Evidence map›Paper›PMID 41840501›Full record

Observational studyBMC cardiovascular disorders2026

A new diagnostic prediction model for infective endocarditis based on the 2023 duke-international society for cardiovascular infectious disease criteria: a multicenter observational study.

Shun Yamashita, Ryosuke Osako, Takanori Yamaguchi, Yoshinori Tokushima, Yukinori Harada, Taiju Miyagami, Yosuke Sasaki, Yasutomo Oda, Masaki Tago

Abstract readMulticenter StudyObservational StudyValidation Study
In one paragraph

Observational study in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Shun YamashitaDepartment of General Medicine, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan. sy.hospitalist.japan@gmail.com.
Ryosuke OsakoDepartment of Cardiovascular Medicine, Saga University, Saga, Japan.
Takanori YamaguchiDepartment of Cardiovascular Medicine, Saga University, Saga, Japan.
Yoshinori TokushimaDepartment of General Medicine, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan.
Yukinori HaradaDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Tochigi, Japan.
Taiju MiyagamiDepartment of General Medicine, Faculty of Medicine, Juntendo University, Tokyo, Japan.
Yosuke SasakiDepartment of General Medicine and Emergency Care, Toho University School of Medicine, Tokyo, Japan.
Yasutomo OdaEducation and Research Center for Community Medicine, Faculty of Medicine, Saga University, Saga, Japan.
Masaki TagoDepartment of General Medicine, Saga University Hospital, 5-1-1 Nabeshima, Saga, 849-8501, Japan.

Funding

Japan Society for the Promotion of Science 23K14739
6 · The paper itself

Abstract

backgroundInfective endocarditis (IE) remains a diagnostically challenging disease with diverse symptoms. Whether IE is suspected frequently depends on clinical judgement. A reliable model to estimate the likelihood of IE at presentation across clinical departments is needed; however, no such model is currently available. We aimed to develop such a diagnostic prediction model using objective and reproducible variables and to validate its performance.

methodsWe included inpatients aged ≥ 20 years who had either a diagnosis code for IE or for undiagnosed fever during hospitalization. The model was developed using data from a single university hospital between 2007 and 2017 (derivation cohort) and validated with data from four university hospitals between 2018 and 2020 (validation cohort). IE was diagnosed according to the 2023 Duke-International Society for Cardiovascular Infectious Diseases (ISCVID) criteria. Variables were selected using the Boruta algorithm and Least Absolute Shrinkage and Selection Operator regression. Multivariable logistic regression analysis was used to estimate odds ratios and 95% confidence intervals and to construct the model. Model performance was assessed in both cohorts using the area under the curve (AUC). In the validation cohort, calibration was also assessed using the calibration slope, Hosmer–Lemeshow test, and stratified likelihood ratio.

resultsThe derivation and validation cohorts included 105 (46 IE) and 286 (106 IE) patients, respectively. Predictors in the final model were the presence of a cardiac murmur, log-transformed platelet count, neutrophil percentage, presence of pleural effusion, and a quick sequential [sepsis-related] organ failure assessment score ≥ 2. The AUC was 0.918 in the derivation cohort. In the validation cohort, the AUC was 0.859, with a Hosmer– Lemeshow test p-value of 0.246 and a calibration slope of 0.759. The stratified likelihood ratio ranged from 0.04 to 9.71 and increased with higher model scores.

conclusionsThis model showed high discrimination and good calibration using objective variables that are readily available early after admission. Furthermore, this is the first model to predict IE based on the 2023 Duke-ISCVID criteria. Further multicenter validation in community hospitals would enhance generalizability.

Indexed as

Clinical Decision RulesDecision Support TechniquesEndocarditisAdultAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk Factors2023 Duke-ISCVID criteriaA diagnostic prediction modelInfective endocarditisMachine learningObjective variables

Identifiers

PMID41840501
PMCPMC13104256

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.