The Revised Identification of Seniors At Risk screening tool predicts readmission in older hospitalized patients: a cohort study
Bibliographic record
Abstract
BACKGROUND: The Identification of Seniors at Risk (ISAR) screening tool is a widely-used risk stratification tool for older adults in the emergency department (ED). Few studies have investigated the use of ISAR to predict outcomes of hospitalized patients. To improve usability a revised version of ISAR (ISAR-R), was developed in a quality improvement project. The ISAR-R is also widely used, although never formally validated. To address these two gaps in knowledge, 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 and geriatric units of these hospitals. Patients (or family 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. The primary metrics of interest were sensitivity and negative predictive value. The Area Under the Curve (AUC) was also computed as an overall measure of performance. RESULTS: Of 711 attempted recruitments, 496 accepted, and ISAR-R was completed for 485. Of these 386 patients were discharged home with a complete ISAR-R, the 90-day readmission rate was 24.9%; the AUC was 0.63 (95% CI 0.57,0.69). Sensitivity and negative predictive value at the recommended cut-point of 2 + were 81% and 87%, respectively. Specificity was low (40%). CONCLUSIONS: The ISAR-R tool is a potentially useful risk stratification tool to predict patients at increased risk of readmission. Its high values of sensitivity and negative predictive value at a cut-point of 2 + make it suitable for rapid screening of patients to identify those suitable for assessment by a clinical geriatric team, who can identify those with geriatric problems requiring further treatment, education, and follow-up to reduce the risk of readmission.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".