Chayangsu Sunee, Suankratay Chusana, Tantraworasin Apichat, Khorana Jiraporn
Department of Internal Medicine, Surin Hospital, Surin 32000, Thailand.
Department of Internal Medicine, Faculty of Medicine, The King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok 10330, Thailand.
Trop Med Infect Dis. 2024 Jun 28;9(7):146. doi: 10.3390/tropicalmed9070146.
Melioidosis, a disease induced by , 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.
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 . The clinical prediction rules were developed using significant risk factors from the multivariable analysis.
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 developing the clinical prediction rules, and the factors were as follows: qSOFA ≥ 2 (odds ratio [OR] = 2.39, = 0.025), abnormal chest X-ray findings (OR = 5.86, < 0.001), creatinine ≥ 1.5 mg/dL (OR = 2.80, = 0.004), aspartate aminotransferase ≥50 U/L (OR = 4.032, < 0.001), and bicarbonate ≤ 20 mEq/L (OR = 2.96, = 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, < 0.001) compared to the low-risk group (scores 0-3) with a lower mortality rate (LR + 0.18, < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.84, indicating good model performance.
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.
类鼻疽病是一种由[病原体名称未给出]引起的疾病，在其流行的热带地区对健康构成重大威胁。尽管有有效的治疗方法，但死亡率仍然显著升高。许多风险因素与死亡率相关。本研究旨在利用现成的临床数据开发一种用于预测类鼻疽病患者住院死亡率的评分系统。
数据收集于2014年4月至2017年3月期间泰国素林府的素林医院。我们纳入了年龄在15岁及以上且[病原体名称未给出]培养检测呈阳性的患者。临床预测规则是根据多变量分析中的显著风险因素制定的。
本研究共纳入282例类鼻疽病患者。在最终分析模型中，251例患者用于确定住院类鼻疽病致死的显著风险因素。确定了五个因素并用于制定临床预测规则，这些因素如下：快速序贯器官衰竭评估（qSOFA）≥2（比值比[OR]=2.39，P = 0.025）、胸部X线检查结果异常（OR = 5.86，P < 0.001）、肌酐≥1.5mg/dL（OR = 2.80，P = 0.004）、天冬氨酸转氨酶≥50U/L（OR = 4.032，P < 0.001）以及碳酸氢盐≤20mEq/L（OR = 2.96，P = 0.002）。预测分数范围为0至7。与低风险组（分数0至3）死亡率较低（阳性似然比[LR +]0.18，P < 0.001）相比，高分数（4至7）患者的死亡率显著升高，超过65.0%（LR + 2.18，P < 0.001）。受试者工作特征曲线（AUC）下面积为0.84，表明模型性能良好。
本研究提出了一种基于易于获取的临床参数的简单评分系统，用于预测类鼻疽病患者的住院死亡率。该工具可能有助于早期识别可从更积极治疗策略中获益的高危患者， potentially improving clinical decision-making and patient outcomes.（原文此处表述有误，应改为“potentially improving clinical decision-making and patient outcomes”，译文：可能改善临床决策和患者预后） 这可能改善临床决策和患者预后。
