基础疾病对脓毒性休克患儿临床特征和预后的影响

目的了解儿童重症监护病房(pediatric intensive care unit,PICU)内不同基础性疾病并发脓毒性休克(septic shock,SS)患儿的临床特征和预后。方法回顾性收集2017年1月1日至2019年12月31日北京儿童医院PICU收治的SS患儿病历资料,按照有无基础疾病、基础疾病种类进行分组,总结不同基础疾病条件下SS的临床特征、预后及病原分布情况。结果研究期间共收治218例SS患儿,总病死率为21.6%(47/218);合并基础疾病者141例[64.7%(141/218)],病死率24.1%(34/141);处于化疗骨髓抑制期的恶性血液病和肿瘤患儿病死率最高(17/45,37.5%),无基础性疾病者病死率最低(13/77,16.9%)。合并基础性疾病的SS患儿以革兰阴性菌感染为主(63.1%,41/65),恶性血液病及肿瘤化疗后骨髓抑制期患儿革兰阴性菌感染最高(80.0%,20/25)。革兰阳性菌感染在无基础疾病组最高(52.1%,25/48)。多器官功能障碍综合征(multiple organ dysfunction syndrome,MODS)在恶性血液病及肿瘤化疗后骨髓抑制期者的发生率最高(95.6%,43/45),无基础疾病组最低(59.7%,46/77)。结论伴发基础疾病,尤其是血液肿瘤的患儿化疗后发生SS时病原体以革兰阴性菌最常见,病死率和MODS发生率高;无基础疾病者以革兰阳性菌最常见,病死率和MODS发生率相对较低。

Revisiting classical SIR modelling in light of the COVID-19 pandemic

Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.