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

ABSTRACT: 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).

SUBMITTER: Ge H

PROVIDER: S-EPMC7724969 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Ge Huiqing H Duan Kailiang K Wang Jimei J Jiang Liuqing L Zhang Lingwei L Zhou Yuhan Y Fang Luping L Heunks Leo M A LMA Pan Qing Q Zhang Zhongheng Z

Frontiers in medicine 20201125

<b>Background and objectives:</b> 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. <b>Methods:</b> The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were incl ...[more]

PMID: 33324663

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Project description:BackgroundPatient-ventilator asynchrony (PVA) is a common problem in patients undergoing invasive mechanical ventilation (MV) in the intensive care unit (ICU), and may accelerate lung injury and diaphragm mis-contraction. The impact of PVA on clinical outcomes has not been systematically evaluated. Effective interventions (except for closed-loop ventilation) for reducing PVA are not well established.MethodsWe performed a systematic review and meta-analysis to investigate the impact of PVA on clinical outcomes in patients undergoing MV (Part A) and the effectiveness of interventions for patients undergoing MV except for closed-loop ventilation (Part B). We searched the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, ClinicalTrials.gov, and WHO-ICTRP until August 2020. In Part A, we defined asynchrony index (AI) ≥ 10 or ineffective triggering index (ITI) ≥ 10 as high PVA. We compared patients having high PVA with those having low PVA.ResultsEight studies in Part A and eight trials in Part B fulfilled the eligibility criteria. In Part A, five studies were related to the AI and three studies were related to the ITI. High PVA may be associated with longer duration of mechanical ventilation (mean difference, 5.16 days; 95% confidence interval [CI], 2.38 to 7.94; n = 8; certainty of evidence [CoE], low), higher ICU mortality (odds ratio [OR], 2.73; 95% CI 1.76 to 4.24; n = 6; CoE, low), and higher hospital mortality (OR, 1.94; 95% CI 1.14 to 3.30; n = 5; CoE, low). In Part B, interventions involving MV mode, tidal volume, and pressure-support level were associated with reduced PVA. Sedation protocol, sedation depth, and sedation with dexmedetomidine rather than propofol were also associated with reduced PVA.ConclusionsPVA may be associated with longer MV duration, higher ICU mortality, and higher hospital mortality. Physicians may consider monitoring PVA and adjusting ventilator settings and sedatives to reduce PVA. Further studies with adjustment for confounding factors are warranted to determine the impact of PVA on clinical outcomes. Trial registration protocols.io (URL: https://www.protocols.io/view/the-impact-of-patient-ventilator-asynchrony-in-adu-bsqtndwn , 08/27/2020).

Project description:BACKGROUND:Patient-ventilator asynchrony is common in mechanically ventilated patients and may be related to adverse outcomes. Few studies have reported the occurrence of asynchrony in brain-injured patients. We aimed to investigate the prevalence, type and severity of patient-ventilator asynchrony in mechanically ventilated patients with brain injury. METHODS:This prospective observational study enrolled acute brain-injured patients undergoing mechanical ventilation. Esophageal pressure monitoring was established after enrollment. Flow, airway pressure, and esophageal pressure-time waveforms were recorded for a 15-min interval, four times daily for 3 days, for visually detecting asynchrony by offline analysis. At the end of each dataset recording, the respiratory drive was determined by the airway occlusion maneuver. The asynchrony index was calculated to represent the severity. The relationship between the prevalence and the severity of asynchrony with ventilatory modes and settings, respiratory drive, and analgesia and sedation were determined. Association of severe patient-ventilator asynchrony, which was defined as an asynchrony index ???10%, with clinical outcomes was analyzed. RESULTS:In 100 enrolled patients, a total of 1076 15-min waveform datasets covering 330,292 breaths were collected, in which 70,156 (38%) asynchronous breaths were detected. Asynchrony occurred in 96% of patients with the median (interquartile range) asynchrony index of 12.4% (4.3%-26.4%). The most prevalent type was ineffective triggering. No significant difference was found in either prevalence or asynchrony index among different classifications of brain injury (p?>?0.05). The prevalence of asynchrony was significantly lower during pressure control/assist ventilation than during other ventilatory modes (p?<?0.05). Compared to the datasets without asynchrony, the airway occlusion pressure was significantly lower in datasets with ineffective triggering (p?<?0.001). The asynchrony index was significantly higher during the combined use of opioids and sedatives (p?<?0.001). Significantly longer duration of ventilation and hospital length of stay after the inclusion were found in patients with severe ineffective triggering (p?<?0.05). CONCLUSIONS:Patient-ventilator asynchrony is common in brain-injured patients. The most prevalent type is ineffective triggering and its severity is likely related to a long duration of ventilation and hospital stay. Prevalence and severity of asynchrony are associated with ventilatory modes, respiratory drive and analgesia/sedation strategy, suggesting treatment adjustment in this particular population. Trial registration The study has been registered on 4 July 2017 in ClinicalTrials.gov (NCT03212482) ( https://clinicaltrials.gov/ct2/show/NCT03212482 ).

Project description:ObjectiveThis study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association study (GWAS) datasets and compared its performance to classic causal learning algorithms.MethodThe DCL algorithm learns the causes of a single target from passive data using Bayesian-scoring, instead of using independence checks, and a novel deletion algorithm. We generate 14,400 simulated datasets and measure the number of datasets for which DCL correctly and partially predicts the direct causes. We then compare its performance with the constraint-based path consistency (PC) and conservative PC (CPC) algorithms, the Bayesian-score based fast greedy search (FGS) algorithm, and the partial ancestral graphs algorithm fast causal inference (FCI). In addition, we extend our comparison of all five algorithms to both a real GWAS dataset and real breast cancer datasets over various time-points in order to observe how effective they are at predicting the causal influences of Alzheimer's disease and breast cancer survival.ResultsDCL consistently outperforms FGS, PC, CPC, and FCI in discovering the parents of the target for the datasets simulated using a simple network. Overall, DCL predicts significantly more datasets correctly (McNemar's test significance: p<<0.0001) than any of the other algorithms for these network types. For example, when assessing overall performance (simple and complex network results combined), DCL correctly predicts approximately 1400 more datasets than the top FGS method, 1600 more datasets than the top CPC method, 4500 more datasets than the top PC method, and 5600 more datasets than the top FCI method. Although FGS did correctly predict more datasets than DCL for the complex networks, and DCL correctly predicted only a few more datasets than CPC for these networks, there is no significant difference in performance between these three algorithms for this network type. However, when we use a more continuous measure of accuracy, we find that all the DCL methods are able to better partially predict more direct causes than FGS and CPC for the complex networks. In addition, DCL consistently had faster runtimes than the other algorithms. In the application to the real datasets, DCL identified rs6784615, located on the NISCH gene, and rs10824310, located on the PRKG1 gene, as direct causes of late onset Alzheimer's disease (LOAD) development. In addition, DCL identified ER category as a direct predictor of breast cancer mortality within 5 years, and HER2 status as a direct predictor of 10-year breast cancer mortality. These predictors have been identified in previous studies to have a direct causal relationship with their respective phenotypes, supporting the predictive power of DCL. When the other algorithms discovered predictors from the real datasets, these predictors were either also found by DCL or could not be supported by previous studies.ConclusionOur results show that DCL outperforms FGS, PC, CPC, and FCI in almost every case, demonstrating its potential to advance causal learning. Furthermore, our DCL algorithm effectively identifies direct causes in the LOAD and Metabric GWAS datasets, which indicates its potential for clinical applications.

Project description:Introduction and aim:Studies regarding asynchrony in patients in the cardiac postoperative period are still only a few. The main objective of our study was to compare asynchronies incidence and its index (AI) in 3 different modes of ventilation (volume-controlled ventilation [VCV], pressure-controlled ventilation [PCV] and pressure-support ventilation [PSV]) after ICU admission for postoperative care. Methods:A prospective parallel randomised trialin the setting of a non-profitable hospital in Brazil. The participants were patients scheduled for cardiac surgery. Patients were randomly allocated to VCV or PCV modes of ventilation and later both groups were transitioned to PSV mode. Results:All data were recorded for 5 minutes in each of the three different phases: T1) in assisted breath, T2) initial spontaneous breath and T3) final spontaneous breath, a marking point prior to extubation. Asynchronies were detected and counted by visual inspection method by two independent investigators. Reliability, inter-rater agreement of asynchronies, asynchronies incidence, total and specific asynchrony indexes (AIt and AIspecific) and odds of AI ?10% weighted by total asynchrony were analysed. A total of 17 patients randomly allocated to the VCV (n=9) or PCV (n=8) group completed the study. High inter-rated agreement for AIt (ICC 0.978; IC95%, 0,963-0.987) and good reliability (r=0.945; p<0.001) were found. Eighty-two % of patients presented asynchronies, although only 7% of their total breathing cycles were asynchronous. Early cycling and double triggering had the highest rates of asynchrony with no difference between groups. The highest odds of AI ?10% were observed in VCV regardless the phase: OR 2.79 (1.36-5.73) in T1 vs T2, p=0.005; OR 2.61 (1.27-5.37) in T1 vs T3, p=0.009 and OR 4.99 (2.37-10.37) in T2 vs T3, p<0.001. Conclusions:There was a high incidence of breathing asynchrony in postoperative cardiac patients, especially when initially ventilated in VCV. VCV group had a higher chance of AI ?10% and this chance remained high in the following PSV phases.

Dataset Information

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

Publications

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

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