Dataset Information

'Combat' Approach to Cardiogenic Shock.

ABSTRACT: The incidence of cardiogenic shock is rising, patient complexity is increasing and patient survival has plateaued. Mirroring organisational innovations of elite military units, our multidisciplinary medical specialists at the INOVA Heart and Vascular Institute aim to combine the adaptability, agility and cohesion of small teams across our large healthcare system. We advocate for widespread adoption of our 'combat' methodology focused on: increased disease awareness, early multidisciplinary shock team activation, group decision-making, rapid initiation of mechanical circulatory support (as appropriate), haemodynamic-guided management, strict protocol adherence, complete data capture and regular after action reviews, with a goal of ending preventable death from cardiogenic shock.

SUBMITTER: Truesdell AG

PROVIDER: S-EPMC5980650 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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'Combat' Approach to Cardiogenic Shock.

Truesdell Alexander G AG Tehrani Behnam B Singh Ramesh R Desai Shashank S Saulino Patricia P Barnett Scott S Lavanier Stephen S Murphy Charles C

Interventional cardiology (London, England) 20180501 2

The incidence of cardiogenic shock is rising, patient complexity is increasing and patient survival has plateaued. Mirroring organisational innovations of elite military units, our multidisciplinary medical specialists at the INOVA Heart and Vascular Institute aim to combine the adaptability, agility and cohesion of small teams across our large healthcare system. We advocate for widespread adoption of our 'combat' methodology focused on: increased disease awareness, early multidisciplinary shock ...[more]

PMID: 29928313

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Project description:BackgroundCardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.MethodsThe current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster's key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.ResultsThe consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347-0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318-0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452-0.505; P < 0.001).ConclusionsML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

Dataset Information

'Combat' Approach to Cardiogenic Shock.

Publications

'Combat' Approach to Cardiogenic Shock.

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