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Rubulotta F, Bahrami S, Marshall DC, Komorowski M. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction. Crit Care Med 2024:00003246-990000000-00361. [PMID: 39133071 DOI: 10.1097/ccm.0000000000006390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.
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Affiliation(s)
- Francesca Rubulotta
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Sahar Bahrami
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Dominic C Marshall
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
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Ye RZ, Lipatov K, Diedrich D, Bhattacharyya A, Erickson BJ, Pickering BW, Herasevich V. Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks. J Crit Care 2024; 82:154794. [PMID: 38552452 DOI: 10.1016/j.jcrc.2024.154794] [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: 09/18/2023] [Revised: 11/20/2023] [Accepted: 12/01/2023] [Indexed: 06/01/2024]
Abstract
OBJECTIVE This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. MATERIALS AND METHODS A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: "ARDS", "Pneumonia", or "Normal". RESULTS A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS. DISCUSSION The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports. CONCLUSION A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting.
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Affiliation(s)
- Run Zhou Ye
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.; Division of Endocrinology, Department of Medicine, Centre de Recherche du CHUS, Sherbrooke QC J1H 5N4, Canada
| | - Kirill Lipatov
- Critical Care Medicine, Mayo Clinic, Eau Claire, WI, United States
| | - Daniel Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | | | - Bradley J Erickson
- Department of Diagnostic Radiology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
| | - Vitaly Herasevich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA..
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Agarwal A, Marion J, Nagy P, Robinson M, Walkey A, Sevransky J. How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials. Crit Care Clin 2023; 39:733-749. [PMID: 37704337 DOI: 10.1016/j.ccc.2023.03.006] [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] [Indexed: 09/15/2023]
Abstract
Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.
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Affiliation(s)
- Ankita Agarwal
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA
| | | | - Paul Nagy
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew Robinson
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Allan Walkey
- Department of Medicine - Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Jonathan Sevransky
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA.
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Chen JT, Mehrizi R, Aasman B, Gong MN, Mirhaji P. Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort. BMJ Health Care Inform 2023; 30:e100782. [PMID: 37709302 PMCID: PMC10503386 DOI: 10.1136/bmjhci-2023-100782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
OBJECTIVE To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort. METHODS We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts. RESULTS Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively. DISCUSSION Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients. CONCLUSION Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.
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Affiliation(s)
- Jen-Ting Chen
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, UCSF, San Francisco, California, USA
- Department of Medicine, Division of Critical Care Medicine, Montefiore Medical Center, Bronx, New York, USA
| | - Rahil Mehrizi
- Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Boudewijn Aasman
- Center for Health Data Innovations, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Michelle Ng Gong
- Department of Medicine, Division of Critical Care Medicine, Montefiore Medical Center, Bronx, New York, USA
| | - Parsa Mirhaji
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, USA
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Sathe NA, Xian S, Mabrey FL, Crosslin DR, Mooney SD, Morrell ED, Lybarger K, Yetisgen M, Jarvik GP, Bhatraju PK, Wurfel MM. Evaluating construct validity of computable acute respiratory distress syndrome definitions in adults hospitalized with COVID-19: an electronic health records based approach. BMC Pulm Med 2023; 23:292. [PMID: 37559024 PMCID: PMC10413524 DOI: 10.1186/s12890-023-02560-y] [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: 03/08/2023] [Accepted: 07/11/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Evolving ARDS epidemiology and management during COVID-19 have prompted calls to reexamine the construct validity of Berlin criteria, which have been rarely evaluated in real-world data. We developed a Berlin ARDS definition (EHR-Berlin) computable in electronic health records (EHR) to (1) assess its construct validity, and (2) assess how expanding its criteria affected validity. METHODS We performed a retrospective cohort study at two tertiary care hospitals with one EHR, among adults hospitalized with COVID-19 February 2020-March 2021. We assessed five candidate definitions for ARDS: the EHR-Berlin definition modeled on Berlin criteria, and four alternatives informed by recent proposals to expand criteria and include patients on high-flow oxygen (EHR-Alternative 1), relax imaging criteria (EHR-Alternatives 2-3), and extend timing windows (EHR-Alternative 4). We evaluated two aspects of construct validity for the EHR-Berlin definition: (1) criterion validity: agreement with manual ARDS classification by experts, available in 175 patients; (2) predictive validity: relationships with hospital mortality, assessed by Pearson r and by area under the receiver operating curve (AUROC). We assessed predictive validity and timing of identification of EHR-Berlin definition compared to alternative definitions. RESULTS Among 765 patients, mean (SD) age was 57 (18) years and 471 (62%) were male. The EHR-Berlin definition classified 171 (22%) patients as ARDS, which had high agreement with manual classification (kappa 0.85), and was associated with mortality (Pearson r = 0.39; AUROC 0.72, 95% CI 0.68, 0.77). In comparison, EHR-Alternative 1 classified 219 (29%) patients as ARDS, maintained similar relationships to mortality (r = 0.40; AUROC 0.74, 95% CI 0.70, 0.79, Delong test P = 0.14), and identified patients earlier in their hospitalization (median 13 vs. 15 h from admission, Wilcoxon signed-rank test P < 0.001). EHR-Alternative 3, which removed imaging criteria, had similar correlation (r = 0.41) but better discrimination for mortality (AUROC 0.76, 95% CI 0.72, 0.80; P = 0.036), and identified patients median 2 h (P < 0.001) from admission. CONCLUSIONS The EHR-Berlin definition can enable ARDS identification with high criterion validity, supporting large-scale study and surveillance. There are opportunities to expand the Berlin criteria that preserve predictive validity and facilitate earlier identification.
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Affiliation(s)
- Neha A Sathe
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA.
| | - Su Xian
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - F Linzee Mabrey
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA
| | - David R Crosslin
- Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Eric D Morrell
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA
| | - Kevin Lybarger
- Department of Information Sciences and Technology, George Mason University, Fairfax, VA, USA
| | - Meliha Yetisgen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Gail P Jarvik
- Department of Genome Sciences and Division of Medical Genetics, Department of Medicine, University of Washington Medical Center, Seattle, WA, USA
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA
| | - Mark M Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, 325 9th Avenue HMC #359640, Seattle, WA, 98104-2499, USA
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Knighton AJ, Kuttler KG, Ranade-Kharkar P, Allen L, Throne T, Jacobs JR, Carpenter L, Winberg C, Johnson K, Shrestha N, Ferraro JP, Wolfe D, Peltan ID, Srivastava R, Grissom CK. An alert tool to promote lung protective ventilation for possible acute respiratory distress syndrome. JAMIA Open 2022; 5:ooac050. [PMID: 35815095 PMCID: PMC9263532 DOI: 10.1093/jamiaopen/ooac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/26/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Computer-aided decision tools may speed recognition of acute respiratory distress syndrome (ARDS) and promote consistent, timely treatment using lung-protective ventilation (LPV). This study evaluated implementation and service (process) outcomes with deployment and use of a clinical decision support (CDS) synchronous alert tool associated with existing computerized ventilator protocols and targeted patients with possible ARDS not receiving LPV. Materials and Methods We performed an explanatory mixed methods study from December 2019 to November 2020 to evaluate CDS alert implementation outcomes across 13 intensive care units (ICU) in an integrated healthcare system with >4000 mechanically ventilated patients annually. We utilized quantitative methods to measure service outcomes including CDS alert tool utilization, accuracy, and implementation effectiveness. Attitudes regarding the appropriateness and acceptability of the CDS tool were assessed via an electronic field survey of physicians and advanced practice providers. Results Thirty-eight percent of study encounters had at least one episode of LPV nonadherence. Addition of LPV treatment detection logic prevented an estimated 1812 alert messages (41%) over use of disease detection logic alone. Forty-eight percent of alert recommendations were implemented within 2 h. Alert accuracy was estimated at 63% when compared to gold standard ARDS adjudication, with sensitivity of 85% and positive predictive value of 62%. Fifty-seven percent of survey respondents observed one or more benefits associated with the alert. Conclusion Introduction of a CDS alert tool based upon ARDS risk factors and integrated with computerized ventilator protocol instructions increased visibility to gaps in LPV use and promoted increased adherence to LPV.
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Affiliation(s)
- Andrew J Knighton
- Healthcare Delivery Institute, Intermountain Healthcare , Murray, Utah, USA
| | - Kathryn G Kuttler
- Digital Technology Services, Intermountain Healthcare , Salt Lake City, Utah, USA
| | | | - Lauren Allen
- Healthcare Delivery Institute, Intermountain Healthcare , Murray, Utah, USA
| | - Taylor Throne
- Healthcare Delivery Institute, Intermountain Healthcare , Murray, Utah, USA
| | - Jason R Jacobs
- Division of Pulmonary and Critical Care Medicine Department of Medicine, Intermountain Medical Center , Murray, Utah, USA
| | - Lori Carpenter
- Division of Pulmonary and Critical Care Medicine Department of Medicine, Intermountain Medical Center , Murray, Utah, USA
| | - Carrie Winberg
- Division of Pulmonary and Critical Care Medicine Department of Medicine, Intermountain Medical Center , Murray, Utah, USA
| | - Kyle Johnson
- Digital Technology Services, Intermountain Healthcare , Salt Lake City, Utah, USA
| | - Neer Shrestha
- Digital Technology Services, Intermountain Healthcare , Salt Lake City, Utah, USA
| | - Jeffrey P Ferraro
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine , Salt Lake City, Utah, USA
| | - Doug Wolfe
- Healthcare Delivery Institute, Intermountain Healthcare , Murray, Utah, USA
| | - Ithan D Peltan
- Division of Pulmonary and Critical Care Medicine Department of Medicine, Intermountain Medical Center , Murray, Utah, USA
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine , Salt Lake City, Utah, USA
| | - Rajendu Srivastava
- Healthcare Delivery Institute, Intermountain Healthcare , Murray, Utah, USA
- Department of Pediatrics, University of Utah School of Medicine , Salt Lake City, Utah, USA
| | - Colin K Grissom
- Division of Pulmonary and Critical Care Medicine Department of Medicine, Intermountain Medical Center , Murray, Utah, USA
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Utah School of Medicine , Salt Lake City, Utah, USA
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Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition. Crit Care Explor 2021; 3:e0451. [PMID: 34136825 PMCID: PMC8202583 DOI: 10.1097/cce.0000000000000451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Supplemental Digital Content is available in the text. OBJECTIVES: Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients. DESIGN: This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition. SETTING: Twenty multidisciplinary ICUs in Mayo Clinic Health System. SUBJECTS: The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019. INTERVENTIONS: The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard. MEASUREMENTS AND MAIN RESULTS: Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao2/Fio2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao2/Fio2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao2/Fio2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians’ documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001). CONCLUSIONS: Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.
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Sjoding MW, Taylor D, Motyka J, Lee E, Co I, Claar D, McSparron JI, Ansari S, Kerlin MP, Reilly JP, Shashaty MGS, Anderson BJ, Jones TK, Drebin HM, Ittner CAG, Meyer NJ, Iwashyna TJ, Ward KR, Gillies CE. Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation. LANCET DIGITAL HEALTH 2021; 3:e340-e348. [PMID: 33893070 PMCID: PMC8182690 DOI: 10.1016/s2589-7500(21)00056-x] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/06/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023]
Abstract
Background Acute respiratory distress syndrome (ARDS) is a common, but under-recognised, critical illness syndrome associated with high mortality. An important factor in its under-recognition is the variability in chest radiograph interpretation for ARDS. We sought to train a deep convolutional neural network (CNN) to detect ARDS findings on chest radiographs. Methods CNNs were pretrained on 595 506 radiographs from two centres to identify common chest findings (eg, opacity and effusion), and then trained on 8072 radiographs annotated for ARDS by multiple physicians using various transfer learning approaches. The best performing CNN was tested on chest radiographs in an internal and external cohort, including a subset reviewed by six physicians, including a chest radiologist and physicians trained in intensive care medicine. Chest radiograph data were acquired from four US hospitals. Findings In an internal test set of 1560 chest radiographs from 455 patients with acute hypoxaemic respiratory failure, a CNN could detect ARDS with an area under the receiver operator characteristics curve (AUROC) of 0·92 (95% CI 0·89–0·94). In the subgroup of 413 images reviewed by at least six physicians, its AUROC was 0·93 (95% CI 0·88–0·96), sensitivity 83·0% (95% CI 74·0–91·1), and specificity 88·3% (95% CI 83·1–92·8). Among images with zero of six ARDS annotations (n=155), the median CNN probability was 11%, with six (4%) assigned a probability above 50%. Among images with six of six ARDS annotations (n=27), the median CNN probability was 91%, with two (7%) assigned a probability below 50%. In an external cohort of 958 chest radiographs from 431 patients with sepsis, the AUROC was 0·88 (95% CI 0·85–0·91). When radiographs annotated as equivocal were excluded, the AUROC was 0·93 (0·92–0·95). Interpretation A CNN can be trained to achieve expert physician-level performance in ARDS detection on chest radiographs. Further research is needed to evaluate the use of these algorithms to support real-time identification of ARDS patients to ensure fidelity with evidence-based care or to support ongoing ARDS research. Funding National Institutes of Health, Department of Defense, and Department of Veterans Affairs.
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Affiliation(s)
- Michael W Sjoding
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
| | - Daniel Taylor
- Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Jonathan Motyka
- Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Elizabeth Lee
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Ivan Co
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Dru Claar
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jakob I McSparron
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Meeta Prasad Kerlin
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John P Reilly
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Michael G S Shashaty
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Brian J Anderson
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tiffanie K Jones
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Harrison M Drebin
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Caroline A G Ittner
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nuala J Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Center for Translational Lung Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Theodore J Iwashyna
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; VA Center for Clinic Management Research, Ann Arbor, MI, USA; Institute for Social Research, Ann Arbor, MI, USA
| | - Kevin R Ward
- Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA
| | - Christopher E Gillies
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Integrative Research in Critical Care; Ann Arbor, MI, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA
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Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data. Crit Care Explor 2021; 3:e0313. [PMID: 33458681 PMCID: PMC7803688 DOI: 10.1097/cce.0000000000000313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data. Design Retrospective, observational cohort study. Setting Academic medical center ICU. Patients Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure. Interventions None. Measurements and Main Results Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6). Conclusions Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.
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Performance Measure Development, Use, and Measurement of Effectiveness Using the Guideline on Mechanical Ventilation in Acute Respiratory Distress Syndrome. An Official American Thoracic Society Workshop Report. Ann Am Thorac Soc 2020; 16:1463-1472. [PMID: 31774323 PMCID: PMC6956829 DOI: 10.1513/annalsats.201909-665st] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Guideline implementation tools are designed to improve uptake of guideline recommendations in clinical settings but do not uniformly accompany the clinical practice guideline documents. Performance measures are a type of guideline implementation tool with the potential to catalyze behavior change and greater adherence to clinical practice guidelines. However, many performance measures suffer from serious flaws in their design and application, prompting the American Thoracic Society (ATS) to define its own performance measure development standards in a previous workshop in 2012. This report summarizes the proceedings of a follow-up workshop convened to advance the ATS’s work in performance measure development and guideline implementation. To illustrate the application of the ATS’s performance measure development framework, we used the example of a low–tidal volume ventilation performance measure created de novo from the 2017 ATS/European Society of Intensive Care Medicine/Society of Critical Care Medicine mechanical ventilation in acute respiratory distress syndrome clinical practice guideline. We include a detailed explanation of the rationale for the specifications chosen, identification of areas in need of further validity testing, and a preliminary strategy for pilot testing of the performance measure. Pending additional resources and broader performance measure expertise, issuing “preliminary performance measures” and their specifications alongside an ATS clinical practice guideline offers a first step to further the ATS’s guideline implementation agenda. We recommend selectively proceeding with full performance measure development for those measures with positive early user feedback and the greatest potential impact in accordance with ATS leadership guidance.
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Semler MW, Bernard GR, Aaron SD, Angus DC, Biros MH, Brower RG, Calfee CS, Colantuoni EA, Ferguson ND, Gong MN, Hopkins RO, Hough CL, Iwashyna TJ, Levy BD, Martin TR, Matthay MA, Mizgerd JP, Moss M, Needham DM, Self WH, Seymour CW, Stapleton RD, Thompson BT, Wunderink RG, Aggarwal NR, Reineck LA. Identifying Clinical Research Priorities in Adult Pulmonary and Critical Care. NHLBI Working Group Report. Am J Respir Crit Care Med 2020; 202:511-523. [PMID: 32150460 PMCID: PMC7427373 DOI: 10.1164/rccm.201908-1595ws] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 03/06/2020] [Indexed: 12/14/2022] Open
Abstract
Preventing, treating, and promoting recovery from critical illness due to pulmonary disease are foundational goals of the critical care community and the NHLBI. Decades of clinical research in acute respiratory distress syndrome, acute respiratory failure, pneumonia, and sepsis have yielded improvements in supportive care, which have translated into improved patient outcomes. Novel therapeutics have largely failed to translate from promising preclinical findings into improved patient outcomes in late-phase clinical trials. Recent advances in personalized medicine, "big data," causal inference using observational data, novel clinical trial designs, preclinical disease modeling, and understanding of recovery from acute illness promise to transform the methods of pulmonary and critical care clinical research. To assess the current state of, research priorities for, and future directions in adult pulmonary and critical care research, the NHLBI assembled a multidisciplinary working group of investigators. This working group identified recommendations for future research, including 1) focusing on understanding the clinical, physiological, and biological underpinnings of heterogeneity in syndromes, diseases, and treatment response with the goal of developing targeted, personalized interventions; 2) optimizing preclinical models by incorporating comorbidities, cointerventions, and organ support; 3) developing and applying novel clinical trial designs; and 4) advancing mechanistic understanding of injury and recovery to develop and test interventions targeted at achieving long-term improvements in the lives of patients and families. Specific areas of research are highlighted as especially promising for making advances in pneumonia, acute hypoxemic respiratory failure, and acute respiratory distress syndrome.
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Affiliation(s)
| | | | - Shawn D. Aaron
- Division of Respirology, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Michelle H. Biros
- Department of Emergency Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Roy G. Brower
- Division of Pulmonary and Critical Care Medicine and
| | - Carolyn S. Calfee
- Department of Medicine and
- Department of Anesthesia, University of California, San Francisco, San Francisco, California
| | | | - Niall D. Ferguson
- Interdepartmental Division of Critical Care Medicine
- Department of Medicine
- Department of Physiology, and
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Michelle N. Gong
- Department of Epidemiology
- Department of Population Health, and
- Department of Medicine, Montefiore Medical Center, Bronx, New York
| | - Ramona O. Hopkins
- Department of Psychology, Brigham Young University, Provo, Utah
- Pulmonary and Critical Care Division, Intermountain Medical Center, Murray, Utah
| | - Catherine L. Hough
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, Washington
| | - Theodore J. Iwashyna
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan
| | - Bruce D. Levy
- Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Thomas R. Martin
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, Washington
| | - Michael A. Matthay
- Department of Medicine and
- Department of Anesthesia, University of California, San Francisco, San Francisco, California
| | - Joseph P. Mizgerd
- Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts
| | - Marc Moss
- Division of Pulmonary Sciences & Critical Care, University of Colorado, Denver, Colorado
| | | | - Wesley H. Self
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Christopher W. Seymour
- Department of Critical Care Medicine and
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Renee D. Stapleton
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Vermont, Burlington, Vermont
| | - B. Taylor Thompson
- Division of Pulmonary and Critical Care Medicine, Harvard University, Boston, Massachusetts
| | - Richard G. Wunderink
- Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, Illinois; and
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Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS). J Crit Care 2020; 60:96-102. [PMID: 32777759 DOI: 10.1016/j.jcrc.2020.07.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/25/2020] [Accepted: 07/19/2020] [Indexed: 01/28/2023]
Abstract
PURPOSE Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS. MATERIALS AND METHODS 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. RESULTS On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. CONCLUSION Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.
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Silva PL, Pelosi P, Rocco PRM. Personalized pharmacological therapy for ARDS: a light at the end of the tunnel. Expert Opin Investig Drugs 2019; 29:49-61. [PMID: 31778609 DOI: 10.1080/13543784.2020.1699531] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Introduction: Pharmacotherapy for the acute respiratory distress syndrome (ARDS) has been tested in preclinical and clinical studies. However, to date, no pharmacological interventions have proven effective. This may be attributed to lack of proper identification of different ARDS phenotypes.Areas covered: We designed inclusive search strings and searched four bibliographic databases (Cochrane Database of Systematic Reviews, PubMed, Web of Science, and clinicaltrials.gov) to identify relevant research. Search results were mainly restricted to papers published from 2009 through 2019. ARDS is a heterogeneous syndrome, and its different phenotypes - defined according to clinical, radiological, and biological parameters - may affect response to therapy. The most promising pharmacological approaches to date have been based on ARDS pathophysiology. They focus on reducing inflammation and pulmonary edema, promoting selective vasodilation, and repairing alveolar epithelial and endothelial cells.Expert opinion: Pharmacotherapeutic approaches targeting ARDS pathophysiology have failed to exert beneficial effects. Personalized medicine targeting the different ARDS phenotypes has emerged as an option to improve survival. Identification of specific ARDS patient phenotypes that respond to specific therapies seems to be the most important challenge for the next decade. Additional research is warranted before personalized medicine approaches can be applied at bedside for ARDS patients.
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Affiliation(s)
- Pedro Leme Silva
- Laboratory of Pulmonary Investigation, Carlos Chagas Filho Institute of Biophysics, Federal University of Rio de Janeiro, Centro de Ciências da Saúde, Rio de Janeiro, Brazil
| | - Paolo Pelosi
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy.,IRCCS for Oncology and Neurosciences, San Martino Policlinico Hospital, Genoa, Italy
| | - Patricia R M Rocco
- Laboratory of Pulmonary Investigation, Carlos Chagas Filho Institute of Biophysics, Federal University of Rio de Janeiro, Centro de Ciências da Saúde, Rio de Janeiro, Brazil
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