Evaluation of a Previously Developed Predictive Model for Infective Endocarditis in 320 Patients Presenting with Fever at 4 Centers in Japan Between January 2018 and December 2020

BACKGROUND: In our previous single-center study, we developed an infective endocarditis (IE) prediction model among patients with undiagnosed fever (UF) based on 5 factors that can be obtained on admission: ambulance transfer, presence of cardiac murmur or pleural effusion, blood neutrophil percentage, and platelet count. The present study aimed to retrospectively evaluate the prediction model for IE in 320 patients presenting with fever at 4 university hospitals in Japan from January 2018 to December 2020. MATERIAL/METHODS: Patients aged ≥20 years admitted to 4 hospitals with I-330 (IE) or R-50-9 (UF) according to the International Statistical Classification of Diseases and Related Health Problems-10 were enrolled. More than 2 physicians at each hospital reviewed the patient diagnoses using the modified Duke criteria, allocating “definite IE” to IE group (n=119) and “non-definite IE” to UF group (n=201). Five factors on admission were analyzed by multivariate logistic regression. The discriminative ability and calibration of the model were evaluated using the area under the curve (AUC) and the shrinkage coefficient, respectively. RESULTS: A total of 320 patients were enrolled. The odds ratios (95% confidence intervals) were as follows: ambulance transfer 1.81 (0.91–3.55); cardiac murmur 13.13 (6.69–27.36); pleural effusion 2.34 (0.62–2.42); blood neutrophil percentage 1.09 (1.06–1.14); and platelet count 0.96 (0.93–0.99). The AUC was 0.783 (0.732–0.834) with a shrinkage coefficient of 0.961. CONCLUSIONS: The IE prediction model is useful to estimate the probability of IE immediately after admission for fever in patients aged ≥20 years.