The Revised Identification of Seniors at Risk screening tool predicts readmission in older hospitalized patients: a cohort study.
Bibliographic record
Abstract
Abstract Background: The Identification of Seniors at Risk (ISAR) screening tool has been used primarily to predict adverse outcomes among older patients in the emergency department (ED).Few studies have investigated the use of ISAR to predict outcomes of hospitalized patients. To improve the usability of ISAR, the revised ISAR (ISAR-R) was developed in a quality improvement project. Although widely used, the ISAR-R has never been validated. We aimed to assess the ability of the ISAR-R to predict readmission in a cohort of older adults who were hospitalized (admitted from the ED) and discharged home. Methods: This was a secondary analysis of data collected in a pre-post evaluation of a patient discharge education tool. Participants were patients aged 65 and older, admitted to hospital via the ED of two general community hospitals, and discharged home from the medical (4) and geriatric (2) units of these hospitals. Patients (or caregivers for patients with mental or physical impairment) were recruited during their admission. The ISAR-R was administered as part of a short in-hospital interview. Providers were blinded to ISAR-R scores. Among patients discharged home, 90-day readmissions were extracted from hospital administrative data. Performance characteristics were computed for different ISAR-R cut-points. Results: Of 711 attempted recruitments, 496 accepted, and ISAR-R was completed for 485. Among 386 patients discharged home with a complete ISAR-R, the 90-day readmission rate was 24.9%; the Area Under the Curve (AUC) was 0.63 (95% CI 0.57,0.69). Sensitivity and specificity at the conventional cut-point of 2+ were 81% and 40%, respectively.Conclusions: The ISAR-R tool is a potentially useful risk stratification tool to predict patients at increased risk of readmission. It improves on the original ISAR by using more intuitive phrasing and scoring, and by decreasing the proportion of patients who screen positive.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.005 |
| 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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".