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Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery.

ABSTRACT: INTRODUCTION:Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery associated with important morbidity, mortality, and costs. To assess the effectiveness of preventive interventions, an important prerequisite is to have access to accurate measures of POAF incidence. The aim of this study was to develop and validate such a measure. METHODS:A validation study was conducted at two large Canadian university health centers. First, a random sample of 976 (10.4%) patients who had cardiac surgery at these sites between 2010 and 2016 was generated. Then, a reference standard assessment of their medical records was performed to determine their true POAF status on discharge (positive/negative). The accuracy of various algorithms combining diagnostic and procedure codes from: 1) the current hospitalization, and 2) hospitalizations up to 6 years before the current hospitalization was assessed in comparison with the reference standard. Overall and site-specific estimates of sensitivity, specificity, positive (PPV), and negative (NPV) predictive values were generated, along with their 95%CIs. RESULTS:Upon manual review, 324 (33.2%) patients were POAF-positive. Our best-performing algorithm combining data from both sites used a look-back window of 6?years to exclude patients previously known for AF. This algorithm achieved 70.4% sensitivity (95%CI: 65.1-75.3), 86.0% specificity (95%CI: 83.1-88.6), 71.5% PPV (95%CI: 66.2-76.4), and 85.4% NPV (95%CI: 82.5-88.0). However, significant site-specific differences in sensitivity and NPV were observed. CONCLUSION:An algorithm based on administrative data can identify POAF patients with moderate accuracy. However, site-specific variations in coding practices have significant impact on accuracy.

SUBMITTER: Bourgon Labelle J

PROVIDER: S-EPMC7132861 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery.

Bourgon Labelle Jonathan J Farand Paul P Vincelette Christian C Dumont Myriam M Le Blanc Mathilde M Rochefort Christian M CM

BMC medical research methodology 20200405 1

<h4>Introduction</h4>Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery associated with important morbidity, mortality, and costs. To assess the effectiveness of preventive interventions, an important prerequisite is to have access to accurate measures of POAF incidence. The aim of this study was to develop and validate such a measure.<h4>Methods</h4>A validation study was conducted at two large Canadian university health centers. First, a random sample of 976 ...[more]

PMID: 32248798

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Project description:BackgroundAtrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection.ObjectiveThe objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis.MethodsThis is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review.ResultsAF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm.ConclusionsAn automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.

Project description:Although mortality after cardiac surgery has significantly decreased in the last decade, patients still experience clinically relevant postoperative complications. Among others, atrial fibrillation (AF) is a common consequence of cardiac surgery, which is associated with prolonged hospitalization and increased mortality.We retrospectively analyzed data from patients who underwent coronary artery bypass grafting, valve surgery or a combination of both at the University Hospital Muenster between April 2014 and July 2015. We evaluated the incidence of new onset and intermittent/permanent AF (patients with pre- and postoperative AF). Furthermore, we investigated the impact of postoperative AF on clinical outcomes and evaluated potential risk factors.In total, 999 patients were included in the analysis. New onset AF occurred in 24.9% of the patients and the incidence of intermittent/permanent AF was 59.5%. Both types of postoperative AF were associated with prolonged ICU length of stay (median increase approx. 2 days) and duration of mechanical ventilation (median increase 1 h). Additionally, new onset AF patients had a higher rate of dialysis and hospital mortality and more positive fluid balance on the day of surgery and postoperative days 1 and 2. In a multiple logistic regression model, advanced age (odds ratio (OR)?=?1.448 per decade increase, p?<?0.0001), a combination of CABG and valve surgery (OR?=?1.711, p?=?0.047), higher C-reactive protein (OR?=?1.06 per unit increase, p?<?0.0001) and creatinine plasma concentration (OR?=?1.287 per unit increase, p?=?0.032) significantly predicted new onset AF. Higher Horowitz index values were associated with a reduced risk (OR?=?0.996 per unit increase, p?=?0.012). In a separate model, higher plasma creatinine concentration (OR?=?2.125 per unit increase, p?=?0.022) was a significant risk factor for intermittent/permanent AF whereas higher plasma phosphate concentration (OR?=?0.522 per unit increase, p?=?0.003) indicated reduced occurrence of this arrhythmia.New onset and intermittent/permanent AF are associated with adverse clinical outcomes of elective cardiac surgery patients. Different risk factors implicated in postoperative AF suggest different mechanisms might be involved in its pathogenesis. Customized clinical management protocols seem to be warranted for a higher success rate of prevention and treatment of postoperative AF.

Project description:BackgroundSome attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions.ObjectiveThis study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device.MethodsA total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch.ResultsOne of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve.ConclusionsWe were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

Project description:BackgroundPostoperative atrial fibrillation (POAF) is one of the most common complications after cardiac surgery. POAF is associated with a longer hospital stay, higher healthcare resource utilization, and higher risk of morbidity and mortality. As a result, the American and European guidelines recommend the use of beta-blockers and amiodarone for the prevention of POAF, and in turn, avoid the complications associated with POAF.ObjectivesThe purpose of this study was to determine the incidence, risk factors, and prognostic implications of new-onset POAF after cardiac surgery in adults with congenital heart disease (CHD).MethodsA retrospective study was conducted among adults with CHD who underwent cardiac surgery (2003-2019). POAF and late-onset atrial fibrillation (AF) were defined as AF occurring within and after 30 days postoperatively, respectively.ResultsOf 1,598 patients (mean age 39 ± 13 years, 51% men), 335 (21%) developed POAF. Risk factors associated with POAF were older age, hypertension, left atrial (LA) reservoir strain and right atrial (RA) dysfunction, and nonsystemic atrioventricular valve regurgitation. Of 1,291 patients (81%) with follow-up ≥12 months, the annual incidence of late-onset AF was 1.5% and was higher in patients with POAF compared with those without POAF (5.9% vs 0.4%; P < 0.001). Risk factors associated with late-onset AF were POAF, older age, severe CHD, and LA and RA dysfunction. Of the 1,291 patients, 63 (5%) died during follow-up, and the risk factors associated with all-cause mortality were older age, severe CHD, hypertension, left ventricular systolic dysfunction, and LA and RA dysfunction. POAF was not associated with all-cause mortality.ConclusionsPOAF was common in adults with CHD and was associated with late-onset AF but not all-cause mortality. Atrial dysfunction was independently associated with POAF, late-onset AF, and all-cause mortality. These risk factors can be used to identify patients at risk for POAF and provide a foundation for prospective studies assessing the efficacy of prophylactic therapies in this population.

Dataset Information

Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery.

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Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery.

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