A multivariable prediction model to stratify risk of 90-day rehospitalization among adults with ulcerative colitis
Bibliographic record
Abstract
Abstract Background Individuals with ulcerative colitis (UC) are frequently re-hospitalized for persistent or recurrent severe disease flares. Accurate prediction of the risk of early re-hospitalization at the time of discharge could promote targeted outpatient interventions to reduce this risk. Methods We conducted a retrospective study in adults with UC admitted to The Ottawa Hospital between 2009 and 2016 for an acute UC-related indication. We ascertained candidate demographic, clinical, and health services predictors through medical records and administrative health databases. We derived and bootstrap validated a multivariable logistic regression model of 90-day UC-related re-hospitalization risk. We chose a probability cut point that maximized Youden’s index to differentiate high-risk from low-risk individuals and assessed model performance. Results Among 248 UC-related hospitalizations, there were 27 (10.9%) re-hospitalizations within 90 days of discharge. Our multivariable model identified gastroenterologist consultation within the prior year (adjusted odds ratio [aOR] 0.11, 95% confidence interval [CI], 0.04-0.39), male sex (aOR 3.27, 95% CI, 1.33-8.05), length of stay (OR 0.94, 95% CI, 0.88-1.01), and narcotic prescription at discharge (OR 1.96, 95% CI, 0.73-5.27) as significant predictors of 90-day re-hospitalization. The optimism-corrected c-statistic value was 0.78, and the goodness-of-fit test P-value was .09. The chosen probability cut point produced a sensitivity of 77.8%, specificity of 80.9%, positive predictive value (PPV) of 33.0%, and negative predictive value (NPV) of 96.7% in the derivation cohort. Conclusions A limited set of variables accessible at the point of hospital discharge can reasonably discriminate re-hospitalization risk among individuals with UC. Future studies are required to validate our findings.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".