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Article ; Online: Accelerating chest pain evaluation with machine learning.

Thangaraj, Phyllis M / Khera, Rohan

European heart journal. Acute cardiovascular care

2023 Volume 12, Issue 11, Page(s) 753–754

MeSH term(s)	Humans ; Machine Learning ; Chest Pain/diagnosis ; Chest Pain/etiology
Language	English
Publishing date	2023-10-04
Publishing country	England
Document type	Journal Article
ZDB-ID	2663340-1
ISSN	2048-8734 ; 2048-8726
ISSN (online)	2048-8734
ISSN	2048-8726
DOI	10.1093/ehjacc/zuad117
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: RCT-Twin-GAN Generates Digital Twins of Randomized Control Trials Adapted to Real-world Patients to Enhance their Inference and Application.

Thangaraj, Phyllis M / Shankar, Sumukh Vasisht / Oikonomou, Evangelos K / Khera, Rohan

medRxiv : the preprint server for health sciences

2023

Abstract: Background: Randomized clinical trials (RCTs) are designed to produce evidence in selected populations. Assessing their effects in the real-world is essential to change medical practice, however, key populations are historically underrepresented in the ... ...

Abstract	Background: Randomized clinical trials (RCTs) are designed to produce evidence in selected populations. Assessing their effects in the real-world is essential to change medical practice, however, key populations are historically underrepresented in the RCTs. We define an approach to simulate RCT-based effects in real-world settings using RCT digital twins reflecting the covariate patterns in an electronic health record (EHR). Methods: We developed a Generative Adversarial Network (GAN) model, RCT-Twin-GAN, which generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from an EHR cohort. We improved upon a traditional tabular conditional GAN, CTGAN, with a loss function adapted for data distributions and by conditioning on multiple discrete and continuous covariates simultaneously. We assessed the similarity between a Heart Failure with preserved Ejection Fraction (HFpEF) RCT (TOPCAT), a Yale HFpEF EHR cohort, and RCT-Twin. We also evaluated cardiovascular event-free survival stratified by Spironolactone (treatment) use. Results: By applying RCT-Twin-GAN to 3445 TOPCAT participants and conditioning on 3445 Yale EHR HFpEF patients, we generated RCT-Twin datasets between 1141-3445 patients in size, depending on covariate conditioning and model parameters. RCT-Twin randomly allocated spironolactone (S)/ placebo (P) arms like an RCT, was similar to RCT by a multi-dimensional distance metric, and balanced covariates (median absolute standardized mean difference (MASMD) 0.017, IQR 0.0034-0.030). The 5 EHR-conditioned covariates in RCT-Twin were closer to the EHR compared with the RCT (MASMD 0.008 vs 0.63, IQR 0.005-0.018 vs 0.59-1.11). RCT-Twin reproduced the overall effect size seen in TOPCAT (5-year cardiovascular composite outcome odds ratio (95% confidence interval) of 0.89 (0.75-1.06) in RCT vs 0.85 (0.69-1.04) in RCT-Twin). Conclusions: RCT-Twin-GAN simulates RCT-derived effects in real-world patients by translating these effects to the covariate distributions of EHR patients. This key methodological advance may enable the direct translation of RCT-derived effects into real-world patient populations and may enable causal inference in real-world settings.
Language	English
Publishing date	2023-12-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.06.23299464
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Article: A Novel Digital Twin Strategy to Examine the Implications of Randomized Control Trials for Real-World Populations.

Thangaraj, Phyllis M / Shankar, Sumukh Vasisht / Huang, Sicong / Nadkarni, Girish / Mortazavi, Bobak / Oikonomou, Evangelos K / Khera, Rohan

medRxiv : the preprint server for health sciences

2024

Abstract: Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that ... ...

Abstract	Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that leverages relationships between covariates and outcomes and generates a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population. We used RCT-Twin-GAN to reproduce treatment effect outcomes of the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood Pressure Trial, which tested the same intervention but had different treatment effect results. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT patient population, we evaluated the cardiovascular event-free survival of SPRINT digital twins conditioned on the ACCORD cohort and vice versa (SPRINT-conditioned ACCORD twins). The conditioned digital twins were balanced by the intervention arm (mean absolute standardized mean difference (MASMD) of covariates between treatment arms 0.019 (SD 0.018), and the conditioned covariates of the SPRINT-Twin on ACCORD were more similar to ACCORD than a sprint (MASMD 0.0082 SD 0.016 vs. 0.46 SD 0.20). Most importantly, across iterations, SPRINT conditioned ACCORD-Twin datasets reproduced the overall non-significant effect size seen in ACCORD (5-year cardiovascular outcome hazard ratio (95% confidence interval) of 0.88 (0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the SPRINT conditioned ACCORD-Twin), while the ACCORD conditioned SPRINT-Twins reproduced the significant effect size seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86)) in ACCORD conditioned SPRINT-Twin). Finally, we describe the translation of this approach to real-world populations by conditioning the trials on an electronic health record population. Therefore, RCT-Twin-GAN simulates the direct translation of RCT-derived treatment effects across various patient populations with varying covariate distributions.
Language	English
Publishing date	2024-03-26
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.03.25.24304868
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Article: An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials.

Oikonomou, Evangelos K / Thangaraj, Phyllis M / Bhatt, Deepak L / Ross, Joseph S / Young, Lawrence H / Krumholz, Harlan M / Suchard, Marc A / Khera, Rohan

medRxiv : the preprint server for health sciences

2023

Abstract: Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to ... ...

Abstract	Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%,
Language	English
Publishing date	2023-11-01
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.06.18.23291542
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Article ; Online: An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials.

Oikonomou, Evangelos K / Thangaraj, Phyllis M / Bhatt, Deepak L / Ross, Joseph S / Young, Lawrence H / Krumholz, Harlan M / Suchard, Marc A / Khera, Rohan

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 217

Abstract: Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize ...

Abstract	Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, p
Language	English
Publishing date	2023-11-25
Publishing country	England
Document type	Journal Article
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00963-z
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Article: Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods.

Thangaraj, Phyllis M / Kummer, Benjamin R / Lorberbaum, Tal / Elkind, Mitchell S V / Tatonetti, Nicholas P

BioData mining

2020 Volume 13, Issue 1, Page(s) 21

Abstract: Background: Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new ... ...

Abstract	Background: Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR. Materials and methods: Using structured patient data from the EHR at a tertiary-care hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank. Results: Across all models, we found that the mean AUROC for detecting AIS was 0.963 ± 0.0520 and average precision score 0.790 ± 0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832 ± 0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60-150 fold over expected). Conclusions: Our findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models.
Language	English
Publishing date	2020-12-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2438773-3
ISSN	1756-0381
ISSN	1756-0381
DOI	10.1186/s13040-020-00230-x
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Article ; Online: Spatial processing in binocular rivalry

Thangaraj Phyllis M / Vattikuti Shashaank / Chow Carson C

BMC Neuroscience, Vol 13, Iss Suppl 1, p P

2012 Volume 166

Keywords	Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Internal medicine ; RC31-1245 ; Medicine ; R ; DOAJ:Neurology ; DOAJ:Medicine (General) ; DOAJ:Health Sciences
Language	English
Publishing date	2012-07-01T00:00:00Z
Publisher	BioMed Central
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Identification of Immune complement function as a determinant of adverse SARS-CoV-2 infection outcome.

Ramlall, Vijendra / Thangaraj, Phyllis M / Meydan, Cem / Foox, Jonathan / Butler, Daniel / May, Ben / De Freitas, Jessica K / Glicksberg, Benjamin S / Mason, Christopher E / Tatonetti, Nicholas P / Shapira, Sagi D

medRxiv : the preprint server for health sciences

2020

Abstract: Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutics and public health intervention strategies. Viral-host interactions can guide discovery of regulators of disease outcomes, and protein structure function analysis ... ...

Abstract	Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutics and public health intervention strategies. Viral-host interactions can guide discovery of regulators of disease outcomes, and protein structure function analysis points to several immune pathways, including complement and coagulation, as targets of the coronavirus proteome. To determine if conditions associated with dysregulation of the complement or coagulation systems impact adverse clinical outcomes, we performed a retrospective observational study of 11,116 patients who presented with suspected SARS-CoV-2 infection. We found that history of macular degeneration (a proxy for complement activation disorders) and history of coagulation disorders (thrombocytopenia, thrombosis, and hemorrhage) are risk factors for morbidity and mortality in SARS-CoV-2 infected patients - effects that could not be explained by age, sex, or history of smoking. Further, transcriptional profiling of nasopharyngeal (NP) swabs from 650 control and SARS-CoV-2 infected patients demonstrated that in addition to innate Type-I interferon and IL-6 dependent inflammatory immune responses, infection results in robust engagement and activation of the complement and coagulation pathways. Finally, we conducted a candidate driven genetic association study of severe SARS-CoV-2 disease. Among the findings, our scan identified putative complement and coagulation associated loci including missense, eQTL and sQTL variants of critical regulators of the complement and coagulation cascades. In addition to providing evidence that complement function modulates SARS-CoV-2 infection outcome, the data point to putative transcriptional genetic markers of susceptibility. The results highlight the value of using a multi-modal analytical approach, combining molecular information from virus protein structure-function analysis with clinical informatics, transcriptomics, and genomics to reveal determinants and predictors of immunity, susceptibility, and clinical outcome associated with infection.
Keywords	covid19
Language	English
Publishing date	2020-06-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2020.05.05.20092452
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Article ; Online: Immune complement and coagulation dysfunction in adverse outcomes of SARS-CoV-2 infection.

Ramlall, Vijendra / Thangaraj, Phyllis M / Meydan, Cem / Foox, Jonathan / Butler, Daniel / Kim, Jacob / May, Ben / De Freitas, Jessica K / Glicksberg, Benjamin S / Mason, Christopher E / Tatonetti, Nicholas P / Shapira, Sagi D

Nature medicine

2020 Volume 26, Issue 10, Page(s) 1609–1615

Abstract: Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutic and public health strategies. Viral-host interactions can guide discovery of disease regulators, and protein structure function analysis points to several immune ... ...

Abstract	Understanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutic and public health strategies. Viral-host interactions can guide discovery of disease regulators, and protein structure function analysis points to several immune pathways, including complement and coagulation, as targets of coronaviruses. To determine whether conditions associated with dysregulated complement or coagulation systems impact disease, we performed a retrospective observational study and found that history of macular degeneration (a proxy for complement-activation disorders) and history of coagulation disorders (thrombocytopenia, thrombosis and hemorrhage) are risk factors for SARS-CoV-2-associated morbidity and mortality-effects that are independent of age, sex or history of smoking. Transcriptional profiling of nasopharyngeal swabs demonstrated that in addition to type-I interferon and interleukin-6-dependent inflammatory responses, infection results in robust engagement of the complement and coagulation pathways. Finally, in a candidate-driven genetic association study of severe SARS-CoV-2 disease, we identified putative complement and coagulation-associated loci including missense, eQTL and sQTL variants of critical complement and coagulation regulators. In addition to providing evidence that complement function modulates SARS-CoV-2 infection outcome, the data point to putative transcriptional genetic markers of susceptibility. The results highlight the value of using a multimodal analytical approach to reveal determinants and predictors of immunity, susceptibility and clinical outcome associated with infection.
MeSH term(s)	Adult ; Age Factors ; Aged ; Aged, 80 and over ; Betacoronavirus ; Blood Coagulation/genetics ; Blood Coagulation Disorders/epidemiology ; COVID-19 ; Complement Activation/genetics ; Complement Activation/immunology ; Coronavirus Infections/blood ; Coronavirus Infections/genetics ; Coronavirus Infections/immunology ; Coronavirus Infections/mortality ; Diabetes Mellitus, Type 2/epidemiology ; Female ; Gene Expression ; Hemorrhage/blood ; Hemorrhage/epidemiology ; Hemorrhage/immunology ; Hereditary Complement Deficiency Diseases/epidemiology ; Hereditary Complement Deficiency Diseases/immunology ; Humans ; Hypertension/epidemiology ; Intubation, Intratracheal ; Macular Degeneration/epidemiology ; Male ; Middle Aged ; New York City/epidemiology ; Obesity/epidemiology ; Pandemics ; Pneumonia, Viral/blood ; Pneumonia, Viral/genetics ; Pneumonia, Viral/immunology ; Pneumonia, Viral/mortality ; Proportional Hazards Models ; Respiration, Artificial ; Retrospective Studies ; Risk Factors ; SARS-CoV-2 ; Severity of Illness Index ; Sex Factors ; Thrombocytopenia/blood ; Thrombocytopenia/epidemiology ; Thrombosis/blood ; Thrombosis/epidemiology
Keywords	covid19
Language	English
Publishing date	2020-08-03
Publishing country	United States
Document type	Journal Article ; Observational Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1220066-9
ISSN	1546-170X ; 1078-8956
ISSN (online)	1546-170X
ISSN	1078-8956
DOI	10.1038/s41591-020-1021-2
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Article ; Online: ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Glicksberg, Benjamin S / Oskotsky, Boris / Giangreco, Nicholas / Thangaraj, Phyllis M / Rudrapatna, Vivek / Datta, Debajyoti / Frazier, Remi / Lee, Nelson / Larsen, Rick / Tatonetti, Nicholas P / Butte, Atul J

JAMIA open

2019 Volume 2, Issue 1, Page(s) 10–14

Abstract: Objectives: Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common ... ...

Abstract	Objectives: Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods: We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results: ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion: ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).
Language	English
Publishing date	2019-01-04
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooy059
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