Evidence map›Paper›PMID 34625815›Full record

ArticleArchives of orthopaedic and trauma surgery2023

A nomogram to predict postoperative infection for older hip fracture patients.

Xiran Peng, Xuechao Hao, Tao Zhu

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Article in Archives of orthopaedic and trauma surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

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4citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

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

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4 · The record

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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

3 authors.

Xiran Peng *Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, China.
Xuechao Hao *Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, China.
Tao ZhuDepartment of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, China. 739501155@qq.com.ORCID http://orcid.org/0000-0002-4557-8872

Funding

Key Technologies Research and Development Program 2018YFC2001800West China Hospital, Sichuan University Z2018A02
6 · The paper itself

Abstract

introductionPostoperative infection is one of the most common postoperative complications in hip fracture surgery. It is related with increased morbidity and mortality. This study aimed at developing a nomogram to predict the individual probability of postoperative infection to facilitate perioperative decision-making. MATERIALS AND

methodsIn this retrospective study, we included all patients over 65 years old admitted for hip fracture in West China Hospital of Sichuan University from 1 January 2015 to 31 December 2019. Univariate and multivariate logistic regression analyses were used to identify significant predictors. We used all-subsets regression to screen an optimal model, and visualized the model through drawing nomogram. To evaluate the model performance, we applied receiver operating characteristic curve and calibration curve.

resultsWe enrolled 677 older patients. 136 (20.1%) patients developed postoperative infection during hospitalization. Variables retained in the final model were albumin [odds ratio (OR) 0.90, 95% confidence interval (CI) 0.84-0.96], cholesterol (OR 1.49, 95% CI 1.04-2.15), blood phosphorus (OR 0.16, 95% CI 0.05-0.48), high-density lipoprotein (OR 0.42, 95% CI 0.19-0.89), surgery type (OR 2.27, 95% CI 1.35-3.90), smoking (OR 1.95, 95% CI 1.02-3.66), American Society of Anesthesiologists classification [class III (OR 1.02, 95% CI 0.55-1.93); class IV (OR 1.93, 95% CI 0.76-4.82)], and chronic pulmonary disease (OR 2.16, 95% CI 1.25-3.68). The C-index of the nomogram was 0.752 (95% CI 0.697-0.806). Calibration curve showed good agreement between predicted value and observed outcome. In the validation group, our nomogram showed an area under the receiver operating characteristic curve of 0.723 (95% CI 0.639-0.807).

conclusionOur nomogram showed good discrimination ability in predicting individual probability of postoperative infection among older patients with hip fracture surgery. The nomogram could help clinicians identify patients at high risk of postoperative infection before surgery.

Indexed as

NomogramsPostoperative ComplicationsAgedChinaHumansRetrospective StudiesHip fractureNomogramOlder patientsPostoperative infection

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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.