Nursing home infection control strategies during the COVID-19 pandemic.

BACKGROUND: The American Rescue Plan Act of 2021 awarded $500 million toward scaling "strike teams" to mitigate the impact of Coronavirus Disease 2019 (COVID-19) within nursing homes. The Massachusetts Nursing Facility Accountability and Support Package (NFASP) piloted one such model during the first weeks of the pandemic, providing nursing homes financial, administrative, and educational support. For a subset of nursing homes deemed high-risk, the state offered supplemental, in-person technical infection control support. METHODS: Using state death certificate data and federal nursing home occupancy data, we examined longitudinal all-cause mortality per 100,000 residents and changes in occupancy across NFASP participants and subgroups that varied in their receipt of the supplemental intervention. RESULTS: Nursing home mortality peaked in the weeks preceding the NFASP, with a steeper increase among those receiving the supplemental intervention. There were contemporaneous declines in weekly occupancy. The potential for temporal confounding and differential selection across NFASP subgroups precluded estimation of causal effects of the intervention on mortality. CONCLUSIONS: We offer policy and design suggestions for future strike team iterations that could inform the allocation of state and federal funding. We recommend expanded data collection infrastructure and, ideally, randomized assignment to intervention subgroups to support causal inference as strike team models are scaled under the direction of state and federal agencies.

Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

BACKGROUND: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. OBJECTIVE: The aim of this study was to evaluate whether natural language processing (NLP)-powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. METHODS: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. RESULTS: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. CONCLUSIONS: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

Identifying Patients With Delirium Based on Unstructured Clinical Notes: Observational Study.

BACKGROUND: Delirium in hospitalized patients is a syndrome of acute brain dysfunction. Diagnostic (International Classification of Diseases [ICD]) codes are often used in studies using electronic health records (EHRs), but they are inaccurate. OBJECTIVE: We sought to develop a more accurate method using natural language processing (NLP) to detect delirium episodes on the basis of unstructured clinical notes. METHODS: We collected 1.5 million notes from >10,000 patients from among 9 hospitals. Seven experts iteratively labeled 200,471 sentences. Using these, we trained three NLP classifiers: Support Vector Machine, Recurrent Neural Networks, and Transformer. Testing was performed using an external data set. We also evaluated associations with delirium billing (ICD) codes, medications, orders for restraints and sitters, direct assessments (Confusion Assessment Method [CAM] scores), and in-hospital mortality. F1 scores, confusion matrices, and areas under the receiver operating characteristic curve (AUCs) were used to compare NLP models. We used the φ coefficient to measure associations with other delirium indicators. RESULTS: The transformer NLP performed best on the following parameters: micro F1=0.978, macro F1=0.918, positive AUC=0.984, and negative AUC=0.992. NLP detections exhibited higher correlations (φ) than ICD codes with deliriogenic medications (0.194 vs 0.073 for ICD codes), restraints and sitter orders (0.358 vs 0.177), mortality (0.216 vs 0.000), and CAM scores (0.256 vs -0.028). CONCLUSIONS: Clinical notes are an attractive alternative to ICD codes for EHR delirium studies but require automated methods. Our NLP model detects delirium with high accuracy, similar to manual chart review. Our NLP approach can provide more accurate determination of delirium for large-scale EHR-based studies regarding delirium, quality improvement, and clinical trails.

Growth In ACA-Compliant Marketplace Enrollment And Spending Risk Changes During The COVID-19 Pandemic.

In 2020 the COVID-19 pandemic caused millions to lose their jobs and, consequently, their employer-sponsored health insurance. Enacted in 2010, the Affordable Care Act (ACA) created safeguards for such events by expanding Medicaid coverage and establishing Marketplaces through which people could purchase health insurance. Using a novel national data set with information on ACA-compliant individual insurance plans, we found large increases in Marketplace enrollment in 2020 compared with 2019 but with varying percentage increases and spending risk implications across states. States that did not expand Medicaid had enrollment and spending risk increases. States that expanded Medicaid but did not relax 2020 Marketplace enrollment criteria also had spending risk increases. In contrast, states that expanded Medicaid and relaxed 2020 enrollment criteria experienced enrollment increases without spending risk changes. The findings are reassuring with respect to the ability of Marketplaces to buffer employment shocks, but they also provide cautionary signals that risks and premiums could begin to rise either in the absence of Medicaid expansion or when Marketplace enrollment is constrained.

The Effects of Aß1-42 Binding to the SARS-CoV-2 Spike Protein S1 Subunit and Angiotensin-Converting Enzyme 2.

Increasing evidence suggests that elderly people with dementia are vulnerable to the development of severe coronavirus disease 2019 (COVID-19). In Alzheimer's disease (AD), the major form of dementia, ß-amyloid (Aß) levels in the blood are increased; however, the impact of elevated Aß levels on the progression of COVID-19 remains largely unknown. Here, our findings demonstrate that Aß1-42, but not Aß1-40, bound to various viral proteins with a preferentially high affinity for the spike protein S1 subunit (S1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the viral receptor, angiotensin-converting enzyme 2 (ACE2). These bindings were mainly through the C-terminal residues of Aß1-42. Furthermore, Aß1-42 strengthened the binding of the S1 of SARS-CoV-2 to ACE2 and increased the viral entry and production of IL-6 in a SARS-CoV-2 pseudovirus infection model. Intriguingly, data from a surrogate mouse model with intravenous inoculation of Aß1-42 show that the clearance of Aß1-42 in the blood was dampened in the presence of the extracellular domain of the spike protein trimers of SARS-CoV-2, whose effects can be prevented by a novel anti-Aß antibody. In conclusion, these findings suggest that the binding of Aß1-42 to the S1 of SARS-CoV-2 and ACE2 may have a negative impact on the course and severity of SARS-CoV-2 infection. Further investigations are warranted to elucidate the underlying mechanisms and examine whether reducing the level of Aß1-42 in the blood is beneficial to the fight against COVID-19 and AD.

Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction.

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. RESULTS: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24-3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71-1.82). CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

Impact of the COVID-19 Pandemic on the Employment and Educational Outcomes of Individuals in a First-Episode Psychosis Clinic.

OBJECTIVE: A central objective of early psychosis therapy is to restore social functioning (e.g., through employment and education). Employment and educational outcomes during the COVID-19 pandemic were examined in a well-defined cohort of patients receiving care in an early psychosis clinic. METHODS: Data were extracted from the electronic health records of 128 patients receiving care at McLean Hospital's first-episode psychosis (FEP) clinic between January 1 and September 21 in 2019 and 2020. Using a generalized linear model with a Gaussian distribution and robust standard errors, the authors compared the average changes in the weekly employment and education proportions before and after COVID-19 lockdowns with the same changes in 2019. RESULTS: Employment losses among patients with FEP were greater than among the general population and persisted through the end of follow-up. In 2020, average employment after a stay-at-home order was instituted was 33% lower than before the order compared with the change in employment during the same period in 2019. The effect was stronger among men and those who identified as non-White, were age <21 years, or did not have a college education. Although educational engagement recovered in the fall of 2020, it still remained below the 2019 levels. CONCLUSIONS: Employment disruptions were major and persistent among the FEP population, which might affect short- and long-term outcomes. Innovative approaches are needed to help patients transition to remote employment, file unemployment claims, and use online hiring platforms to ameliorate the indirect effects of the COVID-19 pandemic.

Effects of COVID-19 pandemic on mental health outcomes in a cohort of early psychosis patients.

AIM: To evaluate the impact of the COVID-19 pandemic on clinical outcomes, we used data from Electronic Health Records from 128 patients receiving care at a First Episode Psychosis clinic. METHODS: Rates of admission or emergency room (ER) visits from January 2020 to July 2020 were analysed using difference-in-difference regression. We used the same weeks in 2019 to control for seasonality. RESULTS: We found 17 hospitalizations or ER visits between 1 January 2020 and 13 March 2020 (incidence rate: 71.4 events/1000 person-weeks) and 6 between 14 March 2020 and 20 June 2020 (incidence rate: 18.5 events/1000 person-weeks) for an incidence rate ratio of 0.26. The severity of presentation worsened after transition to telemedicine. No signs of significant interruptions of care were found. CONCLUSIONS: We report that patients have avoided accessing higher levels of care, except in extreme cases. We argue that this is not a sustainable trajectory and that public health actions are required.

Massachusetts general hospital Covid-19 registry reveals two distinct populations of hospitalized patients by race and ethnicity.

OBJECTIVE: To evaluate differences by race/ethnicity in clinical characteristics and outcomes among hospitalized patients with Covid-19 at Massachusetts General Hospital (MGH). METHODS: The MGH Covid-19 Registry includes confirmed SARS-CoV-2-infected patients hospitalized at MGH and is based on manual chart reviews and data extraction from electronic health records (EHRs). We evaluated differences between White/Non-Hispanic and Hispanic patients in demographics, complications and 14-day outcomes among the N = 866 patients hospitalized with Covid-19 from March 11, 2020-May 4, 2020. RESULTS: Overall, 43% of patients hospitalized with Covid-19 were women, median age was 60.4 [IQR = (48.2, 75)], 11.3% were Black/non-Hispanic and 35.2% were Hispanic. Hispanic patients, representing 35.2% of patients, were younger than White/non-Hispanic patients [median age 51y; IQR = (40.6, 61.6) versus 72y; (58.0, 81.7) (p<0.001)]. Hispanic patients were symptomatic longer before presenting to care (median 5 vs 3d, p = 0.039) but were more likely to be sent home with self-quarantine than be admitted to hospital (29% vs 16%, p<0.001). Hispanic patients had fewer comorbidities yet comparable rates of ICU or death (34% vs 36%). Nonetheless, a greater proportion of Hispanic patients recovered by 14 days after presentation (62% vs 45%, p<0.001; OR = 1.99, p = 0.011 in multivariable adjusted model) and fewer died (2% versus 18%, p<0.001). CONCLUSIONS: Hospitalized Hispanic patients were younger and had fewer comorbidities compared to White/non-Hispanic patients; despite comparable rates of ICU care or death, a greater proportion recovered. These results have implications for public health policy and the design and conduct of clinical trials.

Diabetes as a Risk Factor for Poor Early Outcomes in Patients Hospitalized With COVID-19.

OBJECTIVE: Diabetes and obesity are highly prevalent among hospitalized patients with coronavirus disease 2019 (COVID-19), but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. RESEARCH DESIGN AND METHODS: We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between 11 March 2020 and 30 April 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. RESULTS: Among 450 patients, 178 (39.6%) had diabetes-mostly type 2 diabetes. Among patients with diabetes versus patients without diabetes, a higher proportion was admitted to the ICU (42.1% vs. 29.8%, respectively, P = 0.007), required mechanical ventilation (37.1% vs. 23.2%, P = 0.001), and died (15.9% vs. 7.9%, P = 0.009). In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (odds ratio 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14 days. Obesity was associated with greater odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. CONCLUSIONS: Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjustment for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk for COVID-19.

Discovery of M Protease Inhibitors Encoded by SARS-CoV-2.

The coronavirus (CoV) disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome CoV-2 (SARS-CoV-2) is a health threat worldwide. Viral main protease (Mpro, also called 3C-like protease [3CLpro]) is a therapeutic target for drug discovery. Herein, we report that GC376, a broad-spectrum inhibitor targeting Mpro in the picornavirus-like supercluster, is a potent inhibitor for the Mpro encoded by SARS-CoV-2, with a half-maximum inhibitory concentration (IC50) of 26.4 ± 1.1 nM. In this study, we also show that GC376 inhibits SARS-CoV-2 replication with a half-maximum effective concentration (EC50) of 0.91 ± 0.03 µM. Only a small portion of SARS-CoV-2 Mpro was covalently modified in the excess of GC376 as evaluated by mass spectrometry analysis, indicating that improved inhibitors are needed. Subsequently, molecular docking analysis revealed that the recognition and binding groups of GC376 within the active site of SARS-CoV-2 Mpro provide important new information for the optimization of GC376. Given that sufficient safety and efficacy data are available for GC376 as an investigational veterinary drug, expedited development of GC376, or its optimized analogues, for treatment of SARS-CoV-2 infection in human is recommended.