Abstract 038: A Novel Histology Based Classification System to Identify Patients at Risk for Postoperative Atrial Fibrillation

2013 ◽  
Vol 113 (suppl_1) ◽  
Author(s):  
Mahek Mirza ◽  
Anton Strunets ◽  
Ekhson Holmuhamedov ◽  
Jasbir Sra ◽  
Paul H Werner ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
At Risk ◽  
Classification System ◽  
Area Under The Curve ◽  
Postoperative Atrial Fibrillation ◽  
Right Atrial ◽  
Class Iii ◽  
Atrial Fibrosis ◽  
Patients At Risk ◽  
The Impact

Postoperative atrial fibrillation (PoAF) is a common complication in up to 40% of patients after cardiac surgery, increasing morbidity, hospital stay and costs. The myocardial substrate underlying PoAF is not fully characterized. The objective was to assess the impact of atrial fibrosis on incident AF and define the fibrosis threshold level predictive of PoAF. Methods: Right atrial appendages removed from patients undergoing elective CABG with no history of AF or class III/IV heart failure were used to characterize the ratio of collagen to myocardium (Masson’s trichrome; NIH ImageJ software; Fig A), which was correlated with incident AF. Percentage burden of fibrosis predictive of PoAF with high sensitivity and specificity was determined by ROC curve. Results: Of 28 patients (67±10 years, 64% males), 15 had PoAF. There were no age, gender or comorbidity differences between groups. Compared to the group that remained in sinus rhythm, patients with PoAF had a significantly higher ratio of extracellular collagen to myocardium (45±16% vs. 5±4%, p <0.001; Fig B). A threshold ratio of 12.7% collagen to myocardium (ROC area under the curve 0.997; z statistic 137; P<0.0001) with 96% sensitivity and 97% specificity identified those with PoAF (Fig C). A classification system based on histological extent of atrial fibrosis is proposed for identifying patients at risk for PoAF (Fig D). Conclusion: Ongoing studies will confirm the predictive value of this new classification system for identifying the atrial substrate predisposing PoAF and correlate with preoperative cardiac imaging and circulatory serum biomarkers to provide a novel noninvasive tool to stratify patients at risk for PoAF.

scholarly journals Intraoperative Identification of Cardiac Patients at Risk to Develop Postoperative Atrial Fibrillation

1991 ◽  
Vol 213 (5) ◽  
pp. 388-392 ◽  
Author(s):  
JAMES E. LOWE ◽  
PAUL J. HENDRY ◽  
STEVEN C. HENDRICKSON ◽  
RANDALL WELLS
Keyword(s):  
Atrial Fibrillation ◽  
At Risk ◽  
Postoperative Atrial Fibrillation ◽  
Cardiac Patients ◽  
Patients At Risk

European Respiratory Society Statement on Sleep Apnoea, Sleepiness and Driving Risk

2020 ◽  
pp. 2001272
Author(s):  
Maria R Bonsignore ◽  
Winfried Randerath ◽  
Sofia Schiza ◽  
Johan Verbraecken ◽  
Mark W Elliott ◽  
...  
Keyword(s):  
At Risk ◽  
Motor Vehicle ◽  
Task Force ◽  
Present Report ◽  
Sleep Apnoea ◽  
Accident Risk ◽  
European Respiratory Society ◽  
Obstructive Sleep ◽  
Patients At Risk ◽  
The Impact

Obstructive sleep apnoea (OSA) is highly prevalent and is a recognised risk factor for motor vehicle accidents (MVA). Effective treatment with continuous positive airway pressure (CPAP) has been associated with a normalisation of this increased accident risk. Thus, many jurisdictions have introduced regulations restricting the ability of OSA patients from driving until effectively treated. However, uncertainty prevails regarding the relative importance of OSA severity determined by the apnoea-hypopnoea frequency per hour and the degree of sleepiness in determining accident risk. Furthermore, the identification of subjects at risk for OSA and/or accident risk remains elusive. The introduction of official European regulations regarding fitness to drive prompted the European Respiratory Society to establish a Task Force to address the topic of sleep apnoea, sleepiness and driving with a view to providing an overview to clinicians involved in treating patients with the disorder. The present report evaluates the epidemiology of MVA in patients with OSA, the mechanisms involved in this association, the role of screening questionnaires, driving simulators and other techniques to evaluate sleepiness and/or impaired vigilance, the impact of treatment on MVA risk in affected drivers, and highlights the evidence gaps regarding the identification of OSA patients at risk for MVA.

Natriuretic peptides and right atrial fibrosis in patients with paroxysmal versus persistent atrial fibrillation

Peptides ◽  
2010 ◽  
Vol 31 (8) ◽  
pp. 1531-1539 ◽  
Author(s):  
Hailong Cao ◽  
Lei Xue ◽  
Yanhu Wu ◽  
Hongtai Ma ◽  
Liang Chen ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Natriuretic Peptides ◽  
Persistent Atrial Fibrillation ◽  
Right Atrial ◽  
Atrial Fibrosis

scholarly journals MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

2020 ◽  
Vol 9 (2) ◽  
pp. 343 ◽  
Author(s):  
Arash Kia ◽  
Prem Timsina ◽  
Himanshu N. Joshi ◽  
Eyal Klang ◽  
Rohit R. Gupta ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Area Under The Curve ◽  
Learning Model ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Support Vector ◽  
Adult Age ◽  
Machine Learning Model ◽  
Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

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