Automated cardiac arrest detection using a photoplethysmography wristband: algorithm performance in relation to sex, skin color type and arm hair density
Authors: Edgar R , Scholte N T B , Ebrahimkheil K , Brouwer M A , Beukema R J , Mafi-Rad M , Vernooy K , Yap S C , Ronner E , Van Mieghem N M , Boersma E , Cetinyurek-Yavuz A , Stas P C , Van Royen N , Bonnes J L 
Published: October 2024

Background

While survival from witnessed out-of-hospital cardiac arrest (OHCA) has improved, this is not the case for unwitnessed cardiac arrest as the event goes unnoticed. Automated cardiac arrest detection and alerting through a wristband is considered a promising technique also facilitating early assistance for this large group. In the DETECT-1 study, a photoplethysmography(PPG) algorithm for circulatory arrest detection was developed in patients with induced circulatory arrest. However, patient characteristics, such as skin color and arm hair density, can influence the accuracy of the PPG signal. Thus, we performed further analyses to study the effect of patient characteristics on the algorithm’s performance.

Purpose

To assess the performance of the developed PPG algorithm for cardiac arrest detection in relation to sex, skin color type and arm hair density.

Methods

Patients who underwent short-lasting circulatory arrest induction during transcatheter aortic valve implantation, ventricular tachycardia ablation or subcutaneous ICD implantation wore a PPG wristband during the procedure. Continuous ECG and invasive blood pressure recordings served as reference standards. A PPG algorithm for circulatory arrest detection was developed. The algorithm’s performance was assessed in subgroups based on sex, skin color type (according to the Fitzpatrick scale), and arm hair density. The sensitivity for the detection of circulatory arrest and false positive alarms were compared between the different subgroups.

Results

In total, 151 patients were included in the current analysis. Analyzed data consisted of 159 hours of PPG data including 176 induced circulatory arrest events. The population consisted of 57 (38%) females and 94 (62%) males. The sensitivity for circulatory arrest detection was 100% in the female group vs. 98% in the male group (p=ns); false positive alarms occurred in 4 females and 9 males, resulting in a positive predictive value (PPV) of 94% and 92% (p=ns), respectively. Within the skin color subgroup analysis, 114 (75%) patients had skin color type I or II, and 37 (25%) patients had a skin color type III, IV, V or VI. There were no differences in sensitivity (p=ns) and PPV (p=ns) in the skin color subgroup (table 1). The population consisted of 106 (71%) patients with nil or sparse arm hair density, 43 (29%) patients with moderate or dense arm hair density, and 2 not reported. There were no differences in sensitivity (p=ns) and PPV (p=ns) within the arm hair density subgroup (table 1).

Conclusions

Automated cardiac arrest detection using a smartwatch is feasible and its accuracy is not affected by sex, skin color type or arm hair density in this study. Further study is needed to assess performance of the algorithm in a larger sample size and a more diverse study population. If proven to be effective, automated cardiac arrest detection has the potential to improve survival from OHCA.Sensitivity/PPV for arrest detection