Risk prediction of hepatitis B or C or HIV among newly diagnosed cancer patients

Background Screening for viral infection in cancer patients is inconsistent. A mechanism to readily identify cancer patients at increased risk of existing or prior viral infection could enhance screening efforts while reducing costs. Methods We identified factors associated with increased risk of past or chronic hepatitis virus B, hepatitis virus C, or HIV infection before initiation of systemic cancer therapy. Data were from a multicenter prospective cohort study of 3051 patients with newly diagnosed cancer (SWOG-S1204) enrolled between 2013 and 2017. Patients completed a survey with questions pertaining to personal history and behavioral, socioeconomic, and demographic risk factors for viral hepatitis or HIV. We derived a risk model to predict the presence of viral infection in a random set of 60% of participants using best subset selection. The derived model was validated in the remaining 40% of participants. Logistic regression was used. Results A model with 7 risk factors was identified, and a risk score with 4 levels was constructed. In the validation cohort, each increase in risk level was associated with a nearly threefold increased risk of viral positivity (odds ratio = 2.85, 95% confidence interval = 2.26 to 3.60, P < .001). Consistent findings were observed for individual viruses. Participants in the highest risk group (with >3 risk factors), comprised of 13.4% of participants, were 18 times more likely to be viral positive compared with participants with no risk factors (odds ratio = 18.18, 95% confidence interval = 8.00 to 41.3, P < .001). Conclusions A risk-stratified screening approach using a limited set of questions could serve as an effective strategy to streamline screening for individuals at increased risk of viral infection.

Automated summary

Researchers have identified a risk model with 7 factors and constructed a risk score with 4 levels to predict the presence of viral infection in cancer patients. Using this model, individuals in the highest risk group were 18 times more likely to be viral positive compared to those with no risk factors. A risk-stratified screening approach using this limited set of questions could be an effective strategy to streamline screening for viral infection.