RESUMEN
Background: Trauma is a significant public health problem. Therefore, many injury scores have been created to predict mortality and triage patients. This study aims to validate the modified Rapid Emergency Medicine Score (mREMS) for in-hospital mortality prediction in road traffic injuries and compare the mREMS with the revised trauma score (RTS) and the mechanisms, Glasgow Coma Scale (GCS), age, and arterial pressure (MGAP) score. Methods: Data were retrospectively collected from the Vajira Hospital (1,033 cases). The mREMS was calculated from six predictors: age, systolic blood pressure, heart rate, respiratory rate, pulse oxygen saturation, and GCS. The receiver operating characteristic curve was plotted, and the area under the curve (AUC) was calculated. The AUC and 95% confidence interval (CI) of the mREMS were compared with the AUCs of other scores. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Results: The mREMS was significantly better than the RTS at predicting death in road traffic injury patients [mREMS: AUCs, 0.909 (95% CI, 0.866-0.951); RTS: AUCs, 0.859 (95% CI, 0.791-0.927] (p = 0.023). However, the difference between the AUCs of the mREMS and MGAP score was not statistically significant (p = 0.150). The mREMS' calibration performance was also satisfactory in this dataset based on the Hosmer-Lemeshow goodness-of-fit test (p = 0.277). Conclusion: In the road traffic injury population, the mREMS is an excellent predictor of in-hospital mortality. These results can be applied to improve triage. However, this score should be further validated in other trauma centers before nationwide implementation.
RESUMEN
Seroma is a common complication after mastectomy. To the best of our knowledge, no prediction models have been developed for this. Henceforth, medical records of total mastectomy patients were retrospectively reviewed. Data consisting of 120 subjects were divided into a training-validation data set (96 subjects) and a testing data set (24 subjects). Data was learned by using a 9-layer artificial neural network (ANN), and the model was validated using 10-fold cross-validation. The model performance was assessed by a confusion matrix in the validating data set. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was also calculated. Pathology type, presence of hypertension, presence of diabetes, receiving of neoadjuvant chemotherapy, body mass index, and axillary lymph node (LN) management (i.e., sentinel LN biopsy and axillary LN dissection) were selected as predictive factors in a model developed from the neural network algorithm. The model yielded an AUC of 0.760, which corresponded with a level of acceptable discrimination. Sensitivity, specificity, accuracy, and positive and negative predictive values were 100%, 52.9%, 66.7%, 46.7%, and 100%, respectively. Our model, which was developed from the ANN algorithm can predict seroma after total mastectomy with high sensitivity. Nevertheless, external validation is still needed to confirm the performance of this model.