Norrman Anders, Hasselström Jan, Ljunggren Gunnar, Wachtler Caroline, Eriksson Julia, Kahan Thomas, Wändell Per, Gudjonsdottir Hrafnhildur, Lindblom Sebastian, Ruge Toralph, Rosenblad Andreas, Brynedal Boel, Carlsson Axel C
Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden.
Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden.
Prev Med Rep. 2024 Jun 30;44:102806. doi: 10.1016/j.pmedr.2024.102806. eCollection 2024 Aug.
Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care.
This sex- and age-matched case-control (1:5) study included patients aged 30-65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010-19 (cases) and individuals without a recorded hypertension diagnosis during 2010-19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis.
The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742-0.753) for females and 0.745 (0.740-0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances.
This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.
许多高血压患者仍未被诊断出来。我们旨在利用瑞典初级保健中现有电子病历的诊断编码开发一种高血压预测模型。
这项性别和年龄匹配的病例对照（1:5）研究纳入了年龄在30 - 65岁之间、居住在瑞典斯德哥尔摩地区、在2010 - 2019年期间新记录有高血压诊断的患者（病例组）以及在2010 - 2019年期间没有记录高血压诊断的个体（对照组），共计507,618人。排除患有心血管疾病或糖尿病诊断的患者。使用高血压诊断前三年初级保健中最常记录的1309个ICD - 10编码构建随机梯度提升机器学习模型。
该模型在预测三年内高血压诊断方面，女性的曲线下面积（95%置信区间）为0.748（0.742 - 0.753），男性为0.745（0.740 - 0.751）。女性和男性的敏感性分别为63%和68%，特异性分别为76%和73%。对女性和男性模型贡献最大的25种诊断的标准化相对影响均>1%。对模型贡献最大的编码，两性的边际效应比值比均>1，分别是血脂异常、肥胖以及在其他情况下接受接受医疗保健服务。
这种使用初级卫生保健中现有记录诊断的机器学习模型可能有助于识别有未被识别高血压风险的患者。该预测模型超出血压信息的附加值值得进一步研究。
