BACKGROUND: Dyspnea is a common symptom among patients with heart failure. Currently, there is no standardized, rapid, precise method to assess dyspnea. METHODS AND RESULTS: From a review of the literature, we pooled questions from various questionnaires assessing dyspnea. A total of 201 patients with heart failure completed all questions in the preliminary item bank. Each item asks how much shortness of breath the patient had when doing an activity. Medical charts were reviewed for hospitalization within 1 or 3 months of completing the questions. We created a dyspnea item bank of 44 items. Computer adaptive tests (CAT) generated from this item bank can assess dyspnea by administering on average 10 questions. Simulation CAT scores were generated to compare with the item bank scores. The CAT scores had a correlation of 0.98 with item bank scores. Logistic regression models predicting the probability of being hospitalized from the dyspnea score were statistically significant (P < .05). A 5-point score increase was associated with a 32% increased odds of hospitalization in 1 month and a 20% increased odds of hospitalization in 3 months. CONCLUSIONS: This computer-based tool for dyspnea assessment obtains similar precision to that of answering the entire dyspnea item bank with less patient burden. Copyright (c) 2010 Elsevier Inc. All rights reserved.
BACKGROUND:Dyspnea is a common symptom among patients with heart failure. Currently, there is no standardized, rapid, precise method to assess dyspnea. METHODS AND RESULTS: From a review of the literature, we pooled questions from various questionnaires assessing dyspnea. A total of 201 patients with heart failure completed all questions in the preliminary item bank. Each item asks how much shortness of breath the patient had when doing an activity. Medical charts were reviewed for hospitalization within 1 or 3 months of completing the questions. We created a dyspnea item bank of 44 items. Computer adaptive tests (CAT) generated from this item bank can assess dyspnea by administering on average 10 questions. Simulation CAT scores were generated to compare with the item bank scores. The CAT scores had a correlation of 0.98 with item bank scores. Logistic regression models predicting the probability of being hospitalized from the dyspnea score were statistically significant (P < .05). A 5-point score increase was associated with a 32% increased odds of hospitalization in 1 month and a 20% increased odds of hospitalization in 3 months. CONCLUSIONS: This computer-based tool for dyspnea assessment obtains similar precision to that of answering the entire dyspnea item bank with less patient burden. Copyright (c) 2010 Elsevier Inc. All rights reserved.
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