Evidence map›Paper›PMID 32143637›Full record

ArticleBMC geriatrics2020

Clinically useful prediction of hospital admissions in an older population.

Jan Marcusson, Magnus Nord, Huan-Ji Dong, Johan Lyth

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05706272 (Secure And Focused Primary Care for Older pEople), which is not on this map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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.

NCT05706272 naunknown statusnot on this mapstarted 2023, after this paper: background citation

Secure And Focused Primary Care for Older pEople (SAFE) - A Proactive Trial

TypeinterventionalSponsorLinkoeping UniversityRan2023 to 2025Enrolled4,000ConditionsFrailtyArmsComprehensive geriatric assessment (CGA), Care as usual
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.

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  10. Role of primary and secondary care data in atrial fibrillation ascertainment: impact on risk factor associations, patient management, and mortality in UK Biobank.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025
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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

4 authors at 1 institution in 1 country.

Jan MarcussonAcute Internal Medicine and Geriatrics, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden. jan.marcusson@liu.se.ORCID 0000-0002-6452-3930
Magnus NordFamily Medicine, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Huan-Ji DongPain and Rehabilitation Centre, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Johan LythResearch and Development Unit in Region Östergötland, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Linköping University · SE

Funding

Linköping University 2016186-14
6 · The paper itself

Abstract

backgroundThe healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data.

methodsWe used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC.

resultsThe prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0.69 [95% confidence interval, CI 0.68-0.70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31 and 29%, respectively.

conclusionA prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.

Indexed as

AgedAged, 80 and overFemaleGeriatric AssessmentHumansMalePatient AdmissionPredictive Value of TestsRetrospective StudiesRisk FactorsROC CurveHospitalizationOlder personsPrediction

Identifiers

PMID32143637
PMCPMC7060558
OpenAlexW3012059038

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.