Journal Article Annotations
2020, 2nd Quarter

Psychooncology

Annotations by ) Elie Isenberg-Grzeda and Carlos Fernandez-Robles, MD
June 24, 2020

Long-term work retention after treatment for cancer: a systematic review and meta-analysis.
Suicidal Ideation in Patients With Cancer: A Systematic Review of Prevalence, Risk Factors, Intervention and Assessment.
Development and Validation of a Prognostic Survival Model With Patient-Reported Outcomes for Patients With Cancer.

PUBLICATION #1 — Psychooncology

Long-term work retention after treatment for cancer: a systematic review and meta-analysis.

Angela GEM de Boer, Steffen Torp, Adela Popa, Trine Horsboel, Vesna Zadnik, Yakir Rottenberg, Edit Bardi, Ute Bultmann, and Linda Sharp .

Abstract: J Cancer Surviv. 2020; 14(2): 135–150. Published online 2020 Mar 11. doi: 10.1007/s11764-020-00862-2

Purpose
Almost half of people diagnosed with cancer are working age. Survivors have increased risk of unemployment, but little is known about long-term work retention. This systematic review and meta-analysis assessed work retention and associated factors in long-term cancer survivors.

Methods
We searched Medline/Pubmed, Embase, PsychINFO, and CINAHL for studies published 01/01/2000–08/01/2019 reporting work retention in adult cancer survivors ≥ 2 years post-diagnosis. Survivors had to be in paid work at diagnosis. Pooled prevalence of long-term work retention was estimated. Factors associated with work retention from multivariate analysis were synthesized.

Results
Twenty-nine articles, reporting 21 studies/datasets including 14,207 cancer survivors, were eligible. Work retention was assessed 2–14 years post-diagnosis. Fourteen studies were cross-sectional, five were prospective, and two contained both cross-sectional and prospective elements. No studies were scored as high quality. The pooled estimate of prevalence of long-term work retention in cancer survivors working at diagnosis was 0.73 (95%CI 0.69–0.77). The proportion working at 2–2.9 years was 0.72; at 3–3.9 years 0.80; at 4–4.9 years 0.75; at 5–5.9 years 0.74; and 6+ years 0.65. Pooled estimates did not differ by cancer site, geographical area, or study design. Seven studies assessed prognostic factors for work retention: older age, receiving chemotherapy, negative health outcomes, and lack of work adjustments were associated with not working.

Conclusion
Almost three-quarters of long-term cancer survivors working at diagnosis retain work.

Implications for Cancer Survivors
These findings are pertinent for guidelines on cancer survivorship care. Professionals could focus support on survivors most likely to have poor long-term work outcomes.

Electronic supplementary material
The online version of this article (10.1007/s11764-020-00862-2) contains supplementary material, which is available to authorized users.

Keywords
Cancer, Work retention, Employment, Work ability, Return-to-work, Longitudinal studies, Prospective studies, Meta-analysis

PMCID: PMC7182621

PMID: 32162193

Annotation

The finding
In a systematic review assessing work retention and associated factors among long-term (greater than 2 years) cancer survivors, the authors found that 73% of cancer survivors who had been employed at the time of diagnosis were working beyond 2 years post-diagnosis. Factors associated with not working included old age, lower income at the time of diagnosis, receiving chemotherapy, having comorbidities, having a new cancer event, having a poor prognosis, and depression.

Strength and weaknesses
The study followed standard methodology for systematic reviews. Major strengths distinguishing this review from other studies are the exclusion of patients who were not employed at the time of diagnosis and the inclusion of studies examining cancer survivors beyond 2 years. Limitations are that the included studies were heavily skewed towards North America and Europe, as well as skewed towards breast cancer, which may not be generalizable to other populations.

Relevance
Issues relating to returning to work are a serious concern for many patients in the survivorship phase of cancer. Previous research has studied prevalence and predictors of returning to work, but often limited to the short-term following a cancer diagnosis which may underestimate the true rates of returning to work among survivors at longer-term durations. The high rates of continued functioning ought to instill hope among newly diagnosed patients and may also help clinicians focus on patients with the greatest risk factors for not returning to work.

Type of study (http://ebm.bmj.com/content/early/2016/06/23/ebmed-2016-110401)
systematic review.

PUBLICATION #2 — Psychooncology

Suicidal Ideation in Patients With Cancer: A Systematic Review of Prevalence, Risk Factors, Intervention and Assessment.

Elissa Kolva, Lilian Hoffecker, Emily Cox-Martin. .

Abstract: Palliat Support Care. 2020 Apr;18(2):206-219.doi: 10.1017/S1478951519000610.

Objectives
Suicidal ideation (SI) underlies risk of death by suicide. It is well established that patients with cancer are at increased risk of death by suicide. Therefore, understanding SI in patients with cancer is critically important. The goal of this systematic review was to investigate the prevalence, risk factors, intervention, and assessment of SI in patients with cancer.

Methods
This systematic review was registered with the PROSPERO database (CRD42018115405) and was guided by the PRISMA statement. We searched Medline, PsycInfo, Embase, CINAHL, the Cochrane Database of Systematic Reviews, and Cochrane Central. Two reviewers independently screened abstracts and assessed for quality assurance using a revised Newcastle-Ottawa Scale.

Results
We identified 439 studies to screen for eligibility. Eligible studies included adults with cancer diagnoses and listed SI as an outcome. Ultimately, 44 studies were included in the analyses. Prevalence of SI ranged greatly from 0.7% to 46.3%. Single items drawn from validated measures were the most frequent method of assessing SI (n = 20, 45.5%); additional methods included validated measures and psychological interviews. Commonly identified risk factors for SI included age, sex, and disease/treatment-related characteristics, as well as psychological constructs including depression, anxiety, hopelessness, existential distress, and social support.

Significance of results
Assessment of SI in patients with cancer is the concern of researchers worldwide. Prevalence of SI varied with study population and was likely influenced by the method of assessment. Psychological distress consistently predicted SI. Increasing awareness of demographic, clinical, and psychological associations is critical for risk assessment and intervention development.

Keywords
Assessment; Cancer; Prevalence; Risk factors; Suicidal Ideation.

Annotation

The finding
Several significant findings emerged from this systematic review; the prevalence of suicide ideation (SI) varied widely among studies (0.7% to 46.3%), and the wide range can be attributed to study design and the presence of different risk factors. The investigators identified 33 risk factors; in addition to demographic factors such as male gender, employment, and marital status, the presence of pain, low-performance status, severe physical symptoms, depression, anxiety, and other psychiatric diagnoses, were considered to have a large effect size on suicide risk. Most studies relied on a single-item to assess suicide risk; the authors note that this method assesses the presence of SI over the past 2 weeks but does not capture the episodic nature of SI. Finally, this review also looked at interventions for SI and found effects for problem-solving and behavioral activation interventions as well as spiritual care and nursing interventions.

Strength and weaknesses
That this systematic review includes all studies in the last decade accounts for both its strengths and weaknesses. Having a large sample strengthens the validity of the findings, and the authors’ use of an adapted version of the Newcastle-Ottawa Scale to assess quality gave flexibility when evaluating each study. However, the variability on focus and methodology of each study gave a wide range of variables and focus, making it difficult to generalize findings across studies.

Relevance
Patients with cancer are at higher risk for suicidal ideation, attempt, and completion. This systematic review provides an essential update on the topic. It provides valuable information on the effect size of each risk factor and comments on the impact of instruments used on its assessment (single-item, clinical interview, collection of individual items). Also, it highlights the need for psychological interventions that effectively addressed suicidal ideation in this population.

Type of study
Systematic Review

PUBLICATION #3 — Psychooncology

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

Hsien Seow, Peter Tanuseputro, Lisa Barbera, Craig Earle, Dawn Guthrie, Sarina Isenberg, Rosalyn Juergens, Jeffrey Myers, Melissa Brouwers, Rinku Sutradhar .

Abstract: JAMA Netw Open. 2020 Apr 1;3(4):e201768.doi: 10.1001/jamanetworkopen.2020.1768.

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.

PMID: 32236529

PMCID: PMC7113728

DOI: 10.1001/jamanetworkopen.2020.1768

Annotation

The finding
This study looked at 4 years of data from 255,494 patients with cancer in order to develop a prognostic model that found several risk factors associated with increased risk of death by more than 10%. These factors included hospitalization, congestive heart failure, chronic obstructive pulmonary disease, dementia, moderate to high pain, worse well-being, functional status in the transitional or end-of-life phase, appetite problems, receiving end-of-life home care, and living in a nursing home.

Strength and weaknesses
The sample size from large, longitudinal, population-based databases gives power and credibility to these findings, notably as it encompassed a 4-year period. The authors identified the lack of data on genetic biomarkers and specific targeted therapies as a limitation that could have otherwise increased the model’s predictive power. The authors’ decision to use variable that are easily known and reported by patients may allow the findings to be more accessible to patients, though perhaps at the cost of greater validity. For example, the authors noted how ‘response to chemotherapy’ is not factored into the predictive model, which may make the model more useful for patients and families who do not always have information on treatment response, but the trade-off is reduced precision of the model. However, the authors are planning to test, validate, and refine the tool by making it available online to patients and families.

Relevance
There has been a recent focus on developing prognostic tools to help predict death in patients with cancer. This tool appears to predict the changing cancer survival risk accurately, and therefore it can inform both provider and patient decisions, such as timely access to palliative care. However, the widespread use of these tools has halted for several reasons, including providers’ beliefs and conflicts, poor instrument performance beyond the time of diagnosis, and lack of accessibility to patients.

The fact that this study’s predictive survival model relies on patient-reported outcomes of performance status and symptom severity, instead of biomarkers used by oncologists developed models, increases its potential to be patient-centered, user-friendly, and accessible to patients. This is reflected in the authors’ plans to create a patient-completed online tool, which, they hope, will also allow for adjustment of risk as patients’ conditions change.

Type of study
Retrospective population based prognostic study