Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective
Bibliographic record
Abstract
BACKGROUND: High healthcare costs could arise from unmet needs. This study used random forest (RF) and regression methods to identify predictors of high costs from a US payer perspective in patients newly diagnosed with generalized myasthenia gravis (gMG). METHODS: Plus database (2017-2021). Predictors of high healthcare costs were measured 12 months pre-index (main cohort) and during both the 12 months pre- and post-index (subgroup). Top 50 predictors of high costs [≥ $9404 (main cohort) and ≥ $9159 (subgroup) per-patient-per-month] were identified with RF models; the magnitude and direction of association were estimated with multivariable modified Poisson regression models. RESULTS: The main cohort and subgroup included 2739 and 1638 patients, respectively. In RF analysis, the most important predictors of high costs before/on the index date were index MG exacerbation, all-cause inpatient admission, and number of days with corticosteroids. After the index date, these were immunoglobulin and monoclonal antibody use and number of all-cause outpatient visits and MG-related encounters. Adjusting for the top 50 predictors, post-index immunoglobulin use increased the risk of high costs by 261%, monoclonal antibody use by 135%, index MG exacerbation by 78%, and pre-index all-cause inpatient admission by 27% (all p < 0.05). CONCLUSIONS: This analysis links patient characteristics both before the formal MG diagnosis and in the first year to high future healthcare costs. Findings may help inform payers on cost-saving strategies, and providers can potentially shift to targeted treatment approaches to reduce the clinical and economic burden of gMG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".