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Impact of oral vancomycin treatment duration on rate of Clostridioides difficile recurrence in patients requiring concurrent systemic antibiotics

Kwiatkowski, Diana; Marsh, Kassandra; Katz, Alyson; Papadopoulos, John; So, Jonathan; Major, Vincent J; Sommer, Philip M; Hochman, Sarah; Dubrovskaya, Yanina; Arnouk, Serena
BACKGROUND:infection (CDI) in patients requiring concomitant systemic antibiotics. OBJECTIVES/OBJECTIVE:To evaluate prescribing practices of vancomycin for CDI in patients that required concurrent systemic antibiotics and to determine whether a prolonged duration of vancomycin (>14 days), compared to a standard duration (10-14 days), decreased CDI recurrence. METHODS:(VRE). RESULTS:= .083) were not significantly different between groups. Discontinuation of vancomycin prior to completion of antibiotics was an independent predictor of 8-week recurrence on multivariable logistic regression (OR, 4.8; 95% CI, 1.3-18.1). CONCLUSIONS:Oral vancomycin prescribing relative to the systemic antibiotic end date may affect CDI recurrence to a greater extent than total vancomycin duration alone. Further studies are needed to confirm these findings.
PMID: 38288606
ISSN: 1559-6834
CID: 5627432

Improving Information Extraction from Pathology Reports using Named Entity Recognition

Zeng, Ken G; Dutt, Tarun; Witowski, Jan; Kranthi Kiran, G V; Yeung, Frank; Kim, Michelle; Kim, Jesi; Pleasure, Mitchell; Moczulski, Christopher; Lopez, L Julian Lechuga; Zhang, Hao; Harbi, Mariam Al; Shamout, Farah E; Major, Vincent J; Heacock, Laura; Moy, Linda; Schnabel, Freya; Pak, Linda M; Shen, Yiqiu; Geras, Krzysztof J
Pathology reports are considered the gold standard in medical research due to their comprehensive and accurate diagnostic information. Natural language processing (NLP) techniques have been developed to automate information extraction from pathology reports. However, existing studies suffer from two significant limitations. First, they typically frame their tasks as report classification, which restricts the granularity of extracted information. Second, they often fail to generalize to unseen reports due to variations in language, negation, and human error. To overcome these challenges, we propose a BERT (bidirectional encoder representations from transformers) named entity recognition (NER) system to extract key diagnostic elements from pathology reports. We also introduce four data augmentation methods to improve the robustness of our model. Trained and evaluated on 1438 annotated breast pathology reports, acquired from a large medical center in the United States, our BERT model trained with data augmentation achieves an entity F1-score of 0.916 on an internal test set, surpassing the BERT baseline (0.843). We further assessed the model's generalizability using an external validation dataset from the United Arab Emirates, where our model maintained satisfactory performance (F1-score 0.860). Our findings demonstrate that our NER systems can effectively extract fine-grained information from widely diverse medical reports, offering the potential for large-scale information extraction in a wide range of medical and AI research. We publish our code at
PMID: 37461545
CID: 5588752

Ten Years of Health Informatics Education for Physicians

Chapter by: Major, Vincent J.; Plottel, Claudia S.; Aphinyanaphongs, Yindalon
in: Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023 by
[S.l.] : Institute of Electrical and Electronics Engineers Inc., 2023
pp. 637-644
ISBN: 9798350302639
CID: 5630952

Enabling AI-Augmented Clinical Workflows by Accessing Patient Data in Real-Time with FHIR

Chapter by: Major, Vincent J.; Wang, Walter; Aphinyanaphongs, Yindalon
in: Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023 by
[S.l.] : Institute of Electrical and Electronics Engineers Inc., 2023
pp. 531-533
ISBN: 9798350302639
CID: 5630942

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

Major, Vincent J; Jones, Simon A; Razavian, Narges; Bagheri, Ashley; Mendoza, Felicia; Stadelman, Jay; Horwitz, Leora I; Austrian, Jonathan; Aphinyanaphongs, Yindalon
BACKGROUND: We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. OBJECTIVES/OBJECTIVE: The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). METHODS: We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. RESULTS: Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. CONCLUSION/CONCLUSIONS: An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. TRIAL REGISTRATION/BACKGROUND: identifier: NCT04570488.
PMID: 35896506
ISSN: 1869-0327
CID: 5276672

Generalizing an Antibiotic Recommendation Algorithm for Treatment of Urinary Tract Infections to an Urban Academic Medical Center [Editorial]

Yoon, Garrett; Matulewicz, Richard S; Major, Vincent J
PMID: 35344386
ISSN: 1527-3792
CID: 5219832

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

Wong, Andrew; Cao, Jie; Lyons, Patrick G; Dutta, Sayon; Major, Vincent J; Ötles, Erkin; Singh, Karandeep
PMID: 34797372
ISSN: 2574-3805
CID: 5049692

Supporting Acute Advance Care Planning with Precise, Timely Mortality Risk Predictions

Wang, Erwin; Major, Vincent J; Adler, Nicole; Hauck, Kevin; Austrian, Jonathan; Aphinyanaphongs, Yindalon; Horwitz, Leora I
ISSN: n/a
CID: 5000212

Probing automated treatment of urinary tract infections for bias: A case-study where machine learning perpetuates structural differences and racial disparities

Chapter by: Yoon, Garrett; Major, Vincent J.
in: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2021 by
[S.l.] : Association for Computing Machinery, Inc, 2021
pp. ?-?
ISBN: 9781450384506
CID: 5002242

Mechanical ventilation in cardiac arrest: Association between hyperoxia, hypercarbia and positive end-expiratory pressure with mortality [Meeting Abstract]

Alviar, Restrepo C; Lui, A Y; Jaramillo-Restrepo, V; Celi, L; Rico, Mesa J S; Quien, M; Vargas, A; Aiad, N; Alabdallah, K; Li, B; Major, V; Maselli, D J
Background: Optimization of mechanical ventilation (MV) in patients with cardiac arrest (CA) may help improve outcomes in these patients. We sought to investigate the association between hyperoxia, PCO2, and positive end-expiratory pressure (PEEP) with mortality in patients with CA.
Method(s): Patients admitted to our medical center CICU from 2001 through 2012 (MIMIC-III database) who received MV with available information on MV parameters and had arterial blood gases sampling were included. Hyperoxia was defined as time-weighted mean of PaO2 >120 mmHg and non-hyperoxia as PaO2 <=120 mmHg, while Hypercarbia was defined as PCO2 >35 mmHg during CICU admission. The primary outcome was inhospital mortality. Multivariable logistic regression was used to assess the association between hyperoxia and in-hospital mortality adjusted for age, female sex, Oxford Acute Severity of Illness Score, creatinine, lactate, pH, PaO2/FiO2 ratio, PCO2, PEEP, and time spent on PEEP.
Result(s): Among 136 patients, PaO2 = 139+/-55 mmHg, PCO2 = 39+/-10 mmHg, and PEEP = 6.4+/-2.2cmH2O. Unadjusted mortality was higher in the hyperoxic group (51.4%) compared to the non-hyperoxic group (29.0%) (long rank test p=0.0034, figure). In multivariable analysis, hyperoxia was independently associated with higher in-hospital mortality (OR 4.046, 95% CI: 1.501-10.907, p=0.0057). Additionally, there was no association between the presence of hypercarbia and in-hospital mortality (OR 0.896, 95% CI: 0.319 to 2.521, p=0.836) nor when PCO2 was analyzed as a continuous variable (OR 1.063 per 1 mmHg increase in CO2, 95% CI: 0.111- 10.145, p=0.957). Similarly, there was no assocation between PEEP and in-hospital mortality (OR 1.012 per 1cmH2O increase, 95% CI: 0.807 to 1.270, p=0.917). Post-hoc analysis with PaO2 as a continuous variable was consistent with the primary analysis (OR 1.214 per 10 mmHg increase in PaO2, 95% CI: 1.059-1.391, p=0.005).
Conclusion(s): In patients with CA, hyperoxia was associated with increased mortality, while PCO2 and PEEP levels were not. Optimal MV parameters are important in the management of patients with CA. Further research is warranted to confirm this association and explore the mechanisms behind these observations. These studies can help establish the best MV strategies for patients with CA
ISSN: 1522-9645
CID: 4811392