Wang, Xi

Objectives: Early identification of patients living with cancer at higher risk of death from an infection is critical in infection mortality prevention. We characterize patients living with cancer at the highest risk of dying from an infection. Methods: 7,529,481 US cancer survivors (1992 to 2015) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Standardized mortality ratios (SMRs) and 95% CIs were calculated. Fine-gray survival analysis was performed to calculate hazard ratios by adjusting for the effects of competing risks (eg, deaths due to causes other than infection). Results: Among 7,529,481 patients living with cancer (1992 to 2015), 101,167 (1.3%) died of infection. The rate of infection-specific mortality was 27.19/10,000 person-years, with an SMR of 3.29 (95% CI: 3.26-3.32, P <0.001). Patients who were older, male, black, and unmarried were at a greater risk of fatal infection. Overall, the risk of infection-specific mortality for patients living with cancer is greatest 1 year after diagnosis compared with the general population (SMR: 8.68, 95% CI: 8.53-8.84; P< 0.0001), and this risk decreases with follow-up time (SMR at >10 y after diagnosis: 2.93, 95% CI: 2.87-3.00; P< 0.0001). Among patients with Hodgkin Lymphoma, Non-Hodgkin Lymphoma, and Kaposi Sarcoma, 9.2%, 11.5%, and 82.2% of all deaths within the first year after cancer diagnosis occurred due to acute infectious disease. In contrast, for patients with liver cancer, the relative percentage of infection-specific mortality increases with follow-up time from 3.5% at <1 year after cancer diagnosis and 10.4% at 10+ years of follow-up. Conclusion: The results of this study characterize infection mortality in patients living with cancer, which can guide more targeted research and interventions in this population.

AbstractAlthough cancer is highly heterogeneous, all metastatic cancer is considered American Joint Committee on Cancer (AJCC) Stage IV disease. The purpose of this project was to redefine staging of metastatic cancer. Internal validation of nationally representative patient data from the National Cancer Database (n = 461 357; 2010‐2013), and external validation using the Surveillance, Epidemiology and End Results database (n = 106 595; 2014‐2015) were assessed using the concordance index for evaluation of survival prediction. A Cox proportional hazards model was used for overall survival by considering identified phenotypes (latent classes) and other confounding variables. Latent class analysis was performed for phenotype identification, where Bayesian information criterion (BIC) and sample‐size‐adjusted BIC were used to select the optimal number of distinct clusters. Kappa coefficients assessed external cluster validation. Latent class analysis identified five metastatic phenotypes with differences in overall survival (P < .0001): (Stage IVA) nearly exclusive bone‐only metastases (n = 59 049, 12.8%; median survival 12.7 months; common in lung, breast and prostate cancers); (IVB) predominant lung metastases (n = 62 491, 13.5%; 11.4 months; common in breast, stomach, kidney, ovary, uterus, thyroid, cervix and soft tissue cancers); (IVC) predominant liver/lung metastases (n = 130 014, 28.2%; 7.0 months; common in colorectum, pancreatic, lung, esophagus and stomach cancers); (IVD) bone/liver/lung metastases predominant over brain (n = 61 004, 13.2%; 5.9 months; common in lung and breast cancers); and (IVE) brain/lung metastases predominant over bone/liver (n = 148 799, 32.3%; 5.7 months; lung cancer and melanoma). Long‐term survivors were identified, particularly in Stages IVA‐B. A pan‐cancer nomogram model to predict survival (STARS: site, tumor, age, race, sex) was created, validated and provides 13% better prognostication than AJCC: 1‐month concordance index of 0.67 (95% confidence interval [CI]: 0.66‐0.67) vs 0.61 (95% CI: 0.60‐0.61). STARS is simple, uses easily accessible variables, better prognosticates survival outcomes and provides a platform to develop novel metastasis‐directed clinical trials.