{
  "abstract": "Objectives To review the application of prediction models and risk factors identified by prediction models for invasive fungal infection (IFI) in children, and assess model performance, methodological rigour and applicability.Design This is a systematic review of diagnostic prediction models and a meta-analysis of the risk factors. This study was registered on PROSPERO and performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis and Prediction model risk of bias assessment tool.Data sources PubMed, Embase (Ovid), Medline, Cochrane Library and four Chinese Databases were searched on 10 Mar 2025.Eligibility criteria We included original studies that developed diagnostic prediction models for IFI in children and excluded the informal records.Data extraction and synthesis Odds ratio (OR) with 95% confidence interval (CI) was calculated for risk factors, and a random-effects meta-analysis was applied to factors reported in at least two studies. For prediction models, a descriptive analysis was conducted to summarise model characteristics, model performance and the risk of bias.Results Nine studies were included from 4069 articles. Nine studies developed ten diagnostic prediction models, and logistic regression was the most commonly used method. The predictive performance showed an area under receiver operating curves (AUROC) ranging from 0.76 to 0.95, but meta-analysis of AUROC was not conducted due to heterogeneity. All studies were identified as having a high risk of bias in critical appraisal, particularly in the analysis, mainly due to the lack of validation, as well as the failure to appropriately evaluate model performance and overfitting. Only two of nine studies that developed prediction models used internal or external validation.Conclusions Logistic regression is a common method for predicting IFI in children, although machine learning methods have been popular in prediction models. Our study identified all studies as high risk of bias. To reduce bias, studies should use calibration measures, internal and external validation more frequently, and consider shrinkage methods when developing models.",
  "authors": [
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "West China School of Medicine, Sichuan University, chengdu, China",
        "Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, chengdu, China"
      ],
      "name": "Jianing Liu"
    },
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "West China School of Medicine, Sichuan University, chengdu, China"
      ],
      "name": "Ruonan Gao"
    },
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China"
      ],
      "name": "Liang Huang"
    },
    {
      "affiliations": [
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "Department of Paediatrics, West China Second Hospital, Sichuan University, Chengdu, China"
      ],
      "name": "Yu Zhu"
    },
    {
      "affiliations": [
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "Department of Paediatrics, West China Second Hospital, Sichuan University, Chengdu, China",
        "Laboratory of Molecular Translational Medicine, Center for Translational Medicine, Sichuan University, Chengdu, Sichuan, China"
      ],
      "name": "Guo Cheng"
    },
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China"
      ],
      "name": "Linan Zeng"
    },
    {
      "affiliations": [
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "National Drug Clinical Trial institute, West China Second University Hospital, Sichuan University, Chengdu, China"
      ],
      "name": "Qin Yu"
    },
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China"
      ],
      "name": "Hailong Li"
    },
    {
      "affiliations": [
        "Department of Pharmacy/Evidence-Based Pharmacy Center, West China Second University Hospital, Sichuan University",
        "Children’s Medicine Key Laboratory of Sichuan Province, Chengdu, China",
        "NMPA Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, Chengdu, China",
        "Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China",
        "Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, chengdu, China",
        "West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China"
      ],
      "name": "Lingli Zhang"
    }
  ],
  "title": "Systematic review of prediction models and meta-analysis of risk factors for invasive fungal infection in children",
  "uid": "1a75b72d-c96f-5244-a516-aa84bd84cced"
}