Patient-Reported Functional Status in Outpatients With Advanced Cancer: Correlation With Physician-Reported Scores and Survival

Popović, Gordana; Harhara, Thana; Pope, Ashley; Al-Awamer, Ahmed; Banerjee, Subrata; Bryson, John; Mak, Ernie; Lau, Jenny; Hannon, Breffni; Swami, Nadia; Le, Lisa W.; Zimmermann, Camilla

doi:10.1016/j.jpainsymman.2018.02.015

Cited by 30 publications

(33 citation statements)

References 45 publications

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“…Thirty-two articles were identified for inclusion in level 2 screening, after which 16 articles underwent data extraction. Ultimately, 14 articles21–34 were included in our review (figure 1).…”

Section: Resultsmentioning

confidence: 99%

Inter-rater reliability in performance status assessment between clinicians and patients: a systematic review and meta-analysis

Chow

Zimmermann

Bruera

et al. 2019

BMJ Support Palliat Care

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IntroductionPerformance status is an essential consideration for clinical practice and for patient eligibility for clinical trials in oncology. Assessment of performance status is traditionally done by clinicians, but there is an increasing interest in patient-completed assessment. The aim of this systematic review and meta-analysis was to summarise inter-rater concordance between patient and clinician ratings of performance status.MethodsA search strategy was developed and executed in the databases of Ovid MEDLINE, Embase and Cochrane Central Register of Controlled Trials, from inception until 15 August 2019. Articles were eligible for inclusion if there was mention of both (1) use of performance status tool Karnofsky Performance Status (KPS) or Eastern Cooperative Oncology Group Performance Status (ECOG), and (2) assessment of performance status by both clinicians and patients. Pearson correlation coefficients were calculated for each study and were meta-analysed according to a random-effect analysis model. Analyses were conducted using Comprehensive Meta-Analysis (V.3) by Biostat.ResultsSixteen articles were included in our review, reporting on a cumulative sample size of 6619 patients. The quality of evidence was moderate, as determined by the GRADE tool.Concordance ranged from fair to moderate for both the KPS and ECOG tools. The Pearson correlation coefficient was 0.449 for KPS and 0.584 for ECOG.ConclusionsThere is fair to moderate concordance of patient and clinician performance status ratings. Future studies should examine the reasoning behind clinician and patient ratings to better understand discrepancies between ratings.

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Section: Resultsmentioning

confidence: 99%

Inter-rater reliability in performance status assessment between clinicians and patients: a systematic review and meta-analysis

Chow

Zimmermann

Bruera

et al. 2019

BMJ Support Palliat Care

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“…Although a poor performance status may be a good trigger because it indicates greater functional need and shorter survival, the interrater reliability of performance status assessment is suboptimal . Patient completion of performance status tools is one option to overcome this, but patients' self‐rated scores tend to be worse than those of physicians …”

Section: Innovative Models Of Care Deliverymentioning

confidence: 99%

“…128 Patient completion of performance status tools is one option to overcome this, but patients' selfrated scores tend to be worse than those of physicians. 129 The strengths and weakness of automatic referral models are highlighted in Table 3. In addition to streamlining referral and triaging resources, automatic referral can help to standardize care and normalize the process of referral.…”

Section: Automatic Referral With Standardized Criteriamentioning

confidence: 99%

Improving patient and caregiver outcomes in oncology: Team‐based, timely, and targeted palliative care

Hui

Hannon

Zimmermann

et al. 2018

CA A Cancer J Clinicians

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Over the past decade, a large body of evidence has accumulated supporting the integration of palliative care into oncology practice for patients with advanced cancer. The question is no longer whether palliative care should be offered, but what is the optimal model of delivery, when is the ideal time to refer, who is in greatest need of a referral, and how much palliative care should oncologists themselves be providing. These questions are particularly relevant given the scarcity of palliative care resources internationally. In this state-of-science review directed at the practicing cancer clinician, we will first discuss the contemporary literature examining the impact of specialist palliative care on various health outcomes. We will then provide conceptual models to support team-based, timely and targeted palliative care. Team-based palliative care allows the interdisciplinary members to address comprehensively the multi-dimensional care needs of patients and their caregivers. Timely palliative care, at its best, is preventative care to minimize crises at the end-of-life. Targeted palliative care involves identifying patients most likely to benefit from specialist palliative care interventions, akin to the concept of targeted cancer therapies. Finally, we will summarize the strengths and weaknesses of innovative care models, such as outpatient clinics, embedded clinics, nurse-led palliative care, primary palliative care provided by oncology teams, and automatic referral. Moving forward, more research is needed to determine how different health systems can best personalize palliative care to provide the right level of intervention, for the right patient, in the right setting, at the right time.

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“…In 2013, Ontario also began collecting functional scores using a patient-completed Eastern Cooperative Oncology Group score, which is comparable and highly correlated with the physician-reported PPS. 21,22 The interRAI database began in 2002, when Ontario mandated the use of the Resident Assessment Instrument for Home Care, a standardized tool for patients receiving publicly funded home care services for an expected 60 days or more. The assessment is akin to the Minimum Data Set used internationally and is valid and reliable.…”

Section: Data Sourcesmentioning

confidence: 99%

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

et al. 2020

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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 PARTICIPANTSThis 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. Question Is it possible to develop a risk prediction model of survival for patients with cancer that incorporates patientreported outcomes over time? Findings In this prognostic study of data from 255 494 patients with cancer, the mean) time to death from diagnosis was 567 days. The model fou...

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Patient-Reported Functional Status in Outpatients With Advanced Cancer: Correlation With Physician-Reported Scores and Survival

Abstract: Patients tend to rate their performance status worse than physicians, particularly if they are younger or have greater symptom burden. Prognostic ability of performance status could be improved by using the average of patients and physician scores.

Cited by 30 publications

References 45 publications

Inter-rater reliability in performance status assessment between clinicians and patients: a systematic review and meta-analysis

Inter-rater reliability in performance status assessment between clinicians and patients: a systematic review and meta-analysis

Improving patient and caregiver outcomes in oncology: Team‐based, timely, and targeted palliative care

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

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