Autor: |
Jine Wang, Nan Zheng, Xinyi Chang, Huitao Qian, Yi Han |
Jazyk: |
angličtina |
Rok vydání: |
2022 |
Předmět: |
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Zdroj: |
BMJ Open, Vol 12, Iss 11 (2022) |
Druh dokumentu: |
article |
ISSN: |
2044-6055 |
DOI: |
10.1136/bmjopen-2022-066015 |
Popis: |
Objectives This study aimed to explore the predictive value of single and multiple risk factors for the clinical outcomes of critically ill patients receiving enteral nutrition and to establish an effective evaluation model.Design Retrospective cohort study.Setting Data from the 2020–2021 period were collected from the electronic records of the First Affiliated Hospital, Nanjing Medical University.Participants 459 critically ill patients with enteral nutrition in the geriatric intensive care unit were included in the study.Primary and secondary outcome measures The primary outcome was 28-day mortality. The secondary outcomes were 28-day invasive mechanical ventilation time, intensive care unit stay, Nutrition Risk Screening 2002 (NRS2002) score and Acute Physiology and Chronic Health Evaluation II (APACHE II) score.Results Independent prognostic factors, including prealbumin/procalcitonin (PCT) ratio and APACHE II score, were identified using a logistic regression model and used in the nomogram. The area under the receiver operating characteristic curve and concordance index indicated that the predictive capacity of the model was 0.753. Moreover, both the prealbumin/PCT ratio and the combination model of PCT, prealbumin and NRS2002 had a higher predictive value for clinical outcomes. Subgroup analysis also identified that a higher inflammatory state (PCT >0.5 ng/mL) and major nutritional risk (NRS2002 >3) led to worse clinical outcomes. In addition, patients on whole protein formulae bore less nutritional risk than those on short peptide formulae.Conclusions This nomogram had a good predictive value for 28-day mortality in critically ill patients receiving enteral nutrition. Both the prealbumin/PCT ratio and the combination model (PCT, prealbumin and NRS2002), as composite models of inflammation and nutrition, could better predict the prognosis of critically ill patients. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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