Evidence map›Paper›PMID 31306461›Full record

Observational studyPloS one2019

Development of a predictive score for potentially avoidable hospital readmissions for general internal medicine patients.

Anne-Laure Blanc, Thierry Fumeaux, Jérôme Stirnemann, Elise Dupuis Lozeron, Aimad Ourhamoune, Jules Desmeules, Pierre Chopard, Arnaud Perrier, Nicolas Schaad, Pascal Bonnabry

Abstract readObservational Study
In one paragraph

Observational study in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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.

Anne-Laure BlancPharmacy, Geneva University Hospitals, Geneva, Switzerland.ORCID 0000-0002-2295-8290
Thierry FumeauxGroupement hospitalier de l'ouest lémanique (GHOL), Nyon, Switzerland.ORCID 0000-0002-7427-6902
Jérôme StirnemannDepartment of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland.
Elise Dupuis LozeronDivision of Clinical Epidemiology, Geneva University Hospitals, Geneva, Switzerland.
Aimad OurhamouneDepartment of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland.
Jules DesmeulesClinical Pharmacology and Toxicology, Geneva University Hospitals, Geneva, Switzerland.
Pierre ChopardDepartment of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland.
Arnaud PerrierDepartment of General Internal Medicine, Geneva University Hospitals, Geneva, Switzerland.
Nicolas SchaadPharmacie Interhospitalière de la Côte, Morges, Switzerland.
Pascal BonnabryPharmacy, Geneva University Hospitals, Geneva, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions.

objectiveDerive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication. DESIGN/SETTING/

participantsRetrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011-2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013-2014. Variables were identified at hospital discharge.

resultsThe derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1-1.7)], admission in previous 6 months [2.3 (1.9-2.8)], heart failure [1.3 (1.0-1.7)], chronic ischemic heart disease [1.7 (1.2-2.3)], diabetes with organ damage [2.2 (1.3-3.8)], cancer [1.4 (1.0-1.9)], metastatic carcinoma [1.9 (1.3-3.0)], anemia [1.2 (1.0-1.5)], hypertension [1.3 (1.1-1.7)], arrhythmia [1.3 (1.0-1.6)], hyperkalemia [1.4 (1.0-1.7)], opioid drug prescription [1.3 (1.1-1.6)], and acute myocardial infarction [0.6 (0.4-0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort's operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064).

conclusionThis study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.

Indexed as

AgedAged, 80 and overAlgorithmsDiabetes MellitusFemaleHeart FailureHumansInternal MedicineLength of StayLogistic ModelsMaleMultivariate AnalysisMyocardial IschemiaNeoplasmsPatient DischargePatient Readmission

Identifiers

PMID31306461
PMCPMC6629067

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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.