Changes in Inpatient Electroconvulsive Therapy Utilization Between 2019 and 2020: A National Inpatient Sample Analysis.

OBJECTIVES: Electroconvulsive therapy (ECT) is an essential procedure for a range of psychiatric conditions. Multiple single-center studies have documented reduction in ECT administration in 2020 because of the coronavirus disease 2019 pandemic, but there have been little nationally representative data from the United States. The aim of this study was to examine the demographics of patients receiving ECT in 2019 and 2020 and to characterize temporal and regional variations in ECT utilization. METHODS: The 2019 and 2020 National Inpatient Sample, an administrative database of inpatient hospitalizations in the United States, was queried for hospitalizations involving the delivery of ECT based on procedural codes. Overall number of ECT procedures was calculated based on the overall number of ECT procedural claims. RESULTS: In the 2019 NIS, 14,230 inpatient hospitalizations (95% confidence interval, 12,936-15,524) involved the use of ECT, with a cumulative 52,450 inpatient ECT procedures administered. In 2020, the number of inpatient hospitalizations with ECT decreased to 12,055 (95% confidence interval, 10,878-13,232), with a 10.0% reduction in overall procedures to 47,180. Whereas January and February ECT hospitalizations were comparable in both years, ECT hospitalizations decreased by more than 25% in March through May 2020 relative to 2019 volume. There was regional variability in the change in ECT utilization between 2019 and 2020. CONCLUSIONS: Electroconvulsive therapy use among general hospital inpatients declined between 2019 and 2020, with regional variability in the magnitude of change. Further study is warranted into the root causes and optimal responses to these changes.

Co-Occurring Catatonia and COVID-19 Diagnoses Among Hospitalized Individuals in 2020: A National Inpatient Sample Analysis.

BACKGROUND: COVID-19 is associated with a range of neuropsychiatric manifestations. While case reports and case series have reported catatonia in the setting of COVID-19 infection, its rate has been poorly characterized. OBJECTIVE: This study reports the co-occurrence of catatonia and COVID-19 diagnoses among acute care hospital discharges in the United States in 2020. METHODS: The National Inpatient Sample, an all-payors database of acute care hospital discharges, was queried for patients of any age discharged with a diagnosis of catatonia and COVID-19 in 2020. RESULTS: Among 32,355,827 hospitalizations in the 2020 National Inpatient Sample, an estimated 15,965 (95% confidence interval: 14,992-16,938) involved a diagnosis of catatonia without COVID-19 infection, 1,678,385 (95% confidence interval: 1,644,738-1,712,022) involved a diagnosis of COVID-19 without a co-occurring catatonia diagnosis, and 610 (95% confidence interval: 578-642) involved both catatonia and COVID-19 infection. In an adjusted model, a diagnosis of COVID-19, but not a diagnosis of catatonia or the combination of catatonia and COVID-19, was associated with increased mortality. Patients with catatonia and COVID-19 were frequently diagnosed with encephalopathy and delirium codes. CONCLUSIONS: Catatonia and COVID-19 were rarely co-diagnosed in 2020, and catatonia diagnosis was not associated with increased mortality in patients with COVID-19. Further research is needed to better characterize the phenomenology of catatonia in the setting of COVID-19 infection and its optimal treatment.

The association between non-English primary language and COVID-19 clinical trial eligibility and enrollment: A retrospective cohort study.

BACKGROUND: Establishing equitable access to COVID-19 clinical trials is an important step in mitigating outcomes disparities. Historically, language has served as a barrier to equitable clinical trial participation. METHODS: A centralized research infrastructure was established at our institution to screen potential trial participants and to promote efficient and equitable access to COVID-19 clinical trials. Rates of eligibility and enrollment in COVID-19 clinical trials by primary language between April 9 and July 31, 2020 (during the first regional COVID-19 surge) were evaluated using logistic regression. Estimates were adjusted for potential confounders including age, sex, and time. RESULTS: A total of 1245 patients were admitted to the hospital with COVID-19 during the study period and screened for clinical trial eligibility. Among all screened patients, 487 (39%) had a non-English primary language. After adjustment, patients with a non-English primary language had 1.98 times higher odds (CI 1.51 to 2.59) of being eligible for 1 or more COVID-19 clinical trials. Among eligible patients, those with a non-English primary language had 1.83 times higher odds (CI 1.36 to 2.47) of enrolling in COVID-19 clinical trials than patients with English as the primary language. CONCULSION: These findings suggest that there are modifiable barriers that can be addressed to lessen the impact of language discordance on access to clinical trials and provide an opportunity to further investigate factors associated with clinical trial participation for patients whose primary language is not English.

Case-control study of neuropsychiatric symptoms in electronic health records following COVID-19 hospitalization in 2 academic health systems.

Neuropsychiatric symptoms may persist following acute COVID-19 illness, but the extent to which these symptoms are specific to COVID-19 has not been established. We utilized electronic health records across 6 hospitals in Massachusetts to characterize cohorts of individuals discharged following admission for COVID-19 between March 2020 and May 2021, and compared them to individuals hospitalized for other indications during this period. Natural language processing was applied to narrative clinical notes to identify neuropsychiatric symptom domains up to 150 days following hospitalization, in addition to those reflected in diagnostic codes as measured in prior studies. Among 6619 individuals hospitalized for COVID-19 drawn from a total of 42,961 hospital discharges, the most commonly-documented symptom domains between 31 and 90 days after initial positive test were fatigue (13.4%), mood and anxiety symptoms (11.2%), and impaired cognition (8.0%). In regression models adjusted for sociodemographic features and hospital course, none of these were significantly more common among COVID-19 patients; indeed, mood and anxiety symptoms were less frequent (adjusted OR 0.72 95% CI 0.64-0.92). Between 91 and 150 days after positivity, most commonly-detected symptoms were fatigue (10.9%), mood and anxiety symptoms (8.2%), and sleep disruption (6.8%), with impaired cognition in 5.8%. Frequency was again similar among non-COVID-19 post-hospital patients, with mood and anxiety symptoms less common (aOR 0.63, 95% CI 0.52-0.75). Propensity-score matched analyses yielded similar results. Overall, neuropsychiatric symptoms were common up to 150 days after initial hospitalization, but occurred at generally similar rates among individuals hospitalized for other indications during the same period. Post-acute sequelae of COVID-19 may benefit from standard if less-specific treatments developed for rehabilitation after hospitalization.

An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes.

OBJECTIVE: The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models. MATERIALS AND METHODS: Using data from over 56 000 Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in 4 AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. Models were evaluated both retrospectively and prospectively using model-level metrics of discrimination, accuracy, and reliability, and a novel individual-level metric for error. RESULTS: We found inconsistent instances of model-level bias in the prediction models. From an individual-level aspect, however, we found most all models performing with slightly higher error rates for older patients. DISCUSSION: While a model can be biased against certain protected groups (ie, perform worse) in certain tasks, it can be at the same time biased towards another protected group (ie, perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. CONCLUSION: Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

Clinical Ethics Consultation During the First COVID-19 Pandemic Surge at an Academic Medical Center: A Mixed Methods Analysis.

While a significant literature has appeared discussing theoretical ethical concerns regarding COVID-19, particularly regarding resource prioritization, as well as a number of personal reflections on providing patient care during the early stages of the pandemic, systematic analysis of the actual ethical issues involving patient care during this time is limited. This single-center retrospective cohort mixed methods study of ethics consultations during the first surge of the COVID 19 pandemic in Massachusetts between March 15, 2020 through June 15, 2020 aim to fill this gap. Results indicate that there was no significant difference in the median number of monthly consultation cases during the first COVID-19 surge compared to the same period the year prior and that the characteristics of the ethics consults during the COVID-19 surge and same period the year prior were also similar. Through inductive analysis, we identified four themes related to ethics consults during the first COVID-19 surge including (1) prognostic difficulty for COVID-19 positive patients, (2) challenges related to visitor restrictions, (3) end of life scenarios, and (4) family members who were also positive for COVID-19. Cases were complex and often aligned with multiple themes. These patient case-related sources of ethical issues were managed against the backdrop of intense systemic ethical issues and a near lockdown of daily life. Healthcare ethics consultants can learn from this experience to enhance training to be ready for future disasters.

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

OBJECTIVE: To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). METHOD: Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. RESULTS: Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. CONCLUSION: This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing.

Mood Disorders and Outcomes of COVID-19 Hospitalizations.

OBJECTIVE: The authors sought to characterize the association between prior mood disorder diagnosis and hospital outcomes among individuals admitted with COVID-19 to six Eastern Massachusetts hospitals. METHODS: A retrospective cohort was drawn from the electronic health records of two academic medical centers and four community hospitals between February 15 and May 24, 2020. Associations between history of mood disorder and in-hospital mortality and hospital discharge home were examined using regression models among any hospitalized patients with positive tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). RESULTS: Among 2,988 admitted individuals, 717 (24.0%) had a prior mood disorder diagnosis. In Cox regression models adjusted for age, sex, and hospital site, presence of a mood disorder prior to admission was associated with greater in-hospital mortality risk beyond hospital day 12 (crude hazard ratio=2.156, 95% CI=1.540, 3.020; fully adjusted hazard ratio=1.540, 95% CI=1.054, 2.250). A mood disorder diagnosis was also associated with greater likelihood of discharge to a skilled nursing facility or other rehabilitation facility rather than home (crude odds ratio=2.035, 95% CI=1.661, 2.493; fully adjusted odds ratio=1.504, 95% CI=1.132, 1.999). CONCLUSIONS: Hospitalized individuals with a history of mood disorder may be at risk for greater COVID-19 morbidity and mortality and are at increased risk of need for postacute care. Further studies should investigate the mechanism by which these disorders may confer elevated risk.

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.

Background: The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts. Objectives: To develop an incident delirium predictive model among coronavirus disease 2019 patients. Methods: We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings. Results: Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals. Conclusion: Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Laboratory Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With Coronavirus Disease 2019 in Eastern Massachusetts.

Importance: The coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed. Objective: To quantify admission laboratory and comorbidity features associated with critical illness and mortality risk across 6 Eastern Massachusetts hospitals. Design, Setting, and Participants: Retrospective cohort study of all individuals admitted to the hospital who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by polymerase chain reaction across these 6 hospitals through June 5, 2020, using hospital course, prior diagnoses, and laboratory values in emergency department and inpatient settings from 2 academic medical centers and 4 community hospitals. The data were extracted on June 11, 2020, and the analysis was conducted from June to July 2020. Exposures: SARS-CoV-2. Main Outcomes and Measures: Severe illness defined by admission to intensive care unit, mechanical ventilation, or death. Results: Of 2511 hospitalized individuals who tested positive for SARS-CoV-2 (of whom 50.9% were male, 53.9% White, and 27.0% Hispanic, with a mean [SD ]age of 62.6 [19.0] years), 215 (8.6%) were admitted to the intensive care unit, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded an area under the receiver operating characteristic curve of 0.807 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 212 of 292 deaths (72.6%) occurred in the highest-risk mortality quintile. Conclusions and Relevance: In this cohort, specific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitated risk stratification among individuals hospitalized for COVID-19.

Modified Anesthesia Protocol for Electroconvulsive Therapy Permits Reduction in Aerosol-Generating Bag-Mask Ventilation during the COVID-19 Pandemic.

INTRODUCTION: Electroconvulsive therapy (ECT) is a critical procedure in psychiatric treatment, but as typically delivered involves the use of bag-mask ventilation (BMV), which during the COVID-19 pandemic exposes patients and treatment staff to potentially infectious aerosols. OBJECTIVE: To demonstrate the utility of a modified anesthesia protocol for ECT utilizing preoxygenation by facemask and withholding the use of BMV for only those patients who desaturate during the apneic period. METHODS: This chart review study analyzes patients who were treated with ECT using both the traditional and modified anesthesia protocols. RESULTS: A total of 106 patients were analyzed, of whom 51 (48.1%) required BMV using the new protocol. Of clinical factors, only patient BMI was significantly associated with the requirement for BMV. Mean seizure duration reduced from 52.0 ± 22.4 to 46.6 ± 17.1 s, but seizure duration was adequate in all cases. No acute physical, respiratory, or psychiatric complications occurred during treatment. CONCLUSIONS: A modified anesthesia protocol reduces the use of BMV by more than 50%, while retaining adequate seizure duration.