The Factors Related to Death of Trauma Preventable Death Patients in Rajavithi Hospital

Aims: To study the factors related to death of traumatic preventable death patients with probability of survival score more than 0.75 Methodology: A 1:4 case-control study was conducted on traumatic preventable death patients with probability of survival score more than 0.75 who received treatment at the Emergency Department and was admitted in Rajavithi Hospital between 2015 and 2018. Data were retrieved from Rajavithi trauma registry. Statistical analysis using Chi-square test, student t-test, and Multiple logistic regression was employed for factors associated with death of trauma. Results: There were 36 cases (death) and 150 controls (survivors). In cases group, 21 (61.1%) were male with mean age of 61.36±20.23 years. 26 (72.2%) had underlying diseases. 22 (61.10%) of these injuries occurred at home. The cause of accidents are categorized to fall injury occurring 21 (58.3%), and blunt mechanism of injury 35 (97.20%). The mean Injury Severity Score was 17.81±9.66. Factors significantly associated with increased death are age (Adjust OR: 1.05 (1.01-1.08), P = .02), pulse rate (Adjusted OR): 1.05 (1.01-1.08), P = .01), underlying disease (Adjusted OR): 12.0 (2.29-62.88), and Injury Severity Score (Adjusted OR): 1.29 (1.16-1.43), P < .001) Conclusion: The factors related to death of traumatic preventable death patients with probability of survival score more than 0.75 were age, pulse rate, underlying disease, and Injury Severity Score.

A gene-based survival score for lung adenocarcinoma by multiple transcriptional datasets analysis

Background Lung adenocarcinoma (LUAD) remains a crucial factor endangering human health. Gene-based clinical predictions could be of great help for cancer intervention strategies. Here, we tried to build a gene-based survival score (SS) for LUAD via analyzing multiple transcriptional datasets. Methods We first acquired differentially expressed genes between tumors and normal tissues from intersections of four LUAD datasets. Next, survival-related genes were preliminarily unscrambled by univariate Cox regression and further filtrated by LASSO regression. Then, we applied PCA to establish a comprehensive SS based on survival-related genes. Subsequently, we applied four independent LUAD datasets to evaluate prognostic prediction of SS. Moreover, we explored associations between SS and clinicopathological features. Furthermore, we assessed independent predictive value of SS by multivariate Cox analysis and then built prognostic models based on clinical stage and SS. Finally, we performed pathway enrichments analysis and investigated immune checkpoints expression underlying SS in four datasets. Results We established a 13 gene-based SS, which could precisely predict OS and PFS of LUAD. Close relations were elicited between SS and canonical malignant indictors. Furthermore, SS could serve as an independent risk factor for OS and PFS. Besides, the predictive efficacies of prognostic models were also reasonable (C-indexes: OS, 0.7; PFS, 0.7). Finally, we demonstrated enhanced cell proliferation and immune escape might account for high clinical risk of SS. Conclusions We built a 13 gene-based SS for prognostic prediction of LUAD, which exhibited wide applicability and could contribute to LUAD management.

Evaluation of prognostic prediction models for out-of-hospital cardiac arrest

Introduction: Accurate prognostic prediction of out-of-hospital cardiac arrest is challenging but important for the emergency team and patient’s family members. A number of prognostic prediction models specifically designed for out-of-hospital cardiac arrest are developed and validated worldwide. Objective: This narrative review provides an overview of the prognostic prediction models out-of-hospital cardiac arrest patients for use in the emergency department. Discussion: Out-of-hospital cardiac arrest prognostic prediction models are potentially useful in clinical, administrative and research settings. Development and validation of such models require prehospital and hospital predictor and outcome variables which are best in the standardised Utstein Style. Logistic regression analysis is traditionally employed for model development but machine learning is emerging as the new tool. Examples of such models available for use in the emergency department include ROSC After Cardiac Arrest, CaRdiac Arrest Survival Score, Utstein-Based Return of Spontaneous Circulation, Out-of-Hospital Cardiac Arrest, Cardiac Arrest Hospital Prognosis and Cardiac Arrest Survival Score. The usefulness of these models awaits future studies.