Evidence map›Paper›PMID 33964888›Full record

SynthesisBMC geriatrics2021

The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients.

Patricia Jepma, Lotte Verweij, Arno Tijssen, Martijn W Heymans, Isabelle Flierman, Corine H M Latour, Ron J G Peters, Wilma J M Scholte Op Reimer, Bianca M Buurman, Gerben Ter Riet

Abstract readMeta-Analysis
In one paragraph

Synthesis in BMC geriatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
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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

10 authors.

Patricia JepmaDepartment of Cardiology, Amsterdam UMC, Amsterdam, the Netherlands. p.jepma@amsterdamumc.nl.
Lotte VerweijDepartment of Cardiology, Amsterdam UMC, Amsterdam, the Netherlands.
Arno TijssenCentre of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands.
Martijn W HeymansDepartment of Epidemiology and Data Science, Amsterdam UMC, Amsterdam, the Netherlands.
Isabelle FliermanDepartment of Internal Medicine, section of Geriatric Medicine, Amsterdam UMC, Amsterdam, the Netherlands.
Corine H M LatourCentre of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands.
Ron J G PetersDepartment of Cardiology, Amsterdam UMC, Amsterdam, the Netherlands.
Wilma J M Scholte Op ReimerDepartment of Cardiology, Amsterdam UMC, Amsterdam, the Netherlands.
Bianca M BuurmanCentre of Expertise Urban Vitality, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands.
Gerben Ter RietDepartment of Cardiology, Amsterdam UMC, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown.

aimTo estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients.

methodsAn individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (P

resultsThe population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; P DISCUSSION: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.

Indexed as

FrailtyAgedAged, 80 and overFemaleGeriatric AssessmentHumansMaleNetherlandsPatient ReadmissionProspective StudiesRisk AssessmentRisk FactorsSafety ManagementAgedCardiovascular diseasesFrailtyMortalityPatient readmissionPredictive value of testsRisk assessment

Identifiers

PMID33964888
PMCPMC8105911

What Socratic holds

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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 Socratic graph.