丹麦处方定义和医院诊断的高血压与自述高血压的有效性比较
IF 3.4
2区 医学
Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
引用次数: 0
摘要
目的：高血压是心脏流行病学研究中的一个重要风险因素，但数据质量仍是一个令人担忧的问题。我们验证了基于登记册的不同高血压定义：研究对象包括丹麦国家健康调查（2010 年、2013 年或 2017 年）的所有首次应答者。处方定义的高血压是指在调查回答前 90 天、180 天或 365 天内，≥ 1 或≥ 2 个已开具的≥ 1 或≥ 2 个不同降压药物类别的降压药物处方。医院诊断的高血压是指调查回答前五年内诊断出的高血压。以自我报告的高血压为参考，我们计算了处方定义的高血压和医院诊断的高血压的阳性预测值（PPV）、阴性预测值（NVP）、灵敏度和特异性：在 442 490 名调查对象中，127 247 人（29%）自述患有高血压。对于处方定义的高血压（365 天回溯），≥ 2 种药物类别中≥ 2 个处方的 PPV 最高（94%），≥ 1 种药物类别中≥ 1 个处方的 PPV 最低（85%）。≥2类药物中≥1张处方的NPV最高（94%），≥2类药物中≥1张处方的NPV最低（80%）。≥1类药物中≥1张处方的灵敏度最高（79%），≥2类药物中≥2张处方的灵敏度最低（30%）。所有算法的特异性均≥94%。PPV 和特异性没有随回溯期的长短发生显著变化，而 NPV 和灵敏度通常在回溯期较长时较高。回溯期为 365 天、≥ 1 个处方≥ 2 个药物类别的算法在所有有效性衡量标准中都是最均衡的（PPV=88%，NPV=94%，灵敏度=75%，特异性=96%）。对于医院诊断的高血压，PPV 为 90%，NPV 为 76%，灵敏度为 22%，特异性为 99%：与自我报告的高血压相比，处方定义的高血压和医院诊断的高血压的算法具有较高的预测值和特异性，但灵敏度较低。 关键词：流行病学研究；流行病学；高血压；检测的预测值；灵敏度和特异性；验证研究本文章由计算机程序翻译，如有差异，请以英文原文为准。
Validity of Prescription-Defined and Hospital-Diagnosed Hypertension Compared with Self-Reported Hypertension in Denmark
Purpose: Hypertension is an important risk factor in cardio-epidemiological research, but data quality remains a concern. We validated different registry-based definitions of hypertension.
Patients and Methods: The cohort included all first-time responders of the Danish National Health Surveys (2010, 2013, or 2017). Prescription-defined hypertension was defined as ≥ 1 or ≥ 2 filled prescriptions of antihypertensive specific drugs in ≥ 1 or ≥ 2 different antihypertensive drug classes within 90, 180, or 365 days before survey response. Hospital-diagnosed hypertension was defined from hypertension diagnoses within five years before the survey response. Considering self-reported hypertension as the reference, we calculated the positive predictive value (PPV), the negative predictive value (NVP), the sensitivity, and the specificity of prescription-defined and hospital-diagnosed hypertension.
Results: Among 442,490 survey responders, 127,247 (29%) had self-reported hypertension. For prescription-defined hypertension with 365-day lookback, the PPV was highest for ≥ 2 prescriptions in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 1 drug class (85%). The NPV was highest for ≥ 1 prescription in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 2 drug classes (80%). The sensitivity was highest for ≥ 1 prescription in ≥ 1 drug class (79%) and lowest for ≥ 2 prescriptions in ≥ 2 drug classes (30%). The specificity was ≥ 94% for all algorithms. The PPV and specificity did not change noteworthy with length of lookback period, whereas the NPV and the sensitivity generally were higher for longer lookback. The algorithm ≥ 1 prescription in ≥ 2 drug classes with 365-day lookback was among the best balanced across all measures of validity (PPV=88%, NPV=94%, sensitivity=75%, specificity=96%). For hospital-diagnosed hypertension, the PPV was 90%, the NPV was 76%, the sensitivity was 22%, and the specificity was 99%.
Conclusion: Compared with self-reported hypertension, the algorithms for prescription-defined and hospital-diagnosed hypertension had high predictive values and specificity, but low sensitivity.
Keywords: epidemiologic studies, epidemiology, hypertension, predictive value of tests, sensitivity and specificity, validation study
Patients and Methods: The cohort included all first-time responders of the Danish National Health Surveys (2010, 2013, or 2017). Prescription-defined hypertension was defined as ≥ 1 or ≥ 2 filled prescriptions of antihypertensive specific drugs in ≥ 1 or ≥ 2 different antihypertensive drug classes within 90, 180, or 365 days before survey response. Hospital-diagnosed hypertension was defined from hypertension diagnoses within five years before the survey response. Considering self-reported hypertension as the reference, we calculated the positive predictive value (PPV), the negative predictive value (NVP), the sensitivity, and the specificity of prescription-defined and hospital-diagnosed hypertension.
Results: Among 442,490 survey responders, 127,247 (29%) had self-reported hypertension. For prescription-defined hypertension with 365-day lookback, the PPV was highest for ≥ 2 prescriptions in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 1 drug class (85%). The NPV was highest for ≥ 1 prescription in ≥ 2 drug classes (94%) and lowest for ≥ 1 prescription in ≥ 2 drug classes (80%). The sensitivity was highest for ≥ 1 prescription in ≥ 1 drug class (79%) and lowest for ≥ 2 prescriptions in ≥ 2 drug classes (30%). The specificity was ≥ 94% for all algorithms. The PPV and specificity did not change noteworthy with length of lookback period, whereas the NPV and the sensitivity generally were higher for longer lookback. The algorithm ≥ 1 prescription in ≥ 2 drug classes with 365-day lookback was among the best balanced across all measures of validity (PPV=88%, NPV=94%, sensitivity=75%, specificity=96%). For hospital-diagnosed hypertension, the PPV was 90%, the NPV was 76%, the sensitivity was 22%, and the specificity was 99%.
Conclusion: Compared with self-reported hypertension, the algorithms for prescription-defined and hospital-diagnosed hypertension had high predictive values and specificity, but low sensitivity.
Keywords: epidemiologic studies, epidemiology, hypertension, predictive value of tests, sensitivity and specificity, validation study
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来源期刊
Clinical Epidemiology
Medicine-Epidemiology
CiteScore
6.30
自引率
5.10%
发文量
169
审稿时长
16 weeks
期刊介绍：
Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment.
Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews.
Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews.
When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes.
The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.
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