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

Risk prediction and effect evaluation of complicated appendicitis based on XGBoost modeling

PURPOSE

The distinction between complicated appendicitis (CAP) and uncomplicated appendicitis (UAP) remains challenging. The purpose of this study was to construct a safe and economical diagnostic model that can accurately and rapidly differentiate between CAP and UAP.

METHODS

Patient data from 773 appendectomies were retrospectively collected, important features were selected using random forests, and the data were divided into training and test sets in a 3:1 ratio. An integrated learning algorithm, Extreme Gradient Boosting (XGBoost), was introduced to predict the risk of CAP and compared with Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (CART) algorithms. A comprehensive comparison of the four algorithms was performed using model performance metrics such as the area under the receiver's operating characteristic curve (AUC), sensitivity, specificity, accuracy, precision, negative predictive value(NPV), positive predictive value(PPV),calibration curves, and clinical decision curve analysis (DCA).

RESULT

The results show that all four prediction models exhibit some predictive ability. The XGBoost model showed the best prediction with AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.914, 0.855, 0.865, 0.846,0.848 and 0.897, respectively, followed by the SVM model with results of AUC, accuracy, sensitivity, specificity,NPV and PPV of 0.882, 0.819, 0.865, 0.779, 0.770 and 0.871, respectively. XGBoost and SVM models show very good calibration. The XGBoost model showed better net clinical benefit compared to the DCA curves of the other models.

CONCLUSION

Predictive models based on the XGBoost algorithm have good performance in predicting the risk of acute appendicitis progressing to complicated appendicitis, which helps to optimize clinical decision making.

C-Reactive Protein, International Normalized Ratio, and Fibrinogen in Diagnostic Scale of Complicated Acute Appendicitis

Background/Objectives: Differentiating complicated acute appendicitis (CA) and uncomplicated acute appendicitis (UC) is essential to guide clinical management. While CA requires urgent surgical management, UC can be treated with antibiotic therapy in selected cases. However, accurate identification of CA remains a clinical challenge. This study aimed to identify factors associated with CA and to develop a diagnostic severity scale. Methods: In this retrospective study, we included 132 adult patients (>16 years) with a confirmed postsurgical diagnosis of appendicitis, of whom 52 had CA and 80 had UA. Signs, symptoms, comorbidities, laboratory values, and ultrasonographic findings were evaluated to determine predictive factors and construct a diagnostic scale. Results: The factors most significantly associated with CA were elevated plasma concentrations of C-reactive protein (>7.150 mg/dL), fibrinogen (481.5 mg/dL), International Normalized Ratio (INR) (>1.150), and the presence of free fluid periappendicular. The combination of these factors within one scale showed an area under the curve (AUC) of 0.84, with a sensitivity of 78.75% and a specificity of 82.69%. Conclusions: Serum C-reactive protein concentration, fibrinogen, and INR can be employed individually or as part of a scale as important indicators in diagnosing CA.

Validation and comparison of two new scoring systems for the prediction of complicated versus uncomplicated appendicitis

OBJECTIVES

To validate the Atema and APSI scoring systems in the diagnosis of complicated vs uncomplicated appendicitis. To compare these scoring systems with computed tomography (CT) imaging alone to establish which method provides most accurate prediction of complicated vs uncomplicated appendicitis.

METHODS

This was a retrospective review of a sample of 160 patients that underwent appendicectomy and CT imaging for suspected appendicitis between 2018 and 2021 in a tertiary university teaching hospital. Each scoring system was applied to all patients and results analysed and compared with the effectiveness of CT imaging, RESULTS: 32.5% (n = 52) were found to have complicated appendicitis and 67.5% (n = 108) uncomplicated appendicitis. Application of the Atema score to our cohort of patients resulted in a sensitivity 76.9% [CI (64.2, 87.5), specificity 58.7% [CI (48.9, 68.1)], PPV 47.1% [CI (40.5, 53.8) and NPV 84.2% [CI (76.0, 89.9)]. By comparison, the APSI yielded a sensitivity 50.9% [CI (36.6, 65.4)], specificity 76.1% [CI (67.0, 87.8)], PPV 50% [CI (39.2, 60.6)] and NPV 76% [CI (71.1, 81.7)]. Radiology prediction of complicated vs uncomplicated appendicitis with CT imaging showed sensitivity 46% [CI (32.2, 60.5)], specificity 79%; [CI (69.8, 86)], PPV 51% [CI (39.6, 62.5)] and NPV 75% [CI (69.8, 79.9)].

CONCLUSION

By comparing the APSI and Atema et al. scoring systems with CT reporting in our hospital, it appears that the Atema may confer some benefit in stratifying patient risk of complicated versus uncomplicated appendicitis. Further larger scale prospective studies are required.

Development and Validation of a Deep Learning and Radiomics Combined Model for Differentiating Complicated From Uncomplicated Acute Appendicitis

RATIONALE AND OBJECTIVES

This study aimed to develop and validate a deep learning and radiomics combined model for differentiating complicated from uncomplicated acute appendicitis (AA).

MATERIALS AND METHODS

This retrospective multicenter study included 1165 adult AA patients (training cohort, 700 patients; validation cohort, 465 patients) with available abdominal pelvic computed tomography (CT) images. The reference standard for complicated/uncomplicated AA was the surgery and pathology records. We developed our combined model with CatBoost based on the selected clinical characteristics, CT visual features, deep learning features, and radiomics features. We externally validated our combined model and compared its performance with that of the conventional combined model, the deep learning radiomics (DLR) model, and the radiologist's visual diagnosis using receiver operating characteristic (ROC) curve analysis.

RESULTS

In the training cohort, the area under the ROC curve (AUC) of our combined model in distinguishing complicated from uncomplicated AA was 0.816 (95% confidence interval [CI]: 0.785-0.844). In the validation cohort, our combined model showed robust performance across the data from three centers, with AUCs of 0.836 (95% CI: 0.785-0.879), 0.793 (95% CI: 0.695-0.872), and 0.723 (95% CI: 0.632-0.802). In the total validation cohort, our combined model (AUC = 0.799) performed better than the conventional combined model, DLR model, and radiologist's visual diagnosis (AUC = 0.723, 0.755, and 0.679, respectively; all P < 0.05). Decision curve analysis showed that our combined model provided greater net benefit in predicting complicated AA than the other three models.

CONCLUSION

Our combined model allows the accurate differentiation of complicated and uncomplicated AA.

A CT-based radiomics nomogram involving the cystic fluid area for differentiating appendiceal mucinous neoplasms from appendicitis with intraluminal fluid

OBJECTIVE

To develop and validate a radiomics nomogram based on computed tomography (CT) to distinguish appendiceal mucinous neoplasms (AMNs) from appendicitis with intraluminal fluid (AWIF).

METHOD

A total of 211 patients from two medical institutions were retrospectively analysed, of which 109 were pathologically confirmed as having appendicitis with concomitant CT signs of intraluminal fluid and 102 as having AMN. All patients were randomly assigned to a training (147 patients) or validation cohort (64 patients) at a 7:3 ratio. Radiomics features of the cystic fluid area of the appendiceal lesions were extracted from nonenhanced CT images using 3D Slicer software. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods were employed to screen the radiomics features and develop a radiomics model. Combined radiomics nomogram and clinical-CT models were further developed based on the corresponding features selected after multivariate analysis. Lastly, receiver operating characteristic curves, and decision curve analysis (DCA) were used to assess the models' performances in the training and validation cohorts.

RESULTS

A total of 851 radiomics features were acquired from the nonenhanced CT images. Subsequently, a radiomics model consisting of eight selected features was developed. The combined radiomics nomogram model comprised rad-score, age, and mural calcification, while the clinical-CT model contained age and mural calcification. The combined model achieved area under the curves (AUCs) of 0.945 (95% confidence interval [CI]: 0.895, 0.976) and 0.933 (95% CI: 0.841, 0.980) in the training and validation cohorts, respectively, which were larger than those obtained by the radiomics (training cohort: AUC, 0.915 [95% CI: 0.865, 0.964]; validation cohort: AUC, 0.912 [95% CI: 0.843, 0.981]) and clinical-CT models (training cohort: AUC, 0.884 [95% CI: 0.820, 0.931]; validation cohort: AUC, 0.767 [95% CI: 0.644, 0.863]). Finally, DCA showed that the clinical utility of the combined model was superior to that of the clinical CT and radiomics models.

CONCLUSION

Our combined radiomics nomogram model constituting radiomics, clinical, and CT features exhibited good performance for differentiating AMN from AWIF, indicating its potential application in clinical decision-making.

Validation of scoring systems for the prediction of complicated appendicitis in adults using clinical and computed tomographic findings

OBJECTIVES

The study aimed to evaluate scoring systems for predicting complicated appendicitis in adults diagnosed with acute appendicitis on computed tomography.

METHODS

Three hundred twenty-five consecutive adult patients (mean age 51.9 ± 19.6 years, 212 women) diagnosed with acute appendicitis on computed tomography were retrospectively included. Clinical and imaging findings were compared between patients with and without complicated appendicitis, and independent associations were identified. As C-reactive protein was not available for most patients, 5 out of 8 scoring systems were modified. They, and a newly proposed system, were compared via area under the receiver operating characteristics (ROC) curve (AUC), Additionally, the latter was internally validated. Pairwise comparison was performed, and diagnostic performance of these scoring systems was obtained.

RESULTS

One hundred twenty-seven patients (36.8%) had complicated appendicitis. Significant independent associations were found between complicated appendicitis and duration of symptoms > 12 h, appendicolith, periappendiceal fat stranding, periappendiceal fluid, and extraluminal air (p values < 0.001 to 0.037; AUCs of 0.824-0.829). AUCs of 9 scoring systems ranged from 0.692 to 0.831. Of these, modified Atema, Kim HY, and proposed scores had similarly high and non-significantly different AUCs (0.793-0.831) on pairwise comparison. Their sensitivities, specificities, and accuracies were 73.0-90.6%, 48.5-70.6%, and 64.3-72.3%, respectively. Internal validity test demonstrated high AUCs (0.826-0.844) with one of the proposed scores using odds ratio having 100% sensitivity and 100% negative predictive value.

CONCLUSIONS

Few scoring systems, including proposed ones, had high AUCs, sensitivity, and reasonable specificities, which could potentially aid in safely selecting adult patients with acute appendicitis for nonoperative management.

CRITICAL RELEVANCE STATEMENT

The study suggests few scoring systems for predicting complicated appendicitis with high AUCs and reasonable sensitivities, potentially aiding in selecting patients for nonoperative management.

KEY POINTS

• The study evaluated existing and proposed new scoring systems to predict complicated appendicitis in adults with acute appendicitis on computed tomography. • Several factors were found to be significantly associated with complicated appendicitis, including duration of symptoms, appendicolith, periappendiceal fat stranding, periappendiceal fluid, and extraluminal air. • The modified Atema, Kim HY, and newly proposed scoring systems performed well, potentially aiding in nonoperative management selection.

The predictive value of computerized tomography-assessed sarcopenia for complicated appendicitis in geriatric patients

Objective

Geriatric patients have more complicated appendicitis, which leads to higher morbidity and mortality rates. Sarcopenia has been shown to have a negative impact on patients undergoing surgery. This study aims to reveal the predictive value of computerized tomography-assessed (CT-assessed) sarcopenia for complicated appendicitis in geriatric patients.

Methods

One-hundred fifty-four patients' with acute appendicitis age, gender, co-morbidities, appendicitis status, and body mass index (BMI) values were analyzed. The skeletal muscle index (SMI) and related measurements were evaluated.

Results

Fifty-two percent of the patients had complicated, and 48% had uncomplicated appendicitis. There was a statistically significant difference between uncomplicated and complicated cases regarding BMI, SMI, and muscle area values (P < 0.05). The cutoff point by Receiver Operating Characteristic Curve analysis was conducted for SMI and showed 71% sensitivity and 52% specificity (P = 0.042). Multivariate analysis has shown that comorbidities are significantly more associated with complicated appendicitis than sarcopenia.

Conclusion

Geriatric patients with lower BMI, decreased muscle area, and CT-detected sarcopenia have an increased risk of complicated appendicitis. Comorbidities are also important risk factors. Surgeons should be aware of factors leading to complicated appendicitis, which may cause higher morbidity and mortality rates in elderly patients.

Is acute appendicitis complicated or uncomplicated? Approaching the question via computed tomography

Background

The distinction between complicated and uncomplicated appendicitis is very important for the selection of the treatment method.

Purpose

To investigate the sensitivity and specificity of computed tomography (CT) in differentiating between complicated and uncomplicated appendicitis to demonstrate that false negativity in differentiating these cases can be reduced when CT findings are incorporated into the clinical evaluation of patients.

Material and Methods

All patients aged ≥18 years who underwent appendectomy at Malatya Training and Research Hospital in 2020 and 2021 were retrospectively screened. Of them, 283 patients were included in the study who had undergone CT before the operation. Patients with appendicitis were divided into two groups: complicated and uncomplicated, according to the results of their pathology tests. Demographic data, laboratory results, and CT images of the patients were evaluated.

Results

The patients with complicated appendicitis had a significantly higher mean age ( P<0.001). The most common CT findings in patients with complicated appendicitis were moderate or severe peri-appendiceal fat stranding (PFS) and appendix wall enhancement defect (AWD). The findings with the highest sensitivity were PFS (77.9%) and AWD (69.4%). Although abscess, phlegmon, and peri-appendiceal air had the highest specificity (100%), these findings were the ones with the lowest sensitivity. According to the scoring system was developed for the differential diagnosis, CT had a sensitivity of 83.3% and a specificity of 79.2%.

Conclusion

Based on the sensitivity and specificity values measured for CT according to the findings of our study, the scoring system may be useful for the differential diagnosis of complicated appendicitis.

Factors Influencing the Difficulty and Need for External Help during Laparoscopic Appendectomy: Analysis of 485 Procedures from the Resident-1 Multicentre Trial

Purpose: To identify preoperative predictive factors for technically challenging laparoscopic appendectomy (LA) and the need for external help to laparoscopically complete the procedure. Methods: We analysed data from a two-year data lock on the Resident-1 multicentre registry. The operator classified each procedure following a five-grade Likert scale to define technical difficulty. We performed univariate analysis comparing Grade 1−3 versus 4−5 procedures and then built a logistic regression model to identify independent predictors of Grade 4−5 procedures defined as needing external help to complete a LA. Results: 561 patients were recruited from 2019 to 2021, and 485 patients were included in the final analysis due to missing data. A BMI > 30 kg/m2, preoperative CT scan, and the AIR score were independent preoperative predictors of complex LA with the need for external help to be completed. Patients undergoing such procedures were more affected by CA, had longer operative times, and had the worst postoperative outcomes. Conclusion: The preoperative identification of technically demanding LA could be helpful in optimising the preoperative planning, maximise surgeons’ preparedness, and include expert surgeons in the procedure earlier. Creating a scoring system for the technical difficulty of LA is desirable.