Mamun Gazi Md Salahuddin, Zou Michael, Sarmin Monira, Brintz Ben J, Rahman Abu Sayem Mirza Md Hasibur, Parvin Irin, Ackhter Mst Mahmuda, Chisti Mohammod Jobayer, Leung Daniel T, Shahrin Lubaba
Infectious Diseases Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City, Utah, United States of America.
PLOS Glob Public Health. 2023 Aug 1;3(8):e0002216. doi: 10.1371/journal.pgph.0002216. eCollection 2023.
Children with severe pneumonia in low- and middle-income countries (LMICs) suffer from high rates of treatment failure despite appropriate World Health Organization (WHO)-directed antibiotic treatment. Developing a clinical prediction rule for treatment failure may allow early identification of high-risk patients and timely intervention to decrease mortality. We used data from two separate studies conducted at the Dhaka Hospital of the International Centre for Diarrheal Disease Research, Bangladesh (icddr,b) to derive and externally validate a clinical prediction rule for treatment failure of children hospitalized with severe pneumonia. The derivation dataset was from a randomized clinical trial conducted from 2018 to 2019, studying children aged 2 to 59 months hospitalized with severe pneumonia as defined by WHO. Treatment failure was defined by the persistence of danger signs at the end of 48 hours of antibiotic treatment or the appearance of any new danger signs within 24 hours of enrollment. We built a random forest model to identify the top predictors. The top six predictors were the presence of grunting, room air saturation, temperature, the presence of lower chest wall indrawing, the presence of respiratory distress, and central cyanosis. Using these six predictors, we created a parsimonious model with a discriminatory performance of 0.691, as measured by area under the receiving operating curve (AUC). We performed external validation using a temporally distinct dataset from a cohort study of 191 similarly aged children with severe acute malnutrition and pneumonia. In external validation, discriminatory performance was maintained with an improved AUC of 0.718. In conclusion, we developed and externally validated a parsimonious six-predictor model using random forest methods to predict treatment failure in young children with severe pneumonia in Bangladesh. These findings can be used to further develop and validate parsimonious and pragmatic prognostic clinical prediction rules for pediatric pneumonia, particularly in LMICs.
在低收入和中等收入国家（LMICs），患有严重肺炎的儿童尽管接受了世界卫生组织（WHO）指导的适当抗生素治疗，但治疗失败率仍然很高。制定治疗失败的临床预测规则可能有助于早期识别高危患者并及时进行干预以降低死亡率。我们使用了在孟加拉国腹泻疾病国际研究中心达卡医院（icddr,b）进行的两项独立研究的数据，来推导并外部验证针对因严重肺炎住院儿童治疗失败的临床预测规则。推导数据集来自2018年至2019年进行的一项随机临床试验，研究对象为2至59个月大、因WHO定义的严重肺炎住院的儿童。治疗失败定义为抗生素治疗48小时结束时危险体征持续存在，或入院后24小时内出现任何新的危险体征。我们构建了一个随机森林模型来确定最重要的预测因素。最重要的六个预测因素是呻吟声的存在、室内空气饱和度、体温、下胸壁凹陷的存在、呼吸窘迫的存在以及中心性发绀。使用这六个预测因素，我们创建了一个简约模型，其鉴别性能通过接受操作曲线下面积（AUC）衡量为0.691。我们使用来自一项对191名年龄相仿的患有严重急性营养不良和肺炎儿童的队列研究的时间上不同的数据集进行了外部验证。在外部验证中，鉴别性能得以维持，AUC提高到了0.718。总之，我们使用随机森林方法开发并外部验证了一个简约的六预测因素模型，以预测孟加拉国患有严重肺炎幼儿的治疗失败情况。这些发现可用于进一步开发和验证针对小儿肺炎的简约且实用的预后临床预测规则，尤其是在低收入和中等收入国家。
