[Nomogram to predict a poor outcome in emergency patients with sepsis and at low risk of organ damage according to Sepsis-related Organ Failure Assessment (SOFA)].
García-Villalba Eva,Cano-Sánchez Alfredo,Alcaraz-García Antonia,Cinesi-Gómez César,Piñera-Salmerón Pascual,Marín Irene,Muñoz Ángeles,Vicente Vera Tomás,Bernal-Morell Enrique
Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVES:To develop a nomograph to predict a poor outcome (death during hospitalization or a hospital stay longer than 15 days) in emergency patients with sepsis and at low risk of organ damage according to Sepsis-related Organ Failure Assessment (SOFA). MATERIAL AND METHODS:Prospective, observational study carried out in a single universitary hospital. All patients admitted from the emergency department with sepsis and SOFA scores of 6 or lower were enrolled. We used bivariate logistic regression analysis to develop a predictive nomogram. RESULTS:A total of 174 patients were included. Seventeen patients (9.8%) died during hospitalization and the average hospital stay was greater than 15 days in 29 (16.7%) patient. The outcome was poor in a total of 42 patients (24.1%);. Independent variables that were significantly associated with a poor outcome were SOFA score (odds ratio [OR], 1.3; 95% CI, 1.06-1.71; P<.05), C-reactive protein (CRP) concentration (OR, 1.04; 95% CI, 1.0-1.09; P<.05), N-terminal fragment of brain natriuretic peptide (NT-proBNP) concentration over 1330 ng/mL (OR, 2.64; 95% CI, 1.17-6.22; P<.05), and septic shock (OR, 8.3; 95% CI, 1.16-166.5; P<.05). For a SOFA score of 2 or more the crude OR was 4.44 (95%, CI, 1.91-10.34) and the OR adjusted for other variables was 3.08 (95% CI, 1.24-7.69). CONCLUSION:A high percentage of patients predicted to be at low risk of organ failure had poor outcomes, associated with SOFA score, the presence of septic shock, CRP concentration, and elevated NT-proBNP concentration. The SOFA score by itself is an inadequate prognostic tool in patients at low risk of organ damage. Other clinical and analytical variables are required to complement the SOFA score.
Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients.
Liu Chao,Zhao Zeyin,Gu Xi,Sun Lisha,Chen Guanglei,Zhang Hao,Jiang Yanlin,Zhang Yixiao,Cui Xiaoyu,Liu Caigang
Frontiers in oncology
Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application. The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC). Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%). Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.
A Computed Tomography-Based Radiomics Nomogram to Preoperatively Predict Tumor Necrosis in Patients With Clear Cell Renal Cell Carcinoma.
Jiang Yi,Li Wuchao,Huang Chencui,Tian Chong,Chen Qi,Zeng Xianchun,Cao Yin,Chen Yi,Yang Yintong,Liu Heng,Bo Yonghua,Luo Chenggong,Li Yiming,Zhang Tijiang,Wang Rongping
Frontiers in oncology
To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87-0.96]) and validation (AUC 0.87 [95% CI 0.79-0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.