title: A clinical model to estimate the pretest probability of lung cancer, based on 1198 pedigrees in China.
abstract: BACKGROUND
  Computed tomography screening can detect lung cancer that is curable. However, some studies demonstrated that the risk for false-positives was about 50%. To make screening more efficient, we sought to create a forecasting model for individuals with different risks for lung cancer.
  
  
  METHODS
  We used multiple logistic regression analysis to identify independent predictors and to develop a prediction model. The pathological diagnoses in Guangdong Lung Cancer Institute were consecutively chosen as probands. All first-degree relatives of probands and their spouses were included as subjects. We divided the probands and their spouses into three subgroups according to the odds ratios (ORs), and the accuracy of lung cancer predictions for patients within the subgroups increased synchronously.
  
  
  RESULTS
  There were 633 proband pedigrees and 565 spouse pedigrees. Independent predictors of lung cancer included sex (OR, 1.6; 95% confidence interval [CI], 1.1-2.3), smoking history (light smoker: OR, 1.1; 95% CI, 0.7-1.8; heavy smoker: OR, 4.7; 95% CI, 3.1-7.1), lung disease history (OR, 5.3; 95% CI, 2.8-10.0), occupational exposure (OR, 1.6; 95% CI, 1.1-2.2), and number of affected individuals among first-degree relatives (n = 1: OR, 2.1; 95% CI, 1.3-3.4; n ≥ 2: OR, 4.7; 95% CI, 0.5-41.2). The accuracy of the pretest probability increased for those with higher ORs: low-OR subgroup, 68.3%; mid-OR subgroup, 84.0%; and high-OR subgroup, 91.9%.
  
  
  CONCLUSIONS
  Our prediction rule is recommended for estimating the pretest probability of lung cancer, thereby facilitating early screening.
authors.affiliation: Guangdong Lung Cancer Institute, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China.
authors.forename: Huan
authors.initials: H
authors.lastname: Lin
authors.forename: Wen-Zhao
authors.initials: WZ
authors.lastname: Zhong
authors.forename: Xue-Ning
authors.initials: XN
authors.lastname: Yang
authors.forename: Xu-Chao
authors.initials: XC
authors.lastname: Zhang
authors.forename: Jin-Ji
authors.initials: JJ
authors.lastname: Yang
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhou
authors.forename: Hong-Hong
authors.initials: HH
authors.lastname: Yan
authors.forename: Ri-Qiang
authors.initials: RQ
authors.lastname: Liao
authors.forename: Qiang
authors.initials: Q
authors.lastname: Nie
authors.forename: Song
authors.initials: S
authors.lastname: Dong
authors.forename: Yi-Long
authors.initials: YL
authors.lastname: Wu
mesh_terms: D000230:Adenocarcinoma / Q000453:epidemiology* / Q000209:etiology; D002289:Carcinoma, Non-Small-Cell Lung / Q000453:epidemiology* / Q000209:etiology; D002294:Carcinoma, Squamous Cell / Q000453:epidemiology* / Q000209:etiology; D002681:China / Q000453:epidemiology; D015331:Cohort Studies; D005260:Female; D005500:Follow-Up Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000209:etiology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009367:Neoplasm Staging; D016273:Occupational Exposure / Q000009:adverse effects; D010375:Pedigree; D011336:Probability; D011379:Prognosis; D012307:Risk Factors; D055752:Small Cell Lung Carcinoma / Q000453:epidemiology* / Q000209:etiology; D012907:Smoking / Q000009:adverse effects