Development and validation of a clinical prediction rule for angiotensin-converting enzyme inhibitor-induced cough

建立并验证血管紧张素转换酶抑制剂诱发咳嗽的临床预测规则

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Abstract

BACKGROUND: Angiotensin-converting enzyme inhibitors are effective for many cardiovascular diseases and are widely prescribed, but cough sometimes necessitates their withdrawal. OBJECTIVE: To develop and validate a model that predicts, by using information available at first prescription, whether a patient will develop cough within 6 months. DESIGN: Retrospective cohort study with derivation and validation sets. SETTING: Outpatient clinics affiliated with an urban tertiary care hospital. PATIENTS: Clinical data were collected from electronic charts. The derivation set included 1125 patients and the validation set included 567 patients. INTERVENTIONS: None. MEASUREMENTS: Angiotensin-converting enzyme inhibitor-induced cough assessed by predetermined criteria. RESULTS: In the total cohort, 12% of patients developed angiotensin-converting enzyme inhibitor-induced cough. Independent multivariate predictors of cough were older age, female gender, non-African American (with East Asian having highest risk), no history of previous angiotensin-converting enzyme inhibitor use, and history of cough due to another angiotensin-converting enzyme inhibitor. Patients with a history of angiotensin-converting enzyme inhibitor-induced cough were 29 times more likely to develop a cough than those without this history. These factors were used to develop a model stratifying patients into 4 risk groups. In the derivation set, low-risk, average-risk, intermediate-risk, and high-risk groups had a 6%, 9%, 22%, and 55% probability of cough, respectively. In the validation set, 4%, 14%, 20%, and 60% of patients in these 4 groups developed cough, respectively. CONCLUSIONS: This model may help clinicians predict the likelihood of a particular patient developing cough from an angiotensin-converting enzyme inhibitor at the time of prescribing, and may also assist with subsequent clinical decisions.

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