Zaccardi Francesco, Webb David R, Davies Melanie J, Dhalwani Nafeesa N, Gray Laura J, Chatterjee Sudesna, Housley Gemma, Shaw Dominick, Hatton James W, Khunti Kamlesh
Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK.
Department of Health Sciences, University of Leicester, University Road, Leicester, UK.
Diabetologia. 2017 Jun;60(6):1007-1015. doi: 10.1007/s00125-017-4235-1. Epub 2017 Mar 17.
AIMS/HYPOTHESIS: Hospital admissions for hypoglycaemia represent a significant burden on individuals with diabetes and have a substantial economic impact on healthcare systems. To date, no prognostic models have been developed to predict outcomes following admission for hypoglycaemia. We aimed to develop and validate prediction models to estimate risk of inpatient death, 24 h discharge and one month readmission in people admitted to hospital for hypoglycaemia.
We used the Hospital Episode Statistics database, which includes data on all hospital admission to National Health Service hospital trusts in England, to extract admissions for hypoglycaemia between 2010 and 2014. We developed, internally and temporally validated, and compared two prognostic risk models for each outcome. The first model included age, sex, ethnicity, region, social deprivation and Charlson score ('base' model). In the second model, we added to the 'base' model the 20 most common medical conditions and applied a stepwise backward selection of variables ('disease' model). We used C-index and calibration plots to assess model performance and developed a calculator to estimate probabilities of outcomes according to individual characteristics.
In derivation samples, 296 out of 11,136 admissions resulted in inpatient death, 1789/33,825 in one month readmission and 8396/33,803 in 24 h discharge. Corresponding values for validation samples were: 296/10,976, 1207/22,112 and 5363/22,107. The two models had similar discrimination. In derivation samples, C-indices for the base and disease models, respectively, were: 0.77 (95% CI 0.75, 0.80) and 0.78 (0.75, 0.80) for death, 0.57 (0.56, 0.59) and 0.57 (0.56, 0.58) for one month readmission, and 0.68 (0.67, 0.69) and 0.69 (0.68, 0.69) for 24 h discharge. Corresponding values in validation samples were: 0.74 (0.71, 0.76) and 0.74 (0.72, 0.77), 0.55 (0.54, 0.57) and 0.55 (0.53, 0.56), and 0.66 (0.65, 0.67) and 0.67 (0.66, 0.68). In both derivation and validation samples, calibration plots showed good agreement for the three outcomes. We developed a calculator of probabilities for inpatient death and 24 h discharge given the low performance of one month readmission models.
CONCLUSIONS/INTERPRETATION: This simple and pragmatic tool to predict in-hospital death and 24 h discharge has the potential to reduce mortality and improve discharge in people admitted for hypoglycaemia.
目的/假设：低血糖导致的住院治疗给糖尿病患者带来了沉重负担，并对医疗系统产生了重大经济影响。迄今为止，尚未开发出预测低血糖住院治疗后结局的预后模型。我们旨在开发并验证预测模型，以估计低血糖住院患者的住院死亡风险、24小时出院风险和1个月再入院风险。
我们使用医院事件统计数据库（该数据库包含英格兰国民健康服务医院信托机构所有住院治疗的数据），提取2010年至2014年间因低血糖住院的病例。我们针对每个结局开发、进行内部和时间验证并比较了两个预后风险模型。第一个模型包括年龄、性别、种族、地区、社会剥夺程度和查尔森评分（“基础”模型）。在第二个模型中，我们在“基础”模型中加入了20种最常见的疾病状况，并采用逐步向后变量选择法（“疾病”模型）。我们使用C指数和校准图来评估模型性能，并开发了一个计算器，根据个体特征估计结局的概率。
在推导样本中，11136例住院病例中有296例导致住院死亡，33825例中有1789例在1个月内再入院，33803例中有8396例在24小时内出院。验证样本的相应数值分别为：10976例中有296例、22112例中有1207例、22107例中有5363例。这两个模型具有相似的辨别能力。在推导样本中，基础模型和疾病模型的死亡C指数分别为：0.77（95%CI 0.75，0.80）和0.78（0.75，0.80）；1个月再入院的C指数分别为0.57（0.56，0.59）和0.57（0.56，0.58）；24小时出院的C指数分别为0.68（0.67，0.69）和0.69（0.68，0.69）。验证样本中的相应数值分别为：0.74（0.71，0.76）和0.74（0.72，0.77）；0.55（0.54，0.57）和0.55（0.53，0.56）；0.66（0.65，0.67）和0.67（0.66，0.68）。在推导样本和验证样本中，校准图显示这三种结局的一致性良好。由于1个月再入院模型性能较差，我们开发了住院死亡和24小时出院概率计算器。
结论/解读：这个预测住院死亡和24小时出院的简单实用工具，有可能降低低血糖住院患者的死亡率并改善出院情况。
