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1.
Preprint | EuropePMC | ID: ppcovidwho-296642

ABSTRACT

Importance: Epidemiologic studies suggest maternal immune activation during pregnancy may be associated with neurodevelopmental effects in offspring. Objective: To 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. Design: Retrospective cohort Participants: Live offspring of all mothers who delivered between March and September 2020 at one of six Massachusetts hospitals across two health systems. Exposure: SARS-CoV-2 infection confirmed by PCR during pregnancy Main Outcome and Measures: Neurodevelopmental 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. Results: The 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 2.31, 95% CI 1.16-4.21, p=0.01) Conclusion and Relevance: Our 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.

2.
Gen Hosp Psychiatry ; 74: 9-17, 2021 Nov 02.
Article in English | MEDLINE | ID: covidwho-1568701

ABSTRACT

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.

4.
Am J Psychiatry ; 178(6): 541-547, 2021 06.
Article in English | MEDLINE | ID: covidwho-1169925

ABSTRACT

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.


Subject(s)
COVID-19/psychology , Mood Disorders/complications , Aged , COVID-19/mortality , Cohort Studies , Female , Hospitalization , Humans , Male , Retrospective Studies , Risk Assessment , Treatment Outcome
5.
J Acad Consult Liaison Psychiatry ; 62(3): 298-308, 2021.
Article in English | MEDLINE | ID: covidwho-1117177

ABSTRACT

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.


Subject(s)
COVID-19/complications , Delirium/diagnosis , Delirium/etiology , Adult , Aged , Aged, 80 and over , Area Under Curve , Cohort Studies , Delirium/prevention & control , Electronic Health Records , Female , Humans , Machine Learning , Male , Middle Aged , Models, Statistical , Patient Admission , Risk Assessment/methods , SARS-CoV-2 , Sensitivity and Specificity
6.
J Am Med Inform Assoc ; 28(7): 1411-1420, 2021 07 14.
Article in English | MEDLINE | ID: covidwho-1075534

ABSTRACT

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.


Subject(s)
COVID-19 , Electronic Health Records , Severity of Illness Index , COVID-19/classification , Hospitalization , Humans , Machine Learning , Prognosis , ROC Curve , Sensitivity and Specificity
8.
Saude Coletiva ; - (59):4133-4139, 2020.
Article in English | CINAHL | ID: covidwho-976745

ABSTRACT

Objective: To estimate the Potential Years of Life Lost (APVP) by Covid 19 in Ceará, Rio de Janeiro and São Paulo, according to sex and age, from March to August 2020. Methods: This is an epidemiological study of the type descriptive, comparative. Data analysis occurred through the calculation of APVP, proposed by Romeder and McWhinnie (1977), and the method was adapted for this research. Results: The most significant results of APVP by Covid-19 come from the states of São Paulo and Rio de Janeiro. In these same locations, the age group with the highest APVP was 55 to 59 years. In Ceará, in contrast, APVPs were concentrated in greater numbers in the 50-54 age group, with the male gender standing out in relation to the female. Conclusion: The quantification of APVP is essential to guide public health priorities. Objetivo: Estimar los Años Potenciales de Vida Perdidos (APVP) por Covid 19 en Ceará, Rio de Janeiro y São Paulo, según sexo y edad, de marzo a agosto de 2020. Métodos: Se trata de un estudio epidemiológico del tipo descriptivo, comparativo. El análisis de datos se realizó mediante el cálculo de APVP, propuesto por Romeder y McWhinnie (1977), y el método fue adaptado para esta investigación. Resultados: Los resultados más significativos de APVP por Covid-19 provienen de los estados de São Paulo y Río de Janeiro. En estos mismos lugares, el grupo de edad con mayor APVP fue de 55 a 59 años. En Ceará, en cambio, las APVP se concentraron en mayor número en el grupo de 50 a 54 años, destacando el género masculino en relación al femenino. Conclusión: La cuantificación de APVP es fundamental para orientar las prioridades de salud pública. Objetivo: Estimar os Anos Potenciais de Vida Perdidos (APVP) pela Covid 19 no Ceará, Rio de Janeiro e São Paulo, segundo sexo e idade, no período de março a agosto de 2020. Métodos: Trata-se de um estudo epidemiológico do tipo descritivo, comparativo. A análise dos dados ocorreu por meio do cálculo de APVP, proposto por Romeder e McWhinnie (1977), sendo o método adaptado para esta pesquisa. Resultados: Os resultados mais significativos de APVP por Covid-19 são advindos dos estados de São Paulo e Rio de Janeiro. Nestas mesmas localidades, a faixa etária com maior APVP foi a de 55 a 59 anos. Diferentemente, no Ceará, os APVP concentraram-se em maior número na faixa etária 50-54 anos, com o sexo masculino destacando-se em relação ao feminino. Conclusão: A quantificação dos APVP é essencial para nortear as prioridades em saúde pública.

9.
JAMA Netw Open ; 3(10): e2023934, 2020 10 01.
Article in English | MEDLINE | ID: covidwho-893183

ABSTRACT

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.


Subject(s)
Coronavirus Infections/complications , Critical Illness , Hospital Mortality/trends , Pneumonia, Viral/complications , Adult , Aged , Aged, 80 and over , Area Under Curve , Betacoronavirus/pathogenicity , Blood Urea Nitrogen , C-Reactive Protein/analysis , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques , Cohort Studies , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/physiopathology , Coronavirus Infections/urine , Creatinine/analysis , Creatinine/blood , Critical Illness/epidemiology , Eosinophils , Erythrocyte Count/methods , Female , Glucose/analysis , Hospitalization/statistics & numerical data , Humans , Hydro-Lyases/analysis , Hydro-Lyases/blood , Lymphocyte Count/methods , Male , Massachusetts/epidemiology , Middle Aged , Monocytes , Neutrophils , Pandemics , Platelet Count/methods , Pneumonia, Viral/epidemiology , Pneumonia, Viral/physiopathology , Polymerase Chain Reaction/methods , ROC Curve , Retrospective Studies , SARS-CoV-2 , Troponin T/analysis , Troponin T/blood
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