Quantitative CT Analysis to Predict Esophageal Fistula in Patients With Advanced Esophageal Cancer Treated by Chemotherapy or Chemoradiotherapy

Abstract BackgroundEsophageal fistula is one of the most serious complications of chemotherapy or chemoradiotherapy (CRT) for advanced esophageal cancer. This study aimed to evaluate the performance of quantitative computed tomography (CT) analysis and to establish a practical imaging model for predicting esophageal fistula in esophageal cancer patients administered chemotherapy or chemoradiotherapy. MethodsThis study retrospectively enrolled 204 esophageal cancer patients (54 patients with fistula, 150 patients without fistula) and all patients were allocated to the test and validation cohorts according to the time of inclusion in a 1:1 ratio. Ulcer depth, tumor thickness and length, and minimum and maximum enhanced values for esophageal cancer were measured in pretreatment CT imaging. Logistic regression analysis was used to evaluate the associations of CT quantitative measurements with esophageal fistula. Receiver operating characteristic curve (ROC) analysis was also used. ResultsLogistic regression analysis showed that independent predictors of esophageal fistula included tumor thickness [odds ratio (OR)=1.167; p = 0.037], the ratio of ulcer depth to adjacent tumor thickness (OR=164.947; p < 0.001), and the ratio of minimum to maximum enhanced CT value (OR=0.006; p = 0.039) in the test cohort at baseline CT imaging. These predictors were used to establish a predictive model for predicting esophageal fistula, with areas under the receiver operating characteristic curves (AUCs) of 0.946 and 0.841 in the test and validation groups, respectively. ConclusionQuantitative pretreatment CT analysis has excellent performance for predicting fistula formation in esophageal cancer patients who treated by chemotherapy or chemoradiotherapy.

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Predicting Burn Mortality Using a Simple Novel Prediction Model

Indian Journal of Plastic Surgery ◽

10.1055/s-0040-1721867 ◽

2021 ◽

Author(s):

Sneha Sharma ◽

Raman Tandon

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Receiver Operating Characteristic ◽

Logistic Regression Analysis ◽

Operating Characteristic ◽

Binary Logistic Regression ◽

Apache Ii ◽

Apache Ii Score ◽

Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

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