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Article ; Online: Using public clinical trial reports to probe non-experimental causal inference methods.

Steinberg, Ethan / Ignatiadis, Nikolaos / Yadlowsky, Steve / Xu, Yizhe / Shah, Nigam

2023 Volume 23, Issue 1, Page(s) 204

Abstract: Background: Non-experimental studies (also known as observational studies) are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in non-experimental studies require ... ...

Abstract	Background: Non-experimental studies (also known as observational studies) are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in non-experimental studies require untestable assumptions. This lack of intrinsic verifiability makes it difficult both to compare different non-experimental study methods and to trust the results of any particular non-experimental study. Methods: We introduce TrialProbe, a data resource and statistical framework for the evaluation of non-experimental methods. We first collect a dataset of pseudo "ground truths" about the relative effects of drugs by using empirical Bayesian techniques to analyze adverse events recorded in public clinical trial reports. We then develop a framework for evaluating non-experimental methods against that ground truth by measuring concordance between the non-experimental effect estimates and the estimates derived from clinical trials. As a demonstration of our approach, we also perform an example methods evaluation between propensity score matching, inverse propensity score weighting, and an unadjusted approach on a large national insurance claims dataset. Results: From the 33,701 clinical trial records in our version of the ClinicalTrials.gov dataset, we are able to extract 12,967 unique drug/drug adverse event comparisons to form a ground truth set. During our corresponding methods evaluation, we are able to use that reference set to demonstrate that both propensity score matching and inverse propensity score weighting can produce estimates that have high concordance with clinical trial results and substantially outperform an unadjusted baseline. Conclusions: We find that TrialProbe is an effective approach for probing non-experimental study methods, being able to generate large ground truth sets that are able to distinguish how well non-experimental methods perform in real world observational data.
MeSH term(s)	Humans ; Bayes Theorem ; Causality ; Propensity Score ; Research Design
Language	English
Publishing date	2023-09-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-023-02025-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Clinical utility gains from incorporating comorbidity and geographic location information into risk estimation equations for atherosclerotic cardiovascular disease.

Xu, Yizhe / Foryciarz, Agata / Steinberg, Ethan / Shah, Nigam H

Journal of the American Medical Informatics Association : JAMIA

2023 Volume 30, Issue 5, Page(s) 878–887

Abstract: Objective: There are over 363 customized risk models of the American College of Cardiology and the American Heart Association (ACC/AHA) pooled cohort equations (PCE) in the literature, but their gains in clinical utility are rarely evaluated. We build ... ...

Abstract	Objective: There are over 363 customized risk models of the American College of Cardiology and the American Heart Association (ACC/AHA) pooled cohort equations (PCE) in the literature, but their gains in clinical utility are rarely evaluated. We build new risk models for patients with specific comorbidities and geographic locations and evaluate whether performance improvements translate to gains in clinical utility. Materials and methods: We retrain a baseline PCE using the ACC/AHA PCE variables and revise it to incorporate subject-level information of geographic location and 2 comorbidity conditions. We apply fixed effects, random effects, and extreme gradient boosting (XGB) models to handle the correlation and heterogeneity induced by locations. Models are trained using 2 464 522 claims records from Optum©'s Clinformatics® Data Mart and validated in the hold-out set (N = 1 056 224). We evaluate models' performance overall and across subgroups defined by the presence or absence of chronic kidney disease (CKD) or rheumatoid arthritis (RA) and geographic locations. We evaluate models' expected utility using net benefit and models' statistical properties using several discrimination and calibration metrics. Results: The revised fixed effects and XGB models yielded improved discrimination, compared to baseline PCE, overall and in all comorbidity subgroups. XGB improved calibration for the subgroups with CKD or RA. However, the gains in net benefit are negligible, especially under low exchange rates. Conclusions: Common approaches to revising risk calculators incorporating extra information or applying flexible models may enhance statistical performance; however, such improvement does not necessarily translate to higher clinical utility. Thus, we recommend future works to quantify the consequences of using risk calculators to guide clinical decisions.
MeSH term(s)	Humans ; Arthritis, Rheumatoid ; Cardiovascular Diseases/epidemiology ; Comorbidity ; Renal Insufficiency, Chronic ; Risk Assessment ; Risk Factors ; United States ; Atherosclerosis/epidemiology
Language	English
Publishing date	2023-02-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocad017
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Book ; Online: MOTOR

Steinberg, Ethan / Fries, Jason / Xu, Yizhe / Shah, Nigam

A Time-To-Event Foundation Model For Structured Medical Records

2023

Abstract: We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims. TTE models ...

Abstract	We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims. TTE models are used for estimating the probability distribution of the time until a specific event occurs, which is an important task in medical settings. TTE models provide many advantages over classification using fixed time horizons, including naturally handling censored observations, but are challenging to train with limited labeled data. MOTOR addresses this challenge by pretraining on up to 55M patient records (9B clinical events). We evaluate MOTOR's transfer learning performance on 19 tasks, across 3 patient databases (a private EHR system, MIMIC-IV, and Merative claims data). Task-specific models adapted from MOTOR improve time-dependent C statistics by 4.6% over state-of-the-art, improve label efficiency by up to 95% ,and are more robust to temporal distributional shifts. We further evaluate cross-site portability by adapting our MOTOR foundation model for six prediction tasks on the MIMIC-IV dataset, where it outperforms all baselines. MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL].
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-01-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting patients who are likely to develop Lupus Nephritis of those newly diagnosed with Systemic Lupus Erythematosus.

Bechler, Katelyn K / Stolyar, Liya / Steinberg, Ethan / Posada, Jose / Minty, Evan / Shah, Nigam H

AMIA ... Annual Symposium proceedings. AMIA Symposium

2023 Volume 2022, Page(s) 221–230

Abstract: Patients diagnosed with systemic lupus erythematosus (SLE) suffer from a decreased quality of life, an increased risk of medical complications, and an increased risk of death. In particular, approximately 50% of SLE patients progress to develop lupus ... ...

Abstract	Patients diagnosed with systemic lupus erythematosus (SLE) suffer from a decreased quality of life, an increased risk of medical complications, and an increased risk of death. In particular, approximately 50% of SLE patients progress to develop lupus nephritis, which oftentimes leads to life-threatening end stage renal disease (ESRD) and requires dialysis or kidney transplant
MeSH term(s)	Humans ; Kidney Failure, Chronic/etiology ; Kidney Failure, Chronic/prevention & control ; Lupus Erythematosus, Systemic/complications ; Lupus Erythematosus, Systemic/diagnosis ; Lupus Nephritis/complications ; Lupus Nephritis/diagnosis ; Lupus Nephritis/prevention & control ; Quality of Life ; Renal Dialysis ; Prognosis ; Biopsy ; Preventive Medicine/methods ; Datasets as Topic ; Electronic Health Records ; California ; Male ; Female ; Adult ; Middle Aged ; Cohort Studies ; ROC Curve ; Reproducibility of Results
Language	English
Publishing date	2023-04-29
Publishing country	United States
Document type	Journal Article
ISSN	1942-597X
ISSN (online)	1942-597X
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Article ; Online: Learning decision thresholds for risk stratification models from aggregate clinician behavior.

Patel, Birju S / Steinberg, Ethan / Pfohl, Stephen R / Shah, Nigam H

Journal of the American Medical Informatics Association : JAMIA

2021 Volume 28, Issue 10, Page(s) 2258–2264

Abstract: Using a risk stratification model to guide clinical practice often requires the choice of a cutoff-called the decision threshold-on the model's output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always ... ...

Abstract	Using a risk stratification model to guide clinical practice often requires the choice of a cutoff-called the decision threshold-on the model's output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.
MeSH term(s)	Cardiovascular Diseases ; Clinical Decision-Making ; Humans ; Risk Assessment
Language	English
Publishing date	2021-07-28
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocab159
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Article ; Online: A network paradigm predicts drug synergistic effects using downstream protein-protein interactions.

Wilson, Jennifer L / Steinberg, Ethan / Racz, Rebecca / Altman, Russ B / Shah, Nigam / Grimes, Kevin

CPT: pharmacometrics & systems pharmacology

2022 Volume 11, Issue 11, Page(s) 1527–1538

Abstract: In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through protein-protein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result ... ...

Abstract	In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through protein-protein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result from long-range network effects. We first used PPI network analysis to classify drugs based on proteins downstream of their targets and next predicted drug combination effects where drugs shared network proteins but had distinct binding proteins (e.g., targets, enzymes, or transporters). By classifying drugs using their downstream proteins, we had an 80.7% sensitivity for predicting rare drug combination effects documented in gold-standard datasets. We further measured the effect of predicted drug combinations on adverse outcome phenotypes using novel observational studies in the electronic health record. We tested predictions for 60 network-drug classes on seven adverse outcomes and measured changes in clinical outcomes for predicted combinations. These results demonstrate a novel paradigm for anticipating drug synergistic effects using proteins downstream of drug targets.
MeSH term(s)	Drug Combinations ; Proteins ; Drug Delivery Systems ; Drug Interactions
Chemical Substances	Drug Combinations ; Proteins
Language	English
Publishing date	2022-10-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2697010-7
ISSN	2163-8306 ; 2163-8306
ISSN (online)	2163-8306
ISSN	2163-8306
DOI	10.1002/psp4.12861
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Article ; Online: A Natural Language Processing Model to Identify Confidential Content in Adolescent Clinical Notes.

Rabbani, Naveed / Bedgood, Michael / Brown, Conner / Steinberg, Ethan / Goldstein, Rachel L / Carlson, Jennifer L / Pageler, Natalie / Morse, Keith E

Applied clinical informatics

2023 Volume 14, Issue 3, Page(s) 400–407

Abstract: Background: The 21st Century Cures Act mandates the immediate, electronic release of health information to patients. However, in the case of adolescents, special consideration is required to ensure that confidentiality is maintained. The detection of ... ...

Abstract	Background: The 21st Century Cures Act mandates the immediate, electronic release of health information to patients. However, in the case of adolescents, special consideration is required to ensure that confidentiality is maintained. The detection of confidential content in clinical notes may support operational efforts to preserve adolescent confidentiality while implementing information sharing. Objectives: This study aimed to determine if a natural language processing (NLP) algorithm can identify confidential content in adolescent clinical progress notes. Methods: A total of 1,200 outpatient adolescent progress notes written between 2016 and 2019 were manually annotated to identify confidential content. Labeled sentences from this corpus were featurized and used to train a two-part logistic regression model, which provides both sentence-level and note-level probability estimates that a given text contains confidential content. This model was prospectively validated on a set of 240 progress notes written in May 2022. It was subsequently deployed in a pilot intervention to augment an ongoing operational effort to identify confidential content in progress notes. Note-level probability estimates were used to triage notes for review and sentence-level probability estimates were used to highlight high-risk portions of those notes to aid the manual reviewer. Results: The prevalence of notes containing confidential content was 21% (255/1,200) and 22% (53/240) in the train/test and validation cohorts, respectively. The ensemble logistic regression model achieved an area under the receiver operating characteristic of 90 and 88% in the test and validation cohorts, respectively. Its use in a pilot intervention identified outlier documentation practices and demonstrated efficiency gains over completely manual note review. Conclusion: An NLP algorithm can identify confidential content in progress notes with high accuracy. Its human-in-the-loop deployment in clinical operations augmented an ongoing operational effort to identify confidential content in adolescent progress notes. These findings suggest NLP may be used to support efforts to preserve adolescent confidentiality in the wake of the information blocking mandate.
MeSH term(s)	Humans ; Adolescent ; Natural Language Processing ; Confidentiality ; Language ; Algorithms ; Documentation ; Electronic Health Records
Language	English
Publishing date	2023-03-10
Publishing country	Germany
Document type	Journal Article
ISSN	1869-0327
ISSN (online)	1869-0327
DOI	10.1055/a-2051-9764
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Article ; Online: EHR foundation models improve robustness in the presence of temporal distribution shift.

Guo, Lin Lawrence / Steinberg, Ethan / Fleming, Scott Lanyon / Posada, Jose / Lemmon, Joshua / Pfohl, Stephen R / Shah, Nigam / Fries, Jason / Sung, Lillian

Scientific reports

2023 Volume 13, Issue 1, Page(s) 3767

Abstract: Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global ... ...

Abstract	Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global patterns that can improve the robustness of task-specific models. The objective was to evaluate the utility of EHR foundation models in improving the in-distribution (ID) and out-of-distribution (OOD) performance of clinical prediction models. Transformer- and gated recurrent unit-based foundation models were pretrained on EHR of up to 1.8 M patients (382 M coded events) collected within pre-determined year groups (e.g., 2009-2012) and were subsequently used to construct patient representations for patients admitted to inpatient units. These representations were used to train logistic regression models to predict hospital mortality, long length of stay, 30-day readmission, and ICU admission. We compared our EHR foundation models with baseline logistic regression models learned on count-based representations (count-LR) in ID and OOD year groups. Performance was measured using area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve, and absolute calibration error. Both transformer and recurrent-based foundation models generally showed better ID and OOD discrimination relative to count-LR and often exhibited less decay in tasks where there is observable degradation of discrimination performance (average AUROC decay of 3% for transformer-based foundation model vs. 7% for count-LR after 5-9 years). In addition, the performance and robustness of transformer-based foundation models continued to improve as pretraining set size increased. These results suggest that pretraining EHR foundation models at scale is a useful approach for developing clinical prediction models that perform well in the presence of temporal distribution shift.
MeSH term(s)	Humans ; Electric Power Supplies ; Electronic Health Records ; Hospital Mortality ; Hospitalization
Language	English
Publishing date	2023-03-07
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-30820-8
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Article ; Online: The shaky foundations of large language models and foundation models for electronic health records.

Wornow, Michael / Xu, Yizhe / Thapa, Rahul / Patel, Birju / Steinberg, Ethan / Fleming, Scott / Pfeffer, Michael A / Fries, Jason / Shah, Nigam H

NPJ digital medicine

2023 Volume 6, Issue 1, Page(s) 135

Abstract: The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical ... ...

Abstract	The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.
Language	English
Publishing date	2023-07-29
Publishing country	England
Document type	Journal Article ; Review
ISSN	2398-6352
ISSN (online)	2398-6352
DOI	10.1038/s41746-023-00879-8
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Article ; Online: The Prevalence of Confidential Content in Adolescent Progress Notes Prior to the 21st Century Cures Act Information Blocking Mandate.

Bedgood, Michael / Rabbani, Naveed / Brown, Conner / Goldstein, Rachel / Carlson, Jennifer L / Steinberg, Ethan / Powell, Austin / Pageler, Natalie M / Morse, Keith

Applied clinical informatics

2023 Volume 14, Issue 2, Page(s) 337–344

Abstract: Background: The 21st Century Cures Act information blocking final rule mandated the immediate and electronic release of health care data in 2020. There is anecdotal concern that a significant amount of information is documented in notes that would ... ...

Abstract	Background: The 21st Century Cures Act information blocking final rule mandated the immediate and electronic release of health care data in 2020. There is anecdotal concern that a significant amount of information is documented in notes that would breach adolescent confidentiality if released electronically to a guardian. Objectives: The purpose of this study was to quantify the prevalence of confidential information, based on California laws, within progress notes for adolescent patients that would be released electronically and assess differences in prevalence across patient demographics. Methods: This is a single-center retrospective chart review of outpatient progress notes written between January 1, 2016, and December 31, 2019, at a large suburban academic pediatric network. Notes were labeled into one of three confidential domains by five expert reviewers trained on a rubric defining confidential information for adolescents derived from California state law. Participants included a random sampling of eligible patients aged 12 to 17 years old at the time of note creation. Secondary analysis included prevalence of confidentiality across age, gender, language spoken, and patient race. Results: Of 1,200 manually reviewed notes, 255 notes (21.3%) (95% confidence interval: 19-24%) contained confidential information. There was a similar distribution among gender and age and a majority of English speaking (83.9%) and white or Caucasian patients (41.2%) in the cohort. Confidential information was more likely to be found in notes for females ( Conclusion: This study demonstrates that there is a significant risk to breach adolescent confidentiality if historical progress notes are released electronically to proxies without further review or redaction. With increased sharing of health care data, there is a need to protect the privacy of the adolescents and prevent potential breaches of confidentiality.
MeSH term(s)	Female ; Humans ; Adolescent ; Child ; Prevalence ; Retrospective Studies ; Confidentiality ; Privacy ; Health Facilities
Language	English
Publishing date	2023-05-03
Publishing country	Germany
Document type	Journal Article
ISSN	1869-0327
ISSN (online)	1869-0327
DOI	10.1055/s-0043-1767682
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