Long-Term Survival in Patients With Tracheostomy and Prolonged Mechanical Ventilation in Olmsted County, Minnesota

Kojičić, Marija; Li, Guangxi; Ahmed, Adil; Thakur, Lokendra; Trillo-Alvarez, Cesar; Cartin‐Ceba, Rodrigo; Gay, Peter C.; Gajić, Ognjen

doi:10.4187/respcare.01096

Cited by 25 publications

(25 citation statements)

References 43 publications

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“…Meanwhile, high-volume physicians in high-volume hospitals are most likely to achieve good outcomes for patients because such physicians are usually assisted by highly skilled and interdisciplinary care teams. [23][24][25] Predictors of in-hospital mortality identified in the current study included CCI and LOS, similar to the findings in other previous reports of ANN models for predicting patient mortality. 7,25 Morbid patients receiving mechanical ventilation are typically burdened by a host of comorbid conditions related to mechanical ventilation that increase their risk of medical complications.…”

Section: Discussionsupporting

confidence: 88%

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: Discussionsupporting

confidence: 88%

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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“…Of the 23 patients (7.1%) who were discharged home, 22 (96%) did not have an infection. The percentage of patients discharged home (7.1%) was lower than that reported in other studies, 33,34 a finding that may be attributed to a higher severity of illness in our sample. Compared with patients who had infection after tracheostomy, the percentage of patients discharged to a rehabilitation facility was higher for patients who had no infection or who had infection before the tracheostomy.…”

Section: Discussioncontrasting

confidence: 83%

Comparison of Respiratory Infections before and after Percutaneous Tracheostomy

Sole

Talbert

Penoyer

et al. 2014

American Journal of Critical Care

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Background A tracheostomy is often performed when patients cannot be weaned from mechanical ventilation. Respiratory infections (ventilator-associated pneumonia and infection of the lower respiratory tract) complicate the course of hospitalization in patients receiving mechanical ventilation. Objectives To evaluate respiratory infections before and after a percutaneous tracheostomy and to describe their outcomes. Methods Medical records of adults who had percutaneous tracheostomy during a 1-year period at a tertiary care hospital in the southeastern United States were reviewed retrospectively. Results Data for 322 patients were analyzed. Patients were predominately male (63.0%) and white (57.8%), with a mean age of 57.4 years. Ventilator-associated pneumonia or infection of the lower respiratory tract was identified in 90 patients (28.0%); the majority of infections were lower respiratory infections. Of all infections, 52% occurred before the tracheostomy, and 48% occurred after the procedure. Respiratory infections were associated with longer stays and higher costs, which were significantly higher in patients in whom the infection developed after the tracheostomy. Gram-negative organisms were responsible for the majority of infections. Conclusions Data related to respiratory infections that occurred before a tracheostomy were similar to data related to infections that occurred after a tracheostomy. Most infections were classified as lower respiratory infection rather than pneumonia. Infection, before or after a tracheostomy, resulted in longer stays and higher costs for care. Interventions focused on preventing infection before and after tracheostomy are warranted.

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“…1 The study confirmed that approximately 10% of patients with invasive mechanical ventilation undergo tracheotomy and that overall 1-year mortality in that population is still substantial. Older age and the presence of COPD negatively impacted survival, whereas survival was better among neuromuscular patients who continued to receive mechanical ventilation even after discharge.…”

supporting

confidence: 54%

Prolonged Mechanical Ventilation: Are You a Lumper or a Splitter?

OʼConnor

2011

Respir Care

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In this issue of RESPIRATORY CARE, Kojicic and colleagues describe long-term survival in patients who underwent tracheostomy for anticipated or prolonged mechanical ventilation (PMV) in Olmsted County, Minnesota. 1 The study confirmed that approximately 10% of patients with invasive mechanical ventilation undergo tracheotomy and that overall 1-year mortality in that population is still substantial. Older age and the presence of COPD negatively impacted survival, whereas survival was better among neuromuscular patients who continued to receive mechanical ventilation even after discharge. Importantly, Kojicic et al note that chronic conditions that relate to the underlying cause of mechanical ventilation should be taken into account when considering PMV outcomes.

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Long-Term Survival in Patients With Tracheostomy and Prolonged Mechanical Ventilation in Olmsted County, Minnesota

Cited by 25 publications

References 43 publications

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

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

Comparison of Respiratory Infections before and after Percutaneous Tracheostomy

Prolonged Mechanical Ventilation: Are You a Lumper or a Splitter?

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