[持续肾替代治疗对脓毒症急性肾损伤患者预后预测模型的构建]。

Yalin Li, Dongfeng Li, Jing Wang, Hao Li, Xiao Wang
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引用次数: 0

摘要

目的:探讨脓毒症致急性肾损伤患者接受持续肾替代治疗(CRRT)后预后的影响因素,并建立死亡风险预测模型。方法:采用回顾性研究方法,纳入阜阳市人民医院2021年2月至2023年9月接受CRRT治疗的败血症性急性肾损伤患者。收集患者入组后24小时内的一般信息、合并症、生命体征、实验室指标、疾病严重程度评分、治疗状况、重症监护病房(ICU)住院时间和28天预后。采用Cox回归模型确定脓毒症急性肾损伤患者预后的影响因素,并建立nomogram模型预测患者的死亡率。采用接收算子特征曲线(ROC曲线)、校正曲线和Hosmer-Lemeshow检验验证nomogram模型的预测效果。结果:共纳入146例败血症性急性肾损伤患者,治疗28 d后存活98例,死亡48例,死亡率32.88%。死亡组血乳酸、白细胞介素-6 (IL-6)、血清胱抑素C、急性生理与慢性健康评估ⅱ(APACHEⅱ)、序事性器官衰竭评估(SOFA)、机械通气比例均显著高于生存组。ICU住院时间明显长于生存组,肾小球滤过率明显低于生存组。Cox回归分析表明,血乳酸(比值比(或)= 2.992,95%置信区间1.023 - -8.754(95%置信区间)],il - 6 (OR = 3.522, 95% ci 1.039 - -11.929),血清半胱氨酸蛋白酶抑制物C (OR = 3.999, 95% ci 1.367 - -11.699),机械通气(OR = 4.133, 95% ci 1.413 - -12.092), APACHE II评分(OR = 5.013, 95% ci 1.713 - -14.667),沙发上得分(OR = 3.404, 95% ci 1.634 - -9.959)死亡率的危险因素在sepsis-induced急性肾损伤患者(P < 0.05),肾小球滤过率(OR = 0.294, 95%CI为0.101 ~ 0.860)是脓毒症致急性肾损伤患者死亡的保护因素(P < 0.05)。ROC曲线显示柱状图模型预测脓毒症急性肾损伤患者28天死亡率的敏感性为80.0% (95%CI为69.1% ~ 89.2%),特异性为89.3% (95%CI为83.1% ~ 95.2%)。结论:血乳酸、IL-6、机械通气、APACHEII评分、SOFA评分、肾小球滤过率、血清胱抑素C与脓毒症所致急性肾损伤患者死亡风险相关。nomogram模型可以帮助早期识别这些患者的死亡风险。
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[Construction of prognostic prediction model for patients with sepsis-induced acute kidney injury treated with continuous renal replacement therapy].

Objective: To explore the influencing factors of prognosis in patients with sepsis-induced acute kidney injury undergoing continuous renal replacement therapy (CRRT), and to construct a mortality risk prediction model.

Methods: A retrospective research method was adopted, patients with sepsis-induced acute kidney injury who received CRRT at Fuyang People's Hospital from February 2021 to September 2023 were included in this study. Collect general information, comorbidities, vital signs, laboratory indicators, disease severity scores, treatment status, length of stay in the intensive care unit (ICU), and 28-day prognosis were collected within 24 hours of patient enrollment. The Cox regression model was used to identify the factors influencing prognosis in patients with sepsis-induced acute kidney injury, and a nomogram model was developed to predict mortality in these patients. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the nomogram model.

Results: A total of 146 patients with sepsis-induced acute kidney injury were included, of which 98 survived and 48 died (with a mortality of 32.88%) after 28 days of treatment. The blood lactic acid, interleukin-6 (IL-6), serum cystatin C, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), and proportion of mechanical ventilation in the death group were significantly higher than those in the survival group. The ICU stay was significantly longer than that in the survival group, and the glomerular filtration rate was significantly lower than that in the survival group. Cox regression analysis showed that blood lactic acid [odds ratio (OR) = 2.992, 95% confidence interval (95%CI) was 1.023-8.754], IL-6 (OR = 3.522, 95%CI was 1.039-11.929), serum cystatin C (OR = 3.999, 95%CI was 1.367-11.699), mechanical ventilation (OR = 4.133, 95%CI was 1.413-12.092), APACHE II score (OR = 5.013, 95%CI was 1.713-14.667), SOFA score (OR = 3.404, 95%CI was 1.634-9.959) were risk factors for mortality in patients with sepsis-induced acute kidney injury (all P < 0.05), glomerular filtration rate (OR = 0.294, 95%CI was 0.101-0.860) was a protective factor for mortality in patients with sepsis-induced acute kidney injury (P < 0.05). The ROC curve showed that the column chart model has a sensitivity of 80.0% (95%CI was 69.1%-89.2%) and a specificity of 89.3% (95%CI was 83.1%-95.2%) in predicting 28-day mortality in patients with acute kidney injury caused by sepsis.

Conclusions: Blood lactic acid, IL-6, mechanical ventilation, APACHEII score, SOFA score, glomerular filtration rate, and serum cystatin C are associated with the risk of death in patients with sepsis-induced acute kidney injury. The nomogram model could help early identification of mortality risk in these patients.

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来源期刊
Zhonghua wei zhong bing ji jiu yi xue
Zhonghua wei zhong bing ji jiu yi xue Medicine-Critical Care and Intensive Care Medicine
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发文量
42
期刊最新文献
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