Healthcare costs of patients with cancer stratified by Khorana score risk levels
Bibliographic record
Abstract
AIMS: Patients with cancer are at high risk of venous thromboembolism (VTE), which entails a high economic burden. The risk of cancer-associated VTE can be assessed using the Khorana score (KS), a validated VTE risk prediction algorithm. This study compared healthcare costs associated with different KS in a population of patients newly diagnosed with cancer. METHODS: The Optum Clinformatics DataMart database (01/01/2012-09/30/2017) was used to select adult patients with ≥1 hospitalization or ≥2 outpatient claims with a cancer diagnosis (index date) initiated on systemic therapy or radiation therapy. Patients were classified in mutually exclusive cohorts based on KS (i.e. KS = 0, 1, 2 or ≥3). The observation period spanned from index to the earliest among the end of data availability, death, end of insurance coverage, or 12 months. RESULTS: In total 6,194 patients (KS = 0: 2,488; KS = 1: 2,125; KS = 2: 1,074; KS ≥ 3: 507) were included. On average, patients were aged 68 years, 48-52% were female, and the Quan-Charlson comorbidity index ranged between 1.1 and 1.4. Over the observation period, all-cause total healthcare costs per patient per month (PPPM) were $8,826 (KS = 0), $11,598 (KS = 1), $14,028 (KS = 2), and $16,211 (KS ≥ 3). Using the KS = 0 cohort as a reference, adjusted PPPM costs were $2,506, $4,775, and $6,452 higher in the KS = 1, KS = 2, and KS ≥ 3 cohorts, respectively. Hospitalization and outpatient costs were the main drivers of these differences. Similar results were found for VTE-related costs, which represented 4-11% of the total all-cause cost difference between KS cohorts. LIMITATIONS: Residual confounders; results may not be generalized to patients with other insurance plans or those who received treatments other than systemic therapy or radiation therapy. CONCLUSIONS: This real-world analysis found that cancer patients at higher risk of VTE (based on KS) incurred significantly greater all-cause and VTE-related healthcare costs compared with cancer patients at lower risk of VTE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".