Evidence map›Paper›PMID 35321894›Full record

ArticleBMJ open2022

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.

Timothy Simon Walsh, Ellen Pauley, Eddie Donaghy, Joanne Thompson, Lucy Barclay, Richard Anthony Parker, Christopher Weir, James Marple

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07466446 (Post Intensive Care Accelerometery to Study and Support Recovery Outcomes), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07466446 recruitingnot on this mapstarted 2026, after this paper: background citation

Post Intensive Care Accelerometery to Study and Support Recovery Outcomes

TypeobservationalSponsorUniversity of EdinburghRan2026 to 2027Enrolled40ConditionsPost Intensive Care Syndrome (PICS)
3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 3 countries.

Timothy Simon WalshCritical Care Medicine; Usher Institute of Population Health Sciences, University of Edinburgh Division of Clinical and Surgical Sciences, Edinburgh, UK twalsh@staffmail.ed.ac.uk.ORCID 0000-0002-3590-8540
Ellen PauleyDepartment of Anaesthesia, Critical Care & Pain Medicine, University of Edinburgh Division of Clinical and Surgical Sciences, Edinburgh, UK.
Eddie DonaghyDepartment of Anaesthesia, Critical Care & Pain Medicine, NHS Lothian, Edinburgh, UK.
Joanne ThompsonDepartment of Anaesthesia, Critical Care & Pain Medicine, NHS Lothian, Edinburgh, UK.
Lucy BarclayDepartment of Anaesthesia, Critical Care & Pain Medicine, NHS Lothian, Edinburgh, UK.
Richard Anthony ParkerUsher Institute of Population Health Sciences, University of Edinburgh, Edinburgh, UK.
Christopher WeirUsher Institute of Population Health Sciences, University of Edinburgh, Edinburgh, UK.
James MarpleDepartment of Anaesthesia, Critical Care & Pain Medicine, NHS Lothian, Edinburgh, UK.
NHS Lothian · GBInstitute of Population and Public Health · CAPain and Rehabilitation Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIntensive 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.

designA 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.

settingICU, Royal Infirmary, Edinburgh, UK.

participantsICU survivors over a 12-month period (1 June 2018 and 31 May 2019).

interventionsNone. OUTCOME MEASURE: Readmission or death in the community within 3 months postindex hospital discharge.

resultsOf 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).

conclusionsEarly 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.

Indexed as

ChecklistPatient ReadmissionActivities of Daily LivingCritical CareHumansIntensive Care UnitsPatient DischargeProspective StudiesReproducibility of ResultsSocial SupportSurvivorscritical illnessintensive carereadmissionrecoveryscreeningtransitions of caretriage

Identifiers

PMID35321894
PMCPMC8943772
OpenAlexW4221014188

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.