Dataset Information

Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer.

ABSTRACT:

Importance

Existing prognostic cancer tools include biological and laboratory variables. However, patients often do not know this information, preventing them from using the tools and understanding their prognosis.

Objective

To develop and validate a prognostic survival model for all cancer types that incorporates information on symptoms and performance status over time.

Design, setting, and participants

This is a retrospective, population-based, prognostic study of data from patients diagnosed with cancer from January 1, 2008, to December 31, 2015, in Ontario, Canada. Patients were randomly selected for model derivation (60%) and validation (40%). The derivation cohort was used to develop a multivariable Cox proportional hazards regression model with baseline characteristics under a backward stepwise variable selection process to predict the risk of mortality as a function of time. Covariates included demographic characteristics, clinical information, symptoms and performance status, and health care use. Model performance was assessed on the validation cohort by C statistics and calibration plots. Data analysis was performed from February 6, 2018, to November 6, 2019.

Main outcomes and measures

Time to death from diagnosis (year 0) recalculated at each of 4 annual survivor marks after diagnosis (up to year 4).

Results

A total of 255 494 patients diagnosed with cancer were identified (135 699 [53.1%] female; median age, 65 years [interquartile range, 55-73 years]). The cohort decreased to 217 055, 184 822, 143 649, and 109 569 patients for each of the 4 years after diagnosis. In the derivation cohort year 0, and the most common cancers were breast (30 855 [20.1%]), lung (19 111 [12.5%]), and prostate (18 404 [12.0%]). A total of 47 614 (31.1%) had stage III or IV disease. The mean (SD) time to death in year 0 was 567 (715) days. After backward stepwise selection in year 0, the following factors were associated with increased risk of death by more than 10%: being hospitalized; having congestive heart failure, chronic obstructive pulmonary disease, or dementia; having moderate to high pain; having worse well-being; having functional status in the transitional or end-of-life phase; having any problems with appetite; receiving end-of-life home care; and living in a nursing home. Model discrimination was high for all models (C statistic: 0.902 [year 0], 0.912 [year 1], 0.912 [year 2], 0.909 [year 3], and 0.908 [year 4]).

Conclusions and relevance

The model accurately predicted changing cancer survival risk over time using clinical, symptom, and performance status data and appears to have the potential to be a useful prognostic tool that can be completed by patients. This knowledge may support earlier integration of palliative care.

SUBMITTER: Seow H

PROVIDER: S-EPMC7113728 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAbemaciclib is a CDK4/6 inhibitor used to treat hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer. The prognostic value of patient-reported outcomes (PROs) has been minimally explored for treatment outcomes with CDK4/6 inhibitors. The performance of PROs compared with Eastern Cooperative Oncology Group performance status (ECOG-PS) is unknown.Materials and methodsThis study pooled data from single-arm trial, MONARCH 1, and randomized trials, MONARCH 2 and 3. In total, 900 patients initiated abemaciclib and 384 comparator therapy. Pretreatment PRO association with progression-free survival (PFS) was modeled using Cox proportional hazards regression. Prediction performance was assessed via the C-statistic (c). PROs were recorded via the European Organisation for Research and Treatment of Cancer QLQ-C30.ResultsPatient-reported physical function, pain, role function, fatigue, and appetite loss were associated with PFS on univariable and adjusted analysis (p < .05). Physical function (c = 0.55) was most predictive, superior to ECOG-PS (c = 0.54), with multivariable analysis indicating both provide independent information (p < .02). In the pooled randomized arms of MONARCH 2 and 3, the PFS treatment benefit (hazard ratio [95% confidence interval]) of abemaciclib (vs. comparators) was 0.75 (0.57-1.0) for low physical function, compared with 0.48 (0.40-0.59) for intermediate/high (p[interaction] = .01).ConclusionPROs were identified as prognostic factors for PFS in patients initiating abemaciclib, with patient-reported physical function containing independent predictive information beyond ECOG-PS. Low physical function was associated with a decrease in the magnitude of PFS benefit from abemaciclib. PROs should be explored as prognostic, predictive, and stratification factors for clinical use and research trials of CDK4/6 inhibitors.Implications for practiceFor the first time, pretreatment patient-reported outcomes have been shown to be independent prognostic markers for progression-free survival (PFS) in patients diagnosed with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer treated with abemaciclib. Importantly, patients with low physical function had a smaller PFS benefit from abemaciclib (vs. comparator) than patients with intermediate/high physical function. The present study demonstrates patient-reported outcomes as a simple, effective, inexpensive, and independent prognostic marker for patients with HR+/HER2- advanced breast cancer treated with abemaciclib.