Evaluating the Impact of Local Genetic Testing for Hereditary Hematologic Malignancies on Patient Reported Outcomes
Bibliographic record
Abstract
Introduction: Since the initial discovery of deleterious germline variants in RUNX1 as the cause of familial platelet disorder with associated myeloid malignancies, germline variants in numerous other genes have been found which predispose individuals to myeloid and/or lymphoid malignancies. Despite growing awareness, genetic testing for hereditary hematologic malignancies (HHMs) can be difficult and costly to access for many patients. Identifying persons with or at high risk of developing HHMs can better inform prognosis, cancer screening recommendations, and personalized treatment plans; benefiting patients and their families. However, in the case of HHMs, there is a paucity of literature on the impacts of testing and genetic counselling on patient-reported outcomes. The main objective of this study was to evaluate the impact on patient reported outcomes of recently implemented local genetic testing for patients with suspected HHMs using surveys co-developed by members of a patient advisory panel. Methods: Individuals over the age of 16 years undergoing genetic testing for HHMs were eligible for participation. Patients were consented to the study at the time of their pre-test genetic counselling visit. Patients completed one pre-test survey (PreT) immediately following their pre-test counselling and two post-test surveys (PT1 and PT2 administered by a research coordinator not involved in the patient's clinical care 24 hours and 2-4 weeks after results disclosure and post-test counselling, respectively). Surveys used a 5-point Likert scale to assess patient-reported outcomes including: quality of life; mental health aspects such as family stress, fear, anxiety, anger, and sadness as a result of their personal and/or family history of cancer; and satisfaction with pre- and post-test counselling. Survey data were analyzed using SPSS statistical software. Percentages were reported as percentage agree/strongly agree (agree), disagree/strongly disagree (disagree), or neutral. Responses for each Likert scale question were assigned a score of 1 through 5 and p-values were calculated using the Wilcoxon Signed Rank Test. Results: Thirteen individuals participated in this study. One had a pathogenic variant, 6 had a Variant of Uncertain Significance (VUS) detected, and 6 received a non-diagnostic (i.e. negative) result. Patient reported experiences of anxiety, sadness, and fear as a result of their and/or their family's history of cancer were unchanged pre- and post-testing, with about 50% of patients experiencing these symptoms. Anger was not a common patient-reported outcome (8% Pre-T and 15% post-T (PT1 & PT2). The majority (77%) of patients did not feel that the results of their testing created additional stress, worry, or guilt in the family. Patients' worry about cancer risk in family members as a result of their and/or their family's history of cancer decreased from 100% agree Pre-T to 54% and 46% agree PT1 and PT2, respectively (mean score 4.46 pre-T vs 3.31 PT2; p = 0.007). Overall, individuals' wonder of why they got cancer did not change despite having genetic testing performed ((50% Pre-T, 61% PT1, and 70% PT2 (mean score 3.85 pre-T vs 3.85 PT1, and 3.77 PT2; p = 1.0 and 0.76, respectively)).The result of the testing (pathogenic, VUS, or non-diagnostic) did not impact patients' understanding of why they got cancer. There were no significant differences in responses for any of the questions between the two post-test surveys. Nearly all patients agreed that their results might benefit others in the future (92% PT1 & 100% PT2). 92% of patients reported being satisfied with their genetic testing discussion, 77% (PT1 & PT2) reported they understood the limitations of testing, and 100% agreed that they understood the results. Overall, 100% of the patients were glad to have had genetic testing done, and 100% and 92% reported that it was helpful for themselves and their families, respectively. Conclusion: Our findings show that from the patient perspective, genetic testing was beneficial for themselves and their families and did not result in additional mental health burden, independent of the actual test results. Our findings underscore the importance of pre-and post-test genetic counselling and provide patient-reported outcome data, which would support expanding access to genetic testing for HHMs for at-risk patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".