Evidence map›Paper›PMID 30899054›Full record

ArticleScientific reports2019

A risk stratification tool for hospitalisation in Australia using primary care data.

Sankalp Khanna, David A Rolls, Justin Boyle, Yang Xie, Rajiv Jayasena, Marienne Hibbert, Michael Georgeff

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

7 authors.

Sankalp KhannaCSIRO Australian e-Health Research Centre, Brisbane, QLD 4029, Australia. Sankalp.Khanna@csiro.au.
David A RollsCSIRO Australian e-Health Research Centre, Melbourne, VIC 3052, Australia.ORCID 0000-0003-0397-9923
Justin BoyleCSIRO Australian e-Health Research Centre, Brisbane, QLD 4029, Australia.
Yang XieCSIRO Australian e-Health Research Centre, Sydney, NSW 2122, Australia.
Rajiv JayasenaCSIRO Australian e-Health Research Centre, Melbourne, VIC 3052, Australia.
Marienne HibbertPrecedence Health Care, Melbourne, VIC 3000, Australia.
Michael GeorgeffPrecedence Health Care, Melbourne, VIC 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predictive risk models using general practice (GP) data to predict the risk of hospitalisation have the potential to identify patients for targeted care. Effective use can help deliver significant reductions in the incidence of hospitalisation, particularly for patients with chronic conditions, the highest consumers of hospital resources. There are currently no published validated risk models for the Australian context using GP data to predict hospitalisation. In addition, published models for other contexts typically rely on a patient's history of prior hospitalisations, a field not commonly available in GP information systems, as a predictor. We present a predictive risk model developed for use by GPs to assist in targeting coordinated healthcare to patients most in need. The algorithm was developed and validated using a retrospective primary care cohort, linked to records of hospitalisation in Victoria, Australia, to predict the risk of hospitalisation within one year. Predictors employed include demographics, prescription history, pathology results and disease diagnoses. Prior hospitalisation information was not employed as a predictor. Our model shows good performance and has been implemented within primary care practices participating in Health Care Homes, an Australian Government initiative being trialled for providing ongoing comprehensive care for patients with chronic and complex conditions.

Indexed as

Risk FactorsAlgorithmsAustraliaGeneral PracticeHospitalizationHumansMultiple Chronic ConditionsPatientsRisk Assessment

Identifiers

PMID30899054
PMCPMC6428894

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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.