Unsupervised learning of early post-arrest brain injury phenotypes

RESUSCITATION

卷:153

Article

Elmer, Jonathan^1,2,3  Coppler, Patrick J.¹  May, Teresa L.⁴  Hirsch, Karen⁵  Faro, John⁶  Solanki, Pawan⁷  Brown, McKenzie¹  Puyana, Jacob S.¹  Rittenberger, Jon C.⁸  Callaway, Clifton W.¹ 

[1] Univ Pittsburgh, Sch Med, Dept Emergency Med, Pittsburgh, PA USA

[2] Univ Pittsburgh, Dept Crit Care Med, Sch Med, Pittsburgh, PA USA

[3] Univ Pittsburgh, Sch Med, Dept Neurol, Pittsburgh, PA 15261 USA

[4] Maine Med Ctr, Dept Crit Care Med, Portland, ME 04102 USA

[5] Stanford Univ, Dept Neurol, Sch Med, Stanford, CA 94305 USA

[6] Univ Cincinnati, Coll Med, Cincinnati, OH USA

[7] Univ Buffalo, Dept Anesthesiol, Buffalo, NY USA

[8] Guthrie Robert Packer Hosp Emergency Med Residenc, Sayre, PA USA

关键词: Cardiac arrest;    Precision medicine;    Unsupervised learning;    Clustering;    Phenotype;    Outcomes;   

DOI  :  10.1016/j.resuscitation.2020.05.051

来源: Elsevier

【 摘 要 】

Introduction: Trials may be neutral when they do not appropriately target the experimental intervention. We speculated multimodality assessment of early hypoxic-ischemic brain injury would identify phenotypes likely to benefit from therapeutic interventions. Methods: We performed a retrospective study including comatose patients resuscitated from out-of-hospital cardiac arrest (OHCA) by one of 126 emergency medical services or in-hospital arrest at one of 26 hospitals from 2011 to 2019. All patients were ultimately transported to a single tertiary center for care including standardized initial neurological examination, brain imaging and electroencephalography; targeted temperature management (TTM); hemodynamic optimization targeting mean arterial pressure (MAP) >80 mmHg; and, coronary angiography for clinical suspicion for acute coronary syndrome. We used unsupervised learning to identify brain injury phenotypes defined by admission neurodiagnostics. We tested for interactions between phenotype and TTM, hemodynamic management and cardiac catheterization in models predicting recovery. Results: We included 1086 patients with mean (SD) age 58 (17) years of whom 955 (88%) were resuscitated from OHCA. Survival to hospital discharge was 27%, and 248 (23%) were discharged with Cerebral Performance Category (CPC) 1-3. We identified 5 clusters defining distinct brain injury phenotypes, each comprising 14% to 30% of the cohort with discharge CPC 1-3 in 59% to <1%. We found significant interactions between cluster and TTM strategy (P = 0.01), MAP (P < 0.001) and coronary angiography (P = 0.04) in models predicting outcomes. Conclusions: We identified patterns of early hypoxic-ischemic injury based on multiple diagnostic modalities that predict responsiveness to several therapeutic interventions recently tested in neutral clinical trials.

【 授权许可】

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