Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings.

Background: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.

Methods: An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately.

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.

Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI).

Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model.

Subtyping hospitalized patients with hypokalemia by machine learning consensus clustering and associated mortality risks.

Background: Hospitalized patients with hypokalemia are heterogeneous and cluster analysis, an unsupervised machine learning methodology, may discover more precise and specific homogeneous groups within this population of interest. Our study aimed to cluster patients with hypokalemia at hospital admission using an unsupervised machine learning approach and assess the mortality risk among these distinct clusters.

Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities and laboratory data among 4763 hospitalized adult patients with admission serum potassium ≤3.

Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.

Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.

Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.

Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.

Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU.

Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L).

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.

Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster's key features.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements.

Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters.

Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.

Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival.

Background: The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters.

Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of > 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster's key features.

Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.

: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The objective of this study was to characterize hyperchloremic patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. : We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,394 hospitalized adult patients with admission serum chloride of >108 mEq/L.

Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia.

Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters.

Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster's key features.

Hypotension within one-hour from starting CRRT is associated with in-hospital mortality.

Purpose: To investigate early hemodynamic instability and its implications on adverse outcomes in patients who require continuous renal replacement therapy (CRRT).

Materials And Methods: A retrospective study of patients admitted to the intensive care unit (ICU) and underwent CRRT at Mayo Clinic, Rochester, Minnesota between December 2006 through November 2015.

Results: Multivariate logistic regression was performed to identify predictors of in-hospital mortality and major adverse kidney events (MAKE) at 90 days.

Safety and compatibility of smart device heart rhythm monitoring in patients with cardiovascular implantable electronic devices.

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.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram.

Importance: For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.

Objective: To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD.

End of Life, Withdrawal, and Palliative Care Utilization among Patients Receiving Maintenance Hemodialysis Therapy.

Background And Objectives: Withdrawal from maintenance hemodialysis before death has become more common because of high disease and treatment burden. The study objective was to identify patient factors and examine the terminal course associated with hemodialysis withdrawal, and assess patterns of palliative care involvement before death among patients on maintenance hemodialysis.

Design, Setting, Participants, & Measurements: We designed an observational cohort study of adult patients on incident hemodialysis in a midwestern United States tertiary center, from January 2001 to November 2013, with death events through to November 2015.

Errors of Classification With Potassium Blood Testing: The Variability and Repeatability of Critical Clinical Tests.

Objective: To understand the performance of a currently used clinical blood test with regard to the frequency and size of variation of the results.

Patients And Methods: From November 29, 2012, through November 29, 2013, patients were recruited at 65 sites as part of a previously reported clinical trial (ClinicalTrials.gov Identifier: NCT01737697).

Prospective Study of Routine Heparin Avoidance Hemodialysis in a Tertiary Acute Care Inpatient Practice.

Introduction: Extracorporeal circuit (EC) anticoagulation with heparin is a key advance in hemodialysis (HD), but anticoagulation is problematic in inpatients at risk of bleeding. We prospectively evaluated a heparin-avoidance HD protocol, clotting of the EC circuit (CEC), impact on dialysis efficiency, and associated risk factors in our acute care inpatients who required HD (January 17, 2014 to May 31, 2015).

Methods: HD sessions without routine EC heparin were performed using airless dialysis tubing.

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone.

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 2min intervals during hemodialysis.

High-dose hydroxocobalamin for vasoplegic syndrome causing false blood leak alarm.

Blood leak alarms are important safety features in a hemodialysis machine to protect patients from loss of blood through a rupture in the dialyzer membrane (true alarms). A false blood leak alarm can be triggered by air bubbles or detector malfunction (such as deposits of grease or scale). Hydroxocobalamin is an injectable form of vitamin B12 approved by the US Food and Drug Administration for the treatment of confirmed or suspected cyanide toxicity.

Continuous renal replacement therapy: a potential source of calories in the critically ill.

Overfeeding can lead to multiple metabolic and clinical complications and has been associated with increased mortality in the critically ill. Continuous venovenous hemofiltration (CVVH) represents a potential source of calories that is poorly recognized and may contribute to overfeeding complications. We aimed to quantify the systemic caloric contribution of acid-citrate-dextrose regional anticoagulation and dextrose-containing replacement fluids in the CVVH circuit.

Early Gains in Renal Function Following Implantation of HeartMate II Left Ventricular Assist Devices May Not Persist to One Year.

Renal function improves early after left ventricular assist device (LVAD) implantation but later decline has been observed. We sought to determine the occurrence and evaluate possible causes for this decline. In 62 consecutive patients with HeartMateII LVAD with available calculated glomerular filtration rate (GFR, ml/min/1.

Calciphylaxis: A Disease of Pannicular Thrombosis.

Objective: To identify coagulation risk factors in patients with calciphylaxis and the relationship between anticoagulation use and overall survival.

Patients And Methods: Study subjects were 101 patients with calciphylaxis seen at Mayo Clinic from 1999 to September 2014. Data including thrombophilia profiles were extracted from the medical records of each patient.

Survival, Risk Factors, and Effect of Treatment in 101 Patients With Calciphylaxis.

Objective: To report on the survival and the associations of treatments upon survival of patients with calciphylaxis seen at a single center.

Patients And Methods: Using the International Classification of Diseases, Ninth Revision diagnosis code of 275.49 and the keyword "calciphylaxis" in the dismissal narrative, we retrospectively identified 101 patients with calciphylaxis seen at our institution between January 1, 1999, through September 20, 2014, using a predefined, consensus-developed classification scheme.

Total Red Blood Cell Transfusions for Chronic Hemodialysis Patients in a Single Center, 2009-2013.

Background: Anemia management in chronic hemodialysis (HD) has been affected by the implementation of the prospective payment system (PPS) and changes in clinical guidelines. These factors could impact red blood cell (RBC) transfusion in HD patients. Our distinctive care system contains complete records for all RBC transfusions among our HD patients.