김준모 (서울대학교)

한빛사논문

조회133

서울대학교

npj Digit. Med., Aug 24 2024, 7 (1) | https://doi.org/10.1038/s41746-024-01215-4 Deep learning-based prediction of Clostridioides difficile infection caused by antibiotics using longitudinal electronic health records

Junmo Kim^1,7, Joo Seong Kim^2,7, Sae-Hoon Kim³, Sooyoung Yoo⁴, Jun Kyu Lee² & Kwangsoo Kim^5,6

¹Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.

²Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea.

³Division of Allergy & Clinical Immunology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

⁴Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

⁵Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea.

⁶Department of Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea.

⁷These authors contributed equally: Junmo Kim, Joo Seong Kim.

Corresponding authors

Correspondence to Jun Kyu Lee or Kwangsoo Kim.

Abstract

Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and colitis. It is recognized as one of the most significant hospital-acquired infections. Although CDI can develop severe complications and spores of Clostridioides difficile can be transmitted by the fecal-oral route, CDI is occasionally overlooked in clinical settings. Thus, it is necessary to monitor high CDI risk groups, particularly those undergoing antibiotic treatment, to prevent complications and spread. We developed and validated a deep learning-based model to predict the occurrence of CDI within 28 days after starting antibiotic treatment using longitudinal electronic health records. For each patient, timelines of vital signs and laboratory tests with a 35-day monitoring period and a patient information vector consisting of age, sex, comorbidities, and medications were constructed. Our model achieved the prediction performance with an area under the receiver operating characteristic curve of 0.952 (95% CI: 0.932-0.973) in internal validation and 0.972 (95% CI: 0.968-0.975) in external validation. Platelet count and body temperature emerged as the most important features. The risk score, the output value of the model, exhibited a consistent increase in the CDI group, while the risk score in the non-CDI group either maintained its initial value or decreased. Using our CDI prediction model, high-risk patients requiring symptom monitoring can be identified. This could help reduce the underdiagnosis of CDI, thereby decreasing transmission and preventing complications.

논문정보

형식Research article
게재일2024년 08월 (BRIC 등록일 2024-08-28)
연구진국내 연구진
분야 바이오·의료융합 > 바이오·의료융합 기타

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