A Nomogram-based Model to Predict Neoplastic Risk for Patients with Gallbladder Polyps

Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study

BACKGROUND

The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level.

METHODS

According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs.

RESULTS

Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05).

CONCLUSION

The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

A nomogram for predicting mortality risk within 30 days in sepsis patients admitted in the emergency department: A retrospective analysis

OBJECTIVE

To establish and validate an individualized nomogram to predict mortality risk within 30 days in patients with sepsis from the emergency department.

METHODS

Data of 1205 sepsis patients who were admitted to the emergency department in a tertiary hospital between Jun 2013 and Sep 2021 were collected and divided into a training group and a validation group at a ratio of 7:3. The independent risk factors related to 30-day mortality were identified by univariate and multivariate analysis in the training group and used to construct the nomogram. The model was evaluated by receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis. The model was validated in patients of the validation group and its performance was confirmed by comparing to other models based on SOFA score and machine learning methods.

RESULTS

The independent risk factors of 30-day mortality of sepsis patients included pro-brain natriuretic peptide, lactic acid, oxygenation index (PaO2/FiO2), mean arterial pressure, and hematocrit. The AUCs of the nomogram in the training and verification groups were 0.820 (95% CI: 0.780-0.860) and 0.849 (95% CI: 0.783-0.915), respectively, and the respective P-values of the calibration chart were 0.996 and 0.955. The DCA curves of both groups were above the two extreme curves, indicating high clinical efficacy. The AUC values were 0.847 for the model established by the random forest method and 0.835 for the model established by the stacking method. The AUCs of SOFA model in the model and validation groups were 0.761 and 0.753, respectively.

CONCLUSION

The sepsis nomogram can predict the risk of death within 30 days in sepsis patients with high accuracy, which will be helpful for clinical decision-making.

Advances in the management of gallbladder polyps: establishment of predictive models and the rise of gallbladder-preserving polypectomy procedures

Gallbladder polyps are a common biliary tract disease whose treatment options have yet to be fully established. The indication of "polyps ≥ 10 mm in diameter" for cholecystectomy increases the possibility of gallbladder excision due to benign polyps. Compared to enumeration of risk factors in clinical guidelines, predictive models based on statistical methods and artificial intelligence provide a more intuitive representation of the malignancy degree of gallbladder polyps. Minimally invasive gallbladder-preserving polypectomy procedures, as a combination of checking and therapeutic approaches that allow for eradication of lesions and preservation of a functional gallbladder at the same time, have been shown to maximize the benefits to patients with benign polyps. Despite the reported good outcomes of predictive models and gallbladder-preserving polypectomy procedures, the studies were associated with various limitations, including small sample sizes, insufficient data types, and unknown long-term efficacy, thereby enhancing the need for multicenter and large-scale clinical studies. In conclusion, the emergence of predictive models and minimally invasive gallbladder-preserving polypectomy procedures has signaled an ever increasing attention to the role of the gallbladder and clinical management of gallbladder polyps.

A Nomogram-Based Model for Predicting the Risk of Severe Acute Cholangitis Occurrence

Background

Acute cholangitis is a severe inflammatory disease associated with an infection of the biliary system, which can lead to complications and adverse outcomes. The existing nomogram-based risk assessment methods largely rely on a limited set of clinical features and laboratory indicators, and are mostly constructed using univariable models, which have limitations in predicting the severity. This study aims to develop a nomogram-based model that integrates multiple variables to improve risk prediction for acute cholangitis.

Methods

Data were retrospectively collected from 152 patients with acute cholangitis who attended the People's Hospital of Jiangsu University between January 2019 and March 2022, and were graded as having mild to moderate versus severe cholangitis according to the 2018 Tokyo guidelines. Univariate and multivariate analyses were employed to discern independent risk factors associated with severe acute cholangitis, which were subsequently integrated into a nomogram model. The efficacy of the model was appraised by leveraging Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA).

Results

Aspartate to alanine transaminase ratio (Transaminase ratio or TR), Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), and D-dimer (DD) levels were independent risk factors for severe acute cholangitis. A nomogram model was constructed based on these 4 risk factors. ROC and calibration curves were well differentiated and calibrated. DCA had a high net gain in the range of 7% to 83%. The above model was tested internally. According to the nomogram model when patients using characteristic curve critical values were divided into a low-risk group and a high-risk group, the incidence in the high-risk group was significantly higher than in the low-risk group.

Conclusion

This nomogram model may provide clinicians with an effective tool to predict the potential risk of severe acute cholangitis in patients and guide informed intervention measures and treatment decisions.

Analysis of Pathogenic Bacteria Distribution and Related Factors in Recurrent Acute Cholangitis

Background

To evaluate the risk factors and prognosis of patients with acute cholangitis recurrence.

Methods

A total of 503 patients with acute cholangitis admitted to the First Affiliated Hospital of Chongqing Medical University between July 2013 and January 2022 were included in this retrospective observational study, who were followed up for 360 days and divided into relapse group and non-recurrence group according to the recurrence of acute cholangitis. Risk factors and prognosis of patients with acute cholangitis recurrence were analyzed by univariate, multivariate analyses and proportional hazards model.

Results

A total of 161 patients with recurrent acute cholangitis were identified. Recurrent acute cholangitis usually occurred within 125 days; Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Enterococcus faecalis, and Enterococcus faecium was the most common positive record both in blood and bile culture. In the multivariate analysis, abdominal pain (OR = 2.448, 95% CI = 1.196-5.010, P = 0.014), bile stones (OR = 2.429, 95% CI = 1.024-5.762, P = 0.044), diabetes (OR = 1.790, 95% CI = 1.007-3.182, P = 0.047), pathogen (OR = 3.305, 95% CI = 1.932-5.654, P<0.001), and chronic kidney disease (OR = 2.500, 95% CI = 1.197-5.221, P = 0.015) may be ascertained as the risk factors of acute cholangitis recurrence. The recurrence of acute cholangitis was identified as an independent risk factor for patient death (HR = 4.524, 95% CI = 1.426-14.357, P = 0.010) by Cox proportional-hazards regression.

Conclusion

Abdominal pain, bile stones, diabetes and chronic kidney disease may be risk factors of acute cholangitis recurrence. Patients with recurrent acute cholangitis have poor prognosis and high mortality. Early control of recurrent risk factors and active intervention are beneficial to high-risk patients.

Value of Micro Flow Imaging in the Prediction of Adenomatous Polyps

OBJECTIVE

The aim of this study was to assess the value of micro flow imaging (MFI) in distinguishing adenomatous polyps from cholesterol polyps.

METHODS

A total of 143 patients who underwent cholecystectomy for gallbladder polyps were retrospectively analyzed. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), MFI and contrast-enhanced ultrasound (CEUS) were performed before cholecystectomy. The weighted kappa consistency test was used to evaluate the agreement of vascular morphology among CDFI, MFI and CEUS. Ultrasound image characteristics, including BUS, CDFI and MFI images, were compared between adenomatous polyps and cholesterol polyps. The independent risk factors for adenomatous polyps were selected. The diagnostic performance of MFI combined with BUS in determining adenomatous polyps was compared with CDFI combined with BUS.

RESULTS

Of the 143 patients, 113 cases were cholesterol polyps, and 30 cases were adenomatous polyps. The vascular morphology of gallbladder polyps was more clearly depicted by MFI than CDFI, and it had better agreement with CEUS. Differences in maximum size, height/width ratio, hyperechoic spot and vascular intensity on CDFI and MFI images were significant between adenomatous polyps and cholesterol polyps (p < 0.05). The maximum size, height/width ratio, and vascular intensity on MFI images were independent risk factors for adenomatous polyps. For MFI combined with BUS, sensitivity, specificity and accuracy were 90.00%, 94.69% and 93.70%, respectively. Area under the receiver operating characteristic curve (AUC) of MFI combined with BUS was significantly higher than that of CDFI combined with BUS (AUC = 0.923 vs. 0.784).

CONCLUSION

Compared with CDFI combined with BUS, MFI combined with BUS had higher diagnostic performance in determining adenomatous polyps.

A Bayesian network model to predict neoplastic risk for patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features

BACKGROUND

Polyp size of 10 mm is insufficient to discriminate neoplastic and non-neoplastic risk in patients with gallbladder polyps (GPs). The aim of the study is to develop a Bayesian network (BN) prediction model to identify neoplastic polyps and create more precise criteria for surgical indications in patients with GPs lager than 10 mm based on preoperative ultrasound features.

METHODS

A BN prediction model was established and validated based on the independent risk variables using data from 759 patients with GPs who underwent cholecystectomy from January 2015 to August 2022 at 11 tertiary hospitals in China. The area under receiver operating characteristic curves (AUCs) were used to evaluate the predictive ability of the BN model and current guidelines, and Delong test was used to compare the AUCs.

RESULTS

The mean values of polyp cross-sectional area (CSA), long, and short diameter of neoplastic polyps were higher than those of non-neoplastic polyps (P < 0.0001). Independent neoplastic risk factors for GPs included single polyp, polyp CSA ≥ 85 mm ², fundus with broad base, and medium echogenicity. The accuracy of the BN model established based on the above independent variables was 81.88% and 82.35% in the training and testing sets, respectively. Delong test also showed that the AUCs of the BN model was better than that of JSHBPS, ESGAR, US-reported, and CCBS in training and testing sets, respectively (P < 0.05).

CONCLUSION

A Bayesian network model was accurate and practical for predicting neoplastic risk in patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.