Longitudinal Electronic Health Record Research Articles

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 < 0.001) and +1.0 years in the CUMC cohort (P < 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.

High Burden of Ileus and Pneumonia in Clozapine-Treated Individuals With Schizophrenia: A Finnish 25-Year Follow-Up Register Study.

The authors used longitudinal biobank data with up to 25 years of follow-up on over 2,600 clozapine users to derive reliable estimates of the real-world burden of clozapine adverse drug events (ADEs). A total of 2,659 participants in the FinnGen biobank project had a schizophrenia diagnosis and clozapine purchases with longitudinal electronic health record follow-up for up to 25 years after clozapine initiation. Diseases and health-related events enriched during clozapine use were identified, adjusting for disease severity. The incidence and recurrence of ADEs over years of clozapine use, their effect on clozapine discontinuation and deaths, and their pharmacogenetics were studied. Median follow-up time after clozapine initiation was 12.7 years. Across 2,157 diseases and health-related events, 27 were enriched during clozapine use, falling into five disease categories: gastrointestinal hypomotility, seizures, pneumonia, other acute respiratory tract infections, and tachycardia, along with a heterogeneous group including neutropenia and type 2 diabetes, among others. Cumulative incidence estimates for ileus (severe gastrointestinal hypomotility) and pneumonia were 5.3% and 29.5%, respectively, 20 years after clozapine initiation. Both events were significantly associated with increased mortality among clozapine users (ileus: odds ratio=4.5; pneumonia: odds ratio=2.8). Decreased genotype-predicted CYP2C19 and CYP1A2 activities were associated with higher pneumonia risk. Clozapine-induced ileus and pneumonia were notably more frequent than has previously been reported and were associated with increased mortality. Two CYP genes influenced pneumonia risk. Pneumonia and ileus call for improved utilization of available preventive measures.

Navigating a "Good Death" During COVID-19: Understanding Real-Time End-of-Life Care Structures, Processes, and Outcomes Through Clinical Notes.

The coronavirus disease 2019 (COVID-19) pandemic severely disrupted hospice care, yet there is little research regarding how widespread disruptions affected clinician and family decision-making. We aimed to understand how the pandemic affected structures, processes, and outcomes of end-of-life care. Retrospective narrative chart review of electronic health records of 61 patients referred and admitted to hospice from 3 New York City geriatrics practices who died between March 1, 2020, and March 31, 2021. We linked longitudinal, unstructured medical, and hospice electronic health record notes to create a real-time, multiperspective trajectory of patients' interactions with providers using directed content analysis. Most patients had dementia and were enrolled in hospice for 11 days. Care processes were shaped by structural factors (staffing, supplies, and governmental/institutional policies), and outcomes were prioritized by care teams and families (protecting safety, maintaining high-touch care, honoring patient values, and supporting patients emotionally and spiritually). Processes used to achieve these outcomes were decision-making, care delivery, supporting a "good death," and emotional and spiritual support. Care processes were negotiated throughout the end of life, with clinicians and families making in-the-moment decisions. Some adaptations were effective but also placed extraordinary pressure on paid and family caregivers. Healthcare teams' and families' goals to meet patients' end-of-life priorities can be supported by ongoing assessment of patient goals and process changes needed to support them, stronger structural supports for paid and family caregivers, incentivizing relationships across primary care and hospice teams, and extending social work and spiritual care.

ChatGPT-Assisted Classification of Postoperative Bleeding Following Microinvasive Glaucoma Surgery Using Electronic Health Record Data

PurposeTo evaluate the performance of a large language model (LLM) in classifying electronic health record (EHR) text, and to use this classification to evaluate the type and resolution of hemorrhagic events (HE) following micro-invasive glaucoma surgery (MIGS). DesignRetrospective cohort study. ParticipantsEyes from the Bascom Palmer Glaucoma Repository. MethodsEyes that underwent MIGS between July 1, 2014 and February 1, 2022 were analyzed. ChatGPT was used to classify deidentified EHR anterior chamber examination text into HE categories (no hyphema, microhyphema, clot, and hyphema). Agreement between classifications by ChatGPT and a glaucoma specialist was evaluated using Cohen’s Kappa and precision-recall (PR) curve. Time to resolution of HEs was assessed using Cox proportional-hazards models. Goniotomy HE resolution was evaluated by degree of angle treatment (90-179º, 180-269º, 270-360º). Logistic regression was used to identified HE risk factors. Main Outcome MeasuresAccuracy of ChatGPT HE classification and incidence and resolution of HEs. ResultsThe study included 434 goniotomy eyes (368 patients) and 528 Schlemm’s Canal Stent (SCS) eyes (390 patients). ChatGPT facilitated excellent HE classification (Cohen’s kappa 0.93, area under PR curve 0.968). Using ChatGPT classifications, at postoperative day 1, HEs occurred in 67.8% of goniotomy and 25.2% of SCS eyes (p<0.001). The 270-360º goniotomy group had the highest HE rate (84.0%, p<0.001). At postoperative week 1, HEs were observed in 43.4% and 11.3% of goniotomy and SCS eyes respectively (p<0.001). By postoperative month 1, HE rates were 13.3% and 1.3% among goniotomy and SCS eyes respectively (p<0.001). Time to HE resolution differed between the goniotomy angle groups (log-rank p=0.034); median time to resolution was 10, 10, and 15 days for the 90-179º, 180-269º, and 270-360º groups respectively. Risk factor analysis demonstrated greater goniotomy angle was the only significant predictor of HEs (OR for 270-360º: 4.08, p<0.001). ConclusionsLLMs can be effectively used to classify longitudinal EHR free-text exam data with high accuracy, highlighting a promising direction for future LLM-assisted research and clinical decision support. HEs are relatively common, self-resolving complications that occur more often in goniotomy cases and with larger goniotomy treatments. Time to HE resolution differs significantly between goniotomy groups.

On the evaluation of synthetic longitudinal electronic health records

BackgroundSynthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs.MethodsWe recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset.ResultsTo support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository.ConclusionsThe discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs.

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.

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.

Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records.

Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.

CO115 Long-Term Post-Recurrence Survival (PRS) Outcomes for Stage IIB/C Melanoma Patients in Real-World (RW) Settings: Analyses from a Longitudinal Electronic Health Record Database in the United States

CO115 Long-Term Post-Recurrence Survival (PRS) Outcomes for Stage IIB/C Melanoma Patients in Real-World (RW) Settings: Analyses from a Longitudinal Electronic Health Record Database in the United States

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.

Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data.

Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being. This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use. The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use. The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes. SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.

Enhancing healthcare decision support through explainable AI models for risk prediction

Electronic health records (EHRs) are a valuable source of information that can aid in understanding a patient’s health condition and making informed healthcare decisions. However, modelling longitudinal EHRs with heterogeneous information is a challenging task. Although recurrent neural networks (RNNs) are frequently utilized in artificial intelligence (AI) models for capturing longitudinal data, their explanatory capabilities are limited. Predictive clustering stands as the most recent advancement within this domain, offering interpretable indications at the cluster level for predicting disease risk. Nonetheless, the challenge of determining the optimal number of clusters has put a brake on the widespread application of predictive clustering for disease risk prediction. In this paper, we introduce a novel non-parametric predictive clustering-based risk prediction model that integrates the Dirichlet Process Mixture Model (DPMM) with predictive clustering via neural networks. To enhance the model’s interpretability, we integrate attention mechanisms that enable the capture of local-level evidence in addition to the cluster-level evidence provided by predictive clustering. The outcome of this research is the development of a multi-level explainable artificial intelligence (AI) model. We evaluated the proposed model on two real-world datasets and demonstrated its effectiveness in capturing longitudinal EHR information for disease risk prediction. Moreover, the model successfully produced interpretable evidence to bolster its predictions.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records

ObjectiveAlthough the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation. MethodsWe analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals. ResultsOut of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals. ConclusionThe fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic text generation tasks for healthcare remains challenging. Existing question answering datasets for electronic health record (EHR) data fail to capture the complexity of information needs and documentation burdens experienced by clinicians. To address these challenges, we introduce MedAlign, a benchmark dataset of 983 natural language instructions for EHR data. MedAlign is curated by 15 clinicians (7 specialities), includes clinician-written reference responses for 303 instructions, and provides 276 longitudinal EHRs for grounding instruction-response pairs. We used MedAlign to evaluate 6 general domain LLMs, having clinicians rank the accuracy and quality of each LLM response. We found high error rates, ranging from 35% (GPT-4) to 68% (MPT-7B-Instruct), and 8.3% drop in accuracy moving from 32k to 2k context lengths for GPT-4. Finally, we report correlations between clinician rankings and automated natural language generation metrics as a way to rank LLMs without human review. We make MedAlign available under a research data use agreement to enable LLM evaluations on tasks aligned with clinician needs and preferences.

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.

Multi-Branching Temporal Convolutional Network With Tensor Data Completion for Diabetic Retinopathy Prediction.

Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of vision loss among working-aged adults. However, due to the low compliance rate of DR screening and expensive medical devices for ophthalmic exams, many DR patients did not seek proper medical attention until DR develops to irreversible stages (i.e., vision loss). Fortunately, the widely available electronic health record (EHR) databases provide an unprecedented opportunity to develop cost-effective machine-learning tools for DR detection. This paper proposes a Multi-branching Temporal Convolutional Network with Tensor Data Completion (MB-TCN-TC) model to analyze the longitudinal EHRs collected from diabetic patients for DR prediction. Experimental results demonstrate that the proposed MB-TCN-TC model not only effectively copes with the imbalanced data and missing value issues commonly seen in EHR datasets but also captures the temporal correlation and complicated interactions among medical variables in the longitudinal clinical records, yielding superior prediction performance compared to existing methods. Specifically, our MB-TCN-TC model provides AUROC and AUPRC scores of 0.949 and 0.793 respectively, achieving an improvement of 6.27% on AUROC, 11.85% on AUPRC, and 19.3% on F1 score compared with the traditional TCN model.

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.

Genomic data in the All of Us Research Program

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1–4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.