Economic Burden of Delayed Diagnosis in Patients with Pulmonary Arterial Hypertension (PAH)
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess health care resource utilization (HRU) and costs associated with delayed pulmonary arterial hypertension (PAH) diagnosis in the United States. METHODS: Data Mart Database (2016-2021) were assigned to mutually exclusive cohorts based on time between first PAH-related symptom and first PAH diagnosis (i.e., ≤12 months' delay, >12 to ≤24 months' delay, >24 months' delay). All-cause HRU and health care costs per patient per month (PPPM) were assessed during the first year following diagnosis and compared across cohorts using regression analysis adjusted for baseline covariates. Sensitivity analyses were conducted to assess outcomes during all available follow-up post-diagnosis. RESULTS: Among 538 patients (mean age: 65.6 years; 60.6% female), 60.8% had ≤12 months' delay, 23.4% had a delay of >12 to ≤24 months, and 15.8% had >24 months' delay. Compared with ≤12 months, delays of >12 to ≤24 months and >24 months were associated with increased hospitalizations (incidence rate ratio [95% confidence interval]: 1.40 [1.11-1.71] vs 1.71 [1.29-2.12]) and outpatient visits (1.17 [1.06-1.30] vs 1.26 [1.08-1.41]). Longer delays were also associated with more intensive care unit (ICU) stays and 30-day readmissions. Diagnosis delays translated into excess costs PPPM of US$3986 [1439-6436] for >12 to ≤24 months and US$5366 [2107-8524] for >24 months compared with ≤12 months' delay; increased hospitalization costs (US$3248 [1108-5135] and US$4048 [1401-6342], respectively) being the driver. Sensitivity analyses yielded similar trends. CONCLUSIONS: Delayed PAH diagnosis is associated with significant incremental economic burden post-diagnosis, driven by hospitalizations including ICU stays and 30-day readmissions, highlighting the need for increased awareness and a potential benefit of earlier screening.
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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 | 0.001 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".