Predictors of High-cost Patients With Noninfectious Inflammatory Eye Diseases
Bibliographic record
Abstract
PURPOSE: Noninfectious inflammatory eye diseases (NIIEDs), such as uveitis, is a general term used to describe a complex mix of acute, chronic, allergic, and inflammatory disorders. Prior literature has established that, in addition to severe clinical burden, NIIEDs is associated with significant economic burden for US payers; however, no literature provides a current estimate of the economic burden associated with patients with high-cost NIIEDs. This study aimed to better understand the cost and resource use distribution and predictors of patients with high-cost NIIEDs. METHODS: This retrospective cohort study selected adult patients with NIIEDs from a large US administrative claims database between 2006 and 2015. Among the included patients, total all-cause health care costs were calculated for a randomly selected 12-month period. Patients in the top 20% of total all-cause health care costs were identified as high-cost patients; the remaining patients were identified as lower-cost patients. Patient demographic characteristics, clinical characteristics, cost, and health care resource utilization (HRU) were compared. Logistic regression models were used to determine characteristics associated with high-cost patients. FINDINGS: Patients with NIIEDs (n = 14,879) were categorized into 2976 high-cost and 11,903 lower-cost patients. High-cost patients with NIIEDs were significantly more likely to experience blindness, cataract, cystoid macular degeneration, retinal detachment, and visual disturbances during the follow-up period than the lower-cost patients (all P < 0.05). The high-cost patients accounted for ~77% of the total all-cause health care spend. High-cost patients incurred an average annual total health care cost of $59,873, and the top 1 percentile incurred $349,967 during the follow-up period. Hospitalization was a key cost driver among the high-cost patients, accounting for 50% of the total cost among the top 1 percentile of patients. High-cost patients were more likely to have specific autoimmune diseases, inpatient admission, and use of biologic and immunosuppressant agents. IMPLICATIONS: A small segment of patients with NIIEDs consumed most resources. This study identified several predictors based on patient characteristics and HRU that may help inform the profile of patients with NIIEDs with the highest health care needs. As such, patients with a given profile can be selected for targeted interventions by clinicians to potentially help improve quality of care and to reduce 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".