Hospitalization and Survival of Medicare Patients Treated With Carboplatin Plus Paclitaxel or Pemetrexed for Metastatic, Nonsquamous, Non-Small Cell Lung Cancer

Survival in non-small cell lung cancer patients with versus without prior cancer

Clinical trials on cancer treatments frequently exclude patients with prior cancer, but more evidence is needed to understand their possible effects on outcomes. This study analyzed the prognostic impact of prior cancer in newly diagnosed non-small cell lung cancer (NSCLC) patients while accounting for various patient and cancer characteristics. Using population-based cancer registry data linked with administrative claims data, this retrospective cohort study examined patients aged 15-84 years diagnosed with NSCLC between 2010 and 2015 in Japan. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality in patients with versus without prior cancer. The analysis was stratified according to NSCLC stage and diagnostic time intervals between prior cancers and the index NSCLC. We analyzed 9103 patients (prior cancer: 1416 [15.6%]; no prior cancer: 7687 [84.4%]). Overall, prior cancer had a non-significant mortality HR of 1.07 (95% CI: 0.97-1.17). Furthermore, prior cancer had a significantly higher mortality hazard for diagnostic time intervals of 3 years (HR: 1.23, 95% CI: 1.06-1.43) and 5 years (1.18, 1.04-1.33), but not for longer intervals. However, prior cancer in patients with more advanced NSCLC did not show a higher mortality risk for any diagnostic time interval. Smoking-related prior cancers and prior cancers with poorer prognosis were associated with poorer survival. NSCLC patients with prior cancer do not have an invariably higher risk of mortality, and should be considered for inclusion in clinical trials depending on their cancer stage.

Increasing trends in the prevalence of prior cancer in newly diagnosed lung, stomach, colorectal, breast, cervical, and corpus uterine cancer patients: a population-based study

Background

Cancer survivors are frequently excluded from clinical research, resulting in their omission from the development of many cancer treatment strategies. Quantifying the prevalence of prior cancer in newly diagnosed cancer patients can inform research and clinical practice. This study aimed to describe the prevalence, characteristics, and trends of prior cancer in newly diagnosed cancer patients in Japan.

Methods

Using Osaka Cancer Registry data, we examined the prevalence, characteristics, and temporal trends of prior cancer in patients who received new diagnoses of lung, stomach, colorectal, female breast, cervical, and corpus uterine cancer between 2004 and 2015. Site-specific prior cancers were examined for a maximum of 15 years before the new cancer was diagnosed. Temporal trends were evaluated using the Cochran-Armitage trend test.

Results

Among 275,720 newly diagnosed cancer patients, 21,784 (7.9%) had prior cancer. The prevalence of prior cancer ranged from 3.3% (breast cancer) to 11.1% (lung cancer). In both sexes, the age-adjusted prevalence of prior cancer had increased in recent years (P values for trend < 0.001), especially in newly diagnosed lung cancer patients. The proportion of smoking-related prior cancers exceeded 50% in patients with newly diagnosed lung, stomach, colorectal, breast, and cervical cancer.

Conclusions

The prevalence of prior cancer in newly diagnosed cancer patients is relatively high, and has increased in recent years. Our findings suggest that a deeper understanding of the prevalence and characteristics of prior cancer in cancer patients is needed to promote more inclusive clinical research and support the expansion of treatment options.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08011-3.

Rethinking clinical oncology drug research in an era of value-based cancer care: A role for chemotherapy pathways

The United States spends nearly 1/5th of its GDP on healthcare. Yet, to achieve value-based care, the Economist describes the US healthcare system as handicapped by multiple, disparate silos that prevent the organization and sharing of data. This paper explores the current state of clinical oncology drug research and its relationship to value-based cancer care. Clinical Chemotherapy Pathways are proposed as a unifying structure to bring together disparate sources of data to increase value.

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

PURPOSE

Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients.

PATIENTS AND METHODS

We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%).

RESULTS

There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group.

CONCLUSION

A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.