Clinical Prediction Rules for In-Hospital Mortality Outcome in Melioidosis Patients

Background: Melioidosis, a disease induced by Burkholderia pseudomallei, poses a significant health threat in tropical areas where it is endemic. Despite the availability of effective treatments, mortality rates remain notably elevated. Many risk factors are associated with mortality. This study aims to develop a scoring system for predicting the in-hospital mortality from melioidosis using readily available clinical data. Methods: The data were collected from Surin Hospital, Surin, Thailand, during the period from April 2014 to March 2017. We included patients aged 15 years and above who had cultures that tested positive for Burkholderia pseudomallei. The clinical prediction rules were developed using significant risk factors from the multivariable analysis. Results: A total of 282 patients with melioidosis were included in this study. In the final analysis model, 251 patients were used for identifying the significant risk factors of in-hospital fatal melioidosis. Five factors were identified and used for develo** the clinical prediction rules, and the factors were as follows: qSOFA ≥ 2 (odds ratio [OR] = 2.39, p= 0.025), abnormal chest X-ray findings (OR = 5.86, p < 0.001), creatinine ≥ 1.5 mg/dL (OR = 2.80, p = 0.004), aspartate aminotransferase ≥50 U/L (OR = 4.032, p < 0.001), and bicarbonate ≤ 20 mEq/L (OR = 2.96, p = 0.002). The prediction scores ranged from 0 to 7. Patients with high scores (4–7) exhibited a significantly elevated mortality rate exceeding 65.0% (likelihood ratio [LR+] 2.18, p < 0.001) compared to the low-risk group (scores 0–3) with a lower mortality rate (LR + 0.18, p < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.84, indicating good model performance. Conclusions: This study presents a simple scoring system based on easily obtainable clinical parameters to predict in-hospital mortality in melioidosis patients. This tool may facilitate the early identification of high-risk patients who could benefit from more aggressive treatment strategies, potentially improving clinical decision-making and patient outcomes.