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<p>Pediatric fungemia in pediatric intensive care units (PICUs) carries high mortality. We evaluated whether the Candida Score, combined with clinical variables, predicts mortality after diagnosis using a prespecified multivariable logistic regression (primary model) and benchmarked discrimination against Random Forest and Gradient Boosting Machine. We analyzed 85 pediatric fungemia cases from a PICU (2016–2020). The prespecified primary model was multivariable logistic regression with predefined covariates; Random Forest and Gradient Boosting Machine were exploratory comparators. Discrimination was evaluated on a held-out test set and by 10-fold cross-validation and bootstrapping. In 85 cases, the median age was 6 months and median weight 4.8&#xa0;kg; 62.4% were male. <i>Candida albicans</i> was the most prevalent species (37.6%). Of the subjects, 39 (45.9%) died and 46 (54.1%) survived. On the held-out test set (<i>n</i> = 17), logistic regression achieved accuracy 0.735 and AUC 0.800. Random Forest achieved AUC 0.861 (precision 1.000; recall 0.778), and Gradient Boosting achieved AUC 0.847 (precision 0.875; recall 0.778). Internal validation (10-fold cross-validation and bootstrap resampling) supported model stability.</p><p>Conclusion Integrating the Candida Score with clinical predictors shows potential for mortality risk stratification after fungemia diagnosis. In this single-center cohort, Random Forest yielded the highest discrimination on the test set. Findings are exploratory and require external validation in larger, multicenter studies before clinical use. </p>

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Machine learning prediction of mortality in pediatric fungemia using the Candida score

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月13日

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  • Khouloud Abdulrahman Al-Sofyani,
  • Ibrahim Hussain Ali Muzaffar,
  • Abdulrahman Mohammedsaeed Baqasi,
  • Saleh Al Fulayyih,
  • Mohammed Shahab Uddin

摘要

Pediatric fungemia in pediatric intensive care units (PICUs) carries high mortality. We evaluated whether the Candida Score, combined with clinical variables, predicts mortality after diagnosis using a prespecified multivariable logistic regression (primary model) and benchmarked discrimination against Random Forest and Gradient Boosting Machine. We analyzed 85 pediatric fungemia cases from a PICU (2016–2020). The prespecified primary model was multivariable logistic regression with predefined covariates; Random Forest and Gradient Boosting Machine were exploratory comparators. Discrimination was evaluated on a held-out test set and by 10-fold cross-validation and bootstrapping. In 85 cases, the median age was 6 months and median weight 4.8 kg; 62.4% were male. Candida albicans was the most prevalent species (37.6%). Of the subjects, 39 (45.9%) died and 46 (54.1%) survived. On the held-out test set (n = 17), logistic regression achieved accuracy 0.735 and AUC 0.800. Random Forest achieved AUC 0.861 (precision 1.000; recall 0.778), and Gradient Boosting achieved AUC 0.847 (precision 0.875; recall 0.778). Internal validation (10-fold cross-validation and bootstrap resampling) supported model stability.

Conclusion Integrating the Candida Score with clinical predictors shows potential for mortality risk stratification after fungemia diagnosis. In this single-center cohort, Random Forest yielded the highest discrimination on the test set. Findings are exploratory and require external validation in larger, multicenter studies before clinical use.