Sex-specific neurodevelopmental outcomes in offspring of mothers with SARS-CoV-2 in pregnancy: an electronic health records cohort

ImportancePrior studies using large registries 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. ObjectiveTo determine whether in utero exposure to the novel coronavirus SARS-CoV-2 is associated with sex-specific risk for neurodevelopmental disorders up to 18 months after birth, compared to unexposed offspring born during or prior to the pandemic period. DesignRetrospective cohort. ParticipantsLive offspring of all mothers who delivered between March 2018 and May 2021 at any of eight hospitals across two health systems in Massachusetts. ExposurePCR evidence of maternal SARS-CoV-2 infection during pregnancy. Main Outcome and MeasuresElectronic health record documentation of ICD-10 diagnostic codes corresponding to neurodevelopmental disorders. ResultsThe pandemic cohort included 18,323 live births, including 877 (4.8%) to individuals with SARS-CoV-2 positivity during pregnancy. The cohort included 1806 (9.9%) Asian individuals, 1634 (8.9%) Black individuals, 1711 (9.3%) individuals of another race, and 12,694 (69%) White individuals; 2614 (14%) were of Hispanic ethnicity. Mean maternal age was 33.0 years (IQR 30.0-36.0). In adjusted regression models accounting for race, ethnicity, insurance status, hospital type (academic center vs. community), maternal age, and preterm status, SARS-CoV-2 positivity was associated with statistically significant elevation in risk for neurodevelopmental diagnoses among male offspring (adjusted OR 1.99, 95% CI 1.19-3.34; p=0.009) but not female offspring (adjusted OR 0.90, 95% CI 0.43-1.88; p=0.8). Similar effects were identified using matched analyses in lieu of regression. Conclusion and RelevanceSARS-CoV-2 exposure in utero was associated with greater magnitude of risk for neurodevelopmental diagnoses among male offspring in the 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. Trial RegistrationNA Key PointsO_ST_ABSQuestionC_ST_ABSAre rates of neurodevelopmental disorder diagnoses greater among male or female children with COVID-19 exposure in utero compared to those with no such exposure? FindingsIn a cohort of 18,323 infants delivered after February 2020, males but not females born to mothers with a positive SARS-CoV-2 PCR test during pregnancy were more likely to receive a neurodevelopmental diagnosis in the first 12 months after delivery, even after accounting for preterm delivery. MeaningThese findings suggest that male offspring exposed to COVID-19 in utero may be at increased risk for neurodevelopmental disorders.

Neurodevelopmental outcomes at one year in offspring of mothers who test positive for SARS-CoV-2 during pregnancy

ImportanceEpidemiologic studies suggest maternal immune activation during pregnancy may be associated with neurodevelopmental effects in offspring. ObjectiveTo determine whether in utero exposure to the novel coronavirus SARS-CoV-2 is associated with risk for neurodevelopmental disorders in the first 12 months after birth. DesignRetrospective cohort ParticipantsLive offspring of all mothers who delivered between March and September 2020 at one of six Massachusetts hospitals across two health systems. ExposureSARS-CoV-2 infection confirmed by PCR during pregnancy Main Outcome and MeasuresNeurodevelopmental disorders determined from ICD-10 diagnostic codes over 12 months; sociodemographic and clinical features of mothers and offspring; all drawn from the electronic health record. ResultsThe cohort included 7,772 live births (7,466 pregnancies, 96% singleton, 222 births to SARS-CoV-2 positive mothers), with mean maternal age of 32.9 years; offspring were 9.9% Asian, 8.4% Black, and 69.0% white; 15.1% were of Hispanic ethnicity. Preterm delivery was more likely among exposed mothers (14% versus 8.7%; p=.003). Maternal SARS-CoV-2 positivity during pregnancy was associated with greater rate of neurodevelopmental diagnoses (crude OR 2.17 [95% CI 1.24-3.79, p=0.006]) as well as models adjusted for race, ethnicity, insurance status, offspring sex, maternal age, and preterm status (adjusted OR 1.86 [95% CI 1.03-3.36, p=0.04]). Third-trimester infection was associated with effects of larger magnitude (adjusted OR 2.34, 95% CI 1.23-4.44, p=0.01) Conclusion and RelevanceOur results provide 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 effects. Trial RegistrationNA Key PointsO_ST_ABSQuestionC_ST_ABSDoes COVID-19 exposure in utero increase the risk for neurodevelopmental disorders in the first year of life? FindingsIn a cohort of babies delivered during COVID-19, those born to mothers with a positive SARS-CoV-2 PCR test during pregnancy were more likely to receive a neurodevelopmental diagnosis in the first 12 months after delivery, even after accounting for preterm delivery. MeaningThese preliminary findings suggest that COVID-19 exposure may impact neurodevelopment, and highlight the need for prospective investigation of outcomes in children exposed to COVID-19 in utero.

Persistence of neuropsychiatric symptoms associated with SARS-CoV-2 positivity among a cohort of children and adolescents

BackgroundPost-acute sequelae of COVID-19 are common among adults. The prevalence of such syndromes among community samples of children and adolescents remains less well characterized. MethodWe identified all individuals age 5-18 across 2 New England health systems who had a positive SARS-CoV-2 PCR test between 3/12/2020 and 4/18/2021 and at least 90 days of follow-up visits documented in electronic health records. We identified neuropsychiatric symptoms in intervals prior to, and following, this testing using a previously-derived set of ICD-10 codes and natural language processing terms. Primary analysis examined sociodemographic features associated with presence of at least one incident (i.e., new-onset) neuropsychiatric symptom between 90 and 150 days after an initial positive test for COVID-19. ResultsAmong 5058 children (50% female, 2.9% Asian, 6.3% Black, and 63% White; 30% Hispanic; mean age was 12.4 (IQR 8.9-15.6), 366 (7.2%) exhibited at least one new-onset neuropsychiatric symptom between 90 and 150 days following initial SARS-CoV-2 test positivity. The most common incident symptoms at 90-150 days were headache (2.4%), mood and anxiety symptoms (2.4%), cognitive symptoms (2.3%), and fatigue (1.1%). In regression models, older children, girls, those with Hispanic ethnicity, those with public versus private insurance, and those with greater overall burden of medical comorbidity were more likely to exhibit subsequent symptoms. ConclusionThe prevalence of neuropsychiatric symptoms between 3- and 5-months following SARS-CoV-2 test positivity is similar to that observed in the period prior to infection. Prospective controlled studies will be needed to further refine these estimates.

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

ObjectiveTo provide high-quality data for COVID-19 research, we validated COVID-19 clinical indicators and 22 associated computed phenotypes, which were derived by machine learning algorithms, in the Mass General Brigham (MGB) COVID-19 Data Mart. Materials and MethodsFifteen reviewers performed a 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 the Digital Analytic Patient Reviewer (DAPR). DAPR is a web-based chart review tool that integrates patient notes and provides note search functionalities and a patient-specific summary view linked with relevant notes. Within DAPR, we developed a COVID-19 validation task-oriented view and information extraction logic, enabled fast access to data, and considered privacy and security issues. ResultsThe 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. For phenotypes, the overall specificities, PPVs, and NPVs were high. However, sensitivities were relatively low. Based on these results, we removed 3 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 ConclusionDAPRs patient summary view accelerated the validation process. We are in the process of automating the workflow to use DAPR for chart reviews. Moreover, we will extend its use case to other domains.

Consistency of performance of adverse outcome prediction models for hospitalized COVID-19 patients

BackgroundWe previously reported and validated a risk prediction tool based on COVID-19 hospitalizations prior to June 2020. Here, we report performance of that model on subsequent data from 6 hospitals and among individual patient subgroups. MethodWe included individuals age 18 or older hospitalized at one of 2 academic medical centers and 4 community hospitals from 6/7/2020 through 1/22/2021 with positive PCR test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 5 days of admission. Coefficients from our previously reported least absolute shrinkage and selection operator (Lasso) risk models were applied to estimate probability of a mortality, as well as a composite severe illness outcome, including admission to the ICU, mechanical ventilation or mortality. ResultsOverall model performance for mortality included AUC of 0.83 (95% CI:0.80-0.87) for mortality, with a PPV 0.55 and NPV of 0.95 when using a cutoff corresponding to the highest 20% of predicted risk derived in the training set. For all adverse outcomes, AUC was 0.79 (95% CI:0.75-0.81) and PPV 0.48 and NPV 0.98 in the top 20% risk group. Model discrimination was generally similar between genders and race/ethnicity groups, but markedly poorer for younger age groups. ConclusionAlthough the population of individuals hospitalized for COVID-19 has shifted and outcomes have improved overall, prediction models derived earlier in the pandemic may maintain utility.

Validation of a Derived International Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) includes hundreds of hospitals internationally using a federated computational approach to COVID-19 research using the EHR. ObjectiveWe sought to develop and validate a standard definition of COVID-19 severity from readily accessible EHR data across the Consortium. MethodsWe developed an EHR-based severity algorithm and validated it on patient hospitalization data from 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also used a machine learning approach to compare selected predictors of severity to the 4CE algorithm at one site. ResultsThe 4CE severity algorithm performed with pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of single code categories for acuity were unacceptably inaccurate - varying by up to 0.65 across sites. A multivariate machine learning approach identified codes resulting in mean AUC 0.956 (95% CI: 0.952, 0.959) compared to 0.903 (95% CI: 0.886, 0.921) using expert-derived codes. Billing codes were poor proxies of ICU admission, with 49% precision and recall compared against chart review at one partner institution. DiscussionWe developed a proxy measure of severity that proved resilient to coding variability internationally by using a set of 6 code classes. In contrast, machine-learning approaches may tend to overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold standard outcomes, possibly due to pandemic conditions. ConclusionWe developed an EHR-based algorithm for COVID-19 severity and validated it at 12 international sites.

Laboratory findings associated with mechanical ventilation requirement and mortality among hospitalized individuals with Covid-19 in Eastern Massachusetts

ImportanceThe 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. ObjectiveTo quantify admission laboratory and comorbidity features associated with critical illness and mortality risk across 6 Eastern Massachusetts hospitals. DesignRetrospective cohort study using hospital course, prior diagnoses, and laboratory values. SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals. ParticipantsAll individuals with hospital admission and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR testing across these 6 hospitals through June 5, 2020. ExposureCoronavirus 2 (SARS-CoV-2). Main Outcome MeasuresSevere illness defined by ICU admission, mechanical ventilation, or death. ResultsAmong 2,511 hospitalized individuals who tested positive for SARS-CoV-2 (of whom 50.9% were male, 53.9% white, and 27.0% Hispanic, with mean age 62.6 years), 215 (8.6%) were admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded area under ROC curve (AUC) of 0.807 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 212/292 (78%) of the deaths occurred in the highest-risk mortality quintile. Conclusions and RelevanceIn this cohort, specific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitated risk stratification among individuals hospitalized for COVID-19. Funding1R56MH115187-01 Trial RegistrationNone Key PointsO_ST_ABSQuestionC_ST_ABSHow well can sociodemographic features, laboratory values, and comorbidities of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020 predict severe illness course? FindingsIn this cohort study of 2,511 hospitalized individuals positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR who were admitted to one of six hospitals, 215 (8.6%) were admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 212 (78%) deaths occurred in the top mortality-risk quintile. MeaningSimple prediction models may assist in risk stratification among hospitalized COVID-19 patients.

Identifying common pharmacotherapies associated with reduced COVID-19 morbidity using electronic health records

ObjectiveAbsent a vaccine or any established treatments for the novel and highly infectious coronavirus-19 (COVID-19), rapid efforts to identify potential therapeutics are required. We sought to identify commonly prescribed medications that may be associated with lesser risk of morbidity with COVID-19 across 6 Eastern Massachusetts hospitals. DesignIn silico cohort using electronic health records from individuals evaluated in the emergency department between March 4, 2020 and July 12, 2020. SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals. ParticipantsAll individuals presenting to an emergency department and undergoing COVID-19 testing. Main Outcome or MeasureInpatient hospitalization; documented requirement for mechanical ventilation. ResultsAmong 7,360 individuals with COVID-19 positive test results by PCR, 3,693 (50.2%) were hospitalized in one of 6 hospitals. In models adjusted for sociodemographic features and overall burden of medical illness, medications significantly associated with diminished risk for hospitalization included ibuprofen and sumatriptan. Among individuals who were hospitalized, 962(26.0%) were admitted to the intensive care unit and 608 (16.5%) died; ibuprofen and naproxen were also more commonly prescribed among individuals not requiring intensive care. ConclusionsThese preliminary findings suggest that electronic health records may be applied to identify medications associated with lower risk of morbidity with COVID-19, but larger cohorts will be required to address the substantial problem of confounding by indication, such that extreme caution is warranted in interpreting nonrandomized results. Trial RegistrationNone Summary BoxesO_ST_ABSSection 1: What is already known on this topicC_ST_ABSAbsent a vaccine or any established treatments for the novel and highly infectious coronavirus-19 (COVID-19), rapid efforts to identify potential therapeutics are required. Section 2: What this study addsThis cohort study across 6 hospitals identified medications enriched among individuals positive for COVID-19 who are less likely to experience adverse outcomes including hospitalization, intensive care, or death.

Impact of COVID-19 on psychiatric assessment in emergency and outpatient settings measured using electronic health records

Key PointsO_ST_ABSQuestionC_ST_ABSHow did documentation of psychiatric symptoms in outpatient and emergency room settings change with onset of COVID-19 infection in Eastern Massachusetts? FindingsIn this cohort study spanning 2 academic medical centers and 3 community hospitals, prevalence of narrative notes referencing depression or anxiety decreased 75-81% in outpatient settings following onset of coronavirus in March 2019, and by 44-45% in emergency departments. MeaningThe observation that documentation of psychiatric symptoms declined sharply with increasing coronavirus infection in Massachusetts, even as prevalence of such symptoms is anticipated to increase, suggests additional efforts may be required to address these symptoms in the context of COVID-19.