False alarms and the positive predictive value of smartphone-based hyperacuity home monitoring for the progression of macular disease: a prospective cohort study

Abstract Background Home monitoring of hyperacuity allows early detection of progression in exudative neovascular age-related macular degeneration (nvAMD) and diabetic macular oedema (DMO). However, false alarms may pose a significant burden to both patients and healthcare professionals alike. Purpose To assess the false alarm rate and positive predictive value of smartphone-based home monitoring of nvAMD and DMO. Methods Patients treated with anti-angiogenic therapy in a pro re nata scheme for nvAMD or DMO at the Medical Retina service (Lucerne, Switzerland) between March and June 2016 were included in this prospective cohort study. The home monitoring test Alleye (Oculocare Ltd, Switzerland) provided a session score from 0–100 in addition to a traffic-light system feedback via the smartphone application. Three consecutive “red” scores were considered as a positive test or alarm signal. Specificity, 1-specificity (false alarm rate) and the predictive value for optical coherence tomography-based disease progression were analysed. Results 73 eyes of 56 patients performed 2258 tests in 222 “follow-up periods”. Progression was observed in 141 periods (63.5%). The specificity of the test was 93.8% (95% CI: 86.2–98.0%), the false alarm rate 6.1% (95% CI: 2.0–13.8%), and the positive predictive value 80.0% (95% CI: 59.3–93.2%) for the detection of progression. Conclusion False alarm rates for the detection of progression in macular disease via home monitoring is low. These findings suggest that home monitoring may be a useful adjunct for remote management of nvAMD and DMO.

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Correction: False alarms and the positive predictive value of smartphone-based hyperacuity home monitoring for the progression of macular disease: a prospective cohort study

Eye ◽

10.1038/s41433-021-01512-2 ◽

2021 ◽

Author(s):

Livia Faes ◽

Meriam Islam ◽

Lucas M. Bachmann ◽

Kenny R. Lienhard ◽

Martin K. Schmid ◽

...

Keyword(s):

Cohort Study ◽

Positive Predictive Value ◽

Prospective Cohort Study ◽

Predictive Value ◽

Prospective Cohort ◽

Home Monitoring ◽

False Alarms ◽

Macular Disease

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Correlating data from different sensors to increase the positive predictive value of alarms: an empiric assessment

F1000Research ◽

10.12688/f1000research.1-45.v1 ◽

2012 ◽

Vol 1 ◽

pp. 45 ◽

Cited By ~ 9

Author(s):

Yuval Bitan ◽

Michael F O’Connor

Keyword(s):

Positive Predictive Value ◽

False Alarm ◽

False Alarm Rate ◽

Predictive Value ◽

Left Ventricular ◽

False Alarms ◽

Expert Clinician ◽

Correlated Information ◽

Sensitivity Specificity ◽

High False Alarm Rate

Objectives: Alarm fatigue from high false alarm rate is a well described phenomenon in the intensive care unit (ICU). Progress to further reduce false alarms must employ a new strategy. Highly sensitive alarms invariably have a very high false alarm rate. Clinically useful alarms have a high Positive-Predictive Value. Our goal is to demonstrate one approach to suppressing false alarms using an algorithm that correlates information across sensors and replicates the ways that human evaluators discriminate artifact from real signal.Methods: After obtaining IRB approval and waiver of informed consent, a set of definitions, (hypovolemia, left ventricular shock, tamponade, hemodynamically significant ventricular tachycardia, and hemodynamically significant supraventricular tachycardia), were installed in the monitors in a 10 bed cardiothoracic ICU and evaluated over an 85 day study period. The logic of the algorithms was intended to replicate the logic of practitioners, and correlated information across sensors in a way similar to that used by practitioners. The performance of the alarms was evaluated via a daily interview with the ICU attending and review of the tracings recorded over the previous 24 hours in the monitor. True alarms and false alarms were identified by an expert clinician, and the performance of the algorithms evaluated using the standard definitions of sensitivity, specificity, positive predictive value, and negative predictive value.Results: Between 1 and 221 instances of defined events occurred over the duration of the study, and the positive predictive value of the definitions varied between 4.1% and 84%.Conclusions: Correlation of information across alarms can suppress artifact, increase the positive predictive value of alarms, and can employ more sophisticated definitions of alarm events than present single-sensor based systems.

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