ESC Heart Failure (Dec 2021)

Diamond–Forrester classification using echocardiography haemodynamic assessment in cardiac intensive care unit patients

  • Kyung‐Hee Kim,
  • Jacob C. Jentzer,
  • Brandon M. Wiley,
  • William R. Miranda,
  • Courtney Bennett,
  • Gregory W. Barsness,
  • Jae K. Oh

DOI
https://doi.org/10.1002/ehf2.13527
Journal volume & issue
Vol. 8, no. 6
pp. 4933 – 4943

Abstract

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Abstract Aims We sought to determine whether the Diamond–Forrester classification using non‐invasive haemodynamic measurements by 2‐D and Doppler echocardiography would predict hospital mortality in cardiac intensive care unit (CICU) patients. Methods and results We retrospectively analysed unique patients admitted to the CICU at Mayo Clinic Rochester from 2007 to 2018. Doppler‐derived cardiac index (CI) and ratio of mitral valve E velocity to medial mitral annulus e′ velocity (E/e′ ratio) were used to classify patients into four profiles: Profile I (warm/dry), Profile II (warm/wet), Profile III (cold/dry), and Profile IV (cold/wet). Logistic regression was used to determine predictors of hospital mortality, and Cox proportional‐hazards analysis was used to determine predictors of mortality during one year of follow‐up. We included 4563 patients with a mean age of 68.3 ± 14.3 years, including 36.2% female patients. The distribution of each profile was as follows: I, 47.4%; II, 36.2%; III, 7.9%; IV, 8.5%. A total of 5.8% patients died during hospitalization, and 18.1% died by 1 year. Patients with either low CI or elevated E/e′ ratio had higher in‐hospital and 1 year mortality. Patients with elevated E/e′ ratio (i.e. Profiles II and IV) had an increased risk of death during hospitalization and at 1 year after multivariate adjustment (adjusted hazard ratio 1.72 and 2.17 for 1 year mortality, respectively, compared with Profile I, P < 0.01). Conclusions Simple Doppler echocardiographic assessment can be used to identify haemodynamic profiles defined by the Diamond–Forester classification in patients admitted in CICU. These profiles predict outcomes and may be used to guide therapy in critically ill patients.

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