Development of a risk stratification-based model for prediction of acute kidney injury in critically ill patients

Chen, Yu; Feng, Fang; Li, Min; Chang, Xue-Ni; Wei, Bao-Hua; Dong, Chenming

doi:10.1097/md.0000000000016867

Cited by 12 publications

(19 citation statements)

References 24 publications

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“…If the cut-off value of Nomogram score was 119.22, the AUC of this model was 0.9409 with the sensitivity of 85.71%, the specificity of 89.66%, the positive predictive value of 89.29%, and the negative predictive value of 87.10% in the diagnosis of AKI (Figure 3). Its AUC was higher than that reported in previous studies [13][14][15][16][17][20][21][22][23][24][25]. Thus, our model is promising to be used as a tool to identify high-risk patients clinically.…”

Section: Discussionmentioning

confidence: 51%

Activating Transcription Factor 3 Based Early Alarm Model of Acute Kidney Injury after Cardiopulmonary Bypass in Adults

Jin

Liu

et al. 2022

Disease Markers

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Acute kidney injury (AKI) is a common complication after cardiopulmonary bypass (CPB) for cardiac surgery, and there is no effective treatment. This study was aimed at constructing an early warning model of AKI after CPB in adults and investigating the performance of this model. Patients who underwent CPB in the Department of Cardiac Surgery, Shanghai Tenth People’s Hospital, from January 2018 to December 2019 were recruited into the present study. Blood and urine samples were collected preoperatively (0 h) and 2 h, 6 h, 12 h, 24 h, and 48 h after surgery, and the creatinine and activating transcription factor 3 (ATF3) were detected. According to the diagnostic criteria of AKI, patients were divided into the AKI group and the non-AKI group, and the risk factors for AKI after CPB were screened. The receiver operating characteristic (ROC) curve analysis was used to identify the optimal biomarkers for the establishment of early warning model of AKI after CPB. Finally, the performance of this model was further verified. A total of 83 patients were included in this study, 42 of whom developed AKI after surgery. After CPB, the serum and urine levels of creatinine and ATF3 increased to different degrees, and the increase in urine ATF3 was the most obvious in the AKI group. The area under ROC (AUC) of urine ATF3 at 12 h after surgery was 0.691 (95% CI: 0.576-0.807). When ATF3 was higher than 1216 pg/mL, the sensitivity and specificity of ATF3 in the diagnosis of AKI were 0.43 and 0.85, respectively. The height, conjugated bilirubin on the surgery day, urine ATF3 12 h after surgery, and serum creatinine 24 h after surgery were independent risk factors for postoperative AKI. Urine ATF3 and other factors were used to establish AKI warning model after CPB, which showed good fitting and accuracy. In conclusion, ATF3 is an early biomarker of post-CPB AKI. Addition of urine ATF3 to AKI risk factors can improve the accuracy of early AKI prediction.

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Section: Discussionmentioning

confidence: 51%

Activating Transcription Factor 3 Based Early Alarm Model of Acute Kidney Injury after Cardiopulmonary Bypass in Adults

Jin

Liu

et al. 2022

Disease Markers

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“… 27 28 Another study found that advanced age was an independent risk factor for AKI in hospitalised patients without COVID-19. 29 The viral clearance ability of male patients with SARS-CoV-2 infection is significantly lower than that of female patients with SARS-CoV-2 infection, which may represent one potential reason for the increased severity of symptoms and incidence of complications observed in male patients with SARS-CoV-2 infection. 30 Higher rates of smoking and alcohol consumption, as well as biological differences in the immune system between the sexes, could make men more vulnerable to AKI during SARS-CoV-2 infection.…”

Section: Discussionmentioning

confidence: 97%

“…27 28 Another study found that advanced age was an independent risk factor for AKI in hospitalised patients without COVID-19. 29 The viral clearance ability of male patients with SARS-CoV-2 Figure 4 Forest plot showing the subgroup analysis of risk of death. The Q test showed p>0.1, indicating no heterogeneity existed between studies.…”

Section: Discussionmentioning

confidence: 99%

Risk factors and prognosis for COVID-19-induced acute kidney injury: a meta-analysis

et al. 2020

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ObjectiveTo analyse the incidence, risk factors and impact of acute kidney injury (AKI) on the prognosis of patients with COVID-19.DesignMeta-analysis.Data sourcesPubMed, Embase, CNKI and MedRxiv of Systematic Reviews from 1 January 2020 to 15 May 2020.Study selectionStudies examining the following demographics and outcomes were included: patients’ age; sex; incidence of and risk factors for AKI and their impact on prognosis; COVID-19 disease type and incidence of continuous renal replacement therapy (CRRT) administration during COVID-19 infection.ResultsA total of 79 research articles, including 49 692 patients with COVID-19, met the systemic evaluation criteria. The mortality rate and incidence of AKI in patients with COVID-19 in China were significantly lower than those in patients with COVID-19 outside China. A significantly higher proportion of patients with COVID-19 from North America were aged ≥65 years and also developed AKI. European patients with COVID-19 had significantly higher mortality and a higher CRRT rate than patients from other regions. Further analysis of the risk factors for COVID-19 combined with AKI showed that age ≥60 years and severe COVID-19 were independent risk factors for AKI, with an OR of 3.53, 95% CI (2.92–4.25) and an OR of 6.07, 95% CI (2.53–14.58), respectively. The CRRT rate in patients with severe COVID-19 was significantly higher than in patients with non-severe COVID-19, with an OR of 6.60, 95% CI (2.83–15.39). The risk of death in patients with COVID-19 and AKI was significantly increased, with an OR of 11.05, 95% CI (9.13–13.36).ConclusionAKI was a common and serious complication of COVID-19. Older age and having severe COVID-19 were independent risk factors for AKI. The risk of in-hospital death was significantly increased in patients with COVID-19 complicated by AKI.

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“…Some studies that have included biomarkers do not report a discriminatory capacity for the prediction of kidney injury outcomes. Recently, Chen et al [ 33 ] and Malhotra et al [ 34 ] developed predictive models based mainly on clinical characteristics such as sex, age, hypertension, diabetes, coronary heart disease, heart failure, sepsis, mechanical ventilation, total bilirubin, hypoalbuminemia, emergency surgery, cancer, chronic kidney disease, and exposure to nephrotoxic agents, finding an adequate discriminatory capacity (area under the curve of 0.81). In our study, in addition to the comorbidities included in the Charlson index, variables such as diagnosis of sepsis, heart failure or trauma at admission to the ICU, and the use of contrast media during ICU stay were considered.…”

Section: Discussionmentioning

confidence: 99%

Effect of renal support therapy on 5-year survival in patients discharged from the intensive care unit

Oliveros

Buitrago

2020

j intensive care

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Background: Between 30 and 70% of patients admitted to the intensive care unit (ICU) have acute kidney injury (AKI), and 10% of these patients will require renal replacement therapy (RRT). A significant number of studies have compared the mortality of patients who require RRT versus those who do not require it, finding an increase in mortality rates in the short and medium term; however, few studies have evaluated the long-term survival in a mixture of patients admitted to the ICU. Objective: To evaluate the impact of RRT on 5-year survival in patients with AKI admitted to the ICU Methods: Using administrative databases of insurers of the Colombian health system, a cohort of patients admitted to the ICU between 1 January 2012 and 31 December 2013 was followed until 31 December 2018. ICD-10 diagnoses, procedure codes, and prescribed medications were used to establish the frequencies of the comorbidities included in the Charlson index. Patients were followed for at least 5 years to evaluate survival and establish the adjusted risks by propensity score matching. Results: Of the 150,230 patients admitted to the ICU, 4366 (2.9%) required RRT in the ICU. Mortality rates for patients with RRT vs no RRT evaluated at ICU discharge, 1 year, and 5 years were 35%, 57.4%, and 67.9% vs 7.4%, 17.6%, and 30.1%, respectively. After propensity score matching, the hazard ratio was calculated for patients who received RRT and those who did not (HR, 2.46; 95% CI 2.37 to 2.56; p < 0.001), with a lower difference in years of survival for patients with RRT (mean effect in the treated) of − 1.86 (95% CI − 2.01 to to1.65; p < 0.001). Conclusions: The impact of acute renal failure with the consequent need for RRT in patients admitted to the ICU is reflected in a decrease of approximately one quarter in 5-year survival, regardless of the different comorbidities.

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Development of a risk stratification-based model for prediction of acute kidney injury in critically ill patients

Cited by 12 publications

References 24 publications

Activating Transcription Factor 3 Based Early Alarm Model of Acute Kidney Injury after Cardiopulmonary Bypass in Adults

Activating Transcription Factor 3 Based Early Alarm Model of Acute Kidney Injury after Cardiopulmonary Bypass in Adults

Risk factors and prognosis for COVID-19-induced acute kidney injury: a meta-analysis

Effect of renal support therapy on 5-year survival in patients discharged from the intensive care unit

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