The epidemiology and prognosis of patients with massive burns: A multicenter study of 2483 cases

ObjectiveEpidemiological features of massively burned patients in China remains unclear. This study was designed to investigate the epidemiological characteristics and evaluate the burn index (BI) and other risk factors associated with the prognosis of massively burned patients.MethodsData of patients with ≥30% total body surface area burned admitted in 2014 were retrieved from 106 burn centers in the mainland of China. Information of epidemiological features and the outcome were collected for retrospective analysis.ResultsA total of 2483 massively burned patients were included in this study, with a male-to-female ratio of 2.29:1, the mean age of 49.23 ± 16.67 years, mean TBSA of 55.53 ± 21.39% and the mean BI of 39.75 ± 21.59. Scald accounted for 81.07% of the injuries in children, while flame accounted for 66.89% and 74.31% of the injuries in adults and seniors. Approximately 17.76% of the patients were admitted to the local burn center after 6 h of injury, and the wound areas of 1154 (46.48%) patients were covered with folk remedies. The mortality was 9.79%, and the area under the receiver operating characteristic (ROC) curve for BI was 0.941 (95% CI, 0.929–0.954). When the value of BI was above a threshold of 29 in the 0–14 years age group, 43.5 in the 15–59 years age group and 35.5 in the 60 years or older age group, the mortality increased significantly. Multivariate logistic regression analyses showed that the odds ratio (OR) of death increased 6% with an increase in the BI of 1.0. Patients older than 60 years, the admission time longer than 6 h after-injury (adjusted OR, 1.797; 95% CI, 1.179–2.740; adjusted p < 0.001), and patients with a combined inhalation injury (adjusted OR, 6.649; 95% CI, 4.517–9.789; adjusted p < 0.000), were at higher risk of death.ConclusionsThere are etiological characteristics of the different age groups that should be considered for prevention. BI can be a reliable index of prognosis in severely burned patients. The results of the study showed that a large BI, elderly age, delayed admission after injury and combined inhalation injury are the main risk factors for extensively burned patients.     

Venous thromboembolism following inpatient pediatric surgery: Analysis of 153,220 patients

Purpose

To evaluate venous thromboembolism (VTE) rates and risk factors following inpatient pediatric surgery.

Development and validation of a nomogram for predicting survival on the base of modified lymph node ratio in breast cancer patients

BackgroundAxillary lymph node status is one of the most important prognostic factors in breast cancer and previous studies indicated that lymph node ratio (LNR) could better predict the outcome than the counting of positive lymph nodes. In the current study, we evaluated the prognostic effect of modified LNR in breast cancer patients.MethodsA total of 3339 breast cancer patients undergoing axillary lymph nodes dissection were enrolled and respectively analyzed. Seventy five percent of participants were randomly selected as training cohort and the remaining 25% were as validation cohort. Univariate and multivariate analyses were performed and the prognostic impact of mLNR was compared with pN staging. A prognostic nomogram was established and externally validated in the validation cohort.ResultIn multivariate analysis, both the mLNR and pN staging were independent prognostic factors for breast cancer patients, and the mLNR manifested superior discrimination power than the pN stages regardless of the total number of lymph nodes retrieved and the lymph node status. The nomogram was built including the identified independent prognostic factors and the calibration curves indicated optimal agreement between nomogram prediction and actual observation. The Concordance index (C-index) of the nomogram was statistically higher than that of the TNM system (0.747 vs. 0.711 in training cohort, 0.789 vs. 0.760 in validation cohort, both p < 0.05).ConclusionModified LNR is an important prognostic parameter and can predict survival more accurately than pN staging. The novel nomogram could provide individual prediction for breast cancer patients and help clinicians in treatment option making and prognosis evaluation.     

The time-course of the inflammatory response to major burn injury and its relation to organ failure and outcome

Burn injury causes major inflammatory activation and cytokine release, however, the temporal resolution of the acute and sub-acute inflammatory response has not yet been fully delineated. To this end, we have quantified 20 inflammatory mediators in plasma from 44 adult patients 0–21 days after burn injury and related the time course of these mediators to % total body surface area (TBSA) burned, clinical parameters, organ failure and outcome. Of the cytokines analyzed in these patients, interleukin 6 (IL-6), IL-8, IL-10 and monocyte chemoattractant protein 1 (MCP-1) correlated to the size of the injury at 24–48 h after burn injury. In our study, the concentration of IL-10 had prognostic value in patients with burn injury both measured at admission and at 24–48 h after injury. However, simple demographic data such as age, % burned TBSA, inhalation injury and their combination, the Baux score and modified Baux score, outperform most of the cytokines, with the exception of IL-8 and MCP-1 levels on admission, in predicting death.     

Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes

ObjectiveFor patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors.MethodsThis retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022. The radiomic features were extracted from preoperative unenhanced computed tomography images. After strict feature selection, random forest and random survival forest models were fitted to predict pathological and survival outcomes, respectively. The model performance was assessed by the area under the curve (AUC) and validated internally by the bootstrap method.ResultsIn total, 124 patients with a median age of 61 years were included. The radiomics random forest models of WHO type and TNM stage showed satisfactory performance with an AUC_WHO of 0.898 (95% CI, 0.753-1.000) and an AUC_TNM of 0.766 (95% CI, 0.642-0.886). For overall survival and progression-free survival prediction, the radiomics random survival forest models showed good performance (integrated AUCs, 0.923; 95% CI, 0.691-1.000 and 0.702; 95% CI, 0.513-0.875, respectively), and the integrated AUCs increased to 0.935 (95% CI, 0.705-1.000) and 0.811 (95% CI, 0.647-0.942), respectively, when combined with clinicopathological features.ConclusionsMachine learning-based radiomic computed tomography phenotyping might allow for the satisfactory prediction of pathological and survival outcomes and further improve prognostic performance when integrated with clinicopathological features in patients with thymic epithelial tumors.