International Perspectives on the Influence of Structure and Process of Weaning From Mechanical Ventilation

Rose, Louise; Blackwood, Bronagh; Burns, Suzanne M.; Frazier, Susan K.; Egerod, Ingrid

doi:10.4037/ajcc2011430

Cited by 30 publications

(30 citation statements)

References 48 publications

(48 reference statements)

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“…Compared with data obtained by single-center series studies, data from registry studies provide a better overview of practices in large populations while avoiding referral bias or bias reflecting the practices of individual surgeons or institutions. 10,17,18 Because ANNs use a dynamic approach to analyzing mortality risk, they can modify their internal structure in relation to a functional objective by bottom-up computation (ie, by using the data themselves to generate the model). Although they cannot deal with missing data, ANNs can simultaneously handle numerous variables by building models with reference to outliers and nonlinear interactions among variables.…”

Section: Discussionmentioning

confidence: 99%

“…[4][5][6] Previous comparisons of logistic regression and ANN models for predicting outcomes of mechanical ventilation have shown major shortcomings. [7][8][9][10] First, few have used pooled data analysis for more than 2 years. Second, most studies have analyzed data for patients receiving mechanical ventilation in the United States or in Organization for Economic Cooperation and Development countries, which may differ substantially from patients receiving mechanical ventilation in Taiwan in terms of culture and health system delivery.…”

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confidence: 99%

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In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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Background Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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“…For example, differences exist in nurse and physician staffing models, specialty education for nurses, decision-making hierarchy, and assignment of roles and responsibilities for weaning. 56,57 Perceptions of nurses' experience and competence were key themes related to physicians' perspectives Most studies with a focus on patients were appropriately phenomenological, and the focus was lived experience of being weaned from mechanical ventilation. These studies elucidated the extreme physical and psychological challenges of weaning and underscored how horrible the experience can be for patients, regardless of how well weaning proceeds clinically.…”

Section: Methodsmentioning

confidence: 99%

Weaning From Mechanical Ventilation: A Scoping Review of Qualitative Studies

Rose

Dainty

Jordan

et al. 2014

American Journal of Critical Care

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A closed-book, multiple-choice examination following this article tests your under standing of the following objectives:1. List 3 key factors that influence ventilator weaning. 2. Define a scoping review. 3. Describe the steps in conducting a scoping review.To read this article and take the CNE test online, visit www.ajcconline.org and click "CNE Articles in This Issue." No CNE test fee for AACN members.By Louise Rose, PhD, Katie N. Dainty, PhD, Joanne Jordan, PhD, and Bronagh Blackwood, PhDBackground Weaning from mechanical ventilation is influenced by patient, clinician, and organizational factors.Objective To identify factors that may influence weaning and adoption of weaning strategies and tools, clinicians' perceptions of weaning strategies, and weaning experiences of patients and patients' families. Method A scoping review of indexed and nonindexed publications was done. Qualitative studies of health care providers, patients, and patients' families involved in weaning were included. Two investigators independently screened 8350 publications and extracted data from 43 studies. Study themes were content analyzed to identify common categories and themes within the categories. Results The study sample consisted of nurses in 15 studies, nurses and patients in 1 study, various health care providers in 11, patients in 10, and physicians in 4. Categories identified were as follows: for nurses, role or scope of practice, informing decision making, and influence on weaning outcome; for health care providers, factors influencing weaning decisions or use of protocols, role or scope of practice related to weaning, and organizational structure or practice environment; for patients, experience of mechanical ventilation and weaning, experience of the intensive care environment, psychological phenomena, and enabling success in weaning; and for physicians, tools or factors to facilitate weaning decisions and perceptions of nurses' role and scope of practice. However, even for these easy-to-wean patients, organizational and clinician factors such as staffing levels, skill mix, experience, and decision-making hierarchy influence the weaning process and can delay weaning and extubation. Such delays may result in prolonged duration of mechanical ventilation and stay in the intensive care unit (ICU) and increased costs.3 Approximately 30% of patients treated with mechanical ventilation experience difficult or prolonged weaning. 4 For these patients, similar organizational and clinician factors influence the duration and success of weaning. Because of the risk of further prolongation of mechanical ventilation, reintubation, and increased mortality, 5,6 identification of potentially modifiable factors that cause delays in weaning, unsuccessful weaning trials, and unsuccessful attempts in extubation is needed.An important step in ascertaining patient, clinician, and organizational factors that influence weaning is obtaining the views and perceptions of clinicians, patients, and patients' families who experience the process. Using...

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“…They consist of multiple linked elements: [8] context and setting (workload, resources, staffing); ICU staff characteristics (skill mix, training); and clinical processes (complexity of proocols, Intensive Care Med (2016) 42:278-281 DOI 10.1007/s00134-015-4119-0 EDITORIAL algorithms, decision-making and perceived risk) [9]. Understanding these components is critical to interpreting and generalising study findings [10].…”

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Tolerance to sedative drugs in PICU: can it be moderated or is it immutable?

2015

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Dear Editor, Nienke Vet and colleagues have evaluated the influence of daily sedation interruptions (DSI) in PICU [1]. This was ambitious: bias is hard to avoid and recruitment rates in such studies are poor, which ultimately restrict the strength of conclusions. Nevertheless, their results suggest that, if behavioural tools already drive sedation delivery, then DSI will not substantially influence the duration of ventilation. These findings are consistent with recent reports from adult intensive care [2].A pause in drug delivery allows plasma concentration to fall followed by reduction in effect site concentration and subsequent arousal. However, the lag time can be significant (Fig. 1a). Recovery may be further delayed by the presence and influence of midazolam's alpha-hydroxy metabolite that has half the activity of the parent drug [3]. Other drugs used for sedation also have influence: a three-

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International Perspectives on the Influence of Structure and Process of Weaning From Mechanical Ventilation

Cited by 30 publications

References 48 publications

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Weaning From Mechanical Ventilation: A Scoping Review of Qualitative Studies

Tolerance to sedative drugs in PICU: can it be moderated or is it immutable?

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