Faculty Bibliography

BACKGROUND:Understanding the current risk of inpatient mortality following hip fracture in the United States is of significant value to patient families and the health system. Currently, the literature lacks a national representation of the inpatient mortality following hip fracture. PURPOSE/OBJECTIVE:The purpose of this study was to investigate the incidence of inpatient mortality following hip fracture using Epic Cosmos-an aggregated, de-identified, multi-institutional data that includes over 280 million patients in the United States. METHODS:A "Cosmos hip fracture cohort" that included all adults (18 years or older) who sustained a femoral neck, intertrochanteric, or subtrochanteric hip fracture (ICD 72.0, S72.1, S72.2) between January 2019 and December 2024 was created. The dataset was queried for demographic data including age, sex, geographic location, incidence of inpatient mortality, and bone health medication use at the time of admission. RESULTS:The Cosmos database included 284,455,033 patients, of which 1,232,250 hip fracture hospital admissions between January 2019 and December 2024 were identified. Of these patients, 47,773 (3.9%) expired during their hip fracture hospital admission. The most common age bracket was 85 years or older (39.8%), followed by 75-85 (30.0%), and 65-75 (17.8%). Most patients were white (91%) females (55.5%). Most inpatient mortalities occurred in the South (38.4%), followed by the Midwest (31.8%), followed by the Northeast (23.6%), and last by the West (6.2%). CONCLUSION/CONCLUSIONS:The current inpatient mortality following hip fracture is 3.9%. Most inpatient mortalities occurred in white females above the age of 85 in the South of the country. LEVEL OF EVIDENCE/METHODS:Level III.

OBJECTIVE:Continuous glucose monitoring (CGM) is essential in diabetes care and research; however, extracting key data (e.g., time above, in, or below range) from CGM reports is manual, time-consuming, and inefficient. Natural language processing (NLP) can extract data from unstructured sources (e.g., images), but its application in CGM remains unexplored. We aimed to evaluate the accuracy of extracting CGM data using NLP. RESEARCH DESIGN AND METHODS/METHODS:We analyzed CGM reports stored as PDF files from the electronic health record at New York University Langone Health. The steps of our algorithm pipeline consist of 1) performing optical character recognition (OCR) to obtain glucose matrix data from CGM reports, 2) determining the type of CGM documents based on keywords in OCR results, 3) extracting variables of glucose based on CGM document type, and 4) storing the extracted glucose data in a structured database. Two experts with experience in CGM research and clinical practice conducted an independent manual review of 1% of the documents (n = 226). We calculated accuracy (correct extraction of CGM data) by comparing the algorithm's results with the manual review. RESULTS:Of the documents analyzed, 36.8% were Freestyle Libre and 63.2% were Dexcom. For information extraction, the agreement in evaluating Libre results between two experts was 99.93%. When comparing algorithm accuracy with manual review, the accuracy for Libre was 99.87% and, for Dexcom, 100.00%. CONCLUSIONS:Using an NLP approach to extract valuable glucose data from CGM PDF files is feasible and accurate, which can benefit clinical practice and diabetes research.

BACKGROUND:Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events. METHODS:A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both non-contrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization). RESULTS:Our cohort included 3599 patients (median age 60 years, 62% male, 74% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r=0.56, P<0.001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683 to 0.718]; AUC for PREVENT plus AAC 0.782 [0.767 to 0.797]; P<0.001). There were 324 MACE, of which 246 were MCE. Following adjustment for the 10-year cardiovascular disease PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P<0.001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P<0.001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE. CONCLUSIONS:Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.

Liver transplant (LT) recipients experience a wide range of comorbidities, leading to frequent healthcare encounters. Until now, national registries, which have limited exposures and outcomes, and laborious small cohort studies have been the main data sources for LT research. Cosmos database offers electronic health record (EHR)-based insights into LT recipients at the national level with granular data. We evaluated if Cosmos data is representative of the entire US LT recipient population. Using Cosmos (N=20,235) and the national Scientific Registry of Transplant Recipients (SRTR) (N=51,281), we identified adult, first-time LT recipients between 7/2016-12/2022. We compared demographics, clinical data, and mortality across datasets, calculating Kaplan-Meier survival estimates and multi-variable Cox regressions. Recipient characteristics were highly comparable (e.g., female: Cosmos=36.5% vs. SRTR=36.4%, Black: 6.8% vs. 7.2%; BMI: 28.5 kg/m2 [24.8-32.9] vs. 28.2 [24.6-32.4]). Lab values were similar across cohorts, including MELD (24 [17-30] vs. 23 [16-30]). Transplant indications, donor characteristics, and 5-year survival (Cosmos 83.1% [82.3-83.8) vs. SRTR 80.9% [80.4-81.3]) were similar. The associations of clinical factors with survival were similar across both groups. Cosmos database demonstrated acceptable generalizability to the general US LT recipient population, which may advance LT research through a better understanding about LT recipients' experiences and outcomes.

Developing real-world evidence from electronic health records (EHR) is vital to advance kidney transplantation (KT). We assessed the feasibility of studying KT using the Epic Cosmos aggregated EHR dataset, which includes 274 million unique individuals cared for in 238 U.S. health systems, by comparing it with the Scientific Registry of Transplant Recipients (SRTR). We identified 69,418 KT recipients transplanted between January 2014 and December 2022 in Cosmos (39.4% of all US KT transplants during this period). Demographics and clinical characteristics of recipients captured in Cosmos were consistent with the overall SRTR cohort. Survival estimates were generally comparable, although there were some differences in long-term survival. At 7 years post-transplant, patient survival was 80.4% in Cosmos and 77.8% in SRTR. Multivariable Cox regression showed consistent associations between clinical factors and mortality in both cohorts, with minor discrepancies in the associations between death and both age and race. In summary, Cosmos provides a reliable platform for KT research, allowing EHR-level clinical granularity not available with either the transplant registry or healthcare claims. Consequently, Cosmos will enable novel analyses to improve our understanding of KT management on a national scale.

BACKGROUND:Clinical use of continuous glucose monitoring (CGM) is increasing storage of CGM-related documents in electronic health records (EHR); however, the standardization of CGM storage is lacking. We aimed to evaluate the sensitivity and specificity of CGM Ambulatory Glucose Profile (AGP) classification criteria. METHODS:We randomly chose 2244 (18.1%) documents from NYU Langone Health. Our document classification algorithm: (1) separated multiple-page documents into a single-page image; (2) rotated all pages into an upright orientation; (3) determined types of devices using optical character recognition; and (4) tested for the presence of particular keywords in the text. Two experts in using CGM for research and clinical practice conducted an independent manual review of 62 (2.8%) reports. We calculated sensitivity (correct classification of CGM AGP report) and specificity (correct classification of non-CGM report) by comparing the classification algorithm against manual review. RESULTS:Among 2244 documents, 1040 (46.5%) were classified as CGM AGP reports (43.3% FreeStyle Libre and 56.7% Dexcom), 1170 (52.1%) non-CGM reports (eg, progress notes, CGM request forms, or physician letters), and 34 (1.5%) uncertain documents. The agreement for the evaluation of the documents between the two experts was 100% for sensitivity and 98.4% for specificity. When comparing the classification result between the algorithm and manual review, the sensitivity and specificity were 95.0% and 91.7%. CONCLUSION/CONCLUSIONS:Nearly half of CGM-related documents were AGP reports, which are useful for clinical practice and diabetes research; however, the remaining half are other clinical documents. Future work needs to standardize the storage of CGM-related documents in the EHR.

BACKGROUND:Despite the central role of experiential learning in residency training, the actual clinical experiences residents participate in are not well characterized. A better understanding of the type, volume, and variation in residents' clinical experiences is essential to support precision medical education strategies. OBJECTIVE:We sought to characterize the entirety of the clinical experiences had by individual internal medicine residents throughout their time in training. METHOD/METHODS:We evaluated the clinical experiences of medicine residents (n = 51) who completed training at NYU Grossman School of Medicine's Brooklyn campus between 2020 and 2023. Residents' inpatient and outpatient experiences were identified using notes written, orders placed, and care team sign-ins; principal ICD-10 codes for each encounter were converted into medical content categories using a previously described crosswalk tool. RESULTS:Of 152,426 clinical encounters with available ICD-10 codes, 132,284 were mapped to medical content categories (94.5% capture). Residents' clinical experiences were particularly enriched in infectious and cardiovascular disease; most had very little exposure to allergy, dermatology, oncology, or rheumatology. Some trainees saw twice as many cases in a given content area as did others. There was little concordance between actual frequency of clinical experience and expected content frequency on the ABIM certification exam. CONCLUSIONS:Individual residents' clinical experiences in training vary widely, both in number and in type. Characterizing these experiences paves the way for exploration of the relationships between clinical exposure and educational outcomes, and for the implementation of precision education strategies that could fill residents' experiential gaps and complement strengths with targeted educational interventions.