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.
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.
Authors: L Goldman; E F Cook; D A Brand; T H Lee; G W Rouan; M C Weisberg; D Acampora; C Stasiulewicz; J Walshon; G Terranova Journal: N Engl J Med Date: 1988-03-31 Impact factor: 91.245
Authors: Eric S Johnson; Jessica R Weinstein; Micah L Thorp; Robert W Platt; Amanda F Petrik; Xiuhai Yang; Sharon Anderson; David H Smith Journal: Pharmacoepidemiol Drug Saf Date: 2010-03 Impact factor: 2.890
Authors: Wolfgang C Winkelmayer; Michael A Fischer; Sebastian Schneeweiss; Raisa Levin; Jerry Avorn Journal: J Gen Intern Med Date: 2006-12 Impact factor: 5.128
Authors: Antonio L Dans; Koon Teo; Peggy Gao; Jyh-Hong Chen; Kim Jae-Hyung; Khalid Yusoff; Suphachai Chaithiraphan; Jun Zhu; Liu Lisheng; Salim Yusuf Journal: PLoS One Date: 2010-12-21 Impact factor: 3.240