Does a screening checklist for complex health and social care needs have potential clinical usefulness for predicting unplanned hospital readmissions in intensive care survivors: development and prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Intensive care (ICU) survivors are at high risk of long-term physical and psychosocial problems. Unplanned hospital readmission rates are high, but the best way to triage patients for interventions is uncertain. We aimed to develop and evaluate a screening checklist to help predict subsequent readmissions or deaths. DESIGN: A checklist for complex health and social care needs (CHSCNs) was developed based on previous research, comprising six items: multimorbidity; polypharmacy; frequent previous hospitalisations; mental health issues; fragile social circumstances and impaired activities of daily living. Patients were considered to have CHSCNs if two or more were present. We prospectively screened all ICU discharges for CHSCNs for 12 months. SETTING: ICU, Royal Infirmary, Edinburgh, UK. PARTICIPANTS: ICU survivors over a 12-month period (1 June 2018 and 31 May 2019). INTERVENTIONS: None. OUTCOME MEASURE: Readmission or death in the community within 3 months postindex hospital discharge. RESULTS: Of 1174 ICU survivors, 937 were discharged alive from the hospital. Of these 253 (27%) were classified as having CHSCNs. In total 28% (266/937) patients were readmitted (N=238) or died (N=28) within 3 months. Among CHSCNs patients 45% (n=115) patients were readmitted (N=105) or died (N=10). Patients without CHSCNs had a 22% readmission (N=133) or death (N=18) rate. The checklist had: sensitivity 43% (95% CI 37% to 49%), specificity 79% (95% CI 76% to 82%), positive predictive value 45% (95% CI 41% to 51%), and negative predictive value 78% (95% CI 76% to 80%). Relative risk of readmission/death for patients with CHSCNs was 2.06 (95% CI 1.69 to 2.50), indicating a pretest to post-test probability change of 28%-45%. The checklist demonstrated high inter-rater reliability (percentage agreement ≥87% for all domains; overall kappa, 0.84). CONCLUSIONS: Early evaluation of a screening checklist for CHSCNs at ICU discharge suggests potential clinical usefulness, but this requires further evaluation as part of a care pathway.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".