Real-world clinical outcomes of patients with metastatic renal cell carcinoma receiving pembrolizumab + axitinib vs. ipilimumab + nivolumab.

BACKGROUND: Immune-Oncology (IO) therapies have changed first-line (1L) treatment paradigm for metastatic renal cell carcinoma (mRCC) in last few years with robust clinical trial data. We examined clinical outcomes among clear cell mRCC (mccRCC) patients who received pembrolizumab + axitinib (pembro-axi) or ipilimumab + nivolumab (ipi-nivo) in the US community oncology setting. METHODS: This retrospective cohort study utilized data from electronic health records and chart review within The US Oncology Network to identify adult patients with mccRCC initiating 1L pembro-axi or ipi-nivo from January 01, 2019 to December 31, 2020 and followed through March 31, 2021. Physician-recorded response (real-world overall response rate [rwORR] and real-world disease control rate [rwDCR]) was assessed descriptively. Real-world progression-free survival (rwPFS), real-world time to next treatment (rwTTNT) and time on treatment (rwToT) were estimated using Kaplan-Meier analysis. Association of 1L systemic treatment with time-to-event outcomes was examined using multivariable cox proportional hazards models. RESULTS: Study included 331 mccRCC patients (pembro-axi:44%, ipi-nivo:56%). Median age was 65 years, 75.5% were male, and 82.5% had intermediate/poor (I/P) IMDC risk score. RwORR and rwDCR were 71.0% and 80.0% for pembro-axi and 45.2% and 58.6% for ipi-nivo. In multivariable analysis, pembro-axi was associated with longer rwToT (aHR, 0.53 [95% CI, 0.40, 0.71]), rwTTNT (aHR, 0.60 [95% CI, 0.42, 0.87]), and rwPFS (aHR, 0.70 [95% CI, 0.49, 0.99]) compared to ipi-nivo (P < 0.01). CONCLUSIONS: Our study provides insight into newer mccRCC treatment tolerability and effectiveness in the real-world US community setting. Our real-world results were comparable to data from clinical trials, which is encouraging for mccRCC patients.

Real-world Treatment Patterns and Clinical Outcomes for Metastatic Renal Cell Carcinoma in the Current Treatment Era.

Background: Immuno-oncology (IO) agents and tyrosine kinase inhibitors (TKIs) have revolutionized the treatment paradigm for metastatic renal cell carcinoma (mRCC). Data on real-world usage and outcomes are limited. Objective: To examine real-world treatment patterns and clinical outcomes for mRCC. Design setting and participants: This retrospective cohort study included 1538 patients with mRCC who received first-line treatment with pembrolizumab + axitinib (P + A; n = 279, 18%), ipilimumab + nivolumab (I + N; n = 618, 40%), or TKI monotherapy (TKIm; cabozantinib, sunitinib, pazopanib, or axitinib; n = 641, 42%) between January 1, 2018 and September 30, 2020 in US Oncology Network/non-network practices. Outcome measurements and statistical analysis: The relationship with outcomes, time on treatment (ToT), time to next treatment (TTNT), and overall survival (OS) was analyzed using multivariable Cox proportional-hazards models. Results and limitations: The median age of the cohort was 67 yr (interquartile range 59.5-74.4), 70% were male, 79% had clear cell RCC, and 87% had an intermediate or poor International mRCC Database Consortium risk score. The median ToT was 13.6 for P + A versus 5.8 for I + N versus 3.4 mo for TKIm (p < 0.001) and the median TTNT was 16.4 for P + A versus 8.3 for I + N versus 8.4 mo for TKIm (p < 0.001) . Median OS was not reached for P + A, 27.6 mo for I + N, and 26.9 mo for TKIm (p = 0.237). On adjusted multivariable analysis, treatment with P + A was associated with better ToT (adjusted hazard ratio [aHR] 0.59, 95% confidence interval [CI] 0.47-0.72 vs I + N; 0.37, 95% CI, 0.30-0.45 vs TKIm; p < 0.0001) and better TTNT (aHR 0.61, 95% CI 0.49-0.77 vs I + N; 0.53, 95% CI 0.42-0.67 vs TKIm; p < 0.0001). Limitations include the retrospective design and the limited follow-up for characterization of survival. Conclusions: We noted substantial uptake of IO-based therapies in the first-line community oncology setting since their approval. In addition, the study provides insights into clinical effectiveness, tolerability, and/or compliance of IO-based therapies. Patient summary: We examined the use of immunotherapy for patients with metastatic kidney cancer. The findings suggest rapid implementation of these new treatments by oncologists working in the community setting, which is reassuring for patients with this disease.

Development of comorbidity score for patients undergoing major surgery.

OBJECTIVE: To develop and validate a claims-based comorbidity score for patients undergoing major surgery, and compare its performance with established comorbidity scores. DATA SOURCE: Five percent Medicare data from 2007 to 2014. STUDY DESIGN: Retrospective cohort study of patients aged ≥65 years undergoing six major operations (N = 99 250). DATA COLLECTION: One-year mortality was the primary outcome. Secondary outcomes were hospital mortality, 30-day mortality, 30-day readmission, and length of stay. The comorbidity score was developed in the derivation cohort (70 percent sample) using logistic regression model. The comorbidity score was calibrated and validated in the validation cohort (30 percent sample), and compared against the Charlson, Elixhauser, and Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) comorbidity scores using c-statistic, net reclassification improvement, and integrated discrimination improvement. PRINCIPAL FINDINGS: In the validation cohort, the surgery-specific comorbidity score was well calibrated and performed better than the Charlson, Elixhauser, and CMS-HCC comorbidity scores for all outcomes; the performance was comparable to the CMS-HCC for 30-day readmission. For example, the surgery-specific comorbidity score (c-statistic = 0.792; 95% CI, 0.785-0.799) had greater discrimination than the Charlson (c-statistic = 0.747; 95% CI, 0.739-0.755), Elixhauser (c-statistic = 0.747; 95% CI, 0.735-0.755), or CMS-HCC (c-statistic = 0.755; 95% CI, 0.747-0.763) scores in predicting 1-year mortality. The net reclassification improvement and integrated discrimination improvement were greater for surgery-specific comorbidity score compared to the Charlson, Elixhauser, and CMS-HCC scores. CONCLUSIONS: Compared to commonly used comorbidity measures, a surgery-specific comorbidity score better predicted outcomes in the surgical population.

Adapting the Elixhauser comorbidity index for cancer patients.

BACKGROUND: This study was designed to adapt the Elixhauser comorbidity index for 4 cancer-specific populations (breast, prostate, lung, and colorectal) and compare 3 versions of the Elixhauser comorbidity score (individual comorbidities, summary comorbidity score, and cancer-specific summary comorbidity score) with 3 versions of the Charlson comorbidity score for predicting 2-year survival with 4 types of cancer. METHODS: This cohort study used Texas Cancer Registry-linked Medicare data from 2005 to 2011 for older patients diagnosed with breast (n = 19,082), prostate (n = 23,044), lung (n = 26,047), or colorectal cancer (n = 16,693). For each cancer cohort, the data were split into training and validation cohorts. In the training cohort, competing risk regression was used to model the association of Elixhauser comorbidities with 2-year noncancer mortality, and cancer-specific weights were derived for each comorbidity. In the validation cohort, competing risk regression was used to compare 3 versions of the Elixhauser comorbidity score with 3 versions of the Charlson comorbidity score. Model performance was evaluated with c statistics. RESULTS: The 2-year noncancer mortality rates were 14.5% (lung cancer), 11.5% (colorectal cancer), 5.7% (breast cancer), and 4.1% (prostate cancer). Cancer-specific Elixhauser comorbidity scores (c = 0.773 for breast cancer, c = 0.772 for prostate cancer, c = 0.579 for lung cancer, and c = 0.680 for colorectal cancer) performed slightly better than cancer-specific Charlson comorbidity scores (ie, the National Cancer Institute combined index; c = 0.762 for breast cancer, c = 0.767 for prostate cancer, c = 0.578 for lung cancer, and c = 0.674 for colorectal cancer). Individual Elixhauser comorbidities performed best (c = 0.779 for breast cancer, c = 0.783 for prostate cancer, c = 0.587 for lung cancer, and c = 0.687 for colorectal cancer). CONCLUSIONS: The cancer-specific Elixhauser comorbidity score performed as well as or slightly better than the cancer-specific Charlson comorbidity score in predicting 2-year survival. If the sample size permits, using individual Elixhauser comorbidities may be the best way to control for confounding in cancer outcomes research. Cancer 2018;124:2018-25. © 2018 American Cancer Society.

Comparative Performance of Diagnosis-based and Prescription-based Comorbidity Scores to Predict Health-related Quality of Life.

OBJECTIVES: To compare the performance of the health-related quality of life-comorbidity index (HRQoL-CI) with the diagnosis-based Charlson, Elixhauser, and combined comorbidity scores and the prescription-based chronic disease score (CDS) in predicting HRQoL in Agency of Healthcare Research and Quality priority conditions (asthma, breast cancer, diabetes, and heart failure). METHODS: The Medical Expenditure Panel Survey (2005 and 2007-2011) data was used for this retrospective study. Four disease-specific cohorts were developed that included adult patients (age 18 y and above) with the particular disease condition. The outcome HRQoL [physical component score (PCS) and mental component score (MCS)] was measured using the Short Form Health Survey, Version 2 (SF-12v2). Multiple linear regression analyses were conducted with the PCS and MCS as dependent variables. Comorbidity scores were compared using adjusted R. RESULTS: Of 140,046 adult participants, the study cohort included 7436 asthma (5.3%), 1054 breast cancer (0.8%), 13,829 diabetes (9.9%), and 937 heart failure (0.7%) patients. Among individual scores, HRQoL-CI was best at predicting PCS and MCS. Adding prescription-based comorbidity scores to HRQoL-CI in the same model improved prediction of PCS and MCS. HRQoL-CI+CDS performed the best in predicting PCS (adjusted R): asthma (43.7%), breast cancer (31.7%), diabetes (32.7%), and heart failure (20.0%). HRQoL-CI+CDS and Elixhauser+CDS had superior and comparable performance in predicting MCS (adjusted R): asthma (HRQoL-CI+CDS=20.1%; Elixhauser+CDS=19.6%), breast cancer (HRQoL-CI+CDS=12.9%; Elixhauser+CDS=14.1%), diabetes (HRQoL-CI+CDS=17.7%; Elixhauser+CDS=17.7%), and heart failure (HRQoL-CI+CDS=18.1%; Elixhauser+CDS=17.7%). CONCLUSIONS: HRQoL-CI performed best in predicting HRQoL. Combining prescription-based scores to diagnosis-based scores improved the prediction of HRQoL.