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Ruiz-Botella M, Manrique S, Gomez J, Bodí M. Advancing ICU patient care with a Real-Time predictive model for mechanical Power to mitigate VILI. Int J Med Inform 2024; 189:105511. [PMID: 38851133 DOI: 10.1016/j.ijmedinf.2024.105511] [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: 03/06/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Invasive Mechanical Ventilation (IMV) in Intensive Care Units (ICU) significantly increases the risk of Ventilator-Induced Lung Injury (VILI), necessitating careful management of mechanical power (MP). This study aims to develop a real-time predictive model of MP utilizing Artificial Intelligence to mitigate VILI. METHODOLOGY A retrospective observational study was conducted, extracting patient data from Clinical Information Systems from 2018 to 2022. Patients over 18 years old with more than 6 h of IMV were selected. Continuous data on IMV variables, laboratory data, monitoring, procedures, demographic data, type of admission, reason for admission, and APACHE II at admission were extracted. The variables with the highest correlation to MP were used for prediction and IMV data was grouped in 15-minute intervals using the mean. A mixed neural network model was developed to forecast MP 15 min in advance, using IMV data from 6 h before the prediction and current patient status. The model's ability to predict future MP was analyzed and compared to a baseline model predicting the future value of MP as equal to the current value. RESULTS The cohort consisted of 1967 patients after applying inclusion criteria, with a median age of 63 years and 66.9 % male. The deep learning model achieved a mean squared error of 2.79 in the test set, indicating a 20 % improvement over the baseline model. It demonstrated high accuracy (94 %) in predicting whether MP would exceed a critical threshold of 18 J/min, which correlates with increased mortality. The integration of this model into a web platform allows clinicians real-time access to MP predictions, facilitating timely adjustments to ventilation settings. CONCLUSIONS The study successfully developed and integrated in clinical practice a predictive model for MP. This model will assist clinicians allowing for the adjustment of ventilatory parameters before lung damage occurs.
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Affiliation(s)
- M Ruiz-Botella
- Departament of Chemical Engineering, Universitat Rovira I Virgili, Tarragona, Spain; Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain.
| | - S Manrique
- Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain
| | - J Gomez
- Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain
| | - M Bodí
- Instituto de Investigación Sanitaria Pere i Virgili, Universidad Rovira i Virgili, Tarragona, Spain; Critical Care department, Hospital Universitario Joan XXIII, Tarragona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES). Instituto de Salud Carlos III, Spain
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2
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Nguyen M, Amanian A, Wei M, Prisman E, Mendez-Tellez PA. Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis. Otolaryngol Head Neck Surg 2024. [PMID: 39077854 DOI: 10.1002/ohn.919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 06/12/2024] [Accepted: 07/06/2024] [Indexed: 07/31/2024]
Abstract
OBJECTIVE It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and further ventilation-related complications such as subglottic stenosis. In this study, we aimed to develop a machine learning tool to predict which patients need a tracheostomy at the onset of admission to the intensive care unit (ICU). STUDY DESIGN Retrospective Cohort Study. SETTING Multicenter Study of 335 Intensive Care Units between 2014 and 2015. METHODS The eICU Collaborative Research Database (eICU-CRD) was utilized to obtain the patient cohort. Inclusion criteria included: (1) Age >18 years and (2) ICU admission requiring mechanical ventilation. The primary outcome of interest included tracheostomy assessed via a binary classification model. Models included logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost). RESULTS Of 38,508 invasively mechanically ventilated patients, 1605 patients underwent a tracheostomy. The XGBoost, RF, and LR models had fair performances at an AUROC 0.794, 0.780, and 0.775 respectively. Limiting the XGBoost model to 20 features out of 331, a minimal reduction in performance was observed with an AUROC of 0.778. Using Shapley Additive Explanations, the top features were an admission diagnosis of pneumonia or sepsis and comorbidity of chronic respiratory failure. CONCLUSIONS Our machine learning model accurately predicts the probability that a patient will eventually require a tracheostomy upon ICU admission, and upon prospective validation, we have the potential to institute earlier interventions and reduce the complications of prolonged ventilation.
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Affiliation(s)
| | - Ameen Amanian
- Department of Surgery, Division of Otolaryngology-Head & Neck Surgery, University of British Columbia, Vancouver, Canada
| | - Meihan Wei
- Department of Biomedical Engineering-Whiting School of Engineering, Johns Hopkins University, Baltimore, USA
| | - Eitan Prisman
- Department of Surgery, Division of Otolaryngology-Head & Neck Surgery, University of British Columbia, Vancouver, Canada
| | - Pedro Alejandro Mendez-Tellez
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, USA
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3
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Huerta N, Rao SJ, Isath A, Wang Z, Glicksberg BS, Krittanawong C. The premise, promise, and perils of artificial intelligence in critical care cardiology. Prog Cardiovasc Dis 2024:S0033-0620(24)00094-X. [PMID: 38936757 DOI: 10.1016/j.pcad.2024.06.006] [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: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/29/2024]
Abstract
Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in real-time would assist clinicians in making more informed decisions. This systematic review aimed to explore current research on AI as it pertains to the CICU. A PRISMA search strategy was carried out to identify the pertinent literature on topics including vascular access, heart failure care, circulatory support, cardiogenic shock, ultrasound, and mechanical ventilation. Thirty-eight studies were included. Although AI is still in its early stages of development, this review illustrates its potential to yield numerous benefits in the CICU.
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Affiliation(s)
- Nicholas Huerta
- Department of Medicine, MedStar Union Memorial Hospital, Baltimore, MD, USA
| | - Shiavax J Rao
- Department of Medicine, MedStar Union Memorial Hospital, Baltimore, MD, USA
| | - Ameesh Isath
- Department of Cardiology, Westchester Medical Center and New York Medical College, Valhalla, NY, USA
| | - Zhen Wang
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Tandon P, Nguyen KAN, Edalati M, Parchure P, Raut G, Reich DL, Freeman R, Levin MA, Timsina P, Powell CA, Fayad ZA, Kia A. Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation. Bioengineering (Basel) 2024; 11:626. [PMID: 38927862 PMCID: PMC11200686 DOI: 10.3390/bioengineering11060626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 05/27/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.
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Affiliation(s)
- Pranai Tandon
- Department of Medicine Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kim-Anh-Nhi Nguyen
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - Masoud Edalati
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - Prathamesh Parchure
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - Ganesh Raut
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - David L. Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (M.A.L.)
| | - Robert Freeman
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - Matthew A. Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (M.A.L.)
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Prem Timsina
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
| | - Charles A. Powell
- Department of Medicine Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zahi A. Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Arash Kia
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (K.-A.-N.N.); (M.E.); (P.P.); (G.R.); (R.F.); (P.T.); (A.K.)
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (M.A.L.)
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Zhang G, Xie Q, Wang C, Xu J, Liu G, Su C. Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases. Med Biol Eng Comput 2024:10.1007/s11517-024-03143-7. [PMID: 38861056 DOI: 10.1007/s11517-024-03143-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 05/27/2024] [Indexed: 06/12/2024]
Abstract
The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.
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Affiliation(s)
- Guang Zhang
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China
| | - Qingyan Xie
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Chengyi Wang
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Jiameng Xu
- School of Life Sciences, Tiangong University, Tianjin, 300387, China
| | - Guanjun Liu
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China
| | - Chen Su
- Systems Engineering Institute, People's Liberation Army, Academy of Military Sciences, Tianjin, 300161, China.
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6
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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [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: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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7
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Ortiz-Barrios M, Petrillo A, Arias-Fonseca S, McClean S, de Felice F, Nugent C, Uribe-López SA. An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study. Int J Emerg Med 2024; 17:45. [PMID: 38561694 PMCID: PMC10986051 DOI: 10.1186/s12245-024-00626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 03/28/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Shortages of mechanical ventilation have become a constant problem in Emergency Departments (EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES) to design effective interventions ensuring the high availability of ventilators for patients needing these devices. METHODS First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European hospital group was used to validate the proposed methodology. RESULTS The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity of the AI model was 93.08% (95% confidence interval, [88.46 - 96.26%]), whilst the specificity was 85.45% [77.45 - 91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 - 95.13%) and 87.85% (80.12 - 93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 - 100%). Finally, the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource capacity strategy. CONCLUSIONS Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.
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Affiliation(s)
- Miguel Ortiz-Barrios
- Centro de Investigación en Gestión e Ingeniería de Producción (CIGIP), Universitat Politecnica de Valencia, Camino de Vera, s/n, Valencia, 46022, Spain.
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla, 080002, Colombia.
| | - Antonella Petrillo
- Department of Engineering, University of Naples "Parthenope", Naples, Italy
| | - Sebastián Arias-Fonseca
- Department of Productivity and Innovation, Universidad de la Costa CUC, Barranquilla, 080002, Colombia
| | - Sally McClean
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Fabio de Felice
- Department of Engineering, University of Naples "Parthenope", Naples, Italy
| | - Chris Nugent
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Sheyla-Ariany Uribe-López
- Academic Multidisciplinary Division of Jalpa de Mendez, Juarez Autonomous University of Tabasco, Jalpa de Mendez, Tabasco, Mexico
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Su L, Liu S, Long Y, Chen C, Chen K, Chen M, Chen Y, Cheng Y, Cui Y, Ding Q, Ding R, Duan M, Gao T, Gu X, He H, He J, Hu B, Hu C, Huang R, Huang X, Jiang H, Jiang J, Lan Y, Li J, Li L, Li L, Li W, Li Y, Lin J, Luo X, Lyu F, Mao Z, Miao H, Shang X, Shang X, Shang Y, Shen Y, Shi Y, Sun Q, Sun W, Tang Z, Wang B, Wang H, Wang H, Wang L, Wang L, Wang S, Wang Z, Wang Z, Wei D, Wu J, Wu Q, Xing X, Yang J, Yang X, Yu J, Yu W, Yu Y, Yuan H, Zhai Q, Zhang H, Zhang L, Zhang M, Zhang Z, Zhao C, Zheng R, Zhong L, Zhou F, Zhu W. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne) 2024; 10:1174429. [PMID: 38264049 PMCID: PMC10804886 DOI: 10.3389/fmed.2023.1174429] [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: 02/26/2023] [Accepted: 11/09/2023] [Indexed: 01/25/2024] Open
Abstract
The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.
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Affiliation(s)
- Longxiang Su
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shengjun Liu
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yun Long
- Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chaodong Chen
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Kai Chen
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Ming Chen
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yaolong Chen
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Yisong Cheng
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Yating Cui
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Qi Ding
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Renyu Ding
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Meili Duan
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Tao Gao
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Xiaohua Gu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Hongli He
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jiawei He
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Hu
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Rui Huang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaobo Huang
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Huizhen Jiang
- Department of Information Center, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Jing Jiang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Yunping Lan
- Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China
| | - Jun Li
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - Linfeng Li
- Medical Data Research Institute, Chongqing Medical University, Chongqing, China
| | - Lu Li
- Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wenxiong Li
- Department of Surgical Intensive Critical Unit, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Yongzai Li
- Information Network Center, QiLu Hospital, ShanDong University, Jinan, China
| | - Jin Lin
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xufei Luo
- Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Feng Lyu
- Department of Computer Science and Engineering, Central South University, Changsha, China
| | - Zhi Mao
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - He Miao
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Xiaopu Shang
- Department of Information Management, Beijing Jiaotong University, Beijing, China
| | - Xiuling Shang
- Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China
| | - You Shang
- Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuwen Shen
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Yinghuan Shi
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Qihang Sun
- British Chinese Society of Health Informatics, Beijing, China
| | - Weijun Sun
- Faculty of Automation, Guangdong University of Technology, Guangzhou, China
| | - Zhiyun Tang
- Department of Intensive Care Unit, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Emergency and Intensive Care Unit Center, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Bo Wang
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Haijun Wang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongliang Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Li Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Luhao Wang
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Sicong Wang
- Department of Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Zhanwen Wang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Zhong Wang
- Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Dong Wei
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Jianfeng Wu
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
| | - Qin Wu
- Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Xuezhong Xing
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences; School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jin Yang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Xianghong Yang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiangquan Yu
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wenkui Yu
- Department of Critical Care Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yuan Yu
- Intensive Care Unit of Cardiovascular Surgery Department, Qilu Hospital of Shandong University, Jinan, China
| | - Hao Yuan
- Department of Critical Care Medicine, Sun Yat-Sen University First Affiliated Hospital, Guangzhou, China
| | - Qian Zhai
- National Institute of Healthcare Data Science, Nanjing University, Nanjing, China
| | - Hao Zhang
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lina Zhang
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Meng Zhang
- Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China
| | - Zhongheng Zhang
- Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunguang Zhao
- Intensive Care Unit, XiangYa Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiang Ya Hospital, Central South University, Changsha, China
- Hunan Provincial Clinical Research Center for Critical Care Medicine, Xiang Ya Hospital, Central South University, Changsha, China
| | - Ruiqiang Zheng
- Department of Critical Care Medicine, Northern Jiangsu People’s Hospital; Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lei Zhong
- Department of Intensive Care Unit, National Cancer Center/National Clinical Research Center, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feihu Zhou
- Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Weiguo Zhu
- Department of General Medicine, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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9
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Gilaed A, Shorbaji N, Katzir O, Ankol S, Badarni K, Andrawus E, Roimi M, Katz A, Bar-Lavie Y, Raz A, Epstein D. Early risk factors for prolonged mechanical ventilation in patients with severe blunt thoracic trauma: A retrospective cohort study. Injury 2024; 55:111194. [PMID: 37978015 DOI: 10.1016/j.injury.2023.111194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 10/14/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND A significant proportion of patients with severe chest trauma require mechanical ventilation (MV). Early prediction of the duration of MV may influence clinical decisions. We aimed to determine early risk factors for prolonged MV among adults suffering from severe blunt thoracic trauma. METHODS This retrospective, single-center, cohort study included all patients admitted between January 2014 and December 2020 due to severe blunt chest trauma. The primary outcome was prolonged MV, defined as invasive MV lasting more than 14 days. Multivariable logistic regression was performed to identify independent risk factors for prolonged MV. RESULTS The final analysis included 378 patients. The median duration of MV was 9.7 (IQR 3.0-18.0) days. 221 (58.5 %) patients required MV for more than 7 days and 143 (37.8 %) for more than 14 days. Male gender (aOR 3.01, 95 % CI 1.63-5.58, p < 0.001), age (aOR 1.40, 95 % CI 1.21-1.63, p < 0.001, for each category above 30 years), presence of severe head trauma (aOR 3.77, 95 % CI 2.23-6.38, p < 0.001), and transfusion of >5 blood units on admission (aOR 2.85, 95 % CI 1.62-5.02, p < 0.001) were independently associated with prolonged MV. The number of fractured ribs and the extent of lung contusions were associated with MV for more than 7 days, but not for 14 days. In the subgroup of 134 patients without concomitant head trauma, age (aOR 1.63, 95 % CI 1.18-2.27, p = 0.004, for each category above 30 years), respiratory comorbidities (aOR 9.70, 95 % CI 1.49-63.01, p = 0.017), worse p/f ratio during the first 24 h (aOR 1.55, 95 % CI 1.15-2.09, p = 0.004), and transfusion of >5 blood units on admission (aOR 5.71 95 % CI 1.84-17.68, p = 0.003) were independently associated with MV for more than 14 days. CONCLUSIONS Several predictors have been identified as independently associated with prolonged MV. Patients who meet these criteria are at high risk for prolonged MV and should be considered for interventions that could potentially shorten MV duration and reduce associated complications. Hemodynamically stable, healthy young patients suffering from severe thoracic trauma but no head injury, including those with extensive lung contusions and rib fractures, have a low risk of prolonged MV.
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Affiliation(s)
- Aran Gilaed
- Department of General Thoracic Surgery, Rambam Health Care Campus, Israel
| | - Nadeem Shorbaji
- Department of Diagnostic Imaging, Rambam Health Care Center, Haifa, Israel
| | - Ori Katzir
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shaked Ankol
- Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel
| | - Karawan Badarni
- Critical Care Division, Rambam Health Care Campus, Haifa, Israel
| | - Elias Andrawus
- Critical Care Division, Rambam Health Care Campus, Haifa, Israel
| | - Michael Roimi
- Critical Care Division, Rambam Health Care Campus, Haifa, Israel
| | - Amit Katz
- Department of General Thoracic Surgery, Rambam Health Care Campus, Israel
| | - Yaron Bar-Lavie
- Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel; Critical Care Division, Rambam Health Care Campus, Haifa, Israel
| | - Aeyal Raz
- Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel; Department of Anesthesiology, Rambam Health Care Campus, Haifa, Israel
| | - Danny Epstein
- Critical Care Division, Rambam Health Care Campus, Haifa, Israel.
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10
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Kim J, Kim YK, Kim H, Jung H, Koh S, Kim Y, Yoon D, Yi H, Kim HJ. Machine Learning Algorithms Predict Successful Weaning From Mechanical Ventilation Before Intubation: Retrospective Analysis From the Medical Information Mart for Intensive Care IV Database. JMIR Form Res 2023; 7:e44763. [PMID: 37962939 PMCID: PMC10685278 DOI: 10.2196/44763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/23/2023] [Accepted: 10/08/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND The prediction of successful weaning from mechanical ventilation (MV) in advance of intubation can facilitate discussions regarding end-of-life care before unnecessary intubation. OBJECTIVE We aimed to develop a machine learning-based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation. METHODS We used the Medical Information Mart for Intensive Care IV database, which is an open-access database covering 524,740 admissions of 382,278 patients in Beth Israel Deaconess Medical Center, United States, from 2008 to 2019. We selected adult patients who underwent MV in the intensive care unit (ICU). Clinical and laboratory variables that are considered relevant to the prognosis of the patient in the ICU were selected. Data collected before or within 24 hours of intubation were used to develop machine learning models that predict the probability of successful weaning within 14 days of ventilator support. Developed models were integrated into an ensemble model. Performance metrics were calculated by 5-fold cross-validation for each model, and a permutation feature importance and Shapley additive explanations analysis was conducted to better understand the impacts of individual variables on outcome prediction. RESULTS Of the 23,242 patients, 19,025 (81.9%) patients were successfully weaned from MV within 14 days. Using the preselected 46 clinical and laboratory variables, the area under the receiver operating characteristic curve of CatBoost classifier, random forest classifier, and regularized logistic regression classifier models were 0.860 (95% CI 0.852-0.868), 0.855 (95% CI 0.848-0.863), and 0.823 (95% CI 0.813-0.832), respectively. Using the ensemble voting classifier using the 3 models above, the final model revealed the area under the receiver operating characteristic curve of 0.861 (95% CI 0.853-0.869), which was significantly better than that of Simplified Acute Physiology Score II (0.749, 95% CI 0.742-0.756) and Sequential Organ Failure Assessment (0.588, 95% CI 0.566-0.609). The top features included lactate and anion gap. The model's performance achieved a plateau with approximately the top 21 variables. CONCLUSIONS We developed machine learning algorithms that can predict successful weaning from MV in advance to intubation in the ICU. Our models can aid the appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.
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Affiliation(s)
- Jinchul Kim
- Division of Hematology-Oncology, Department of Internal Medicine, Inha University College of Medicine and Hospital, Incheon, Republic of Korea
| | - Yun Kwan Kim
- Department of the Technology Development, Seers Technology Co, Ltd, Seongnam, Republic of Korea
| | - Hyeyeon Kim
- Crowdworks Co, Ltd, Seoul, Republic of Korea
| | - Hyojung Jung
- Healthcare Artificial Intelligence Team, National Cancer Center, Goyang, Republic of Korea
| | - Soonjeong Koh
- Department of Preventive Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Yujeong Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Hahn Yi
- Asan Medical Center, Asan Institute for Life Sciences, Seoul, Republic of Korea
| | - Hyung-Jun Kim
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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11
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Chander S, Kumari R, Sadarat F, Luhana S. The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence. Curr Probl Cardiol 2023; 48:101805. [PMID: 37209793 DOI: 10.1016/j.cpcardiol.2023.101805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/13/2023] [Indexed: 05/22/2023]
Abstract
Critical care practice has been embodied in the healthcare system since the institutionalization of intensive care units (ICUs) in the late '50s. Over time, this sector has experienced many changes and improvements in providing immediate and dedicated healthcare as patients requiring intensive care are often frail and critically ill with high mortality and morbidity rates. These changes were aided by innovations in diagnostic, therapeutic, and monitoring technologies, as well as the implementation of evidence-based guidelines and organizational structures within the ICU. In this review, we examine these changes in intensive care management over the past 40 years and their impact on the quality of care available to patients. Moreover, the current state of intensive care management is characterized by a multidisciplinary approach and the use of innovative technologies and research databases. Advancements such as telecritical care and artificial intelligence are being increasingly explored, especially since the COVID-19 pandemic, to reduce the length of hospitalization and ICU mortality. With these advancements in intensive care and ever-changing patient needs, critical care experts, hospital managers, and policymakers must also explore appropriate organizational structures and future enhancements within the ICU.
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Affiliation(s)
- Subhash Chander
- Department of Internal Medicine, Mount Sinai Beth Israel Hospital, New York, NY.
| | - Roopa Kumari
- Department of Internal Medicine, Mount Sinai Morningside and West, New York, NY
| | - Fnu Sadarat
- Department of Internal Medicine, University of Buffalo, NY, USA
| | - Sindhu Luhana
- Department of Internal Medicine, Aga Khan University Hospital, Karachi, Pakistan
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12
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Atallah L, Nabian M, Brochini L, Amelung PJ. Machine Learning for Benchmarking Critical Care Outcomes. Healthc Inform Res 2023; 29:301-314. [PMID: 37964452 PMCID: PMC10651403 DOI: 10.4258/hir.2023.29.4.301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 08/23/2023] [Accepted: 09/25/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVES Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML. METHODS We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles). We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective. RESULTS Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results. CONCLUSIONS Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.
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Affiliation(s)
- Louis Atallah
- Clinical Integration and Insights, Philips, Cambridge, MA,
USA
| | - Mohsen Nabian
- Clinical Integration and Insights, Philips, Cambridge, MA,
USA
| | - Ludmila Brochini
- Clinical Integration and Insights, Philips, Eindhoven, The
Netherlands
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13
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Pennati F, Aliverti A, Pozzi T, Gattarello S, Lombardo F, Coppola S, Chiumello D. Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan. Ann Intensive Care 2023; 13:60. [PMID: 37405546 PMCID: PMC10322807 DOI: 10.1186/s13613-023-01154-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/11/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND To develop and validate classifier models that could be used to identify patients with a high percentage of potentially recruitable lung from readily available clinical data and from single CT scan quantitative analysis at intensive care unit admission. 221 retrospectively enrolled mechanically ventilated, sedated and paralyzed patients with acute respiratory distress syndrome (ARDS) underwent a PEEP trial at 5 and 15 cmH2O of PEEP and two lung CT scans performed at 5 and 45 cmH2O of airway pressure. Lung recruitability was defined at first as percent change in not aerated tissue between 5 and 45 cmH2O (radiologically defined; recruiters: Δ45-5non-aerated tissue > 15%) and secondly as change in PaO2 between 5 and 15 cmH2O (gas exchange-defined; recruiters: Δ15-5PaO2 > 24 mmHg). Four machine learning (ML) algorithms were evaluated as classifiers of radiologically defined and gas exchange-defined lung recruiters using different models including different variables, separately or combined, of lung mechanics, gas exchange and CT data. RESULTS ML algorithms based on CT scan data at 5 cmH2O classified radiologically defined lung recruiters with similar AUC as ML based on the combination of lung mechanics, gas exchange and CT data. ML algorithm based on CT scan data classified gas exchange-defined lung recruiters with the highest AUC. CONCLUSIONS ML based on a single CT data at 5 cmH2O represented an easy-to-apply tool to classify ARDS patients in recruiters and non-recruiters according to both radiologically defined and gas exchange-defined lung recruitment within the first 48 h from the start of mechanical ventilation.
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Affiliation(s)
- Francesca Pennati
- Ipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Andrea Aliverti
- Ipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Tommaso Pozzi
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Simone Gattarello
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Fabio Lombardo
- Department of Anesthesiology, University Medical Center Göttingen, Göttingen, Germany
| | - Silvia Coppola
- Department of Anesthesia and Intensive Care, ASST Santi Paolo e Carlo, San Paolo University Hospital, Via Di Rudini 9, Milan, Italy
| | - Davide Chiumello
- Department of Health Sciences, University of Milan, Milan, Italy.
- Department of Anesthesia and Intensive Care, ASST Santi Paolo e Carlo, San Paolo University Hospital, Via Di Rudini 9, Milan, Italy.
- Coordinated Research Center on Respiratory Failure, University of Milan, Milan, Italy.
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14
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Vali M, Paydar S, Seif M, Sabetian G, Abujaber A, Ghaem H. Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach. Sci Rep 2023; 13:5925. [PMID: 37045979 PMCID: PMC10097728 DOI: 10.1038/s41598-023-33159-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 04/07/2023] [Indexed: 04/14/2023] Open
Abstract
The goal of this study was to develop a predictive machine learning model to predict the risk of prolonged mechanical ventilation (PMV) in patients admitted to the intensive care unit (ICU), with a focus on laboratory and Arterial Blood Gas (ABG) data. This retrospective cohort study included ICU patients admitted to Rajaei Hospital in Shiraz between 2016 and March 20, 2022. All adult patients requiring mechanical ventilation and seeking ICU admission had their data analyzed. Six models were created in this study using five machine learning models (PMV more than 3, 5, 7, 10, 14, and 23 days). Patients' demographic characteristics, Apache II, laboratory information, ABG, and comorbidity were predictors. This study used Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and C.5 decision tree (C.5 DT) to predict PMV. The study enrolled 1138 eligible patients, excluding brain-dead patients and those without mechanical ventilation or a tracheostomy. The model PMV > 14 days showed the best performance (Accuracy: 83.63-98.54). The essential ABG variables in our two optimal models (artificial neural network and decision tree) in the PMV > 14 models include FiO2, paCO2, and paO2. This study provides evidence that machine learning methods outperform traditional methods and offer a perspective for achieving a consensus definition of PMV. It also introduces ABG and laboratory information as the two most important variables for predicting PMV. Therefore, there is significant value in deploying such models in clinical practice and making them accessible to clinicians to support their decision-making.
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Affiliation(s)
- Mohebat Vali
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Paydar
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mozhgan Seif
- Non-Communicable Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Golnar Sabetian
- Anesthesiology and Critical Care Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Haleh Ghaem
- Non-Communicable Diseases Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
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15
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Giri J, Al-Lohedan HA, Mohammad F, Soleiman AA, Chadge R, Mahatme C, Sunheriya N, Giri P, Mutyarapwar D, Dhapke S. A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach. Bioengineering (Basel) 2023; 10:bioengineering10040418. [PMID: 37106605 PMCID: PMC10136217 DOI: 10.3390/bioengineering10040418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023] Open
Abstract
Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.
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Affiliation(s)
- Jayant Giri
- Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
- Correspondence:
| | - Hamad A. Al-Lohedan
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Faruq Mohammad
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Ahmed A. Soleiman
- Department of Chemistry, College of Science, Southern University and A&M College, Baton Rouge, LA 70813, USA
| | - Rajkumar Chadge
- Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
| | - Chetan Mahatme
- Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
| | - Neeraj Sunheriya
- Mechanical Department, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India
| | - Pallavi Giri
- Laxminarayan Institute of Technology, Nagpur 440033, India
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16
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Wang Z, Zhang L, Huang T, Yang R, Cheng H, Wang H, Yin H, Lyu J. Developing an explainable machine learning model to predict the mechanical ventilation duration of patients with ARDS in intensive care units. Heart Lung 2023; 58:74-81. [PMID: 36423504 PMCID: PMC9678346 DOI: 10.1016/j.hrtlng.2022.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/25/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS) is common in intensive care units with high mortality rate and mechanical ventilation (MV) is the most important related treatment. Early prediction of MV duration has benefit for patients risk stratification and care strategies support. OBJECTIVE To develop an explainable model for predicting mechanical ventilation (MV) duration in patients with ARDS using the machine learning (ML) approach. METHOD The number of 1,148, 1,697, and 29 ARDS patients admitted to intensive care units (ICU) in the MIMIC-IV, eICU-CRD, and AmsterdamUMCdb databases were included in the study. Features at MV initiation from the MIMIC-IV dataset were used to train prediction models based on seven supervised machine learning algorithms. After 5-fold cross-validation for hyperparameters tuning, the hyperparameters- optimized model of different algorithms was tested by external datasets extracted from eICU-CRD and Amsterdamumcdb. Finally, three descriptive machine learning explanation methods were conducted for the model explanation. RESULT The XGBoosting model showed the most stable and accurate performance among two testing datasets (RMSE= 5.57 and 5.46 days in eICU-CRD and AmsterdamUMCdb) and was selected as the optimal model. The model explanation based on SHAP, LIME, and DALEX results showed a consistent result, vasopressor, PH, and SOFA score had the highest effect on MV duration prediction. CONCLUSION ML models with features at MV initiation can accurate predict MV duration in patients with ARDS in ICUs. Among seven algorithms, XGB models showed the best performance (RMSE= 5.57 and 5.46 in two external datasets). LIME, SHAP, and Breakdown methods showed good performance as AXI methods.
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Affiliation(s)
- Zichen Wang
- Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China; Department of Public Health, University of California, Irvine, Irvine, California, United State
| | - Luming Zhang
- Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China; Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China
| | - Tao Huang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China
| | - Rui Yang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China; School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Statistics, Iowa State University, Ames, Iowa, Unite States
| | - Haiyan Yin
- Department of Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China; Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, Guangdong, China.
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17
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Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13030357. [PMID: 36766460 PMCID: PMC9914063 DOI: 10.3390/diagnostics13030357] [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: 12/22/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
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Affiliation(s)
- Gerui Zhang
- Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Lin Luo
- Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Zhuo Liu
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
- Correspondence:
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Polz M, Bergmoser K, Horn M, Schörghuber M, Lozanović J, Rienmüller T, Baumgartner C. A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients. Front Physiol 2023; 14:1101966. [PMID: 37123264 PMCID: PMC10133509 DOI: 10.3389/fphys.2023.1101966] [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: 11/18/2022] [Accepted: 04/04/2023] [Indexed: 05/02/2023] Open
Abstract
Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8-16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.
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Affiliation(s)
- Mathias Polz
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria
| | - Katharina Bergmoser
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria
- CBmed Center for Biomarker Research in Medicine, Graz, STM, Austria
| | - Martin Horn
- Institute of Automation and Control, Graz University of Technology, Graz, STM, Austria
| | - Michael Schörghuber
- Department of Anesthesiology and Intensive Care Medicine, Medical University of Graz, Graz, STM, Austria
| | - Jasmina Lozanović
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria
| | - Theresa Rienmüller
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria
| | - Christian Baumgartner
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, STM, Austria
- *Correspondence: Christian Baumgartner,
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Wang H, Wang C, Xu J, Yuan J, Liu G, Zhang G. Invasive mechanical ventilation probability estimation using machine learning methods based on non-invasive parameters. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lee KC, Hsu CC, Lin TC, Chiang HF, Horng GJ, Chen KT. Prediction of Prognosis in Patients with Trauma by Using Machine Learning. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58101379. [PMID: 36295540 PMCID: PMC9606956 DOI: 10.3390/medicina58101379] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 09/21/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022]
Abstract
Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.
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Affiliation(s)
- Kuo-Chang Lee
- Emergency Department, Chi-Mei Medical Center, Tainan 710402, Taiwan
| | - Chien-Chin Hsu
- Emergency Department, Chi-Mei Medical Center, Tainan 710402, Taiwan
- Department of Biotechnology, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
| | - Tzu-Chieh Lin
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
| | - Hsiu-Fen Chiang
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
| | - Gwo-Jiun Horng
- Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
| | - Kuo-Tai Chen
- Emergency Department, Chi-Mei Medical Center, Tainan 710402, Taiwan
- Correspondence: ; Tel.: +886-6-2812811 (ext. 57196); Fax: +886-6-2816161
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Mortality Risk Factors in Patients Admitted with the Primary Diagnosis of Tracheostomy Complications: An Analysis of 8026 Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159031. [PMID: 35897404 PMCID: PMC9332357 DOI: 10.3390/ijerph19159031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/16/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Background: Tracheostomy is a procedure commonly conducted in patients undergoing emergency admission and requires prolonged mechanical ventilation. In the present study, the aim was to determine the prevalence and risk factors of mortality among emergently admitted patients with tracheostomy complications, during the years 2005−2014. Methods: This was a retrospective cohort study. Demographics and clinical data were obtained from the National Inpatient Sample, 2005−2014, to evaluate elderly (65+ years) and non-elderly adult patients (18−64 years) with tracheostomy complications (ICD-9 code, 519) who underwent emergency admission. A multivariable logistic regression model with backward elimination was used to identify the association between predictors and in-hospital mortality. Results: A total of 4711 non-elderly and 3315 elderly patients were included. Females included 44.5% of the non-elderly patients and 47.6% of the elderly patients. In total, 181 (3.8%) non-elderly patients died, of which 48.1% were female, and 163 (4.9%) elderly patients died, of which 48.5% were female. The mean (SD) age of the non-elderly patients was 50 years and for elderly patients was 74 years. The mean age at the time of death of non-elderly patients was 53 years and for elderly patients was 75 years. The odds ratio (95% confidence interval, p-value) of some of the pertinent risk factors for mortality showed by the final regression model were older age (OR = 1.007, 95% CI: 1.001−1.013, p < 0.02), longer hospital length of stay (OR = 1.008, 95% CI: 1.001−1.016, p < 0.18), cardiac disease (OR = 3.21, 95% CI: 2.48−4.15, p < 0.001), and liver disease (OR = 2.61, 95% CI: 1.73−3.93, p < 0.001). Conclusion: Age, hospital length of stay, and several comorbidities have been shown to be significant risk factors in in-hospital mortality in patients admitted emergently with the primary diagnosis of tracheostomy complications. Each year of age increased the risk of mortality by 0.7% and each additional day in the hospital increased it by 0.8%.
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Bihani P, Jaju R, Saxena M, Paliwal N, Tharu V. “The show must go on”: Aftermath of Covid-19 on anesthesiology residency programs. Saudi J Anaesth 2022; 16:452-456. [DOI: 10.4103/sja.sja_563_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 11/04/2022] Open
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Sayed M, Riaño D, Villar J. Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning. J Clin Med 2021; 10:jcm10173824. [PMID: 34501270 PMCID: PMC8432117 DOI: 10.3390/jcm10173824] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/16/2021] [Accepted: 08/23/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4–9.8 days) in MIMIC-III and 5.0 days (IQR 3.0–9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10–6.41 days, and it was externally validated in eICU with RMSE of 5.87–6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.
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Affiliation(s)
- Mohammed Sayed
- Department of Computer Engineering, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Spain; (M.S.); (D.R.)
| | - David Riaño
- Department of Computer Engineering, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007 Tarragona, Spain; (M.S.); (D.R.)
| | - Jesús Villar
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Monforte de Lemos 3-5, Pabellón 11, 28029 Madrid, Spain
- Multidisciplinary Organ Dysfunction Evaluation Research Network, Research Unit, Hospital Universitario Dr. Negrín, Barranco de la Ballena s/n, 4th Floor-South Wing, 35019 Las Palmas de Gran Canaria, Spain
- Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, 38 Shuter St., Toronto, ON M5B 1A6, Canada
- Correspondence:
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Sharma M, Taweesedt PT, Surani S. Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium. Cureus 2021; 13:e15531. [PMID: 34268051 PMCID: PMC8266146 DOI: 10.7759/cureus.15531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2021] [Indexed: 11/06/2022] Open
Abstract
We have witnessed rapid advancement in technology over the last few decades. With the advent of artificial intelligence (AI), newer avenues have opened for researchers. AI has added an entirely new dimension to this technological boom. Researchers in medical science have been excited about the tantalizing prospect of utilizing AI for the benefit of patient care. Lately, we have come across studies trying to test and validate various models based on AI to improve patient care strategies in critical care medicine as well. Thus, in this review, we will attempt to succinctly review current literature discussing AI in critical care medicine and analyze its future utility based on prevailing evidence.
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Affiliation(s)
- Munish Sharma
- Internal Medicine, Corpus Christi Medical Center, Corpus Christi, USA
| | | | - Salim Surani
- Internal Medicine, Corpus Christi Medical Center, Corpus Christi, USA
- Internal Medicine, University of North Texas, Dallas, USA
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Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients. J Clin Med 2021; 10:jcm10102172. [PMID: 34069799 PMCID: PMC8157228 DOI: 10.3390/jcm10102172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/14/2021] [Accepted: 05/15/2021] [Indexed: 12/13/2022] Open
Abstract
Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.
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Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data. Crit Care Explor 2021; 3:e0402. [PMID: 34079945 PMCID: PMC8162520 DOI: 10.1097/cce.0000000000000402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Background Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes. Objectives The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased. Derivation Cohort All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699). Validation Cohort External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389). Prediction Model All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score. Results Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives. Conclusions Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.
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Mamandipoor B, Frutos-Vivar F, Peñuelas O, Rezar R, Raymondos K, Muriel A, Du B, Thille AW, Ríos F, González M, del-Sorbo L, del Carmen Marín M, Pinheiro BV, Soares MA, Nin N, Maggiore SM, Bersten A, Kelm M, Bruno RR, Amin P, Cakar N, Suh GY, Abroug F, Jibaja M, Matamis D, Zeggwagh AA, Sutherasan Y, Anzueto A, Wernly B, Esteban A, Jung C, Osmani V. Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation. BMC Med Inform Decis Mak 2021; 21:152. [PMID: 33962603 PMCID: PMC8102841 DOI: 10.1186/s12911-021-01506-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/26/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid-base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. METHODS We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. RESULTS Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. CONCLUSION The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes. TRIAL REGISTRATION NCT02731898 ( https://clinicaltrials.gov/ct2/show/NCT02731898 ), prospectively registered on April 8, 2016.
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Affiliation(s)
| | - Fernando Frutos-Vivar
- Hospital Universitario de Getafe & Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Oscar Peñuelas
- Hospital Universitario de Getafe & Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Richard Rezar
- Clinic of Internal Medicine II, Department of Cardiology, Paracelsus Medical University of Salzburg, 5020 Salzburg, Austria
| | | | - Alfonso Muriel
- Clinic of Internal Medicine II, Department of Cardiology, Paracelsus Medical University of Salzburg, 5020 Salzburg, Austria
- Unidad de Bioestadística Clinica Hospital Ramón y Cajal, Instituto Ramón y Cajal de Investigaciones Sanitarias (IRYCIS) & Centro de Investigación en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Bin Du
- Peking Union Medical College Hospital, Beijing, People’s Republic of China
| | | | - Fernando Ríos
- Hospital Nacional Alejandro Posadas, Buenos Aires, Argentina
| | - Marco González
- Clínica Medellín & Universidad Pontificia Bolivariana, Medellín, Colombia
| | - Lorenzo del-Sorbo
- Interdepartmental Division of Critical Care Medicine, Toronto, ON Canada
| | - Maria del Carmen Marín
- Hospital Regional 1° de Octubre, Instituto de Seguridad Y Servicios Sociales de Los Trabajadores del Estado (ISSSTE), México, DF México
| | - Bruno Valle Pinheiro
- Pulmonary Research Laboratory, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | | | | | | | - Andrew Bersten
- Department of Critical Care Medicine, Flinders University, Adelaide, South Australia Australia
| | - Malte Kelm
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, University of Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Raphael Romano Bruno
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, University of Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Pravin Amin
- Bombay Hospital Institute of Medical Sciences, Mumbai, India
| | - Nahit Cakar
- Istanbul Faculty of Medicine, Istanbul, Turkey
| | - Gee Young Suh
- Department of Critical Care Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | | | - Manuel Jibaja
- Hospital de Especialidades Eugenio Espejo, Quito, Ecuador
| | | | - Amine Ali Zeggwagh
- Centre Hospitalier Universitarie Ibn Sina - Mohammed V University, Rabat, Morocco
| | - Yuda Sutherasan
- Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Antonio Anzueto
- South Texas Veterans Health Care System and University of Texas Health Science Center, San Antonio, TX USA
| | - Bernhard Wernly
- Clinic of Internal Medicine II, Department of Cardiology, Paracelsus Medical University of Salzburg, 5020 Salzburg, Austria
| | - Andrés Esteban
- Hospital Universitario de Getafe & Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Christian Jung
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, University of Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Venet Osmani
- Fondazione Bruno Kessler Research Institute, Trento, Italy
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Ossai CI, Wickramasinghe N. Intelligent decision support with machine learning for efficient management of mechanical ventilation in the intensive care unit - A critical overview. Int J Med Inform 2021; 150:104469. [PMID: 33906020 DOI: 10.1016/j.ijmedinf.2021.104469] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/16/2021] [Accepted: 04/18/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Effective management of Mechanical Ventilation (MV) is vital for reducing morbidity, mortality, and cost of healthcare. OBJECTIVE This study aims to synthesize evidence for effective MV management through Intelligent decision support (IDS) with Machine Learning (ML). METHOD Databases that include EBSCO, IEEEXplore, Google Scholar, SCOPUS, and the Web of Science were systematically searched to identify studies on IDS for effective MV management regarding Tidal Volume (TV), asynchrony, weaning, and other outcomes such as the risk of Prolonged Mechanical ventilation (PMV). The quality of the articles identified was assessed with a modified Joanna Briggs Institute (JBI) critical appraisal checklist for cross-sessional research. RESULTS A total of 26 articles were identified for the study that has IDS for TV (n = 2, 7.8 %), asynchrony (n = 9, 34.6 %), weaning (n = 12, 46.2 %), and others (n = 3, 11.5 %). It was affirmed that implementing IDS in MV management will enhance seamless ICU patient management following the utilization of various Machine Learning (ML) algorithms in decision support. The studies relied on (n = 14) ML algorithms to predict the TV, asynchrony, weaning, risk of PMV and Positive End-Expiratory Pressure (PEEP) changes of 11-20262 ICU patients records with model inputs ranging from (n = 1) for timeseries analysis of TV to (n = 47) for weaning prediction. CONCLUSIONS The small data size, poor study design, and result reporting, with the heterogeneity of techniques used in the various studies, hampered the development of a unified approach for managing MV efficiency in TV monitoring, asynchrony, and weaning predictions. Notwithstanding, the ensemble model was able to predict TV, asynchrony, and weaning to a higher accuracy than the other algorithms.
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Affiliation(s)
- Chinedu I Ossai
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia.
| | - Nilmini Wickramasinghe
- Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John street Hawthorn, Victoria, 3122, Australia; Epworth Healthcare Australia, Australia.
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Wong AKI, Cheung PC, Kamaleswaran R, Martin GS, Holder AL. Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome. Front Big Data 2020; 3:579774. [PMID: 33693419 PMCID: PMC7931901 DOI: 10.3389/fdata.2020.579774] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/22/2020] [Indexed: 12/23/2022] Open
Abstract
Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.
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Affiliation(s)
- An-Kwok Ian Wong
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, United States
| | | | | | - Greg S. Martin
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Andre L. Holder
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, United States
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Hagan R, Gillan CJ, Spence I, McAuley D, Shyamsundar M. Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units. Comput Biol Med 2020; 126:104030. [PMID: 33068808 PMCID: PMC7543875 DOI: 10.1016/j.compbiomed.2020.104030] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Mechanical ventilation is a lifesaving tool and provides organ support for patients with respiratory failure. However, injurious ventilation due to inappropriate delivery of high tidal volume can initiate or potentiate lung injury. This could lead to acute respiratory distress syndrome, longer duration of mechanical ventilation, ventilator associated conditions and finally increased mortality. In this study, we explore the viability and compare machine learning methods to generate personalized predictive alerts indicating violation of the safe tidal volume per ideal body weight (IBW) threshold that is accepted as the upper limit for lung protective ventilation (LPV), prior to application to patients. We process streams of patient respiratory data recorded per minute from ventilators in an intensive care unit and apply several state-of-the-art time series prediction methods to forecast the behavior of the tidal volume metric per patient, 1 hour ahead. Our results show that boosted regression delivers better predictive accuracy than other methods that we investigated and requires relatively short execution times. Long short-term memory neural networks can deliver similar levels of accuracy but only after much longer periods of data acquisition, further extended by several hours computing time to train the algorithm. Utilizing Artificial Intelligence, we have developed a personalized clinical decision support tool that can predict tidal volume behavior within 10% accuracy and compare alerts recorded from a real world system to highlight that our models would have predicted violations 1 hour ahead and can therefore conclude that the algorithms can provide clinical decision support.
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Affiliation(s)
- Rachael Hagan
- School of Electrical and Electronic Engineering and Computer Science, Queen's University Belfast, Queen's Road, Queen's Island, Belfast, Northern Ireland, BT9 3DT, United Kingdom.
| | - Charles J Gillan
- School of Electrical and Electronic Engineering and Computer Science, Queen's University Belfast, Queen's Road, Queen's Island, Belfast, Northern Ireland, BT9 3DT, United Kingdom
| | - Ivor Spence
- School of Electrical and Electronic Engineering and Computer Science, Queen's University Belfast, Queen's Road, Queen's Island, Belfast, Northern Ireland, BT9 3DT, United Kingdom
| | - Danny McAuley
- The Centre for Experimental Medicine, School of Medicine, Dentistry and Biological Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast, Northern Ireland, BT9 7BL, United Kingdom
| | - Murali Shyamsundar
- The Centre for Experimental Medicine, School of Medicine, Dentistry and Biological Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast, Northern Ireland, BT9 7BL, United Kingdom
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31
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Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106612] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Johnson KB, Wei W, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci 2020; 14:86-93. [PMID: 32961010 PMCID: PMC7877825 DOI: 10.1111/cts.12884] [Citation(s) in RCA: 290] [Impact Index Per Article: 72.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/11/2020] [Indexed: 12/16/2022] Open
Abstract
The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
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Affiliation(s)
- Kevin B. Johnson
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of PediatricsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Wei‐Qi Wei
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | | | - Mark E. Frisse
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Karl Misulis
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Clinical NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kyu Rhee
- IBM Watson HealthCambridgeMassachusettsUSA
| | - Juan Zhao
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
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33
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Nguyen D, Ngo B, vanSonnenberg E. AI in the Intensive Care Unit: Up-to-Date Review. J Intensive Care Med 2020; 36:1115-1123. [PMID: 32985324 DOI: 10.1177/0885066620956620] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential lies in its ability to process large volumes of data and perform complex pattern analyses. The ICU is an area of medicine that is particularly conducive to AI applications. Much AI ICU research currently is focused on improving high volumes of data on high-risk patients and making clinical workflow more efficient. Emerging topics of AI medicine in the ICU include AI sensors, sepsis prediction, AI in the NICU or SICU, and the legal role of AI in medicine. This review will cover the current applications of AI medicine in the ICU, potential pitfalls, and other AI medicine-related topics relevant for the ICU.
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Affiliation(s)
- Diep Nguyen
- University of Arizona College of Medicine Phoenix, AZ, USA
| | - Brandon Ngo
- University of Arizona College of Medicine Phoenix, AZ, USA
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34
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Data-driven ICU management: Using Big Data and algorithms to improve outcomes. J Crit Care 2020; 60:300-304. [PMID: 32977139 DOI: 10.1016/j.jcrc.2020.09.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/17/2020] [Accepted: 09/02/2020] [Indexed: 12/13/2022]
Abstract
The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic. In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care.
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35
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The present and future role of artificial intelligence and machine learning in anesthesiology. Int Anesthesiol Clin 2020; 58:7-16. [PMID: 32841964 DOI: 10.1097/aia.0000000000000294] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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36
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Iqbal U, Celi LA, Li YCJ. How Can Artificial Intelligence Make Medicine More Preemptive? J Med Internet Res 2020; 22:e17211. [PMID: 32780024 PMCID: PMC7448175 DOI: 10.2196/17211] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 06/05/2020] [Accepted: 06/25/2020] [Indexed: 12/21/2022] Open
Abstract
In this paper we propose the idea that Artificial intelligence (AI) is ushering in a new era of "Earlier Medicine," which is a predictive approach for disease prevention based on AI modeling and big data. The flourishing health care technological landscape is showing great potential-from diagnosis and prescription automation to the early detection of disease through efficient and cost-effective patient data screening tools that benefit from the predictive capabilities of AI. Monitoring the trajectories of both in- and outpatients has proven to be a task AI can perform to a reliable degree. Predictions can be a significant advantage to health care if they are accurate, prompt, and can be personalized and acted upon efficiently. This is where AI plays a crucial role in "Earlier Medicine" implementation.
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Affiliation(s)
- Usman Iqbal
- Master Program in Global Health & Development, PhD Program in Global Health & Health Security, College of Public Health, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States.,Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.,Department of Biostatistics, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Yu-Chuan Jack Li
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Department of Dermatology, Taipei Municipal Wan-Fang Hospital, Taipei, Taiwan.,International Medical Informatics Association, Geneva, Switzerland
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The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery. Eur J Trauma Emerg Surg 2020; 47:757-762. [DOI: 10.1007/s00068-020-01444-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/16/2020] [Indexed: 12/11/2022]
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38
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Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach. PLoS One 2020; 15:e0235231. [PMID: 32639971 PMCID: PMC7343348 DOI: 10.1371/journal.pone.0235231] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/10/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI). METHODS This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients' demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV. RESULTS The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power. CONCLUSION This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV.
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Parreco J, Soe-Lin H, Parks JJ, Byerly S, Chatoor M, Buicko JL, Namias N, Rattan R. Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury. Am Surg 2020. [DOI: 10.1177/000313481908500731] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurements per patient. Patients with AKI were identified and trends in vital signs and laboratory values were determined by calculating the slope of the least-squares-fit linear equation using three days for each value. Different machine learning classifiers (gradient boosted trees [GBT], logistic regression, and deep learning) were trained to predict AKI using the laboratory values, vital signs, and slopes. There were 151,098 ICU stays identified and the rate of AKI was 5.6 per cent. The best performing algorithm was GBT with an AUC of 0.834 ± 0.006 and an F-measure of 42.96 per cent ± 1.26 per cent. Logistic regression performed with an AUC of 0.827 ± 0.004 and an F-measure of 28.29 per cent ± 1.01 per cent. Deep learning performed with an AUC of 0.817 ± 0.005 and an F-measure of 42.89 per cent ± 0.91 per cent. The most important variable for GBT was the slope of the minimum creatinine (30.32%). This study identifies the best performing machine learning algorithms for predicting AKI using trends in laboratory values in ICU patients. Early identification of these patients using readily available data indicates that incorporating machine learning predictive models into electronic medical record systems is an inevitable requisite for improving patient outcomes.
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Affiliation(s)
- Joshua Parreco
- Ryder Trauma Center, Jackson Memorial Hospital, Miami, Florida
| | - Hahn Soe-Lin
- Ryder Trauma Center, Jackson Memorial Hospital, Miami, Florida
| | | | - Saskya Byerly
- Ryder Trauma Center, Jackson Memorial Hospital, Miami, Florida
| | - Matthew Chatoor
- Ryder Trauma Center, Jackson Memorial Hospital, Miami, Florida
| | - Jessica L. Buicko
- Division of Endocrine Surgery, Weil Cornell Medical Center, New York, New York
| | - Nicholas Namias
- Division of Trauma Surgery and Surgical Critical Care, Dewitt Daughtry Family Department of Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Rishi Rattan
- Division of Trauma Surgery and Surgical Critical Care, Dewitt Daughtry Family Department of Surgery, University of Miami Miller School of Medicine, Miami, Florida
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Lal A, Pinevich Y, Gajic O, Herasevich V, Pickering B. Artificial intelligence and computer simulation models in critical illness. World J Crit Care Med 2020; 9:13-19. [PMID: 32577412 PMCID: PMC7298588 DOI: 10.5492/wjccm.v9.i2.13] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 04/21/2020] [Accepted: 05/12/2020] [Indexed: 02/06/2023] Open
Abstract
Widespread implementation of electronic health records has led to the increased use of artificial intelligence (AI) and computer modeling in clinical medicine. The early recognition and treatment of critical illness are central to good outcomes but are made difficult by, among other things, the complexity of the environment and the often non-specific nature of the clinical presentation. Increasingly, AI applications are being proposed as decision supports for busy or distracted clinicians, to address this challenge. Data driven "associative" AI models are built from retrospective data registries with missing data and imprecise timing. Associative AI models lack transparency, often ignore causal mechanisms, and, while potentially useful in improved prognostication, have thus far had limited clinical applicability. To be clinically useful, AI tools need to provide bedside clinicians with actionable knowledge. Explicitly addressing causal mechanisms not only increases validity and replicability of the model, but also adds transparency and helps gain trust from the bedside clinicians for real world use of AI models in teaching and patient care.
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Affiliation(s)
- Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Rochester, Mayo Clinic, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
| | - Yuliya Pinevich
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Rochester, Mayo Clinic, MN 55905, United States
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
| | - Vitaly Herasevich
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
| | - Brian Pickering
- Multidisciplinary Epidemiology and Translational Research in Intensive Care Group, Mayo Clinic, Rochester, MN 55905, United States
- Department of Anesthesiology and Perioperative Medicine, Division of Critical Care, Mayo Clinic, Rochester, MN 55905, United States
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Yuan KC, Tsai LW, Lee KH, Cheng YW, Hsu SC, Lo YS, Chen RJ. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform 2020; 141:104176. [PMID: 32485555 DOI: 10.1016/j.ijmedinf.2020.104176] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/16/2020] [Accepted: 05/11/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of storing a large quantity of clinical data that can facilitate the development of artificial intelligence (AI) in medicine. However, several difficulties, such as poor structure and heterogenicity of the raw EMR data, are encountered when introducing AI with ICU data. Labor-intensive work, including manual data entry, personal medical records sorting, and laboratory results interpretation may hinder the progress of AI. In this article, we introduce the developing of an AI algorithm designed for sepsis diagnosis using pre-selected features; and compare the performance of the AI algorithm with SOFA score based diagnostic method. MATERIALS AND METHODS This is a prospective open-label cohort study. A specialized EMR, named TED_ICU, was implemented for continuous data recording. One hundred six clinical features relevant to sepsis diagnosis were selected prospectively. A labeling work to allocate SEPSIS or NON_SEPSIS status for each ICU patient was performed by the in-charge intensivist according to SEPSIS-3 criteria, along with the automatic recording of selected features every day by TED_ICU. Afterward, we use de-identified data to develop the AI algorithm. Several machine learning methods were evaluated using 5-fold cross-validation, and XGBoost, a decision-tree based algorithm was adopted for our AI algorithm development due to best performance. RESULTS The study was conducted between August 2018 and December 2018 for the first stage of analysis. We collected 1588 instances, including 444 SEPSIS and 1144 NON-SEPSIS, from 434 patients. The 434 patients included 259 (59.6%) male patients and 175 female patients. The mean age was 67.6-year-old, and the mean APACHE II score was 13.8. The SEPSIS cohort had a higher SOFA score and increased use of organ support treatment. The AI algorithm was developed with a shuffle method using 80% of the instances for training and 20% for testing. The established AI algorithm achieved the following: accuracy = 82% ± 1%; sensitivity = 65% ± 5%; specificity = 88% ± 2%; precision = 67% ± 3%; and F1 = 0.66 ± 0.02. The area under the receiver operating characteristic curve (AUROC) was approximately 0.89. The SOFA score was used on the same 1588 instances for sepsis diagnosis, and the result was inferior to our AI algorithm (AUROC = 0.596). CONCLUSION Using real-time data, collected by EMR, from the ICU daily practice, our AI algorithm established with pre-selected features and XGBoost can provide a timely diagnosis of sepsis with an accuracy greater than 80%. AI algorithm also outperforms the SOFA score in sepsis diagnosis and exhibits practicality as clinicians can deploy appropriate treatment earlier. The early and precise response of this AI algorithm will result in cost reduction, outcome improvement, and benefit for healthcare systems, medical staff, and patients as well.
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Affiliation(s)
- Kuo-Ching Yuan
- Department of Emergency and Critical Care Medicine, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Lung-Wen Tsai
- Department of Medicine Education, Taipei Medical University Hospital, Taipei, Taiwan
| | | | | | | | - Yu-Sheng Lo
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Ray-Jade Chen
- Division of General Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan; Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
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Shao D, Straub J, Matrka L. Obesity as a Predictor of Prolonged Mechanical Ventilation. Otolaryngol Head Neck Surg 2020; 163:750-754. [DOI: 10.1177/0194599820923601] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Objective To examine the effect of including obesity with parameters of the I-TRACH scale in predicting the need for prolonged mechanical ventilation. Study Design A retrospective cohort study. Setting Tertiary care academic medical center. Subjects and Methods Consecutive patients were identified retrospectively over a 45-month period based on need for mechanical ventilation in the medical intensive care unit. Chart review was performed to collect demographic information as well as clinical data, including duration of mechanical ventilation, body mass index (BMI), and I-TRACH parameters (heart rate >110, serum urea nitrogen >25, serum pH <7.25, serum creatinine >2, serum bicarbonate <20). Statistical analysis was performed to identify any predictors of prolonged mechanical ventilation, defined as ≥14 days and as ≥10 days. Results In total, 455 patients were identified, with an average duration of mechanical ventilation of 10.4 days (range, 0-248 days). On univariate and multivariate regression analysis, only BMI >30 reached statistical significance with respect to prolonged mechanical ventilation ( P < .05). The I-TRACH parameters—either alone or in combination—were not significantly predictive. Conclusion This study challenges previous findings regarding the I-TRACH scale and the relation of its parameters to prolonged mechanical ventilation. Furthermore, BMI >30 alone was predictive of prolonged intubation. Inclusion of BMI in predictive models could assist current decision making in determining the likelihood of prolonged mechanical ventilation in medical intensive care unit patients going forward, and obesity should be considered a predictor of prolonged mechanical ventilation.
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Affiliation(s)
- Diana Shao
- The Ohio State University College of Medicine, Columbus, Ohio, USA
| | - Jeffrey Straub
- Department of Otolaryngology—Head and Neck Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
| | - Laura Matrka
- Department of Otolaryngology—Head and Neck Surgery, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA
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Lhommet C, Garot D, Grammatico-Guillon L, Jourdannaud C, Asfar P, Faisy C, Muller G, Barker KA, Mercier E, Robert S, Lanotte P, Goudeau A, Blasco H, Guillon A. Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation? BMC Pulm Med 2020; 20:62. [PMID: 32143620 PMCID: PMC7060632 DOI: 10.1186/s12890-020-1089-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 02/17/2020] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia? METHODS We included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant. RESULTS We included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia). CONCLUSION Neither experts nor an AI algorithm can predict the microbial etiology of CAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.
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Affiliation(s)
- Claire Lhommet
- CHRU Tours, Service de Médecine Intensive Réanimation, 2 Bd Tonnellé, F-37044, Tours Cedex 9, France
| | - Denis Garot
- CHRU Tours, Service de Médecine Intensive Réanimation, 2 Bd Tonnellé, F-37044, Tours Cedex 9, France
| | - Leslie Grammatico-Guillon
- CHRU Tours, Service d'Information Médicale, d'Epidémiologie et d'Economie de la Santé, Tours, France
| | | | - Pierre Asfar
- CHRU Angers, Service médecine intensive et réanimation, Angers, France
| | - Christophe Faisy
- UPRES EA220, Laboratoire de recherche en pharmacologie respiratoire, Université Versailles Saint-Quentin, Suresnes, France
| | - Grégoire Muller
- CHR Orléans, Service de Médecine Intensive Réanimation, Orléans, France
| | - Kimberly A Barker
- Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
| | - Emmanuelle Mercier
- CHRU Tours, Service de Médecine Intensive Réanimation, 2 Bd Tonnellé, F-37044, Tours Cedex 9, France
| | - Sylvie Robert
- CHRU Tours, Service de bactériologie, virologie et hygiène hospitalière, Tours, France
| | - Philippe Lanotte
- CHRU Tours, Service de bactériologie, virologie et hygiène hospitalière, Tours, France
| | - Alain Goudeau
- CHRU Tours, Service de bactériologie, virologie et hygiène hospitalière, Tours, France
| | - Helene Blasco
- CHRU Tours, Laboratoire de Biochimie et Biologie Moléculaire, Tours, France.,INSERM U 930, Université de Tours, Tours, France
| | - Antoine Guillon
- CHRU Tours, Service de Médecine Intensive Réanimation, 2 Bd Tonnellé, F-37044, Tours Cedex 9, France. .,INSERM, centre d'étude des pathologies respiratoires (CEPR), U1100, Université de Tours, Tours, France.
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Mlodzinski E, Stone DJ, Celi LA. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review. Pulm Ther 2020; 6:67-77. [PMID: 32048244 PMCID: PMC7229087 DOI: 10.1007/s41030-020-00110-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Indexed: 01/22/2023] Open
Abstract
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
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Affiliation(s)
- Eric Mlodzinski
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
| | - David J Stone
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.,Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Leo A Celi
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
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Could "Big Brother" Be Joining the Early Mobilization Team? Crit Care Med 2019; 47:1274-1276. [PMID: 31415314 DOI: 10.1097/ccm.0000000000003886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Michard F, Teboul JL. Predictive analytics: beyond the buzz. Ann Intensive Care 2019; 9:46. [PMID: 30976934 PMCID: PMC6459443 DOI: 10.1186/s13613-019-0524-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 04/08/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
| | - Jean Louis Teboul
- Bicetre University Hospital, Paris South University, Le Kremlin Bicetre, France
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Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. Crit Care Clin 2019; 35:483-495. [PMID: 31076048 DOI: 10.1016/j.ccc.2019.02.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
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Lovejoy CA, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:7. [PMID: 30630492 PMCID: PMC6327423 DOI: 10.1186/s13054-018-2301-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 12/21/2018] [Indexed: 11/10/2022]
Affiliation(s)
- Christopher A Lovejoy
- Cera Care, London, UK. .,St George's Hospital, Blackshaw Road, London, SW17 0QT, UK.
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