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Objective <p>To identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI.</p> Methods <p>Blood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature &gt; 38&#xa0;°C) or hypothermia (body temperature &lt; 36&#xa0;°C), chills, hypotension, oliguria, or high lactic acid levels. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. Independent risk factors identified by multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.</p> Results <p>A total of 195 bacterial BSI patients were enrolled. gender, Acute Physiology and Chronic Health Evaluation-II (APACHEII) score, nCD64 index, erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), lymphocyte count, T-cell count, B-cell count, NK-cell count, Interleukin-8 (IL-8), Interleukin-10 (IL-10) and Interleukin-17A(IL-17A) were independent risk factors for BSI. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.836 (95% CI 0.653–0.874) in the training set and 0.871 (95% CI 0.793–0.861) in the validation set. The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI.</p> Conclusion <p>Overall, this study successfully identified five risk factors for BSI patients and developed a nomogram, offering individualized diagnosis and risk assessment to predict bacterial BSI in infected patients.</p>

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Development and validation of a nomogram to predict bacterial blood stream infection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月20日

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  • Yu Huan Jiang,
  • Rui Zhao,
  • Yun Xue Bai,
  • Hui Ming Li,
  • Jun Liu,
  • Shi Xuan Wang,
  • Xing Xie,
  • Yang Liu,
  • Qiang Chen

摘要

Objective

To identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI.

Methods

Blood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature > 38 °C) or hypothermia (body temperature < 36 °C), chills, hypotension, oliguria, or high lactic acid levels. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. Independent risk factors identified by multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.

Results

A total of 195 bacterial BSI patients were enrolled. gender, Acute Physiology and Chronic Health Evaluation-II (APACHEII) score, nCD64 index, erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), lymphocyte count, T-cell count, B-cell count, NK-cell count, Interleukin-8 (IL-8), Interleukin-10 (IL-10) and Interleukin-17A(IL-17A) were independent risk factors for BSI. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.836 (95% CI 0.653–0.874) in the training set and 0.871 (95% CI 0.793–0.861) in the validation set. The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI.

Conclusion

Overall, this study successfully identified five risk factors for BSI patients and developed a nomogram, offering individualized diagnosis and risk assessment to predict bacterial BSI in infected patients.