Evidence map›Paper›PMID 34383814›Full record

ArticlePloS one2021

A comparison of comorbidity measures for predicting mortality after elective hip and knee replacement: A cohort study of data from the National Joint Registry in England and Wales.

Chris M Penfold, Michael R Whitehouse, Ashley W Blom, Andrew Judge, J Mark Wilkinson, Adrian Sayers

Abstract readComparative Study
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

6 authors.

Chris M PenfoldMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Southmead Hospital, Bristol, United Kingdom.ORCID 0000-0001-8654-353X
Michael R WhitehouseMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Southmead Hospital, Bristol, United Kingdom.
Ashley W BlomMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Southmead Hospital, Bristol, United Kingdom.
Andrew JudgeMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Southmead Hospital, Bristol, United Kingdom.
J Mark WilkinsonDepartment of Oncology and Metabolism, University of Sheffield, Sorby Wing, Northern General Hospital, Sheffield, United Kingdom.
Adrian SayersMusculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School, Southmead Hospital, Bristol, United Kingdom.ORCID 0000-0001-7452-5043

Funding

Medical Research Council MR/L01226X/1
6 · The paper itself

Abstract

backgroundThe risk of mortality following elective total hip (THR) and knee replacements (KR) may be influenced by patients' pre-existing comorbidities. There are a variety of scores derived from individual comorbidities that can be used in an attempt to quantify this. The aims of this study were to a) identify which comorbidity score best predicts risk of mortality within 90 days or b) determine which comorbidity score best predicts risk of mortality at other relevant timepoints (30, 45, 120 and 365 days). PATIENTS AND

methodsWe linked data from the National Joint Registry (NJR) on primary elective hip and knee replacements performed between 2011-2015 with pre-existing conditions recorded in the Hospital Episodes Statistics. We derived comorbidity scores (Charlson Comorbidity Index-CCI, Elixhauser, Hospital Frailty Risk Score-HFRS). We used binary logistic regression models of all-cause mortality within 90-days and within 30, 45, 120 and 365-days of the primary operation using, adjusted for age and gender. We compared the performance of these models in predicting all-cause mortality using the area under the Receiver-operator characteristics curve (AUROC) and the Index of Prediction Accuracy (IPA).

resultsWe included 276,594 elective primary THRs and 338,287 elective primary KRs for any indication. Mortality within 90-days was 0.34% (N = 939) after THR and 0.26% (N = 865) after KR. The AUROC for the CCI and Elixhauser scores in models of mortality ranged from 0.78-0.81 after THR and KR, which slightly outperformed models with ASA grade (AUROC = 0.77-0.78). HFRS performed similarly to ASA grade (AUROC = 0.76-0.78). The inclusion of comorbidities prior to the primary operation offers no improvement beyond models with comorbidities at the time of the primary. The discriminative ability of all prediction models was best for mortality within 30 days and worst for mortality within 365 days.

conclusionsComorbidity scores add little improvement beyond simpler models with age, gender and ASA grade for predicting mortality within one year after elective hip or knee replacement. The additional patient-specific information required to construct comorbidity scores must be balanced against their prediction gain when considering their utility.

Indexed as

AgedArthroplasty, Replacement, HipArthroplasty, Replacement, KneeCohort StudiesComorbidityElective Surgical ProceduresEnglandFemaleHip FracturesHospital MortalityHumansMaleMiddle AgedRegistriesRisk FactorsROC Curve

Identifiers

PMID34383814
PMCPMC8360555

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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.