Use of photoplethysmography to predict mortality in intensive care units
Kelser de Souza Kock,1 Jefferson Luiz Brum Marques2 1Graduate Program in Medical Sciences, Federal University of Santa Catarina, Florianópolis, SC, Brazil; 2Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Flor...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2018-10-01
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| Series: | Vascular Health and Risk Management |
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| Online Access: | https://www.dovepress.com/use-of-photoplethysmography-to-predict-mortality-in-intensive-care-uni-peer-reviewed-article-VHRM |
| _version_ | 1818158920338767872 |
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| author | Kock KS Marques JLB |
| author_facet | Kock KS Marques JLB |
| author_sort | Kock KS |
| collection | DOAJ |
| description | Kelser de Souza Kock,1 Jefferson Luiz Brum Marques2 1Graduate Program in Medical Sciences, Federal University of Santa Catarina, Florianópolis, SC, Brazil; 2Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil Purpose: The 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 methods: A 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.Results: A 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).Conclusion: The 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. Keywords: photoplethysmography, intensive care units, computer-aided signal processing, pulse wave analysis, prognosis, hemodynamic monitoring |
| first_indexed | 2024-12-11T15:37:45Z |
| format | Article |
| id | doaj.art-46dde4b7a1124e00b296807d6c489c3b |
| institution | Directory Open Access Journal |
| issn | 1178-2048 |
| language | English |
| last_indexed | 2024-12-11T15:37:45Z |
| publishDate | 2018-10-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Vascular Health and Risk Management |
| spelling | doaj.art-46dde4b7a1124e00b296807d6c489c3b2022-12-22T00:59:54ZengDove Medical PressVascular Health and Risk Management1178-20482018-10-01Volume 1431132041936Use of photoplethysmography to predict mortality in intensive care unitsKock KSMarques JLBKelser de Souza Kock,1 Jefferson Luiz Brum Marques2 1Graduate Program in Medical Sciences, Federal University of Santa Catarina, Florianópolis, SC, Brazil; 2Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil Purpose: The 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 methods: A 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.Results: A 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).Conclusion: The 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. Keywords: photoplethysmography, intensive care units, computer-aided signal processing, pulse wave analysis, prognosis, hemodynamic monitoringhttps://www.dovepress.com/use-of-photoplethysmography-to-predict-mortality-in-intensive-care-uni-peer-reviewed-article-VHRMPhotoplethysmographyIntensive Care UnitsComputer-Aided Signal ProcessingPulse Wave AnalysisPrognosisHemodynamic monitoring |
| spellingShingle | Kock KS Marques JLB Use of photoplethysmography to predict mortality in intensive care units Vascular Health and Risk Management Photoplethysmography Intensive Care Units Computer-Aided Signal Processing Pulse Wave Analysis Prognosis Hemodynamic monitoring |
| title | Use of photoplethysmography to predict mortality in intensive care units |
| title_full | Use of photoplethysmography to predict mortality in intensive care units |
| title_fullStr | Use of photoplethysmography to predict mortality in intensive care units |
| title_full_unstemmed | Use of photoplethysmography to predict mortality in intensive care units |
| title_short | Use of photoplethysmography to predict mortality in intensive care units |
| title_sort | use of photoplethysmography to predict mortality in intensive care units |
| topic | Photoplethysmography Intensive Care Units Computer-Aided Signal Processing Pulse Wave Analysis Prognosis Hemodynamic monitoring |
| url | https://www.dovepress.com/use-of-photoplethysmography-to-predict-mortality-in-intensive-care-uni-peer-reviewed-article-VHRM |
| work_keys_str_mv | AT kockks useofphotoplethysmographytopredictmortalityinintensivecareunits AT marquesjlb useofphotoplethysmographytopredictmortalityinintensivecareunits |