Assessing the cost & cost-effectiveness of next-generation sequencing for the diagnosis of hematological malignancies in children, adolescence & young-adults: A systematic literature review
Bibliographic record
Abstract
Abstract Next-generation sequencing (NGS) is a cumulation of novel diagnostic modalities that can sequence entire parts of a cell in one sample. NGS is quicker and more accurate than standard-of-care (SoC) diagnostic methods, making it beneficial to cancer patients, particularly pediatric cancer, where a timely diagnosis is of the essence to improve survival outcomes, and because acquiring the adequate tissue samples needed for SoC modalities may be difficult, resulting in a compromised diagnosis. However, the integration of NGS into clinical settings has been limited due to the lack of existing data on its costs. We conducted a systematic literature review to measure the average total costs (ATC) and clinical outcomes of applying NGS modalities in comparison to SoC when diagnosing children, adolescents, and young-adults (AYA, 0-40 year) with cancer. We gathered English-language literature published beyond 2010. Study data had to be primary, focus on patients 0-40 years diagnosed with hematological malignancies via a next-generation sequencing modality, and to have included the ATC of their modality. Studies including older populations or those with other types of cancers were included if data regarding pediatric and AYA age group with hematological malignancies were provided. We gathered studies from MEDLINE, Embase, ResearchGate, and Google Scholar, and they were voted on Covidence. Nine studies were pulled to be reviewed, and we modelled their data on Microsoft Excel, Google Sheets, and RStudio. We modelled ATC per patient and ATC per sample, and we assessed clinically beneficial outcomes by determining the clinical validity, clinical impact, and potentially actionable findings (PAFs) derived from NGS. We also conducted a sensitivity analysis that calculated costs per patient for leukemia diagnoses after a 50% reduction in consumables prices, as well as predicted future costs of NGS for pediatric cancer diagnoses through a linearized exponential decay model. All nine studies report outcomes that were advantageous to patients and their treatments. Our findings show that on average, ATC per patient (across all NGS modalities) are approximately $6890 (in 2023 U.S. dollars), although separating the cost data from hematological versus non-hematological malignancies was not possible. The NGS modality with the lowest ATC per patient was RNA sequencing. Sensitivity analysis showed that ATC per patient for acute leukemia diagnoses can be lower than SoC modalities if the price of consumables decreases by 50%. The linearized exponential decay model constructed from the predictive data by Owens et al. demonstrates that ATC per patient could decrease by 82% from 2020 to 2030 (Adj R2=0.89, F=17.69, p=0.1486), although the high p-value indicates that more observations are needed to produce stronger results. A small number of studies and variations in reported units of measurement for clinical and cost data are major limitations in this review. Further, studies did not separate cost data for the different types of malignancies, nor for different age groups pediatric and AYA (0-40 year) versus adult (>40 year) patients. This may result in estimates for ATC to be over or underinflated. Although, at present, NGS costs are higher than the SoC methods for cancer diagnoses, available data predicts a reduction of ATC of NGS in the future. This systematic literature review highlights the importance of homogeneity among data collection methods to ensure comparability for future costing analyses. We recommend the development of uniform reporting guidelines for standardizing units of measurement for cost and clinical outcomes, and the application of different methods (such as the reduction in consumables prices) to reduce average total costs.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".