Reading discordant bedside indices in sepsis-induced cardiomyopathy: an evidence-mapping framework for diagnosis and...

Sepsis-induced cardiomyopathy (SICM) affects 20%-30% of patients with septic shock and independently predicts mortality, yet it lacks a consensus definition or validated reference standard. Bedside diagnostic indices-left ventricular ejection fraction (LVEF), global longitudinal strain, E/e', troponin level, and cardiac index-are frequently discordant, and no principled framework exists for interpreting this discordance. In parallel, every major pharmacological intervention targeting SICM, including levosimendan, corticosteroids, and beta-blockers, has failed to demonstrate consistent mortality benefit across randomised trials. Herein, we propose that both impasses may share a single root cause: a construct validity failure in which bedside indices used for diagnosis and trial enrolment do not reliably identify the specific pathophysiological processes that they are assumed to represent. We present a structured evidence-mapping framework that organises six bedside diagnostic methods against seven pathophysiological trait dimensions of SICM, grading the evidence for each combination against explicit criteria and identifying structural blind spots. The framework explains why index discordance is a diagnostic signal rather than a measurement error, identifies two dimensions-metabolic dysfunction and cardiac-specific inflammatory signalling-that are structurally invisible to all current bedside diagnostics and whose invisibility offers a testable explanation for repeated trial failures, and translates these insights into a three-step bedside reasoning framework. When LVEF is depressed but longitudinal function is preserved, the framework directs assessment towards ventriculo-arterial uncoupling rather than inotrope escalation; when right ventricular dysfunction predominates, it directs assessment towards reverse resuscitation. The framework reframes index discordance from a source of diagnostic paralysis into a source of clinical precision and defines the enrolment biomarker agenda required to make future SICM trials mechanistically interpretable.