Longitudinal Electronic Health Record Data Research Articles

U.S. weight trends: a longitudinal analysis of an NIH-partnered dataset.

Obesity is a major public health challenge in the U.S. Existing datasets utilized for calculating obesity prevalence, such as the National Health and Nutrition Examination Survey (NHANES) and Behavioral Risk Factor Surveillance System (BRFSS), have limitations. Our objective was to analyze weight trends in the U.S. using a nationally representative dataset that incorporates longitudinal electronic health record data. Using the National Institutes of Health All of Us Research Program (AoU) dataset, we identified patients aged 18-70 years old who had at least two height and weight measurements within a 5-year period from 2008 to 2021. Baseline and most recent BMI values were used to calculate total body weight (%TBW) changes. %TBW change predictors were determined using multivariable linear regression. We included 30,862 patients (mean age 48.9 [ ± 12.6] years; 60.5% female). At the 5-year follow-up, the prevalences of obesity and severe obesity were 37.4% and 20.7%, respectively. The frequency of patients with normal weight or overweight BMI who gained ≥5% TBW at follow-up was 37.8% and 33.1%, respectively. Nearly 24% of the cohort lost ≥ 5% TBW, and 6.5% with severe obesity lost weight to achieve a BMI < 30 kg/m2. In adjusted analyses, male sex (-1.10%, 95% CI [-1.36, -0.85]), non-Hispanic Asian race/ethnicity (-1.69% [-2.44, -0.94]), and type 2 diabetes (-1.58% [-1.95, -1.22]) were associated with weight loss, while obstructive sleep apnea (1.80% [1.40, 2.19]) was associated with weight gain. This evaluation of an NIH-partnered dataset suggests that patients are continuing to gain weight in the U.S. AoU represents a unique tool for obesity prediction, prevention, and treatment given its longitudinal nature and unique behavioral and genetic data.

Identification of Novel Genetic Risk Variants Associated with Hidradenitis Suppurativa in an Exome Sequencing Cohort of 92,455 Individuals

Introduction: Hidradenitis suppurativa (HS) is a prevalent and persistent inflammatory skin disorder, lacking a known cure or effective biomarkers for early diagnosis at present. The genetic determinants of HS have not been fully documented, but it is believed to result from a combination of genetic and environmental factors. Methods: To identify relevant HS gene variants in sporadic HS patients, this study utilized longitudinal electronic health records (EHRs) and whole-exome sequencing. DNA exome sequencing data from 92,455 participant samples in the MyCode biobank, linked to Geisinger’s EHR, were analyzed. This cohort included 1,092 HS cases and 91,363 healthy controls. The MyCode EHR has a median longitudinal follow-up of 15 years per participant, with an average of 87 clinical encounters, 687 laboratory tests, and 7 procedures. Results: There were 1,092 (901 females and 191 males) participants aged 14–89 years (median 47 years) with HS (L73.2), indicating a 1.18% prevalence and accounting for a 4.7:1 female-to-male ratio among the individuals presenting for clinical care. γ-secretase complex, syndromic, and autoinflammatory gene variants were assessed. Potential pathogenic variants were identified among 66 individuals in the HS genes studied. Molecularly, the estimated HS variant prevalence was 1:1,400 in the cohort, 12.3% of variant carriers had HS diagnosis in EHR. Conclusions: Using longitudinal EHR data, genomic screening identified HS-associated gene variants in a defined group of sporadic HS patients to augment the clinical diagnosis, particularly in cases of ambiguity. Based on this study, the field of skin disorders can benefit from a personalized approach to HS diagnosis using large-scale sequencing.

Heparin treatment is associated with a delayed diagnosis of Alzheimer’s dementia in electronic health records from two large United States health systems

AbstractRecent studies suggest that heparan sulfate proteoglycans (HSPG) contribute to the predisposition to, protection from, and potential treatment and prevention of Alzheimer’s disease (AD). Here, we used electronic health records (EHR) from two different health systems to examine whether heparin therapy was associated with a delayed diagnosis of AD dementia. Longitudinal EHR data from 15,183 patients from the Mount Sinai Health System (MSHS) and 6207 patients from Columbia University Medical Center (CUMC) were used in separate survival analyses to compare those who did or did not receive heparin therapy, had a least 5 years of observation, were at least 65 years old by their last visit, and had subsequent diagnostic code or drug treatment evidence of possible AD dementia. Analyses controlled for age, sex, comorbidities, follow-up duration and number of inpatient visits. Heparin therapy was associated with significant delays in age of clinical diagnosis of AD dementia, including +1.0 years in the MSMS cohort (P &lt; 0.001) and +1.0 years in the CUMC cohort (P &lt; 0.001). While additional studies are needed, this study supports the potential roles of heparin-like drugs and HSPGs in the protection from and prevention of AD dementia.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Leveraging EHR data from 11627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Prevalence, incidence, mortality and healthcare resource use for generalized pustular psoriasis, palmoplantar pustulosis and plaque psoriasis in England: a population-based cohort study.

Generalized pustular psoriasis (GPP) and palmoplantar pustulosis (PPP) are chronic inflammatory skin conditions. Accumulating evidence shows that GPP and PPP have different characteristics to plaque psoriasis and are distinct clinical entities. To assess the epidemiology, comorbidities, mortality and healthcare use for patients in England with GPP and PPP versus those with plaque psoriasis. We carried out a cohort study involving analyses of longitudinal electronic health record data in the Clinical Practice Research Datalink Aurum database and linked hospital and mortality data between 2008 and 2019. The primary study outcome was the incidence and prevalence rates of GPP, PPP and plaque psoriasis in England. Secondary outcomes included survival rates and healthcare resource use (HCRU) by disease type. We identified 373 patients with GPP, 1828 with PPP and 224 223 with plaque psoriasis. Mean (SD) age was 55.9 (18.6) years for patients with GPP, 51.5 (16.4) years for those with PPP and 48.5 (19.1) years for those with plaque psoriasis; 62.5% and 65.9% of patients with GPP and PPP, respectively, were women, vs. 49.4% of those with plaque psoriasis. About half of patients were overweight or obese at baseline (GPP 48.6%, PPP 56.0%, plaque psoriasis 45.9%). The incidence rates for GPP, PPP and plaque psoriasis were 0.25 [95% confidence interval (CI) 0.21-0.28], 2.01 (95% CI 1.92-2.11) and 103.2 (95% CI 102.5-103.9) per 100 000 person-years, respectively. From 2008 to 2019, the prevalence rates per 100 000 persons ranged from 1.61 to 3.0 for GPP, from 1.1 to 18.7 for PPP and from 1771.0 to 1903.8 for plaque psoriasis. Survival rates were lower for patients with GPP, particularly those who were > 55 years of age and those with a history of one or more comorbidities in each cohort. HCRU was lower in the cohort with plaque psoriasis and highest in the cohort with GPP, particularly among those who had more than one GPP flare. Our results provide further evidence that, in England, GPP is a distinct disease with different epidemiology, lower survival and higher HCRU than plaque psoriasis.

Abstract 3821: Real world outcomes among patients with newly diagnosed acute myeloid leukemia

Abstract Introduction: Treatment options for patients with acute myeloid leukemia (AML) have expanded in recent years. Although new treatments have demonstrated efficacy in clinical trials, less is known about their effect on outcomes in routine clinical practice. We examined outcomes for patients with newly-diagnosed AML (ND-AML) using real world data (RWD). Methods: This retrospective observational study was conducted using the COTA real-world (rw) database, a de-identified source of longitudinal electronic health record data pertaining to the diagnosis, management, and outcomes of patients with cancer. Adult patients with ND-AML, diagnosed January 1, 2013 to March 20, 2023 were included. First-line treatment regimens were grouped into therapeutic categories and outcomes were summarized. The index date was defined as date of initiation of first-line therapy. Results: Among 2516 patients with ND-AML included (Table), the most common regimens used were intensive chemotherapy with cytarabine (IC w/ara-C), hypomethylating agents with venetoclax (HMA+ven), and HMA alone. Complete response rate was highest in patients treated with IC w/ara-C or investigational regimen(s). Median overall survival (OS) was 16 mo overall, with highest median OS seen in patients treated with IC w/ara-C and investigational regimen(s). Median OS for patients treated with HMA+ven was 13.5 mo. Thirty-day mortality was lowest in patients treated with IC w/ara-C, CPX-351 (liposomal daunorubicin + cytarabine), and HMA+ven. Conclusions: Results suggest that IC w/ara-C remains the standard of care for most patients with ND-AML and this regimen has a higher CR rate and median OS. Variation of patient demographics and clinical characteristics across treatment groups did not permit cross-treatment comparisons, though future analyses evaluating these subgroups may provide further insight into effectiveness and safety of AML regimens in routine care for populations underrepresented in clinical trials. rwCR rate Median rwOS 30-day mortality N % (95% CI) Months (95% CI) % (95% CI) Overall 2516 45.3 (43.2, 47.3) 16.0 (14.9, 17.3) 5.9 (5.0, 6.8) IC w/ara-C 1171 60.6 (57.8, 63.4) 36.4 (31.0, 46.0) 1.6 (0.9, 2.3) HMA+ ven 300 33.7 (28.3, 39.3) 13.5 (11.4, 15.5) 3 (1.1, 4.9) Decitabine 242 12.8 (8.9, 17.7) 8.4 (6.7, 9.9) 10.4 (6.5, 14.2) Azacitidine 206 7.8 (4.5, 12.3) 7.2 (5.3, 9.6) 10.7 (6.4, 14.8) Investigational 206 61.6 (54.6, 68.3) 21.3 (15.2, 27.8) 3.4 (0.9, 5.9) Lower dose cytarabine 62 12.9 (5.7, 23.9) 8.7 (5.4, 16.1) 14.9 (5.4, 23.4) CPX-351 based 61 55.7 (42.4, 68.5) 9.3 (7.3, 15.1) 0.0 No treatment 188 NA 1.9 (1.5, 2.8) 30 (22.7, 36.6) Citation Format: Elizabeth D. Pulte, Laura L. Fernandes, Joseph Wynne, Eric Hansen, Andrew J. Belli, Anna Barcellos, Christina M. Zettler, Rebecca Bystrom, Jonathon Vallejo, Wenjuan Gu, Catherine Lerro, Kelly Norsworthy, Angelo DeClaro, Marc R. Theoret, Donna Rivera, Ching-Kun Wang. Real world outcomes among patients with newly diagnosed acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3821.

Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.

Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences. We performed a retrospective, longitudinal analysis using records of patients with cancer (n = 208) hospitalized between 2008 and 2014. A long short-term memory (LSTM)-based recurrent neural network, a linear regression, and random forest models were trained on previous symptoms experienced and used to predict future symptom trajectories. We found that at least one of three tested models (LSTM, linear regression, and random forest) outperform predictions based solely on the previous clinical observation. LSTM models significantly outperformed linear regression and random forest models in predicting nausea (P < .1) and psychosocial status (P < .01). Linear regression outperformed all models when predicting oral health (P < .01), while random forest outperformed all models when predicting mobility (P < .01) and nutrition (P < .01). We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.

A deep clustering-based state-space model for improved disease risk prediction in personalized healthcare

AbstractDecision support systems are being developed to assist clinicians in complex decision-making processes by leveraging information from clinical knowledge and electronic health records (EHRs). One typical application is disease risk prediction, which can be challenging due to the complexity of modelling longitudinal EHR data, including unstructured medical notes. To address this challenge, we propose a deep state-space model (DSSM) that simulates the patient’s state transition process and formally integrates latent states with risk observations. A typical DSSM consists of three parts: a prior module that generates the distribution of the current latent state based on previous states; a posterior module that approximates the latent states using up-to-date medical notes; and a likelihood module that predicts disease risks using latent states. To efficiently and effectively encode raw medical notes, our posterior module uses an attentive encoder to better extract information from unstructured high-dimensional medical notes. Additionally, we couple a predictive clustering algorithm into our DSSM to learn clinically useful representations of patients’ latent states. The latent states are clustered into multiple groups, and the weighted average of the cluster centres is used for prediction. We demonstrate the effectiveness of our deep clustering-based state-space model using two real-world EHR datasets, showing that it not only generates better risk prediction results than other baseline methods but also clusters similar patient health states into groups.

Temporal Phenotyping for End-Stage Renal Disease Using Longitudinal Electronic Health Records.

End Stage Renal Disease (ESRD) is a highly heterogeneous disease with significant differences in prevalence, mortality, complications, and treatment modalities across age, sex, race, and ethnicity. An improved knowledge of disease characteristics results from the use of a data-driven phenotypic classification strategy to identify patients of different subtypes and expose the clinical traits of different subtypes. This study used topic models and process mining techniques to perform subtyping of ESRD patients on hemodialysis based on real-world longitudinal electronic health record data. The mined subtypes are interpretable and clinically significant, and they can reflect differences in the progression of the disease state and clinical outcomes.

DOP33 Distinct perturbances in metabolic pathways associate with disease progression in patients with Inflammatory Bowel Disease

Abstract Background Patients with inflammatory bowel diseases (IBD) exhibit distinct shifts in circulating metabolite levels that have been associated with clinical disease activity and disease phenotype. However, there are limited data on the impact of metabolomic changes on disease progression in IBD. We aimed to understand the association of circulating metabolite levels and related pathways with the risk of disease progression in patients with IBD. Methods We conducted an observational cohort study using the Mount Sinai Crohn’s and Colitis Registry (MSCCR). Patients were prospectively enrolled with baseline questionnaires and biospecimens that were then linked to longitudinal electronic health record data. A total of 1,456 serum metabolites were measured in 277 patients with UC and 375 patients with CD using untargeted metabolomics. Disease progression was defined as any new steroid or biological prescriptions, IBD-related hospitalizations, or surgery. Multivariable Cox proportional hazards regression models, adjusted for age, sex, smoking, biologic use, and surgery were used to explore metabolite associations with risk of disease progression. Metabolite set enrichment analysis (MSEA) was performed to identify underlying metabolic pathways associated with disease progression. Results During a median 2-year follow-up time (IQR 0.3-5.1 years), 375 patients (57.5%) had a disease progression event. Among CD, 151 metabolites significantly correlated with the composite outcome (FDR&amp;lt;0.1). Of these, 81 (53.6%) were linked to higher risk, mainly enriched in amino acids, purine, and pyrimidine metabolism, fibrinogen cleavage peptides, and bile acid pathways. Additionally, 70 (46.4%) metabolites associated with lower risk with enrichment in fatty acid metabolism, steroid biosynthesis, histidine, tryptophan, and antioxidant pathways. In CD, aspartate metabolism was most significantly associated with increased risk and linoleic metabolism with decreased risk. In UC, 84 metabolites were significantly associated with disease progression (FDR&amp;lt;0.1), with 29 (34.5%) associated with increased and 55 (65.5%) with decreased risk. Metabolic pathways linked to higher risk were sphingolipid, benzoate, hydrogen sulfide, polyamine, and tyrosine metabolism, while protective metabolites were enriched in branched-chain amino acid (BCAA), steroid biosynthesis, histidine, and phenylalanine pathways. In UC, sphingolipid metabolism was prominently associated with increased risk and BCAA metabolism with decreased risk. Conclusion Specific metabolites and metabolic pathways are associated with risk of disease progression in patients with IBD, highlighting potential prognostic metabolite biomarkers and pathways that may be important in modulating risk of disease progression.

Household Health-Related Social Needs in Newborns and Infant Behavioral Functioning at 6 Months

Dysfunctional patterns of behavior during infancy can predict the emergence of mental health disorders later in childhood. The Baby Pediatric Symptom Checklist (BPSC) can identify indicators of behavioral disorders among children aged 0 to 18 months. Understanding the association of early health-related social needs (HRSNs) with poor infant behavioral functioning can inform interventions to promote early childhood mental well-being. To examine the association between household HRSNs in the first 4 months of life and BPSC results at 6 months. This was a retrospective cohort analysis of longitudinal electronic health record data. Covariates were selected based on the biopsychosocial ecological model. Logistic regression analyses examined the association of HRSN domains and the number of HRSNs with the 6-month BPSC outcomes. Participants were recruited from 6 primary care clinics within 1 health system. Children aged 5 to 8 months who were evaluated for 6-month well-child visits between March 30, 2021, and June 30, 2022, were included in the study. Responses to the first HRSN screening tool that a caregiver completed for infants between 0 and 4 months of age. HRSN domains were examined individually and as the number of positive HRSNs. BPSC screen identified for clinical review due to 1 or more elevated subscales (inflexibility, irritability, and difficulty with routines) at 6 months. A total of 1541 children (mean [SD] age, 6.1 [0.5] months; 775 female [50.3%]) were included in the study. A total of 405 children (26.3%) had a BPSC screen identified for clinical review, and 328 caregivers (21.3%) reported at least 1 HRSN. Food insecurity (174 [11.3%]) and benefits issues (169 [11.0%]) were the most frequently reported HRSN. Children in households with food insecurity had statistically significant higher odds of inflexibility (adjusted odds ratio [aOR], 1.73; 95% CI, 1.14-2.63), difficulty with routines (aOR, 1.64; 95% CI, 1.05-2.57), and irritability (aOR, 1.86; 95% CI, 1.13-3.08) than children in households without food insecurity. Children in households with benefits issues had statistically significant higher odds of difficulty with routines (aOR, 1.70; 95% CI, 1.10-2.65) and irritability (aOR, 1.70; 95% CI, 1.03-2.82). Children in households with 2 or more HRSNs had consistently higher odds of having a BPSC screen identified for clinical review (aOR, 2.16; 95% CI, 1.38-3.39) compared with children with no HRSNs. Results of this cohort study suggest that household food insecurity, benefits issues, and the number of HRSNs were significantly associated with a BPSC screen identified for clinical review at 6 months of age. These findings highlight the urgency of intervening on HRSNs in the newborn period to prevent adverse infant behavioral outcomes.

Insurance coverage and discontinuity during pregnancy: Frequency and associations documented in the PROMISE cohort.

To describe insurance patterns and discontinuity during pregnancy, which may affect the experiences of the pregnant person: their timely access to care, continuity of care, and health outcomes. Data are from the PROMISE study, which utilizes data from community-based health care organizations (CHCOs) (e.g., federally qualified health centers that serve patients regardless of insurance status or ability to pay) in the United States from 2005 to 2021. This descriptive study was a cohort utilizing longitudinal electronic health record data. Insurance type at each encounter was recorded in the clinical database and coded as Private, Public, and Uninsured. Pregnant people were categorized into one of several insurance patterns. We analyzed the frequency and timing of insurance changes and care utilization within each group. Continuous public insurance was the most common insurance pattern (69.2%), followed by uninsured/public discontinuity (11.8%), with 6.4% experiencing uninsurance throughout the entirety of pregnancy. Insurance discontinuity was experienced by 16.6% of pregnant people; a majority of these reflect people transitioning to public insurance. Those with continuous public insurance had the highest frequency of inadequate prenatal care (19.5%), while those with all three types of insurance during pregnancy had the highest percentage of intensive prenatal care (16.5%). The majority (71.7%-81.2%) of those with a discontinuous pattern experienced a single insurance change. Insurance discontinuity and uninsurance are common within our population of pregnant people seeking care at CHCOs. Our findings suggest that insurance status should be regarded as a dynamic rather than a static characteristic during pregnancy and should be measured accordingly. Future research is needed to assess the drivers of perinatal insurance discontinuity and if and how these discontinuities may affect health care access, utilization, and birth outcomes.

Harnessing Artificial Intelligence for Risk Stratification in Acute Myeloid Leukemia (AML): Evaluating the Utility of Longitudinal Electronic Health Record (EHR) Data Via Graph Neural Networks

Background: AML is a life-threatening disease, and to determine which patients need allogeneic stem cell transplantation, hematologists risk-stratify each case. However, standard risk stratification using the European LeukemiaNet (ELN) criteria is focused on baseline mutations and chromosomal aberrations, and the risk estimate is not updated during a patient's course. In other blood cancers, recalculating the risk with treatment response data can help guide the need for more intensive therapy (Kurtz, et al, Cell, 2019). Furthermore, deep learning graph neural networks (GNN) applied to EHR data have strong predictive power in a hematology context (Fouladvand, et al, J Biomed Inform, 2023). Thus, we evaluated the power of a GNN to predict survival in AML using longitudinal EHR data, specifically with labs and histological features that are not included in the ELN but may capture the treatment response. Methods: Patients who were seen at the Stanford Cancer Institute, had EHR data available within six months of diagnosis, and were diagnosed with AML between June 1998 and January 2021 were included in this retrospective analysis. The GNN was trained to predict survival at two years from diagnosis using the first six months of clinical data. Patients were excluded if they were lost to follow-up before two years or died before six months. Data were collected from structured databases associated with Stanford's EHR, except that diagnosis dates were from Stanford's Cancer Registry, and survival data was supplemented with other databases including the Social Security Death Index. Dysplasia, bone marrow cellularity, and bone marrow blast percentages from pathology reports (“pathology report data”) were extracted using text processing algorithms and weakly supervised machine learning (Ratner, et al, ArXiv, 2017). To represent time series information, we framed each patient's timeline as a network (or “graph”) of events. The primary GNN model was a heterogenous graph transformer classifier with two node types: complete blood count (CBC) data and pathology report data (Hu, et al, ArXiv, 2020). Data from the same week were assumed to be from the same timeframe and connected with bidirectional edges. Data separated by longer time periods were connected with unidirectional edges of a separate edge type. The independent test dataset consisted of patients whose ELN 2022 classification was available, and to train the model, the remaining data were divided into train/validation splits of 0.9/0.1. Results: Of the 2,535 patients with survival data, 1,029 met inclusion criteria. Table 1 summarizes the data available in the EHR for each variable, and nearly all patients had CBC and pathology report data. The area under the receiver operating characteristic (AUROC) using the ELN 2022 criteria for predicting survival in the test dataset was 0.79. The AUROC curve for the GNN model was comparable at 0.76, despite not using any variables from the ELN criteria, and the model effectively stratified patients' disease into high- and low-risk in the independent test dataset (hazard ratio [HR] 3.0, log-rank p = 0.0009). Interestingly, despite not having access to mutation or cytogenetic data, the high-risk cases were enriched in known high-risk mutations, like TP53 and RUNX1, and in high-risk chromosomal aberrations, like 5q deletion (Table 1). Although the model predictions correlated with the ELN criteria in some ways, they also stratified the ELN intermediate-risk AML cases into high and low risk (HR 6.1 for model-predicted high risk among ELN intermediate cases, p = 0.07). Conclusions: Risk stratification using artificial intelligence and longitudinal data from the EHR performed comparably to the ELN 2022 criteria and has the potential to further stratify the ELN categories. The model performed well despite only using histological features and lab values, which are more readily available and more frequently updated than next-generation sequencing results. In the future, this approach may further improve with a larger sample size and additional variables, such as measurable residual disease and treatment information. Given the heterogeneity and increasing complexity of AML classification, leveraging artificial intelligence to assist with classification will be crucial, and these results are a step towards a future where data are automatically extracted from the EHR and used for continuously updated risk stratification.

Predicting multiple sclerosis severity with multimodal deep neural networks

Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients’ multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient’s MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.

Abstract 12687: Evaluating a Coronary Artery Disease Polygenic Risk Score Screening Strategy in a Diverse Patient Cohort at UCLA

Introduction: Recent efforts to predict and mitigate coronary artery disease (CAD) risk have incorporated polygenic risk scores (PGS). Few studies examine the clinical impact of these PGS screening strategies or quantify their performance in clinical terms. Here we estimate the number needed to screen (NNS) of a PGS screening strategy for CAD based using a diverse electronic health record (EHR) linked biobank at UCLA Health. Methods: We leveraged longitudinal EHR data to conduct a synthetic trial. We assumed PGS screening was performed on 6/1/2013 for patients established prior to 1/1/2013 or 6 months into follow time if established later. Patients with preexisting cardiovascular disease or strong statin indications (LDL &gt; 190, diabetes or already on statins) are excluded. Patients with PGS scores in the top 5% of their genetic ancestry groupings (European, East Asian, African, Hispanic) are assumed to have been started on statins for the median 6.9 years of follow up. CAD diagnosis during follow up time was determined by ICD codes. NNS to prevent one CV event is calculated from cohort size and events assuming a statin number needed to treat of 50. Results: Of 33,789 adults, 17,074 were low risk and eligible for PGS screening (35% male with a mean age of 44). NNS to detect one CAD diagnosis was 351, 359, 814, 159 in the European, Hispanic, East Asian and African ancestry groups and NNS to prevent one cardiovascular event was 17,550, 17,950, 40,700, 7,950 respectively. Conclusion: Although PGS accuracy is lower in African ancestry individuals as compared to Europeans, the clinical utility of this PGS-based screening is highest in African ancestries due to increased baseline rates of disease in the African genetic ancestry grouping. Our results demonstrate that PGS accuracy is distinct from clinical utility of PGS-based strategies. Evaluation of screening strategies rather than accuracy alone will be required before PGS screening implementation.

An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD.

Dynamic Delirium Prediction in the Intensive Care Unit using Machine Learning on Electronic Health Records.

Delirium is a syndrome of acute brain failure which is prevalent amongst older adults in the Intensive Care Unit (ICU). Incidence of delirium can significantly worsen prognosis and increase mortality, therefore necessitating its rapid and continual assessment in the ICU. Currently, the common approach for delirium assessment is manual and sporadic. Hence, there exists a critical need for a robust and automated system for predicting delirium in the ICU. In this work, we develop a machine learning (ML) system for real-time prediction of delirium using Electronic Health Record (EHR) data. Unlike prior approaches which provide one delirium prediction label per entire ICU stay, our approach provides predictions every 12 hours. We use the latest 12 hours of ICU data, along with patient demographic and medical history data, to predict delirium risk in the next 12-hour window. This enables delirium risk prediction as soon as 12 hours after ICU admission. We train and test four ML classification algorithms on longitudinal EHR data pertaining to 16,327 ICU stays of 13,395 patients covering a total of 56,297 12-hour windows in the ICU to predict the dynamic incidence of delirium. The best performing algorithm was Categorical Boosting which achieved an area under receiver operating characteristic curve (AUROC) of 0.87 (95% Confidence Interval; C.I, 0.86-0.87). The deployment of this ML system in ICUs can enable early identification of delirium, thereby reducing its deleterious impact on long-term adverse outcomes, such as ICU cost, length of stay and mortality.

Prenatal Health Care Outcomes Before and During the COVID-19 Pandemic Among Pregnant Individuals and Their Newborns in an Integrated US Health System

The COVID-19 pandemic accelerated the use of telemedicine. However, data on the integration of telemedicine in prenatal health care and health outcomes are sparse. To evaluate a multimodal model of in-office and telemedicine prenatal health care implemented during the COVID-19 pandemic and its association with maternal and newborn health outcomes. This cohort study of pregnant individuals using longitudinal electronic health record data was conducted at Kaiser Permanente Northern California, an integrated health care system serving a population of 4.5 million people. Individuals who delivered a live birth or stillbirth between July 1, 2018, and October 21, 2021, were included in the study. Data were analyzed from January 2022 to May 2023. Exposure levels to the multimodal prenatal health care model were separated into 3 intervals: unexposed (T1, birth delivery between July 1, 2018, and February 29, 2020), partially exposed (T2, birth delivery between March 1, 2020, and December 5, 2020), and fully exposed (T3, birth delivery between December 6, 2020, and October 31, 2021). Primary outcomes included rates of preeclampsia and eclampsia, severe maternal morbidity, cesarean delivery, preterm birth, and neonatal intensive care unit (NICU) admission. The distributions of demographic and clinical characteristics, care processes, and health outcomes for birth deliveries within each of the 3 intervals of interest were assessed with standardized mean differences calculated for between-interval contrasts. Interrupted time series analyses were used to examine changes in rates of perinatal outcomes and its association with the multimodal prenatal health care model. Secondary outcomes included gestational hypertension, gestational diabetes, depression, venous thromboembolism, newborn Apgar score, transient tachypnea, and birth weight. The cohort included 151 464 individuals (mean [SD] age, 31.3 [5.3] years) who delivered a live birth or stillbirth. The mean (SD) number of total prenatal visits was similar in T1 (9.41 [4.75] visits), T2 (9.17 [4.50] visits), and T3 (9.15 [4.66] visits), whereas the proportion of telemedicine visits increased from 11.1% (79 214 visits) in T1 to 20.9% (66 726 visits) in T2 and 21.3% (79 518 visits) in T3. NICU admission rates were 9.2% (7014 admissions) in T1, 8.3% (2905 admissions) in T2, and 8.6% (3615 admissions) in T3. Interrupted time series analysis showed no change in NICU admission risk during T1 (change per 4-week interval, -0.22%; 95% CI, -0.53% to 0.09%), a decrease in risk during T2 (change per 4-week interval, -0.91%; 95% CI, -1.77% to -0.03%), and an increase in risk during T3 (change per 4-week interval, 1.75%; 95% CI, 0.49% to 3.02%). There were no clinically relevant changes between T1, T2, and T3 in the rates of risk of preeclampsia and eclampsia (change per 4-week interval, 0.76% [95% CI, 0.39% to 1.14%] for T1; -0.19% [95% CI, -1.19% to 0.81%] for T2; and -0.80% [95% CI, -2.13% to 0.55%] for T3), severe maternal morbidity (change per 4-week interval , 0.12% [95% CI, 0.40% to 0.63%] for T1; -0.39% [95% CI, -1.00% to 1.80%] for T2; and 0.99% [95% CI, -0.88% to 2.90%] for T3), cesarean delivery (change per 4-week interval, 0.06% [95% CI, -0.11% to 0.23%] for T1; -0.03% [95% CI, -0.49% to 0.44%] for T2; and -0.05% [95% CI, -0.68% to 0.59%] for T3), preterm birth (change per 4-week interval, 0.23% [95% CI, -0.11% to 0.57%] for T1; -0.37% [95% CI, -1.29% to 0.55%] for T2; and -0.15% [95% CI, -1.41% to 1.13%] for T3), or secondary outcomes. These findings suggest that a multimodal prenatal health care model combining in-office and telemedicine visits performed adequately compared with in-office only prenatal health care, supporting its continued use after the pandemic.

Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.

Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows. We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC). The best-performing model was built using a combination of all 7 sets of variables and the time window of 4days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69). This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4days before the event, allowing for earlier targeted interventions.

Leveraging electronic health record data for endometriosis research.

Endometriosis is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease-often identified during (in)fertility consultations-to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.7-3.6 years) and misdiagnosis is common. Early and accurate diagnosis of endometriosis remains a research priority for patient advocates and healthcare providers. Electronic health records (EHRs) have been widely adopted as a data source in biomedical research. However, they remain a largely untapped source of data for endometriosis research. EHRs capture diverse, real-world patient populations and care trajectories and can be used to learn patterns of underlying risk factors for endometriosis which, in turn, can be used to inform screening guidelines to help clinicians efficiently and effectively recognize and diagnose the disease in all patient populations reducing inequities in care. Here, we provide an overview of the advantages and limitations of using EHR data to study endometriosis. We describe the prevalence of endometriosis observed in diverse populations from multiple healthcare institutions, examples of variables that can be extracted from EHRs to enhance the accuracy of endometriosis prediction, and opportunities to leverage longitudinal EHR data to improve our understanding of long-term health consequences for all patients.