Evidence map›Paper›PMID 36754375›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2023

Development and Validation of Multivariable Prediction Models for In-Hospital Death, 30-Day Death, and Change in Residence After Hip Fracture Surgery and the "Stratify-Hip" Algorithm.

Aicha Goubar, Finbarr C Martin, Catherine Sackley, Nadine E Foster, Salma Ayis, Celia L Gregson, Ian D Cameron, Nicola E Walsh, Katie J Sheehan

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Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed, 1 synthesis or guideline 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

9 authors.

Aicha GoubarSchool of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.
Finbarr C MartinSchool of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.ORCID 0000-0002-6911-3498
Catherine SackleySchool of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.
Nadine E FosterSurgical Treatment and Rehabilitation Service (STARS) Education and Research Alliance, The University of Queensland and Metro North Health, Brisbane, Queensland, Australia.
Salma AyisSchool of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.
Celia L GregsonMusculoskeletal Research Unit, Translation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID 0000-0001-6414-0529
Ian D CameronJohn Walsh Centre for Rehabilitation Research, Northern Sydney Local Health District and University of Sydney, Ryde, New South Wales, Australia.
Nicola E WalshCentre for Health and Clinical Research, University of the West of England Bristol, Bristol, UK.
Katie J SheehanSchool of Life Course and Population Sciences, Faculty of Life Science and Medicine, King's College London, London, UK.ORCID 0000-0002-5325-7454

Funding

Medical Research Council MR/S032819/1
6 · The paper itself

Abstract

backgroundTo develop and validate the stratify-hip algorithm (multivariable prediction models to predict those at low, medium, and high risk across in-hospital death, 30-day death, and residence change after hip fracture).

methodsMultivariable Fine-Gray and logistic regression of audit data linked to hospital records for older adults surgically treated for hip fracture in England/Wales 2011-14 (development n = 170 411) and 2015-16 (external validation, n = 90 102). Outcomes included time to in-hospital death, death at 30 days, and time to residence change. Predictors included age, sex, pre-fracture mobility, dementia, and pre-fracture residence (not for residence change). Model assumptions, performance, and sensitivity to missingness were assessed. Models were incorporated into the stratify-hip algorithm assigning patients to overall low (low risk across outcomes), medium (low death risk, medium/high risk of residence change), or high (high risk of in-hospital death, high/medium risk of 30-day death) risk.

resultsFor complete-case analysis, 6 780 of 141 158 patients (4.8%) died in-hospital, 8 693 of 149 258 patients (5.8%) died by 30 days, and 4 461 of 119 420 patients (3.7%) had residence change. Models demonstrated acceptable calibration (observed:expected ratio 0.90, 0.99, and 0.94), and discrimination (area under curve 73.1, 71.1, and 71.5; Brier score 5.7, 5.3, and 5.6) for in-hospital death, 30-day death, and residence change, respectively. Overall, 31%, 28%, and 41% of patients were assigned to overall low, medium, and high risk. External validation and missing data analyses elicited similar findings. The algorithm is available at https://stratifyhip.co.uk.

conclusionsThe current study developed and validated the stratify-hip algorithm as a new tool to risk stratify patients after hip fracture.

Indexed as

Hip FracturesAgedAlgorithmsEnglandHospital MortalityHumansClassificationFracture neck of femurFragility fractureRecoveryStratification

Identifiers

PMID36754375
PMCPMC10460557

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