Do Longitudinally Collected Symptom Scores Predict Time to Death in Advanced Breast Cancer: A Joint Modeling Analysis

Cox model and decision trees: an application to breast cancer data

Objective.

To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer.

Methods.

A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan–Meier method, Cox regression, and conditional decision tree.

Results.

Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days.

Conclusions.

This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient.

Assessment of Changes in Symptoms Is Feasible and Prognostic in the Last Weeks of Life: An International Multicenter Cohort Study

Background: Symptoms are not typically part of established various prognostic factors and scoring systems but are among the most frequently assessed issues in patient care. Objectives: To evaluate that, changes in symptoms can provide additional useful prognostic information. Design: A secondary analysis of an international cohort study in Japan, Korea, and Taiwan. Setting/Subjects: Subjects were adult patients with advanced cancer (n = 2074) who were admitted to 37 palliative care units (PCUs) in 3 countries from January 2017 to September 2018. Measurements: Symptoms (dyspnea, fatigue, dry mouth, and drowsiness) were assessed at admission and one-week later. Dyspnea was assessed by the presence of resting and exertional dyspnea, whereas other symptoms were assessed using the Integrated Palliative care Outcome Scales (IPOS) (range 0-4). For analysis, we grouped patients by symptom change, as either Improved, Stable, or Worsened (by having at least a one increment decrease, no change, or at least a one increment increase, respectively). Results: Worsened groups had the shortest survival (median survival 15-21 days) compared with those with Improved (median survival 23-31 days) and Stable symptoms (median survival 27-29 days) across all four symptoms (dyspnea, fatigue, dry mouth, and drowsiness). Survival differences were statistically significantly different across all three groups for all symptoms (all p < 0.001). Interestingly, Improved symptoms were associated with similar survival compared with Stable groups, with no statistical differences. Conclusions: Worsened symptoms at one week after admission were useful predictors of survival for patients with advanced cancer in PCUs during the final weeks of life. Longitudinal assessments are needed to reflect passage of time as well as impact of treatments.

Longitudinal tumor fraction trajectories predict risk of progression in metastatic HR+ breast cancer patients undergoing CDK4/6 treatment

Despite improved clinical outcomes, intrinsic or acquired resistance to CDK4/6 inhibitor treatment has limited the success of this treatment in HR+ HER2- metastatic breast cancer patients. Biomarkers are urgently needed, and longitudinal biomarker measurements may harbor more dynamic predictive and prognostic information compared to single time point measurements. The aim of this study was to explore the longitudinal evolution of circulating tumor fractions within cell-free DNA assessed by an untargeted sequencing approach during CDK4/6 therapy and to quantify the potential association between longitudinal z-score measurements and clinical outcome by using joint models. Forty-nine HR+ HER2- metastatic breast cancer patients were enrolled, and z-score levels were measured at baseline and during 132 follow-up visits (median number of measurements per patient = 3, 25th -75th percentile: 3-5, range: 1-8). We observed higher baseline z-score levels (estimated difference 0.57, 95% CI: 0.147-0.983, P-value = 0.008) and a constant increase of z-score levels over follow-up time (overall P-value for difference in log z-score over time = 0.024) in patients who developed progressive disease. Importantly, the joint model revealed that elevated z-score trajectories were significantly associated with higher progression risk (HR of log z-score at any time of follow-up = 3.3, 95% CI, 1.44-7.55, P = 0.005). In contrast, single z-score measurement at CDK4/6 inhibitor treatment start did not predict risk of progression. In this prospective study, we demonstrate proof-of-concept that longitudinal z-score trajectories rather than single time point measurements may harbor important dynamic information on the development of disease progression in HR+ HER2- breast cancer patients undergoing CDK4/6 inhibitor treatment.

Prediction of breast cancer-related outcomes with the Edmonton Symptom Assessment Scale: A literature review

PURPOSE

The Edmonton Symptom Assessment Scale (ESAS) is a validated tool used in patients with varied cancer diagnoses to measure patient symptoms. The present manuscript will review the literature assessing the ability of the ESAS to predict patient-related outcomes in breast cancer patients.

METHODS

A literature search was conducted of Cochrane Central Register of Controlled Trials databases, Ovid MEDLINE, and Embase for English articles that investigated the use of predictive modelling with the ESAS in the breast cancer population. Study type, publication year, sample size, patient demographics, predicted outcomes, and strongest predictive factors/symptoms were summarized for each study.

RESULTS

A total of nine articles were included in this review. Five articles used the ESAS in predictive models to determine patient time to death. ESAS was also used to predict emergency department visits, determine symptoms associated with decreased quality of life, and generate a Health Utility Score. Lack of appetite was the most common ESAS symptom, as it was reported in five studies to be associated with decreased survival. In four of the nine articles, an additional survey investigating physical functioning was used in combination with ESAS to strengthen the predictive models.

CONCLUSIONS

Included studies support the use of ESAS in predictive models, particularly for predicting survival. Using the ESAS as a predictive tool allows for more accurate time to death predictions, potentially improving symptom management and preventing overtreatment of palliative patients near the end of life.