The ROX, shock and diastolic shock indexes in the prediction of mortality in COVID-19.

OBJECTIVES: The aim of this study is to determine the role of Respiratory Rate Oxygenation (ROX), shock, and diastolic shock indexes ​​in predicting mortality in coronavirus disease 2019 (COVID-19) patients admitted to the emergency department. BACKGROUND: The COVID-19 spread worldwide in a short time and caused a major pandemic. The ROX, shock, and diastolic shock indexes are used in various life-threatening clinical situations. The use of these indexes in triage at emergency departments can accelerate the determination of COVID-19 patients' severity. METHODS: The ROX, shock and diastolic shock indices were calculated and recorded. Patients were divided into three groups; 1) who were discharged from the hospital, 2) who were admitted to the hospital and 3) who were admitted to the intensive care unit. RESULTS: Increased diastolic shock index and decreased ROX index were found to be independent risk factors for mortality. In the prediction of mortality, the sensitivity and specificity of the diastolic shock index were 61.2% and 60.8%, respectively. However, the sensitivity and specificity of ROX index was 73.1% and 71.5%, respectively. CONCLUSION: In conclusion, we found that the ROX index had higher sensitivity and specificity than other indexes in predicting mortality in the evaluation of COVID-19 patients (Tab. 3, Fig. 2, Ref. 18).

Comparative analysis of ChatGPT, Gemini and emergency medicine specialist in ESI triage assessment.

INTRODUCTION: The term Artificial Intelligence (AI) was first coined in the 1960s and has made significant progress up to the present day. During this period, numerous AI applications have been developed. GPT-4 and Gemini are two of the best-known of these AI models. As a triage system The Emergency Severity Index (ESI) is currently one of the most commonly used for effective patient triage in the emergency department. The aim of this study is to evaluate the performance of GPT-4, Gemini, and emergency medicine specialists in ESI triage against each other; furthermore, it aims to contribute to the literature on the usability of these AI programs in emergency department triage. METHODS: Our study was conducted between February 1, 2024, and February 29, 2024, among emergency medicine specialists in Turkey, as well as with GPT-4 and Gemini. Ten emergency medicine specialists were included in our study but as a limitation the emergency medicine specialists participating in the study do not frequently use the ESI triage model in daily practice. In the first phase of our study, 100 case examples related to adult or trauma patients were extracted from the sample and training cases found in the ESI Implementation Handbook. In the second phase of our study, the provided responses were categorized into three groups: correct triage, over-triage, and under-triage. In the third phase of our study, the questions were categorized according to the correct triage responses. RESULTS: In the results of our study, a statistically significant difference was found between the three groups in terms of correct triage, over-triage, and under-triage (p < 0.001). GPT-4 was found to have the highest correct triage rate with an average of 70.60 (±3.74), while Gemini had the highest over-triage rate with an average of 35.2 (±2.93) (p < 0.001). The highest under-triage rate was observed in emergency medicine specialists (32.90 (±11.83)). In the ESI 1-2 class, Gemini had a correct triage rate of 87.77%, GPT-4 had 85.11%, and emergency medicine specialists had 49.33%. CONCLUSION: In conclusion, our study shows that both GPT-4 and Gemini can accurately triage critical and urgent patients in ESI 1&2 groups at a high rate. Furthermore, GPT-4 has been more successful in ESI triage for all patients. These results suggest that GPT-4 and Gemini could assist in accurate ESI triage of patients in emergency departments.