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. 2024 Mar 4:14:1348896.
doi: 10.3389/fcimb.2024.1348896. eCollection 2024.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit

Zhili Qi  1 , Lei Dong  1 , Jin Lin  1 , Meili Duan  1
Affiliations

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit

Zhili Qi et al. Front Cell Infect Microbiol. .

Abstract

Purpose: This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU).

Patients and methods: Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility.

Results: The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness.

Conclusion: The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Keywords: bacteremia; bloodstream infections; critically ill; early diagnosis; intensive care unit; nomogram; prediction model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer ZZ declared a past co-authorship with the authors JL and MD to the handling editor.

Figures

Figure 1
Figure 1
Flow diagram of blood culture data processing. ICU, Intensive Care Unit; ICU LOS, Length of stay in the ICU. Age<18, ICU LOS <48.
Figure 2
Figure 2
Nomogram to predict the outcomes of blood culture.
Figure 3
Figure 3
ROC curves of the Training dataset (A), Internal validation dataset (B), eICU validation dataset (C) and BFH validation dataset (D).
Figure 4
Figure 4
Calibration curves of the nomogram in the Training dataset (A), Internal validation dataset (B), eICU validation dataset (C) and BFH validation dataset (D).
Figure 5
Figure 5
Decision curve analysis (DCA) for the nomogram in theTraining dataset (A), Internal validation dataset (B), eICU validation dataset (C) and BFH validation dataset (D).
Figure 6
Figure 6
The clinical impact curve in the Training dataset (A), Internal validation dataset (B), eICU validation dataset (C) and BFH validation dataset (D).
See this image and copyright information in PMC

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