Crescent calculator: A webtool enabling objective decision-making for assessment of IgA nephropathy immune activity throughout the disease course

IgA nephropathy (IgAN) is an immune-mediated glomerulonephritis, posing a challenge for the long-term management. It is crucial to monitor the disease's activity over the disease course. Crescent lesions have been known as an active lesion associated with immune activity. We aimed to develop the Crescent Calculator to aid clinicians in making timely and well-informed decisions throughout the long-term disease course, such as renal biopsies and immunosuppressive therapy. 1,761 patients with biopsy-proven IgAN were recruited from four medical centers in Zhejiang Province, China. 16.9% presented crescent lesions. UPCR, URBC, eGFR and C4 were independently associated with the crescent lesions. By incorporating these variables, the Crescent Calculator was constructed to estimate the likelihood of crescent lesions. The predictor achieved AUC values of over 0.82 in two independent testing datasets. In addition, to fulfill varied clinical needs, multiple classification modes were established. The Crescent Calculator was developed to estimate the risk of crescent lesions for patients with IgAN, assisting clinicians in making timely, objective, and well-informed decisions regarding the need for renal biopsies and more appropriate use of immunosuppressive therapy in patients with IgAN.

Letter to editor ‘Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness’

This letter to the editor relates to the study entitled "Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness". Acute bleeding caused by esophageal varices is a life-threatening complication in patients with liver cirrhosis. Due to the discomfort, contraindications, and associated complications of upper gastrointestinal endoscopy screening, it is crucial to identify an imaging-based non-invasive model for predicting high-risk esophageal varices in patients with cirrhosis.

Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness

BACKGROUND

Acute bleeding due to esophageal varices (EVs) is a life-threatening complication in patients with cirrhosis. The diagnosis of EVs is mainly through upper gastrointestinal endoscopy, but the discomfort, contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance. According to the bleeding risk of EVs, the Baveno VI consensus divides varices into high bleeding risk EVs (HEVs) and low bleeding risk EVs (LEVs). We sought to identify a non-invasive prediction model based on spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) as an alternative to EVs screening.

AIM

To develop a safe, simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy.

METHODS

Data from 200 patients with viral cirrhosis were included in this study, with 140 patients as the modelling group and 60 patients as the external validation group, and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno VI consensus. Those patients were divided into the HEVs group (66 patients) and the LEVs group (74 patients). The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses, and a non-invasive prediction model was established. Finally, the discrimination ability, calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group.

RESULTS

Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis. On this basis, logistic regression analysis was used to construct a prediction model: Ln [P/(1-P)] = -8.184 -0.228 × SSM + 0.642 × LSM. The area under the curve of the new model was 0.965. When the cut-off value was 0.27, the sensitivity, specificity, positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%, 82.43%, 83.52%, and 100%, respectively. Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score, variceal risk index, aspartate aminotransferase to alanine aminotransferase ratio, and Baveno VI, the established model can better predict HEVs in patients with viral cirrhosis.

CONCLUSION

Based on the SSM and LSM measured by transient elastography, we established a non-invasive prediction model for HEVs. The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening, which is helpful for clinical decision making.

Non-invasive prediction model for high-risk esophageal varices in the Chinese population

BACKGROUND

There are two types of esophageal varices (EVs): high-risk EVs (HEVs) and low-risk EVs, and HEVs pose a greater threat to patient life than low-risk EVs. The diagnosis of EVs is mainly conducted by gastroscopy, which can cause discomfort to patients, or by non-invasive prediction models. A number of non-invasive models for predicting EVs have been reported; however, those that are based on the formula for calculation of liver and spleen volume in HEVs have not been reported.

AIM

To establish a non-invasive prediction model based on the formula for liver and spleen volume for predicting HEVs in patients with viral cirrhosis.

METHODS

Data from 86 EV patients with viral cirrhosis were collected. Actual liver and spleen volumes of the patients were determined by computed tomography, and their calculated liver and spleen volumes were calculated by standard formulas. Other imaging and biochemical data were determined. The impact of each parameter on HEVs was analyzed by univariate and multivariate analyses, the data from which were employed to establish a non-invasive prediction model. Then the established prediction model was compared with other previous prediction models. Finally, the discriminating ability, calibration ability, and clinical efficacy of the new model was verified in both the modeling group and the external validation group.

RESULTS

Data from univariate and multivariate analyses indicated that the liver-spleen volume ratio, spleen volume change rate, and aspartate aminotransferase were correlated with HEVs. These indexes were successfully used to establish the non-invasive prediction model. The comparison of the models showed that the established model could better predict HEVs compared with previous models. The discriminating ability, calibration ability, and clinical efficacy of the new model were affirmed.

CONCLUSION

The non-invasive prediction model for predicting HEVs in patients with viral cirrhosis was successfully established. The new model is reliable for predicting HEVs and has clinical applicability.

A novel index for preoperative, non-invasive prediction of macro-radical primary surgery in patients with stage IIIC-IV ovarian cancer-a part of the Danish prospective pelvic mass study

The purpose of this study was to develop a novel index for preoperative, non-invasive prediction of complete primary cytoreduction in patients with FIGO stage IIIC-IV epithelial ovarian cancer. Prospectively collected clinical data was registered in the Danish Gynecologic Cancer Database. Blood samples were collected within 14 days of surgery and stored by the Danish CancerBiobank. Serum human epididymis protein 4 (HE4), serum cancer antigen 125 (CA125), age, performance status, and presence/absence of ascites at ultrasonography were evaluated individually and combined to predict complete tumor removal. One hundred fifty patients with advanced epithelial ovarian cancer were treated with primary debulking surgery (PDS). Complete PDS was achieved in 41 cases (27 %). The receiver operating characteristic curves demonstrated an area under the curve of 0.785 for HE4, 0.678 for CA125, and 0.688 for age. The multivariate model (Cancer Ovarii Non-invasive Assessment of Treatment Strategy (CONATS) index), consisting of HE4, age, and performance status, demonstrated an AUC of 0.853. According to the Danish indicator level, macro-radical PDS should be achieved in 60 % of patients admitted to primary surgery (positive predictive value of 60 %), resulting in a negative predictive value of 87.5 %, sensitivity of 68.3 %, specificity of 83.5 %, and cutoff of 0.63 for the CONATS index. Non-invasive prediction of complete PDS is possible with the CONATS index. The CONATS index is meant as a supplement to the standard preoperative evaluation of each patient. Evaluation of the CONATS index combined with radiological and/or laparoscopic findings may improve the assessment of the optimal treatment strategy in patients with advanced epithelial ovarian cancer.