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Risk Prediction Model for Incontinence-Associated Dermatitis in Critically Ill Patients: A Systematic Review and

Tao Li1, Dan Yao1, Jing Li1

  • 1Department of Intensive Care Unit, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Nursing in Critical Care
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

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Risk prediction models for incontinence-associated dermatitis (IAD) in critically ill patients show good accuracy but suffer from methodological flaws. Future research must prioritize high-quality data and validation for improved preventive care in ICUs.

Area of Science:

  • Critical Care Medicine
  • Dermatology
  • Health Informatics

Background:

  • Incontinence-associated dermatitis (IAD) is a common and serious skin issue in critically ill patients, increasing pain and infection risk.
  • Existing risk prediction models for IAD lack clear methodological quality, performance evaluation, and clinical utility.
  • A systematic review and evaluation of these models are needed to guide prevention strategies.

Purpose of the Study:

  • To systematically review and meta-analyze existing risk prediction models for IAD in critically ill adult ICU patients.
  • To critically appraise the methodological rigor of these prediction models.
  • To summarize the predictive performance of identified models.

Main Methods:

  • Searched multiple databases (PubMed, Embase, Cochrane, Web of Science, CINAHL, Chinese databases) up to January 2026.
Keywords:
incontinence‐associated dermatitisintensive care unitrisk prediction modelsystematic review

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  • Included studies developing or validating multivariable prediction models for IAD in adult ICU patients.
  • Assessed risk of bias using the PROBAST tool and performed meta-analysis of pooled AUC values.

    • Main Results:

    • 14 studies with 14 prediction models were included after screening 834 records.
    • Models showed good to excellent discrimination (AUC 0.810–0.993), with a pooled AUC of 0.88.
    • Common predictors included PAT score, stool frequency, age, and serum albumin; however, all studies had a high risk of bias.

    Conclusions:

    • Current IAD risk prediction models for critically ill patients demonstrate strong discriminative ability but are hampered by methodological weaknesses.
    • Future models require high-quality prospective data and robust external validation for clinical utility.
    • Validated tools are essential for early identification of high-risk patients and enhancing preventive care in ICUs.