Volume 22 Issue 3
Mar.  2024
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Article Navigation >  Chinese Journal of General Practice  > 2024  >  22(3): 384-388
YANG Ling, ZHANG Li, PENG Hu, GU Xin, YU Xiangyou. Development of a prediction model for enteral feeding intolerance in patients with sepsis[J]. Chinese Journal of General Practice, 2024, 22(3): 384-388. doi: 10.16766/j.cnki.issn.1674-4152.003408
Citation: YANG Ling, ZHANG Li, PENG Hu, GU Xin, YU Xiangyou. Development of a prediction model for enteral feeding intolerance in patients with sepsis[J]. Chinese Journal of General Practice, 2024, 22(3): 384-388. doi: 10.16766/j.cnki.issn.1674-4152.003408
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Development of a prediction model for enteral feeding intolerance in patients with sepsis

doi: 10.16766/j.cnki.issn.1674-4152.003408
  • 1.

    School of Nursing, Xinjiang Medical University, Urumqi, Xinjiang 830054, China

Funds:

 82160360

 320.6750.2023-02-3

  • Received Date: 2023-11-28
    Available Online: 2024-05-27
    Abstract
  •   Objective  To explore the risk factors of enteral feeding intolerance (EFI) in sepsis patients and to understand the occurrence of EFI in sepsis patients. To construct a risk prediction model for the occurrence of EFI in sepsis patients to facilitate the screening of high-risk groups to prevent and control the occurrence of EFI.  Methods  Convenience sampling method was used to select sepsis patients admitted to the Department of Critical Care Medicine of a tertiary-level hospital in Xinjiang who underwent enteral nutrition therapy from January 2022 to June 2023 as study subjects and were divided into the feeding tolerance group and feeding intolerance group by whether or not EFI occurred in the patients, and a one-way analysis was carried out on the patients ' general, clinical, and feeding intolerance-related data, and LASSO regression was applied to solve the covariance problem among variables, then the variables in the results of LASSO regression analysis were included in logistic regression analysis to derive the risk factors for EFI in patients with sepsis, on the basis of which we constructed a prediction model for the risk of EFI in patients with sepsis, and plotted a Nomogram, ROC curve, decision curve analysis, and calibration curve to evaluate and validate the diagnostic efficacy of the prediction model.  Results  A total of 199 sepsis patients with enteral nutrition therapy were included, including 93 cases in the intolerant group and 106 cases in the tolerant group, and the incidence rate of EFI was 46.7%. Multifactorial analysis showed that sequential organ failure assessment (SOFA) score, nutritional risk screening (NRS-2002) score, analgesics, and ICU length of stay were independent risk factors for the occurrence of EFI in patients with sepsis. The area under the ROC curve was 0.885 (95% CI: 0.833-0.933), and the maximal Youden index was 0.689, with a sensitivity of 0.849, and a specificity of 0.839.  Conclusion  SOFA score, NRS-2002 score, analgesics, and ICU length of stay are risk factors for EFI in septic patients. The risk prediction model constructed in this study based on this was validated and found to be a more accurate predictor of the risk of EFI in patients with sepsis, which can help clinical healthcare professionals provide scientific and individualized enteral nutrition therapy to patients.

     

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