Introduction: Skin tear (ST) will prolong the hospitalisation time of an older person, increase the cost of medical expenses and the difficulty in care for nursing staff, and seriously affect the quality of life of the older person. Early identification and intervention of the elderly at risk of ST are key factors in preventing the occurrence of ST in older persons. At present, risk factors for ST in older persons have not been systematically evaluated, let alone summarised to analyse risk factors for ST in older persons. Therefore, this systematic review and meta-analysis aims to synthesise existing research on risk factors for ST in older populations. Methods and analysis: The protocol is being reported by the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement. On 17 September 2023, we will start literature search in PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, Medline, Chinese Scientific Journal Database, Wan Fang Data Knowledge Service Platform, China National Knowledge Infrastructure, and Chinese Biomedical Literature Database. The language of the included literature is Chinese or English. Using RevMan V.5.4 software, we will perform a systematic review and meta-analysis of the final set of included studies to synthesise the data and draw meaningful conclusions. The Newcastle-Ottawa Quality Assessment Scale and the Agency for Healthcare Research and Quality will be used to assess the quality of the literature. The I ² test will be used to test heterogeneity. Ethics and dissemination: Ethical approval is not needed for this systematic review, as the study will not directly use information from human participants, and the data we use will be extracted from original studies. This systematic review and meta-analysis has been registered at the International Prospective Register of Systematic Reviews (PROSPERO). Once the systematic review and meta-analysis have been completed, we will publish our study in an academic journal. PROSPERO registration number: CRD42023460810.

Successful placement of nasoenteric tubes in critically ill patients remains challenging. Anatomical parameters such as gastropyloric angle and tube-to-iliac-crest distance may influence success, but their combined predictive value has not been systematically evaluated using machine learning approaches. This study aimed to investigate the independent predictive roles of the gastropyloric angle and the vertical distance from the lowest point of the nasojejunal tube to the highest point of the iliac crest for successful nasojejunal tube placement, and to develop and compare machine learning models for individualized risk prediction. A retrospective study was conducted on 287 critically ill patients who underwent nasojejunal tube placement. Eight candidate predictors were analyzed, including the gastropyloric angle and the vertical distance from the lowest point of the nasojejunal tube to the highest point of the iliac crest (distance_tube_to_iliac_crest). Five models—Logistic Regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were developed and compared. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Feature importance was derived from the RF and LR models. The overall success rate was 80.13% (230/287). LR achieved the highest AUC (0.7464), and KNN achieved the highest accuracy (0.8276) and F1 score (0.8980). No single model outperformed others across all metrics, and all AUC values remained below 0.75, indicating only moderate discriminative ability. SVM showed perfect recall (1.00) but low AUC (0.5851), suggesting overfitting. Feature importance analysis revealed that distance_tube_to_iliac_crest was the most important predictor (0.2424), followed by the gastropyloric angle (0.2244) and insertion depth (0.1430). These three features collectively accounted for more than 60% of the predictive information. Five-fold cross-validation confirmed the stability of the LR model (mean AUC 0.7348 ± 0.0668). The gastropyloric angle and the vertical distance from the lowest tube point to the iliac crest are independent anatomical predictors of successful nasojejunal tube placement, supporting the “turning resistance hypothesis”. LR was selected for the web-based risk calculator primarily due to its interpretability, although its AUC of 0.7464 indicates only moderate predictive performance. KNN showed superior classification accuracy and F1 score, suggesting it may be preferred when the clinical goal is to maximize correct classification. External validation is warranted before clinical deployment. Based on the model, a three-tier risk stratification strategy was proposed to guide the selection of blind versus image-guided placement, aiming to improve first-attempt success and reduce complications.