A risk factor‐based model for upper aerodigestive tract cancers in India: predicting and validating the receiver operating characteristic curve. (24th November 2016)

• Record Type: Journal Article
• Authors: Gupta, Bhawna; Kumar, Narinder; Johnson, Newell W.
• Is Part Of: Journal of oral pathology & medicine. Volume 46:Number 6(2017)
• Page Start: 465
• Page End: 471
• Publication Date: 2016-11-24
• Subjects: case–control study -- high‐risk population -- receiver operating characteristic curve -- risk factors -- screening -- upper aerodigestive tract cancers; Dentistry -- Periodicals; Teeth -- Diseases -- Periodicals; 617
• DOI: 10.1111/jop.12520
• Languages: English
• ISSNs: 0904-2512

Abstract

Abstract : Background: A study was conducted to develop and validate a screening model using risk scores to identify individuals at high risk for developing upper aerodigestive tract (UADT) cancers in an Indian population. Methods: A hospital‐based case–control study ( n = 480) was conducted in Pune, India. We assessed risk factors for UADT cancers by administering a questionnaire through face‐to‐face interviews. We developed a risk factor model based on the statistically significant risk factors in multiple logistic regression. A total, single risk score was calculated per individual based on the adjusted odds ratio for each of their risk factors. Standard receiver operator characteristic curve was plotted for the total score and the presence of UADT cancers. The stratification ability of the model was determined using the c‐statistic. The optimal criterion value was determined at the point on curve at which the Youden's index was maximal. Confidence intervals were calculated by bootstrapping. Results: Total risk score for each individual ranged from 0 to 26. Area under the receiver operating characteristic curve (95.8; P < 0.001) suggests strong predictive ability. A risk score criterion value of ≤10 produced optimal sensitivity (93.5%), specificity (71.1%), false‐positive rate (28.8%), false‐negative rate (6.4%), positive predictive value (74.8%), and negative predictive value (96.6%). Conclusion: This risk factor‐based model has the potential of satisfactorily screening and detection of UADT cancers at its early stage in a high‐risk population like India. The identified at‐risk individuals can then be targeted for clinical examination and for focused preventive/treatment measures at the hospital.