Reliability of standardized reporting system of acute appendicitis in adults at low-dose 320-rows CT

Mahankali, Shravan Kumar; Razek, Ahmed Abdel Khalek Abdel; Ahamed, Shefeek Abubacker

doi:10.1016/j.ejro.2019.11.001

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…The median AppD was significantly higher in the perforated AA group, which is consistent with recent studies. PALC was observed significantly more often in the perforated AA group, and this is also supported by the existing literature [ 40 , 42 ].…”

Section: Discussionsupporting

confidence: 89%

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

Yazici,

Ugurlu,

Aygul

et al. 2024

BMC Emerg Med

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Backgrounds Acute Appendicitis (AA) is one of the most common surgical emergencies worldwide. This study aims to investigate the predictive performances of 6 different Machine Learning (ML) algorithms for simple and complicated AA. Methods Data regarding operated AA patients between 2012 and 2022 were analyzed retrospectively. Based on operative findings, patients were evaluated under two groups: perforated AA and none-perforated AA. The features that showed statistical significance (p < 0.05) in both univariate and multivariate analysis were included in the prediction models as input features. Five different error metrics and the area under the receiver operating characteristic curve (AUC) were used for model comparison. Results A total number of 1132 patients were included in the study. Patients were divided into training (932 samples), testing (100 samples), and validation (100 samples) sets. Age, gender, neutrophil count, lymphocyte count, Neutrophil to Lymphocyte ratio, total bilirubin, C-Reactive Protein (CRP), Appendix Diameter, and PeriAppendicular Liquid Collection (PALC) were significantly different between the two groups. In the multivariate analysis, age, CRP, and PALC continued to show a significant difference in the perforated AA group. According to univariate and multivariate analysis, two data sets were used in the prediction model. K-Nearest Neighbors and Logistic Regression algorithms achieved the best prediction performance in the validation group with an accuracy of 96%. Conclusion The results showed that using only three input features (age, CRP, and PALC), the severity of AA can be predicted with high accuracy. The developed prediction model can be useful in clinical practice.

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Section: Discussionsupporting

confidence: 89%

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

Yazici,

Ugurlu,

Aygul

et al. 2024

BMC Emerg Med

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“…Many factors may contribute to the improved prognosis of IBD, such as increased awareness of early detection and intervention, improved access to healthcare providers, improved surveillance system, and evolved treatment strategies. Image techniques including magnetic resonance enterography, virtual computed tomography colonoscopy have been upgraded to better facilitated IBD clinical management [10][11][12]. At the start of our study period, treatment options and strategies were different from now.…”

Section: Discussionmentioning

confidence: 99%

Increasing newly diagnosed inflammatory bowel disease and improving prognosis in China: a 30-year retrospective study from a single centre

Jin

Zhang

et al. 2020

BMC Gastroenterol

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Background We aimed to characterize the trends of prognosis in ulcerative colitis (UC) and Crohn’s disease (CD) in a Chinese tertiary hospital. Methods A 30-year retrospective cohort analysis was conducted at Peking Union Medical College Hospital. Consecutive patients newly diagnosed with UC or CD from 1985 to 2014 were included. The primary outcome was in-hospital mortality. The secondary outcomes included surgery and length of stay in hospital. The Pearson correlation coefficient was applied to determine the relationship between time and prognosis. Multivariable logistic regression analysis was performed to determine the risk factors for in-hospital mortality and surgery. Results In total, 1467 patients were included in this study (898 cases with UC and 569 cases with CD). Annual admissions for UC and CD have increased significantly over the last 30 years (UC, r = 0.918, P < 0.05; CD, r = 0.898, P < 0.05). Decreased in-hospital mortality was observed both in patients with UC and CD (UC, from 2.44 to 0.27%, r = − 0.827, P < 0.05; CD, from 12.50 to 0.00%, r = − 0.978, P < 0.05). A decreasing surgery rate was observed in patients with CD (r = − 0.847, P < 0.05), while an increasing surgery rate was observed in patients with UC (r = 0.956, P < 0.05). Shortened average lengths of hospital stay were observed in both UC and CD patients (UC, from 47.83 ± 34.35 to 23.58 ± 20.05 days, r = − 0.970, P < 0.05; CD, from 65.50 ± 50.57 to 26.41 ± 18.43 days, r = − 0.913, P < 0.05). Toxic megacolon and septic shock were independent risk factors for in-hospital mortality in patients with UC. Intestinal fistula and intestinal perforation were independent risk factors for in-hospital mortality in patients with CD. Conclusions In this cohort, the admissions of patients with UC and CD were increased, with significantly improved prognoses during the past 30 years.

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CT based Acute Appendicitis Severity Index for acute appendicitis and validate its effectiveness in predicting complicated appendicitis

Mahankali¹,

Ahamed²,

Gupta³

et al. 2021

Emerg Radiol

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Reliability of standardized reporting system of acute appendicitis in adults at low-dose 320-rows CT

Cited by 4 publications

References 34 publications

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

Predicting severity of acute appendicitis with machine learning methods: a simple and promising approach for clinicians

Increasing newly diagnosed inflammatory bowel disease and improving prognosis in China: a 30-year retrospective study from a single centre

CT based Acute Appendicitis Severity Index for acute appendicitis and validate its effectiveness in predicting complicated appendicitis

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