes. Nevertheless, we found gaps in current research and its implications for future work, including the need for a HIT-based care transition model for guiding theory-driven design and evaluation of HIT-based discharge CTIs. Conclusions We appraised and aggregated empirical evidence on the cumulative effectiveness of HIT-based interventions to support discharge transitions from hospital to home, and we highlighted the implications for evidence-based practice and informatics research.

BACKGROUND: Burnout is a significant public health concern affecting more than half of the healthcare workforce; however, passive screening tools to detect burnout are lacking. We investigated the ability of machine learning (ML) techniques to identify burnout using passively collected electronic health record (EHR)-based audit log data. METHOD: Physician trainees participated in a longitudinal study where they completed monthly burnout surveys and provided access to their EHR-based audit logs. Using the monthly burnout scores as the target outcome, we trained ML models using combinations of features derived from audit log data—aggregate measures of clinical workload, time series-based temporal measures of EHR use, and the baseline burnout score. Five ML models were constructed to predict burnout as a continuous score: penalized linear regression, support vector machine, neural network, random forest, and gradient boosting machine. RESULTS: 88 trainee physicians participated and completed 416 surveys; >10 million audit log actions were collected (Mean [Standard Deviation]=25,691 [14,331] actions per month, per physician). The workload feature set predicted burnout score with a mean absolute error (MAE) of 0.602 (95% Confidence Interval (CI), 0.412–0.826), and was able to predict burnout status with an average AUROC of 0.595 (95% CI 0.355–0.808) and average accuracy 0.567 (95% CI 0.393–0.742). The temporal feature set had a similar performance, with MAE 0.596 (95% CI 0.391–0.826), and AUROC 0.581 (95% CI 0.343–0.790). The addition of the baseline burnout score to the workload features improved the model performance to a mean AUROC of 0.829 (95% CI 0.607–0.996) and mean accuracy of 0.781 (95% CI 0.587–0.936); however, this performance was not meaningfully different than using the baseline burnout score alone. CONCLUSIONS: Current findings illustrate the complexities of predicting burnout exclusively based on clinical work activities as captured in the EHR, highlighting its multi-factorial and individualized nature. Future prediction studies of burnout should account for individual factors (e.g., resilience, physiological measurements such as sleep) and associated system-level factors (e.g., leadership).

Aim: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed, and delayed diagnosis is associated with substantial morbidity and mortality. At three large academic medical centers, combinations of phenotypic features were implemented in electronic health record (EHR) systems to identify patients with heart failure at risk for ATTRwt-CM. Methods: Phenotypes/phenotype combinations were selected based on strength of correlation with ATTRwt-CM versus non-amyloid heart failure; different clinical decision support and reporting approaches and data sources were evaluated on Cerner and Epic EHR platforms. Results: Multiple approaches/sources showed potential usefulness for incorporating predictive analytics into the EHR to identify at-risk patients. Conclusion: These preliminary findings may guide other medical centers in building and implementing similar systems to improve recognition of ATTRwt-CM in patients with heart failure.

BACKGROUND: Real-time prediction of surgical duration can inform perioperative decisions and reduce surgical costs. We developed a machine learning approach that continuously incorporates preoperative and intraoperative information for forecasting surgical duration. METHODS: Preoperative (e.g. procedure name) and intraoperative (e.g. medications and vital signs) variables were retrieved from anaesthetic records of surgeries performed between March 1, 2019 and October 31, 2019. A modular artificial neural network was developed and compared with a Bayesian approach and the scheduled surgical duration. Continuous ranked probability score (CRPS) was used as a measure of time error to assess model accuracy. For evaluating clinical performance, accuracy for each approach was assessed in identifying cases that ran beyond 15:00 (commonly scheduled end of shift), thus identifying opportunities to avoid overtime labour costs. RESULTS: The analysis included 70 826 cases performed at eight hospitals. The modular artificial neural network had the lowest time error (CRPS: mean=13.8; standard deviation=35.4 min), which was significantly better (mean difference=6.4 min [95% confidence interval: 6.3–6.5]; P

Background: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. Methods: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. Results: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. Conclusion: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive.

Male sex is associated with severe COVID-19. It is not known whether the risk of hospitalization differs between men with hypogonadism, men with eugonadism, and those receiving testosterone therapy (TTh).To compare COVID-19 hospitalization rates for men with hypogonadism who were not receiving TTh, men with eugonadism, and men receiving TTh.This cohort study was conducted in 2 large academic health systems in St Louis, Missouri, among 723 men with a history of COVID-19 who had testosterone concentrations measured between January 1, 2017, and December 31, 2021.The primary exposure was gonadal status (hypogonadism, eugonadism, and TTh). Hypogonadism was defined as a total testosterone concentration below the limit of normal provided by the laboratory (which varied from 175 to 300 ng/dL [to convert to nanomoles per liter, multiply by 0.0347]).The primary outcome was rate of hospitalization for COVID-19. Statistical adjustments were made for group differences in age, body mass index, race and ethnicity, immunosuppression, and comorbid conditions.Of the 723 study participants (mean [SD] age, 55 [14] years; mean [SD] body mass index, 33.5 [7.3]), 116 men had hypogonadism, 427 had eugonadism, and 180 were receiving TTh. Men with hypogonadism were more likely than men with eugonadism to be hospitalized with COVID-19 (52 of 116 [45%] vs 53 of 427 [12%]; P .001). After multivariable adjustment, men with hypogonadism had higher odds than men with eugonadism of being hospitalized (odds ratio, 2.4; 95% CI, 1.4-4.4; P .003). Men receiving TTh had a similar risk of hospitalization as men with eugonadism (odds ratio, 1.3; 95% CI, 0.7-2.3; P = .35). Men receiving inadequate TTh (defined as subnormal testosterone concentrations while receiving TTh) had higher odds of hospitalization compared with men who had normal testosterone concentrations while receiving TTh (multivariable adjusted odds ratio, 3.5; 95% CI, 1.5-8.6; P = .003).This study suggests that men with hypogonadism were more likely to be hospitalized after COVID-19 infection compared with those with eugonadism, independent of other known risk factors. This increased risk was not observed among men receiving adequate TTh. Screening and appropriate therapy for hypogonadism need to be evaluated as a strategy to prevent severe COVID-19 outcomes among men.

We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on a future sequence of low SpO2 (i.e., blood oxygen saturation) instances, we propose the hybrid inference network (hiNet) that makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes. hiNet integrates 1) a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and 2) two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learn contextual latent representations that capture the transition from present states to future states. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms strong baselines including the model used by the state-of-the-art hypoxemia prediction system. With its capability to make real-time predictions of near-term hypoxemic at clinically acceptable alarm rates, hiNet shows promise in improving clinical decision making and easing burden of perioperative care., Accepted for CIKM '22

BACKGROUND The response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created an unprecedented disruption in work conditions. This study describes the mental health and well-being of workers both with and without clinical exposure to patients with coronavirus disease (COVID-19). Objective: The aim of this study is to measure the prevalence of stress, anxiety, depression, work exhaustion, burnout, and decreased well-being among faculty and staff at a university and academic medical center during the SARS-CoV-2 pandemic and describe work-related and personal factors associated with their mental health and well-being. Methods: All faculty, staff, and postdoctoral fellows of a university, including its medical school, were invited in April 2020 to complete an online questionnaire measuring stress, anxiety, depression, work exhaustion, burnout, and decreased well-being. We examined associations between these outcomes and factors including work in high-risk clinical settings and family/home stressors. Results: There were 5550 respondents (overall response rate of 34.3%). Overall, 38% of faculty and 14% of staff (n=915) were providing clinical care, while 57% of faculty and 77% of staff were working from home. Among all workers, anxiety (prevalence ratio 1.37, 95% CI 1.09-1.73), depression (prevalence ratio 1.28, 95% CI 1.03-1.59), and high work exhaustion (prevalence ratio 1.24, 95% CI 1.13-1.36) were independently associated with community or clinical exposure to COVID-19. Poor family-supportive behaviors by supervisors were also associated with these outcomes (prevalence ratio 1.40, 95% CI 1.21-1.62; prevalence ratio 1.69, 95% CI 1.48-1.92; and prevalence ratio 1.54, 95% CI 1.44-1.64, respectively). Age

BACKGROUND The rapid spread of the Coronavirus disease 2019 (COVID-19) has created considerable strain on the physical and mental health of healthcare workers around the world. The effects have been acute for physician trainees—a unique group functioning simultaneously as learners and care providers with limited autonomy. OBJECTIVE To investigate the longitudinal effects of physician trainee exposure to patients being tested for COVID-19 on stress, anxiety, depression and burnout using three surveys conducted during the early phase of the pandemic. METHODS All physician trainees (N=1375) at an academic medical center were invited to participate in a web-based survey. Multivariable models were used to assess the relationship between repeated exposure to patients being tested for COVID-19 and stress, anxiety, depression and burnout. RESULTS 389 trainees completed the baseline survey (28.3%). Of these, 191 and 136 responded to the ensuing surveys. Mean stress, anxiety and burnout decreased by 21% (95% Confidence Interval (CI): -28% to -12%; P < 0.001), 25% (95% CI: -36% to -11%; P < 0.001) and 13% (95% CI: -18% to -7%; P < 0.001), respectively per survey. However, for each survey time point, there was mean increase in stress, anxiety and burnout per additional exposure: stress [24% (95% CI: +12% to +38%; P < 0.001)], anxiety [22%, (95% CI: +2% to +46%; P = 0.026)], burnout [18%, (95% CI: +10% to +28%; P < 0.001)]. For depression, the association between exposure was strongest for the third survey, where mean depression scores increased by 33% per additional exposure (95% CI: +18% to +50%; P = CONCLUSIONS Training programs should adapt to address the detrimental effects of the “pile up” of distress associated with persistent exposure through adaptive programs that allow flexibility for time off and recovery.

BACKGROUND The response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has created an unprecedented disruption in work conditions. This study describes the mental health and well-being of workers both with and without clinical exposure to patients with coronavirus disease (COVID-19). OBJECTIVE The aim of this study is to measure the prevalence of stress, anxiety, depression, work exhaustion, burnout, and decreased well-being among faculty and staff at a university and academic medical center during the SARS-CoV-2 pandemic and describe work-related and personal factors associated with their mental health and well-being. METHODS All faculty, staff, and postdoctoral fellows of a university, including its medical school, were invited in April 2020 to complete an online questionnaire measuring stress, anxiety, depression, work exhaustion, burnout, and decreased well-being. We examined associations between these outcomes and factors including work in high-risk clinical settings and family/home stressors. RESULTS There were 5550 respondents (overall response rate of 34.3%). Overall, 34% of faculty and 14% of staff (n=915) were providing clinical care, while 61% of faculty and 77% of staff were working from home. Among all workers, anxiety (prevalence ratio 1.37, 95% CI 1.09-1.73), depression (prevalence ratio 1.28, 95% CI 1.03-1.59), and high work exhaustion (prevalence ratio 1.24, 95% CI 1.13-1.36) were independently associated with community or clinical exposure to COVID-19. Poor family-supportive behaviors by supervisors were also associated with these outcomes (prevalence ratio 1.40, 95% CI 1.21-1.62; prevalence ratio 1.69, 95% CI 1.48-1.92; and prevalence ratio 1.54, 95% CI 1.44-1.64, respectively). Age CONCLUSIONS Our findings suggest that the pandemic has had negative effects on the mental health and well-being of both clinical and nonclinical employees. Mitigating exposure to COVID-19 and increasing supervisor support are modifiable risk factors that may protect mental health and well-being for all workers.

Background : Anterior cranial base can be taken as a reference line (SN) to determine the steepness of mandibular plane. Subjects with high mandibular plane angle tend to have a long face and one with low MP-SN angle has a shorter face. Objective : This study was done to investigate if dental arch widths correlated with vertical facial types and if there are any differences in arch widths between untreated male and female adults in South Indian population. Materials and Methods : Lateral cephalogram and dental casts were obtained from 180 untreated South Indian adults (90 males and 90 females) above 18 year old with no cross bite, minimal crowding and spacing. The angle between the anterior cranial base and the mandibular plane was measured on lateral cephalogram of each patient. Dental casts were used to obtain comprehensive dental measurements including maxillary and mandibular inter canine, inter premolar and inter molar widths, as well as amount of crowding or spacing. Results : The results showed that male arch widths were significantly larger than those of females (P < 0.05) and there was a significant decrease in inter arch width as the MP-SN angle increased in untreated adult South Indian population. The results obtained in our study when compared with studies done in other population groups showed that there is difference in inter arch widths according to ethnicity and race. Conclusion : It was concluded that the dental arch width is associated with gender, race and vertical facial morphology. Thus using individualized arch wires according to each patient's pre treatment arch form and width is suggested during orthodontic treatment.