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To systematically review published studies on fall risk prediction models for inpatients.
A systematic review and meta-analysis of prognostic model studies.
A literature search was carried out in Web of Science, the Cochrane Library, PubMed, Embase, CINAHL, SinoMed, VIP Database, CNKI and Wanfang Database. The search covered studies on risk prediction models for falls in inpatients from inception to March 9, 2024.
The research question was formulated using the PICOTS framework. Data extraction was performed following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The quality of studies related to risk prediction models was evaluated with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was conducted using STATA 18.0 software.
A total of 15 studies were included, with 13 eligible for meta-analysis. Only 2 of these 15 studies had external validation. The reported AUC values ranged from 0.681 to 0.900. The overall risk of bias was high, mainly attributed to inappropriate data sources and improper processing in the analysis domain. The pooled AUC from the meta-analysis was 0.799. After reviewing the predictors included in various models, FRIDs, fall history, age, gait, mental status, gender and incontinence were relatively common.
The fall risk prediction model for inpatients performs well overall, but it has a high risk of bias. Future development of risk prediction models should strictly adhere to the PROBAST, combine clinical reality, optimise study design and improve methodological quality.
This study provides medical professionals with a clear overview of constructing fall risk prediction models for inpatients. The fall-related predictors in these models help healthcare providers identify high-risk patients and implement preventive strategies. It also offers valuable insights for the development of future prediction models.
This study did not include patient or public involvement in its design, conduct, or reporting.
To investigate the physical activity levels of lung cancer survivors, analyse the influencing factors, and construct a predictive model for the physical activity levels of lung cancer survivors based on machine learning algorithms.
This was a cross-sectional study.
Convenience sampling was used to survey lung cancer survivors across 14 hospitals in eastern, central, and western China. Data on demographic, disease-related, health-related, physical, and psychosocial factors were also collected. Descriptive analyses were performed using SPSS 25.0, and predictors were identified through multiple logistic regression analyses. Four machine learning models—random forest, gradient boosting tree, support vector machine, and logistic regression—were developed and evaluated based on the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), accuracy, precision, recall, and F1 score. The best model was used to create an online computational tool using Python 3.11 and Flask 3.0.3. This study was conducted and reported in accordance with the TRIPOD guidelines and checklist.
Among the 2231 participants, 670 (30%), 1185 (53.1%), and 376 (16.9%) exhibited low, moderate, and high physical activity levels, respectively. Multivariate logistic regression identified 15 independent influencing factors: residential location, geographical region, religious beliefs, histological type, treatment modality, regional lymph node stage, grip strength, 6-min walking distance, globulin, white blood cells, aspartate aminotransferase, blood urea, MDASI score, depression score, and SRAHP score. The random forest model performed best among the four algorithms, achieving AUC-ROC values of 0.86, 0.70, 0.72, and 0.67, respectively, and was used to develop an online predictive tool (URL: http://10.60.32.178:5000).
This study developed a machine learning model to predict physical activity levels in lung cancer survivors, with the random forest model demonstrating the highest accuracy and clinical utility. This tool enables the early identification of low-activity survivors, facilitating timely, personalised rehabilitation and health management.
The development of a predictive model for physical activity levels in lung cancer survivors can help clinical medical staff identify survivors with relatively low physical activity levels as early as possible. Thus, personalised rehabilitation plans can be formulated to optimise quality of life during their survival period.
Physical activity has been used as a nonpharmacological intervention in cancer patient rehabilitation plans. However, a review of past studies has shown that lung cancer survivors generally have low physical activity levels. In this study, we identified the key factors influencing physical activity among lung cancer survivors through a literature review. We constructed a prediction model for their physical activity levels using machine learning algorithms. Clinical medical staff can use this model to identify patients with low physical activity levels early and to develop personalised intervention plans to improve their quality of life during survival.
The study adhered to the relevant EQUATOR reporting guidelines, the TRIPOD Checklist for Prediction Model Development and Validation.
During the data collection phase, participants were recruited to complete the questionnaires.
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