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Keim-Malpass J, Moorman LP, Moorman JR, Hamil S, Yousevfand G, Monfredi OJ, Ratcliffe SJ, Krahn KN, Jones MK, Clark MT, Bourque JM. Prospective validation of clinical deterioration predictive models prior to intensive care unit transfer among patients admitted to acute care cardiology wards. Physiol Meas 2024; 45:065004. [PMID: 38772399 DOI: 10.1088/1361-6579/ad4e90] [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: 01/03/2024] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Objective. Very few predictive models have been externally validated in a prospective cohort following the implementation of an artificial intelligence analytic system. This type of real-world validation is critically important due to the risk of data drift, or changes in data definitions or clinical practices over time, that could impact model performance in contemporaneous real-world cohorts. In this work, we report the model performance of a predictive analytics tool developed before COVID-19 and demonstrate model performance during the COVID-19 pandemic.Approach. The analytic system (CoMETⓇ, Nihon Kohden Digital Health Solutions LLC, Irvine, CA) was implemented in a randomized controlled trial that enrolled 10 422 patient visits in a 1:1 display-on display-off design. The CoMET scores were calculated for all patients but only displayed in the display-on arm. Only the control/display-off group is reported here because the scores could not alter care patterns.Main results.Of the 5184 visits in the display-off arm, 311 experienced clinical deterioration and care escalation, resulting in transfer to the intensive care unit, primarily due to respiratory distress. The model performance of CoMET was assessed based on areas under the receiver operating characteristic curve, which ranged from 0.725 to 0.737.Significance.The models were well-calibrated, and there were dynamic increases in the model scores in the hours preceding the clinical deterioration events. A hypothetical alerting strategy based on a rise in score and duration of the rise would have had good performance, with a positive predictive value more than 10-fold the event rate. We conclude that predictive statistical models developed five years before study initiation had good model performance despite the passage of time and the impact of the COVID-19 pandemic.
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Affiliation(s)
- Jessica Keim-Malpass
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Pediatrics, Hematology-Oncology Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Liza P Moorman
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Susan Hamil
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Gholamreza Yousevfand
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Oliver J Monfredi
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Sarah J Ratcliffe
- Department of Public Health Sciences, Biostatistics Division, University of Virginia, Charlottesville, VA, United States of America
| | - Katy N Krahn
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Marieke K Jones
- Department of Public Health Sciences, Biostatistics Division, University of Virginia, Charlottesville, VA, United States of America
| | - Matthew T Clark
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States of America
| | - Jamieson M Bourque
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
- Department of Internal Medicine, Cardiovascular Division, School of Medicine, University of Virginia, Charlottesville, VA, United States of America
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Leenen JP, Schoonhoven L, Patijn GA. Wearable wireless continuous vital signs monitoring on the general ward. Curr Opin Crit Care 2024; 30:275-282. [PMID: 38690957 DOI: 10.1097/mcc.0000000000001160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
PURPOSE OF REVIEW Wearable wireless sensors for continuous vital signs monitoring (CVSM) offer the potential for early identification of patient deterioration, especially in low-intensity care settings like general wards. This study aims to review advances in wearable CVSM - with a focus on the general ward - highlighting the technological characteristics of CVSM systems, user perspectives and impact on patient outcomes by exploring recent evidence. RECENT FINDINGS The accuracy of wearable sensors measuring vital signs exhibits variability, especially notable in ambulatory patients within hospital settings, and standard validation protocols are lacking. Usability of CMVS systems is critical for nurses and patients, highlighting the need for easy-to-use wearable sensors, and expansion of the number of measured vital signs. Current software systems lack integration with hospital IT infrastructures and workflow automation. Imperative enhancements involve nurse-friendly, less intrusive alarm strategies, and advanced decision support systems. Despite observed reductions in ICU admissions and Rapid Response Team calls, the impact on patient outcomes lacks robust statistical significance. SUMMARY Widespread implementation of CVSM systems on the general ward and potentially outside the hospital seems inevitable. Despite the theoretical benefits of CVSM systems in improving clinical outcomes, and supporting nursing care by optimizing clinical workflow efficiency, the demonstrated effects in clinical practice are mixed. This review highlights the existing challenges related to data quality, usability, implementation, integration, interpretation, and user perspectives, as well as the need for robust evidence to support their impact on patient outcomes, workflow and cost-effectiveness.
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Affiliation(s)
- Jobbe Pl Leenen
- Connected Care Centre, Isala, Zwolle
- Research Group IT Innovations in Healthcare, Windesheim University of Applied Sciences, Zwolle
| | - Lisette Schoonhoven
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
- School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK
| | - Gijs A Patijn
- Connected Care Centre, Isala, Zwolle
- Department of Surgery, Isala, Zwolle, The Netherlands
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Chae S, Davoudi A, Song J, Evans L, Hobensack M, Bowles KH, McDonald MV, Barrón Y, Rossetti SC, Cato K, Sridharan S, Topaz M. Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model. J Am Med Inform Assoc 2023; 30:1622-1633. [PMID: 37433577 PMCID: PMC10531127 DOI: 10.1093/jamia/ocad129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/24/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
OBJECTIVES Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows. MATERIALS AND METHODS We used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC). RESULTS The best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69). DISCUSSION AND CONCLUSION This prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.
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Affiliation(s)
- Sena Chae
- College of Nursing, The University of Iowa, Iowa City, Iowa, USA
| | - Anahita Davoudi
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Jiyoun Song
- Columbia University School of Nursing, New York City, New York, USA
| | - Lauren Evans
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Mollie Hobensack
- Columbia University School of Nursing, New York City, New York, USA
| | - Kathryn H Bowles
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
- Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | | | - Yolanda Barrón
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Sarah Collins Rossetti
- Columbia University School of Nursing, New York City, New York, USA
- Department of Biomedical Informatics, Columbia University, New York City, New York, USA
| | - Kenrick Cato
- Department of Family and Community Health, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA
| | - Sridevi Sridharan
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
| | - Maxim Topaz
- Center for Home Care Policy & Research, VNS Health, New York, New York, USA
- Columbia University School of Nursing, New York City, New York, USA
- Data Science Institute, Columbia University, New York City, New York, USA
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Douglas MJ, Callcut R, Celi LA, Merchant N. Interpretation and Use of Applied/Operational Machine Learning and Artificial Intelligence in Surgery. Surg Clin North Am 2023; 103:317-333. [PMID: 36948721 DOI: 10.1016/j.suc.2022.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.
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Affiliation(s)
- Molly J Douglas
- Department of Surgery, University of Arizona, 1501 N Campbell Avenue, Tucson, AZ 85724, USA.
| | - Rachel Callcut
- Trauma, Acute Care Surgery and Surgical Critical Care, University of California, Davis, 2335 Stockton Boulevard, Sacramento, CA 95817, USA. https://twitter.com/callcura
| | - Leo Anthony Celi
- Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA; Beth Israel Deaconess Medical Center. https://twitter.com/MITCriticalData
| | - Nirav Merchant
- Data Science Institute, University of Arizona, 1230 North Cherry Avenue, Tucson, AZ 85721, USA
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Hajipour M, Baumann B, Azarbarzin A, Allen AH, Liu Y, Fels S, Goodfellow S, Singh A, Jen R, Ayas NT. Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review. J Clin Sleep Med 2023; 19:225-242. [PMID: 36106591 PMCID: PMC9892740 DOI: 10.5664/jcsm.10298] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/09/2022] [Accepted: 09/12/2022] [Indexed: 02/04/2023]
Abstract
STUDY OBJECTIVES Polysomnograms (PSGs) collect a plethora of physiologic signals across the night. However, few of these PSG data are incorporated into standard reports, and hence, ultimately, under-utilized in clinical decision making. Recently, there has been substantial interest regarding novel alternative PSG metrics that may help to predict obstructive sleep apnea (OSA)-related outcomes better than standard PSG metrics such as the apnea-hypopnea index. We systematically review the recent literature for studies that examined the use of alternative PSG metrics in the context of OSA and their association with health outcomes. METHODS We systematically searched EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews for studies published between 2000 and 2022 for those that reported alternative metrics derived from PSG in adults and related them to OSA-related outcomes. RESULTS Of the 186 initial studies identified by the original search, data from 31 studies were ultimately included in the final analysis. Numerous metrics were identified that were significantly related to a broad range of outcomes. We categorized the outcomes into 2 main subgroups: (1) cardiovascular/metabolic outcomes and mortality and (2) cognitive function- and vigilance-related outcomes. Four general categories of alternative metrics were identified based on signals analyzed: autonomic/hemodynamic metrics, electroencephalographic metrics, oximetric metrics, and respiratory event-related metrics. CONCLUSIONS We have summarized the current landscape of literature for alternative PSG metrics relating to risk prediction in OSA. Although promising, further prospective observational studies are needed to verify findings from other cohorts, and to assess the clinical utility of these metrics. CITATION Hajipour M, Baumann B, Azarbarzin A, et al. Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review. J Clin Sleep Med. 2023;19(2):225-242.
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Affiliation(s)
- Mohammadreza Hajipour
- Department of Experimental Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - Brett Baumann
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - Ali Azarbarzin
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts
| | - A.J. Hirsch Allen
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - Yu Liu
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
- Department of Pharmacology, Shanxi Medical University, Taiyuan, China
| | - Sidney Fels
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Sebastian Goodfellow
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Amrit Singh
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Rachel Jen
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
| | - Najib T. Ayas
- Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, Canada
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van der Stam JA, Mestrom EHJ, Nienhuijs SW, de Hingh IHJT, Boer AK, van Riel NAW, de Groot KTJ, Verhaegh W, Scharnhorst V, Bouwman RA. A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients. Eur J Surg Oncol 2023; 49:278-284. [PMID: 36085116 DOI: 10.1016/j.ejso.2022.08.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/30/2022] [Accepted: 08/26/2022] [Indexed: 01/24/2023] Open
Abstract
INTRODUCTION The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS). MATERIALS AND METHODS The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch. MAIN RESULTS A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed. CONCLUSIONS The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.
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Affiliation(s)
- Jonna A van der Stam
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Laboratory, Catharina Hospital, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands.
| | - Eveline H J Mestrom
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, the Netherlands
| | - Simon W Nienhuijs
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands
| | - Ignace H J T de Hingh
- Department of Surgery, Catharina Hospital, Eindhoven, the Netherlands; GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Arjen-Kars Boer
- Clinical Laboratory, Catharina Hospital, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Koen T J de Groot
- Department of AI, Data Science & Digital Twin, Philips Research, Eindhoven, the Netherlands
| | - Wim Verhaegh
- Department of AI, Data Science & Digital Twin, Philips Research, Eindhoven, the Netherlands
| | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Clinical Laboratory, Catharina Hospital, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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Rappold BA. Review of the Use of Liquid Chromatography-Tandem Mass Spectrometry in Clinical Laboratories: Part II-Operations. Ann Lab Med 2022; 42:531-557. [PMID: 35470272 PMCID: PMC9057814 DOI: 10.3343/alm.2022.42.5.531] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/08/2022] [Accepted: 04/13/2022] [Indexed: 11/19/2022] Open
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is increasingly utilized in clinical laboratories because it has advantages in terms of specificity and sensitivity over other analytical technologies. These advantages come with additional responsibilities and challenges given that many assays and platforms are not provided to laboratories as a single kit or device. The skills, staff, and assays used in LC-MS/MS are internally developed by the laboratory, with relatively few exceptions. Hence, a laboratory that deploys LC-MS/MS assays must be conscientious of the practices and procedures adopted to overcome the challenges associated with the technology. This review discusses the post-development landscape of LC-MS/MS assays, including validation, quality assurance, operations, and troubleshooting. The content knowledge of LC-MS/MS users is quite broad and deep and spans multiple scientific fields, including biology, clinical chemistry, chromatography, engineering, and MS. However, there are no formal academic programs or specific literature to train laboratory staff on the fundamentals of LC-MS/MS beyond the reports on method development. Therefore, depending on their experience level, some readers may be familiar with aspects of the laboratory practices described herein, while others may be not. This review endeavors to assemble aspects of LC-MS/MS operations in the clinical laboratory to provide a framework for the thoughtful development and execution of LC-MS/MS applications.
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Affiliation(s)
- Brian A Rappold
- Laboratory Corporation of America Holdings, Research Triangle Park, NC, USA
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8
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Flick M, Bergholz A, Sierzputowski P, Vistisen ST, Saugel B. What is new in hemodynamic monitoring and management? J Clin Monit Comput 2022; 36:305-313. [PMID: 35394584 PMCID: PMC9122861 DOI: 10.1007/s10877-022-00848-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/10/2022] [Indexed: 01/20/2023]
Affiliation(s)
- Moritz Flick
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Bergholz
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Pawel Sierzputowski
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon T Vistisen
- Department of Anaesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark
| | - Bernd Saugel
- Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Outcomes Research Consortium, Cleveland, Ohio, USA.
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Kant N, Peters GM, Voorthuis BJ, Groothuis-Oudshoorn CGM, Koning MV, Witteman BPL, Rinia-Feenstra M, Doggen CJM. Continuous vital sign monitoring using a wearable patch sensor in obese patients: a validation study in a clinical setting. J Clin Monit Comput 2021; 36:1449-1459. [PMID: 34878613 DOI: 10.1007/s10877-021-00785-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/27/2021] [Indexed: 10/19/2022]
Abstract
Our aim was to determine the agreement of heart rate (HR) and respiratory rate (RR) measurements by the Philips Biosensor with a reference monitor (General Electric Carescape B650) in severely obese patients during and after bariatric surgery. Additionally, sensor reliability was assessed. Ninety-four severely obese patients were monitored with both the Biosensor and reference monitor during and after bariatric surgery. Agreement was defined as the mean absolute difference between both monitoring devices. Bland Altman plots and Clarke Error Grid analysis (CEG) were used to visualise differences. Sensor reliability was reflected by the amount, duration and causes of data loss. The mean absolute difference for HR was 1.26 beats per minute (bpm) (SD 0.84) during surgery and 1.84 bpm (SD 1.22) during recovery, and never exceeded the 8 bpm limit of agreement. The mean absolute difference for RR was 1.78 breaths per minute (brpm) (SD 1.90) during surgery and 4.24 brpm (SD 2.75) during recovery. The Biosensor's RR measurements exceeded the 2 brpm limit of agreement in 58% of the compared measurements. Averaging 15 min of measurements for both devices improved agreement. CEG showed that 99% of averaged RR measurements resulted in adequate treatment. Data loss was limited to 4.5% of the total duration of measurements for RR. No clear causes for data loss were found. The Biosensor is suitable for remote monitoring of HR, but not RR in morbidly obese patients. Future research should focus on improving RR measurements, the interpretation of continuous data, and development of smart alarm systems.
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Affiliation(s)
- Niels Kant
- Department of Anesthesiology and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | - Guido M Peters
- Scientific Bureau, Rijnstate Hospital, Rijnstate Research Center, Wagnerlaan 55, PO Box 9555, 6800 TA, Arnhem, The Netherlands.,Technical Medical Centre, Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | - Brenda J Voorthuis
- Technical Medical Centre, Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | | | - Mark V Koning
- Department of Anesthesiology and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | | | - Myra Rinia-Feenstra
- Department of Anesthesiology and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | - Carine J M Doggen
- Scientific Bureau, Rijnstate Hospital, Rijnstate Research Center, Wagnerlaan 55, PO Box 9555, 6800 TA, Arnhem, The Netherlands. .,Technical Medical Centre, Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands.
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10
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Diane C, Sebastian D, Reegan K, Alison Y, Michelle M. Identification of nutritional risk in the acute care setting: progress towards a practice and evidence informed systems level approach. BMC Health Serv Res 2021; 21:1288. [PMID: 34847947 PMCID: PMC8638168 DOI: 10.1186/s12913-021-07299-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/15/2021] [Indexed: 01/09/2023] Open
Abstract
Background To improve nutritional assessment and care pathways in the acute care setting, it is important to understand the indicators that may predict nutritional risk. Informed by a review of systematic reviews, this project engaged stakeholders to prioritise and reach consensus on a list of evidence based and clinically contextualised indicators for identifying malnutrition risk in the acute care setting. Methods A modified Delphi approach was employed which consisted of four rounds of consultation with 54 stakeholders and 10 experts to reach consensus and refine a list of 57 risk indicators identified from a review of systematic reviews. Weighted mean and variance scores for each indicator were evaluated. Consistency was tested with intra class correlation coefficient. Cronbach's alpha was used to determine the reliability of the indicators. The final list of indicators was subject to Cronbach’s alpha and exploratory principal component analysis. Results Fifteen indicators were considered to be the most important in identifying nutritional risk. These included difficulty self-feeding, polypharmacy, surgery and impaired gastro-intestinal function. There was 82% agreement for the final 15 indicators that they collectively would predict malnutrition risk in hospital inpatients. Conclusion The 15 indicators identified are supported by evidence and are clinically informed. This represents an opportunity for translation into a novel and automated systems level approach for identifying malnutrition risk in the acute care setting. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-021-07299-y.
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Affiliation(s)
- Chamberlain Diane
- Caring Futures Institute, College of Nursing and Health Sciences, GPO Box 2100, Adelaide, 5001, Australia.
| | - Doeltgen Sebastian
- Caring Futures Institute, College of Nursing and Health Sciences, GPO Box 2100, Adelaide, 5001, Australia
| | - Knowles Reegan
- College of Nursing and Health Sciences, GPO Box 2100, Adelaide, 5001, Australia
| | - Yaxley Alison
- Caring Futures Institute, College of Nursing and Health Sciences, GPO Box 2100, Adelaide, 5001, Australia
| | - Miller Michelle
- Caring Futures Institute, College of Nursing and Health Sciences, GPO Box 2100, Adelaide, 5001, Australia
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11
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Keim-Malpass J, Moorman LP. Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond. INTERNATIONAL JOURNAL OF NURSING STUDIES ADVANCES 2021; 3:100019. [PMID: 33426534 PMCID: PMC7781904 DOI: 10.1016/j.ijnsa.2021.100019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 12/16/2020] [Accepted: 12/29/2020] [Indexed: 12/23/2022] Open
Abstract
As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.
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Affiliation(s)
- Jessica Keim-Malpass
- School of Nursing, Department of Acute and Specialty Care, University of Virginia, Charlottesville, VA, USA,Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, USA,Corresponding author at: University of Virginia School of Nursing, P.O. Box 800782, Charlottesville, VA 22908 USA
| | - Liza P. Moorman
- AMP3D: Advanced Medical Predictive Devices, Diagnostics and Displays, Inc., Charlottesville, VA, USA
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12
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Mann KD, Good NM, Fatehi F, Khanna S, Campbell V, Conway R, Sullivan C, Staib A, Joyce C, Cook D. Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting. J Med Internet Res 2021; 23:e28209. [PMID: 34591017 PMCID: PMC8517822 DOI: 10.2196/28209] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 07/14/2021] [Accepted: 07/27/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. METHODS An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. RESULTS A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. CONCLUSIONS Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.
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Affiliation(s)
- Kay D Mann
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Norm M Good
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Farhad Fatehi
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Sankalp Khanna
- The Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Australia
| | - Victoria Campbell
- Sunshine Coast University Hospital, Sunshine Coast Hospital and Health Service, Birtinya, Australia.,Clinical Excellence Queensland, Queensland Health, Queensland, Australia.,School of Medicine, Griffith University, Nathan Campas, Australia
| | - Roger Conway
- Sunshine Coast University Hospital, Sunshine Coast Hospital and Health Service, Birtinya, Australia
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia.,Metro North Hospital and Health Service, Brisbane, Australia
| | - Andrew Staib
- Clinical Excellence Queensland, Queensland Health, Queensland, Australia.,Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Christopher Joyce
- Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - David Cook
- Princess Alexandra Hospital, Metro South Hospital and Health Service, Brisbane, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, Australia.,School of Computer Science, Faculty of Science, Queensland University of Technology, Brisbane, Australia
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Keim-Malpass J, Ratcliffe SJ, Moorman LP, Clark MT, Krahn KN, Monfredi OJ, Hamil S, Yousefvand G, Moorman JR, Bourque JM. Predictive Monitoring-Impact in Acute Care Cardiology Trial (PM-IMPACCT): Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2021; 10:e29631. [PMID: 34043525 PMCID: PMC8285742 DOI: 10.2196/29631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/23/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Patients in acute care wards who deteriorate and are emergently transferred to intensive care units (ICUs) have poor outcomes. Early identification of patients who are decompensating might allow for earlier clinical intervention and reduced morbidity and mortality. Advances in bedside continuous predictive analytics monitoring (ie, artificial intelligence [AI]-based risk prediction) have made complex data easily available to health care providers and have provided early warning of potentially catastrophic clinical events. We present a dynamic, visual, predictive analytics monitoring tool that integrates real-time bedside telemetric physiologic data into robust clinical models to estimate and communicate risk of imminent events. This tool, Continuous Monitoring of Event Trajectories (CoMET), has been shown in retrospective observational studies to predict clinical decompensation on the acute care ward. There is a need to more definitively study this advanced predictive analytics or AI monitoring system in a prospective, randomized controlled, clinical trial. OBJECTIVE The goal of this trial is to determine the impact of an AI-based visual risk analytic, CoMET, on improving patient outcomes related to clinical deterioration, response time to proactive clinical action, and costs to the health care system. METHODS We propose a cluster randomized controlled trial to test the impact of using the CoMET display in an acute care cardiology and cardiothoracic surgery hospital floor. The number of admissions to a room undergoing cluster randomization was estimated to be 10,424 over the 20-month study period. Cluster randomization based on bed number will occur every 2 months. The intervention cluster will have the CoMET score displayed (along with standard of care), while the usual care group will receive standard of care only. RESULTS The primary outcome will be hours free from events of clinical deterioration. Hours of acute clinical events are defined as time when one or more of the following occur: emergent ICU transfer, emergent surgery prior to ICU transfer, cardiac arrest prior to ICU transfer, emergent intubation, or death. The clinical trial began randomization in January 2021. CONCLUSIONS Very few AI-based health analytics have been translated from algorithm to real-world use. This study will use robust, prospective, randomized controlled, clinical trial methodology to assess the effectiveness of an advanced AI predictive analytics monitoring system in incorporating real-time telemetric data for identifying clinical deterioration on acute care wards. This analysis will strengthen the ability of health care organizations to evolve as learning health systems, in which bioinformatics data are applied to improve patient outcomes by incorporating AI into knowledge tools that are successfully integrated into clinical practice by health care providers. TRIAL REGISTRATION ClinicalTrials.gov NCT04359641; https://clinicaltrials.gov/ct2/show/NCT04359641. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/29631.
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Affiliation(s)
| | | | | | | | - Katy N Krahn
- University of Virginia, Charlottesville, VA, United States
| | | | - Susan Hamil
- University of Virginia, Charlottesville, VA, United States
| | | | - J Randall Moorman
- University of Virginia, Charlottesville, VA, United States.,AMP3D, Charlottesville, VA, United States
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14
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Kowalski RL, Lee L, Spaeder MC, Moorman JR, Keim-Malpass J. Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis. JMIR Pediatr Parent 2021; 4:e25991. [PMID: 33547772 PMCID: PMC8078697 DOI: 10.2196/25991] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. OBJECTIVE The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. METHODS We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. RESULTS During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. CONCLUSIONS This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.
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Affiliation(s)
- Rebecca L Kowalski
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Laura Lee
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Michael C Spaeder
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - J Randall Moorman
- School of Medicine, University of Virginia, Charlottesville, VA, United States
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Sarlabous L, Aquino-Esperanza J, Magrans R, de Haro C, López-Aguilar J, Subirà C, Batlle M, Rué M, Gomà G, Ochagavia A, Fernández R, Blanch L. Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation. Sci Rep 2020; 10:13911. [PMID: 32807815 PMCID: PMC7431581 DOI: 10.1038/s41598-020-70814-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/05/2020] [Indexed: 11/28/2022] Open
Abstract
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm's performance was compared versus the gold standard (the ventilator's waveform recordings for CP-VI were scored visually by three experts; Fleiss' kappa = 0.90 (0.87-0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient's own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78-0.86) and 0.78 (0.78-0.85), and accuracies of 0.93 (0.89-0.93) and 0.89 (0.89-0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
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Affiliation(s)
- Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain.
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - José Aquino-Esperanza
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | | | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Batlle
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Montserrat Rué
- Department of Basic Medical Sciences, Universitat de Lleida-IRBLLEIDA, Lleida, Spain
| | - Gemma Gomà
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
| | - Ana Ochagavia
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
| | - Rafael Fernández
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya , Manresa, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Parc Taulí 1, 08208, Sabadell, Barcelona, Spain
- Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- BetterCare S.L, Sabadell, Spain
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