Did the COVID-19 Pandemic Affect the Management of Patients With Acute Appendicitis?

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

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.

Should we consider Systemic Inflammatory Response Index (SIRI) as a new diagnostic marker for rectal cancer?

PURPOSE

The Systemic Inflammatory Response Index (SIRI), which depends on peripheral neutrophil, monocyte, and lymphocyte count, was found to be an effective prognostic indicator for various malignancies. In this study, we aimed to investigate the diagnostic value and the prognostic impact of SIRI on rectal cancer patients.

METHOD

The medical records of patients underwent sphincter-sparing rectal cancer surgery at general surgery between 2017 and 2022 were examined retrospectively. Patient demographics, operation types, neoadjuvant chemo/radiotherapies, pathological results, and complications were recorded. A total number of 99 patients who operated with diagnoses other than cancer were conducted as a control group. SIRI was calculated from preoperative peripheral blood samples' neutrophil, lymphocyte, and monocyte count. The optimal cut-off value for SIRI was found to be 1.38. The clinicopathological outcomes and Overall Survival (OS) were analyzed under two groups according to the SIRI values lower or higher than 1.38.

RESULTS

The number of eligible patients was 104. The median age of the entire cohort was 62 (31-89). The median follow-up time was 33 (1-62) months. The median SIRI value in the study group was significantly higher compared with the control group. The study group was examined under two groups: SIRI 1.38 and SIRI > 1.38. The male gender was significantly more frequent in the high SIRI group. The remaining patient demographics and operation types were similar between the groups. The pathological outcomes were similar between the two groups. Overall Survival rate was better in the low SIRI group than those higher. The higher group had significantly higher complication rates than the lower SIRI group (p: 0.004).

CONCLUSION

SIRI may be a valuable diagnostic marker in rectal cancer patients. Higher SIRI levels were also associated with poorer prognosis and increased complication rates. Still, further prospective studies with a larger number of patients are needed.