Evidence map›Paper›PMID 39467281›Full record

ArticleJMIR aging2024

Using Existing Clinical Data to Measure Older Adult Inpatients' Frailty at Admission and Discharge: Hospital Patient Register Study.

Boris Wernli, Henk Verloo, Armin von Gunten, Filipa Pereira

Abstract read
In one paragraph

Article in JMIR aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

4 authors.

Boris Wernli *Swiss Centre of Expertise in the Social Sciences (FORS), Lausanne, Switzerland.ORCID 0000-0002-5567-1317
Henk Verloo *University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland.ORCID 0000-0002-5375-3255
Armin von Gunten *Service of Old Age Psychiatry, Lausanne University Hospital, Lausanne, Switzerland.ORCID 0000-0001-7852-3803
Filipa Pereira *University of Applied Sciences and Arts Western Switzerland (HES-SO), Sion, Switzerland.ORCID 0000-0001-9207-4856

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFrailty is a widespread geriatric syndrome among older adults, including hospitalized older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalization in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records could effectively detect frailty, frailty-related problems, and complications as well be a clinical alert. Identifying frailty among older adults using existing patient health data would greatly aid the management and support of frailty identification and could provide a valuable public health instrument without additional costs.

objectiveWe aim to explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge.

methodsA retrospective electronic patient register study included inpatients aged ≥65 years admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalizations was used to customize this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A 2-step hierarchical cluster procedure was applied to compute e-Frail-CH (Switzerland) scores at hospital admission and discharge. Prevalence, central tendency, comparative, and validation statistics were computed.

resultsMean patient age at admission was 78.4 (SD 7.9) years, with more women admitted (28,018/53,690, 52.18%) than men (25,672/53,690, 47.81%). Our 2-step hierarchical clustering approach computed 46,743 inputs of hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.02% (n=19,643) of admissions and 48.23% (n=22,845) of discharges. Within e-Frail-CH's 0-12 range, a score ≥6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3, SD 2.6) and discharge (5.75, SD 2.7; P<.001). Sensitivity and specificity cut point values were 0.82 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (r

conclusionsAn electronic frailty measurement instrument was constructed and validated using patient data routinely collected during hospitalization, especially at admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores during hospitalization could provide very useful clinical alerts on the health trajectories of older adults and help select interventions for preventing or mitigating frailty.

Indexed as

Frail ElderlyFrailtyGeriatric AssessmentPatient DischargeRegistriesAgedAged, 80 and overElectronic Health RecordsFemaleHospitalizationHumansInpatientsMalePatient AdmissionRetrospective Studiesclinical datacluster analysiselectronic patient recordsfrailtyfrailty assessmentfunctional independence measurehierarchical clusteringhospital registermedical recordsolder adultspatient recordsroutinely collected data

Identifiers

PMID39467281
PMCPMC11555450

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Registered trials

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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.