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Hinrichs N, Roeschl T, Lanmueller P, Balzer F, Eickhoff C, O'Brien B, Falk V, Meyer A. Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery. PLOS DIGITAL HEALTH 2024; 3:e0000598. [PMID: 39264979 PMCID: PMC11392423 DOI: 10.1371/journal.pdig.0000598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 07/30/2024] [Indexed: 09/14/2024]
Abstract
Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. Forecasting of vital parameters is a more general approach to ML-based patient monitoring, but the literature on its feasibility and robust benchmarks of achievable accuracy are scarce. We implemented five univariate statistical models (the naïve model, the Theta method, exponential smoothing, the autoregressive integrated moving average model, and an autoregressive single-layer neural network), two univariate neural networks (N-BEATS and N-HiTS), and two multivariate neural networks designed for sequential data (a recurrent neural network with gated recurrent unit, GRU, and a Transformer network) to produce forecasts for six vital parameters recorded at five-minute intervals during intensive care monitoring. Vital parameters were the diastolic, systolic, and mean arterial blood pressure, central venous pressure, peripheral oxygen saturation (measured by non-invasive pulse oximetry) and heart rate, and forecasts were made for 5 through 120 minutes into the future. Patients used in this study recovered from cardiothoracic surgery in an ICU. The patient cohort used for model development (n = 22,348) and internal testing (n = 2,483) originated from a heart center in Germany, while a patient sub-set from the eICU collaborative research database, an American multicenter ICU cohort, was used for external testing (n = 7,477). The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. With this study, we established an extensive set of benchmarks for forecast performance in the ICU. Our findings suggest that supplying physicians with short-term forecasts of vital parameters in the ICU is feasible, and that multivariate neural networks are most suited for the task due to their ability to learn patterns across thousands of patients.
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Affiliation(s)
- Nils Hinrichs
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Roeschl
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pia Lanmueller
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Eickhoff
- Institute for Bioinformatics and Medical Informatics, Eberhard-Karls-Universität Tübingen, Tübingen, Germany
| | - Benjamin O'Brien
- German Centre for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, Germany
- Department of Cardiac Anesthesiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Department of Perioperative Medicine, St Bartholomew's Hospital and Barts Heart Centre, London, United Kingdom
| | - Volkmar Falk
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, Germany
- Department of Health Sciences and Technology, Translational Cardiovascular Technologies, Eidgenössische Technische Hochschule Zürich, Zürich, Switzerland
| | - Alexander Meyer
- Department of Cardiothoracic and Vascular Surgery, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany
- Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Berlin, Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Technical University of Berlin, Berlin, Germany
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Karaman F, Genc A, Yerebakan Sen AN, Rashidi M, Yildirim G, Unsal Jafarov G, Acar R, Saygin Sahin B. Effects of love glove application on vital signs for COVID-19 patients in the intensive care unit. Nurs Open 2024; 11:e2106. [PMID: 38391100 PMCID: PMC10847619 DOI: 10.1002/nop2.2106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 03/11/2023] [Accepted: 01/11/2024] [Indexed: 02/24/2024] Open
Abstract
AIM To evaluate the effects of love glove application on vital signs for COVID-19 patients in the intensive care unit. DESIGN A single-group pretest-posttest quasi-experimental design was used. TREND Statement Checklist was followed during the present study. METHODS The study was conducted on 30 intubated/extubated adult patients. The gloves were filled with warm water and air to prevent pressure injuries. Then they were tied together and applied to both hands of the patient for 30 min. The patient's vital signs were recorded before and after the application. A Wilcoxon signed-rank test was performed. RESULTS It was determined that respiratory rate, systolic blood pressure, diastolic blood pressure and oxygen saturation were significantly affected after the application of the love glove. The application of love gloves is a cheap and non-pharmacological method with no side effects. PATIENT OR PUBLIC CONTRIBUTION Patients were involved in the design and conduct of this study.
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Affiliation(s)
- Funda Karaman
- Department of Nursing, Faculty of Health SciencesBiruni UniversityIstanbulTurkey
| | - Asli Genc
- Department of Nursing, School of NursingUfuk UniversityAnkaraTurkey
| | - Ayse Nur Yerebakan Sen
- Department of Surgical Nursing, Institute of Graduate StudiesIstanbul University‐CerrahpasaIstanbulTurkey
| | - Mahruk Rashidi
- Department of Nursing, Faculty of Health SciencesIstanbul Gelisim UniversityIstanbulTurkey
| | - Gulay Yildirim
- Department of Nursing, Kesan Hakki Yoruk School of HealthTrakya UniversityEdirneTurkey
| | | | | | - Buse Saygin Sahin
- Department of Mental Health and Diseases Nursing, Institute of Graduate StudiesIstanbul University‐CerrahpasaIstanbulTurkey
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Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters. SENSORS 2021; 21:s21248503. [PMID: 34960595 PMCID: PMC8705488 DOI: 10.3390/s21248503] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 12/26/2022]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.
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