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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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Peters GM, Peelen RV, Gilissen VJ, Koning MV, van Harten WH, Doggen CJM. Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients. J Med Syst 2023; 47:12. [PMID: 36692798 PMCID: PMC9871416 DOI: 10.1007/s10916-022-01898-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks. METHODS When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected. RESULTS In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events. CONCLUSION Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians.
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Affiliation(s)
- Guido M Peters
- Clinical Research Center, Rijnstate Hospital, Arnhem, The Netherlands
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - Roel V Peelen
- Department of Anaesthesiology, Critical Care and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | - Vincent Jhs Gilissen
- Department of Anaesthesiology, Critical Care and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | - Mark V Koning
- Department of Anaesthesiology, Critical Care and Pain Management, Rijnstate Hospital, Arnhem, The Netherlands
| | - Wim H van Harten
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Rijnstate Hospital, Arnhem, The Netherlands
| | - Carine J M Doggen
- Clinical Research Center, Rijnstate Hospital, Arnhem, The Netherlands.
- Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
- Scientific Bureau, Rijnstate Hospital, Wagnerlaan 55, PO Box 9555, 6800 TA, Arnhem, The Netherlands.
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Spaeder MC, Moorman JR, Moorman LP, Adu-Darko MA, Keim-Malpass J, Lake DE, Clark MT. Signatures of illness in children requiring unplanned intubation in the pediatric intensive care unit: A retrospective cohort machine-learning study. Front Pediatr 2022; 10:1016269. [PMID: 36440325 PMCID: PMC9682496 DOI: 10.3389/fped.2022.1016269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/26/2022] [Indexed: 11/09/2022] Open
Abstract
Acute respiratory failure requiring the initiation of invasive mechanical ventilation remains commonplace in the pediatric intensive care unit (PICU). Early recognition of patients at risk for respiratory failure may provide clinicians with the opportunity to intervene and potentially improve outcomes. Through the development of a random forest model to identify patients at risk for requiring unplanned intubation, we tested the hypothesis that subtle signatures of illness are present in physiological and biochemical time series of PICU patients in the early stages of respiratory decompensation. We included 116 unplanned intubation events as recorded in the National Emergency Airway Registry for Children in 92 PICU admissions over a 29-month period at our institution. We observed that children have a physiologic signature of illness preceding unplanned intubation in the PICU. Generally, it comprises younger age, and abnormalities in electrolyte, hematologic and vital sign parameters. Additionally, given the heterogeneity of the PICU patient population, we found differences in the presentation among the major patient groups - medical, cardiac surgical, and non-cardiac surgical. At four hours prior to the event, our random forest model demonstrated an area under the receiver operating characteristic curve of 0.766 (0.738 for medical, 0.755 for cardiac surgical, and 0.797 for non-cardiac surgical patients). The multivariable statistical models that captured the physiological and biochemical dynamics leading up to the event of urgent unplanned intubation in a PICU can be repurposed for bedside risk prediction.
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Affiliation(s)
- Michael C. Spaeder
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - J. Randall Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Liza P. Moorman
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
| | - Michelle A. Adu-Darko
- Department of Pediatrics, Division of Pediatric Critical Care, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Acute and Specialty Care, School of Nursing, University of Virginia, Charlottesville, VA, United States
| | - Douglas E. Lake
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Department of Medicine, Division of Cardiovascular Medicine, School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Matthew T. Clark
- Center for Advanced Medical Analytics, School of Medicine, University of Virginia, Charlottesville, VA, United States
- Nihon Kohden Digital Health Solutions, Irvine, CA, United States
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The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU. NPJ Digit Med 2022; 5:41. [PMID: 35361861 PMCID: PMC8971442 DOI: 10.1038/s41746-022-00584-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.
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