Cardiogenic Shock: Polymorbidity Burden to Treatable Traits in Failure vs Infarction

Tuesday, March 24, 2026

12:30 PM - 1:30 PM Central Time

Location: Connections Central - RST 18

CW

Caroline N. Wojtas, DO (she/her/hers)

Florida

Disclosure(s): No relevant financial relationship(s) to disclose.
Patricia Rivero, MD (she/her/hers)

Florida

Disclosure(s): No relevant financial relationship(s) to disclose.
LF

Lauren Forchette, DO (she/her/hers)

MUS

Disclosure(s): No relevant financial relationship(s) to disclose.
LP

Luis Palma, MD

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.
MR

Manuel Rosario, MD

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.
BB

Benjamin Behers, MD

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.
KM

Karen M. Hamad, MD

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.
WW

Wilhelmine Wiese-Rometsch, MD

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.
RS

Robert A, Smith, PhD, FCCM

Dr.
Florida State University at Sarasota Memorial Hospital

Disclosure information not submitted.

Introduction: Cardiogenic shock (CS) mortality is high, with worse outcomes in decompensated heart failure (CS-HF) versus acute myocardial infarction (CS-AMI). Our proof of principle study hypothesized this gap is driven by baseline comorbidity architecture differences, manifesting as distinct "Treatable Traits" (TTs).

Methods: We studied under IRB exemption 151 CS patients (106 CS-AMI, 45 CS-HF) who underwent ICU >2d censored at >28d between January 2020 and March 2025. We characterized multimorbidity and polymorbidity using principal component analysis (PCA) and multivariate heatmaps derived from Elixhauser Comorbidity Index (v2025.1, n=39). Comorbidity architecture integrated with biomarker signatures to create a functional map of each patient's physiological trajectory over initial 48h. Three key TTs were defined using sequenced machine learning approaches: 1) Persistent Hyper-inflammation (Systemic Immune-Inflammation Index, SII), 2) Impaired Immunonutrition (HALP Score), and 3) Severe Cardiorenal-Neurohormonal Stress (BUN/Albumin Ratio, BAR).

Results: CS-HF patients demonstrated higher baseline comorbidity burden, with mean Elixhauser score of 7 vs. 5 for CS-AMI patients (p=0.0004). Heatmap analysis and PCA revealed an interconnected network of cardio-renal-pulmonary and metabolic comorbidities (polymorbidity) in CS-HF cohort. In contrast, CS-AMI cohort showed sparser pattern of isolated risk factors (multimorbidity). These differences equated a higher prevalence of actionable TTs in CS-HF group. On admission, CS-HF patients exhibited profound Impaired Immunonutrition and Severe Cardiorenal Stress. While both groups expressed high initial inflammation, only CS-AMI group’s resolved by D2, indicating higher prevalence of Persistent Hyper-inflammation trait in CS-HF cohort.

Conclusions: CS-HF and CS-AMI are distinct manifestations by etiology and underlying comorbidity architecture. CS-HF is a state of advanced polymorbidity that predisposes to maladaptive host response characterized by high burden of persistent TTs. Identifying dominant TTs allows for precision medicine strategy, enabling predictive enrichment for adaptive clinical trials and guiding application of targeted therapies (e.g., immunomodulation, specialized nutrition, early renal replacement therapy) to right patient at right time.