Addressing vital sign alarm fatigue using personalized alarm thresholds

Abstract

Alarm fatigue, a condition in which clinical staff become desensitized to alarms due to the high frequency of unnecessary alarms, is a major patient safety concern. Alarm fatigue is particularly prevalent in the pediatric setting, due to the high level of variation in vital signs with patient age. Existing studies have shown that the current default pediatric vital sign alarm thresholds are inappropriate, and lead to a larger than necessary alarm load. This study leverages a large database containing over 190 patient-years of heart rate data to accurately identify the 1st and 99th percentiles of an individual's heart rate on their first day of vital sign monitoring. These percentiles are then used as personalized vital sign thresholds, which are evaluated by comparing to non-default alarm thresholds used in practice, and by using the presence of major clinical events to infer alarm labels. Using the proposed personalized thresholds would decrease low and high heart rate alarms by up to 50% and 44% respectively, while maintaining sensitivity of 62% and increasing specificity to 49%. The proposed personalized vital sign alarm thresholds will reduce alarm fatigue, thus contributing to improved patient outcomes, shorter hospital stays, and reduced hospital costs.

Figure1:

Merging of RDE and STRIDE data using patient Medical Record Numbers (MRNs) where available, and using bed number and time where a unique patient was recorded as occupying the bed at the time of interest. Instances where the two mapping schemes gave different patients were removed.

Figure 2:

Schematic of the model training process. First the training set is used to fit loess models to the outcomes of mean heart rate and heart rate variance, using age as the only feature. The outputs of these models are used as the parameters of a lognormal model to estimate the 1^st and 99^th percentiles of heart rate. These resulting estimates are proposed as personalized thresholds: age only. The output of the loess model fitting to mean heart rate is also used as a feature in a pair of random forest models, one fit to the outcome of mean heart rate, and the other fit to variance of heart rate. These random forest models models also have gender, weight, race, ethnicity, hospital department, and admit diagnosis group (DRG) as additional features. The mean and variance of heart rate for each patient, as predicted by the random forest models, are used as the parameters of a lognormal model, allowing the 1^st and 99^th percentiles of heart rate to be estimated. The trained models are used to estimate the 1^st and 99^th percentiles of heart rate for patients in the test set, which can then be compared to the actual values observed over the first day of monitoring. The previously used original LPCH thresholds and age-grouped thresholds can also be compared to the observed 1^st and 99^th percentiles of heart rate.

Figure 3:

Error using mean and standard deviation of heart rate with lognormal assumption to find 1st (left) and 99^th (right) percentile of heart rate.

Figure 4:

Comparison of alarm thresholds with the 1^st (for low thresholds) and 99^th (for high thresholds)percentiles of heart rate observed over the first 24 hours of monitoring (circles), and comparison of alarm thresholds with the recorded non-default thresholds (triangles).