Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul;44(7):1163-1166.
doi: 10.1017/ice.2022.218. Epub 2022 Sep 19.

Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers

Meghana Kamineni  1 , Erkin Ötleş  2   3 , Jeeheh Oh  4 , Krishna Rao  5 , Vincent B Young  5 , Benjamin Y Li  2   4 , Lauren R West  6 , David C Hooper  6   7   8 , Erica S Shenoy  6   7   8 , John G Guttag  1 , Jenna Wiens  4 , Maggie Makar  1   4
Affiliations

Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers

Meghana Kamineni et al. Infect Control Hosp Epidemiol. 2023 Jul.

Abstract

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models' robustness to data-set shifts.

PubMed Disclaimer

Conflict of interest statement

EÖ: patent pending for the University of Michigan for an artificial intelligence-based approach for the dynamic prediction of health states for patients with occupational injuries. KR: Dr. Rao is supported in part from an investigator-initiated grant from Merck & Co, Inc.; he has consulted for Bio-K+ International, Inc., Roche Molecular Systems, Inc., Seres Therapeutics, Inc. and Summit Therapeutics, Inc.

Figures

Figure 1:
Figure 1:. AUROC at MGH and MM in Retrospective and Prospective Evaluations.
The figures on the left show a comparison of AUROC in retrospective and prospective evaluations MGH (upper) and MM (lower). The 95% confidence intervals (CI) for the AUROC are shaded. The figures on the right show a monthly AUROC comparison at MGH (upper) and MM (lower). The 95% CI for the AUROC are represented by error bars.
Figure 2:
Figure 2:. Confusion Matrices at MGH and MM in Retrospective and Prospective Evaluations.
The figures on the left display confusion matrices, sensitivity, specificity, and positive predictive values for retrospective evaluations at MGH (upper) and MM (lower). The figures on the right display the same metrics for prospective evaluations at MGH (upper) and MM (lower).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019;17:195. - PMC - PubMed
    1. Brajer N, Cozzi B, Gao M, et al. Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission. JAMA Netw Open 2020;3:e1920733. - PubMed
    1. Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med 2020;46:383–400. - PMC - PubMed
    1. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689. - PMC - PubMed
    1. Finlayson SG, Subbaswamy A, Singh K, et al. The Clinician and Dataset Shift in Artificial Intelligence. N Engl J Med 2021;385:283–286. - PMC - PubMed