Use of photoplethysmography to predict mortality in intensive care units

Abstract

PurposeThe aim of this study was to evaluate and compare the capacity to predict hemodynamic variables obtained with photoplethysmography (PPG) and Acute Physiology and Chronic Health Evaluation (APACHE II) in patients hospitalized in the intensive care unit (ICU).Materials and methodsA prospective cohort study was conducted in the adult ICU of Hospital Nossa Senhora da Conceição, located in Tubarão, Santa Catarina, Brazil. The data collected included the diagnosis for hospitalization, age, gender, clinical or surgical profile, PPG pulse curve signal, and APACHE II score in the first 24 hours. A bivariate and a multivariate logistic regressions were performed, with death as an outcome. A mortality model using artificial neural networks (ANNs) was proposed.ResultsA total of 190 individuals were evaluated. Most of them were males (6:5), with a median age of 67 (54–75) years, and the main reasons for hospitalization were cardiovascular and neurological causes; half of them were surgical cases. APACHE II median score was 14 (8–19), with a median length of stay of 6 (3–15) days, and 28.4% of the patients died. The following factors were associated with mortality: age (OR=1.023; 95% CI 1.001–1.044; P=0.039), clinical profile (OR=5.481; 95% CI 2.646–11.354; P<0.001), APACHE II (OR=1.168; 95% CI 1.106–1.234; P<0.001), heart rate in the first 24 hours (OR=1.020; 95% CI 1.001–1.039; P=0.036), and time between the systolic and diastolic peak (∆T) intervals obtained with PPG (OR=0.989; 95% CI 0.979–0.998; P=0.015). Compared with the accuracy (area under the receiver-operating characteristic curve) 0.780 of APACHE II (95% CI 0.711–0.849; P<0.001), the multivariate logistic model showed a larger area of 0.858 (95% CI 0.803–0.914; P<0.001). In the model using ANNs, the accuracy was 0.895 (95% CI 0.851–0.940; P<0.001).ConclusionThe mortality models using variables obtained with PPG, with the inclusion of epidemiological parameters, are very accurate and, if associated to APACHE II, improve prognostic accuracy. The use of ANN was even more accurate, indicating that this tool is important to help in the clinical judgment of the intensivist.

The 2023 wearable photoplethysmography roadmap

“…Furthermore, recent developments in imaging PPG (iPPG), allow further opportunities for hospital monitoring as iPPG offers non-contact sensing using cameras (Kamshilin et al 2022 ). Current ongoing research for clinical monitoring covers a wide range of applications, including neonatal and paediatrics monitoring (Chung et al 2020 ), diabetes screening (Avram et al 2020 ), hospitalised coronavirus (COVID-19) patient health monitoring (Santos et al 2021 ), predicting deteriorations in patients with infectious disease (Tadesse et al 2020 ), assessment of hypertension and vascular ageing (Shin et al 2022 ), prediction of mortality and hospitalisation of patients (de Souza Kock and Marques 2018 ), detection of atrial fibrillation and prediction of cardiovascular risks (Aschbacher et al 2020 ), and sleep monitoring and detection of obstructive sleep apnoea (Radha et al 2021 ). Some notable examples are highlighted in figure 22 .…”