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Surgery Surgery

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.

In Frontiers in medicine

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Ge Huiqing, Pan Qing, Zhou Yong, Xu Peifeng, Zhang Lingwei, Zhang Junli, Yi Jun, Yang Changming, Zhou Yuhan, Liu Limin, Zhang Zhongheng

2020

COVID-19, asynchonized, asynchrony, lung mechanics, mechanical ventilation, prone positioning

oncology Oncology

Corrigendum: Deep Learning for Whole Slide Image Analysis: An Overview.

In Frontiers in medicine

[This corrects the article on p. 264 in vol. 6, PMID: 31824952.].

Dimitriou Neofytos, Arandjelović Ognjen, Caie Peter D

2020

cancer, computer vision, digital pathology, image analysis, machine learning, oncology, personalized pathology

oncology Oncology

Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data.

In International journal of environmental research and public health ; h5-index 73.0

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

Tartaglione Enzo, Barbano Carlo Alberto, Berzovini Claudio, Calandri Marco, Grangetto Marco

2020-Sep-22

COVID-19, chest X-ray, classification, deep learning

Surgery Surgery

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.

In Frontiers in medicine

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Ge Huiqing, Pan Qing, Zhou Yong, Xu Peifeng, Zhang Lingwei, Zhang Junli, Yi Jun, Yang Changming, Zhou Yuhan, Liu Limin, Zhang Zhongheng

2020

COVID-19, asynchonized, asynchrony, lung mechanics, mechanical ventilation, prone positioning

General General

Infections in the Era of Targeted Therapies: Mapping the Road Ahead.

In Frontiers in medicine

Immunosuppressive treatment strategies for autoimmune diseases have changed drastically with the development of targeted therapies. While targeted therapies have changed the way we manage immune mediated diseases, their use has been attended by a variety of infectious complications-some expected, others unexpected. This perspective examines lessons learned from the use of different targeted therapies over the past several decades, and reviews existing strategies to minimize infectious risk. Several of these infectious complications were predictable in the light of preclinical models and early clinical trials (i.e., tuberculosis and TNF inhibitors; meningococcus; and eculizumab). While these scenarios can potentially help us in terms of enhancing our predictive powers (higher vigilance, earlier detection, and risk mitigation), targeted therapies have also revealed unpredictable toxicities (i.e., natalizumab and progressive multifocal leukoencephalopathy). Severe infectious complications, even if rare, can derail a promising therapeutic and highlight the need for increased awareness and meticulous adjudication. Tools are available to help mitigate infectious risks. The first step is to ensure that infection safety is adequately studied at every level of drug development prior to regulatory approval, with adequate post-marketing surveillance including registries that collect real-world adverse events in a collaborative effort. The second step is to identify high risk patients (using risk calculators such as the RABBIT risk score; big data analyses; artificial intelligence). Finally, the most underutilized interventions to prevent severe infections in patients receiving targeted therapies across the spectrum of immune mediated inflammatory diseases are vaccinations.

Calabrese Leonard H, Calabrese Cassandra, Lenfant Tiphaine, Kirchner Elizabeth, Strand Vibeke

2020

PML, TNF inhibitors, infection, natalizumab, targeted therapies, tuberculosis, vaccine

General General

A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.

In PeerJ

Background : Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided.

Methods : This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance.

Results : The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

Kuo Kuang-Ming, Talley Paul, Kao YuHsi, Huang Chi Hsien

2020

Diagnosis, Machine-learning techniques, Predictive models, Type 2 diabetes mellitus