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Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using
Jiyoun Song1, Kyungmi Woo, Jingjing Shang
1Jiyoun Song, PhD, RN, AGACNP-BC, is Postdoctoral Fellow, Columbia University School of Nursing, New York, NY. Kyungmi Woo, PhD, RN, is Assistant Professor, The Research Institute of Nursing Science, Seoul National University College of Nursing, Republic of Korea. Jingjing Shang, PhD, RN, is Associate Professor, Columbia University School of Nursing, New York, NY. Marietta Ojo, MPH, is Research Assistant, Columbia University Mailman School of Public Health, New York, NY. Maxim Topaz, PhD, RN, is Associate Professor, Columbia University School of Nursing, New York, NY. Acknowledgments: This study is funded by the Eugenie and Joseph Doyle Research Partnership Fund from Visiting Nurses Service of New York and the Intramural Pilot Grant from Columbia University School of Nursing. At the time of data analysis and manuscript development, Jiyoun Song was supported in part by the Agency for Healthcare Research and Quality (R01HS024915), Nursing Intensity of Patient Care Needs and Rates of Healthcare-Associated Infections, and The Jonas Center for Nursing and Veterans Healthcare. Kyungmi Woo was supported by the Comparative and Cost-Effectiveness Research (T32 NR014205) grant through the National Institute of Nursing Research. The authors have disclosed no other financial relationships related to this article. Submitted August 28, 2020; accepted in revised form December 8, 2020.
Objective:
Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate description of a patient's condition by extracting risk factors from clinical notes to build predictive models to identify a patient's risk of wound infection in HHC.
Methods:
The structured data (eg, standardized assessments) and unstructured information (eg, narrative-free text charting) were retrospectively reviewed for HHC patients with wounds who were served by a large HHC agency in 2014. Wound infection risk factors were identified through bivariate analysis and stepwise variable selection. Risk predictive performance of three machine learning models (logistic regression, random forest, and artificial neural network) was compared.
Results:
A total of 754 of 54,316 patients (1.39%) had a hospitalization or ED visit related to wound infection. In the bivariate logistic regression, language describing wound type in the patient's clinical notes was strongly associated with risk (odds ratio, 9.94; P < .05). The areas under the curve were 0.82 in logistic regression, 0.75 in random forest, and 0.78 in artificial neural network. Risk prediction performance of the models improved (by up to 13.2%) after adding risk factors extracted from clinical notes.
Conclusions:
Logistic regression showed the best risk prediction performance in prediction of wound infection-related hospitalization or ED visits in HHC. The use of data extracted from clinical notes can improve the performance of risk prediction models.
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