Development of a Laboratory Risk-Score Model to Predict One-Year Mortality in Acute Myocardial Infarction Survivors.

The high post-discharge mortality rate of acute myocardial infarction (AMI) survivors is concerning, indicating a need for reliable, easy-to-use risk prediction tools. We aimed to examine if a combined pre-procedural blood testing risk model predicts one-year mortality in AMI survivors. Overall, 1355 consecutive AMI patients who received primary coronary revascularization were divided into derivation (n = 949) and validation (n = 406) cohorts. A risk-score model of parameters from pre-procedural routine blood testing on admission was generated. In the derivation cohort, multivariable analysis demonstrated that hemoglobin < 11 g/dL (odds ratio (OR) 4.01), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (OR 3.75), albumin < 3.8 mg/dL (OR 3.37), and high-sensitivity troponin I > 2560 ng/L (OR 3.78) were significantly associated with one-year mortality after discharge. An increased risk score, assigned from 0 to 4 points according to the counts of selected variables, was significantly associated with higher one-year mortality in both cohorts (p < 0.001). Receiver-operating characteristics curve analyses of risk models demonstrated adequate discrimination between patients with and without one-year death (area under the curve (95% confidence interval) 0.850 (0.756−0.912) in the derivation cohort; 0.820 (0.664−0.913) in the validation cohort). Our laboratory risk-score model can be useful for predicting one-year mortality in AMI survivors.

Simple risk-score model for in-hospital major bleeding based on multiple blood variables in patients with acute myocardial infarction.

BACKGROUND: In-hospital bleeding is associated with poor prognosis in patients with acute myocardial infarction (AMI). We sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of in-hospital major bleeding in patients with AMI. METHODS AND RESULTS: A total of 1684 consecutive AMI patients who underwent primary percutaneous coronary intervention (PCI) were recruited and randomly divided into derivation (n = 1010) and validation (n = 674) cohorts. A risk-score model was created based on a combination of parameters assessed on routine blood tests on admission. In the derivation cohort, multivariate analysis revealed that the following 5 variables were significantly associated with in-hospital major bleeding: hemoglobin level < 12 g/dL (odds ratio [OR], 3.32), white blood cell count >10,000/µL (OR, 2.58), platelet count <150,000/µL (OR, 2.51), albumin level < 3.8 mg/dL (OR, 2.51), and estimated glomerular filtration rate < 60 mL/min/1.73 m2 (OR, 2.31). Zero to five points were given according to the number of these factors each patient had. Incremental risk scores were significantly associated with a higher incidence of in-hospital major bleeding in both cohorts (P < 0.001). Receiver operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without in-hospital major bleeding (derivation cohort: area under the curve [AUC], 0.807; 95% confidence interval [CI], 0.759-0.848; validation cohort: AUC, 0.793; 95% CI, 0.725-0.847). CONCLUSIONS: Our novel laboratory-based bleeding risk model could be useful for simple and objective prediction of in-hospital major bleeding events in patients with AMI.

Serum Albumin and Bleeding Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction (from the HAGAKURE-ACS Registry).

Low serum albumin (SA) on admission in patients with acute myocardial infarction (AMI) has been reported to be associated with adverse cardiovascular events. The relation between low SA and post-AMI bleeding events is presently unknown. We analyzed 1,724 patients with AMI enrolled in the HAGAKURE-ACS registry who underwent primary percutaneous coronary intervention from January 2014 to December 2018. To assess the influence of low SA at admission, patients were divided into 3 groups according to the albumin tertiles: the low SA group (<3.8 g/100 ml), the middle SA (MSA) group (3.8 to 4.1 g/100 ml), and the normal SA (NSA) group (≥4.2 g/100 ml). The primary end point was the incidence of Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries moderate/severe bleeding. The cumulative 3-year incidence of the primary end point was significantly higher in the low SA group than in the MSA and NSA groups (30.8% and 11.9% vs 7.7%; p <0.001). In the landmark analysis at 30 days, the cumulative incidences of the primary end point were also significantly higher in the low SA group than in the MSA and NSA groups, both within and beyond 30 days (20.1% and 6.1% vs 3.5%; p <0.001, and 12.4% and 6.2% vs 4.5%; p <0.001, respectively). After adjusting for confounders, the low SA group showed excess risk of bleeding events relative to NSA (hazard ratio 1.56; 95% confidence interval 1.06 to 2.30; p = 0.026), whereas risk of bleeding was neutral in MSA relative to NSA (hazard ratio 0.94; 95% confidence interval 0.63 to 1.34; p = 0.752). In conclusion, low SA at admission was independently associated with higher risk for bleeding events in patients with AMI undergoing percutaneous coronary intervention.

A Novel Prediction Model of Acute Kidney Injury Based on Combined Blood Variables in STEMI.

Background: Development of acute kidney injury (AKI) is associated with poor prognosis in patients with ST-segment elevation myocardial infarction (STEMI). Objective: This study sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of AKI in patients with STEMI. Methods: A total of 908 consecutive Japanese patients with STEMI who underwent primary percutaneous coronary intervention within 48 hours of symptom onset were recruited and divided into derivation (n = 617) and validation (n = 291) cohorts. A risk score model was created based on a combination of parameters assessed on routine blood tests on admission. Results: In the derivation cohort, multivariate analysis showed that the following 4 variables were significantly associated with AKI: blood sugar ≥200 mg/dL (odds ratio [OR]: 2.07), high-sensitivity troponin I >1.6 ng/mL (upper limit of normal ×50) (OR: 2.43), albumin ≤3.5 mg/dL (OR: 2.85), and estimated glomerular filtration rate <45 mL/min/1.73 m2 (OR: 2.64). Zero to 4 points were given according to the number of those factors. Incremental risk scores were significantly associated with a higher incidence of AKI in both cohorts (P < 0.001). Receiver-operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without AKI (derivation cohort, area under the curve: 0.754; 95% confidence interval: 0.733-0.846; validation cohort, area under the curve: 0.754; 95% confidence interval: 0.644-0.839). Conclusions: Our novel laboratory-based model might be useful for early prediction of the post-procedural risk of AKI in patients with STEMI.

A Novel Predictive Model for In-Hospital Mortality Based on a Combination of Multiple Blood Variables in Patients with ST-Segment-Elevation Myocardial Infarction.

In emergency clinical settings, it may be beneficial to use rapidly measured objective variables for the risk assessment for patient outcome. This study sought to develop an easy-to-measure and objective risk-score prediction model for in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI). A total of 1027 consecutive STEMI patients were recruited and divided into derivation (n = 669) and validation (n = 358) cohorts. A risk-score model was created based on the combination of blood test parameters obtained immediately after admission. In the derivation cohort, multivariate analysis showed that the following 5 variables were significantly associated with in-hospital death: estimated glomerular filtration rate <45 mL/min/1.73 m2, platelet count <15 × 104/µL, albumin ≤3.5 g/dL, high-sensitivity troponin I >1.6 ng/mL, and blood sugar ≥200 mg/dL. The risk score was weighted for those variables according to their odds ratios. An incremental change in the scores was significantly associated with elevated in-hospital mortality (p < 0.001). Receiver operating characteristic curve analysis showed adequate discrimination between patients with and without in-hospital death (derivation cohort: area under the curve (AUC) 0.853; validation cohort: AUC 0.879), and there was no significant difference in the AUC values between the laboratory-based and Global Registry of Acute Coronary Events (GRACE) score (p = 0.721). Thus, our laboratory-based model might be helpful in objectively and accurately predicting in-hospital mortality in STEMI patients.