Department of General Medicine, Amrita Institute of Medical Sciences and Research Centre, AIMS Ponekkara P. O, Kochi, Ernakulam 682041, Kerala, India.
Department of Biostatistics, Amrita Institute of Medical Sciences and Research Centre, AIMS Ponekkara P. O, Kochi, Ernakulam 682041, Kerala, India.
Trans R Soc Trop Med Hyg. 2023 Oct 3;117(10):741-750. doi: 10.1093/trstmh/trad058.
The study aimed to identify predictors of severe dengue during the 2017 epidemic and to develop and validate a simple predictive score for severity.
A retrospective analytical study was conducted using clinical and laboratory data from adult dengue patients with a confirmed microbiological diagnosis. The study included patients who presented to a tertiary care centre in Kerala, India, during the febrile phase (≤4 d) between June 2017 and February 2019. Using appropriate statistical tests, we derived predictors of severe disease and computed a risk score model.
Of the 153 patients (mean age 50±17 y; 64% males), 31 (20%) had severe dengue and 4 (3%) died. Petechial lesions, hypoalbuminemia (<3.5 g/dl), elevated alanine aminotransferase (>40 IU/l) and urea >40 IU/l were significant predictors. Our scoring system (cut-off: 2) showed excellent performance, with an area under the receiver operating characteristics curve of 0.9741, sensitivity of 100%, specificity of 96% and accuracy of 98%. The risk score was secondarily validated on 48 patients hospitalized from March 2019 to June 2019.
Our scoring system is easy to implement and will help primary healthcare practitioners in promptly identifying severe dengue cases upon hospital presentation.
本研究旨在确定 2017 年流行期间重症登革热的预测因素，并制定和验证一个简单的严重程度预测评分。
采用回顾性分析方法，对 2017 年 6 月至 2019 年 2 月期间在印度喀拉拉邦一家三级保健中心就诊的成年登革热患者的临床和实验室数据进行研究。研究纳入了在发热期（≤4 天）就诊的患者。我们通过适当的统计检验确定疾病严重程度的预测因素，并计算风险评分模型。
153 例患者中（平均年龄 50±17 岁；64%为男性），31 例（20%）为重症登革热，4 例（3%）死亡。瘀点、低白蛋白血症（<3.5 g/dl）、丙氨酸氨基转移酶升高（>40 IU/l）和尿素>40 IU/l 是显著的预测因素。我们的评分系统（截值：2）表现出优异的性能，ROC 曲线下面积为 0.9741，灵敏度为 100%，特异性为 96%，准确性为 98%。该风险评分在 2019 年 3 月至 2019 年 6 月期间住院的 48 例患者中进行了二次验证。
我们的评分系统易于实施，将有助于基层医疗保健工作者在患者入院时及时识别重症登革热病例。
