Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 29
Filtrar
1.
Thromb Haemost ; 2021 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-34758489

RESUMO

BACKGROUND: As inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are at increased risk for venous thromboembolism (VTE), identifying high-risk patients requiring thromboprophylaxis is critical to reduce the mortality and morbidity associated with VTE. This study aimed to evaluate and compare the validities of the Padua Prediction Score and Caprini risk assessment model (RAM) in predicting the risk of VTE in inpatients with AECOPD. METHODS: The inpatients with AECOPD were prospectively enrolled from seven medical centers of China between September 2017 and January 2020. Caprini and Padua scores were calculated on admission, and the incidence of 3-month VTE was investigated. RESULTS: Among the 3277 eligible patients with AECOPD, 128 patients (3.9%) developed VTE within 3 months after admission. The distribution of the study population by the Caprini risk level was as follows: high, 53.6%; moderate, 43.0%; and low, 3.5%. The incidence of VTE increased by risk level as high, 6.1%; moderate, 1.5%; and low, 0%. According to the Padua RAM, only 10.9% of the study population was classified as high risk and 89.1% as low risk, with the corresponding incidence of VTE 7.9% and 3.4%, respectively. The Caprini RAM had higher area under curve (AUC) compared with the Padua RAM (0.713  0.021 vs 0.644 ± 0.023, P = 0.029). CONCLUSION: The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

2.
BMC Infect Dis ; 21(1): 1040, 2021 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-34620102

RESUMO

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. METHODS: This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. RESULTS: Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than < 0.8 × 109 per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747-0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. CONCLUSIONS: This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service.


Assuntos
COVID-19 , Humanos , Masculino , Programas de Rastreamento , Estudos Retrospectivos , Fatores de Risco , SARS-CoV-2
3.
Physiol Meas ; 42(8)2021 08 27.
Artigo em Inglês | MEDLINE | ID: mdl-34384069

RESUMO

Objective. The measurement of the static compliance of the respiratory system (Cstat) during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.Approach. We propose a method to measure the quasi-static respiratory compliance (Cqstat) under pressure control ventilation mode without the inspiratory pause maneuver. First, a screening strategy was applied to filter out breaths affected strongly by spontaneous breathing efforts or artifacts. Then, we performed a virtual extrapolation of the flow-time waveform when the end-inspiratory flow was not zero, to allow for the calculation ofCqstatfor each kept cycle. Finally, the outputCqstatwas obtained as the average of the smallest 40Cqstatmeasurements. The proposed method was validated against the gold standardCstatmeasured from real clinical settings and compared with two reported algorithms. The gold standardCstatwas obtained by applying an end-inspiratory pause maneuver in the volume-control ventilation mode.Main results. Sixty-nine measurements from 36 patients were analyzed. The Bland-Altman analysis showed that the bias of agreement forCqstatversus the gold standard measurement was -0.267 ml/cmH2O (95% limits of agreement was -4.279 to 4.844 ml/cmH2O). The linear regression analysis indicated a strong correlation (R2 = 0.90) between theCqstatand gold standard.Significance. The results showed that theCqstatcan be accurately estimated from continuous ventilator waveforms, including spontaneous breathing without an inspiratory pause maneuver. This method promises to provide continuous measurements compliant with mechanical ventilation.


Assuntos
Respiração Artificial , Sistema Respiratório , Humanos , Ventiladores Mecânicos
5.
Sensors (Basel) ; 21(12)2021 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-34204238

RESUMO

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.


Assuntos
Serviços de Assistência Domiciliar , Redes Neurais de Computação , Humanos , Aprendizado de Máquina , Respiração Artificial , Ventiladores Mecânicos
6.
Front Physiol ; 12: 684927, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34149459

RESUMO

Importance: Postoperative pulmonary complications and cardiovascular complications are major causes of morbidity, mortality, and resource utilization in cardiac surgery patients. Objectives: To investigate the effects of airway pressure release ventilation (APRV) on respiration and hemodynamics in post cardiac surgery patients. Main Outcomes and Measures: A single-center randomized control trial was performed. In total, 138 patients undergoing cardiopulmonary bypass were prospectively screened. Ultimately 39 patients met the inclusion criteria and were randomized into two groups: 19 patients were managed with pressure control ventilation (PCV) and 20 patients were managed with APRV. Respiratory mechanics after 4 h, hemodynamics within the first day, and Chest radiograph score (CRS) and blood gasses within the first three days were recorded and compared. Results: A higher cardiac index (3.1 ± 0.7 vs. 2.8 ± 0.8 L⋅min-1⋅m2; p < 0.05), and shock volume index (35.4 ± 9.2 vs. 33.1 ± 9.7 ml m-2; p < 0.05) were also observed in the APRV group after 4 h as well as within the first day (p < 0.05). Compared to the PCV group, the PaO2/FiO2 was significantly higher after 4 h in patients of APRV group (340 ± 97 vs. 301 ± 82, p < 0.05) and within the first three days (p < 0.05) in the APRV group. CRS revealed less overall lung injury in the APRV group (p < 0.001). The duration of mechanical ventilation and ICU length of stay were not significantly (p = 0.248 and 0.424, respectively). Conclusions and Relevance: Compared to PCV, APRV may be associated with increased cardiac output improved oxygenation, and decreased lung injury in postoperative cardiac surgery patients.

7.
Ann Transl Med ; 9(7): 588, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33987286

RESUMO

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.

8.
EClinicalMedicine ; 36: 100898, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34041461

RESUMO

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. Δ M P 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; p < 0.001) and the largest in class 3 (HR for every 5 Joules/min increase: 1.83; 95% CI: 1.52 to 2.20; p < 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).

9.
Comput Methods Programs Biomed ; 204: 106057, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-33836375

RESUMO

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.


Assuntos
Serviços de Assistência Domiciliar , Respiração Artificial , Humanos , Redes Neurais de Computação , Respiração com Pressão Positiva , Ventiladores Mecânicos
10.
Chest ; 159(4): 1426-1436, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33197403

RESUMO

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 H2O 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.


Assuntos
Respiração com Pressão Positiva , Síndrome do Desconforto Respiratório/terapia , Insuficiência Respiratória/terapia , Idoso , Feminino , Humanos , Intubação Intratraqueal , Masculino , Pessoa de Meia-Idade , Projetos Piloto , Síndrome do Desconforto Respiratório/fisiopatologia , Insuficiência Respiratória/fisiopatologia , Mecânica Respiratória
11.
PeerJ ; 8: e10497, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33312774

RESUMO

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 PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 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]; p = 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]; p = 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.

12.
Front Med (Lausanne) ; 7: 597406, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33324663

RESUMO

Background and objectives: 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. Methods: 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). Results: 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; p < 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; p < 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. Conclusions: 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).

13.
EBioMedicine ; 62: 103081, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33181462

RESUMO

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).


Assuntos
Biomarcadores , Análise por Conglomerados , Aprendizado Profundo , Suscetibilidade a Doenças , Sepse/diagnóstico , Sepse/etiologia , Algoritmos , Biologia Computacional/métodos , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Valor Preditivo dos Testes , Prognóstico , Curva ROC , Transcriptoma
14.
J Evid Based Med ; 13(4): 301-312, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33185950

RESUMO

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.


Assuntos
Inteligência Artificial , Síndrome do Desconforto Respiratório/terapia , Ensaios Clínicos como Assunto/estatística & dados numéricos , Árvores de Decisões , Registros Eletrônicos de Saúde/estatística & dados numéricos , Humanos , Aprendizado de Máquina , Síndrome do Desconforto Respiratório/etiologia , Fatores de Risco , Procedimentos Cirúrgicos Operatórios/efeitos adversos , Reação Transfusional/complicações
15.
Front Med (Lausanne) ; 7: 541, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32974375

RESUMO

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.

16.
JMIR Med Inform ; 8(4): e17642, 2020 Apr 23.
Artigo em Inglês | MEDLINE | ID: mdl-32324148

RESUMO

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.

17.
Comput Biol Med ; 120: 103721, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32250853

RESUMO

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.


Assuntos
Memória de Curto Prazo , Respiração Artificial , Humanos , Aprendizado de Máquina , Redes Neurais de Computação , Ventiladores Mecânicos
18.
PLoS One ; 14(8): e0221577, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31465523

RESUMO

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.


Assuntos
Pesquisas sobre Serviços de Saúde , Padrões de Prática Médica/estatística & dados numéricos , Respiração Artificial/estatística & dados numéricos , Terapia Respiratória/estatística & dados numéricos , Mídias Sociais , China , Humanos , Terapia Respiratória/métodos
19.
Intensive Care Med ; 45(6): 856-864, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31062050

RESUMO

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.


Assuntos
Índice de Massa Corporal , Fenômenos Mecânicos , Síndrome do Desconforto Respiratório/classificação , Síndrome do Desconforto Respiratório/mortalidade , Adulto , Área Sob a Curva , Feminino , Humanos , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Mortalidade , Análise Multivariada , Curva ROC , Respiração Artificial/métodos , Respiração Artificial/estatística & dados numéricos , Síndrome do Desconforto Respiratório/epidemiologia
20.
J Clin Ultrasound ; 47(4): 206-211, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-30671990

RESUMO

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.


Assuntos
Diafragma/diagnóstico por imagem , Diafragma/fisiopatologia , Respiração Artificial , Sepse/fisiopatologia , Ultrassonografia/métodos , Idoso , Estudos de Coortes , Estado Terminal , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Estudos Prospectivos , Reprodutibilidade dos Testes
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...