Development of a Clinical Prediction Model for Infective Endocarditis Among Patients with Undiagnosed Fever: A Pilot Case–Control Study

Abstract

Shun Yamashita, Masaki Tago, So Motomura, Satsuki Oie, Hidetoshi Aihara, Naoko E Katsuki, Shu-ichi Yamashita Department of General Medicine, Saga University Hospital, Saga, JapanCorrespondence: Shun YamashitaDepartment of General Medicine, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, JapanTel [phone]Fax [phone]Email [email protected]: Infective endocarditis (IE) may be diagnosed as fever of unknown origin due to its delusively non-descriptive clinical features, especially in outpatient clinics. Our objective is to develop a prediction model to discriminate patients to be diagnosed as “definite” IE from “non-definite” by modified Duke criteria among patients with undiagnosed fever, using only history and results of physical examinations and common laboratory examinations.Patients and Methods: The study was a single-center case–control study. Inpatients at Saga University Hospital diagnosed with IE from 2007 to 2017 and patients with undiagnosed fever from 2015 to 2017 were enrolled. Patients diagnosed with definite IE according to the modified Duke criteria, except those definitely diagnosed with other disorders responsible for fever, were allocated to the IE group. Patients without IE among those defined as non-definite according to the modified Duke criteria were allocated to the undiagnosed fever group. We developed a prediction model to pick up patients who would be “definite” by modified Duke criteria, which was subsequently assessed by area under the curve (AUC).Results: A total of 144 adult patients were included. Of these, 59 patients comprised the IE group. We developed the prediction model using five indicators, including transfer by ambulance, cardiac murmur, pleural effusion, neutrophil count, and platelet count, with a sensitivity 84.7%, a specificity 84.7%, an AUC 0.893 (95% confidence interval 0.828– 0.959), a shrinkage coefficient 0.635, and a stratum-specific likelihood ratio 0.2– 50.4.Conclusion: Our prediction model, which uses only indicators easy to gain, facilitates prediction of patients with IE. These indicators can be acquired even at common hospitals and clinics, without requiring advanced medical equipment or invasive examinations.Trial Registration Number: UMIN000041344.Keywords: infective endocarditis, undiagnosed fever, case–control study, prediction model, area under the curve

Keywords

• infective endocarditis
• undiagnosed fever
• case–control study
• prediction model
• area under the curve