11 results on '"Jennifer L. Dugan"'
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2. Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
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Zachi I. Attia, Naveen L. Pereira, Anneli Svensson, Francisco Fernández-Avilés, Thomas F Luescher, Raja Sekhar Madathala, Jozef Bartunek, John Halamka, Henrik Jensen, Francisco Lopez Jimenez, Paari Dominic, Pyotr G. Platonov, Domenico Zagari, Pahlajani Db, Nikhita R Chennaiah Gari, Marco Merlo, Darryl D Esakof, Vladan Vukomanovic, John Signorino, Daniel C. DeSimone, Gianfranco Sinagra, Stefan Janssens, Kevin P. Cohoon, Francis J. Alenghat, Jennifer L. Dugan, Karl Dujardin, Melody Hermel, Michael E. Farkouh, Goran Loncar, Sanjiv M. Narayan, Suraj Kapa, Deepak Padmanabhan, Karam Turk-Adawi, Rickey E. Carter, Paul A. Friedman, Carolyn Lam Su Ping, Fahad Gul, Amit Noheria, Nidal Asaad, Arun Sridhar, Gaetano Antonio Lanza, Peter A. Noseworthy, Nicholas S. Peters, Marc K. Lahiri, Jessica Cruz, Brenda D Rodriguez Escenaro, Gaurav A. Upadhyay, Jose Alberto Pardo Gutierrez, Attia, Z. I., Kapa, S., Dugan, J., Pereira, N., Noseworthy, P. A., Jimenez, F. L., Cruz, J., Carter, R. E., Desimone, D. C., Signorino, J., Halamka, J., Chennaiah Gari, N. R., Madathala, R. S., Platonov, P. G., Gul, F., Janssens, S. P., Narayan, S., Upadhyay, G. A., Alenghat, F. J., Lahiri, M. K., Dujardin, K., Hermel, M., Dominic, P., Turk-Adawi, K., Asaad, N., Svensson, A., Fernandez-Aviles, F., Esakof, D. D., Bartunek, J., Noheria, A., Sridhar, A. R., Lanza, G. A., Cohoon, K., Padmanabhan, D., Pardo Gutierrez, J. A., Sinagra, G., Merlo, M., Zagari, D., Rodriguez Escenaro, B. D., Pahlajani, D. B., Loncar, G., Vukomanovic, V., Jensen, H. K., Farkouh, M. E., Luescher, T. F., Su Ping, C. L., Peters, N. S., and Friedman, P. A.
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COVID-19, coronavirus infectious disease 19 ,COVID-19/diagnosis ,Coronavirus disease 2019 (COVID-19) ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Population ,Predictive Value of Test ,ACE2, angiotensin-converting enzyme 2 ,SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 ,Sensitivity and Specificity ,WHO, World Health Organization ,AUC, area under the curve ,Electrocardiography ,COVID-19 ,Case-Control Studies ,Humans ,Predictive Value of Tests ,Artificial Intelligence ,PCR, polymerase chain reaction ,Medicine ,education ,Volunteer ,education.field_of_study ,medicine.diagnostic_test ,business.industry ,Area under the curve ,Case-control study ,AI-ECG, artificial intelligence–enhanced electrocardiogram ,REDCap, Research Electronic Data Capture ,General Medicine ,PPV, positive predictive value ,NPV, negative predictive value ,Predictive value of tests ,Screening ,Original Article ,AI, artificial intelligence ,Artificial intelligence ,business ,Case-Control Studie ,COVID 19 ,Human - Abstract
OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.
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- 2021
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3. B-PO05-149 ASSESSMENT OF DRUG-INDUCED QT PROLONGATION USING AN AI-ECG ALGORITHM ON A MOBILE 6-LEAD ECG PLATFORM
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Jennifer L. Dugan, Dave Albert, Peter A. Noseworthy, Christopher Newton-Cheh, Johan M Bos, Paul A. Friedman, Zachi Itzhak Attia Msee, Matthew Schram, John R. Giudicessi, and Michael J. Ackerman
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medicine.medical_specialty ,business.industry ,Physiology (medical) ,Internal medicine ,medicine ,Cardiology ,Drug-induced QT prolongation ,Cardiology and Cardiovascular Medicine ,Lead (electronics) ,business - Published
- 2021
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4. Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
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Jennifer L. Dugan, Patrick W. Johnson, M. Fernanda Bellolio, Anthony H. Kashou, Zachi I. Attia, Francisco Lopez-Jimenez, Johnathan M. Sheele, Rickey E. Carter, Michael Albus, Paul A. Friedman, Demilade Adedinsewo, and Peter A. Noseworthy
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Male ,Emergency Medical Services ,medicine.medical_specialty ,Systole ,030204 cardiovascular system & hematology ,Ventricular Function, Left ,Electrocardiography ,Ventricular Dysfunction, Left ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Predictive Value of Tests ,Physiology (medical) ,Humans ,Medicine ,Diagnosis, Computer-Assisted ,Aged ,Retrospective Studies ,business.industry ,Reproducibility of Results ,Signal Processing, Computer-Assisted ,Stroke Volume ,030208 emergency & critical care medicine ,Emergency department ,Middle Aged ,medicine.disease ,Dyspnea ,Heart failure ,Emergency medicine ,Female ,Cardiology Service, Hospital ,Cardiology and Cardiovascular Medicine ,business ,Acute dyspnea ,Heart Failure, Systolic - Abstract
Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction 800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.
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- 2020
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5. Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone
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Paul A. Friedman, Jennifer L. Dugan, Samuel J. Asirvatham, John J. Dillon, Virend K. Somers, Amir B. Geva, Michael J. Ackerman, Christopher G. Scott, Christopher V. DeSimone, Kevin E. Bennet, Dan Sadot, Dorothy J. Ladewig, Omar Yasin, Yehu Sapir, and Zachi I. Attia
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Male ,medicine.medical_specialty ,Hyperkalemia ,medicine.medical_treatment ,Potassium ,chemistry.chemical_element ,Blood potassium measurement ,030204 cardiovascular system & hematology ,Signal ,Article ,End stage renal disease ,Electrocardiography ,03 medical and health sciences ,0302 clinical medicine ,Renal Dialysis ,Internal medicine ,Humans ,Medicine ,030212 general & internal medicine ,business.industry ,Signal Processing, Computer-Assisted ,Middle Aged ,chemistry ,Single lead ,Cardiology ,Kidney Failure, Chronic ,Female ,Smartphone ,Hemodialysis ,medicine.symptom ,Cardiology and Cardiovascular Medicine ,business ,Potassium level - Abstract
Objective We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. Patients and methods Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2 min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. Results The mean absolute error between the estimated potassium and blood potassium 0.38 ± 0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. Conclusions A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. Summary A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.
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- 2017
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6. Safety and compatibility of smart device heart rhythm monitoring in patients with cardiovascular implantable electronic devices
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Mark J. Henrich, John J. Dillon, Jennifer L. Dugan, Dorothy J. Ladewig, Paul A. Friedman, Zachi I. Attia, Ameesh Isath, James D. Ryan, and Anas Abudan
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Male ,medicine.medical_specialty ,Pacemaker, Artificial ,Time Factors ,Smart device ,Population ,Electric Countershock ,030204 cardiovascular system & hematology ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,law ,Heart Rate ,Predictive Value of Tests ,Risk Factors ,Physiology (medical) ,Internal medicine ,medicine ,Humans ,In patient ,030212 general & internal medicine ,Prospective Studies ,education ,Cardiac device ,Aged ,Aged, 80 and over ,education.field_of_study ,Clinical events ,business.industry ,Cardiac Pacing, Artificial ,Reproducibility of Results ,Arrhythmias, Cardiac ,Signal Processing, Computer-Assisted ,Middle Aged ,Mobile Applications ,Defibrillators, Implantable ,Heart Rhythm ,Safety profile ,Remote Sensing Technology ,Cardiology ,Female ,Smartphone ,Cardiology and Cardiovascular Medicine ,business ,Artifacts ,Electrophysiologic Techniques, Cardiac - Abstract
Introduction Emerging medical technology has allowed for monitoring of heart rhythm abnormalities using smartphone compatible devices. The safety and utility of such devices have not been established in patients with cardiac implantable electronic devices (CIEDs). We sought to assess the safety and compatibility of the Food and Drug Administration-approved AliveCor Kardia device in patients with CIEDs. Methods and results We prospectively recruited patients with CIED for a Kardia recording during their routine device interrogation. A recording was obtained in paced and nonpaced states. Adverse clinical events were noted at the time of recording. Electrograms (EGMs) from the cardiac device were obtained at the time of recording to assess for any electromagnetic interference (EMI) introduced by Kardia. Recordings were analyzed for quality and given a score of 3 (interpretable rhythm, no noise), 2 (interpretable rhythm, significant noise) or 1 (uninterpretable). A total of 251 patients were recruited (59% with a pacemaker and 41% with ICD). There were no adverse clinical events noted at the time of recording and no changes to CIED settings. Review of all EGMs revealed no EMI introduced by Kardia. Recordings were correctly interpreted in 90% of paced recordings (183 had a score of 3, 43 of 2, and 25 of 1) and 94.7% of nonpaced recordings (147 of 3, 15 of 2, and 9 of 1). Conclusion The AliveCor Kardia device has an excellent safety profile when used in conjunction with most CIEDs. The quality of recordings was preserved in this population. The device, therefore, can be considered for heart rhythm monitoring in patients with CIEDs.
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- 2019
7. AI ENHANCED ECG ENABLED RAPID NON-INVASIVE EXCLUSION OF SEVERE ACUTE RESPIRATORY SYNDROME CORONAVIRUS 2 (SARS-COV-2) INFECTION
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Daniel C. DeSimone, Jennifer L. Dugan, Jessica Cruz, Naveen L. Pereira, John Signorino, John Halamka, Zachi I. Attia, Paul A. Friedman, Peter A. Noseworthy, Suraj Kapa, Francisco Lopez-Jimenez, and Rickey E. Carter
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Spotlight on Special Topics ,business.industry ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Non invasive ,Medicine ,Cardiology and Cardiovascular Medicine ,business ,Virology - Published
- 2021
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8. APPLICATION OF AN ARTIFICIAL INTELLIGENCE-ENABLED ECG ALGORITHM TO IDENTIFY PATIENTS WITH LEFT VENTRICULAR SYSTOLIC DYSFUNCTION PRESENTING TO THE EMERGENCY ROOM WITH DYSPNEA
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Francisco Lopez-Jimenez, Rickey E. Carter, Patrick W. Johnson, Demilade Adedinsewo, Albus Michael, Zachi I. Attia, Peter A. Noseworthy, Jennifer L. Dugan, Sheele Jonathan, Anthony H. Kashou, and Paul A. Friedman
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Operator (computer programming) ,business.industry ,Point of care ultrasound ,Medicine ,Artificial intelligence ,Emergency department ,Cardiology and Cardiovascular Medicine ,Acute dyspnea ,business - Abstract
Assessment of acute dyspnea in the emergency department (ED) is challenging. Performance of a point of care ultrasound in the ER improves diagnostic precision, but is operator dependent and requires training. Our goal was to utilize an artificial intelligence (AI)-enabled ECG to identify patients
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- 2020
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9. ECG-based Potassium Measurement is Unaffected by Errors in Blood Potassium Measurement during Hemodialysis
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Jennifer L. Dugan, John J. Dillon, Michael J. Ackerman, Virend K. Somers, Dorothy J. Ladewig, Bhupinder Singh, Samuel J. Asirvatham, Guarav C Satam, Yehu Sepir, Kevin E. Bennet, Amir Geva, Zachi I. Attia, Dan Sadot, and Paul A. Friedman
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medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,medicine.medical_treatment ,Potassium ,Albumin ,chemistry.chemical_element ,chemistry ,Internal medicine ,Cardiology ,Medicine ,Blood test ,Hemodialysis ,business ,Electrocardiography ,Dialysis ,Venous return curve ,Blood drawing - Abstract
Background: Potassium abnormalities can cause lifethreatening arrhythmias. Measuring potassium requires access to blood. We have developed methods measuring potassium noninvasively using the processed, signalaveraged ECG. Four patients, in a larger study, were found to have unexpected discrepancies between measured blood potassium and ECG-derived estimated potassium values. Methods: Of 240 patients enrolled at 17 sites in the PORTEND (REVEAL-HD) study, 200 wore a continuouslyrecording, single-lead, wireless ECG patch. Blood for chemistries was obtained once before, twice during and once after dialysis. Complete blood test and ECG data were available for 142 subjects. The general potassium pattern during dialysis was an exponential decay throughout the treatment. Four subjects, whose blood potassium values, but not ECG-based potassium values, deviated from this pattern, are the subjects of this analysis. Findings: Among 4 patients, at least one blood potassium value declined to 2.6 mmol/l or less during dialysis, and then rebounded unexpectedly, while the ECG-based potassium values were consistent with the expected exponential delay. Three of these four patients were at a single site, suggesting site-specific likelihood of pattern deviation (p=0.04). In each case, BUN and phosphorous blood levels were markedly low, with albumin and calcium unaffected. Conclusions: These results are compatible with blood drawing errors in which dialyzed blood was obtained from the venous return, rather than from the arterial tubing. A physiologic, ECG-based test that estimates potassium on the basis of the concentration of potassium in the blood surrounding the heart is free from local aberrations and might be a useful potassium monitoring tool in dialysis patients.
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- 2018
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10. Noninvasive potassium determination using a mathematically processed ECG: Proof of concept for a novel 'blood-less, blood test'
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Dan Sadot, Amir Geva, Dorothy J. Ladewig, Susan B. Mikell, Jennifer L. Dugan, Bryan L. Striemer, Charles J. Bruce, John J. Dillon, Virend K. Somers, Christopher V. DeSimone, Emily J. Gilles, Samuel J. Asirvatham, Kevin E. Bennet, Michael J. Ackerman, Jan Bukartyk, Yehu Sapir, Christopher G. Scott, and Paul A. Friedman
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Male ,medicine.medical_specialty ,Hyperkalemia ,medicine.medical_treatment ,Potassium ,chemistry.chemical_element ,Pilot Projects ,Sensitivity and Specificity ,Electrocardiography ,QRS complex ,Cog ,Renal Dialysis ,Internal medicine ,medicine ,Humans ,Repolarization ,Blood test ,Diagnosis, Computer-Assisted ,Hematologic Tests ,medicine.diagnostic_test ,business.industry ,Reproducibility of Results ,Middle Aged ,Amplitude ,chemistry ,Cardiology ,Feasibility Studies ,Female ,Hemodialysis ,medicine.symptom ,Cardiology and Cardiovascular Medicine ,business ,Algorithms ,Biomarkers - Abstract
To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients.Signal-averaged ECGs were obtained from standard ECG leads in 12 patients before, during, and after dialysis. Based on physiological considerations, five repolarization-related ECG measures were chosen and automatically extracted for analysis: the slope of the T wave downstroke (T right slope), the amplitude of the T wave (T amplitude), the center of gravity (COG) of the T wave (T COG), the ratio of the amplitude of the T wave to amplitude of the R wave (T/R amplitude), and the center of gravity of the last 25% of the area under the T wave curve (T4 COG) (Fig. 1).The correlations with potassium were statistically significant for T right slope (P0.0001), T COG (P=0.007), T amplitude (P=0.0006) and T/R amplitude (P=0.03), but not T4 COG (P=0.13). Potassium changes as small as 0.2mmol/L were detectable.Small changes in blood potassium concentrations, within the normal range, resulted in quantifiable changes in the processed, signal-averaged ECG. This indicates that non-invasive, ECG-based potassium measurement is feasible and suggests that continuous or remote monitoring systems could be developed to detect early potassium deviations among high-risk patients, such as those with cardiovascular and renal diseases. The results of this feasibility study will need to be further confirmed in a larger cohort of patients.
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- 2015
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11. Novel Bloodless Potassium Determination Using a Signal‐Processed Single‐Lead ECG
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Charles J. Bruce, Michael J. Ackerman, Jan Bukartyk, Samuel J. Asirvatham, Jennifer L. Dugan, Paul A. Friedman, Kevin E. Bennet, Dan Sadot, Dorothy J. Ladewig, Christopher G. Scott, Emily J. Gilles, Amir B. Geva, John J. Dillon, Virend K. Somers, Christopher V. DeSimone, Bryan L. Striemer, Yehu Sapir, and Zachi I. Attia
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Adult ,Male ,medicine.medical_specialty ,Pathology ,Time Factors ,Hyperkalemia ,medicine.medical_treatment ,Potassium ,chemistry.chemical_element ,Hypokalemia ,Diagnostic Testing ,030204 cardiovascular system & hematology ,Signal ,Electrocardiography ,03 medical and health sciences ,0302 clinical medicine ,Predictive Value of Tests ,Renal Dialysis ,Internal medicine ,medicine ,Humans ,Arrhythmia and Electrophysiology ,waves ,Prospective Studies ,030212 general & internal medicine ,Dialysis ,Original Research ,Aged ,medicine.diagnostic_test ,business.industry ,potassium ,Reproducibility of Results ,Signal Processing, Computer-Assisted ,Middle Aged ,electrophysiology ,chemistry ,Predictive value of tests ,Cardiology ,Regression Analysis ,Female ,Hemodialysis ,medicine.symptom ,Cardiology and Cardiovascular Medicine ,business ,Algorithms ,Biomarkers - Abstract
Background Hyper‐ and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood‐free method for tracking potassium would be an important clinical advance. Methods and Results Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high‐resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single‐channel ECG . In addition, by analyzing the entire development group's first‐visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). Conclusions The signal‐processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost‐effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.
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- 2016
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