Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Zhang, Zhongheng; Navarese, Eliano Pio; Zheng, Bin; Meng, Qinghe; Liu, Nan; Ge, Huiqing; Pan, Qing; Yu, Yuetian; Ma, Xuelei

doi:10.1111/jebm.12418

Cited by 35 publications

(33 citation statements)

References 109 publications

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“…Artificial intelligence technology has created its way in the research field to examine big databases. For example, Zhang et al discussed the use of artificial intelligence technology in improving the patient care with acute lung injury and acute respiratory distress syndrome [22]. Similar things could be applied during COVID-19 pandemic era to examine a bigger dataset.…”

Section: Discussionmentioning

confidence: 99%

Musculoskeletal Corticosteroid Injection during COVID-19 Pandemic in Sabah: Is It Safe?

Aziz

Hanifah

Nahar

2021

Advances in Orthopedics

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Musculoskeletal corticosteroid injection is commonly used as an adjunct to help patients in pain management. In this current COVID-19 pandemic, many clinicians would differ from this treatment as steroid is considered an immunosuppressive drug and could risk the patient of developing severe adverse effects if contracting COVID-19. This is a retrospective study based in Sabah, Malaysia, examining the prevalence of COVID-19 infection following musculoskeletal corticosteroid injection from 1 December 2019 until 30 June 2020 in the sports medicine clinic and the orthopedic clinic. Patients who received musculoskeletal corticosteroid injection were called by telephone and asked about visits to the emergency department or government health clinic for influenza-like illness symptoms or severe acute respiratory infection that would require screening of COVID-19. Thirty-five patients who responded to the call were included, with mean ages of 47.9 years ± 15.1. 52% were male respondents, while 48% were female. 25% of them were diabetics, and 2.9% of them had a history of lymphoproliferative disorders. The mean pain score before injection was 6.74 ± 1.03 and after injection pain was 2.27 ± 1.63. In this study, there were 11.4% (n = 4) with minor complications of steroid injection, that is, skin discoloration. Nonetheless, there were no severe complications due to corticosteroids reported. There were no reported cases of COVID-19 among the respondents following corticosteroid injection. Musculoskeletal pain would affect a person’s well-being and activities; thus, its management requires that careful consideration with risk-benefit analysis be made before administering musculoskeletal corticosteroid injection during COVID-19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Musculoskeletal Corticosteroid Injection during COVID-19 Pandemic in Sabah: Is It Safe?

Aziz

Hanifah

Nahar

2021

Advances in Orthopedics

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“…For some models that are harder to explain, such as integrated tree model and neural network model, SHAP algorithm may be useful. In recent years, increasing efforts have been put into improving the interpretability of black-box artificial intelligence and designing more interpretable models for clinical prediction (40,41). This will be our future direction.…”

Section: Discussionmentioning

confidence: 99%

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Zhang

Hong

et al. 2021

Front. Med.

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Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients.Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan–Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models.Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K–M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores.Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

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“…With the achievement of computer science, AI is involved in clinical practice, including tracking data (6,7), diagnosis (8), and support of decision making (9,10). AI has been widely used in clinical practices, such as in prediction, decision support, and the delivery of personalized health care (11)(12)(13), especially in diagnosis and treatment of acute events (14) to improve outcomes (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Liu

Chen

et al. 2021

Front. Med.

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Objective: Clinical trials contribute to the development of clinical practice. However, little is known about the current status of trials on artificial intelligence (AI) conducted in emergency department and intensive care unit. The objective of the study was to provide a comprehensive analysis of registered trials in such field based on ClinicalTrials.gov.Methods: Registered trials on AI conducted in emergency department and intensive care unit were searched on ClinicalTrials.gov up to 12th January 2021. The characteristics were analyzed using SPSS21.0 software.Results: A total of 146 registered trials were identified, including 61 in emergency department and 85 in intensive care unit. They were registered from 2004 to 2021. Regarding locations, 58 were conducted in Europe, 58 in America, 9 in Asia, 4 in Australia, and 17 did not report locations. The enrollment of participants was from 0 to 18,000,000, with a median of 233. Universities were the primary sponsors, which accounted for 43.15%, followed by hospitals (35.62%), and industries/companies (9.59%). Regarding study designs, 85 trials were interventional trials, while 61 were observational trials. Of the 85 interventional trials, 15.29% were for diagnosis and 38.82% for treatment; of the 84 observational trials, 42 were prospective, 14 were retrospective, 2 were cross-sectional, 2 did not report clear information and 1 was unknown. Regarding the trials' results, 69 trials had been completed, while only 10 had available results on ClinicalTrials.gov.Conclusions: Our study suggest that more AI trials are needed in emergency department and intensive care unit and sponsors are encouraged to report the results.

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Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Cited by 35 publications

References 109 publications

Musculoskeletal Corticosteroid Injection during COVID-19 Pandemic in Sabah: Is It Safe?

Musculoskeletal Corticosteroid Injection during COVID-19 Pandemic in Sabah: Is It Safe?

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

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