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Development of a Nomogram for Predicting Hospital-Acquired Pressure Injuries Risk in Cardiovascular Intensive Care
Hao-Yue Li1,2,3,4,5,6, Yan Zhang1,2,3,4,5,6, Xiao-Lan Shui1,2,3,4,5,6
1Hao-Yue Li , BN, CICU, Renmin Hospital of Wuhan University, Wuhan, Republic of China.
Purpose:
The purpose of this study was to establish a nomogram model for predicting the hospital-acquired pressure injury (HAPI) occurrences in patients receiving care in a cardiovascular intensive care unit (CICU).
Design:
Comparison Cohort study.
Subjects And Setting:
A review of medical records identified 260 patients that were randomly divided into 2 groups. One group provided a dataset for an AI program to determine the nomogram, (n = 208) and a second group (n = 52) used to test the predictive power of the algorithm. The study setting was Wuhan University, located in central China.
Methods:
We examined the electronic medical records of patients cared for in our CICU between January 2023 and June 2023. Factors associated with HAPI occurrences were identified using univariate and multivariate logistic regression analyses, and the screened indicators were integrated into a nomogram. The effectiveness of the nomogram was evaluated and verified using receiver operating characteristic curve and decision curve analysis.
Results:
Univariate and multivariate logistic regression analysis showed that vasopressor use (OR = 2.54, 95% confidence interval [CI] = 1.18-5.46, P = .017), B-type natriuretic peptide (OR = 2.85, 95% Cl = 1.35-6.03, P = .006), elevated lactic acid levels (OR = 12.18, 95% Cl = 4.96-29.95, P < .001), elevated procalcitonin levels (OR = 1.22, 95% Cl = 1.02-1.46, P = .033), and low albumin levels (OR = 0.84, 95% Cl = 0.76-0.93, P < .001) emerged as independent risk factors for HAPI occurrences in the CICU. Based on these factors, we established a nomogram for predicting the risk of HAPI risk in patients cared for in a CICU. The nomogram was well calibrated and showed good discriminative ability (AUC = 0.868).
Conclusion:
The nomogram model developed in this study demonstrated good predictive capability for predicting CICU patients at risk of developing HAPIs. Findings from this study also have the potential to guide preventive interventions.