Victor M Castro

Partners HealthCare · Research Computing

Context. Prior birth cohorts have suggested an association between maternal infection in pregnancy and offspring risk for childhood obesity. Whether maternal SARS-CoV-2 infection is similarly associated with increased cardiometabolic risk for offspring is not known. Objective. To determine whether in utero exposure to SARS-CoV-2 is associated with...

Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily available precise information on the timing of clinical events such as the onset time of heart failure. We propose a LAbel-efficienT incidenT phEnotyping (LATTE) algorithm...

Growing evidence has shown that applying machine learning models to large clinical data sources may exceed clinician performance in suicide risk stratification. However, many existing prediction models either suffer from "temporal bias" (a bias that stems from using case-control sampling) or require training on all available patient visit data. Her...

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 neurodevelopmenta...

Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited...

Several recent studies have applied machine learning techniques to develop risk algorithms that predict subsequent suicidal behavior based on electronic health record data. In this study we used a retrospective cohort study design to test whether developing more tailored predictive models-within specific subpopulations of patients-would improve pre...

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected...

Importance The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides ca...

Importance: Prior 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. Objective: To determine whether in utero exposure to the novel coronavirus SARS-CoV-2 is associated with sex-specific risk for neu...

Objective To provide high-quality data for coronavirus disease 2019 (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. Methods Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19-positive patients in...

Treatment-resistant depression (TRD), often defined by absence of symptomatic remission following at least two adequate treatment trials, occurs in roughly a third of all individuals with major depressive disorder (MDD). Prior work has suggested a significant common variant genetic component of liability to TRD, with heritability estimates of 8% wh...

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 Inc...

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...

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...

Background Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in pra...

Objectives The pathogenesis of intracranial aneurysms is multifactorial and includes genetic, environmental, and anatomic influences. We aimed to identify image-based morphological parameters that were associated with middle cerebral artery (MCA) bifurcation aneurysms. Materials and methods We evaluated three-dimensional morphological parameters o...

Importance: Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients. Objective: To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems. Design, setting, and participants: This p...

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...

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the...

Objective Integrating and harmonizing disparate patient data sources into one consolidated data portal enables researchers to conduct analysis efficiently and effectively. Materials and Methods We describe an implementation of Informatics for Integrating Biology and the Bedside (i2b2) to create the Mass General Brigham (MGB) Biobank Portal data re...

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 de...

Background: Post-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. Method: We 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 le...

This prognostic study reports on the performance of a previously validated COVID-19 severity prediction tool when applied to data during the second wave of the pandemic.

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...

BACKGROUND Interest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in pra...

Objective: To 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 Methods: Fifteen reviewers performed a manual chart review for 150 COVID-19 positive pa...

Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health...

Objective Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating...

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 betwee...

Objective: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Even with a working knowledge of EHR, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use o...

Background We 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. Method We included individuals age 18 or older hospitalized at one of 2 academic medical centers and 4 co...

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 incid...

We present a cohort of patients with anterior communicating artery (ACoA) aneurysms to investigate morphological characteristics and clinical factors associated with rupture of the aneurysms. 505 patients with ACoA aneurysms were identified at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016, with available...

Introduction: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective: We sought to develop and validate a computable phenotype for COVID-19 severity. Methods: Twelve 4CE sites participated. First we dev...

Morphological factors of intracranial aneurysms and the surrounding vasculature could affect aneurysm rupture risk in a location specific manner. Our goal was to identify image-based morphological parameters that correlated with ruptured basilar tip aneurysms. Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital...

Background Hemodynamic stress, conditioned by the morphology of the surrounding vasculature, plays an important role in aneurysm formation. Our goal was to identify image-based location-specific parameters that are associated with posterior communicating artery (PCoA) aneurysms. Methods Three-dimensional morphological parameters obtained from CT a...

Objective We aimed to identify clinical and morphological risk factors that are correlated with anterior communicating artery (ACoA) aneurysm formation. Methods Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital subtraction angiography (DSA) from 504 patients with ACoA aneurysms and 201 patients with aneurysms...

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 Massachuset...

Hemodynamic stress is thought to play an important role in the formation of intracranial aneurysms, which is conditioned by the geometry of the surrounding vasculature. Our goal was to identify image-based morphological parameters that were associated with basilar artery tip aneurysms (BTA) in a location-specific manner. Three-dimensional morpholog...

A bstract Introduction The 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. Objective We sought to develop and validate a standard definition of COVID-19 severity from readily accessible EHR data across the...

Risk of intracranial aneurysm rupture could be affected by geometric features of intracranial aneurysms and the surrounding vasculature in a location specific manner. Our goal is to investigate the morphological characteristics associated with ruptured posterior communicating artery (PCoA) aneurysms, as well as patient factors associated with the m...

Objective: A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled d...

This cohort study investigates the documentation of psychiatric symptoms in narrative clinical notes as coronavirus disease 2019 (COVID-19) prevalence increased in eastern Massachusetts.

Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart fai...

Importance: The Covid-19 pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse outcomes are needed. Objective: To quantify admission laboratory features associated with mechanical ventilation and mortality risk across 5 Eastern Massachusetts hospitals. Design: Retrospective cohort st...

Importance: Absent a vaccine or any established treatments for the novel and highly infectious coronavirus-19 (COVID-19), rapid efforts to identify potential therapeutics are required. Objective: To identify commonly-prescribed medications that may be associated with lesser risk of morbidity with COVID-19 across 5 Eastern Massachusetts hospitals. D...

Objective A major bottleneck hindering utilization of electronic health record (EHR) data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and label...

Importance: As with other traumatic events, pandemics such as coronavirus-19 (COVID-19) may precipitate or exacerbate psychiatric symptoms such as anxiety and depression, while potentially interfering with health systems' capacity to treat such symptoms. Objective: To quantify the impact of increasing COVID-19 infection on extent of psychiatric ass...

Importance Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings. Objective To evaluate the...

Objective: Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Disease...

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variatio...

Importance Quantifying patient-physician cost conversations is challenging but important as out-of-pocket spending by US patients increases and patients are increasingly interested in discussing costs with their physicians. Objective To characterize the prevalence of financial considerations documented in narrative clinical records of primary care...

Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate th...

Objective: To utilize electronic health records (EHRs) to study SLE, algorithms are needed to accurately identify these patients. We used machine learning to generate data-driven SLE EHR algorithms and assessed performance of existing rule-based algorithms. Methods: We randomly selected subjects with ≥ 1 SLE ICD-9/10 codes from our EHR and ident...

Iron and its derivatives play a significant role in various physiological and biochemical pathways, and are influenced by a wide variety of inflammatory, infectious, and immunological disorders. We hypothesized that iron and its related factors play a role in intracranial aneurysm pathophysiology and investigated if serum iron values are associated...

Background Prescription stimulant use (amphetamine and methylphenidate) for the treatment of attention deficit hyperactivity disorder (ADHD) is increasing. In 2007, the US Food and Drug Administration mandated changes to stimulant prescribing labels based on findings of new-onset psychosis in patients without pre-existing disease. Although these ch...

Objective Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Dis...

Background The prescription use of the stimulants methylphenidate and amphetamine for the treatment of attention deficit–hyperactivity disorder (ADHD) has been increasing. In 2007, the Food and Drug Administration mandated changes to drug labels for stimulants on the basis of findings of new-onset psychosis. Whether the risk of psychosis in adolesc...

Background Weight change is a common adverse effect of antidepressant treatment. The purpose of this study was to identify genetic variants associated with change in body weight during antidepressant treatment for Major Depressive Disorder (MDD) with Selective Serotonin Reuptake Inhibitors (SSRIs). Methods Genotyping and objectively measured weigh...

Background Bipolar Disorder (BD) is a heritable mood disorder with about 1% lifetime prevalence in general population. Many BD-associated loci have been identified through Genome-Wide Association Studies (GWAS). However, larger sample size and more detailed clinical/phenotypic information are needed to further understand the etiology of BD. To expa...

OBJECTIVE Individuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health conseq...

Objective: To determine the association between ruptured saccular aneurysms and aspirin use/aspirin dose. Methods: Four thousand seven hundred one patients who were diagnosed at the Massachusetts General Hospital and Brigham and Women's Hospital between 1990 and 2016 with 6,411 unruptured and ruptured saccular intracranial aneurysms were evaluat...

Background and Purpose— The effects of anticoagulation therapy and elevated international normalized ratio (INR) values on the risk of aneurysmal subarachnoid hemorrhage are unknown. We aimed to investigate the association between anticoagulation therapy, elevated INR values, and rupture of intracranial aneurysms. Methods— We conducted a case-cont...

While cocaine use is thought to be associated with aneurysmal rupture, it is not known whether heroin use increases the risk of rupture in patients with non-mycotic saccular aneurysms. Our goal was to investigate the association between heroin and cocaine use and the rupture of saccular non-mycotic aneurysms. The medical records of 4701 patients wi...

Background: Geometric factors of intracranial aneurysms and surrounding vasculature could affect the risk of aneurysm rupture. However, large-scale assessments of morphological parameters correlated with intracranial aneurysm rupture in a location-specific manner are scarce. Objective: To investigate the morphological characteristics associated...

Background and purpose: Both low serum calcium and magnesium levels have been associated with the extent of bleeding in patients with intracerebral hemorrhage, suggesting hypocalcemia- and hypomagnesemia-induced coagulopathy as a possible underlying mechanism. We hypothesized that serum albumin-corrected total calcium and magnesium levels are asso...

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively...

Background and Purpose—Growing evidence from experimental animal models and clinical studies suggests the protective effect of statin use against rupture of intracranial aneurysms; however, results from large studies detailing the relationship between intracranial aneurysm rupture and total cholesterol, HDL (high-density lipoprotein), LDL (low-dens...

Alcohol consumption may be a modifiable risk factor for rupture of intracranial aneurysms. Our aim is to evaluate the association between ruptured aneurysms and alcohol consumption, intensity, and cessation. The medical records of 4701 patients with 6411 radiographically confirmed intracranial aneurysms diagnosed at the Brigham and Women’s Hospital...

Background: Genetic studies of neuropsychiatric disease strongly suggest an overlap in liability. There are growing efforts to characterize these diseases dimensionally rather than categorically, but the extent to which such dimensional models correspond to biology is unknown. Methods: We applied a newly developed natural language processing met...

Background: Relying on diagnostic categories of neuropsychiatric illness obscures the complexity of these disorders. Capturing multiple dimensional measures of neuropathology could facilitate the clinical and neurobiological investigation of cognitive and behavioral phenotypes. Methods: We developed a natural language processing-based approach t...