Development of a novel score to predict the risk of acute kidney injury in patient with acute myocardial infarction

Abusaada, Khalid; Yuan, Cai; Sabzwari, Rafay; Butt, Khurram; Maqsood, Aadil

doi:10.1007/s40620-016-0326-1

Cited by 18 publications

(38 citation statements)

References 19 publications

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“…A majority of the published studies showed that basal renal dysfunction was the major risk factor for AKI [11, 22, 23, 32]. Our study confirmed this also.…”

Section: Discussionsupporting

confidence: 90%

“…In the previous two prediction scores created by Queiroz and Abusaada, tachycardia was an independent risk factor for AKI and was included in the prediction score [22, 23]. The similar conclusion was drawn from our study: we found the risk of AKI increased by 1.75 times when heart rate > 100 bpm at admission.…”

Section: Discussionsupporting

confidence: 89%

“…After the occurrence of AMI, these patients will be suffered from heart and kidney hypoperfusion and strong stress response, thereby their renal function will be damaged heavily. Previous studies have shown that elderly patients who always have a poor renal reserve capacity was a risk factor for AKI [18–21, 23]. However, the same results were not obtained in this study, which may be related to the age factor being revised when the modified MDRD formala was used to eGFR.…”

Section: Discussioncontrasting

confidence: 66%

“…There were some prediction scores of AKI after the percutaneous coronary intervention (PCI) for AMI [18–21]. However, only few studies have developed prediction scores including all the AMI patients which undergoing PCI or not [22, 23]. In 2012, Queiroz et al created a prediction score for AKI secondary to AMI [22].…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the sample size was small (406 patients) and employed only for the clinical manifestation of ST-segment elevation myocardial infarction (STEMI) in the emergency department, thereby exhibiting some limitations. Recently, Abusaoda et al developed a novel score to predict the risk of AKI secondary to AMI [23]. The study included a total of 1107 patients and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.76.…”

Section: Introductionmentioning

confidence: 99%

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Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

Cheng

Tong

et al. 2019

BMC Nephrol

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Background Acute kidney injury (AKI) is a major complication of acute myocardial infarction(AMI), which can significantly increase mortality. This study is to analyze the related risk factors and establish a prediction score of acute kidney injury in order to take early measurement for prevention. Methods The medical records of 6014 hospitalized patients with AMI in Beijing Anzhen Hospital from January 2010 to December 2016 were retrospectively analyzed. These patients were randomly assigned into two cohorts: one was for the derivation of prediction score ( n = 4252) and another for validation ( n = 1762). The criterion for AKI was defined as an increase in serum creatinine of ≥ 0.3 mg/dL or ≥ 50% from baseline within 48 h. On the basis of odds ratio obtained from multivariate logistic regression analysis, a prediction score of acute kidney injury after AMI was built up. Results In this prediction score, risk score 1 point included hypertension history, heart rate > 100 bpm on admission, peak serum troponin I ≥ 100 μg/L, and time from admission to coronary reperfusion > 120 min; risks score 2 points included Killip classification ≥ class 3 on admission; and maximum dosage of intravenous furosemide ≥ 60 mg/d; risks score 3 points only included shock during hospitalization. In addition, when baseline estimated glomerular filtration rate (eGFR) was less than 90 ml/min·1.73 m 2 , every 10 ml/min·1.73 m 2 reduction of eGFR increased risk score 1 point. Youden index showed that the best cut-off value for prediction of AKI was 3 points with a sensitivity of 71.1% and specificity 74.2%. The datasets of derivation and validation both displayed adequate discrimination (an area under the ROC curve, 0.79 and 0.81, respectively) and satisfactory calibration (Hosmer–Lemeshow statistic test, P = 0.63 and P = 0.60, respectively). Conclusions In conclusion, a prediction score for AKI secondary to AMI in Chinese patients was established, which may help to prevent AKI early.

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“…A majority of the published studies showed that basal renal dysfunction was the major risk factor for AKI [11, 22, 23, 32]. Our study confirmed this also.…”

Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 89%

Section: Discussioncontrasting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

Cheng

Tong

et al. 2019

BMC Nephrol

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Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

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ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

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The Incidence and the Prognostic Impact of Acute Kidney Injury in Acute Myocardial Infarction Patients: Current Preventive Strategies

Kaltsas

Chalikias

Tziakas

2018

Cardiovasc Drugs Ther

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Acute kidney injury (AKI) is one of the most common complications during hospitalization in various clinical settings. The goal of this review was to assess the incidence of AKI in acute myocardial infarction patients (AMI), how this incidence is affected by the diverse definitions, and if there is variability in the reported rates over recent years. Additionally, we sought to appraise the impact of AKI on short- and long-term prognosis of these patients. Finally, we report on the current preventive measures as they are suggested in the current guidelines of various societies, we comment on the evidence that support them, and we review the literature for other proposed therapeutic strategies, which either failed to prove their efficacy or they are not adequately confirmed yet. Due to the heterogeneity in AKI definition and in the population studied of the published data, the incidence of AKI ranged from 5.2 to 59%. A recent meta-analysis reported a median value of 15.8%. All studies assessing AKI-related prognosis in AMI patients suggested that presence of AKI has detrimental effect on patients prognosis, raising mortality two- to threefold not only during the 30 first days but also during the first year after the acute event. Various treatment modalities have been proposed for prevention of AKI in AMI patients; however, the majority of them failed to prove their efficacy in the clinical trial arena. Hydration, use of iso- or low-osmolar agents at the lowest possible dose during coronary interventions, and use of statins have been proposed among others. Nonetheless, the prevalence of AKI after an AMI still remains high today and therefore it is crucial for the practicing physician to be aware of its presence and for the scientific community to identify novel measures for a more efficacious prevention.

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Development of a novel score to predict the risk of acute kidney injury in patient with acute myocardial infarction

Abstract: A simple score using clinical and laboratory data available on admission can predict the risk of AKI in patients presenting with acute myocardial infarction.

Cited by 18 publications

References 19 publications

Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients

Characterization of Risk Prediction Models for Acute Kidney Injury

The Incidence and the Prognostic Impact of Acute Kidney Injury in Acute Myocardial Infarction Patients: Current Preventive Strategies

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