High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A collection of 750 data segments was made available for training and a set of 500 was held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program’s performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year’s Challenge.

Atrial fibrillation (AF) is an independent predictor of mortality after acute myocardial infarction (AMI). We analyzed the relationship between biomarkers linked to myocardial stretch [NT-pro-brain natriuretic peptide (NT-proBNP)], myocardial damage [Troponin-T (TnT)] and inflammation [high-sensitivity C-reactive protein (hsCRP)] and new-onset AF during AMI to identify patients at high risk for AF. In a prospective multicenter registry of AMI patients from (TRIUMPH), we measured NT-proBNP, TnT, and hsCRP in patients without a history of AF (N=2370). New-onset AF was defined as AF that occurred during the index hospitalization. Hierarchical multivariable logistic regression models were used to determine the association of biomarkers with new-onset AF, after adjusting for other covariates. New-onset AF was documented in 114 AMI patients (4.8%; mean age 58 years; 32% women). For each 2-fold increase in NT-proBNP, there was an 18% increase in the rate of AF (OR 1.18 95% CI 1.03-1.35; p<0.02). Similarly, for every 2-fold increase in hs-CRP, there was a 15% increase in the rate of AF (OR 1.15 95% CI 1.02-1.30; p=0.02). TnT was not independently associated with new-onset AF (OR 0.96 95% CI 0.85-1.07; p=0.3). NT-proBNP and hs-CRP were independently associated with new in-hospital AF after MI, in both men and women, irrespective of race. Our study suggests that markers of myocardial stretch and inflammation, but not the amount of myocardial necrosis, are important determinants of AF in the setting of AMI.

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High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this Challenge, the design of the Challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.