PEOPLE
- Principal Investigator
JOURNAL
The Lancet eClinical Medicine
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ABSTRACT
Methods
We developed a deep learning model to Predict changes in left ventricULar Systolic function from Electrocardiograms (ECG) of patients who have Heart Failure (PULSE-HF). The model integrates 12-lead ECG waveforms with a patient's history of prior LVEF measurements to calculate the likelihood that the LVEF will be less than 40% during the year after the ECG is obtained. The model is retrospectively developed and tested using data from one hospital and externally validated on retrospective cohorts from two different hospitals. The internal development data was collected between January 1, 2000, and June 30, 2021. The external validation datasets were collected between January 1, 2000, and June 30, 2021 at one hospital and between 2008 and 2019 at the other hospital.
Findings
PULSE–HF demonstrates strong discriminatory ability with respect to forecasting whether the LVEF would be below 40% within the next year, achieving areas under the receiver operating characteristic curve (AUROC) of 87.5–91.4% across all three HF cohorts. Among patients with HF who have a baseline LVEF above 40%, PULSE–HF effectively identified those at risk of worsening LVEF with AUROCs of 81.6–86.3% across all three datasets. PULSE–HF's discriminatory ability remained consistently high across a range of subgroups with different comorbidities and regardless of medical therapy. Assuming an underlying prevalence of LVEF worsening of 10% per year, PULSE-HF's negative predictive values are over 97%, assuming an underlying sensitivity of 80%. Lastly, we demonstrate that a lead I version of PULSE–HF has a performance similar to the performance of the model that uses all 12 ECG leads.
Interpretation
PULSE–HF robustly predicts worsening LVEF in patients who have a prior diagnosis of HF. The method provides a platform for identifying patients who are at an increased risk of worsening systolic dysfunction.
Co-authors: Teya Bergamaschi, Tiffany Yau, Payal Chandak, Abena Kyereme-Tuah, Judy Hung, Hanna Gaggin, Isaac S Kohane, Collin M Stultz
We developed a deep learning model to Predict changes in left ventricULar Systolic function from Electrocardiograms (ECG) of patients who have Heart Failure (PULSE-HF). The model integrates 12-lead ECG waveforms with a patient's history of prior LVEF measurements to calculate the likelihood that the LVEF will be less than 40% during the year after the ECG is obtained. The model is retrospectively developed and tested using data from one hospital and externally validated on retrospective cohorts from two different hospitals. The internal development data was collected between January 1, 2000, and June 30, 2021. The external validation datasets were collected between January 1, 2000, and June 30, 2021 at one hospital and between 2008 and 2019 at the other hospital.
Findings
PULSE–HF demonstrates strong discriminatory ability with respect to forecasting whether the LVEF would be below 40% within the next year, achieving areas under the receiver operating characteristic curve (AUROC) of 87.5–91.4% across all three HF cohorts. Among patients with HF who have a baseline LVEF above 40%, PULSE–HF effectively identified those at risk of worsening LVEF with AUROCs of 81.6–86.3% across all three datasets. PULSE–HF's discriminatory ability remained consistently high across a range of subgroups with different comorbidities and regardless of medical therapy. Assuming an underlying prevalence of LVEF worsening of 10% per year, PULSE-HF's negative predictive values are over 97%, assuming an underlying sensitivity of 80%. Lastly, we demonstrate that a lead I version of PULSE–HF has a performance similar to the performance of the model that uses all 12 ECG leads.
Interpretation
PULSE–HF robustly predicts worsening LVEF in patients who have a prior diagnosis of HF. The method provides a platform for identifying patients who are at an increased risk of worsening systolic dysfunction.
Co-authors: Teya Bergamaschi, Tiffany Yau, Payal Chandak, Abena Kyereme-Tuah, Judy Hung, Hanna Gaggin, Isaac S Kohane, Collin M Stultz
