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Dillon, John J

Mayo Clinic

Division of Nephrology and Hypertension, Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA.

Citations 13

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram
Galloway, Conner, Valys, Alexander , Shreibati, Jacqueline Baras , Treiman, Daniel L., Petterson, Frank L., Gundotra, Vivek P., Albert, D.J. , Carter, Rickey E. , Asirvatham, Samuel J. , Ackerman, Michael J. , Friedman, Paul A. , Dillon, John J, Asirvatham, Samuel J , Carter, Rickey E , Attia, Zachi I. , Noseworthy, Peter A. , Dillon, John

JAMA Cardiology | April 3, 2019 | Volume: 4 | Issue: 5 | Pages: 428

10.1001/jamacardio.2019.0640 Get It

Novel Bloodless Potassium Determination Using a Signal‐Processed Single‐Lead ECG
Sapir, Yehu , Dugan, Jennifer , Ackerman, Michael J. , Asirvatham, Samuel J. , Striemer, Bryan L., Bukartyk, Jan , Bennet, Kevin E. , Ladewig, Dorothy J., Gilles, Emily J., Sadot, Dan , Geva, Amir B. , Friedman, Paul A. , Dillon, John J, Dugan, Jennifer L, Bruce, Charles J. , Attia, Zachi I. , DeSimone, Christopher V. , Dillon, John J., Somers, Virend K. , Scott, Christopher G. , Asirvatham, Samuel J

Journal of the American Heart Association | January 25, 2016 | Volume: 5 | Issue: 1 | Pages: No page available

10.1161/jaha.115.002746 Get It

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.
Ceftriaxone-Induced Acute Encephalopathy in a Peritoneal Dialysis Patient
Safadi, Sami , Mao, Michael, Dillon, John J, Mao, Michael A. , Dillon, John

Case Reports in Nephrology | December 7, 2014 | Volume: 2014 | Issue: No issue available | Pages: 1-4

10.1155/2014/108185 Get It

Encephalopathy is a rare side effect of third and fourth generation cephalosporins. Renal failure and preexisting neurological disease are notable risk factors. Recognition is important as discontinuing the offending agent usually resolves symptoms. We present a case of acute encephalopathy in a patient with end stage renal disease (ESRD) treated with peritoneal dialysis (PD) who received intravenous ceftriaxone for peritonitis. This case illustrates the potential severe neurologic effects of cephalosporins, which are recommended by international guidelines as first-line antimicrobial therapy for spontaneous bacterial peritonitis.