Determining Risk Factors to Develop a Predictive Model of Incontinence-associated Dermatitis Among Critically Ill...

Critically ill patients with fecal incontinence are at high risk of developing incontinence-associated dermatitis (IAD). Scientific prediction and prevention of IAD are essential.

The purpose of this study was to determine the risk factors for IAD among critically ill patients with fecal incontinence. Based on this information, a predictive risk assessment model was developed to provide research evidence for IAD prevention.

A prospective study was conducted from October 2016 to December 2017. Convenience sampling was used to recruit patients with fecal incontinence treated in intensive care units (ICUs) at Tianjin Medical University General Hospital in China. Trained nurses collected demographic data (age, gender, and ICU type), data related to fecal incontinence (Perineal Assessment Tool [PAT] scores, bowel movement frequency, and stool traits per the Bristol Stool Scale), and clinical data (length of ICU hospitalization, body temperature, diabetes history, hypertension history, consciousness, nutrition support, oxygen supply, number of antibiotic species, sedative use, and albumin levels) from participants and their medical records. The PAT was used to assess patient risk of developing IAD, and the Bristol Stool Scale was used to assess patient stool traits. Names were coded anonymously, and data were entered from paper-and-pencil questionnaires into a software program for statistical analysis. Univariate analysis and multivariate logistic regression were performed to identify risk factors for IAD. A predictive risk factor model was established using a receiver operating characteristic curve.

Among 266 critically ill patients with fecal incontinence (182 male, 84 female; mean age 64.18 ± 17.10), IAD incidence was 65.4%. The use of sedative drugs, coma status, higher PAT score, more frequent bowel movements, and loose stool were found to be independent risk factors for IAD (P <.05). The subsequent risk factor predictive model had a sensitivity and specificity of 99.4% and 96.7%, respectively, and the agreement rate was 98.1%.

The identified risk factors and subsequent predictive model may contribute to timely identification and quantitative risk assessment of IAD among critically ill patients. Additional quantitative research could provide a scientific basis for the development of specific preventive interventions.