DOI: 10.3886/e242986
title: Evaluating Risk Factors That Affect Chronic Wound Healing with Logistic Regression and Neural Network Models

description: Chronic wound impose immense burdens and suffering on patients, and the healing is influenced by a complex interplay of physiological, social, and psychological factors. This study aims to explore risk factors affecting chronic wound healing. We collected demographic variables data from 232 chronic wound patients. The rank-sum test was used to analyze the risk factors that affect chronic wound prognosis. Statistically significant parameters were included in binary logistic regression and multilayer perceptron neural network models to identify independent risk factors for chronic wound prognosis and assess the diagnostic performance of related risk factors in predicting chronic wound outcomes. We found significant differences in education level, number of wounds, size of wounds, intervention methods, non-steroidal drug use, pain, hemoglobin, and albumin affecting chronic wound outcomes (P = 0.016, P = 0.018, P = 0.042, P &lt; 0.001, P = 0.009, P = 0.005, P = 0.004, P = 0.002). In the binary logistic regression model, education level, number of wounds, size of wounds, and intervention methods emerged as independent risk factors (P = 0.048, P = 0.046, P = 0.048, P &lt; 0.001). In the multilayer perceptron neural network model, the accuracy of training set and test set were at 70.6% and 72.2%, and the ROC AUC value was 0.772, with normalized importance of 100% and 83.5% for wound size and albumin, respectively. In conclusion, wound healing is influenced by multiple factors, among which wound size and albumin are the main factors affecting wound healing.
description: not involved
description: Response Rates: Among the 232 patients included in this study, 110 (47.5%) were classified into the poor prognosis group, comprising 63 males and 47 females. The remaining 122 patients (52.5%) were categorized as the good prognosis group, consisting of 67 males and 55 females. The average age of participants in the poor prognosis group was 67.78 years (SD 11.07), while the good prognosis group had a lower average age of 65.61 years (SD 10.68). Hemoglobin levels were also significantly different, with the poor prognosis group averaging 118.70 g/L (SD 21.87) compared to 126.93 g/L (SD 17.34) in the good prognosis group. Additionally, albumin levels revealed a similar trend: the poor prognosis group had an average of 36.48 g/dl (SD 9.88), whereas the good prognosis group had a higher average of 38.08 g/dl (SD 4.90). Pain scores reflected these differences as well, with the poor prognosis group's average score at 2.78 (SD 1.23) compared to 3.21 (SD 1.15) in the good prognosis group. The majority of patients resided in rural areas (n=203, 87.5%), and educational attainment was low, with only 11 individuals (5%) having completed high school or higher; the most prevalent education level was junior high school (n=119, 51.1%). Comorbidities were present in 120 subjects (51.7%). Lifestyle factors included 72 smokers (31.1%) and 61 individuals (26.4%) who consumed alcohol. Furthermore, 92 patients (40.2%) reported a history of non-steroidal drug use for chronic wound pain, and 142 (61.2%) experienced emotional disorders
description: Presence of Common Scales: Pain was evaluated using the numerical rating scale (NRS)
description: 19 parametersage were included: gender, smoking status, alcohol consumption, urban/rural residence, education level, wound pathogenic factors, wound location, number of wounds, duration and size of wounds, presence of wound fistulas, comorbidities, intervention methods, emotional issues, pain levels, non-steroidal drug use, hemoglobin, and serum albumin levels
description: Population of Southwest China. Smallest Geographic Unit: cm²
description: face-to-face interview; telephone interview; The 232 patient data were collected from the medical records of hospitalized or outpatientpatients  at Chongqing University Qianjiang Hospital between January 2023 and June 2024 , encompassing comprehensive patient-level information such as age, gender, smoking status, alcohol consumption, urban/rural residence, education level, wound pathogenic factors, wound location, number of wounds, duration and size of wounds, presence of wound fistulas, comorbidities, intervention methods, emotional issues, pain levels, non-steroidal drug use, hemoglobin, and serum albumin levels.