Postangiography Prediction of Renal Replacement Therapy in Acute Myocardial Infarction–Related Cardiogenic Shock: Least Absolute Shrinkage and Selection Operator Nomogram Development and Validation

Abstract 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.