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Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD. A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 2024; 25:232. [PMID: 38834976 DOI: 10.1186/s12931-024-02834-x] [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: 02/13/2024] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
AIM Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS. METHOD In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS. We searched the following databases: Google Scholar, PubMed, and EBSCO from 2009 to 2023. A total of 243 studies was screened, in which, 52 studies were included for review and analysis. We integrated knowledge of previous work providing the state of art and overview of explainable decision models in machine learning and have identified areas for future research. RESULTS Gradient boosting is the most common and successful method utilised in 12 (23.1%) of the studies. Due to limitation of data size available, neural network and its variation is used by only 8 (15.4%) studies. Whilst all studies used cross validating technique or separated database for validation, only 1 study validated the model with clinician input. Explainability methods were presented in 15 (28.8%) of studies with the most common method is feature importance which used 14 times. CONCLUSION For databases of 5000 or fewer samples, extreme gradient boosting has the highest probability of success. A large, multi-region, multi centre database is required to reduce bias and take advantage of neural network method. A framework for validating with and explaining ML model to clinicians involved in the management of ARDS would be very helpful for development and deployment of the ML model.
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Affiliation(s)
- Tu K Tran
- Department of Engineering and Science, University of Oxford, Oxford, UK.
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | - Minh C Tran
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Arun Joseph
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Phi A Phan
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering and Science, University of Oxford, Oxford, UK
| | - Andrew D Farmery
- Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, Oxford Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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Wu L, An J, Li X, Tao Q, Liu Z, Zhang K, Zhou L, Zhang X. Comprehensive Proteomic Profiling of Aqueous Humor in Idiopathic Uveitis and Vogt-Koyanagi-Harada Syndrome. ACS OMEGA 2024; 9:18643-18653. [PMID: 38680323 PMCID: PMC11044210 DOI: 10.1021/acsomega.3c10257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/29/2024] [Accepted: 04/03/2024] [Indexed: 05/01/2024]
Abstract
Idiopathic uveitis (IU) and Vogt-Koyanagi-Harada (VKH) syndrome are common types of uveitis. However, the exact pathological mechanisms of IU and VKH remain unclear. Proteomic analysis of aqueous humor (AH), the most easily accessible intraocular fluid and a key site of uveitis development, may reveal potential biomarkers and elucidate uveitis pathogenesis. In this study, 44 AH samples, including 12 IU cases, 16 VKH cases, and 16 controls, were subjected to label-free quantitative proteomic analysis. We identified 557 proteins from a comprehensive spectral library of 634 proteins across all samples. The AH proteomic profiles of the IU and VKH groups were different from those of the control group. Differential analysis revealed a shared pattern of extracellular matrix disruption and downregulation of retinal cellular proteins in the IU and VKH groups. Enrichment analysis revealed a protein composition indicative of inflammation in the AH of the IU and VKH groups but not in that of the control group. In the IU and VKH groups, innate immunity played an important role, as indicated by complement cascade activation and overexpression of innate immune cell markers. Extreme gradient boosting (XGBoost), an efficient and robust machine learning algorithm, was subsequently used to screen potential biomarkers for classifying the IU, VKH, and control groups. Transferrin and complement factor B were deemed the most important and represent a promising biomarker panel. These proteins were validated by high-resolution multiple reaction monitoring (HR-MRM) in an independent validation cohort. A classification decision tree was subsequently built for the diagnosis. Our findings further the understanding of the underlying molecular mechanisms in IU and VKH and facilitate the development of potential therapeutic and diagnostic strategies.
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Affiliation(s)
- Lingzi Wu
- Tianjin
Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of
National Clinical Research Center for Ocular Disease, Eye Institute
and School of Optometry, Tianjin Medical
University Eye Hospital, Tianjin 300384, China
- Beijing
Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren
Hospital, Capital Medical University, Beijing 100051, China
| | - Jinying An
- Tianjin
Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of
National Clinical Research Center for Ocular Disease, Eye Institute
and School of Optometry, Tianjin Medical
University Eye Hospital, Tianjin 300384, China
| | - Xueru Li
- Tianjin
Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of
National Clinical Research Center for Ocular Disease, Eye Institute
and School of Optometry, Tianjin Medical
University Eye Hospital, Tianjin 300384, China
| | - Qingqin Tao
- Tianjin
Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of
National Clinical Research Center for Ocular Disease, Eye Institute
and School of Optometry, Tianjin Medical
University Eye Hospital, Tianjin 300384, China
| | - Zheng Liu
- Shanxi
Eye Hospital, Taiyuan 030002, Shanxi, China
| | - Kai Zhang
- The
Province and Ministry Co-sponsored Collaborative Innovation Center
for Medical Epigenetics, Key Laboratory of Immune Microenvironment
and Disease (Ministry of Education), Tianjin Key Laboratory of Medical
Epigenetics, Department of Biochemistry and Molecular Biology, School
of Basic Medical Sciences, Tianjin Medical
University, Tianjin 300070, China
| | - Lei Zhou
- School
of Optometry, Department of Applied Biology and Chemical Technology,
and Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Hong Kong 999077, China
- Centre for
Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong 999077, China
| | - Xiaomin Zhang
- Tianjin
Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of
National Clinical Research Center for Ocular Disease, Eye Institute
and School of Optometry, Tianjin Medical
University Eye Hospital, Tianjin 300384, China
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Bickenbach J. [Potential of AI for the Treatment of Acute Respiratory Distress Syndrome (ARDS)]. Anasthesiol Intensivmed Notfallmed Schmerzther 2024; 59:34-44. [PMID: 38190824 DOI: 10.1055/a-2043-8644] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Acute respiratory distress syndrome (ARDS) is still associated with high mortality rates and poses a significant, vital threat to ICU patients because this syndrome is often detected too late (or not at all), and timely therapy and the fastest possible elimination of the underlying causes thus fail to materialize. Artificial Intelligence (AI) solutions can enable clinicians to make every minute in the ICU work for the patient by processing and analyzing all relevant data, thus supporting early diagnosis, adhering to clinical guidelines, and even providing a prognosis for the course of the ICU. This article shows what is already possible and where further challenges lie in this field of digital medicine.
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Srivastava S, Rajan V. ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units. IEEE J Biomed Health Inform 2023; 27:5076-5086. [PMID: 37819834 DOI: 10.1109/jbhi.2023.3295751] [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: 10/13/2023]
Abstract
Risk models play a crucial role in disease prevention, particularly in intensive care units (ICUs). Diseases often have complex manifestations with heterogeneous subpopulations, or subtypes, that exhibit distinct clinical characteristics. Risk models that explicitly model subtypes have high predictive accuracy and facilitate subtype-specific personalization. Such models combine clustering and classification methods but do not effectively utilize the inferred subtypes in risk modeling. Their limitations include tendency to obtain degenerate clusters and cluster-specific data scarcity leading to insufficient training data for the corresponding classifier. In this article, we develop a new deep learning model for simultaneous clustering and classification, ExpertNet, with novel loss terms and network training strategies that address these limitations. The performance of ExpertNet is evaluated on the tasks of predicting risk of (i) sepsis and (ii) acute respiratory distress syndrome (ARDS), using two large electronic medical records datasets from ICUs. Our extensive experiments show that, in comparison to state-of-the-art baselines for combined clustering and classification, ExpertNet achieves superior accuracy in risk prediction for both ARDS and sepsis; and comparable clustering performance. Visual analysis of the clusters further demonstrates that the clusters obtained are clinically meaningful and a knowledge-distilled model shows significant differences in risk factors across the subtypes. By addressing technical challenges in training neural networks for simultaneous clustering and classification, ExpertNet lays the algorithmic foundation for the future development of subtype-aware risk models.
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Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning. INTELLIGENCE-BASED MEDICINE 2023; 7:100087. [PMID: 36624822 PMCID: PMC9812471 DOI: 10.1016/j.ibmed.2023.100087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/22/2022] [Accepted: 01/04/2023] [Indexed: 01/06/2023]
Abstract
Acute Respiratory Distress Syndrome (ARDS) is associated with high morbidity and mortality. Identification of ARDS enables lung protective strategies, quality improvement interventions, and clinical trial enrolment, but remains challenging particularly in the first 24 hours of mechanical ventilation. To address this we built an algorithm capable of discriminating ARDS from other similarly presenting disorders immediately following mechanical ventilation. Specifically, a clinical team examined medical records from 1263 ICU-admitted, mechanically ventilated patients, retrospectively assigning each patient a diagnosis of "ARDS" or "non-ARDS" (e.g., pulmonary edema). Exploiting data readily available in the clinical setting, including patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports, we applied an iterative pre-processing and machine learning framework. The resulting model successfully discriminated ARDS from non-ARDS causes of respiratory failure (AUC = 0.85) among patients meeting Berlin criteria for severe hypoxia. This analysis also highlighted novel patient variables that were informative for identifying ARDS in ICU settings.
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Saab A, Abi Khalil C, Jammal M, Saikali M, Lamy JB. Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations. J Patient Saf 2022; 18:578-586. [PMID: 35985042 DOI: 10.1097/pts.0000000000001069] [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: 11/26/2022]
Abstract
OBJECTIVE The aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients. DESIGN This is a retrospective single-institution study. All consecutive adult patients' cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient's electronic medical record (EMR). SETTING The setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards. PATIENTS The study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 "nonevent" cases to build the training and validation data set. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0-6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0-42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon. CONCLUSIONS In hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.
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Affiliation(s)
| | | | - Mouin Jammal
- Department of Internal Medicine, Faculty of Medical Sciences, Saint Joseph University, Beirut, Lebanon
| | | | - Jean-Baptiste Lamy
- From the LIMICS, Université Sorbonne Paris Nord, INSERM, UMR 1142, Bobigny, France
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Rashid M, Ramakrishnan M, Chandran VP, Nandish S, Nair S, Shanbhag V, Thunga G. Artificial intelligence in acute respiratory distress syndrome: A systematic review. Artif Intell Med 2022; 131:102361. [DOI: 10.1016/j.artmed.2022.102361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/01/2022] [Accepted: 07/11/2022] [Indexed: 11/02/2022]
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Sheu RK, Chen LC, Wu CL, Pardeshi MS, Pai KC, Huang CC, Chen CY, Chen WC. Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP). Diagnostics (Basel) 2022; 12:diagnostics12071706. [PMID: 35885612 PMCID: PMC9317409 DOI: 10.3390/diagnostics12071706] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 07/09/2022] [Indexed: 11/30/2022] Open
Abstract
Evaluating several vital signs and chest X-ray (CXR) reports regularly to determine the recovery of the pneumonia patients at general wards is a challenge for doctors. A recent study shows the identification of pneumonia by the history of symptoms and signs including vital signs, CXR, and other clinical parameters, but they lack predicting the recovery status after starting treatment. The goal of this paper is to provide a pneumonia status prediction system for the early affected patient’s discharge from the hospital within 7 days or late discharge more than 7 days. This paper aims to design a multimodal data analysis for pneumonia status prediction using deep learning classification (MDA-PSP). We have developed a system that takes an input of vital signs and CXR images of the affected patient with pneumonia from admission day 1 to day 3. The deep learning then classifies the health status improvement or deterioration for predicting the possible discharge state. Therefore, the scope is to provide a highly accurate prediction of the pneumonia recovery on the 7th day after 3-day treatment by the SHAP (SHapley Additive exPlanation), imputation, adaptive imputation-based preprocessing of the vital signs, and CXR image feature extraction using deep learning based on dense layers-batch normalization (BN) with class weights for the first 7 days’ general ward patient in MDA-PSP. A total of 3972 patients with pneumonia were enrolled by de-identification with an adult age of 71 mean ± 17 sd and 64% of them were male. After analyzing the data behavior, appropriate improvement measures are taken by data preprocessing and feature vectorization algorithm. The deep learning method of Dense-BN with SHAP features has an accuracy of 0.77 for vital signs, 0.92 for CXR, and 0.75 for the combined model with class weights. The MDA-PSP hybrid method-based experiments are proven to demonstrate higher prediction accuracy of 0.75 for pneumonia patient status. Henceforth, the hybrid methods of machine and deep learning for pneumonia patient discharge are concluded to be a better approach.
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Affiliation(s)
- Ruey-Kai Sheu
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Lun-Chi Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
- Correspondence: ; Tel.: +886-04-2359-0415
| | - Chieh-Liang Wu
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
- Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan
- Department of Automatic Control Engineering, Feng Chia University, Taichung 407102, Taiwan
| | | | - Kai-Chih Pai
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Chien-Chung Huang
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - Chia-Yu Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
| | - Wei-Cheng Chen
- Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; (R.-K.S.); (K.-C.P.); (C.-C.H.); (C.-Y.C.); (W.-C.C.)
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Wu J, Liu C, Xie L, Li X, Xiao K, Xie G, Xie F. Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning. BMC Pulm Med 2022; 22:193. [PMID: 35550064 PMCID: PMC9098141 DOI: 10.1186/s12890-022-01963-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 04/21/2022] [Indexed: 12/02/2022] Open
Abstract
Background Several studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS), in addition, etiology-associated heterogeneity in ARDS has become an emerging topic quite recently; however, the intersection between the two, which is early prediction of target conditions in etiology-specific ARDS, has not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of moderate-to-severe condition of inhalation-induced ARDS. Methods Clinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 h that ended 6 h prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 200 mmHg). Results The trained random forest classifier was validated using two independent validation cohorts, with an area under the curve of 0.9127 (95% confidence interval 0.8713–0.9542) and 0.9026 (95% confidence interval 0.8075–1), respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min < 9, resp_96h_6h_mean ≥ 16.1, HR_96h_6h_mean ≥ 102, and temp_96h_6h_max > 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 h prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test). Conclusions This newly established random forest‑based interpretable model shows good predictive ability for moderate-to-severe inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-01963-7.
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Affiliation(s)
- Junwei Wu
- Library of Graduate School, Chinese People's Liberation Army General Hospital, Beijing, 100853, China
| | - Chao Liu
- Ping An Healthcare Technology, Beijing, China.,Yidu Cloud Technology Inc, Beijing, China
| | - Lixin Xie
- College of Pulmonary and Critical Care Medicine, Chinese People's Liberation Army General Hospital, Beijing, 100853, China
| | - Xiang Li
- Ping An Healthcare Technology, Beijing, China
| | - Kun Xiao
- College of Pulmonary and Critical Care Medicine, Chinese People's Liberation Army General Hospital, Beijing, 100853, China
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing, China. .,Ping An Health Cloud Company Limited, Beijing, China. .,Ping An International Smart City Technology Co., Ltd., Beijing, China.
| | - Fei Xie
- College of Pulmonary and Critical Care Medicine, Chinese People's Liberation Army General Hospital, Beijing, 100853, China.
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Fang Y, Zhang X. A propensity score-matching analysis of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker exposure on in-hospital mortality in patients with acute respiratory failure. Pharmacotherapy 2022; 42:387-396. [PMID: 35344607 PMCID: PMC9322533 DOI: 10.1002/phar.2677] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 03/06/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023]
Abstract
STUDY OBJECTIVE To explore the impact of pre-hospital ACEI and ARB exposure on the prognosis of ARF patients. DESIGN A single-center retrospective cohort study. SETTING Medical Information Mart for Intensive Care-III (MIMIC-III) database. PATIENTS The patients meeting ICD-9 code of acute respiratory failure were enrolled. INTERVENTION The primary exposure was the pre-hospital exposure of ACEI and ARB. MEASUREMENT AND MAIN RESULTS The primary outcome was in-hospital mortality. Multiple logistic regression analysis was conducted to determine the independent effect of ACEI/ARB exposure on mortality. Propensity score matching (PSM) method was adopted to reduce bias of the confounders. Subgroup analysis and sensitivity analysis were used to test the stability of the conclusion. 5335 adult ARF patients were enrolled. Mortality was significantly decreased in patients with ACEI/ARB exposure before and after PSM, and the adjusted odds ratio (OR) of ACEI/ARB exposure was 0.56 (95% CI 0.43-0.72). In the subgroup analysis, ACEI/ARB lost its protective effect in young subgroup, but no significant interaction was found between ACEI/ARB exposure and age (p = 0.082). The point estimation and lower 95% limit of E-value was 2.97 and 2.12. In sensitivity analysis, ACEI/ARB exposure showed similar effect in ARDS cohort, but no significantly difference was found in the MIMIC-IV database, which may be explained by small sample size of the ACEI/ARB group. CONCLUSIONS Among patients with acute respiratory failure, pre-hospital ACEI/ARB exposure was associated with better outcomes and acted as an independent factor. The relationship between ACEI/ARB and prognosis of ARF is worth investigating further.
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Affiliation(s)
- Yi‐Peng Fang
- Laboratory of Molecular CardiologyThe First Affiliated Hospital of Shantou University Medical CollegeShantouChina
- Laboratory of Medical Molecular ImagingThe First Affiliated Hospital of Shantou University Medical CollegeShantouChina
- Shantou University Medical CollegeShantouChina
| | - Xin Zhang
- Laboratory of Molecular CardiologyThe First Affiliated Hospital of Shantou University Medical CollegeShantouChina
- Laboratory of Medical Molecular ImagingThe First Affiliated Hospital of Shantou University Medical CollegeShantouChina
- Shantou University Medical CollegeShantouChina
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Zare S, Meidani Z, Ouhadian M, Akbari H, Zand F, Fakharian E, Sharifian R. Identification of data elements for blood gas analysis dataset: a base for developing registries and artificial intelligence-based systems. BMC Health Serv Res 2022; 22:317. [PMID: 35260155 PMCID: PMC8902269 DOI: 10.1186/s12913-022-07706-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 03/01/2022] [Indexed: 11/23/2022] Open
Abstract
Background One of the challenging decision-making tasks in healthcare centers is the interpretation of blood gas tests. One of the most effective assisting approaches for the interpretation of blood gas analysis (BGA) can be artificial intelligence (AI)-based decision support systems. A primary step to develop intelligent systems is to determine information requirements and automated data input for the secondary analyses. Datasets can help the automated data input from dispersed information systems. Therefore, the current study aimed to identify the data elements required for supporting BGA as a dataset. Materials and methods This cross-sectional descriptive study was conducted in Nemazee Hospital, Shiraz, Iran. A combination of literature review, experts’ consensus, and the Delphi technique was used to develop the dataset. A review of the literature was performed on electronic databases to find the dataset for BGA. An expert panel was formed to discuss on, add, or remove the data elements extracted through searching the literature. Delphi technique was used to reach consensus and validate the draft dataset. Results The data elements of the BGA dataset were categorized into ten categories, namely personal information, admission details, present illnesses, past medical history, social status, physical examination, paraclinical investigation, blood gas parameter, sequential organ failure assessment (SOFA) score, and sampling technique errors. Overall, 313 data elements, including 172 mandatory and 141 optional data elements were confirmed by the experts for being included in the dataset. Conclusions We proposed a dataset as a base for registries and AI-based systems to assist BGA. It helps the storage of accurate and comprehensive data, as well as integrating them with other information systems. As a result, high-quality care is provided and clinical decision-making is improved.
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Affiliation(s)
- Sahar Zare
- Health Information Management Research Center (HIMRC), Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Meidani
- Health Information Management Research Center (HIMRC), Kashan University of Medical Sciences, Kashan, Iran.,Department of Health Information Management & Technology, School of Allied Health Professions, Kashan University of Medical Sciences, Kashan, Iran
| | - Maryam Ouhadian
- Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hosein Akbari
- Department of Epidemiology and Biostatistics, School of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Farid Zand
- Anesthesiology and Critical Care Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. .,Department of Anesthesia and Critical Care Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Esmaeil Fakharian
- Department of Neurosurgery, Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran
| | - Roxana Sharifian
- Health Human Resources Research Center, Department of Health Information Management and Technology, Shiraz University of Medical Sciences, Shiraz, Iran
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12
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Pai KC, Chao WC, Huang YL, Sheu RK, Chen LC, Wang MS, Lin SH, Yu YY, Wu CL, Chan MC. Artificial intelligence–aided diagnosis model for acute respiratory distress syndrome combining clinical data and chest radiographs. Digit Health 2022; 8:20552076221120317. [PMID: 35990108 PMCID: PMC9386858 DOI: 10.1177/20552076221120317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/29/2022] [Indexed: 11/28/2022] Open
Abstract
Objective The aim of this study was to develop an artificial intelligence–based model to detect the presence of acute respiratory distress syndrome (ARDS) using clinical data and chest X-ray (CXR) data. Method The transfer learning method was used to train a convolutional neural network (CNN) model with an external image dataset to extract the image features. Then, the last layer of the model was fine-tuned to determine the probability of ARDS. The clinical data were trained using three machine learning algorithms—eXtreme Gradient Boosting (XGB), random forest (RF), and logistic regression (LR)—to estimate the probability of ARDS. Finally, ensemble-weighted methods were proposed that combined the image model and the clinical data model to estimate the probability of ARDS. An analysis of the importance of clinical features was performed to explore the most important features in detecting ARDS. A gradient-weighted class activation mapping (Grad-CAM) model was used to explain what our CNN sees and understands when making a decision. Results The proposed ensemble-weighted methods improved the performances of the ARDS classifiers (XGB + CNN, area under the curve [AUC] = 0.916; RF + CNN, AUC = 0.920; LR + CNN, AUC = 0.920; XGB + RF + LR + CNN, AUC = 0.925). In addition, the ML model using clinical data to present the top 15 important features to identify the risk factors of ARDS. Conclusion This study developed combined machine learning models with clinical data and CXR images to detect ARDS. According to the results of the Shapley Additive exPlanations values and the Grad-CAM techniques, an explicable ARDS diagnosis model is suitable for a real-life scenario.
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Affiliation(s)
- Kai-Chih Pai
- College of Engineering, Tunghai University, Taichung, Taiwan
| | - Wen-Cheng Chao
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Yu-Len Huang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Ruey-Kai Sheu
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Lun-Chi Chen
- College of Engineering, Tunghai University, Taichung, Taiwan
| | - Min-Shian Wang
- Artificial Intelligence Studio, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shau-Hung Lin
- DDS-THU Artificial Intelligence Center, Tunghai University, Taichung, Taiwan
| | - Yu-Yi Yu
- Division of Critical Care and Respiratory Therapy, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chieh-Liang Wu
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Ming-Cheng Chan
- College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Division of Critical Care and Respiratory Therapy, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
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13
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Bhattarai S, Gupta A, Ali E, Ali M, Riad M, Adhikari P, Mostafa JA. Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome? Cureus 2021; 13:e13529. [PMID: 33786236 PMCID: PMC7996475 DOI: 10.7759/cureus.13529] [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] [Received: 01/15/2021] [Accepted: 02/24/2021] [Indexed: 11/05/2022] Open
Abstract
Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease. Clinical methods alone can result in the under-recognition of this heterogeneous syndrome. The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms. Most of the work in the field of ML in ARDS has been in the development of prediction models that have comparable efficacies to that of traditional models. Prediction algorithms have been useful in identifying new variables that may be important to consider in the future, supplementing the unknown information with the help of available noninvasive parameters, as well as predicting mortality. Phenotype identification using an unsupervised ML algorithm has been pivotal in classifying the heterogeneous population into more homogenous classes. Big data generated from ventilators in the form of ventilator waveform analysis and images in the form of radiomics have also been leveraged for the identification of the syndrome and can be incorporated into a clinical decision support system. Although the results are promising, lack of generalizability, "black box" nature of algorithms and concerns about "alarm fatigue" should be addressed for more mainstream adoption of these models.
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Affiliation(s)
- Sanket Bhattarai
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Ashish Gupta
- Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Eiman Ali
- Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Moeez Ali
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Mohamed Riad
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | - Prakash Adhikari
- Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
- Internal Medicine, Piedmont Athens Regional Medical Center, Athens, USA
| | - Jihan A Mostafa
- Psychiatry, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
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14
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Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. J Med Internet Res 2021; 23:e23026. [PMID: 33534724 PMCID: PMC7901593 DOI: 10.2196/23026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/09/2020] [Accepted: 02/01/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.
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Affiliation(s)
- Shengtian Sang
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Ran Sun
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Jean Coquet
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | | | - Tina Seto
- Technology and Digital Solutions, Stanford University, Stanford, CA, United States
| | - Tina Hernandez-Boussard
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
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15
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McNicholas B, Madden MG, Laffey JG. Machine Learning Classifier Models: The Future for Acute Respiratory Distress Syndrome Phenotyping? Am J Respir Crit Care Med 2020; 202:919-920. [PMID: 32687397 PMCID: PMC7528797 DOI: 10.1164/rccm.202006-2388ed] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Bairbre McNicholas
- Department of Anaesthesia and Intensive Care Medicine, Galway University Hospitals, Galway, Ireland
| | - Michael G Madden
- College of Science and Engineering, National University of Ireland, Galway, Ireland
| | - John G Laffey
- Department of Anaesthesia and Intensive Care Medicine, Galway University Hospitals, Galway, Ireland.,School of Medicine and.,Regenerative Medicine Institute at the CURAM Centre for Medical Devices, National University of Ireland, Galway, Ireland
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16
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Zhang Z, Navarese EP, Zheng B, Meng Q, Liu N, Ge H, Pan Q, Yu Y, Ma X. Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome. J Evid Based Med 2020; 13:301-312. [PMID: 33185950 DOI: 10.1111/jebm.12418] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/21/2020] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.
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Affiliation(s)
- Zhongheng Zhang
- Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Eliano Pio Navarese
- Interventional Cardiology and Cardiovascular Medicine Research, Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland
- Faculty of Medicine, University of Alberta, Edmonton, Canada
| | - Bin Zheng
- Department of Surgery, 2D, Walter C Mackenzie Health Sciences Centre, University of Alberta, Edmonton, Alberta, Canada
| | - Qinghe Meng
- Department of Surgery, State University of New York Upstate Medical University, Syracuse, New York
| | - Nan Liu
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Huiqing Ge
- Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qing Pan
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuetian Yu
- Department of Critical Care Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuelei Ma
- Department of biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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