Machine Learning Algorithms Could Accurately Predict C. Diff Infection

A machine-learning method called XGBoost provided the highest overall accuracy, despite being the least complex model.

Several common machine learning algorithms could accurately predict which hospitalized patients will become infected with C. difficile, according to new research published in the American Journal of Infection Control.

The study authors said the findings could support prevention and early diagnosis of infections, as well as more timely implementation of infection control measures to minimize spread. C. diff is the leading cause of hospital-acquired diarrhea and is associated with significant morbidity, mortality, and health care costs, but despite these problems there is currently no gold standard tool to assess individual patients’ risk of infection.

“Our study findings suggest that [machine learning algorithms] could play a significant role in reducing the clinical and economic impact of health care-associated infections such as C. diff by providing early predictions of at-risk patients prior to them developing serious complications,” said Jana Hoffman, PhD, vice president of science at Dascena Inc, in a press release. “These data are consistent with a growing body of evidence that validates artificial intelligence and [machine learning algorithms] as integral components of health care management that can improve patient outcomes and assist time-constrained clinicians in providing the best patient care.”

Researchers used a database of electronic health record patient data from more than 700 hospitals in the United States to train and then systematically evaluate 3 different, classical machine- and deep-learning methods. The team initially assessed various models of each of these methods to determine whether they could effectively predict C. diff infection among hospitalized patients using early inpatient data. They then used a distinct, external dataset to evaluate the generalizability of the best-performing machine learning algorithm models.

The results suggest that machine learning algorithms can predict C. diff infections with excellent discrimination using just the first 6 hours of inpatient data, according to the study authors. Of the 3 methods studied, a machine-learning method called XGBoost provided the highest overall accuracy, despite being the least complex model. XHBoost also demonstrated generalizability by maintaining its predictive performance in an external dataset.

In the other 2 methods evaluated by the researchers, neural networks called Deep Long Short Term Memory and 1-dimensional convolutional neural network also demonstrated high levels of predictive accuracy; however, these methods were less generalizable according to the study authors.

The best-performing models all used similar features to predict C. diff infection among patients, all of which have previously been identified as risk factors. In this study, age was the leading risk factor for C. diff infection, followed by clinical measurements such as sodium, body mass index, white blood cell count, and heart rate. Active treatment with antibiotics or proton pump inhibitors, glycated hemoglobin, and race were also risk factors.

“This study supports earlier research suggesting that [machine learning algorithms] provide reliable infection-risk prediction that can empower clinical teams to implement appropriate infection control measures at earlier time points and thereby improve health care outcomes,” said Linda Dickey, RN, MPH, CIC, FAPIC, 2022 president of the Association for Professionals in Infection Control and Epidemiology, in the press release.

REFERENCE

New data suggest machine learning algorithms can accurately predict C. Diff infection in hospitalized patients. News release. APIC; January 20, 2022. Accessed January 20, 2022. https://apic.org/news/new-data-suggest-machine-learning-algorithms-can-accurately-predict-c-diff-infection-in-hospitalized-patients/