Journal of clinical nursing · 2025
Prediction Models for Postoperative Pneumonia in Elderly Hip Fracture Patients: A Systematic Review and Critical Appraisal.
СтатьяСистематический обзорМетаанализПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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He Zhiqiang, Zhong Gaoting, Han Wenjin, Han Mengyu, Wu Wenbin, Zhou Xiaoling, Yang Yaru, An Yu, Li Jin
Аннотация
BACKGROUND: Although several models have been developed to predict postoperative pneumonia in elderly hip fracture patients, no systematic review of the model quality and clinical applicability has been reported.
OBJECTIVE: To systematically review and critically appraise existing models for postoperative pneumonia in elderly hip fracture patients.
DESIGN: Systematic review and meta-analysis.
METHODS: 10 databases were systematically searched from inception to April 15, 2024, updated on August 26. Two reviewers independently performed literature selection, information extraction and quality assessment. A narrative synthesis was employed to summarise the characteristics of the models. Meta-analysis was performed using Stata 17.0.
RESULTS: 13 studies containing 25 models were included. The prevalence of pneumonia was 9.62% (95% CI: 7.62%-11.62%). Age (53.8%), hypoproteinemia (46.2%), chronic obstructive pulmonary disease (COPD, 30.8%), gender (30.8%), activity of daily living score (ADL, 30.8%) and American Society of Anesthesiologists (ASA, 30.8%) score were the top six predictors. All models reported area under curve (AUC: 0.617-0.996). 9 studies (69.2%) used the Hosmer-Lemeshow (H-L) test, calibration curves, or Brier scores to evaluate the calibration. 5 studies (38.5%) performed internal validation, 4 studies (30.8%) performed external validation. All studies had a high risk of bias due to single sample source, inappropriate data processing, inadequate model evaluation, and negligence of calibration and validation. 10 studies (76.9%) had good applicability.
CONCLUSIONS: Prediction models for postoperative pneumonia in elderly hip fracture patients are still in the developing stage. The validation and evaluation of existing models are poor. Future studies should focus on robust external validation and updating. Additionally, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + artificial intelligence (TRIPOD+AI) statement should be followed.
RELEVANCE TO CLINICAL PRACTICE: Prediction models are effective in discriminating postoperative pneumonia in elderly hip fracture patients, but further external validation and adjustment are still warranted.
OBJECTIVE: To systematically review and critically appraise existing models for postoperative pneumonia in elderly hip fracture patients.
DESIGN: Systematic review and meta-analysis.
METHODS: 10 databases were systematically searched from inception to April 15, 2024, updated on August 26. Two reviewers independently performed literature selection, information extraction and quality assessment. A narrative synthesis was employed to summarise the characteristics of the models. Meta-analysis was performed using Stata 17.0.
RESULTS: 13 studies containing 25 models were included. The prevalence of pneumonia was 9.62% (95% CI: 7.62%-11.62%). Age (53.8%), hypoproteinemia (46.2%), chronic obstructive pulmonary disease (COPD, 30.8%), gender (30.8%), activity of daily living score (ADL, 30.8%) and American Society of Anesthesiologists (ASA, 30.8%) score were the top six predictors. All models reported area under curve (AUC: 0.617-0.996). 9 studies (69.2%) used the Hosmer-Lemeshow (H-L) test, calibration curves, or Brier scores to evaluate the calibration. 5 studies (38.5%) performed internal validation, 4 studies (30.8%) performed external validation. All studies had a high risk of bias due to single sample source, inappropriate data processing, inadequate model evaluation, and negligence of calibration and validation. 10 studies (76.9%) had good applicability.
CONCLUSIONS: Prediction models for postoperative pneumonia in elderly hip fracture patients are still in the developing stage. The validation and evaluation of existing models are poor. Future studies should focus on robust external validation and updating. Additionally, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + artificial intelligence (TRIPOD+AI) statement should be followed.
RELEVANCE TO CLINICAL PRACTICE: Prediction models are effective in discriminating postoperative pneumonia in elderly hip fracture patients, but further external validation and adjustment are still warranted.