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Evaluation of predictive value of lactate and complex model for prolongation of mechanical ventilation after off-pump coronary bypass grafting
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Shi-Cheng Zhou1, Hong-Guang Han2, *, Jin-Song Han2, Li-Sheng Xu3, Fang Ji4, Shi-Qi Wang5
Medical Journal of Chinese People’s Liberation Army | 2022, 47(5) : 471 - 478
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Medical Journal of Chinese People’s Liberation Army | 2022, 47(5): 471-478
Clinical Research
Evaluation of predictive value of lactate and complex model for prolongation of mechanical ventilation after off-pump coronary bypass grafting
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Shi-Cheng Zhou1, Hong-Guang Han2, *, Jin-Song Han2, Li-Sheng Xu3, Fang Ji4, Shi-Qi Wang5
Affiliations
  • 1Training Base for Graduate of General Hospital of Northern Theater Command, China Medical University, Shenyang 110016, China
  • 2Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, Shenyang 110016, China
  • 3School of Biomedical and Information Engineering, Northeastern University, Shenyang 110169, China
  • 4Medical Management Division, General Hospital of Northern Theater Command, Shenyang 110016, China
  • 5Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang 110016, China
Published: 2022-05-28 doi: 10.11855/j.issn.0577-7402.2022.05.0471
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Objective To investigate the predictive value of lactate (Lac) and complex model for the occurrence of prolonging mechanical ventilation (PMV) after off-pump coronary bypass grafting (OPCABG) based on dose-response analysis and decision curve analysis. Methods A retrospective analysis was conducted on 683 patients who underwent OPCABG from January to December 2019 in the Department of Cardiovascular Surgery of the General Hospital of the Northern Theater Command. These patients were divided into PMV group (n=107) and non-PMV group (n=576) based on whether duration of prolonged mechanical ventilation was longer than 24 h. The arterial blood lactate at 0 h and 6 h after admission to the ICU of cardiovascular surgery,baseline data and other clinical indicators of the patients were recorded. Preoperative, intraoperative and postoperative risk factors affecting PMV time were analyzed by univariate analysis, and a prediction model was established by indicators selected by logistic regression. Restricted cubic spline model, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate the predictive value of arterial blood lactate and complex model. Results There were no significant differences between PMV group and non-PMV group in gender, age, BMI, NYHA cardiac function classification, history of myocardial infarction, history of PCI, smoking history, hypertension, diabetes, hypercholesterolemia, >50% stenosis with of 3 coronary artery branches and left aortic stenosis >50%, number of blood vessel bridge, arterial blood lactate at 0 h after admission to ICU,preoperative serum creatinine, hemoglobin after operation, preoperative total bilirubin, preoperative direct bilirubin (P>0.05). There were statistically significant differences in the use of IABP, left ventricular ejection fraction (LVEF), pulmonary arterial pressure,preoperative red blood cell distribution width (RDW), arterial blood lactate at 6 h after admission to ICU, preoperative hemoglobin,preoperative hypersensitive CRP (hs-CRP), postoperative hs-CRP, preoperative troponin T (TNT) and preoperative amino-terminal pro-brain natriuretic peptide (NT-proBNP) (P<0.05). Multivariate logistic regression analysis showed that preoperative RDW, arterial blood lactate 6 h after admission to ICU, pulmonary arterial pressure, preoperative NT-proBNP were risk predictors of PMV, and use of IABP was protective predictor of PMV (P<0.05), OR of five factors were 1.242 (95%CI 1.001-1.539), 1.370 (95%CI 1.171-1.604), 1.043 (95%CI 1.002-1.087), 2.065 (95%CI 1.333-3.200), 0.146 (95%CI 0.071-0.301), respectively (P<0.05). The area under the receiver operating characteristic curve of arterial blood lactate 6 h after admission to ICU and complex model were 0.582 (95%CI 0.518-0.646), 0.727 (95%CI 0.674-0.781), respectively. Intensity of association between Lac 6 h and the development of PMV exhibited a non-linear dose response relationship (P<0.01). Decision curve analysis showed that compared with Lac 6 h, the complex model had a higher net benefit when the threshold probability was between 0.05 and 0.75. Conclusions Compared with Lac 6 h, complex model has a higher predictive value for the occurrence of PMV after OPCABG.

lactate  /  complex model  /  off-pump coronary artery bypass grafting  /  mechanical ventilation
Shi-Cheng Zhou, Hong-Guang Han, Jin-Song Han, Li-Sheng Xu, Fang Ji, Shi-Qi Wang. Evaluation of predictive value of lactate and complex model for prolongation of mechanical ventilation after off-pump coronary bypass grafting[J]. Medical Journal of Chinese People’s Liberation Army, 2022 , 47 (5) : 471 -478 . DOI: 10.11855/j.issn.0577-7402.2022.05.0471
  • Plan of 2020 Shenyang Science and Technology(20-205-4-016)
  • Youth Training Project of Military Medical Science and Technology(20QNPY090)
Year 2022 volume 47 Issue 5
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Article Info
doi: 10.11855/j.issn.0577-7402.2022.05.0471
  • Receive Date:2021-09-17
  • Online Date:2025-12-17
  • Published:2022-05-28
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History
  • Received:2021-09-17
  • Accepted:2022-02-15
Funding
Plan of 2020 Shenyang Science and Technology(20-205-4-016)
Youth Training Project of Military Medical Science and Technology(20QNPY090)
Affiliations
    1Training Base for Graduate of General Hospital of Northern Theater Command, China Medical University, Shenyang 110016, China
    2Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, Shenyang 110016, China
    3School of Biomedical and Information Engineering, Northeastern University, Shenyang 110169, China
    4Medical Management Division, General Hospital of Northern Theater Command, Shenyang 110016, China
    5Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang 110016, China

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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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