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Prediction Models for Dysphagia in Intensive Care Unit after Mechanical Ventilation: A Systematic Review and Meta-analysis.
Chen, Juan; Lu, Guangyu; Wang, Zhiyao; Zhang, Jingyue; Ding, Jiali; Zeng, Qingping; Chai, Liying; Zhao, Li; Yu, Hailong; Li, Yuping.
Afiliação
  • Chen J; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Lu G; Institute of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China.
  • Wang Z; Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou, China.
  • Zhang J; Neuro Intensive Care Unit, Department of Neurosurgery, Clinical Medical College of Yangzhou University, Yangzhou, China.
  • Ding J; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Zeng Q; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Chai L; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Zhao L; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
  • Yu H; Institute of Public Health, Medical College of Yangzhou University, Yangzhou University, Yangzhou, China.
  • Li Y; School of Nursing and Public Health, Yangzhou University, Yangzhou, China.
Laryngoscope ; 134(2): 517-525, 2024 Feb.
Article em En | MEDLINE | ID: mdl-37543979
OBJECTIVE: Dysphagia is a common condition that can independently lead to death in patients in the intensive care unit (ICU), particularly those who require mechanical ventilation. Despite extensive research on the predictors of dysphagia development, consistency across these studies is lacking. Therefore, this study aimed to identify predictors and summarize existing prediction models for dysphagia in ICU patients undergoing invasive mechanical ventilation. METHODS: We searched five databases: PubMed, EMBASE, Web of Science, Cochrane Library, and the China National Knowledge Infrastructure. Studies that developed a post-extubation dysphagia risk prediction model in ICU were included. A meta-analysis of individual predictor variables was performed with mixed-effects models. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). RESULTS: After screening 1,923 references, we ultimately included nine studies in our analysis. The most commonly identified risk predictors included in the final risk prediction model were the length of indwelling endotracheal tube ≥72 h, Acute Physiology and Chronic Health Evaluation (APACHE) II score ≥15, age ≥65 years, and duration of gastric tube ≥72 h. However, PROBAST analysis revealed a high risk of bias in the performance of these prediction models, mainly because of the lack of external validation, inadequate pre-screening of variables, and improper treatment of continuous and categorical predictors. CONCLUSIONS: These models are particularly susceptible to bias because of numerous limitations in their development and inadequate external validation. Future research should focus on externally validating the existing model in ICU patients with varying characteristics. Moreover, assessing the acceptance and effectiveness of the model in clinical practice is needed. LEVEL OF EVIDENCE: NA Laryngoscope, 134:517-525, 2024.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Respiração Artificial / Transtornos de Deglutição Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Aged / Humans Idioma: En Revista: Laryngoscope Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Respiração Artificial / Transtornos de Deglutição Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Aged / Humans Idioma: En Revista: Laryngoscope Ano de publicação: 2024 Tipo de documento: Article