Publications by authors named "Huiqing Ge"

20 Publications

  • Page 1 of 1

Individualized Mechanical power-based ventilation strategy for acute respiratory failure formalized by finite mixture modeling and dynamic treatment regimen.

EClinicalMedicine 2021 Jun 24;36:100898. Epub 2021 May 24.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.

Background: Mechanical ventilation (MV) is the key to the successful treatment of acute respiratory failure (ARF) in the intensive care unit (ICU). The study aims to formalize the concept of individualized MV strategy with finite mixture modeling (FMM) and dynamic treatment regime (DTR).

Methods: ARF patients requiring MV for over 48 h from 2008 to 2019 were included. FMM was conducted to identify classes of ARF. Static and dynamic mechanical power (MP_static and MP_dynamic) and relevant clinical variables were calculated/collected from hours 0 to 48 at an interval of 8 h. was calculated as the difference between actual and optimal MP.

Findings: A total of 8768 patients were included for analysis with a mortality rate of 27%. FFM identified three classes of ARF, namely, the class 1 (baseline), class 2 (critical) and class 3 (refractory respiratory failure). The effect size of MP_static on mortality is the smallest in class 1 (HR for every 5 Joules/min increase: 1.29; 95% CI: 1.15 to 1.45; < 0.001) and the largest in class 3 (HR for every 5 Joules/min increase: 1.83; 95% CI: 1.52 to 2.20; < 0.001).

Interpretation: MP has differing therapeutic effects for subtypes of ARF. Optimal MP estimated by DTR model may help to improve survival outcome.

Funding: The study was funded by Health Science and Technology Plan of Zhejiang Province (2021KY745), Key Research & Development project of Zhejiang Province (2021C03071) and Yilu "Gexin" - Fluid Therapy Research Fund Project (YLGX-ZZ-2,020,005).
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http://dx.doi.org/10.1016/j.eclinm.2021.100898DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144670PMC
June 2021

Aerosol delivery via invasive ventilation: a narrative review.

Ann Transl Med 2021 Apr;9(7):588

Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

In comparison with spontaneously breathing non-intubated subjects, intubated, mechanically ventilated patients encounter various challenges, barriers, and opportunities in receiving medical aerosols. Since the introduction of mechanical ventilation as a part of modern critical care medicine during the middle of the last century, aerosolized drug delivery by jet nebulizers has become a common practice. However, early evidence suggested that aerosol generators differed in their efficacies, and the introduction of newer aerosol technology (metered dose inhalers, ultrasonic nebulizer, vibrating mesh nebulizers, and soft moist inhaler) into the ventilator circuit opened up the possibility of optimizing inhaled aerosol delivery during mechanical ventilation that could meet or exceed the delivery of the same aerosols in spontaneously breathing patients. This narrative review will catalogue the primary variables associated with this process and provide evidence to guide optimal aerosol delivery and dosing during mechanical ventilation. While gaps exist in relation to the appropriate aerosol drug dose, discrepancies in practice, and cost-effectiveness of the administered aerosol drugs, we also present areas for future research and practice. Clinical practice should expand to incorporate these techniques to improve the consistency of drug delivery and provide safer and more effective care for patients.
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http://dx.doi.org/10.21037/atm-20-5665DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105868PMC
April 2021

An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation.

Comput Methods Programs Biomed 2021 Jun 19;204:106057. Epub 2021 Mar 19.

College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China. Electronic address:

Background And Objective: Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic.

Methods: We proposed an interpretable one-dimensional convolutional neural network (1DCNN) to detect four most manifestation types of PVA (double triggering, ineffective efforts during expiration, premature cycling and delayed cycling) under pressure control ventilation mode and pressure support ventilation mode. A global average pooling (GAP) layer was incorporated with the 1DCNN model to highlight the sections of the respiratory waveform the model focused on when making a classification. Dilation convolution and batch normalization were introduced to the 1DCNN model for compensating the reduction of performance caused by the GAP layer.

Results: The proposed interpretable 1DCNN exhibited comparable performance with the state-of-the-art deep learning model in PVA detection. The F1 scores for the detection of four types of PVA under pressure control ventilation and pressure support ventilation modes were greater than 0.96. The critical sections of the waveform used to detect PVA were highlighted, and found to be well consistent with the understanding of the respective type of PVA by experts.

Conclusions: The findings suggest that the proposed 1DCNN can help detect PVA, and enhance the interpretability of the classification process to help clinicians better understand the results obtained from deep learning technology.
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http://dx.doi.org/10.1016/j.cmpb.2021.106057DOI Listing
June 2021

Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm.

Front Med (Lausanne) 2020 25;7:597406. Epub 2020 Nov 25.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics. The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs). A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90; < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13; < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates. Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).
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http://dx.doi.org/10.3389/fmed.2020.597406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724969PMC
November 2020

Cumulative oxygen deficit is a novel predictor for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress.

PeerJ 2020 27;8:e10497. Epub 2020 Nov 27.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Background And Objectives: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification.

Methods: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models.

Results: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57-16.93]; = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28-85.37]; = 0.029).

Conclusions: The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.
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http://dx.doi.org/10.7717/peerj.10497DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703393PMC
November 2020

Sigh in Patients With Acute Hypoxemic Respiratory Failure and ARDS: The PROTECTION Pilot Randomized Clinical Trial.

Chest 2021 Apr 13;159(4):1426-1436. Epub 2020 Nov 13.

Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Critical Care, Pitié-Salpêtrière Hospital, Paris, France.

Background: Sigh is a cyclic brief recruitment maneuver: previous physiologic studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity, and increase release of surfactant.

Research Question: Is the clinical application of sigh during pressure support ventilation (PSV) feasible?

Study Design And Methods: We conducted a multicenter noninferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or ARDS undergoing PSV. Patients were randomized to the no-sigh group and treated by PSV alone, or to the sigh group, treated by PSV plus sigh (increase in airway pressure to 30 cm HO for 3 s once per minute) until day 28 or death or successful spontaneous breathing trial. The primary end point of the study was feasibility, assessed as noninferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiologic parameters in the first week from randomization, 28-day mortality, and ventilator-free days.

Results: Two-hundred and fifty-eight patients (31% women; median age, 65 [54-75] years) were enrolled. In the sigh group, 23% of patients failed to remain on assisted ventilation vs 30% in the no-sigh group (absolute difference, -7%; 95% CI, -18% to 4%; P = .015 for noninferiority). Adverse events occurred in 12% vs 13% in the sigh vs no-sigh group (P = .852). Oxygenation was improved whereas tidal volume, respiratory rate, and corrected minute ventilation were lower over the first 7 days from randomization in the sigh vs no-sigh group. There was no significant difference in terms of mortality (16% vs 21%; P = .337) and ventilator-free days (22 [7-26] vs 22 [3-25] days; P = .300) for the sigh vs no-sigh group.

Interpretation: Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk.

Trial Registry: ClinicalTrials.gov; No.: NCT03201263; URL: www.clinicaltrials.gov.
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http://dx.doi.org/10.1016/j.chest.2020.10.079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664474PMC
April 2021

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

J Evid Based Med 2020 Nov 13;13(4):301-312. Epub 2020 Nov 13.

Department of biotherapy, State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

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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http://dx.doi.org/10.1111/jebm.12418DOI Listing
November 2020

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

EBioMedicine 2020 Dec 10;62:103081. Epub 2020 Nov 10.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China. Electronic address:

Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets.

Methods: The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance.

Findings: A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211).

Interpretation: Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership.

Funding: The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).
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http://dx.doi.org/10.1016/j.ebiom.2020.103081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658497PMC
December 2020

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

Front Med (Lausanne) 2020 21;7:541. Epub 2020 Aug 21.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

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. 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. 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/cmHO (95% CI: 1.75 to 2.86; < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. 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.
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http://dx.doi.org/10.3389/fmed.2020.00541DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472529PMC
August 2020

Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.

JMIR Med Inform 2020 Apr 23;8(4):e17642. Epub 2020 Apr 23.

Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Background: Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds.

Objective: The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system.

Methods: A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).

Results: The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628.

Conclusions: This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.
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http://dx.doi.org/10.2196/17642DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206519PMC
April 2020

Detection of patient-ventilator asynchrony from mechanical ventilation waveforms using a two-layer long short-term memory neural network.

Comput Biol Med 2020 05 26;120:103721. Epub 2020 Mar 26.

College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou, 310023, China. Electronic address:

Background And Objective: Mismatch between invasive mechanical ventilation and the requirements of patients results in patient-ventilator asynchrony (PVA), which is associated with a series of adverse clinical outcomes. Although the efficiency of the available approaches for automatically detecting various types of PVA from the ventilator waveforms is unsatisfactory, the feasibility of powerful deep learning approaches in addressing this problem has not been investigated.

Methods: We propose a 2-layer long short-term memory (LSTM) network to detect two most frequently encountered types of PVA, namely, double triggering (DT) and ineffective inspiratory effort during expiration (IEE), on two datasets. The performance of the networks is evaluated first using cross-validation on the combined dataset, and then using a cross testing scheme, in which the LSTM networks are established on one dataset and tested on the other.

Results: Compared with the reported rule-based algorithms and the machine learning models, the proposed 2-layer LSTM network exhibits the best overall performance, with the F1 scores of 0.983 and 0.979 for DT and IEE detection, respectively, on the combined dataset. Furthermore, it outperforms the other approaches in cross testing.

Conclusions: The findings suggest that LSTM is an excellent technique for accurate recognition of PVA in clinics. Such a technique can help detect and correct PVA for a better patient ventilator interaction.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103721DOI Listing
May 2020

Practice pattern of aerosol therapy among patients undergoing mechanical ventilation in mainland China: A web-based survey involving 447 hospitals.

PLoS One 2019 29;14(8):e0221577. Epub 2019 Aug 29.

Department of Respiratory Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Background And Objective: Aerosol therapies are widely used for mechanically ventilated patients. However, the practice pattern of aerosol therapy in mainland China remains unknown. This study aimed to determine the current practice of aerosol therapy in mainland China.

Methods: A web-based survey was conducted by the China Union of Respiratory Care (CURC) from August 2018 to January 2019. The survey was disseminated via Email or WeChat to members of CURC. A questionnaire comprising 16 questions related to hospital information and 12 questions related to the practice of aerosol therapy. Latent class analysis was employed to identify the distinct classes of aerosol therapy practice.

Main Results: A total of 693 valid questionnaires were returned by respiratory care practitioners from 447 hospitals. Most of the practitioners used aerosol therapy for both invasive mechanical ventilation (90.8%) and non-invasive mechanical ventilation (91.3%). Practitioners from tertiary care centers were more likely to use aerosol therapy compared with those from non-tertiary care centers (91.9% vs. 85.4%, respectively; p = 0.035). The most commonly used drugs for aerosol therapy were bronchodilators (64.8%) followed by mucolytic agents (44.2%), topical corticosteroids (43.4%) and antibiotics (16.5%). The ultrasonic nebulizer (48.3%) was the most commonly used followed by the jet nebulizer (39.2%), the metered dose inhaler (15.4%) and the vibrating mesh nebulizer (14.6%). Six latent classes were identified via latent class analysis. Class 1 was characterized by the aggressive use of aerosol therapy without a standard protocol, while class 3 was characterized by the absence of aerosol therapy.

Conclusions: Substantial heterogeneity among institutions with regard to the use of aerosol therapy was noted. The implementation of aerosol therapy during mechanical ventilation was inconsistent in light of recent practice guidelines. Additional efforts by the CURC to improve the implementation of aerosol therapy in mainland China are warranted.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221577PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715194PMC
March 2020

Mechanical power normalized to predicted body weight as a predictor of mortality in patients with acute respiratory distress syndrome.

Intensive Care Med 2019 06 6;45(6):856-864. Epub 2019 May 6.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016, Zhejiang, China.

Purpose: Protective mechanical ventilation based on multiple ventilator parameters such as tidal volume, plateau pressure, and driving pressure has been widely used in acute respiratory distress syndrome (ARDS). More recently, mechanical power (MP) was found to be associated with mortality. The study aimed to investigate whether MP normalized to predicted body weight (norMP) was superior to other ventilator variables and to prove that the discrimination power cannot be further improved with a sophisticated machine learning method.

Methods: The study included individual patient data from eight randomized controlled trials conducted by the ARDSNet. The data was split 3:1 into training and testing subsamples. The discrimination of each ventilator variable was calculated in the testing subsample using the area under receiver operating characteristic curve. The gradient boosting machine was used to examine whether the discrimination could be further improved.

Results: A total of 5159 patients with acute onset ARDS were included for analysis. The discrimination of norMP in predicting mortality was significantly better than the absolute MP (p = 0.011 for DeLong's test). The gradient boosting machine was not able to improve the discrimination as compared to norMP (p = 0.913 for DeLong's test). The multivariable regression model showed a significant interaction between norMP and ARDS severity (p < 0.05). While the norMP was not significantly associated with mortality outcome (OR 0.99; 95% CI 0.91-1.07; p = 0.862) in patients with mild ARDS, it was associated with increased risk of mortality in moderate (OR 1.11; 95% CI 1.02-1.23; p = 0.021) and severe (OR 1.13; 95% CI 1.03-1.24; p < 0.008) ARDS.

Conclusions: The study showed that norMP was a good ventilator variable associated with mortality, and its predictive discrimination cannot be further improved with a sophisticated machine learning method. Further experimental trials are needed to investigate whether adjusting ventilator variables according to norMP will significantly improve clinical outcomes.
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http://dx.doi.org/10.1007/s00134-019-05627-9DOI Listing
June 2019

Alterations in diaphragmatic function assessed by ultrasonography in mechanically ventilated patients with sepsis.

J Clin Ultrasound 2019 May 22;47(4):206-211. Epub 2019 Jan 22.

Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Purpose: To assess alteration of diaphragmatic function by ultrasonography in a population of mechanically ventilated patients with or without sepsis.

Methods: We performed a prospective, 6-month, single-center, observational cohort study. Mechanically ventilated septic and nonseptic patients were studied within 24 hours following intubation and before the moment of ventilator liberation. Diaphragm thickness and contractile activity (quantified by diaphragmatic thickening fraction, DTF) were measured by ultrasonography at the zone of apposition. Intraobserver and interobserver reproducibility were measured.

Results: Fifty-two critically ill patients were included, 28 with sepsis and 24 without sepsis. Upon initiation of ventilation, DTF was lower in septic than that in nonseptic patients (P = 0.03). No difference was observed between septic and nonseptic patients for diaphragm thickness. Mean 188 ± 111 hours after the first measurement, both diaphragm thickness and DTF decreased significantly compared with first measurements in septic and nonseptic patients, all P < 0.001. Diaphragm thickness decreased by 9.1 ± 10.7% in nonseptic and by 16.0 ± 13.5% in septic patients, P = 0.049. DTF decreased by 15.2 ± 21.3% in nonseptic and by 30.7 ± 22.0% in septic patients, P = 0.013.

Conclusions: Mechanically ventilated patients with sepsis were associated with an earlier and more severe diaphragm dysfunction compared with patients without sepsis.
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http://dx.doi.org/10.1002/jcu.22690DOI Listing
May 2019

Nomogram for the prediction of postoperative hypoxemia in patients with acute aortic dissection.

BMC Anesthesiol 2018 10 20;18(1):146. Epub 2018 Oct 20.

Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China.

Background: Postoperative hypoxemia is quite common in patients with acute aortic dissection (AAD) and is associated with poor clinical outcomes. However, there is no method to predict this potentially life-threatening complication. The study aimed to develop a regression model in patients with AAD to predict postoperative hypoxemia, and to validate it in an independent dataset.

Methods: All patients diagnosed with AAD from December 2012 to December 2017 were retrospectively screened for potential eligibility. Preoperative and intraoperative variables were included for analysis. Logistic regression model was fit by using purposeful selection procedure. The original dataset was split into training and validating datasets by 4:1 ratio. Discrimination and calibration of the model was assessed in the validating dataset. A nomogram was drawn for clinical utility.

Results: A total of 211 patients, involving 168 in non-hypoxemia and 43 in hypoxemia group, were included during the study period (incidence: 20.4%). Duration of mechanical ventilation (MV) was significantly longer in the hypoxemia than non-hypoxemia group (41(10.5140) vs. 12(3.75,70.25) hours; p = 0.002). There was no difference in the hospital mortality rate between the two groups. The purposeful selection procedure identified 8 variables including hematocrit (odds ratio [OR]: 0.89, 95% confidence interval [CI]: 0.80 to 0.98, p = 0.011), PaO/FiO ratio (OR: 0.99, 95% CI: 0.99 to 1.00, p = 0.011), white blood cell count (OR: 1.21, 95% CI: 1.06 to 1.40, p = 0.008), body mass index (OR: 1.32, 95% CI: 1.15 to 1.54; p = 0.000), Stanford type (OR: 0.22, 95% CI: 0.06 to 0.66; p = 0.011), pH (OR: 0.0002, 95% CI: 2*10 to 0.74; p = 0.048), cardiopulmonary bypass time (OR: 0.99, 95% CI: 0.98 to 1.00; p = 0.031) and age (OR: 1.03, 95% CI: 0.99 to 1.08; p = 0.128) to be included in the model. In an independent dataset, the area under curve (AUC) of the prediction model was 0.869 (95% CI: 0.802 to 0.936). The calibration was good by visual inspection.

Conclusions: The study developed a model for the prediction of postoperative hypoxemia in patients undergoing operation for AAD. The model showed good discrimination and calibration in an independent dataset that was not used for model training.
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http://dx.doi.org/10.1186/s12871-018-0612-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195757PMC
October 2018

Respiratory Care Education and Clinical Practice in Mainland China.

Respir Care 2018 Oct 24;63(10):1239-1245. Epub 2018 Jul 24.

Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Background: Compared with 10 years ago when our last survey was completed, the number of respiratory therapists (RTs) has increased markedly in mainland China. In addition, the education systems for RTs and the working environment have also changed. We aimed to describe the current status of respiratory care in mainland China.

Methods: A nationwide survey was initiated from August 15, 2016, to September 2, 2016, through network platforms.

Results: We obtained responses from 196 RTs, of whom, 30.6% graduated from a bachelor's degree program, 25.5% graduated from an associate's degree program, and 43.9% were nurses who transitioned to be RTs through 6-month on-the-job training programs. Among the 3 groups, no significant differences existed in the basic job responsibility, such as mechanical ventilation and aerosol therapy; however, bachelor's degree RT graduates participated more in bronchoscopy assistance (96% vs 78%, = .002), extracorporeal membrane oxygenation management (42% vs 25%, = .02), and pulmonary ultrasound (40% vs 15%, < .001). There was no RT certification or licensure in mainland China at the time of the survey, so only 23% of bachelor's degree graduates and 42% of associate's degree graduates received a license through other professions. For the respondents' opinions on the obstacles of respiratory care profession development, the lack of licensure was a profound barrier for both degree graduates, whereas on-the-job training RTs deemed that insufficient recognition of the value of the respiratory care profession was the main obstacle.

Conclusions: In mainland China, degree programs for students and on-the-job training for Experienced ICU nurses were 2 major ways to train RTs. The absence of credential and/or licensure and the lack of recognition of the value of an RT were deemed as the 2 key obstacles in the development of respiratory care profession.
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http://dx.doi.org/10.4187/respcare.06217DOI Listing
October 2018

Enhanced recovery care versus traditional care after laparoscopic liver resections: a randomized controlled trial.

Surg Endosc 2018 06 12;32(6):2746-2757. Epub 2017 Dec 12.

The Department of General Surgery, The Sir Run Run Shaw Hospital, Medical College of Zhejiang University, Hangzhou, China.

Background: Enhanced recovery after surgery (ERAS), with several evidence-based elements, has been shown to shorten length of hospital stay and reduce perioperative hospital costs in many operations. This randomized clinical trial was performed to compare complications and hospital stay of laparoscopic liver resection between ERAS and traditional care.

Methods: A randomized controlled trial was performed for laparoscopic liver resection from August 2015 to August 2016. Patients were randomly divided into ERAS group and traditional care group. The primary outcome was length of hospital stay (LOS) after surgery. Second outcomes included postoperative complications, hospital cost, and 30-day readmissions. Elements used in ERAS group included more perioperative education, nurse navigators, nutrition support for liver diseases, respiratory therapy, oral carbohydrate 2 h before operation, early mobilization and oral intake, goal-directed fluid therapy, less drainages, postoperative nausea and vomiting (PONV) prophylaxis and multimodal analgesia.

Results: The study included 58 (two conversion to laparotomy) patients in ERAS group and 61 (three conversion to laparotomy) patients in the traditional care group. Postoperative LOS was significantly shorter in the ERAS group than traditional care group (5 vs. 8 days; p < 0.001). ERAS program significantly reduced the hospital costs (CNY 45413.1 vs. 55794.1; p = 0.006) and complications (36.2 vs. 55.7%; p = 0.033). Duration till first flatus and PONV were significantly reduced in ERAS group. Pain control was better in ERAS (Visual analogue scale (VAS) POD1 (≥ 4) 19.0 vs. 39.3%, p = 0.017; VAS POD1 2.5 vs. 3.1, p = 0.010). There was no difference in the rate of 30-day readmissions (6.9 vs. 8.2%; p = 1.000).

Conclusion: ERAS protocol is feasible and safe for laparoscopic liver resection. Patients in ERAS group have less pain and complications.
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http://dx.doi.org/10.1007/s00464-017-5973-3DOI Listing
June 2018

Rational lung tissue and animal models for rapid breath tests to determine pneumonia and pathogens.

Am J Transl Res 2017 15;9(11):5116-5126. Epub 2017 Nov 15.

Respiratory Department, Sir Run Run Shaw Hospital, Medical School, Zhejiang UniversityHangzhou, Zhejiang, China.

Objective: This study works to develop novel models that may be adopted for earlier non-invasive breathomics tests to determine pneumonia pathogens.

Methods: Two types of pneumonia models were created, both and . Paraneoplasm lung tissue and specific pathogen-free (SPF) rabbits were adopted and separately challenged with sterile saline solution control or three pathogens: , , and . After inoculation, headspace air or exhaled air were absorbed by solid phase micro-extraction (SPME) fibers and subsequently analyzed with gas chromatograph Mass Spectrometer (GCMS).

Results: Pneumonia and pathogen-specific discriminating VOC patterns (1H-Pyrrole-3-carbonitrile, Diethyl phthalate, Cedrol, Decanoic acid, Cyclohexane, Diisooctyl phthalate) were determined.

Conclusion: Our study successfully generated nosocomial pneumonia models for pneumonia diagnosis and pathogen-discriminating breath tests. The tests may allow for earlier pneumonia and pathogen diagnoses, and may transfer empirical therapy to targeted therapy earlier, thus improving clinical outcomes.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714795PMC
November 2017

Diaphragmatic Dysfunction Is Characterized by Increased Duration of Mechanical Ventilation in Subjects With Prolonged Weaning.

Respir Care 2016 Oct;61(10):1316-22

Department of Respiratory Care, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Background: Diaphragmatic dysfunction is often underdiagnosed and is among the risk factors for failed weaning. The purpose of this study was to determine the prevalence of diaphragmatic dysfunction diagnosed by B-mode ultrasonography and to determine whether prolonged weaning subjects with diaphragmatic dysfunction have increased duration of mechanical ventilation compared with those without diaphragmatic dysfunction.

Methods: This was a prospective observational study in mechanically ventilated subjects who failed ≥3 spontaneous breathing trials or required >7 d of weaning after the first spontaneous breathing trial. Diaphragm thickness was measured in the zone of apposition using a 6-13-MHz ultrasound transducer during a spontaneous breathing trial. The diaphragmatic thickening fraction was calculated as a percentage from the formula: (Thickness at peak inspiration - thickness at end expiration)/thickness at end expiration. Intra-observer and inter-observer reliability were also evaluated.

Results: Forty-one subjects (24 males; 62.2 ± 15.9 y old) were included in the study. Of these, the prevalence of ultrasonographic diaphragmatic dysfunction (defined as diaphragmatic thickening fraction of <20% with inspiration) was 34.1% (n = 14). Subjects with diaphragmatic dysfunction had longer ventilation time after inclusion (293.4 ± 194.8 vs 145.1 ± 101.3 h, P = .02) and ICU stay (29.2 ± 11.4 vs 22.4 ± 7.7 d, P = .03) than subjects without diaphragmatic dysfunction.

Conclusions: Diaphragmatic dysfunction as assessed by B-mode ultrasonography is common in subjects with prolonged weaning. Subjects with such diaphragmatic dysfunction show longer mechanical ventilation durations and ICU stays.
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http://dx.doi.org/10.4187/respcare.04746DOI Listing
October 2016

High-Level Pressure Support Ventilation Attenuates Ventilator-Induced Diaphragm Dysfunction in Rabbits.

Am J Med Sci 2015 Dec;350(6):471-8

Departments of Respiratory Care (HG, PX, YY), Pathology (TZ), Respiratory Medicine (KY), and Department of Critical Care (ZL, JZ), Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.

Background: The effects of different modes of mechanical ventilation in the same ventilatory support level on ventilator-induced diaphragm dysfunction onset were assessed in healthy rabbits.

Methods: Twenty New Zealand rabbits were randomly assigned to 4 groups (n = 5 in each group). Group 1: no mechanical ventilation; group 2: controlled mechanical ventilation (CMV) for 24 hours; group 3: assist/control ventilation (A/C) mode for 24 hours; group 4: high-level pressure support ventilation (PSV) mode for 24 hours. Heart rate, mean arterial blood pressure, PH, partial pressure of arterial oxygen/fraction of inspired oxygen and partial pressure of arterial carbon dioxide were monitored and diaphragm electrical activity was analyzed in the 4 groups. Caspase-3 was evaluated by protein analysis and diaphragm ultra structure was assessed by electron microscopy.

Results: The centroid frequency and the ratio of high frequency to low frequency were significantly reduced in the CMV, A/C and PSV groups (P < 0.001). The percent change in centroid frequency was significantly lower in the PSV group than in the CMV and A/C groups (P = 0.001 and P = 0.028, respectively). Electromyography of diaphragm integral amplitude decreased by 90% ± 1.48%, 67.8% ± 3.13% and 70.2% ± 4.72% in the CMV, A/C and PSV groups, respectively (P < 0.001). Caspase-3 protein activation was attenuated in the PSV group compared with the CMV and A/C groups (P = 0.035 and P = 0.033, respectively). Irregular swelling of mitochondria along with fractured and fuzzy cristae was observed in the CMV group, whereas mitochondrial cristae were dense and rich in the PSV group. The mitochondrial injury scores (Flameng scores) in the PSV group were the lowest among the 3 ventilatory groups (0.93 ± 0.09 in PSV versus 2.69 ± 0.05 in the CMV [P < 0.01] and PSV versus A/C groups [2.02 ± 0.08, P < 0.01]).

Conclusions: The diaphragm myoelectric activity was reduced in the PSV group, although excessive oxidative stress and ultra-structural changes of diaphragm were found. However, partial diaphragm electrical activity was retained and diaphragm injury was minimized using the PSV mode.
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http://dx.doi.org/10.1097/MAJ.0000000000000596DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668956PMC
December 2015