目的:通过血流动力学、液体平衡相关参数建立脱机失败预测模型以指导临床脱机。方法:回顾性分析2017年1月1日至2018年12月31日入住天津市第三中心医院重症医学科有创机械通气时间>24 h并进行脱机试验患者的临床资料。搜集患者入重症监护病房（ICU）24 h内的基线资料、脉搏指示连续心排血量监测（PiCCO）的血流动力学参数、B型利钠肽（BNP）、尿量、液体平衡量以及脱机前24 h内PiCCO监测的血流动力学参数、BNP、尿量、液体平衡量、利尿剂使用、去甲肾上腺素使用、机械通气期间连续性肾脏替代治疗（CRRT）使用情况。根据是否脱机成功将纳入患者分为脱机成功组和脱机失败组，比较两组间各变量的差异，将脱机前24 h内差异有统计学意义的变量纳入Logistic回归分析中，建立脱机失败预测模型，并找出造成脱机失败的可能因素。结果:共有159例患者纳入研究，其中脱机成功138例，脱机失败21例。两组入ICU 24 h内PiCCO监测参数、BNP、尿量、液体平衡量比较差异均无统计学意义；两组脱机前24 h内BNP（n χ2＝9.262、n P＝0.026）、中心静脉压（CVP；n χ2＝7.948、n P＝0.047）、左室收缩力指数（dPmx；n χ2＝10.486、n P＝0.015）、尿量（n χ2＝8.921、n P＝0.030）、液体平衡量（n χ2＝9.172、n P＝0.027）差异均有统计学意义。此外，为完善模型和提高预测准确率，将脱机前心排血指数（CI；n χ2＝7.789、n P＝0.051）也纳入预测模型。最终将脱机前24 h内BNP、CVP、CI、dPmx、尿量、液体平衡量纳入Logistic回归模型，其预测脱机失败的准确率为92.9%，敏感度为100%，特异度为76.8%；用年龄和去甲肾上腺素使用进行校正后，其准确率为94.2%，敏感度为100%，特异度为81.2%。n 结论:以PiCCO监测指标联合液体平衡指标建立脱机失败预测模型预测脱机的准确率高，能指导临床脱机。“,”Objective:To establish a model that can predict weaning failure from ventilation through hemodynamic and fluid balance parameters.Methods:A retrospective analysis was conducted. The patients who underwent invasive mechanical ventilation for more than 24 hours and having spontaneous breathing test admitted to intensive care unit (ICU) of Tianjin Third Central Hospital from January 1st, 2017 to December 31st, 2018 were enrolled. The information was collected, which included the baseline data, hemodynamic parameters by pulse indicator continuous cardiac output (PiCCO) monitoring, B-type natriuretic peptide (BNP), urinary output, fluid balance in first 24 hours when patients admitted to ICU, and hemodynamic parameters by PiCCO monitoring, BNP, urinary output, fluid balance, diuretic usage, noradrenalin usage within 24 hours before weaning as well as usage of continuous renal replacement therapy (CRRT) during mechanical ventilation. According to weaning success or failure, the patients were divided into weaning success group and weaning failure group, and the statistical differences between the two groups were calculated. Variables with statistical significance within 24 hours before weaning were included in the multivariate Logistic regression analysis to establish weaning failure prediction model and find out the possible risk factors of weaning failure.Results:A total of 159 patients were included in this study, which included 138 patients in the weaning success group and 21 patients in the weaning failure group. There were no statistical differences in all hemodynamic parameters by PiCCO monitoring, BNP, urinary output, fluid balance within 24 hours into ICU between two groups. There were statistical differences in BNP (n χ2 = 9.262, n P = 0.026), central venous pressure (CVP; n χ2 = 7.948, n P = 0.047), maximum rate of the increase in pressure (dPmx; n χ2 = 10.486, n P = 0.015), urinary output (n χ2 = 8.921, n P = 0.030), fluid balance (n χ2 = 9.172, n P = 0.027) within 24 hours before weaning between two groups. In addition, variable about cardiac index (CI; n χ2 = 7.789, n P = 0.051) was included into multivariate Logistic regression model to improve the prediction model and enhance the accuracy of model. Finally, variables included in the multivariate Logistic regression model were BNP, CVP, CI, dPmx, urinary output, fluid balance volume, and the accuracy of the weaning failure prediction model was 92.9%, the sensitivity was 100%, and the specificity was 76.8%. When the model was adjusted by variables of age and noradrenalin usage, the accuracy of model to predict failure of weaning was 94.2%, the sensitivity was 100%, the specificity was 81.2%.n Conclusion:Weaning failure prediction model based on hemodynamic parameters by PiCCO monitoring and variables about liquid balance has high accuracy and can guide clinical weaning.