Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations. OBJECTIVE: We develop a Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) algorithm based on extensive unlabeled longitudinal Electronic Health Records (EHR) data augmented by a limited set of gold standard labels on the binary status information indicating whether the clinical event of interest occurred during the follow-up period. METHODS: The SeDDLeR algorithm calculates an individualized risk of developing future clinical events over time using each patient's baseline EHR features via the following steps: (1) construction of an initial EHR-derived surrogate as a proxy for the onset status; (2) deep learning calibration of the surrogate along gold-standard onset status; and (3) semi-supervised deep learning for risk prediction combining calibrated surrogates and gold-standard onset status. To account for missing onset time and heterogeneous follow-up, we introduce temporal kernel weighting. We devise a Gated Recurrent Units (GRUs) module to capture temporal characteristics. We subsequently assess our proposed SeDDLeR method in simulation studies and apply the method to the Massachusetts General Brigham (MGB) Biobank to predict type 2 diabetes (T2D) risk. RESULTS: SeDDLeR outperforms benchmark risk prediction methods, including Semi-parametric Transformation Model (STM) and DeepHit, with consistently best accuracy across experiments. SeDDLeR achieved the best C-statistics ( 0.815, SE 0.023; vs STM +.084, SE 0.030, P-value .004; vs DeepHit +.055, SE 0.027, P-value .024) and best average time-specific AUC (0.778, SE 0.022; vs STM + 0.059, SE 0.039, P-value .067; vs DeepHit + 0.168, SE 0.032, P-value <0.001) in the MGB T2D study. CONCLUSION: SeDDLeR can train robust risk prediction models in both real-world EHR and synthetic datasets with minimal requirements of labeling event times. It holds the potential to be incorporated for future clinical trial recruitment or clinical decision-making.

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.

Psychosis with Methylphenidate or Amphetamine in Patients with ADHD.

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 adolescents and young adults with ADHD differs among various stimulants has not been extensively studied. METHODS: We used data from two commercial insurance claims databases to assess patients 13 to 25 years of age who had received a diagnosis of ADHD and who started taking methylphenidate or amphetamine between January 1, 2004, and September 30, 2015. The outcome was a new diagnosis of psychosis for which an antipsychotic medication was prescribed during the first 60 days after the date of the onset of psychosis. To estimate hazard ratios for psychosis, we used propensity scores to match patients who received methylphenidate with patients who received amphetamine in each database, compared the incidence of psychosis between the two stimulant groups, and then pooled the results across the two databases. RESULTS: We assessed 337,919 adolescents and young adults who received a prescription for a stimulant for ADHD. The study population consisted of 221,846 patients with 143,286 person-years of follow up; 110,923 patients taking methylphenidate were matched with 110,923 patients taking amphetamines. There were 343 episodes of psychosis (with an episode defined as a new diagnosis code for psychosis and a prescription for an antipsychotic medication) in the matched populations (2.4 per 1000 person-years): 106 episodes (0.10%) in the methylphenidate group and 237 episodes (0.21%) in the amphetamine group (hazard ratio with amphetamine use, 1.65; 95% confidence interval, 1.31 to 2.09). CONCLUSIONS: Among adolescents and young adults with ADHD who were receiving prescription stimulants, new-onset psychosis occurred in approximately 1 in 660 patients. Amphetamine use was associated with a greater risk of psychosis than methylphenidate. (Funded by the National Institute of Mental Health and others.).

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.

Geometric Features Associated with Middle Cerebral Artery Bifurcation Aneurysm Formation: A Matched Case-Control Study.

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 obtained from CT angiography (CTA) or digital subtraction angiography (DSA) from 317 patients with unilateral MCA bifurcation aneurysms diagnosed at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016. We chose the contralateral unaffected MCA bifurcation as the control group, in order to control for genetic and environmental risk factors. Diameters and angles of surrounding parent and daughter vessels of 634 MCAs were examined. RESULTS: Univariable and multivariable statistical analyses were performed to determine statistical significance. Sensitivity analyses with smaller (≤ 3 mm) aneurysms only and with angles excluded, were also performed. In a multivariable conditional logistic regression model we showed that smaller diameter size ratio (OR 0.0004, 95% CI 0.0001-0.15), larger daughter-daughter angles (OR 1.08, 95% CI 1.06-1.11) and larger parent-daughter angle ratios (OR 4.24, 95% CI 1.77-10.16) were significantly associated with MCA aneurysm presence after correcting for other variables. In order to account for possible changes to the vasculature by the aneurysm, a subgroup analysis of small aneurysms (≤ 3 mm) was performed and showed that the results were similar. CONCLUSIONS: Easily measurable morphological parameters of the surrounding vasculature of the MCA may provide objective metrics to assess MCA aneurysm formation risk in high-risk patients.

Mechanistic Investigation of the Formation of Isoindole N-Oxides in the Electron Transfer-Mediated Oxidative Cyclization of 2'-Alkynylacetophenone Oximes.

This paper describes a joint experiment-theory investigation of the formation and cyclization of 2'-alkynylacetophenone oxime radical cations using photoinduced electron transfer (PET) with DCA as the photosensitizer. Using a combination of experimental 1H and 13C nuclear magnetic resonance (NMR) spectra, high-resolution mass spectrometry, and calculated NMR chemical shifts, we identified the products to be isoindole N-oxides. The reaction was found to be stereoselective; only one of the two possible stereoisomers is formed under these conditions. A detailed computational investigation of the cyclization reaction mechanism suggests facile C-N bond formation in the radical cation leading to a 5-exo intermediate. Back-electron transfer from the DCA radical anion followed by barrierless intramolecular proton transfer leads to the final product. We argue that the final proton transfer step in the mechanism is responsible for the stereoselectivity observed in experiment. As a whole, this work provides new insights into the formation of complex heterocycles through oxime and oxime ether radical cation intermediates produced via PET. Moreover, it represents the first reported formation of isoindole N-oxides.

Elevated International Normalized Ratio Is Associated With Ruptured Aneurysms.

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-control study of 4696 patients with 6403 intracranial aneurysms, including 1198 prospective patients, diagnosed at the Massachusetts General Hospital and the Brigham and Women's Hospital between 1990 and 2016 who were on no anticoagulant therapy or on warfarin for anticoagulation. Patients were divided into ruptured and nonruptured groups. Univariable and multivariable logistic regression analyses were performed to evaluate the association of anticoagulation therapy, INR values, and presentation with a ruptured intracranial aneurysm, taking into account the interaction between anticoagulant use and INR. Inverse probability weighting using propensity scores was used to minimize differences in baseline demographics characteristics. The marginal effects of anticoagulant use on rupture risk stratified by INR values were calculated. Results- In unweighted and weighted multivariable analyses, elevated INR values were significantly associated with rupture status among patients who were not anticoagulated (unweighted odds ratio, 22.78; 95% CI, 10.85-47.81 and weighted odds ratio, 28.16; 95% CI, 12.44-63.77). In anticoagulated patients, warfarin use interacts significantly with INR when INR ≥1.2 by decreasing the effects of INR on rupture risk. Conclusions- INR elevation is associated with intracranial aneurysm rupture, but the effects may be moderated by warfarin. INR values should, therefore, be taken into consideration when counseling patients with intracranial aneurysms.

Lipid-Lowering Agents and High HDL (High-Density Lipoprotein) Are Inversely Associated With Intracranial Aneurysm Rupture.

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-density lipoprotein), and lipid-lowering agent use are lacking. METHODS: The medical records of 4701 patients with 6411 intracranial aneurysms diagnosed at the Massachusetts General Hospital and the Brigham and Women's Hospital between 1990 and 2016 were reviewed and analyzed. Patients were separated into ruptured and nonruptured groups. Univariable and multivariable logistic regression analyses were performed to determine the effects of lipids (total cholesterol, LDL, and HDL) and lipid-lowering medications on intracranial aneurysm rupture risk. Propensity score weighting was used to account for differences in baseline characteristics of the cohorts. RESULTS: Lipid-lowering agent use was significantly inversely associated with rupture status (odds ratio, 0.58; 95% confidence interval, 0.47-0.71). In a subgroup analysis of complete cases that includes both lipid-lowering agent use and lipid values, higher HDL levels (odds ratio, 0.95; 95% confidence interval, 0.93-0.98) and lipid-lowering agent use (odds ratio, 0.41; 95% confidence interval, 0.23-0.73) were both significantly and inversely associated with rupture status, whereas total cholesterol and LDL levels were not significant. A monotonic exposure-response curve between HDL levels and risk of aneurysmal rupture was obtained. CONCLUSIONS: Higher HDL values and the use of lipid-lowering agents are significantly inversely associated with ruptured intracranial aneurysms.

Antihyperglycemic Agents Are Inversely Associated With Intracranial Aneurysm Rupture.

BACKGROUND AND PURPOSE: Previous studies have suggested a protective effect of diabetes mellitus on aneurysmal subarachnoid hemorrhage risk. However, reports are inconsistent, and objective measures of hyperglycemia in these studies are lacking. Our aim was to investigate the association between aneurysmal subarachnoid hemorrhage and antihyperglycemic agent use and glycated hemoglobin levels. METHODS: The medical records of 4701 patients with 6411 intracranial aneurysms, including 1201 prospective patients, diagnosed at the Massachusetts General Hospital and Brigham and Women's Hospital between 1990 and 2016 were reviewed and analyzed. Patients were separated into ruptured and nonruptured groups. Univariate and multivariate logistic regression analyses were performed to determine the association between aneurysmal subarachnoid hemorrhage and antihyperglycemic agents and glycated hemoglobin levels. Propensity score weighting was used to account for selection bias. RESULTS: In both unweighted and weighted multivariate analysis, antihyperglycemic agent use was inversely and significantly associated with ruptured aneurysms (unweighted odds ratio, 0.58; 95% confidence interval, 0.39-0.87; weighted odds ratio, 0.57; 95% confidence interval, 0.34-0.96). In contrast, glycated hemoglobin levels were not significantly associated with rupture status. CONCLUSIONS: Antihyperglycemic agent use rather than hyperglycemia is associated with decreased risk of aneurysmal subarachnoid hemorrhage, suggesting a possible protective effect of glucose-lowering agents in the pathogenesis of aneurysm rupture.

Low Serum Calcium and Magnesium Levels and Rupture of Intracranial Aneurysms.

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 associated with ruptured intracranial aneurysms. METHODS: The medical records of 4701 patients, including 1201 prospective patients, diagnosed at the Brigham and Women's Hospital and Massachusetts General Hospital between 1990 and 2016 were reviewed and analyzed. One thousand two hundred seventy-five patients had available serum calcium, magnesium, and albumin values within 1 day of diagnosis. Individuals were divided into cases with ruptured aneurysms and controls with unruptured aneurysms. Univariable and multivariable logistic regression analyses were performed to determine the association between serum albumin-corrected total calcium and magnesium levels and ruptured aneurysms. RESULTS: In multivariable analysis, both albumin-corrected calcium (odds ratio, 0.33; 95% confidence interval, 0.27-0.40) and magnesium (odds ratio, 0.40; 95% confidence interval, 0.28-0.55) were significantly and inversely associated with ruptured intracranial aneurysms. CONCLUSIONS: In this large case-control study, hypocalcemia and hypomagnesemia at diagnosis were significantly associated with ruptured aneurysms. Impaired hemostasis caused by hypocalcemia and hypomagnesemia may explain this association.

Efficient genome-wide association in biobanks using topic modeling identifies multiple novel disease loci.

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that may be unreliable and fail to capture the relationship between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records (EHR) for 10845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes are included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p<1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than for single phenome-wide diagnostic codes, and incorporation of less strongly-loading diagnostic codes enhanced association. This strategy provides a more efficient means of phenome-wide association in biobanks with coded clinical data.

Genome-wide association study identifies SESTD1 as a novel risk gene for lithium-responsive bipolar disorder.

Lithium is the mainstay prophylactic treatment for bipolar disorder (BD), but treatment response varies considerably across individuals. Patients who respond well to lithium treatment might represent a relatively homogeneous subtype of this genetically and phenotypically diverse disorder. Here, we performed genome-wide association studies (GWAS) to identify (i) specific genetic variations influencing lithium response and (ii) genetic variants associated with risk for lithium-responsive BD. Patients with BD and controls were recruited from Sweden and the United Kingdom. GWAS were performed on 2698 patients with subjectively defined (self-reported) lithium response and 1176 patients with objectively defined (clinically documented) lithium response. We next conducted GWAS comparing lithium responders with healthy controls (1639 subjective responders and 8899 controls; 323 objective responders and 6684 controls). Meta-analyses of Swedish and UK results revealed no significant associations with lithium response within the bipolar subjects. However, when comparing lithium-responsive patients with controls, two imputed markers attained genome-wide significant associations, among which one was validated in confirmatory genotyping (rs116323614, P=2.74 × 10(-8)). It is an intronic single-nucleotide polymorphism (SNP) on chromosome 2q31.2 in the gene SEC14 and spectrin domains 1 (SESTD1), which encodes a protein involved in regulation of phospholipids. Phospholipids have been strongly implicated as lithium treatment targets. Furthermore, we estimated the proportion of variance for lithium-responsive BD explained by common variants ('SNP heritability') as 0.25 and 0.29 using two definitions of lithium response. Our results revealed a genetic variant in SESTD1 associated with risk for lithium-responsive BD, suggesting that the understanding of BD etiology could be furthered by focusing on this subtype of BD.

"Where we put little fish in the water there are no mosquitoes:" a cross-sectional study on biological control of the Aedes aegypti vector in 90 coastal-region communities of Guerrero, Mexico.

BACKGROUND: In the Mexican state of Guerrero, some households place fish in water storage containers to prevent the development of mosquito larvae. Studies have shown that larvivorous fish reduce larva count in household water containers, but there is a lack of evidence about whether the use of fish is associated with a reduction in dengue virus infection. We used data from the follow up survey of the Camino Verde cluster randomised controlled trial of community mobilisation to reduce dengue risk to study this association. METHODS: The survey in 2012, among 90 clusters in the three coastal regions of Guerrero State, included a questionnaire to 10,864 households about socio-demographic factors and self-reported cases of dengue illness in the previous year. Paired saliva samples provided serological evidence of recent dengue infection among 4856 children aged 3-9 years. An entomological survey in the same households looked for larvae and pupae of Aedes aegypti and recorded presence of fish and temephos in water containers. We examined associations with the two outcomes of recent dengue infection and reported dengue illness in bivariate analysis and then multivariate analysis using generalized linear mixed modelling. RESULTS: Some 17% (1730/10,111) of households had fish in their water containers. The presence of fish was associated with lower levels of recent dengue virus infection in children aged 3-9 years (OR 0.64; 95% CI 0.45-0.91), as was living in a rural area (OR 0.57; 95% CI 0.45-0.71), and being aged 3-5 years (OR 0.65; 95% CI 0.51-0.83). Factors associated with lower likelihood of self-reported dengue illness were: the presence of fish (OR 0.79; 95% CI 0.64-0.97), and living in a rural area (OR 0.74; 95% CI 0.65-0.84). Factors associated with higher likelihood of self-reported dengue illness were: higher education level of the household head (OR 1.28; 95% CI 1.07-1.52), living in a household with five people or less (OR 1.33; 95% CI 1.16-1.52) and household use of insecticide anti-mosquito products (OR 1.68; 95% CI 1.47-1.92). CONCLUSIONS: Our study suggests that fish in water containers may reduce the risk of dengue virus infection and dengue illness. This could be a useful part of interventions to control the Aedes aegypti vector.

Pre-birth cohort study of atopic dermatitis and severe bronchiolitis during infancy.

BACKGROUND: Infants hospitalized for bronchiolitis (i.e. severe bronchiolitis) are at increased risk of childhood asthma. There are many known risk factors for severe bronchiolitis, including cardiac and pulmonary diseases. Less is known about the association between atopic diseases and risk of severe bronchiolitis. We sought to further examine risk factors for severe bronchiolitis, focusing on atopic dermatitis (AD). METHODS: We conducted a nested cohort study within the Massachusetts General Hospital Obstetric Maternal Study (MOMS), a prospective cohort of pregnant women enrolled during 1998-2006. Children of mothers enrolled in MOMS were included in the analysis if they received care within our health system (n = 5407). Potential risk factors for bronchiolitis and hospitalization data were extracted from the children's electronic health records; we also examined pregnancy and perinatal risk factors collected from the underlying MOMS data. RESULTS: During the first year of life, 125 infants (2.3%) had severe bronchiolitis. Eighteen of these patients had AD; 11 (61%) were diagnosed with AD prior to bronchiolitis hospitalization. In unadjusted analyses, AD was associated with severe bronchiolitis (χ(2) 14.6; p < 0.001). In multivariable analyses adjusting for nine known risk factors for severe bronchiolitis, including demographics, birth season, disposition at birth, cardiac disease, maternal parity, and delivery mode, AD was associated with increased odds of severe bronchiolitis (odds ratio 2.72, 95% confidence interval 1.60-4.63). CONCLUSIONS: Atopic dermatitis is significantly associated with severe bronchiolitis in infancy. The mechanism of the AD-bronchiolitis association is unclear and merits further study; this research may shed light on the pathogenesis of asthma.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

Evaluation of matched control algorithms in EHR-based phenotyping studies: a case study of inflammatory bowel disease comorbidities.

The success of many population studies is determined by proper matching of cases to controls. Some of the confounding and bias that afflict electronic health record (EHR)-based observational studies may be reduced by creating effective methods for finding adequate controls. We implemented a method to match case and control populations to compensate for sparse and unequal data collection practices common in EHR data. We did this by matching the healthcare utilization of patients after observing that more complete data was collected on high healthcare utilization patients vs. low healthcare utilization patients. In our results, we show that many of the anomalous differences in population comparisons are mitigated using this matching method compared to other traditional age and gender-based matching. As an example, the comparison of the disease associations of ulcerative colitis and Crohn's disease show differences that are not present when the controls are chosen in a random or even a matched age/gender/race algorithm. In conclusion, the use of healthcare utilization-based matching algorithms to find adequate controls greatly enhanced the accuracy of results in EHR studies. Full source code and documentation of the control matching methods is available at https://community.i2b2.org/wiki/display/conmat/.

HistoriView: Implementation and Evaluation of a Novel Approach to Review a Patient Using a Scalable Space-Efficient Timeline without Zoom Interactions.

BACKGROUND: Timelines have been used for patient review. While maintaining a compact overview is important, merged event representations caused by the intricate and voluminous patient data bring event recognition, access ambiguity, and inefficient interaction problems. Handling large patient data efficiently is another challenge. OBJECTIVE: This study aims to develop a scalable, efficient timeline to enhance patient review for research purposes. The focus is on addressing the challenges presented by the intricate and voluminous patient data. METHODS: We propose a high-throughput, space-efficient HistoriView timeline for an individual patient. For a compact overview, it uses nonstacking event representation. An overlay detection algorithm, y-shift visualization, and popup-based interaction facilitate comprehensive analysis of overlapping datasets. An i2b2 HistoriView plugin was deployed, using split query and event reduction approaches, delivering the entire history efficiently without losing information. For evaluation, 11 participants completed a usability survey and a preference survey, followed by qualitative feedback. To evaluate scalability, 100 randomly selected patients over 60 years old were tested on the plugin and were compared with a baseline visualization. RESULTS: Most participants found that HistoriView was easy to use and learn and delivered information clearly without zooming. All preferred HistoriView over a stacked timeline. They expressed satisfaction on display, ease of learning and use, and efficiency. However, challenges and suggestions for improvement were also identified. In the performance test, the largest patient had 32,630 records, which exceeds the baseline limit. HistoriView reduced it to 2,019 visual artifacts. All patients were pulled and visualized within 45.40 seconds. Visualization took less than 3 seconds for all. DISCUSSION AND CONCLUSION: HistoriView allows complete data exploration without exhaustive interactions in a compact overview. It is useful for dense data or iterative comparisons. However, issues in exploring subconcept records were reported. HistoriView handles large patient data preserving original information in a reasonable time.

Offspring cardiometabolic outcomes and postnatal growth trajectories after exposure to maternal SARS-CoV-2 infection.

OBJECTIVE: The objective of this study is to determine whether in utero exposure to SARS-CoV-2 is associated with increased risk for a cardiometabolic diagnosis by 18 months of age. METHODS: This retrospective electronic health record (EHR)-based cohort study included the live-born offspring of all individuals who delivered during the COVID-19 pandemic (April 1, 2020-December 31, 2021) at eight hospitals in Massachusetts. Offspring exposure was defined as a positive maternal SARS-CoV-2 polymerase chain reaction test during pregnancy. The primary outcome was presence of an ICD-10 code for a cardiometabolic disorder in offspring EHR by 18 months. Weight-, length-, and BMI-for-age z scores were calculated and compared at 6-month intervals from birth to 18 months. RESULTS: A total of 29,510 offspring (1599 exposed and 27,911 unexposed) were included. By 18 months, 6.7% of exposed and 4.4% of unexposed offspring had received a cardiometabolic diagnosis (crude odds ratio [OR] 1.47 [95% CI: 1.10 to 1.94], p = 0.007; adjusted OR 1.38 [1.06 to 1.77], p = 0.01). Exposed offspring had a significantly greater mean BMI-for-age z score versus unexposed offspring at 6 months (z score difference 0.19 [95% CI: 0.10 to 0.29], p < 0.001; adjusted difference 0.04 [-0.06 to 0.13], p = 0.4). CONCLUSIONS: Exposure to maternal SARS-CoV-2 infection was associated with an increased risk of receiving a cardiometabolic diagnosis by 18 months preceded by greater BMI-for-age at 6 months.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care. Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%). Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder.

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 mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.