PMID- 38196631
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240210
DP  - 2023 Dec 22
TI  - Utilizing Machine Learning to Predict Neurological Injury in Venovenous 
      Extracorporeal Membrane Oxygenation Patients: An Extracorporeal Life Support 
      Organization Registry Analysis.
LID - rs.3.rs-3779429 [pii]
LID - 10.21203/rs.3.rs-3779429/v1 [doi]
AB  - BACKGROUND: Venovenous extracorporeal membrane oxygenation (VV-ECMO) is 
      associated with acute brain injury (ABI), including central nervous system (CNS) 
      ischemia (defined as ischemic stroke or hypoxic-ischemic brain injury) and 
      intracranial hemorrhage (ICH). There is limited data on prediction models for ABI 
      and neurological outcomes in VV-ECMO. RESEARCH QUESTION: Can machine learning 
      (ML) accurately predict ABI and identify modifiable factors of ABI in VV-ECMO? 
      STUDY DESIGN AND METHODS: We analyzed adult (>/=18 years) VV-ECMO patients in the 
      Extracorporeal Life Support Organization Registry (2009-2021) from 676 centers. 
      ABI was defined as CNS ischemia, ICH, brain death, and seizures. Overall, 65 
      total variables were extracted including clinical characteristics and pre-ECMO 
      and on-ECMO variables. Random Forest, CatBoost, LightGBM, and XGBoost ML 
      algorithms (10-fold leave-one-out cross-validation) were used to predict ABI. 
      Feature Importance Scores were used to pinpoint variables most important for 
      predicting ABI. RESULTS: Of 37,473 VV-ECMO patients (median age=48.1 years, 63% 
      male), 2,644 (7.1%) experienced ABI: 610 (2%) and 1,591 (4%) experienced CNS 
      ischemia and ICH, respectively. The median ECMO duration was 10 days 
      (interquartile range=5-20 days). The area under the receiver-operating 
      characteristics curves to predict ABI, CNS ischemia, and ICH were 0.67, 0.63, and 
      0.70, respectively. The accuracy, positive predictive, and negative predictive 
      values for ABI were 79%, 15%, and 95%, respectively. ML identified pre-ECMO 
      cardiac arrest as the most important risk factor for ABI while ECMO duration and 
      bridge to transplantation as an indication for ECMO were associated with lower 
      risk of ABI. INTERPRETATION: This is the first study to use machine learning to 
      predict ABI in a large cohort of VV-ECMO patients. Performance was sub-optimal 
      due to the low reported prevalence of ABI with lack of standardization of 
      neuromonitoring/imaging protocols and data granularity in the ELSO Registry. 
      Standardized neurological monitoring and imaging protocols may improve machine 
      learning performance to predict ABI.
FAU - Kalra, Andrew
AU  - Kalra A
AUID- ORCID: 0000-0001-8338-019X
AD  - Johns Hopkins University School of Medicine.
FAU - Bachina, Preetham
AU  - Bachina P
AD  - Johns Hopkins University School of Medicine.
FAU - Shou, Benjamin L
AU  - Shou BL
AUID- ORCID: 0000-0003-2825-3301
AD  - Johns Hopkins University School of Medicine.
FAU - Hwang, Jaeho
AU  - Hwang J
AD  - Johns Hopkins University School of Medicine.
FAU - Barshay, Meylakh
AU  - Barshay M
AUID- ORCID: 0000-0001-5611-024X
AD  - Warren Alpert Medical School of Brown University.
FAU - Kulkarni, Shreyas
AU  - Kulkarni S
AUID- ORCID: 0000-0002-6723-515X
AD  - Warren Alpert Medical School of Brown University.
FAU - Sears, Isaac
AU  - Sears I
AUID- ORCID: 0000-0002-3293-4524
AD  - Warren Alpert Medical School of Brown University.
FAU - Eickhoff, Carsten
AU  - Eickhoff C
AUID- ORCID: 0000-0001-9895-4061
AD  - Faculty of Medicine, University of Tubingen.
FAU - Bermudez, Christian A
AU  - Bermudez CA
AD  - Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
FAU - Brodie, Daniel
AU  - Brodie D
AUID- ORCID: 0000-0002-0813-3145
AD  - Johns Hopkins University School of Medicine.
FAU - Ventetuolo, Corey E
AU  - Ventetuolo CE
AUID- ORCID: 0000-0002-4223-4775
AD  - Warren Alpert Medical School of Brown University.
FAU - Whitman, Glenn J R
AU  - Whitman GJR
AUID- ORCID: 0000-0003-3225-2360
AD  - Johns Hopkins University School of Medicine.
FAU - Abbasi, Adeel
AU  - Abbasi A
AD  - Warren Alpert Medical School of Brown University.
FAU - Cho, Sung-Min
AU  - Cho SM
AUID- ORCID: 0000-0002-5132-0958
AD  - Johns Hopkins University School of Medicine.
LA  - eng
GR  - K23 HL157610/HL/NHLBI NIH HHS/United States
PT  - Preprint
DEP - 20231222
PL  - United States
TA  - Res Sq
JT  - Research square
JID - 101768035
PMC - PMC10775358
OTO - NOTNLM
OT  - acute brain injury
OT  - machine learning
OT  - neurological complications
OT  - venovenous extracorporeal membrane oxygenation
EDAT- 2024/01/10 06:41
MHDA- 2024/01/10 06:42
PMCR- 2024/01/09
CRDT- 2024/01/10 03:40
PHST- 2024/01/10 06:41 [pubmed]
PHST- 2024/01/10 06:42 [medline]
PHST- 2024/01/10 03:40 [entrez]
PHST- 2024/01/09 00:00 [pmc-release]
AID - rs.3.rs-3779429 [pii]
AID - 10.21203/rs.3.rs-3779429/v1 [doi]
PST - epublish
SO  - Res Sq [Preprint]. 2023 Dec 22:rs.3.rs-3779429. doi: 10.21203/rs.3.rs-3779429/v1.