Molecular markers emerged as the strongest predictors, whereas conventional clinical variables showed limited value, and the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery is highlighted.
Abstract
Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.
ABSTRACT Wound‐healing complications (WHCs) remain a significant challenge in soft tissue sarcoma (STS) surgery, contributing to increased morbidity, prolonged hospitalisation, and impaired functional outcomes. This study aimed to identify predictors of WHC development in STS patients to improve therapeutic strategies and patient recovery. We conducted a single‐institution, retrospective analysis of 318 STS patients treated between April 2019 and June 2021. Variables assessed included demographic data, tumour characteristics, treatment modalities, surgical interventions, and follow‐up outcomes. Statistical analyses used chi‐square tests, Kaplan–Meier estimates, and multivariate regression. WHCs occurred in 30.8% of patients. Significant negative predictors included high tumour grade (p = 0.018), presence of metastasis (p < 0.001), bone tissue resection (p = 0.005), number of revision procedures (p < 0.001), secondary wound closure (p < 0.001), and tumour relapse frequency (p = 0.012). WHCs were also associated with increased risk of death (p = 0.004) and longer initial hospitalisation (p < 0.001). Chemotherapy showed a protective effect when stratified by tumour grade. Patients with high‐grade tumours and multiple surgical interventions represent a high‐risk group for WHC development. These findings highlight the importance of ongoing treatment reevaluation and suggest that chemotherapy may offer beneficial outcomes in selected cases.
Luisa Kriens, Jendrik Hardes, Wiebke K. Guder et al.· International Wound Journal· 0 citations
AI-based modeling offers a robust, data-driven approach for predicting dental implant success by integrating multifactorial clinical parameters and incorporating such models into clinical workflows can enhance patient-specific risk assessment and improve long-term implant outcomes.
V. Veeraraghavan, A. Jebin A, Isha Dusane et al.· International Arab Journal o...· 0 citations
Chronic wounds impose immense burdens and suffering on patients, with healing governed by a complex blend of physiological, social, and psychological components. This study aimed to comprehensively evaluate risk factors affecting chronic wound healing by integrating traditional statistical methods with machine learning techniques.
We retrospectively collected demographic and clinical data from 232 chronic wound patients treated at our hospital. The Mann-Whitney U test was employed for univariate comparisons of continuous variables, while categorical variables were analyzed using the chi-square or Fisher's exact test, and ordinal variables were assessed with the Cochran-Armitage trend test. Statistically significant parameters were subsequently incorporated into binary logistic regression and multilayer perceptron neural network (MLP) models.
Univariate analysis identified seven factors significantly associated with wound prognosis: number of concurrent wounds (
P
= 0.020), wound size (
P
= 0.043), pain score (
P
= 0.005), intervention modalities (
P
for trend = 0.005), NSAIDs use (
P
= 0.010), hemoglobin (
P
= 0.005), and albumin (
P
= 0.001). In the multivariable logistic regression model (event = good prognosis), albumin (adjusted OR=1.101, 95% CI: 1.024 -1.183,
P
= 0.010) and intervention with two modalities (OR=4.775, 95% CI: 1.215 -18.762,
P
= 0.025) or three modalities (OR=6.360, 95% CI: 1.404 -28.797,
P
= 0.016) were independently associated with good prognosis. The MLP model's AUC was recorded at 0.771 (95% CI: 0.710 -0.832), with wound size (100%) and albumin (69.3%) showing the highest normalized importance. The DeLong test showed no significant difference between the logistic regression and MLP models (
P
= 0.907).
Albumin levels and multimodal interventions were independently associated with favorable chronic wound prognosis, while wound size and albumin emerged as the most influential predictors in the neural network model. These findings underscore the importance of nutritional status and comprehensive wound management, although causal inference is limited by the retrospective design.
Fengmei Zhang, Jie Cui, Hui Li et al.· Frontiers in Health Services· 0 citations
The LASSO-based GBM model demonstrated stable predictive performance and acceptable clinical utility and was selected as the final model for predicting postoperative recurrence of anal fistula.
Yun-Hao Zhou, Da-Wei Wang, Min Tang et al.· Frontiers in Surgery· 0 citations
RF-based models can accurately and equitably predict perioperative complications in diverse spine surgery contexts, supporting personalized counseling, targeted monitoring, and optimized resource allocation.
Andrea Campagner, Francesco Langella, P. Bellosta-López et al.· European spine journal· 0 citations
PURPOSE
Early identification of anastomotic leakage (AL) is critical for safe discharge within enhanced recovery pathways. This study developed and prospectively validated a machine learning (ML) model to predict AL using 24-h postoperative inflammatory biomarkers.
METHODS
We analyzed 1,961 patients undergoing elective minimally invasive colorectal resection (2012-2025). Five ML architectures were developed using a 70/15/15 split. The Regularized Logistic Regression (RLB) model was selected and locked with a pre-specified threshold (0.1258). Global variable importance and directionality were assessed via SHAP analysis. Prospective temporal validation was performed on 250 consecutive patients (February 2024 - December 2025).
RESULTS
AL incidence was 9.8% in the development cohort. The RLB model achieved high discrimination (AUCPR 0.859; AUC-ROC 0.819). Postoperative C-reactive protein (CRP) and the Systemic Inflammation Response Index (SIRI) at 24 h were the strongest predictors. During temporal validation, despite a 70% relative reduction in AL incidence (2.8%), the model maintained a robust negative predictive value (NPV) of 97.9% (95% CI 95.1-99.1% and an AUC of 0.73 (95% CI 0.54-0.92). Calibration was near-optimal (slope 0.987, intercept 0.505). Decision curve analysis demonstrated superior net clinical benefit across risk thresholds of 5-20%.
CONCLUSIONS
ML-based integration of early inflammatory biomarkers provides a reliable "safety filter" for postoperative surveillance. The high NPV supports objective decision-making for early discharge, even in changing clinical environments with decreasing complication rates.
J. Martín-Arévalo, Andreia Guimaraes, Irina Palomo-Lopez et al.· Surgical Endoscopy· 0 citations
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