Artificial Neural Network: A Method for Prediction of Surgery-Related Pressure Injury in Cardiovascular Surgical Patients

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Abstract

PURPOSE:

The aim of this study was to build an artificial neural network (ANN) model for predicting surgery-related pressure injury (SRPI) in cardiovascular surgical patients.

DESIGN:

Prospective cohort study.

SUBJECTS AND SETTING:

One hundred forty-nine patients who had cardiovascular surgery were included in the study. This study was conducted in a 1000-bed teaching hospital in Eastern China where 250 to 350 cardiac surgeries are performed each year.

METHODS:

We performed a prospective cohort study among consecutive patients undergoing cardiovascular surgery between January and December 2015. The ANN model was built based on possible SRPI risk factors. The model performance was tested by a receiver operating characteristic curve and the C-index. A C-index from 0.5 to 0.7 is classified as having low accuracy, 0.7 to 0.9 as having moderate accuracy, and 0.9 to 1.0 as having high accuracy. We also compared the actual SRPI incidences based on the ANN stratification.

RESULTS:

Thirty-seven of 147 patients developed SRPIs, yielding an incidence rate of 24.8% (95% CI, 18.1-32.6). The C-index was 0.815, which showed the ANN model had a moderate prediction value for SRPI. According to the ANN model, the SRPI predicting incidence ranged from 6.4% to 67.7%. Surgery-related pressure injury incidences were significantly different among 3 risk groups stratified by the ANN (P < .05).

CONCLUSION:

We established an ANN model that provides moderate prediction of SRPI in patients undergoing cardiovascular surgical procedures. Identification and additional associated factors should be incorporated into the ANN model to increase its predictive ability.

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