Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias.

Objectives: 1.1Biases inherent in electronic health records (EHRs), and therefore in medical artificial intelligence (AI) models may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature. Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases are further exacerbated, resulting in systems that perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs. Methods: 1.2We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles. Results: 1.3Systematic categorizations of diverse sources of bias are scarce in the literature, while the effects of separate studies are often convoluted and methodologically contestable. Our categorization of published empirical evidence identified the six main sources of bias: a) bias arising from past clinical trials; b) data-related biases arising from missing, incomplete information or poor labeling of data; human-related bias induced by c) implicit clinician bias, d) referral and admission bias; e) diagnosis or risk disparities bias and finally, (f) biases in machinery and algorithms. Conclusions: 1.4Machine learning and data-driven solutions can potentially transform healthcare delivery, but not without limitations. The core inputs in the systems (data and human factors) currently contain several sources of bias that are poorly documented and analyzed for remedies. The current evidence heavily focuses on data-related biases, while other sources are less often analyzed or anecdotal. However, these different sources of biases add to one another exponentially. Therefore, to understand the issues holistically we need to explore these diverse sources of bias. While racial biases in EHR have been often documented, other sources of biases have been less frequently investigated and documented (e.g. gender-related biases, sexual orientation discrimination, socially induced biases, and implicit, often unconscious, human-related cognitive biases). Moreover, some existing studies lack causal evidence, illustrating the different prevalences of disease across groups, which does not per se prove the causality. Our review shows that data-, human- and machine biases are prevalent in healthcare and they significantly impact healthcare outcomes and judgments and exacerbate disparities and differential treatment. Understanding how diverse biases affect AI systems and recommendations is critical. We suggest that researchers and medical personnel should develop safeguards and adopt data-driven solutions with a "bias-in-mind" approach. More empirical evidence is needed to tease out the effects of different sources of bias on health outcomes.

Surgery Duration Prediction using Multitask Feature Selection.

Efficient optimization of operation room (OR) activity poses a significant challenge for hospital managers due to the complex and risky nature of the environment. The traditional "one size fits all" approach to OR scheduling is no longer practical, and personalized medicine is required to meet the diverse needs of patients, care providers, medical procedures, and system constraints within limited resources. This paper aims to introduce a scientific and practical tool for predicting surgery durations and improving OR performance for maximum benefit to patients and the hospital. Previous works used machine-learning models for surgery duration prediction based on preoperative data. The models consider covariates known to the medical staff at the time of scheduling the surgery. However, model selection becomes crucial, where the number of covariates used for prediction depend on the available sample size. Our proposed approach utilizes multitask regression to select a common subset of predicting covariates for all tasks with the same sample size while allowing the model's coefficients to vary between them. A regression task can refer to a single surgeon or operation type or the interaction between them. By considering these diverse factors, our method provides an overall more accurate estimation of the surgery durations, and the selected covariates that enter the model may help to identify the resources required for a specific surgery. We found that when the regression tasks were surgeon-based or based on the pair of operation type and surgeon, our suggested approach outperformed the compared baseline suggested in a previous study. However, our approach failed to reach the baseline for an operation type-based task. By accurately estimating surgery durations, hospital managers can provide care to a greater number of patients, optimize resource allocation and utilization, and reduce waste. This research contributes to the advancement of personalized medicine and provides a valuable tool for improving operational efficiency in the dynamic world of medicine.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Does the "Obesity Paradox" Have an Expiration Date? A Retrospective Cohort Study.

(1) Background: The "obesity paradox" refers to a protective effect of higher body mass index (BMI) on mortality in acute infectious disease patients. However, the long-term impact of this paradox remains uncertain. (2) Methods: A retrospective study of patients diagnosed with community-acquired acute infectious diseases at Shamir Medical Center, Israel (2010-2020) was conducted. Patients were grouped by BMI: underweight, normal weight, overweight, and obesity classes I-III. Short- and long-term mortality rates were compared across these groups. (3) Results: Of the 25,226 patients, diverse demographics and comorbidities were observed across BMI categories. Short-term (90-day) and long-term (one-year) mortality rates were notably higher in underweight and normal-weight groups compared to others. Specifically, 90-day mortality was 22% and 13.2% for underweight and normal weight respectively, versus 7-9% for others (p < 0.001). Multivariate time series analysis revealed underweight individuals had a significantly higher 5-year mortality risk (HR 1.41 (95% CI 1.27-1.58, p < 0.001)), while overweight and obese categories had a reduced risk (overweight-HR 0.76 (95% CI 0.72-0.80, p < 0.001), obesity class I-HR 0.71 (95% CI 0.66-0.76, p < 0.001), obesity class II-HR 0.77 (95% CI 0.70-0.85, p < 0.001), and obesity class III-HR 0.79 (95% CI 0.67-0.92, p = 0.003)). (4) Conclusions: In this comprehensive study, obesity was independently associated with decreased short- and long-term mortality. These unexpected results prompt further exploration of this counterintuitive phenomenon.

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

OBJECTIVE: Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations' variability on patient characteristics. MATERIALS AND METHODS: Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams' cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. RESULTS: On average, the teams' interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts' size varied from one-third of the master cohort size to 10 times the cohort size (2159-63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3-16.2%). The teams' cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. CONCLUSIONS: Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Volume-Outcome Relationship of Resternotomy Coronary Artery Bypass Grafting.

BACKGROUND: We assessed volume-outcome relationships of resternotomy coronary artery bypass grafting (CABG). METHODS: We studied 1,362,218 first-time CABG and 93,985 resternotomy CABG patients reported to The Society of Thoracic Surgeons Adult Cardiac Surgery Database between 2010 and 2019. Primary outcomes were in-hospital mortality and mortality and morbidity (M&M) rates calculated per hospital and per surgeon. Outcomes were compared across 6 total cardiac surgery volume categories. Multivariable generalized linear mixed-effects models were used considering continuous case volume as the main exposure, adjusting for patient characteristics and within-surgeon and hospital variation. RESULTS: We observed a decline in resternotomy CABG unadjusted mortality and M&M from the lowest to the highest case-volume categories (hospital-level mortality, 3.9% ± 0.6% to 3.3% ± 0.1%; M&M, 18.5% ± 1.1% to 15.7% ± 0.4%, P < .001; surgeon-level mortality, 4.1% ± 0.3% to 4.1% ± 1.3%; M&M, 18.5% ± 0.6% to 14.5% ± 2.2%, P < .001). Looking at outcomes vs continuous volume showed that beyond a minimum annual volume (hospital 200-300 cases; surgeon 100-150 cases, approximately), mortality and M&M rates did not further improve. Using individual-level data and adjusting for patient characteristics and clustering within surgeon and hospital, we found higher procedural volume was associated with improved surgeon-level outcomes (mortality adjusted odds ratio, 0.39/100 procedures; 95% CI, 0.24-0.61; M&M adjusted odds ratio, 0.37/100 procedures; 95% CI, 0.28-0.48; P < .001 for both). Hospital-level adjusted volume-outcomes associations were not statistically significant. CONCLUSIONS: We observed an inverse relationship between total cardiac case volume and resternotomy CABG outcomes at the surgeon level only, indicating that individual surgeon's experience, rather than institutional volume, is the key determinant.

Resternotomy Coronary Artery Bypass 1999-2018: Insights From The Society of Thoracic Surgeons Adult Cardiac Surgery Database.

BACKGROUND: We sought to quantify the risk trend of resternotomy coronary artery bypass grafting (CABG) over the past 2 decades. METHODS: We compared the outcomes of 194 804 consecutive resternotomy CABG patients and 1 445 894 randomly selected first-time CABG patients (50% of total) reported to The Society of Thoracic Surgeons Adult Cardiac Surgery Database between 1999 and 2018. Primary outcomes were in-hospital mortality and overall morbidity. Using multiple logistic regression for each outcome for each year, we computed the annual trends of risk-adjusted odds ratios for the primary outcomes in the entire cohort and in 194 776 propensity-matched pairs. RESULTS: The annual resternotomy CABG case volume from participating centers declined by 68%, from a median of 25 (range, 14-44) to a median of 8 (range, 4-15). Compared with first-time CABG, resternotomy CABG patients were consistently older, with higher proportions of comorbidities. After propensity matching, primary outcomes of resternotomy and first-time CABG were similar (mortality: 3.5% vs 2.3%, standardized difference [SDiff], 7.5%; morbidity: 40.7% vs 40.3%, SDiff, 0.9%). Mortality of resternotomy CABG performed after prior CABG was higher than that after prior non-CABG (4.3% vs 2.4%; SDiff, 10.8). Morbidity was similar between these subgroups (41.0% vs 39.1%; SDiff, 2.9). The adjusted odds ratio for mortality after resternotomy CABG declined from 1.93 (95% CI, 1.73-2.16) to 1.22 (95% CI, 0.92-1.62), and that of morbidity declined from 1.13 (95% CI, 1.08-1.18) to 0.91 (95% CI, 0.87-0.95), P < .001 for both. CONCLUSIONS: The risk of resternotomy CABG has decreased substantially over time. Resternotomy CABG performed after a prior CABG is higher risk compared with that performed after a non-CABG operation.

Identification of a pleiotropic effect of ADIPOQ on cardiac dysfunction and Alzheimer's disease based on genetic evidence and health care records.

Observations of comorbidity in heart diseases, including cardiac dysfunction (CD) are increasing, including and cognitive impairment, such as Alzheimer's disease and dementia (AD/D). This comorbidity might be due to a pleiotropic effect of genetic variants shared between CD and AD/D. Here, we validated comorbidity of CD and AD/D based on diagnostic records from millions of patients in Korea and the University of California, San Francisco Medical Center (odds ratio 11.5 [8.5-15.5, 95% Confidence Interval (CI)]). By integrating a comprehensive human disease-SNP association database (VARIMED, VARiants Informing MEDicine) and whole-exome sequencing of 50 brains from individuals with and without Alzheimer's disease (AD), we identified missense variants in coding regions including APOB, a known risk factor for CD and AD/D, which potentially have a pleiotropic role in both diseases. Of the identified variants, site-directed mutation of ADIPOQ (268 G > A; Gly90Ser) in neurons produced abnormal aggregation of tau proteins (p = 0.02), suggesting a functional impact for AD/D. The association of CD and ADIPOQ variants was confirmed based on domain deletion in cardiac cells. Using the UK Biobank including data from over 500000 individuals, we examined a pleiotropic effect of the ADIPOQ variant by comparing CD- and AD/D-associated phenotypic evidence, including cardiac hypertrophy and cognitive degeneration. These results indicate that convergence of health care records and genetic evidences may help to dissect the molecular underpinnings of heart disease and associated cognitive impairment, and could potentially serve a prognostic function. Validation of disease-disease associations through health care records and genomic evidence can determine whether health conditions share risk factors based on pleiotropy.

Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach.

Endometriosis is a condition characterized by implants of endometrial tissues into extrauterine sites, mostly within the pelvic peritoneum. The prevalence of endometriosis is under-diagnosed and is estimated to account for 5-10% of all women of reproductive age. The goal of this study was to develop a model for endometriosis based on the UK-biobank (UKB) and re-assess the contribution of known risk factors to endometriosis. We partitioned the data into those diagnosed with endometriosis (5924; ICD-10: N80) and a control group (142,723). We included over 1000 variables from the UKB covering personal information about female health, lifestyle, self-reported data, genetic variants, and medical history prior to endometriosis diagnosis. We applied machine learning algorithms to train an endometriosis prediction model. The optimal prediction was achieved with the gradient boosting algorithms of CatBoost for the data-combined model with an area under the ROC curve (ROC-AUC) of 0.81. The same results were obtained for women from a mixed ethnicity population of the UKB (7112; ICD-10: N80). We discovered that, prior to being diagnosed with endometriosis, affected women had significantly more ICD-10 diagnoses than the average unaffected woman. We used SHAP, an explainable AI tool, to estimate the marginal impact of a feature, given all other features. The informative features ranked by SHAP values included irritable bowel syndrome (IBS) and the length of the menstrual cycle. We conclude that the rich population-based retrospective data from the UKB are valuable for developing unified machine learning endometriosis models despite the limitations of missing data, noisy medical input, and participant age. The informative features of the model may improve clinical utility for endometriosis diagnosis.

Multi-Dimensional Laboratory Test Score as a Proxy for Health.

The standard of care for a physician to review laboratory tests results is to weigh each individual laboratory test result and compare it to against a standard reference range. Such a method of scanning can lead to missing high-level information. Different methods have tried to overcome a part of the problem by creating new types of reference values. This research proposes looking at test scores in a higher dimension space. And using machine learning approach, determine whether a subject has abnormal tests result that, according to current practice, would be defined as valid - and thus indicating a possible disease or illness. To determine health status, we look both at a disease-specific level and disease-independent level, while looking at several different outcomes.

Deviation of Physiological from Chronological Age Is Associated with Health.

Biological age may be of higher importance than chronological age, yet biological age is not trivial to estimate. This study presents a regression model to predict age using routine clinical tests like laboratory tests using the UK Biobank (UKBB) data. We run different machine learning regression models for this predictions task and compare their performance according to RMSE. The models were trained using data from 472,189 subjects aged 37-82 years old and 61 different laboratory tests results. Our chosen model was an XGboost model, which achieved an RMSE of 6.67 years. Subjects whose the model predicted to be younger than their actual age were found to be healthier as they had fewer diagnoses, fewer operations, and had a lower prevalence of specific diseases than age-matched controls. On the other hand, subjects predicted to be older than their chronological age had no significant differences in the number of diagnoses, number of operations, and specific diseases than age-matched controls.

Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

ProteinBERT: a universal deep-learning model of protein sequence and function.

SUMMARY: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to long sequences. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data. AVAILABILITY AND IMPLEMENTATION: Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bladder Cancer Immunotherapy by BCG Is Associated with a Significantly Reduced Risk of Alzheimer's Disease and Parkinson's Disease.

Bacillus Calmette-Guerin (BCG) is a live attenuated form of Mycobacterium bovis that was developed 100 years ago as a vaccine against tuberculosis (TB) and has been used ever since to vaccinate children globally. It has also been used as the first-line treatment in patients with nonmuscle invasive bladder cancer (NMIBC), through repeated intravesical applications. Numerous studies have shown that BCG induces off-target immune effects in various pathologies. Accumulating data argue for the critical role of the immune system in the course of neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD). In this study, we tested whether repeated exposure to BCG during the treatment of NMIBC is associated with the risk of developing AD and PD. We presented a multi-center retrospective cohort study with patient data collected between 2000 and 2019 that included 12,185 bladder cancer (BC) patients, of which 2301 BCG-treated patients met all inclusion criteria, with a follow-up of 3.5 to 7 years. We considered the diagnosis date of AD and nonvascular dementia cases for BC patients. The BC patients were partitioned into those who underwent a transurethral resection of the bladder tumor followed by BCG therapy, and a disjoint group that had not received such treatment. By applying Cox proportional hazards (PH) regression and competing for risk analyses, we found that BCG treatment was associated with a significantly reduced risk of developing AD, especially in the population aged 75 years or older. The older population (≥75 years, 1578 BCG treated, and 5147 controls) showed a hazard ratio (HR) of 0.726 (95% CI: 0.529-0.996; p-value = 0.0473). While in a hospital-based cohort, BCG treatment resulted in an HR of 0.416 (95% CI: 0.203-0.853; p-value = 0.017), indicating a 58% lower risk of developing AD. The risk of developing PD showed the same trend with a 28% reduction in BCG-treated patients, while no BCG beneficial effect was observed for other age-related events such as Type 2 diabetes (T2D) and stroke. We attributed BCG's beneficial effect on neurodegenerative diseases to a possible activation of long-term nonspecific immune effects. We proposed a prospective study in elderly people for testing intradermic BCG inoculation as a potential protective agent against AD and PD.

A model and test for coordinated polygenic epistasis in complex traits.

Interactions between genetic variants-epistasis-is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work, we develop a model for structured polygenic epistasis, called coordinated epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CE in 18 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.

Do geography and ethnicity play a role in juvenile Spondyloarthritis? A multi-center binational retrospective study.

BACKGROUND: Observations among Israeli pediatric rheumatologists reveal that pediatric Juvenile Spondyloarthritis (JSpA) may present differently compared to patients from the United States (US). This study is aimed to compare the demographic and clinical variables of Israeli and US JSpA patients upon presentation. METHODS: We performed a retrospective, cross-sectional, multicenter comparison of JSpA patients among 3 large Israeli pediatric rheumatology centers and a large US pediatric rheumatology center. Patients with diagnosis of Juvenile Ankylosing Spondylitis (JAS) and/or Enthesitis-related Arthritis (ERA) were included. The demographic, clinical and radiologic features were compared. RESULTS: Overall 87 patients were included (39 Israeli, 48 US patients). Upon presentation, inflammatory back pain, sacroiliac joint tenderness and abnormal modified Schober test, were significantly more prevalent among Israeli patients (59% vs. 35.4, 48.7% vs. 16.7, and 41.2% vs. 21.5%, respectively, all p < 0.05), whereas peripheral arthritis and enthesitis were significantly more prevalent among US patients (43.6% vs. 91.7 and 7.7% vs. 39.6% in Israeli patients vs. US patients, p < 0.05). In addition, 96.7% of the Israeli patients versus 29.7% of the US patients demonstrated sacroiliitis on MRI (p < 0.001, N = 67). Less than one-third of the Israeli patients (32%) were HLA-B27 positive vs. 66.7% of US patients (p = 0.007). CONCLUSION: Israeli children with JSpA presented almost exclusively with axial disease compared to US patients who were more likely to present with peripheral symptoms. HLA B27 prevalence was significantly lower in the Israeli cohort compared to the US cohort. Further studies are needed to unravel the genetic and possibly environmental factors associated with these findings.

Significantly Improved COVID-19 Outcomes in Countries with Higher BCG Vaccination Coverage: A Multivariable Analysis.

The COVID-19 pandemic that started in China has spread within 3 months to the entire globe. We tested the hypothesis that the vaccination against tuberculosis by Bacille Calmette-Guérin vaccine (BCG) correlates with a better outcome for COVID-19 patients. Our analysis covers 55 countries complying with predetermined thresholds on the population size and number of deaths per million (DPM). We found a strong negative correlation between the years of BCG administration and the DPM along with the progress of the pandemic, corroborated by permutation tests. The results from multivariable regression tests with 23 economic, demographic, health-related, and pandemic restriction-related quantitative properties, substantiate the dominant contribution of BCG years to the COVID-19 outcomes. The analysis of countries according to an age-group partition reveals that the strongest correlation is attributed to the coverage in BCG vaccination of the young population (0-24 years). Furthermore, a strong correlation and statistical significance are associated with the degree of BCG coverage for the most recent 15 years, but no association was observed in these years for other broadly used vaccination protocols for measles and rubella. We propose that BCG immunization coverage, especially among the most recently vaccinated population, contribute to attenuation of the spread and severity of the COVID-19 pandemic.

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MOTIVATION: Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. RESULTS: We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. AVAILABILITY AND IMPLEMENTATION: PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A genome-wide association study identifies only two ancestry specific variants associated with spontaneous preterm birth.

Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.