Sex-Specific Neurodevelopmental Outcomes Among Offspring of Mothers With SARS-CoV-2 Infection During Pregnancy.

Importance: Prior studies using large registries have suggested a modest increase in risk for neurodevelopmental diagnoses among children of mothers with immune activation during pregnancy, and such risk may be sex-specific. Objective: To determine whether in utero exposure to SARS-CoV-2 is associated with sex-specific risk for neurodevelopmental disorders up to 18 months after birth, compared with unexposed offspring born during or prior to the COVID-19 pandemic period. Design, Setting, and Participants: This retrospective cohort study included the live offspring of all mothers who delivered between January 1 and December 31, 2018 (born and followed up before the COVID-19 pandemic), between March 1 and December 31, 2019 (born before and followed up during the COVID-19 pandemic), and between March 1, 2020, and May 31, 2021 (born and followed up during the COVID-19 pandemic). Offspring were born at any of 8 hospitals across 2 health systems in Massachusetts. Exposures: Polymerase chain reaction evidence of maternal SARS-CoV-2 infection during pregnancy. Main Outcomes and Measures: Electronic health record documentation of International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnostic codes corresponding to neurodevelopmental disorders. Results: The COVID-19 pandemic cohort included 18 355 live births (9399 boys [51.2%]), including 883 (4.8%) with maternal SARS-CoV-2 positivity during pregnancy. The cohort included 1809 Asian individuals (9.9%), 1635 Black individuals (8.9%), 12 718 White individuals (69.3%), and 1714 individuals (9.3%) who were of other race (American Indian or Alaska Native, Native Hawaiian or other Pacific Islander, more than 1 race); 2617 individuals (14.3%) were of Hispanic ethnicity. Mean maternal age was 33.0 (IQR, 30.0-36.0) years. In adjusted regression models accounting for race, ethnicity, insurance status, hospital type (academic center vs community), maternal age, and preterm status, maternal SARS-CoV-2 positivity was associated with a statistically significant elevation in risk for neurodevelopmental diagnoses at 12 months among male offspring (adjusted OR, 1.94 [95% CI 1.12-3.17]; P = .01) but not female offspring (adjusted OR, 0.89 [95% CI, 0.39-1.76]; P = .77). Similar effects were identified using matched analyses in lieu of regression. At 18 months, more modest effects were observed in male offspring (adjusted OR, 1.42 [95% CI, 0.92-2.11]; P = .10). Conclusions and Relevance: In this cohort study of offspring with SARS-CoV-2 exposure in utero, such exposure was associated with greater magnitude of risk for neurodevelopmental diagnoses among male offspring at 12 months following birth. As with prior studies of maternal infection, substantially larger cohorts and longer follow-up will be required to reliably estimate or refute risk.

The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation.

OBJECTIVE: To provide high-quality data for COVID-19 research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart. MATERIALS AND METHODS: Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a Natural Language Processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes, so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR. RESULTS: The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the Positive Predictive Value (PPV) in the pre-pandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. DISCUSSION AND CONCLUSION: Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization.

OBJECTIVE: The growing availability of electronic health records (EHR) data opens opportunities for integrative analysis of multi-institutional EHR to produce generalizable knowledge. A key barrier to such integrative analyses is the lack of semantic interoperability across different institutions due to coding differences. We propose a Multiview Incomplete Knowledge Graph Integration (MIKGI) algorithm to integrate information from multiple sources with partially overlapping EHR concept codes to enable translations between healthcare systems. METHODS: The MIKGI algorithm combines knowledge graph information from (i) embeddings trained from the co-occurrence patterns of medical codes within each EHR system and (ii) semantic embeddings of the textual strings of all medical codes obtained from the Self-Aligning Pretrained BERT (SAPBERT) algorithm. Due to the heterogeneity in the coding across healthcare systems, each EHR source provides partial coverage of the available codes. MIKGI synthesizes the incomplete knowledge graphs derived from these multi-source embeddings by minimizing a spherical loss function that combines the pairwise directional similarities of embeddings computed from all available sources. MIKGI outputs harmonized semantic embedding vectors for all EHR codes, which improves the quality of the embeddings and enables direct assessment of both similarity and relatedness between any pair of codes from multiple healthcare systems. RESULTS: With EHR co-occurrence data from Veteran Affairs (VA) healthcare and Mass General Brigham (MGB), MIKGI algorithm produces high quality embeddings for a variety of downstream tasks including detecting known similar or related entity pairs and mapping VA local codes to the relevant EHR codes used at MGB. Based on the cosine similarity of the MIKGI trained embeddings, the AUC was 0.918 for detecting similar entity pairs and 0.809 for detecting related pairs. For cross-institutional medical code mapping, the top 1 and top 5 accuracy were 91.0% and 97.5% when mapping medication codes at VA to RxNorm medication codes at MGB; 59.1% and 75.8% when mapping VA local laboratory codes to LOINC hierarchy. When trained with 500 labels, the lab code mapping attained top 1 and 5 accuracy at 77.7% and 87.9%. MIKGI also attained best performance in selecting VA local lab codes for desired laboratory tests and COVID-19 related features for COVID EHR studies. Compared to existing methods, MIKGI attained the most robust performance with accuracy the highest or near the highest across all tasks. CONCLUSIONS: The proposed MIKGI algorithm can effectively integrate incomplete summary data from biomedical text and EHR data to generate harmonized embeddings for EHR codes for knowledge graph modeling and cross-institutional translation of EHR codes.

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.

Neurodevelopmental Outcomes at 1 Year in Infants of Mothers Who Tested Positive for SARS-CoV-2 During Pregnancy.

Importance: Epidemiologic studies suggest maternal immune activation during pregnancy may be associated with neurodevelopmental effects in offspring. Objective: To evaluate whether in utero exposure to SARS-CoV-2 is associated with risk for neurodevelopmental disorders in the first 12 months after birth. Design, Setting, and Participants: This retrospective cohort study examined live offspring of all mothers who delivered between March and September 2020 at any of 6 Massachusetts hospitals across 2 health systems. Statistical analysis was performed from October to December 2021. Exposures: Maternal SARS-CoV-2 infection confirmed by a polymerase chain reaction test during pregnancy. Main Outcomes and Measures: Neurodevelopmental disorders determined from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes over the first 12 months of life; sociodemographic and clinical features of mothers and offspring; all drawn from the electronic health record. Results: The cohort included 7772 live births (7466 pregnancies, 96% singleton, 222 births to SARS-CoV-2 positive mothers), with mean (SD) maternal age of 32.9 (5.0) years; offspring were 9.9% Asian (772), 8.4% Black (656), and 69.0% White (5363); 15.1% (1134) were of Hispanic ethnicity. Preterm delivery was more likely among exposed mothers: 14.4% (32) vs 8.7% (654) (P = .003). Maternal SARS-CoV-2 positivity during pregnancy was associated with greater rate of neurodevelopmental diagnoses in unadjusted models (odds ratio [OR], 2.17 [95% CI, 1.24-3.79]; P = .006) as well as those adjusted for race, ethnicity, insurance status, offspring sex, maternal age, and preterm status (adjusted OR, 1.86 [95% CI, 1.03-3.36]; P = .04). Third-trimester infection was associated with effects of larger magnitude (adjusted OR, 2.34 [95% CI, 1.23-4.44]; P = .01). Conclusions and Relevance: This cohort study of SARS-CoV-2 exposure in utero found preliminary evidence that maternal SARS-CoV-2 may be associated with neurodevelopmental sequelae in some offspring. Prospective studies with longer follow-up duration will be required to exclude confounding and confirm these associations.

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.

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.