ArticleApplied health economics and health policy2024
Predictors of High Healthcare Cost Among Patients with Generalized Myasthenia Gravis: A Combined Machine Learning and Regression Approach from a US Payer Perspective.
Article in Applied health economics and health policy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Health economic evaluations of myasthenia gravis: a systematic review.Croatian medical journal · 2026Pooled it
- Telitacicept in myasthenia gravis: a systematic review of clinical outcomes, steroid-sparing effects, and safety.Frontiers in neurology · 2026Pooled it
- Changes in corticosteroid and non-steroidal immunosuppressive therapy with long-term zilucoplan treatment in generalized myasthenia gravis.Journal of neurology · 2025Trial
- Incremental health care resource utilization, costs, and predictors of high costs in generalized myasthenia gravis.Journal of managed care & specialty pharmacy · 2026Article
- Burden of Disease in Myasthenia Gravis: A Targeted Literature Review.Journal of neurology · 2026Review
- Efgartigimod for Generalized Myasthenia Gravis and Beyond: A Narrative Review of Its Pharmacological Profile, Clinical Utility, and Expanding Applications.Biomedicines · 2025Review
- Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments.Diagnostics (Basel, Switzerland) · 2025Review
- Appropriate use of steroids for patients with generalized Myasthenia Gravis: an international Delphi study.Therapeutic advances in neurological disorders · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh 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).
methodsAdults with gMG (first diagnosis = index) were selected from the IQVIA PharMetrics
resultsThe 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).
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.