Construction and validation of secondary ectopic risk prediction model for PICC catheters in preterm infants based on random forest algorithm.

Objective:To investigate the key risk factors for secondary ectopics of catheter after central venous catheter insertion (PICC) through peripheral veins, and to construct and validate a risk prediction model based on the random forest algorithm. Methods:The data of 590 preterm infants hospitalized i...

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Publicado en:Chinese Nursing Research Vol. 40; no. 9; pp. 1481 - 1488
Autores principales: GUO, Yongqin, DOU, Yingying, LI, Jianli, WANG, Li, HU, Jing
Formato: research tables/charts Journal Article
Publicado: Chinese Nursing Research Editorial Office May2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2026
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      pub: Chinese Nursing Research Editorial Office
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        10.12102/j.issn.1009-6493.2026.09.007
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        atl: Construction and validation of secondary ectopic risk prediction model for PICC catheters in preterm infants based on random forest algorithm.
      aug:
        au:
          GUO, Yongqin
          DOU, Yingying
          LI, Jianli
          WANG, Li
          HU, Jing
        affil: Changzhi Maternal and Child Health Care Hospital, Shanxi 046000 China
      sug:
        subj:
          Catheter-Related Infections Risk Factors
          Peripherally Inserted Central Catheters Adverse Effects
          Prediction Models
          Risk Assessment
          Infant, Premature
          Reproducibility of Results
          Algorithms
          Human
          Random Forest
          Infant, Hospitalized
          China
          Tertiary Health Care China
          Catheterization, Peripheral Adverse Effects
          Retrospective Design
          Random Assignment
          Sensitivity and Specificity
          ROC Curve
          Confidence Intervals
          Child, Preschool
          Infant
          Child, Preschool: 2-5 years
          Infant: 1-23 months
      ab: Objective:To investigate the key risk factors for secondary ectopics of catheter after central venous catheter insertion (PICC) through peripheral veins, and to construct and validate a risk prediction model based on the random forest algorithm. Methods:The data of 590 preterm infants hospitalized in the department of neonatology of a tertiary specialty hospital in Shanxi province with PICC catheterization were retrospectively collected, and the data were randomly divided into training set (n=413) and validation set (n=177) in a ratio of 7:3. Eighteen clinical indicators were selected as predictors for whether secondary ectopic of the catheter occurred at the tip after PICC catheterization in preterm infants. Based on the random forest algorithm, a risk prediction model for secondary ectopic ectopic of PICC catheters in preterm infants was constructed, the importance of risk factors was ranked, and a SHAP plot was plotted to explain the contribution of each variable to the prediction of model output. The confusion matrix analysis of the model was carried out using the validation set data, and the prediction effect of the model was evaluated by using the accuracy, sensitivity, specificity and receiver operating characteristic (ROC) curves. Results: The incidence of secondary ectopic of PICC catheter in 413 preterm infants in the training set was 39.0%, and the key factors screened by the random forest algorithm were the first X-ray localization result, secondary catheter fixation, mechanical ventilation, and weight gain rate, respectively. In the validation set, the accuracy of the risk prediction model was 81.92%, the sensitivity was 71.21%, the specificity was 88.29%, the positive predictive value was 78.33%, the negative predictive value was 83.76%, and the AUC value was 0.870 (95%CI: 0.817-0.923). Conclusions: The risk prediction model constructed based on the random forest algorithm demonstrates good predictive performance. It could potentially provide a scientific basis for the safe and efficient clinical use of PICC in premature infants.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: Chinese
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