ArticleInfection control and hospital epidemiology2018
A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers.
Article in Infection control and hospital epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic prediction models to identify patients at risk for healthcare-facility-onsetInfection control and hospital epidemiology · 2024Pooled it
- Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review.Journal of medical Internet research · 2020Pooled it
- Derivation and validation of a prediction model for primary and recurrentAntimicrobial stewardship & healthcare epidemiology : ASHE · 2026Article
- Predictive Modeling for Clostridioides difficile Infection: Current State of the Science, Clinical Applications, and Future Directions.Infectious disease clinics of North America · 2025Review
- Guiding Clostridioides difficile Infection Prevention Efforts in a Hospital Setting With AI.JAMA network open · 2025Article
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Predicting positiveHeliyon · 2025Article
- Impact of empiric antibiotics on risk ofAntimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Coronary Disease Risk Prediction, Risk Reduction, and Postoperative Myocardial Injury.The Medical clinics of North America · 2024Review
- Deep learning-based prediction of Clostridioides difficile infection caused by antibiotics using longitudinal electronic health records.NPJ digital medicine · 2024Article
- Article
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Development of a Prediction Model to Identify the Risk ofPharmacy (Basel, Switzerland) · 2024Article
- Informing estimates of probability ofAntimicrobial stewardship & healthcare epidemiology : ASHE · 2024Article
- Artificial intelligence applications in the diagnosis and treatment of bacterial infections.Frontiers in microbiology · 2024Review
- Article
- An integrated pipeline for prediction of Clostridioides difficile infection.Scientific reports · 2023Article
- Enhancing Kidney Transplant Care through the Integration of Chatbot.Healthcare (Basel, Switzerland) · 2023Review
- Prospective evaluation of data-driven models to predict daily risk ofInfection control and hospital epidemiology · 2023Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
Abstract
OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80-0.84) and 0.75 ( 95% CI, 0.73-0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425-433.
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