Wang Youquan, Li Yanhua, Wang Huimei, Li Hongxiang, Li Yuting, Zhang Liying, Zhang Chaoyang, Gao Meng, Zhang Nan, Zhang Dong
Department of Critical Care Medicine, The First Hospital of Jilin University, Changchun, 130021, China.
Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China.
Clin Nutr. 2023 Dec;42(12):2293-2301. doi: 10.1016/j.clnu.2023.10.003. Epub 2023 Oct 5.
Developing and validating a clinical prediction nomogram of enteral feeding intolerance (NOFI) in critically ill patients. So as to help clinicians implement pre-intervention for patients with high risk of enteral feeding intolerance (FI), formulate individualized feeding strategies, and reduce the probability of FI occurrence.
From March 2018 to April 2023, patients who met the inclusion criteria but did not meet the exclusion criteria constituted the development cohort for retrospective analysis, and NOFI was developed. Patients recruited consecutively between May 2023 and July 2023 constituted the validation cohort for the prospective analysis for independent external validation of NOFI. Initially, a backward stepwise method was employed to conduct a multivariate logistic regression analysis in the development cohort, aiming to identify the optimal-fit model. Subsequently, a nomogram was derived from this model. The validation of the nomogram was carried out in an independent external validation cohort, where discrimination and calibration were evaluated. Additionally, a decision curve analysis was conducted to assess the net benefit of utilizing the nomogram for decision-making.
A total of 628 and 143 patients, 49.0 % and 51.7 % of patients occurred FI, were included in the development and validation cohort, respectively. We developed a NOFI in severely ill patients and the primary diagnosis, Acute gastrointestinal injury (AGI) grade, and APACHE II score were independent predictors of FI, with the OR of the primary diagnosis of circulatory disease being 2.281 (95 % CI, 1.364-3.816; P = 0.002); The OR of respiratory diseases was 0.424 (95 % CI, 0.259-0.594; P = 0.001); The OR of AGI grade was 4.920 (95 % CI, 3.773-6.416; P < 0.001), OR of APACHE II score was 1.100 (95 % CI, 1.059-1.143; P < 0.001). Independent external validation of the prediction model was performed. This model has good discrimination and calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (full early enteral nutrition or delayed enteral nutrition).
The prediction of enteral feeding intolerance can be conveniently facilitated by the NOFI that integrates primary diagnosis, AGI grade, and APACHE II score in critically ill patients.
开发并验证危重症患者肠内营养不耐受(NOFI)的临床预测列线图。以帮助临床医生对肠内营养不耐受(FI)高风险患者实施预干预,制定个体化喂养策略,并降低FI发生的概率。
2018年3月至2023年4月,符合纳入标准但不符合排除标准的患者构成回顾性分析的开发队列,并开发NOFI。2023年5月至2023年7月连续招募的患者构成验证队列,用于对NOFI进行独立外部验证的前瞻性分析。最初,在开发队列中采用向后逐步法进行多因素逻辑回归分析,旨在确定最优拟合模型。随后,从该模型导出列线图。在独立的外部验证队列中对列线图进行验证,评估其区分度和校准度。此外,进行决策曲线分析以评估使用列线图进行决策的净效益。
分别有628例和143例患者纳入开发队列和验证队列,FI发生率分别为49.0%和51.7%。我们在重症患者中开发了NOFI,主要诊断、急性胃肠损伤(AGI)分级和急性生理与慢性健康状况评分系统(APACHE)II评分是FI的独立预测因素,循环系统疾病主要诊断的比值比(OR)为2.281(95%置信区间[CI],1.364 - 3.816;P = 0.002);呼吸系统疾病的OR为0.424(95% CI,0.259 - 0.594;P = 0.001);AGI分级的OR为4.920(95% CI,3.773 - 6.416;P < 0.001),APACHE II评分的OR为1.100(95% CI,1.059 - 1.143;P < 0.001)。对预测模型进行了独立外部验证。该模型具有良好的区分度和校准度。列线图的决策曲线分析提供了比其他选择(全早期肠内营养或延迟肠内营养)更好的净效益。
整合主要诊断、AGI分级和APACHE II评分的NOFI可方便地促进对危重症患者肠内营养不耐受的预测。