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Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models.

ABSTRACT: Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF-related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3-year new-onset AF prediction (C-statistic, 0.796 [95% CI, 0.785-0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C-statistic, 0.777 [95% CI, 0.766-0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3-year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.

SUBMITTER: Yum Y

PROVIDER: S-EPMC9238645 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAtrial fibrillation (AF) burden on patients and healthcare systems warrants innovative strategies for screening asymptomatic individuals.ObjectiveWe sought to externally validate a predictive model originally developed in a German population to detect unidentified incident AF utilising real-world primary healthcare databases from countries in Europe and Australia.MethodsThis retrospective cohort study used anonymized, longitudinal patient data from 5 country-level primary care databases, including Australia, Belgium, France, Germany, and the UK. The study eligibility included adult patients (≥45 years) with either an AF diagnosis (cases) or no diagnosis (controls) who had continuous enrolment in the respective database prior to the study period. Logistic regression was fitted to a binary response (yes/no) for AF diagnosis using pre-determined risk factors.ResultsAF patients were from Germany (n = 63,562), the UK (n = 42,652), France (n = 7,213), Australia (n = 2,753), and Belgium (n = 1,371). Cases were more likely to have hypertension or other cardiac conditions than controls in all validation datasets compared to the model development data. The area under the receiver operating characteristic (ROC) curve in the validation datasets ranged from 0.79 (Belgium) to 0.84 (Germany), comparable to the German study model, which had an area under the curve of 0.83. Most validation sets reported similar specificity at approximately 80% sensitivity, ranging from 67% (France) to 71% (United Kingdom). The positive predictive value (PPV) ranged from 2% (Belgium) to 16% (Germany), and the number needed to be screened was 50 in Belgium and 6 in Germany. The prevalence of AF varied widely between these datasets, which may be related to different coding practices. Low prevalence affected PPV, but not sensitivity, specificity, and ROC curves.ConclusionsAF risk prediction algorithms offer targeted ways to identify patients using electronic health records, which could improve screening number and the cost-effectiveness of AF screening if implemented in clinical practice.

Project description:BackgroundAtrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm.MethodsWe investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a random forest classifier and an expert-labeled training dataset of 2,532 recordings. The seven top-ranked algorithms are combined by using an optimized weighting approach.ResultsThe proposed fusion algorithm, when validated on a separate test dataset consisting of 4,644 recordings, resulted in an area under the receiver operating characteristic (ROC) curve of 0.99. The sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and F1-score of the proposed algorithm were 0.93, 0.97, 0.87, 0.99, and 0.90, respectively, which were all superior to any single algorithm or any previously published.ConclusionThis study demonstrates how a set of well-chosen independent algorithms and a voting mechanism to fuse the outputs of the algorithms, outperforms any single state-of-the-art algorithm for AFib detection. The proposed framework is a case study for the general notion of crowdsourcing between open-source algorithms in healthcare applications. The extension of this framework to similar applications may significantly save time, effort, and resources, by combining readily existing algorithms. It is also a step toward the democratization of artificial intelligence and its application in healthcare.

Project description:The mouse is a useful preclinical species for evaluating disease etiology due to the availability of a wide variety of genetically modified strains and the ability to perform disease-modifying manipulations. In order to establish an atrial filtration (AF) model in our laboratory, we profiled several commonly used murine AF models. We initially evaluated a pharmacological model of acute carbachol (CCh) treatment plus atrial burst pacing in C57BL/6 mice. In an effort to observe micro-reentrant circuits indicative of authentic AF, we employed optical mapping imaging in isolated mouse hearts. While CCh reduced atrial refractoriness and increased atrial tachyarrhythmia vulnerability, the left atrial (LA) excitation patterns were rather regular without reentrant circuits or wavelets. Therefore, the atrial tachyarrhythmia resembled high frequency atrial flutter, not typical AF per se. We next examined both a chronic angiotensin II (Ang II) infusion model and the surgical model of transverse aortic constriction (TAC), which have both been reported to induce atrial and ventricular structural changes that serve as a substrates for micro-reentrant AF. Although we observed some extent of atrial remodeling such as fibrosis or enlarged LA diameter, burst pacing-induced atrial tachyarrhythmia vulnerability did not differ from control mice in either model. This again suggested that an AF-like pathophysiology is difficult to demonstrate in the mouse. To continue searching for a valid murine AF model, we studied mice with a cardiac-specific deficiency (KO) in liver kinase B1 (Cardiac-LKB1), which has been reported to exhibit spontaneous AF. Indeed, the electrocardiograms (ECG) of conscious Cardiac-LKB1 KO mice exhibited no P waves and had irregular RR intervals, which are characteristics of AF. Histological evaluation of Cardiac-LKB1 KO mice revealed dilated and fibrotic atria, again consistent with AF. However, atrial electrograms and optical mapping revealed that electrical activity was limited to the sino-atrial node area with no electrical conduction into the atrial myocardium beyond. Thus, Cardiac-LKB1 KO mice have severe atrial myopathy or atrial standstill, but not AF. In summary, the atrial tachyarrhythmias we observed in the four murine models were distinct from typical human AF, which often exhibits micro- or macro-reentrant atrial circuits. Our results suggest that the four murine AF models we examined may not reflect human AF well, and raise a cautionary note for use of those murine models to study AF.

Project description:To develop electrocardiogram (ECG) tools to quantify the number of sources for atrial fibrillation (AF), i.e. spatially stable rotors and focal impulses, and whether they lie in right or left atrium. Intracardiac mapping has recently shown that paroxysmal and persistent AF is sustained by rotors or focal sources that are stable in location and thus targets for limited ablation [focal impulse and rotor modulation (FIRM)] to eliminate AF. Importantly, the numbers and locations of concurrent sources determine both the complexity of AF and the approach for ablation.In 36 AF patients (n = 29 persistent, 63 ± 9 years) in the CONventional ablation with or without Focal Impulse and Rotor Modulation (CONFIRM) trial, we developed phase lock (PL) to quantify spatial repeatability of ECG 'F-waves' between leads over time. Phase lock spectrally quantifies the angle ? between F-wave voltages in planes formed by ECG leads I, aVF, and V1 at successive points in time. We compared PL with ECG spectral dominant frequency (DF) and organizational index (OI) to characterize stable rotors and focal sources validated by intracardiac FIRM mapping. Focal impulse and rotor modulation ablation alone at ?3 sources acutely terminated and rendered AF non-inducible or substantially slowed AF in 31 of 36 patients. Receiver operating characteristics of PL for this endpoint had area under the curve (AUC) = 0.72, and the optimum cut-point (PL = 0.09) had 74% sensitivity, 92% positive predictive value (PPV). Receiver operating characteristics areas for OI and DF were 0.50 and 0.58, respectively. Left (n = 28) or right (n = 3) atrial sources were localized by PL with AUC = 0.85, sensitivity 100%, PPV 30%, and negative predictive value 100%. Spectral DF provided AUC = 0.79. Notably, PL did not comigrate with diagnosis of paroxysmal or persistent AF (P = NS), unlike ECG DF.The novel metric of ECG PL identifies patients with fewer (?3) or greater numbers of stable rotors/focal sources for AF, validated by intracardiac FIRM mapping, and localized them to right or left atria. These data open the possibility of using 12-lead ECG analyses to classify AF mechanistically and plan procedures for right- or left-sided FIRM ablation.

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Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models.

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