In resource-limited settings, augmenting primary care provider (PCP)-based referrals with data-derived algorithms could direct scarce resources towards those patients most likely to have a cancer diagnosis and benefit from early treatment. Using data from Botswana, we compared accuracy of predictions of probable cancer using different approaches for identifying symptomatic cancer at primary clinics. We followed cancer suspects until they entered specialized care for cancer treatment (following pathologically confirmed diagnosis), exited from the study following noncancer diagnosis, or died. Routine symptom and demographic data included baseline cancer probability assessed by the primary care provider (low, intermediate, high), age, sex, performance status, baseline cancer probability by study physician, predominant symptom (lump, bleeding, pain or other) and HIV status. Logistic regression with 10-fold cross-validation was used to evaluate classification by different sets of predictors: (1) PCPs, (2) Algorithm-only, (3) External specialist physician review and (4) Primary clinician augmented by algorithm. Classification accuracy was assessed using c-statistics, sensitivity and specificity. Six hundred and twenty-three adult cancer suspects with complete data were retained, of whom 166 (27%) were diagnosed with cancer. Models using PCP augmented by algorithm (c-statistic: 77.2%, 95% CI: 73.4%, 81.0%) and external study physician assessment (77.6%, 95% CI: 73.6%, 81.7%) performed better than algorithm-only (74.9%, 95% CI: 71.0%, 78.9%) and PCP initial assessment (62.8%, 95% CI: 57.9%, 67.7%) in correctly classifying suspected cancer patients. Sensitivity and specificity statistics from models combining PCP classifications and routine data were comparable to physicians, suggesting that incorporating data-driven algorithms into referral systems could improve efficiency.

Keywords: predictive modeling; symptomatic cancer.