Suppr超能文献

PIRO-CIC模型能够预测重症监护病房中重症肝硬化患者的死亡率和治疗的无效性。

PIRO-CIC model can predict mortality and futility of care in critically ill cirrhosis patients in the intensive care unit.

作者信息

Maiwall Rakhi, Pasupuleti Samba Siva Rao, Tevethia Harsh Vardhan, Sarin Shiv Kumar

机构信息

Department of Hepatology, Institute of Liver and Biliary Sciences, New Delhi, 110070, India.

Department of Statistics, Mizoram University (A Central University), Pachhunga University College Campus, Aizawl, India.

出版信息

Hepatol Int. 2023 Apr;17(2):476-487. doi: 10.1007/s12072-022-10426-4. Epub 2022 Oct 13.

Abstract

BACKGROUND

Dynamic assessment of critically ill patients with cirrhosis (CICs) is required for accurate prognostication.

OBJECTIVE

Development of a dynamic model for prediction of mortality and decision on futility of care in CICs.

DESIGN AND SETTING

In a prospective cohort study, we developed the PIRO-CIC model (predisposition, injury, response, organ failure for critically ill cirrhotics)] in a derivative cohort (n = 360) and validated it (n = 240) for patients admitted to the Liver ICU.

PATIENTS

Decompensated cirrhosis admitted to ICU. The model was developed using Cox-regression analysis, and futility was performed by decision-curve analysis.

RESULTS

CICs aged 48 ± 11.5 years, 87% males, majority being alcoholics, were enrolled, of which 73.5% were alive at one month. Factors significant for P component were INR [hazard ratio 1.12, 95% confidence interval 1.07-1.18] and CystatinC [2.25, 1.70-2.97]; for I component were sepsis [4.69, 1.90-11.57], arterial lactate[1.40, 1.02-1.93] and alcohol as etiology [2.78, 1.85-4.18]; for R component-systemic inflammatory response syndrome [1.97, 1.14-3.42] and urine neutrophil-gelatinase-associated lipocalin [HR 2.37, 1.59-3.53]; for O component-low PaO2/FiO2 ratio and need of mechanical ventilation [7.41, 4.63-11.86]. The PIRO-CIC model predicted one-month mortality with a C-index of 0.83 in the derivation and 0.80 in the validation cohorts. It predicted futility of care better than other prognostic scores. The immediate risk of mortality increased by 39% with each unit increase in PIRO-CIC score.

LIMITATIONS

Not applicable for acute-on-chronic liver failure and patients requiring emergency liver transplant.

CONCLUSIONS

Assessment and stratification of CICs with the dynamic PIRO-CIC model could determine one-month mortality and futility in the first week. Targeted and aggressive management of coagulation, kidneys, sepsis, and severe systemic inflammation may improve outcomes of CICs.

摘要

背景

对肝硬化危重症患者(CICs)进行动态评估对于准确预后判断至关重要。

目的

开发一种动态模型,用于预测CICs的死亡率并决定治疗的无效性。

设计与研究地点

在一项前瞻性队列研究中,我们在一个衍生队列(n = 360)中开发了PIRO-CIC模型(易感性、损伤、反应、肝硬化危重症患者的器官功能衰竭),并在入住肝脏重症监护病房的患者中对其进行了验证(n = 240)。

患者

入住重症监护病房的失代偿期肝硬化患者。该模型通过Cox回归分析开发,治疗无效性通过决策曲线分析进行评估。

结果

纳入的CICs患者年龄为48±11.5岁,男性占87%,大多数为酗酒者,其中73.5%在1个月时存活。对P组分有显著影响的因素是国际标准化比值(INR)[风险比1.12,95%置信区间1.07 - 1.18]和胱抑素C[2.25,1.70 - 2.97];对I组分有显著影响的因素是脓毒症[4.69,1.90 - 11.57]、动脉血乳酸[1.40,1.02 - 1.93]以及酒精作为病因[2.78,1.85 - 4.18];对R组分有显著影响的因素是全身炎症反应综合征[1.97,1.14 - 3.42]和尿中性粒细胞明胶酶相关脂质运载蛋白[风险比2.37,1.59 - 3.53];对O组分有显著影响的因素是低动脉血氧分压/吸入氧分数比以及需要机械通气[7.41,4.63 - 11.86]。PIRO-CIC模型在衍生队列中预测1个月死亡率的C指数为0.83,在验证队列中为0.80。它在预测治疗无效性方面优于其他预后评分。PIRO-CIC评分每增加一个单位,即刻死亡风险增加39%。

局限性

不适用于急性慢性肝衰竭患者和需要紧急肝移植的患者。

结论

使用动态PIRO-CIC模型对CICs进行评估和分层可以在第一周确定1个月死亡率和治疗无效性。针对性地积极处理凝血、肾脏、脓毒症和严重全身炎症可能改善CICs的预后。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验