背景:虽然先前的研究已经确定了糖尿病酮症酸中毒(DKA)的多种危险因素,临床医生仍然缺乏临床准备模型来预测DKA的危险和昂贵的发作。我们问我们是否可以应用深度学习,特别是使用长期短期(LSTM)模型,准确预测青少年1型糖尿病(T1D)患者DKA相关住院180天风险。
目的:描述LSTM模型的发展,以预测T1D青年DKA相关住院的180天风险。
方法:我们使用了17个连续日历季度的临床数据(2016年01月10日-2020年03月18日),来自美国中西部儿童糖尿病临床网络的1745名8至18岁的T1D青年。输入数据包括人口统计,离散临床观察(实验室结果,生命体征,人体测量,诊断和程序代码),药物,按相遇类型划分的访问计数,历史DKA事件的数量,自上次DKA入院以来的天数,患者报告的结果(诊所摄入量问题的答案),以及通过自然语言处理(NLP)从与糖尿病和非糖尿病相关的临床笔记中得出的数据特征。我们使用来自季度1-7的输入数据(n=1377)训练模型,使用部分样本外队列(OOS-P;n=1505)中第3-9季度的输入进行验证,并在一个完整的样本外队列(OOS-F;n=354)中进一步验证,输入来自10-15季度。
结果:在两个OOS队列中,DKA入院的发生率为每180天5%。对于OOS-P和OOS-F队列,分别为:中位年龄13.7岁(IQR=11.3,15.8)和13.1岁(10.7,15.5);纳入时HbA1c分别为8.6%(7.6,9.8)[70(60,84)mmol/mol]和8.1%(6.9,9.5)[65(52,80)mmol/mol];14%和13%曾有DKA入院经历(T1D诊断后);召回率分别为0.33和第对于按住院概率排序的列表,OOS-P队列中位置1-80,1-25和1-10的精确度从0.33提高到0.56至1.0,OOS-F队列中位置1-18,1-10和1-5的精确度从0.50提高到0.60至0.80.
结论:提出的用于预测180天DKA相关住院的LSTM模型在本样本中是有效的。未来的工作应评估模型在多个人群和环境中的有效性,以解决可能存在于不同人群中的健康不平等现象(例如,种族和/或社会经济多样化的群体)。按DKA相关住院的概率对青年进行排序将使诊所能够识别出风险最高的青年。这样做的临床意义是,诊所可以根据可用资源创建和评估新的预防性干预措施。
BACKGROUND: Although prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D).
OBJECTIVE: We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D.
METHODS: We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory results, vital signs, anthropometric measures, diagnosis, and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15.
RESULTS: DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively.
CONCLUSIONS: The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially or socioeconomically diverse cohorts). Rank ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.