Journal Article Annotations
2020, 2nd Quarter
Annotations by ) Elie Isenberg-Grzeda and Carlos Fernandez-Robles, MD
June 24, 2020
PUBLICATION #1 — Psychooncology
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.
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)
PUBLICATION #2 — Psychooncology
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.
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
PUBLICATION #3 — Psychooncology
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.
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