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Utilizing Percutaneous Ventricular Assist Devices in Acute Myocardial Infarction Complicated by Cardiogenic Shock
Published on: June 12, 2021
Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction-Related Cardiogenic Shock:
Amin Daoulah1, Julia M Ladna2, Abdulrahman Arabi3
1King Faisal Specialist Hospital & Research Centre, Jeddah, Makkah, 23423, Saudi Arabia, 966 555042073.
Insights
A new nomogram accurately predicts the need for renal replacement therapy (RRT) in patients with cardiogenic shock due to myocardial infarction (CS-AMI). This tool aids early intervention and may improve survival rates for high-risk patients.
Area of Science:
- Cardiology
- Nephrology
- Intensive Care Medicine
Background:
- Acute kidney injury (AKI) significantly worsens outcomes and increases mortality in patients with cardiogenic shock secondary to acute myocardial infarction (CS-AMI).
- Early initiation of renal replacement therapy (RRT) is associated with improved survival in CS-AMI patients, yet comprehensive prediction models for RRT are lacking.
- Predicting the need for RRT is crucial for timely intervention in CS-AMI patients experiencing AKI.
Purpose of the Study:
- To develop and internally validate a prediction model using Least Absolute Shrinkage and Selection Operator (LASSO) regression for in-hospital RRT in CS-AMI patients.
- To create a clinical nomogram based on the LASSO model to aid in predicting RRT requirements.
- To compare the performance of the developed model against a simpler, established model.
Main Methods:
- A multicenter retrospective cohort study involving 1431 CS-AMI patients from the Gulf Cardiogenic Shock (Gulf-CS) registry (2020-2022).
- LASSO logistic regression was employed on a training set (80%) to identify baseline predictors of RRT, with performance validated on a testing set (20%).
- Internal validation included 10-fold cross-validation and bootstrapping; a clinical nomogram was constructed from the final model.
Main Results:
- Of 1431 patients, 190 (13.3%) required RRT. Patients needing RRT were older, with higher rates of diabetes, peripheral arterial disease, prior cerebrovascular accident, lower creatinine clearance, higher lactate, and more advanced shock stages.
- The LASSO model identified 15 baseline predictors and achieved an AUC of 0.714 on the testing set, significantly outperforming a parsimonious model (AUC: 0.624; P<.001).
- RRT initiation was associated with higher in-hospital mortality (75.8% vs 38.8%), longer hospital stays, and increased rates of major bleeding and cerebrovascular accidents.
Conclusions:
- A robust 15-variable nomogram, the Gulf-CS-Nomogram, was developed and validated for predicting RRT need in CS-AMI patients using baseline data.
- This tool is intended for use after coronary angiography, facilitating early nephrology consultation and timely RRT initiation.
- The Gulf-CS-Nomogram has the potential to improve outcomes for CS-AMI patients requiring RRT.
Background:
Acute kidney injury critically impacts outcomes in cardiogenic shock secondary to acute myocardial infarction (CS-AMI). Acute kidney injury is one of the strongest independent predictors of in-hospital mortality in CS-AMI. Despite evidence that early renal replacement therapy (RRT) initiation improves survival, comprehensive prediction models for RRT in this population remain lacking.
Objective:
This study aimed to develop and internally validate a Least Absolute Shrinkage and Selection Operator (LASSO) regression-based prediction model and clinical nomogram for in-hospital RRT in patients with CS-AMI.
Methods:
This multicenter retrospective cohort study included 1431 patients with CS-AMI from the Gulf Cardiogenic Shock (Gulf-CS) registry across 13 centers in 6 Gulf countries (2020-2022). LASSO logistic regression was applied to a training set (1071/1431, 80%) to select baseline predictors of RRT; performance was evaluated on a held-out testing set (268/1431, 20%). Internal validation included 10-fold cross-validation and bootstrapping (1000 iterations). Cluster-robust SEs accounted for center effects. The model was compared to a parsimonious model (age+creatinine clearance), and a clinical nomogram was developed.
Results:
Of 1431 patients, 190 (13.3%) required RRT. Patients requiring RRT were significantly older (mean 64.17, SD 12.14 y vs mean 59.75, SD 11.77 y; P<.001), with higher prevalences of diabetes mellitus (72.1% vs 61.9%; P=.008), peripheral arterial disease (11.6% vs 3.7%; P<.001), and prior cerebrovascular accident (11.1% vs 5.7%; P=.005). The RRT group had lower creatinine clearance (46 vs 72 mL/min; P<.001), higher baseline lactate (2.7 vs 2.1 mmol/L; P<.001), and more advanced Society for Cardiovascular Angiography and Interventions (SCAI) shock stages (stages D and E: 90.5% vs 64.9%; P<.001). LASSO selected 15 baseline predictors. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.714 on the testing set, significantly outperforming the parsimonious model (AUC: 0.624; P<.001). Bootstrap-corrected AUC was 0.745 (95% CI 0.730-0.756). In-hospital mortality was markedly higher in the RRT group (75.8% vs 38.8%; P<.001), with longer hospital stay (10 vs 6 d; P<.001), more major bleeding (16.8% vs 7.3%; P<.001), and cerebrovascular accidents (11.1% vs 4.9%; P=.001).
Conclusions:
We have developed and internally validated a robust 15-variable nomogram (Gulf-CS-Nomogram) that accurately predicts the need for RRT in patients with CS-AMI using baseline data intended for use after coronary angiography. This tool may facilitate early nephrology consultation and timely RRT initiation to improve outcomes.
