Dataset Information

Identification and predicting short-term prognosis of early cardiorenal syndrome type 1: KDIGO is superior to RIFLE or AKIN.

ABSTRACT:

Objective

Acute kidney injury (AKI) in patients hospitalized for acute heart failure (AHF) is usually type 1 of the cardiorenal syndrome (CRS) and has been associated with increased morbidity and mortality. Early recognition of AKI is critical. This study was to determine if the new KDIGO criteria (Kidney Disease: Improving Global Outcomes) for identification and short-term prognosis of early CRS type 1 was superior to the previous RIFLE and AKIN criteria.

Methods

The association between AKI diagnosed by KDIGO but not by RIFLE or AKIN and in-hospital mortality was retrospectively evaluated in 1005 Chinese adult patients with AHF between July 2008 and May 2012. AKI was defined as RIFLE, AKIN and KDIGO criteria, respectively. Cox regression was used for multivariate analysis of in-hospital mortality.

Results

Within 7 days on admission, the incidence of CRS type 1 was 38.9% by KDIGO criteria, 34.7% by AKIN, and 32.1% by RIFLE. A total of 110 (10.9%) cases were additional diagnosed by KDIGO criteria but not by RIFLE or AKIN. 89.1% of them were in Stage 1 (AKIN) or Stage Risk (RIFLE). They accounted for 18.4% (25 cases) of the overall death. After adjustment, this proportion remained an independent risk factor for in-hospital mortality [odds ratios (OR)3.24, 95% confidence interval(95%CI) 1.97-5.35]. Kaplan-Meier curve showed AKI patients by RIFLE, AKIN, KDIGO and [K(+)R(-)+K(+)A(-)] had lower hospital survival than non-AKI patients (Log Rank P<0.001).

Conclusion

KDIGO criteria identified significantly more CRS type 1 episodes than RIFLE or AKIN. AKI missed diagnosed by RIFLE or AKIN criteria was an independent risk factor for in-hospital mortality, indicating the new KDIGO criteria was superior to RIFLE and AKIN in predicting short-term outcomes in early CRS type 1.

SUBMITTER: Li Z

PROVIDER: S-EPMC4277271 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Identification and predicting short-term prognosis of early cardiorenal syndrome type 1: KDIGO is superior to RIFLE or AKIN.

Li Zhilian Z Cai Lu L Liang Xinling X Du Zhiming Z Chen Yuanhan Y An Shengli S Tan Ning N Xu Lixia L Li Ruizhao R Li Liwen L Shi Wei W

PloS one 20141226 12

<h4>Objective</h4>Acute kidney injury (AKI) in patients hospitalized for acute heart failure (AHF) is usually type 1 of the cardiorenal syndrome (CRS) and has been associated with increased morbidity and mortality. Early recognition of AKI is critical. This study was to determine if the new KDIGO criteria (Kidney Disease: Improving Global Outcomes) for identification and short-term prognosis of early CRS type 1 was superior to the previous RIFLE and AKIN criteria.<h4>Methods</h4>The association ...[more]

PMID: 25542014

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Project description:BACKGROUND AND AIMS:Follow-up observation was performed on elderly acute kidney injury (AKI) patients to analyze the short-term prognosis and risk factors of AKI patients in the 48-h diagnostic window and 7-day diagnostic window of the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. METHODS:Inpatients aged???75 years in the geriatric ward of the People's Liberation Army General Hospital, China, between January 2007 and December 2015 were selected as the research subjects. According to two diagnostic criteria in the KDIGO guidelines, patients were divided into a 48-h diagnostic window group and a 7-day diagnostic window group. The medical data of the patients were divided into the death group and the survival group for analysis based on the survival condition of the patients after 90 days of AKI. Factors that affected the 90-day survival of patients in the 48-h diagnostic window and 7-day diagnostic window groups were analyzed using multivariate Cox regression. RESULTS:During the follow-up period, a total of 652 patients were enrolled in this study. Among them, 623 cases were men, accounting for 95.6% of the patients. The median age was 87 (84-91) years. According to the KDIGO staging criteria, there were 308 (47.2%) cases in AKI stage 1, 164 (25.2%) cases in stage 2, and 180 (27.6%) cases in stage 3. Among the 652 patients, 334 (51.2%) were diagnosed with AKI based on the 48-h diagnostic criteria window, and 318 (48.8%) were diagnosed with AKI based on the baseline 7-day diagnostic criteria. The 90-day mortality of AKI patients was 42.5% in the 48-h diagnostic window and 24.2% in the 7-day diagnostic window. The multivariate Cox analysis results showed that low mean arterial pressure (HR?=?0.966; P?<?0.001), low serum prealbumin level (HR?=?0.932; P?<?0.001), infection (HR?=?1.448; P?=?0.047), mechanical ventilation (HR?=?1.485; P?=?0.038), high blood urea nitrogen (BUN) level (HR?=?1.026; P?<?0.001), blood magnesium level (HR?=?2.560; P?=?0.024), and more severe AKI stage (stage 2: HR?=?3.482; P?<?0.001 and stage 3: HR?=?6.267; P?<?0.001) were independent risk factors affecting the 90-day mortality of elderly patients in the 48-h diagnostic window, whereas low body mass index (HR?=?0.851; P?<?0.001), low mean arterial pressure (HR?=?0.980; P?=?0.036), low serum prealbumin level (HR?=?0.950; P?=?0.048), low serum albumin level (HR?=?0.936; P?=?0.015), high BUN level (HR?=?1.046; P?<?0.001), and more severe AKI stage (stage 2: HR?=?4.249; P?=?0.001 and stage 3: HR?=?9.230; P?<?0.001) were independent risk factors affecting the 90-day mortality of elderly patients in the 7-day diagnostic window. CONCLUSIONS:The clinical differences of AKI and risk factors for 90-day mortality in elderly AKI individuals vary depending on the definition used. An increment of Scr???26.5 ?mol/L in 48 h (48-h KDIGO window) alone predicts adverse clinical outcomes.

Dataset Information

Identification and predicting short-term prognosis of early cardiorenal syndrome type 1: KDIGO is superior to RIFLE or AKIN.

Objective

Methods

Results

Conclusion

Publications

Identification and predicting short-term prognosis of early cardiorenal syndrome type 1: KDIGO is superior to RIFLE or AKIN.

Similar Datasets

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