PMID- 31404280
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220409
IS  - 2059-8661 (Electronic)
IS  - 2059-8661 (Linking)
VI  - 2
IP  - 6
DP  - 2018 Dec
TI  - An example of medical device-based projection of clinical trial enrollment: Use 
      of electrocardiographic data to identify candidates for a trial in acute coronary 
      syndromes.
PG  - 377-383
LID - 10.1017/cts.2019.365 [doi]
AB  - BACKGROUND: To identify potential participants for clinical trials, electronic 
      health records (EHRs) are searched at potential sites. As an alternative, we 
      investigated using medical devices used for real-time diagnostic decisions for 
      trial enrollment. METHODS: To project cohorts for a trial in acute coronary 
      syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or 
      ST elevation myocardial infarction (STEMI) that prompt clinicians to offer 
      patients trial enrollment. We searched six hospitals' electrocardiograph systems 
      for electrocardiograms (ECGs) meeting the planned trial's enrollment criterion: 
      ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia 
      time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI 
      regression to require only data directly from the electrocardiograph, the 
      e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n 
      = 3,453; test set n = 2,315). We also tested both on data from emergency 
      department electrocardiographs from across the US (n = 8,556). We then used 
      ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and 
      compared performance to cohorts from EHR data at the hospitals. RESULTS: 
      Receiver-operating characteristic (ROC) curve areas on the test set were 
      excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On 
      the national electrocardiographic database, ROC areas were 0.78 and 0.69, 
      respectively, and with very good calibration. When tested for detection of 
      patients with > 75% ACS probability, both electrocardiograph-based methods 
      identified eligible patients well, and better than did EHRs. CONCLUSION: Using 
      data from medical devices such as electrocardiographs may provide accurate 
      projections of available cohorts for clinical trials.
FAU - Selker, Harry P
AU  - Selker HP
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
AD  - Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 
      Boston, Massachusetts, USA.
FAU - Kwong, Manlik
AU  - Kwong M
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
AD  - Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 
      Boston, Massachusetts, USA.
FAU - Ruthazer, Robin
AU  - Ruthazer R
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
FAU - Gorman, Sheeona
AU  - Gorman S
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
FAU - Green, Giuliana
AU  - Green G
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
FAU - Patchen, Elizabeth
AU  - Patchen E
AD  - Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 
      Boston, Massachusetts, USA.
FAU - Udelson, James E
AU  - Udelson JE
AD  - Division of Cardiology, Tufts Medical Center, Boston, Massachusetts, USA.
FAU - Smithline, Howard A
AU  - Smithline HA
AD  - Department of Emergency Medicine, Baystate Medical Center, Springfield, 
      Massachusetts, USA.
FAU - Baumann, Michael R
AU  - Baumann MR
AD  - Department of Emergency Medicine, Maine Medical Center, Portland, Maine, USA.
FAU - Harris, Paul A
AU  - Harris PA
AD  - Department of Biomedical Informatics and Department of Biomedical Engineering, 
      Vanderbilt University Medical Center, Nashville, Tennessee, USA.
FAU - Shah, Rashmee U
AU  - Shah RU
AD  - Division of Cardiovascular Medicine, Univerity of Utah School of Medicine, Salt 
      Lake City, Utah, USA.
FAU - Nelson, Sarah J
AU  - Nelson SJ
AD  - Vanderbilt University Medical Center, Nashville, Tennessee, USA.
FAU - Cohen, Theodora
AU  - Cohen T
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
AD  - Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 
      Boston, Massachusetts, USA.
FAU - Jones, Elizabeth B
AU  - Jones EB
AD  - Department of Emergency Medicine, University of Texas Health Science Center at 
      Houston, Houston, Texas, USA.
FAU - Barnewolt, Brien A
AU  - Barnewolt BA
AD  - Department of Emergency Medicine, Tufts Medical Center, Boston, Massachusetts, 
      USA.
FAU - Williams, Andrew E
AU  - Williams AE
AD  - Tufts Clinical and Translational Science Institute, Tufts University, Boston, 
      Massachusetts, USA.
LA  - eng
GR  - K08 HL136850/HL/NHLBI NIH HHS/United States
PT  - Journal Article
PL  - England
TA  - J Clin Transl Sci
JT  - Journal of clinical and translational science
JID - 101689953
PMC - PMC6676436
OTO - NOTNLM
OT  - Cohort discovery
OT  - acute coronary syndromes
OT  - clinical trial enrollment
OT  - electrocardiograph
OT  - medical device
EDAT- 2019/08/14 06:00
MHDA- 2019/08/14 06:01
PMCR- 2019/05/14
CRDT- 2019/08/13 06:00
PHST- 2019/08/13 06:00 [entrez]
PHST- 2019/08/14 06:00 [pubmed]
PHST- 2019/08/14 06:01 [medline]
PHST- 2019/05/14 00:00 [pmc-release]
AID - 00365 [pii]
AID - 10.1017/cts.2019.365 [doi]
PST - ppublish
SO  - J Clin Transl Sci. 2018 Dec;2(6):377-383. doi: 10.1017/cts.2019.365.