Evidence map›Paper›PMID 38872086›Full record

Observational studyBMC geriatrics2024

Reweighting and validation of the hospital frailty risk score using electronic health records in Germany: a retrospective observational study.

Klaus Kaier, Adrian Heidenreich, Markus Jäckel, Vera Oettinger, Alexander Maier, Ingo Hilgendorf, Philipp Breitbart, Tau Hartikainen, Till Keller, Dirk Westermann and 1 more

Abstract readObservational StudyValidation Study
In one paragraph

Observational study in BMC geriatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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

11 authors.

Klaus KaierInstitute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Str. 49, Freiburg, 79106, Germany. klaus.kaier@googlemail.com.
Adrian HeidenreichCentre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Markus JäckelCentre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Vera OettingerCentre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Alexander MaierCentre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Ingo HilgendorfMedical Centre, Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, University Heart Centre Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Philipp BreitbartMedical Centre, Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, University Heart Centre Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Tau HartikainenMedical Centre, Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, University Heart Centre Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Till KellerMedical Centre, Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, University Heart Centre Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Dirk WestermannMedical Centre, Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, University Heart Centre Freiburg - Bad Krozingen, University of Freiburg, Freiburg, Germany.
Constantin von Zur MühlenCentre for Big Data Analysis in Cardiology (CeBAC), Department of Cardiology and Angiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the hospital setting, frailty is a significant risk factor, but difficult to measure in clinical practice. We propose a reweighting of an existing diagnoses-based frailty score using routine data from a tertiary care teaching hospital in southern Germany.

methodsThe dataset includes patient characteristics such as sex, age, primary and secondary diagnoses and in-hospital mortality. Based on this information, we recalculate the existing Hospital Frailty Risk Score. The cohort includes patients aged ≥ 75 and was divided into a development cohort (admission year 2011 to 2013, N = 30,525) and a validation cohort (2014, N = 11,202). A limited external validation is also conducted in a second validation cohort containing inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251). In the development cohort, LASSO regression analysis was used to select the most relevant variables and to generate a reweighted Frailty Score for the German setting. Discrimination is assessed using the area under the receiver operating characteristic curve (AUC). Visualization of calibration curves and decision curve analysis were carried out. Applicability of the reweighted Frailty Score in a non-elderly population was assessed using logistic regression models.

resultsReweighting of the Frailty Score included only 53 out of the 109 frailty-related diagnoses and resulted in substantially better discrimination than the initial weighting of the score (AUC = 0.89 vs. AUC = 0.80, p < 0.001 in the validation cohort). Calibration curves show a good agreement between score-based predictions and actual observed mortality. Additional external validation using inpatient cases aged ≥ 75 in 2022 throughout Germany (N = 491,251) confirms the results regarding discrimination and calibration and underlines the geographic and temporal validity of the reweighted Frailty Score. Decision curve analysis indicates that the clinical usefulness of the reweighted score as a general decision support tool is superior to the initial version of the score. Assessment of the applicability of the reweighted Frailty Score in a non-elderly population (N = 198,819) shows that discrimination is superior to the initial version of the score (AUC = 0.92 vs. AUC = 0.87, p < 0.001). In addition, we observe a fairly age-stable influence of the reweighted Frailty Score on in-hospital mortality, which does not differ substantially for women and men.

conclusionsOur data indicate that the reweighted Frailty Score is superior to the original Frailty Score for identification of older, frail patients at risk for in-hospital mortality. Hence, we recommend using the reweighted Frailty Score in the German in-hospital setting.

Indexed as

Electronic Health RecordsFrail ElderlyFrailtyHospital MortalityAgedAged, 80 and overFemaleGeriatric AssessmentGermanyHospitalizationHumansMaleRetrospective StudiesRisk AssessmentRisk FactorsAgedClinical decision makingClinical frailty scaleMachine learningRisk adjustmentSupervised learning

Identifiers

PMID38872086
PMCPMC11177354

What OpenQuestion holds

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LicenceCC BY
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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.