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A Clinical Model To Estimate the Pretest Probability of Lung Cancer in Patients With Solitary Pulmonary Nodules^*

^* From the Veterans Affairs Palo Alto Health Care System (Dr. Gould), Palo Alto, CA; and the Veterans Affairs Health Economics Resource Center (Ms. Ananth and Dr. Barnett), Menlo Park, CA.

Correspondence to: Michael K. Gould, MD, MS, FCCP, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Ave (111P), Palo Alto, CA 94304; e-mail: gould{at}stanford.edu

Abstract

Background: Estimating the clinical probability of malignancy in patients with a solitary pulmonary nodule (SPN) can facilitate the selection and interpretation of subsequent diagnostic tests.

Methods: We used multiple logistic regression analysis to identify independent clinical predictors of malignancy and to develop a parsimonious clinical prediction model to estimate the pretest probability of malignancy in a geographically diverse sample of 375 veterans with SPNs. We used data from Department of Veterans Affairs (VA) administrative databases and a recently completed VA Cooperative Study that evaluated the accuracy of positron emission tomography (PET) scans for the diagnosis of SPNs.

Results: The mean (± SD) age of subjects in the sample was 65.9 ± 10.7 years. The prevalence of malignant SPNs was 54%. Most participants were either current smokers (n = 177) or former smokers (n = 177). Independent predictors of malignant SPNs included a positive smoking history (odds ratio [OR], 7.9; 95% confidence interval [CI], 2.6 to 23.6), older age (OR, 2.2 per 10-year increment; 95% CI, 1.7 to 2.8), larger nodule diameter (OR, 1.1 per 1-mm increment; 95% CI, 1.1 to 1.2), and time since quitting smoking (OR, 0.6 per 10-year increment; 95% CI, 0.5 to 0.7). Model accuracy was very good (area under the curve of the receiver operating characteristic, 0.79; 95% CI, 0.74 to 0.84), and there was excellent agreement between the predicted probability and the observed frequency of malignant SPNs.

Conclusions: Our prediction rule can be used to estimate the pretest probability of malignancy in patients with SPNs, and thereby facilitate clinical decision making when selecting and interpreting the results of diagnostic tests such as PET imaging.

Key Words: coin lesion • diagnosis • lung neoplasms • pulmonary • receiver operating characteristic curve • statistical models • veterans