PMID- 37268103
OWN - NLM
STAT- Publisher
LR  - 20230709
IS  - 1097-685X (Electronic)
IS  - 0022-5223 (Linking)
DP  - 2023 Jun 1
TI  - In search of similarity in adverse events journeys of patients with left 
      ventricular assist devices.
LID - S0022-5223(23)00449-X [pii]
LID - 10.1016/j.jtcvs.2023.05.025 [doi]
AB  - OBJECTIVE: The Interagency Registry for Mechanically Assisted Circulatory Support 
      (INTERMACS) Event data set contains an expansive collection of longitudinal 
      evidence of the course of adverse events (AEs) of >15,000 patients who have 
      received a left ventricular assist device (LVAD). Buried in the huge Event data 
      set is knowledge that can provide a deeper understanding of the patterns of the 
      "AE journey" of patients with LVAD. Thus, the goal of this study was to examine 
      the Event data set from a comprehensive perspective to identify unique 
      relationships and patterns of AEs, alert potential challenges, and suggest future 
      research directions. METHODS: A sequential pattern mining algorithm called SPADE 
      (ie, Sequential PAttern Discovery using Equivalence classes) was applied to 
      86,912 recorded AEs of 15,820 patients with a continuous-flow LVAD between 2008 
      and 2016, extracted from the publicly accessible INTERMACS registry. The patterns 
      of AE journey were investigated by posing 5 descriptive research questions about 
      most common types of AE, concomitant AEs, AE sequences, AE subsequences, and 
      interesting relations between AEs. RESULTS: The analysis revealed several 
      characteristics of patterns of the AE journey of patients who received an LVAD 
      that accounts for the types and temporal ordering of successive AEs, combinations 
      of AEs, and their timing after surgery. CONCLUSIONS: The high diversity and 
      sparsity of the types and timing of AE occurrences make the AE journeys of 
      patients dissimilar from each other, impeding the discovery of highly-patterned 
      AE journeys among the patients. This study suggests 2 salient directions for 
      future studies to tackle this issue using cluster analysis to cluster patients 
      into more similar groups and translate these results into a practical clinical 
      tool to forecast the next AE based on the history of previous AEs.
CI  - Copyright (c) 2023. Published by Elsevier Inc.
FAU - Movahedi, Faezeh
AU  - Movahedi F
AD  - Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pa.
FAU - Pagani, Francis D
AU  - Pagani FD
AD  - Department of Cardiac Surgery, University of Michigan, Ann Arbor, Mich.
FAU - Antaki, James F
AU  - Antaki JF
AD  - Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY. 
      Electronic address: antaki@cornell.edu.
LA  - eng
PT  - Journal Article
DEP - 20230601
PL  - United States
TA  - J Thorac Cardiovasc Surg
JT  - The Journal of thoracic and cardiovascular surgery
JID - 0376343
SB  - IM
OTO - NOTNLM
OT  - INTERMACS
OT  - LVAD
OT  - adverse events
OT  - patterns
EDAT- 2023/06/03 11:42
MHDA- 2023/06/03 11:42
CRDT- 2023/06/02 19:23
PHST- 2023/01/02 00:00 [received]
PHST- 2023/05/05 00:00 [revised]
PHST- 2023/05/22 00:00 [accepted]
PHST- 2023/06/03 11:42 [pubmed]
PHST- 2023/06/03 11:42 [medline]
PHST- 2023/06/02 19:23 [entrez]
AID - S0022-5223(23)00449-X [pii]
AID - 10.1016/j.jtcvs.2023.05.025 [doi]
PST - aheadofprint
SO  - J Thorac Cardiovasc Surg. 2023 Jun 1:S0022-5223(23)00449-X. doi: 
      10.1016/j.jtcvs.2023.05.025.