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Wijayaratne D, Weeratunga P, Jayasinghe S. Exploring synthesis as a vital cognitive skill in complex clinical diagnosis. Diagnosis (Berl) 2024; 11:121-124. [PMID: 38294360 DOI: 10.1515/dx-2023-0139] [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: 10/07/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024]
Abstract
Clinicians employ two main cognitive approaches for diagnoses, depending on their expertise. Novices typically use linear hypothetico-deductive methods, while experts rely more on intuitive pattern recognition. These closely correspond to System 1 and System 2 thinking described in behavioral economics. We propose that complex cases additionally require the cognitive skill of synthesis, to visualize and understand the connections between various elements. To illustrate the concept, we describe a 60-year-old individual with a 6 h history of chest pain, fever, cough, accompanying chronic heart failure, atrial fibrillation, COPD, thyrotoxicosis, and ischemic heart disease. Faced with such a scenario, a bedside approach adapted by clinicians is to generate a list of individual diagnoses or pathways of pathogenesis, and address them individually. For example, this cluster could include: smoking causing COPD, IHD leading to chest pain and heart failure, and thyrotoxicosis causing atrial fibrillation (AF). However, other interconnections across pathways could be considered: smoking contributing to IHD; COPD exacerbating heart failure; IHD and pneumonia triggering atrial fibrillation; thyrotoxicosis and AF, independently worsening heart failure; COPD causing hypoxemia and worsening ventricular function. The second cluster of explanation offers a richer network of relationships and connections across disorders and pathways of pathogenesis. This cognitive process of creatively identifying these relationships is synthesis, described in Bloom's taxonomy of the cognitive domain. It is a crucial skill required for visualizing a comprehensive and holistic view of a patient. The concept of synthesis as a cognitive skill in clinical reasoning warrants further exploration.
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Affiliation(s)
- Dilushi Wijayaratne
- Department of Clinical Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| | - Praveen Weeratunga
- Department of Clinical Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
| | - Saroj Jayasinghe
- Department of Clinical Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka
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Villar J, González-Martin JM, Añón JM, Ferrando C, Soler JA, Mosteiro F, Mora-Ordoñez JM, Ambrós A, Fernández L, Montiel R, Vidal A, Muñoz T, Pérez-Méndez L, Rodríguez-Suárez P, Fernández C, Fernández RL, Szakmany T, Burns KEA, Steyerberg EW, Slutsky AS. Clinical relevance of timing of assessment of ICU mortality in patients with moderate-to-severe Acute Respiratory Distress Syndrome. Sci Rep 2023; 13:1543. [PMID: 36707634 PMCID: PMC9883467 DOI: 10.1038/s41598-023-28824-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/25/2023] [Indexed: 01/28/2023] Open
Abstract
Mortality is a frequently reported outcome in clinical studies of acute respiratory distress syndrome (ARDS). However, timing of mortality assessment has not been well characterized. We aimed to identify a crossing-point between cumulative survival and death in the intensive care unit (ICU) of patients with moderate-to-severe ARDS, beyond which the number of survivors would exceed the number of deaths. We hypothesized that this intersection would occur earlier in a successful clinical trial vs. observational studies of moderate/severe ARDS and predict treatment response. We conducted an ancillary study of 1580 patients with moderate-to-severe ARDS managed with lung-protective ventilation to assess the relevance and timing of measuring ICU mortality rates at different time-points during ICU stay. First, we analyzed 1303 patients from four multicenter, observational cohorts enrolling consecutive patients with moderate/severe ARDS. We assessed cumulative ICU survival from the time of moderate/severe ARDS diagnosis to ventilatory support discontinuation within 7-days, 28-days, 60-days, and at ICU discharge. Then, we compared these findings to those of a successful randomized trial of 277 moderate/severe ARDS patients. In the observational cohorts, ICU mortality (487/1303, 37.4%) and 28-day mortality (425/1102, 38.6%) were similar (p = 0.549). Cumulative proportion of ICU survivors and non-survivors crossed at day-7; after day-7, the number of ICU survivors was progressively higher compared to non-survivors. Measures of oxygenation, lung mechanics, and severity scores were different between survivors and non-survivors at each point-in-time (p < 0.001). In the trial cohort, the cumulative proportion of survivors and non-survivors in the treatment group crossed before day-3 after diagnosis of moderate/severe ARDS. In clinical ARDS studies, 28-day mortality closely approximates and may be used as a surrogate for ICU mortality. For patients with moderate-to-severe ARDS, ICU mortality assessment within the first week of a trial might be an early predictor of treatment response.
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Affiliation(s)
- Jesús Villar
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain. .,Research Unit, Hospital Universitario Dr. Negrín, Barranco de La Ballena S/N, 4th Floor - South wing, 35019, Las Palmas de Gran Canaria, Spain. .,Li Ka Shing Knowledge Institute at St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada.
| | - Jesús M González-Martin
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Research Unit, Hospital Universitario Dr. Negrín, Barranco de La Ballena S/N, 4th Floor - South wing, 35019, Las Palmas de Gran Canaria, Spain
| | - José M Añón
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Intensive Care Unit, Hospital Universitario La Paz, IdiPaz, 28046, Madrid, Spain
| | - Carlos Ferrando
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Surgical Intensive Care Unit, Department of Anesthesia, Hospital Clinic, IDIBAPS, 08036, Barcelona, Spain
| | - Juan A Soler
- Intensive Care Unit, Hospital Universitario Virgen de Arrixaca, 30120, Murcia, Spain
| | - Fernando Mosteiro
- Intensive Care Unit, Hospital Universitario de A Coruña, 15006, La Coruña, Spain
| | - Juan M Mora-Ordoñez
- Intensive Care Unit, Hospital Universitario Regional Carlos Haya, 29010, Málaga, Spain
| | - Alfonso Ambrós
- Intensive Care Unit, Hospital General Universitario de Ciudad Real, 13005, Ciudad Real, Spain
| | - Lorena Fernández
- Intensive Care Unit, Hospital Universitario Río Hortega, 47012, Valladolid, Spain
| | - Raquel Montiel
- Intensive Care Unit, Hospital Universitario NS de Candelaria, 38010, Santa Cruz de Tenerife, Spain
| | - Anxela Vidal
- Intensive Care Unit, Hospital Universitario Fundación Jiménez Díaz, 28040, Madrid, Spain
| | - Tomás Muñoz
- Intensive Care Unit, Hospital Universitario de Cruces, 48903, Barakaldo, Vizcaya, Spain
| | - Lina Pérez-Méndez
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Research Unit, Hospital Universitario NS de Candelaria, 38010, Santa Cruz de Tenerife, Spain
| | - Pedro Rodríguez-Suárez
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Thoracic Surgery, Hospital Universitario Dr. Negrín, 35019, Las Palmas de Gran Canaria, Spain
| | - Cristina Fernández
- Research Unit, Hospital Universitario Dr. Negrín, Barranco de La Ballena S/N, 4th Floor - South wing, 35019, Las Palmas de Gran Canaria, Spain
| | - Rosa L Fernández
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029, Madrid, Spain.,Research Unit, Hospital Universitario Dr. Negrín, Barranco de La Ballena S/N, 4th Floor - South wing, 35019, Las Palmas de Gran Canaria, Spain
| | - Tamas Szakmany
- Department of Intensive Care Medicine and Anesthesia, Bevan University Health Board, Newport, NP20 2UB, UK.,Honorary Professor in Intensive Care, Cardiff University, Cardiff, CF14 4XW, Wales, UK
| | - Karen E A Burns
- Li Ka Shing Knowledge Institute at St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada.,Critical Care Medicine, Unity Health Toronto-St. Michael's Hospital, Toronto, M5B 1W8, Canada.,Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ewout W Steyerberg
- Department Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Arthur S Slutsky
- Li Ka Shing Knowledge Institute at St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada.,Division of Critical Care Medicine, University of Toronto, Toronto, ON, M5T 3A1, Canada
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Monfredi OJ, Moore CC, Sullivan BA, Keim-Malpass J, Fairchild KD, Loftus TJ, Bihorac A, Krahn KN, Dubrawski A, Lake DE, Moorman JR, Clermont G. Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration. J Electrocardiol 2023; 76:35-38. [PMID: 36434848 PMCID: PMC10061545 DOI: 10.1016/j.jelectrocard.2022.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/01/2022] [Accepted: 10/22/2022] [Indexed: 11/24/2022]
Abstract
The idea that we can detect subacute potentially catastrophic illness earlier by using statistical models trained on clinical data is now well-established. We review evidence that supports the role of continuous cardiorespiratory monitoring in these predictive analytics monitoring tools. In particular, we review how continuous ECG monitoring reflects the patient and not the clinician, is less likely to be biased, is unaffected by changes in practice patterns, captures signatures of illnesses that are interpretable by clinicians, and is an underappreciated and underutilized source of detailed information for new mathematical methods to reveal.
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Affiliation(s)
- Oliver J Monfredi
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Christopher C Moore
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Brynne A Sullivan
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Jessica Keim-Malpass
- Center for Advanced Medical Analytics, University of Virginia, United States of America; School of Nursing, University of Virginia, United States of America
| | - Karen D Fairchild
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Pediatrics, University of Virginia, United States of America
| | - Tyler J Loftus
- Department of Surgery, University of Florida, United States of America
| | - Azra Bihorac
- Department of Medicine, University of Florida, United States of America
| | - Katherine N Krahn
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - Artur Dubrawski
- Robotics Institute, Carnegie Mellon University, United States of America
| | - Douglas E Lake
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America
| | - J Randall Moorman
- Center for Advanced Medical Analytics, University of Virginia, United States of America; Department of Medicine, University of Virginia, United States of America.
| | - Gilles Clermont
- Department of Critical Care, University of Pittsburgh, United States of America
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