Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model

Abstract

Background Surgery-related pressure injury (SRPI) is a serious problem in patients who undergo cardiovascular surgery. Identifying patients at a high risk of SRPI is important for clinicians to recognize and prevent it expeditiously. Machine learning (ML) has been widely used in the field of healthcare and is well suited to predictive analysis. Purpose The aim of this study was to develop an ML-based predictive model for SRPI in patients undergoing cardiovascular surgery. Methods This secondary analysis of data was based on a single-center, prospective cohort analysis of 149 patients who underwent cardiovascular surgery. Data were collected from a 1,000-bed university-affiliated hospital. We developed the ML model using the XGBoost algorithm for SRPI prediction in patients undergoing cardiovascular surgery based on major potential risk factors. Model performance was tested using a receiver operating characteristic curve and the C-index. Results Of the sample of 149 patients, SRPI developed in 37, an incidence rate of 24.8%. The five most important predictors included duration of surgery, patient weight, duration of the cardiopulmonary bypass procedure, patient age, and disease category. The ML model had an area under the receiver operating characteristic curve of 0.806, which indicates that the ML model has a moderate prediction value for SRPI. Conclusions Applying ML to clinical data may be a reliable approach to the assessment of the risk of SRPI in patients undergoing cardiovascular surgical procedures. Future studies may deploy the ML model in the clinic and focus on applying targeted interventions for SRPI and related diseases.

Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital

“…As we all know, the traditional XGBoost algorithm aims to reduce the overall error, so it pays more attention to the classification and prediction performance of most class samples in the process of model learning, which will lead to the insufficient training of the classification performance of a few class samples [ 28 , 29 ]. In the problem of length-of-stay prediction, this will also affect the prediction effect of the model for the allocation of hospital beds with relatively less frequency but more serious practical impact.…”

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

“…Studies included in the review ranged from 2011 to 2020, one being from 2011, one from 2015, two from 2018, one from 2019, and three from 2020, of which three were from China, two from USA, one from Mexico, and one from Thailand, as can be seen in Table 4. These studies are aimed at recognizing body posture in bed (e.g., [6,7,11,12]) and identifying potential risk of developing pressure ulcers (e.g., [8][9][10]) and providing some type of alerts or recommendations.…”

“…Data analysis was performed using distinct algorithms. According to Tables 5 and 6, neural networks were used in two studies [8,11], support vector machines (SVM) were used in three studies [6,7,11], and XGBoost [9], RF [10], DT [11] and deep learning [12] were each used in one study. Some studies did not describe how they used the algorithm output, although other studies referred alerts or warnings to caregivers to intervene when the patient was absent or when he or she stayed in the same position for too long.…”

“…Acquiring this data usually depends exclusively on human intervention. Recently, researchers have been exploring ways to integrate machine learning (ML) and the internet of things (IoT) into healthcare systems, to produce indicators/alerts useful to healthcare professionals [6][7][8][9][10][11][12]. Healthcare professionals cannot care for PU patients based only on predisposing factors, nor based only on humanless data.…”

“…Healthcare professionals cannot care for PU patients based only on predisposing factors, nor based only on humanless data. Posture recognition can identify when localized parts of the body are under continuous pressure, such as when patients are recovering from surgery, so blood supply to those areas is hindered and a pressure sore develops consequently [2,[5][6][7][8][9][10][11][12]. For example, after identifying that a bedridden pressure ulcer patient is in the same position for a specific amount of time, based on sensors attached in the mattress (humanless data acquisition), the adequate time to change his position may differ based on gender, age, Informatics 2021, 8, 76 2 of 13 comorbidities, and other aspects (human intervention data acquisition).…”