New-Onset Atrial Fibrillation in the Critically Ill.

OBJECTIVE: To determine the association of new-onset atrial fibrillation with outcomes, including ICU length of stay and survival. DESIGN: Retrospective cohort of ICU admissions. We found atrial fibrillation using automated detection (≥ 90 s in 30 min) and classed as new-onset if there was no prior diagnosis of atrial fibrillation. We identified determinants of new-onset atrial fibrillation and, using propensity matching, characterized its impact on outcomes. SETTING: Tertiary care academic center. PATIENTS: A total of 8,356 consecutive adult admissions to either the medical or surgical/trauma/burn ICU with available continuous electrocardiogram data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From 74 patient-years of every 15-minute observations, we detected atrial fibrillation in 1,610 admissions (19%), with median burden less than 2%. Most atrial fibrillation was paroxysmal; less than 2% of admissions were always in atrial fibrillation. New-onset atrial fibrillation was subclinical or went undocumented in 626, or 8% of all ICU admissions. Advanced age, acute respiratory failure, and sepsis were the strongest predictors of new-onset atrial fibrillation. In propensity-adjusted regression analyses, clinical new-onset atrial fibrillation was associated with increased hospital mortality (odds ratio, 1.63; 95% CI, 1.01-2.63) and longer length of stay (2.25 d; CI, 0.58-3.92). New-onset atrial fibrillation was not associated with survival after hospital discharge (hazard ratio, 0.99; 95% CI, 0.76-1.28 and hazard ratio, 1.11; 95% CI, 0.67-1.83, respectively, for subclinical and clinical new-onset atrial fibrillation). CONCLUSIONS: Automated analysis of continuous electrocardiogram heart rate dynamics detects new-onset atrial fibrillation in many ICU patients. Though often transient and frequently unrecognized, new-onset atrial fibrillation is associated with poor hospital outcomes.

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

OBJECTIVES: Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. DESIGN: We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). SETTING: University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. PATIENTS: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. CONCLUSIONS: Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

Incidence of Atrial Fibrillation following Alcohol Septal Ablation for Hypertrophic Cardiomyopathy.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at a fourfold to sixfold higher risk of developing atrial fibrillation (AF) compared to the general population, though incidence rates among patients undergoing alcohol septal ablation (ASA) are not well characterized. The purpose of this study was to evaluate atrial fibrillation incidence following ASA. METHODS: We studied 132 consecutive HCM patients without comorbid AF that underwent 154 ASA procedures. The incidence of AF in follow-up was assessed through chart abstraction including electrocardiography. Survival free of AF was estimated using Kaplan-Meier methodology. RESULTS: Over a mean follow-up of 3.6 ± 2.7 years (maximum 11.3 years), 10 (7.6%) patients developed new-onset AF. Of those who developed AF, both resting and provoked left ventricular outflow tract (LVOT) gradients had improved significantly (difference -79.78 mm Hg, P ≤ 0.005). Severity of mitral regurgitation improved in 7 (70%) patients. Survival free of AF was estimated to be 99.1%, 93.7%, and 91.7% at 1, 3, and 5 years. CONCLUSIONS: Despite relieving LVOT obstruction and improving mitral regurgitation severity via ASA, new-onset AF remained a common complication of hypertrophic cardiomyopathy.

Heart rate dynamics preceding hemorrhage in the intensive care unit.

Occult hemorrhage in surgical/trauma intensive care unit (STICU) patients is common and may lead to circulatory collapse. Continuous electrocardiography (ECG) monitoring may allow for early identification and treatment, and could improve outcomes. We studied 4,259 consecutive admissions to the STICU at the University of Virginia Health System. We collected ECG waveform data captured by bedside monitors and calculated linear and non-linear measures of the RR interbeat intervals. We tested the hypothesis that a transfusion requirement of 3 or more PRBC transfusions in a 24 hour period is preceded by dynamical changes in these heart rate measures and performed logistic regression modeling. We identified 308 hemorrhage events. A multivariate model including heart rate, standard deviation of the RR intervals, detrended fluctuation analysis, and local dynamics density had a C-statistic of 0.62. Earlier detection of hemorrhage might improve outcomes by allowing earlier resuscitation in STICU patients.

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.

BACKGROUND: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. GOAL: Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases. METHODS: We tested algorithms on the NSR, AF and arrhythmia databases. RESULTS: When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms. CONCLUSION: Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.

Acute Noncardiovascular Illness in the Cardiac Intensive Care Unit.

BACKGROUND: Fifty years after the inception of the cardiac intensive care unit (CICU), noncardiovascular illnesses have become more prevalent and may contribute to morbidity and mortality. OBJECTIVES: The authors performed multivariate statistical analyses to determine the association of acute noncardiovascular illnesses with outcomes, including length of stay (LOS), mortality, and hospital readmission. METHODS: We studied 1,042 admissions between October 12, 2013 and November 28, 2014 to the CICU at the University of Virginia Health System, a tertiary-care academic medical center. Through systematic inspection of individual charts, we identified primary and secondary diagnoses, vital sign measurements, length of stay (LOS), hospital readmissions, and mortality. RESULTS: The most common primary diagnosis was acute coronary syndrome (25%), which consisted of both non-ST-segment elevation acute coronary syndrome (14%) and ST-segment elevation myocardial infarction (11%). Sepsis was the most frequent noncardiovascular primary diagnosis (5%), but it only occurred in 16% of all admissions. Acute kidney injury and acute respiratory failure each occurred in 30% of admissions. One-half of all admissions (n = 524; 50%) were marked by acute respiratory failure, acute kidney injury, or sepsis. Median LOS in the CICU and the hospital were 2 days (interquartile range [IQR]: 1 to 5 days) and 6 days (IQR: 3 to 11 days). Mortality was 7% in the CICU and 12% in the hospital. Of the 920 patients who survived to hospital discharge, 171 (19%) were readmitted within 30 days. Sepsis, acute kidney injury, and acute respiratory failure were associated with mortality. Acute kidney injury, acute respiratory failure, and new-onset subclinical atrial fibrillation, which occurred in 8% of admissions, were all associated with CICU LOS. CONCLUSIONS: Many patients in the modern CICU have acute noncardiovascular illnesses that are associated with mortality and increased LOS.

Hemorrhage Prediction Models in Surgical Intensive Care: Bedside Monitoring Data Adds Information to Lab Values.

Hemorrhage is a frequent complication in surgery patients; its identification and management have received increasing attention as a target for quality improvement in patient care in the Intensive Care Unit (ICU). The purposes of this work were 1) to find an early detection model for hemorrhage by exploring the range of data mining methods that are currently available, and 2) to compare prediction models utilizing continuously measured physiological data from bedside monitors to those using commonly obtained laboratory tests. We studied 3766 patients admitted to the University of Virginia Health System Surgical Trauma Burn ICU. Hemorrhage was defined as three or more units of red blood cells transfused within 24 h without red blood cell transfusion in the preceding 24 h. 222 patients (5.9%) experienced a hemorrhage, and multivariate models based on vital signs and their trends showed good results (AUC = 76.1%). The hematocrit, not surprisingly, had excellent performance (AUC = 87.7%). Models that included both continuous monitoring and laboratory tests had the best performance (AUC = 92.2%). The results point to a combined strategy of continuous monitoring and intermittent lab tests as a reasonable clinical approach to the early detection of hemorrhage in the surgical ICU.

External validation in an intermediate unit of a respiratory decompensation model trained in an intensive care unit.

BACKGROUND: Preventing urgent intubation and upgrade in level of care in patients with subclinical deterioration could be of great utility in hospitalized patients. Early detection should result in decreased mortality, duration of stay, and/or resource use. The goal of this study was to externally validate a previously developed, vital sign-based, intensive care unit, respiratory instability model on a separate population, intermediate care patients. METHODS: From May 2014 to May 2016, the model calculated relative risk of adverse events every 15 minutes (n = 373,271 observations) for 2,050 patients in a surgical intermediate care unit. RESULTS: We identified 167 upgrades and 57 intubations. The performance of the model for predicting upgrades within 12 hours was highly significant with an area under the curve of 0.693 (95% confidence interval, 0.658-0.724). The model was well calibrated with relative risks in the highest and lowest deciles of 2.99 and 0.45, respectively (a 6.6-fold increase). The model was effective at predicting intubation, with a demonstrated area under the curve within 12 hours of the event of 0.748 (95% confidence interval, 0.685-0.800). The highest and lowest deciles of observed relative risk were 3.91 and 0.39, respectively (a 10.1-fold increase). Univariate analysis of vital signs showed that transfer upgrades were associated, in order of importance, with rising respiration rate, rising heart rate, and falling pulse-oxygen saturation level. CONCLUSION: The respiratory instability model developed previously is valid in intermediate care patients to predict both urgent intubations and requirements for upgrade in level of care to an intensive care unit.

Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study.

BACKGROUND: Charted vital signs and laboratory results represent intermittent samples of a patient's dynamic physiologic state and have been used to calculate early warning scores to identify patients at risk of clinical deterioration. We hypothesized that the addition of cardiorespiratory dynamics measured from continuous electrocardiography (ECG) monitoring to intermittently sampled data improves the predictive validity of models trained to detect clinical deterioration prior to intensive care unit (ICU) transfer or unanticipated death. METHODS AND FINDINGS: We analyzed 63 patient-years of ECG data from 8,105 acute care patient admissions at a tertiary care academic medical center. We developed models to predict deterioration resulting in ICU transfer or unanticipated death within the next 24 hours using either vital signs, laboratory results, or cardiorespiratory dynamics from continuous ECG monitoring and also evaluated models using all available data sources. We calculated the predictive validity (C-statistic), the net reclassification improvement, and the probability of achieving the difference in likelihood ratio χ2 for the additional degrees of freedom. The primary outcome occurred 755 times in 586 admissions (7%). We analyzed 395 clinical deteriorations with continuous ECG data in the 24 hours prior to an event. Using only continuous ECG measures resulted in a C-statistic of 0.65, similar to models using only laboratory results and vital signs (0.63 and 0.69 respectively). Addition of continuous ECG measures to models using conventional measurements improved the C-statistic by 0.01 and 0.07; a model integrating all data sources had a C-statistic of 0.73 with categorical net reclassification improvement of 0.09 for a change of 1 decile in risk. The difference in likelihood ratio χ2 between integrated models with and without cardiorespiratory dynamics was 2158 (p value: <0.001). CONCLUSIONS: Cardiorespiratory dynamics from continuous ECG monitoring detect clinical deterioration in acute care patients and improve performance of conventional models that use only laboratory results and vital signs.

Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy.

Atrial fibrillation (AF) is usually detected by inspection of the electrocardiogram waveform, a task made difficult when the signal is distorted by noise. The RR interval time series is more frequently available and accurate, yet linear and nonlinear time series analyses that detect highly varying and irregular AF are vulnerable to the common finding of frequent ectopy. We hypothesized that different nonlinear measures might capture characteristic features of AF, normal sinus rhythm (NSR), and sinus rhythm (SR) with frequent ectopy in ways that linear measures might not. To test this, we studied 2722 patients with 24 h ECG recordings in the University of Virginia Holter database. We found dynamical phenotypes for the three rhythm classifications. As expected, AF records had the highest variability and entropy, and NSR the lowest. SR with ectopy could be distinguished from AF, which had higher entropy, and from NSR, which had different fractal scaling, measured as higher detrended fluctuation analysis slope. With these dynamical phenotypes, we developed successful classification strategies, and the nonlinear measures improved on the use of mean and variability alone, even after adjusting for age. Final models using all variables had excellent performance, with positive predictive values for AF, NSR and SR with ectopy as high as 97, 98 and 90%, respectively. Since these classifiers can reliably detect rhythm changes utilizing segments as short as 10 min, we envision their application in noisy settings and in personal monitoring devices where only RR interval time series may be available.

Local dynamics of heart rate: detection and prognostic implications.

The original observation that reduced heart rate variability (HRV) confers poor prognosis after myocardial infarction has been followed by many studies of heart rate dynamics. We tested the hypothesis that an entropy-based local dynamics measure gave prognostic information in ambulatory patients undergoing 24-h electrocardiography. In this context, entropy is the probability that short templates will find matches in the time series. We studied RR interval time series from 24-h Holter monitors of 1564 consecutive patients over age 39. We generated histograms of the count of templates as a function of the number of templates matches in short RR interval time series, and found characteristic appearance of histograms for atrial fibrillation, sinus rhythm with normal HRV, and sinus rhythm with reduced HRV and premature ventricular contractions (PVCs). We developed statistical models to detect the abnormal dynamic phenotype of reduced HRV with PVCs and fashioned a local dynamics score (LDs) that, after controlling for age, added more prognostic information than other standard risk factors and common HRV metrics, including, to our surprise, the PVC count and the HRV of normal-to-normal intervals. Addition of the LDs to a predictive model using standard risk factors significantly increased the ROC area and the net reclassification improvement was 27%. We conclude that abnormal local dynamics of heart rate confer adverse prognosis in patients undergoing 24-h ambulatory electrocardiography.

Left ventricular systolic function following alcohol septal ablation for symptomatic hypertrophic cardiomyopathy.

Because alcohol septal ablation (ASA) for the treatment of symptomatic hypertrophic cardiomyopathy (HC) with left ventricular (LV) outflow tract (LVOT) obstruction results in a myocardial infarct of up to 10% of ventricular mass, LV systolic function could decline over time. We evaluated LV function during longitudinal follow-up in a cohort of patients who underwent ASA. We studied 145 consecutive patients with HC that underwent 167 ASA procedures from 2002 to 2011. Echocardiographic follow-up was available in 139 patients (96%). Echocardiographic indexes included LV ejection fraction (LVEF), mitral regurgitation severity, systolic anterior motion of the anterior mitral leaflet, and resting and provoked LVOT gradients. All patients had a baseline LVEF of >55%. LVEF was preserved in 97.1% of patients over a mean follow-up time of 3.1±2.3 years (maximum 9.7). Mild LV systolic dysfunction was observed (LVEF range 44% to 54%) in only 4 patients. Mitral regurgitation severity improved in 67% (n=112 of 138 with complete data). Resting LVOT gradient declined from a mean of 75 to 19 mm Hg (p<0.001), and provoked gradient declined from a mean of 101 to 33 mm Hg (p<0.001). New York Heart Association class improved from a mean of 2.9±0.4 to 1.3±0.5 (p<0.001). In conclusion, LV systolic function is only mildly reduced in a minority of patients after ASA for symptomatic HC; other echocardiographic and functional measures were significantly improved.

Dynamic analysis of cardiac rhythms for discriminating atrial fibrillation from lethal ventricular arrhythmias.

BACKGROUND: Implantable cardioverter-defibrillators (ICDs), the first line of therapy for preventing sudden cardiac death in high-risk patients, deliver appropriate shocks for termination of ventricular tachycardia (VT)/ventricular fibrillation. A common shortcoming of ICDs is imperfect rhythm discrimination, resulting in the delivery of inappropriate shocks for atrial fibrillation (AF). An underexplored area for rhythm discrimination is the difference in dynamic properties between AF and VT/ventricular fibrillation. We hypothesized that the higher entropy of rapid cardiac rhythms preceding ICD shocks distinguishes AF from VT/ventricular fibrillation. METHODS AND RESULTS: In a multicenter, prospective, observational study of patients with primary prevention ICDs, 119 patients received shocks from ICDs with stored, retrievable intracardiac electrograms. Blinded adjudication revealed shocks were delivered for VT/ventricular fibrillation (62%), AF (23%), and supraventricular tachycardia (15%). Entropy estimation of only 9 ventricular intervals before ICD shocks accurately distinguished AF (receiver operating characteristic curve area, 0.98; 95% confidence intervals, 0.93-1.0) and outperformed contemporary ICD rhythm discrimination algorithms. CONCLUSIONS: This new strategy for AF discrimination based on entropy estimation expands on simpler concepts of variability, performs well at fast heart rates, and has potential for broad clinical application.