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• W2322684862 abstract "Introduction: Atrial fibrillation (AF) is the second most common cardiac arrhythmia in the intensive care unit (ICU) and is associated with significant morbidity and mortality. Early predictors of AF can guide treatment and monitoring for patients admitted to the ICU. Hypothesis: Sophisticated computational tools can be used to predict AF in a fully-automated manner using vital signs data collected early (e.g., first 8 hours) during ICU admission. Methods: The complete census of patients admitted to the medical, surgical, cardiac and neurological ICUs at a single, tertiary care center between 1/1/11 and 5/19/12 was evaluated retrospectively. Heart rate, respiration rate, oxygen saturation, and systolic, diastolic and mean arterial blood pressures were sampled every minute within the first 8 hours of admission when the signal was present. AF was recognized through automated rhythm analysis and verified by a nurse. Patients were stratified by data availability (q1 minute) as >90%, >50%, all patients. An area under the receiver operating characteristic curve (AUROC) maximizing support vector machine (SVM) model was trained using data collected before 12/14/11 to predict AF using summary features derived from the signals, in addition to demographic data (age, race, and gender). The model was validated on data from patients admitted after 12/14/11. Results: The study cohort comprised 5,145 patients [mean age of 60 (+/- 15.6); 55.6% male]. The incidence of AF was 9.3% over an average ICU stay of 8.68 days. The overall mortality rate was 12.6% with a rate of 23.6% in patients experiencing AF. The AUROC of the SVM model based on data availability was: All patients 0.713, >50% data 0.766, >90% data 0.861, with a sensitivity of 0.923 in this final group. Conclusions: Computational algorithms can be applied to data already collected routinely from patients early during ICU admission to robustly predict AF during subsequent admission. AUROC needs to be greater than 0.7 and in this case our early work exceeds 0.86 when sufficient data is available. This represents a promising approach to predict new onset atrial fibrillation and the hope to label those most likely to benefit from prophylactic measures." @default.
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• W2322684862 date "2012-12-01" @default.
• W2322684862 modified "2023-10-18" @default.
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• W2322684862 doi "https://doi.org/10.1097/01.ccm.0000424480.02801.f0" @default.
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