Evidence map›Paper›PMID 40128899›Full record

ArticleEuropean journal of medical research2025

Risk factors and prediction model for carbapenem-resistant organism infection in sepsis patients.

Ronghua Liu, Xiang Li, Jie Yang, Yue Peng, Xiaolu Liu, Chanchan Tian

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Article in European journal of medical research, 2025. 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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ronghua LiuDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China.
Xiang LiDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China.
Jie YangDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China.
Yue PengDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China.
Xiaolu LiuDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China.
Chanchan TianDepartment of Laboratory Medicine, The Second People's Hospital of China Three Gorges University, The Second People's Hospital of Yichang, Third Floor, No. 21, Xiling 1st Road, Yichang, 443000, Hubei, China. 271242178@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt aimed to identify the key risk factors associated with carbapenem-resistant organism (CRO) infections in septic patients, and subsequently develop a nomogram and assess its predictive accuracy.

methodsThe study population comprised adult critically ill patients with sepsis, drawn from the MIMIC-IV 2.0 data set. The data were split into a training set and a validation set at a 7:3 ratio. Independent predictors were identified using both univariate and multivariate logistic regression models, followed by the construction of a nomogram. The predictive performance of the model was evaluated using the C-index, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve.

resultsWe enrolled 8814 patients, with 529 (6%) CRO-infected and 8285 (94%) non-CRO-infected. Using risk factors such as age, gender, laboratory values (WBC_max, Creatinine_max, BUN_max, Hemoglobin_min, Sodium_max), and medical conditions (COPD, hypoimmunity, diabetes), along with medications (meropenem, ceftriaxone), we developed a predictive model for CRO infection in septic patients. The model demonstrated good performance, with AUC values of 0.747 for the training set and 0.725 for the validation set. The calibration curve indicates that predicted outcomes align well with observed outcomes. The clinical decision curve results indicate that the nomogram prediction model has a high net benefit, which is clinically beneficial.

conclusionsThe nomogram we have developed for predicting the risk of CRO infection in sepsis patients is reasonably accurate and reliable. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Anti-Bacterial AgentsCarbapenemsSepsisAdultAgedFemaleHumansMaleMiddle AgedNomogramsRisk FactorsROC CurveAnti-Bacterial AgentsCarbapenemsCROMIMIC-IVNomogramSepsis

Identifiers

PMID40128899
PMCPMC11934461

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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.