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

Abstract

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.

Acute Kidney Injury Following Admission with Acute Coronary Syndrome: The Role of Diabetes Mellitus

“…The significantly increased mortality rates among patients with AKI versus patients without in the setting of ACS and following PCI are consistent with previous reports [5][6][7][8][9][10][11]25,37,38]. However, the mortality rates among ACS patients who developed AKI in our study are somewhat higher than those reported in most previous studies (up to 44%).…”

“…Among patients presenting with an acute coronary syndrome (ACS), those with DM are at increased risk of in-hospital morbidity and mortality compared with those without DM [3,4]. Acute kidney injury (AKI) is a common complication in patients presenting with acute coronary syndrome (ACS), particularly following percutaneous coronary intervention (PCI) [5][6][7][8][9]. Furthermore, when AKI occurs in patients with ACS, it is associated with significantly worse short-and long-term outcomes that include increased risk for renal replacement therapy, prolonged hospitalization, greater mortality and economic burden [5][6][7][8][9][10].…”

“…Acute kidney injury (AKI) is a common complication in patients presenting with acute coronary syndrome (ACS), particularly following percutaneous coronary intervention (PCI) [5][6][7][8][9]. Furthermore, when AKI occurs in patients with ACS, it is associated with significantly worse short-and long-term outcomes that include increased risk for renal replacement therapy, prolonged hospitalization, greater mortality and economic burden [5][6][7][8][9][10]. Patients with DM are considered to be at increased risk for AKI in the setting of ACS and PCI [10][11][12][13].…”

Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records

“…The Table shows a comparison of the AUROC for the models in 20 well-studied AKI subgroups from the literature (eTable 13 in the Supplement ). 17 , 20 , 22 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 The personalized model with transfer learning was superior to each of the current models, significantly outperforming the global model in 16 subgroups, the subgroup model in 11 subgroups, and the subgroup model with transfer learning in 9 subgroups. For example, among patients older than 65 years, AUROC was 0.76 (95% CI, 0.74-0.77) for the personalized model with transfer learning, 0.73 (95% CI, 0.72-0.75; P < .001) for the global model, 0.71 (95% CI, 0.70-0.72; P < .001) for the subgroup model, and 0.73 (95% CI, 0.72-0.75; P < .001) for the subgroup model with transfer learning.…”

Machine learning approach to predict acute kidney injury after liver surgery

“…With the continuous expansion of artificial intelligence (AI) techniques, machine learning and clinical medicine are gradually overlapping, as illustrated by numerous studies performed on both[ 9 ]. In clinical medicine, machine learning has demonstrated its value in analyzing postoperative complications and long-term outcomes due to its powerful data processing capabilities[ 10 - 13 ]. For example, in contrast to traditional regression models, machine learning performed better at screening patients at high-risk of sepsis[ 14 ].…”