Development and Validation of a Clinical Prediction Model for Complicated Appendicitis in the Elderly

Feng, Hui; Yu, Qingsheng; Wang, Jingxing; Yuan, Yang; Yu, Shushan; Wei, Feisheng; Zheng, Zhou; Peng, Hui; Zhang, Wanzong

doi:10.3389/fsurg.2022.905075

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…The data on rectal cancer in the elderly used in this paper were obtained from the article “Development and Validation of a Clinical Prediction Model for Complicated Appendicitis in the Elderly” 29 . The dataset collected clinical data of elderly patients with acute appendicitis who attended the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022.…”

Section: Methodsmentioning

confidence: 99%

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Wei,

Tongping,

Jiaming

2024

Sci Rep

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Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

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Section: Methodsmentioning

confidence: 99%

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Wei,

Tongping,

Jiaming

2024

Sci Rep

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“…However, it can be challenging for surgeons to determine the pathological type of appendicitis before surgery using imaging modalities such as CT and ultrasound. Previous studies have explored the establishment of predictive models using various clinical information, and there are also studies that investigate the construction of clinical prediction models using CT imaging features or with the combination of CT and clinical information 6 , 7 . The results show their potential application in distinguishing between uncomplicated and complicated appendicitis 8 .…”

Section: Introductionmentioning

confidence: 99%

Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images

Zhao,

Wang,

Zhang

et al. 2024

Sci Rep

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To investigate the radiomics models for the differentiation of simple and non-simple acute appendicitis. This study retrospectively included 334 appendectomy cases (76 simple and 258 non-simple cases) for acute appendicitis. These cases were divided into training (n = 106) and test cohorts (n = 228). A radiomics model was developed using the radiomic features of the appendix area on CT images as the input variables. A CT model was developed using the clinical and CT features as the input variables. A combined model was developed by combining the radiomics model and clinical information. These models were tested, and their performance was evaluated by receiver operating characteristic curves and decision curve analysis (DCA). The variables independently associated with non-simple appendicitis in the combined model were body temperature, age, percentage of neutrophils and Rad-score. The AUC of the combined model was significantly higher than that of the CT model (P = 0.041). The AUC of the radiomics model was also higher than that of the CT model but did not reach a level of statistical significance (P = 0.053). DCA showed that all three models had a higher net benefit (NB) than the default strategies, and the combined model presented the highest NB. A nomogram of the combined model was developed as the graphical representation of the final model. It is feasible to use the combined information of clinical and CT radiomics models for the differentiation of simple and non-simple acute appendicitis.

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Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters

Yadalam,

Thirukkumaran,

Natarajan

et al. 2024

Front. Oral. Health

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IntroductionUntreated periodontitis significantly increases the risk of tooth loss, often delaying treatment due to asymptomatic phases. Recent studies have increasingly associated poor dental health with conditions such as rheumatoid arthritis, diabetes, obesity, pneumonia, cardiovascular disease, and renal illness. Despite these connections, limited research has investigated the relationship between appendicitis and periodontal disease. This study aims to predict appendicitis in patients with periodontal disease using biochemical and clinical parameters through the application of a light gradient boost tree classifier.MethodsData from 125 patient records at Saveetha Institute of Dental College and Medical College were pre-processed and analyzed. We utilized data preprocessing techniques, feature selection methods, and model development approaches to estimate the risk of appendicitis in patients with periodontitis. Both Random Forest and Light Gradient Boosting algorithms were evaluated for accuracy using confusion matrices to assess their predictive performance.ResultsThe Random Forest model achieved an accuracy of 94%, demonstrating robust predictive capability in this context. In contrast, the Light Gradient Boost algorithms achieved a significantly higher accuracy of 98%, underscoring their superior predictive efficiency. This substantial difference highlights the importance of algorithm selection and optimization in developing reliable predictive models. The higher accuracy of Light Gradient Boost algorithms suggests effective minimization of prediction errors and improved differentiation between appendicitis with periodontitis and healthy states. Our study identifies age, white blood cell count, and symptom duration as pivotal predictors for detecting concurrent periodontitis in acute appendicitis cases.ConclusionsThe newly developed prediction model introduces a novel and promising approach, providing valuable insights into distinguishing between periodontitis and acute appendicitis. These findings highlight the potential to improve diagnostic accuracy and support informed clinical decision-making in patients presenting with both conditions, offering new avenues for optimizing patient care strategies.

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Development and Validation of a Clinical Prediction Model for Complicated Appendicitis in the Elderly

Cited by 3 publications

References 31 publications

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Combination of clinical information and radiomics models for the differentiation of acute simple appendicitis and non simple appendicitis on CT images

Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters

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