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Using routine data to inform routine clinical problems
Allan J Walkey, MD, MSc Associate Professor of Medicine The Pulmonary Center Evans Center of Implementation and Improvement Sciences
Using routine data to inform routine clinical problems Allan J - - PowerPoint PPT Presentation
Using routine data to inform routine clinical problems Allan J - - PowerPoint PPT Presentation
Using routine data to inform routine clinical problems Allan J Walkey, MD, MSc Associate Professor of Medicine The Pulmonary Center Evans Center of Implementation and Improvement Sciences ICU Medicine Poorly- Well- Well-managed
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Poorly- characterized Clinical Problems Well- Characterized Clinical Problems Well-managed clinical problems
ICU Medicine
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What does it mean and what should I do when patients with sepsis develop AF?
Intersection of well-characterized problems = poorly characterized problem Intersection of well-characterized problems = poorly characterized problem
New-Onset Atrial Fibrillation (AF) during Pneumonia and Sepsis
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My First Forays into “Big Data”
Good data: Data that exists!
AHRQ State Inpatient Databases include 240 data elements from every hospitalization California = 3.9 million hospitalizations/year ~1 billion data points
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Walkey AJ et al. JAMA 2011; 306 (20) 1615
Is risk of new-onset AF increased in sepsis? YES! By nearly 7-fold; 10% of patients Is it bad? YES! Increased risk of death and stroke. How long so those risks last? A long time!
Walkey AJ et al. Chest. 2014;146(5):1187-1195.
Characterizing aClinical Problem: Epidemiology
New-onset AF in Sepsis
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Could we learn more about AF if we could accurately detect AF in banked ECG waveforms within EHR data? What if we could predict AF onset…could we intervene beforehand to prevent AF? With what?
CharacterizingProblems: Better data
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Database: MIMIC III Open Source ICU data ~ 2000 sepsis patients with electronic health records linked to banked waveform data
CharacterizingProblems: Better data
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More Data, More Problems
Noise!
Variable frequency complex demodulation (VFCDM) based time-frequency spectral (TFS) method to detect and remove noisy segments
Bashar S, Walkey AJ, Mcmanus DD, Chon K. IEEE Access 2019; 7: 88357 - 88368
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AF Feature Detection
P-Waves QRS Complexes
Hossain B, Bashar S, Walkey AJ, McManus, DD Chon K. IEEE Access 2019: 128869
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AF Prediction
WIP
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Question: What is most effective treatment to reduce heart rate during AF in sepsis? Need: Really granular data Philips eICU open source: 200,000 admissions,100 ICUs Vital signs resolution: 1 min.
Better-managed clinical problems
norepinephrine phenylephrine
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- Predicting cardiovascular events after sepsis using
EMR data to better target treatments
- Use of novel continuous regression discontinuity
designs to evaluate implementation effectiveness
- Automated extraction of EMR data for outcome
ascertainment in pragmatic trials of AF treatments
Other Ongoing Studies
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Summary
- So many poorly defined clinical problems + so
much underused data = learning opportunity
- Diverse methodological + subject expertise =
necessity in data science
- AF + Sepsis = Bad…
but maybe we can change that using routinely collected data to enable a Learning Healthcare System
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