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Joshi M, Archer S, Morbi A, Ashrafian H, Arora S, Khan S, Cooke G, Darzi A. Perceptions on the Use of Wearable Sensors and Continuous Monitoring in Surgical Patients: Interview Study Among Surgical Staff. JMIR Form Res 2022; 6:e27866. [PMID: 35147503 PMCID: PMC8881779 DOI: 10.2196/27866] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 10/08/2021] [Accepted: 11/22/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Continuous vital sign monitoring by using wearable sensors may result in the earlier detection of patient deterioration and sepsis. Few studies have explored the perspectives of surgical team members on the use of such sensors in surgical patients. OBJECTIVE This study aims to understand the views of surgical team members regarding novel wearable sensors for surgical patients. METHODS Wearable sensors that monitor vital signs (heart rate, respiratory rate, and temperature) continuously were used by acute surgical patients. The opinions of surgical staff who were treating patients with these sensors were collated through in-depth semistructured interviews to thematic saturation. Interviews were audio recorded, transcribed, and analyzed via thematic analysis. RESULTS A total of 48 interviews were performed with senior and junior surgeons and senior and junior nurses. The main themes of interest that emerged from the interviews were (1) problems with current monitoring, (2) the anticipated impact of wearables on patient safety, (3) the impact on staff, (4) the impact on patients overall, (5) potential new changes, and (6) the future and views on technology. CONCLUSIONS Overall, the feedback from staff who were continuously monitoring surgical patients via wearable sensors was positive, and relatively few concerns were raised. Surgical staff members identify problems with current monitoring and anticipate that sensors will both improve patient safety and be the future of monitoring.
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Affiliation(s)
- Meera Joshi
- Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Stephanie Archer
- Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Abigail Morbi
- Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Hutan Ashrafian
- Department of Surgery & Cancer, Imperial College London, London, United Kingdom
| | - Sonal Arora
- Chelsea and Westminster Hospital, National Health Service Foundation Trust, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster Hospital, National Health Service Foundation Trust, London, United Kingdom
| | - Graham Cooke
- Department of Infectious Diseases, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Department of Surgery & Cancer, Imperial College London, London, United Kingdom
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Thrombin Generation in Trauma Patients: How Do we Navigate Through Scylla and Charybdis? CURRENT ANESTHESIOLOGY REPORTS 2022. [DOI: 10.1007/s40140-021-00502-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Gambo IP, Massenon R, Kolawole BA, Ikono R. Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2021. [DOI: 10.4018/ijhisi.289461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.
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Affiliation(s)
| | | | | | - Rhoda Ikono
- Obafemi Awolowo University, Ile-Ife, Nigeria
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Abstract
OBJECTIVES Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information. DATA SOURCES We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care. STUDY SELECTION Three reviewers independently screened preliminary citation lists. DATA EXTRACTION Summary data were tabulated for review. DATA SYNTHESIS To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices. CONCLUSIONS Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.
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Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, Shaw GM, Benyo B, Moeller K, Safaei S, Tawhai M, Hunter P, Desaive T. Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomed Eng Online 2018; 17:24. [PMID: 29463246 PMCID: PMC5819676 DOI: 10.1186/s12938-018-0455-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 02/12/2018] [Indexed: 01/17/2023] Open
Abstract
Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.
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Affiliation(s)
- J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jean-Charles Preiser
- Department of Intensive Care, Erasme University of Hospital, 1070 Brussels, Belgium
| | - Jennifer L. Dickson
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Antoine Pironet
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, School of Engineering, Monash University Malaysia, 47500 Selangor, Malaysia
| | - Christopher G. Pretty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M. Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
| | - Balazs Benyo
- Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary
| | - Knut Moeller
- Department of Biomedical Engineering, Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Soroush Safaei
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Peter Hunter
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Thomas Desaive
- GIGA In Silico Medicine, University of Liege, 4000 Liege, Belgium
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Holder AL, Clermont G. Using what you get: dynamic physiologic signatures of critical illness. Crit Care Clin 2015; 31:133-64. [PMID: 25435482 DOI: 10.1016/j.ccc.2014.08.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The development and resolution of cardiopulmonary instability take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in physiologic signatures. When primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to develop physiologic signatures. The creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exist. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve outcomes.
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Affiliation(s)
- Andre L Holder
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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Bohanon FJ, Mrazek AA, Shabana MT, Mims S, Radhakrishnan GL, Kramer GC, Radhakrishnan RS. Heart rate variability analysis is more sensitive at identifying neonatal sepsis than conventional vital signs. Am J Surg 2015. [PMID: 26212391 DOI: 10.1016/j.amjsurg.2015.06.002] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Sepsis remains the largest preventable source of neonatal mortality in the world. Heart rate variability (HRV) analysis and noninvasive cardiac output have been shown to be useful adjuncts to sepsis detection in many patient groups. METHODS With Institutional Review Board approval, 4 septic and 6 nonseptic extremely low birth weight patients were enrolled. Data from septic and healthy patients were collected for 5 hours. Electrocardiogram waveform and traditional vital signs were collected and the RR intervals were calculated; then HRV analysis was performed in both the time and frequency domain. RESULTS HRV measurements in time domain, heart rate, and pulse oximetry (SpO2) were significantly different in septic patients vs nonseptic controls. CONCLUSIONS These results indicate that nonconventional vital signs such as HRV are more sensitive than traditionally used vital signs, such as cardiac output and mean arterial pressure, in the confirmation of sepsis in extremely low birth weight neonates. HRV may allow for earlier identification of septic physiology.
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Affiliation(s)
- Fredrick J Bohanon
- Department of Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - Amy A Mrazek
- Department of Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - Mohamed T Shabana
- Department of Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - Sarah Mims
- Department of Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - Geetha L Radhakrishnan
- Department of Pediatrics, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - George C Kramer
- Department of Anesthesiology, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA
| | - Ravi S Radhakrishnan
- Department of Surgery, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA; Department of Pediatrics, University of Texas Medical Branch, 301 University Boulevard, Galveston, TX, 77555, USA.
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Søreide K, Thorsen K, Søreide JA. Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease. Eur J Trauma Emerg Surg 2014; 41:91-8. [PMID: 25621078 PMCID: PMC4298653 DOI: 10.1007/s00068-014-0417-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 05/26/2014] [Indexed: 12/27/2022]
Abstract
Purpose Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored artificial neural networks (ANNs) to predict the complex interactions between the risk factors of PPU and death among patients with this condition. Methods ANN modelling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer and an output layer) was used to predict the 30-day mortality of consecutive patients from a population-based cohort undergoing surgery for PPU. A receiver-operating characteristic (ROC) analysis was used to assess model accuracy. Results Of the 172 patients, 168 had their data included in the model; the data of 117 (70 %) were used for the training set, and the data of 51 (39 %) were used for the test set. The accuracy, as evaluated by area under the ROC curve (AUC), was best for an inclusive, multifactorial ANN model (AUC 0.90, 95 % CIs 0.85–0.95; p < 0.001). This model outperformed standard predictive scores, including Boey and PULP. The importance of each variable decreased as the number of factors included in the ANN model increased. Conclusions The prediction of death was most accurate when using an ANN model with several univariate influences on the outcome. This finding demonstrates that PPU is a highly complex disease for which clinical prognoses are likely difficult. The incorporation of computerised learning systems might enhance clinical judgments to improve decision making and outcome prediction.
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Affiliation(s)
- K Søreide
- Department of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - K Thorsen
- Department of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - J A Søreide
- Department of Gastrointestinal Surgery, Stavanger University Hospital, P.O. Box 8100, 4068 Stavanger, Norway ; Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Scheff JD, Calvano SE, Androulakis IP. Predicting critical transitions in a model of systemic inflammation. J Theor Biol 2013; 338:9-15. [PMID: 23973206 DOI: 10.1016/j.jtbi.2013.08.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Revised: 05/13/2013] [Accepted: 08/12/2013] [Indexed: 12/14/2022]
Abstract
The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.
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Affiliation(s)
- Jeremy D Scheff
- Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA.
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