Dataset Information

Health-related quality of life as predictor for mortality in patients treated with long-term mechanical ventilation.

ABSTRACT:

Background

The Severe Respiratory Insufficiency (SRI) questionnaire is a specific measure of health-related quality of life (HRQoL) in patients treated with long-term mechanical ventilation (LTMV). The aim of the present study was to examine whether SRI sum scores and related subscales are associated with mortality in LTMV patients.

Methods

The study included 112 LTMV patients (non-invasive and invasive) from the Norwegian LTMV registry in Western Norway from 2008 with follow-up in August 2014. SRI data were obtained through a postal questionnaire, whereas mortality data were obtained from the Norwegian Cause of Death Registry. The SRI questionnaire contains 49 items and seven subscales added into a summary score (range 0-100) with higher scores indicating a better HRQoL. The association between the SRI score and mortality was estimated as hazard ratios (HRs) with 95% confidence intervals (95% CI) using Cox regression models and HRs were estimated per one unit change in the SRI score.

Results

Of the 112 participating patients in 2008, 52 (46%) had died by August 2014. The mortality rate was the highest in patients with chronic obstructive pulmonary disease (75%), followed by patients with neuromuscular disease (46%), obesity hypoventilation syndrome (31%) and chest wall disease (25%) (p?ConclusionsSRI score is associated with mortality in LTMV-treated patients. We propose the use of SRI in the daily clinic with repeated measurements as part of individual follow-up. Randomized clinical trials with interventions aimed to improve HRQoL in LTMV patients should consider the SRI questionnaire as the standard HRQoL measurement.

SUBMITTER: Markussen H

PROVIDER: S-EPMC6330471 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Health-related quality of life as predictor for mortality in patients treated with long-term mechanical ventilation.

Markussen Heidi H Lehmann Sverre S Nilsen Roy M RM Natvig Gerd K GK

BMC pulmonary medicine 20190111 1

<h4>Background</h4>The Severe Respiratory Insufficiency (SRI) questionnaire is a specific measure of health-related quality of life (HRQoL) in patients treated with long-term mechanical ventilation (LTMV). The aim of the present study was to examine whether SRI sum scores and related subscales are associated with mortality in LTMV patients.<h4>Methods</h4>The study included 112 LTMV patients (non-invasive and invasive) from the Norwegian LTMV registry in Western Norway from 2008 with follow-up i ...[more]

PMID: 30635052

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Project description:BackgroundAmong patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.MethodsRetrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.ResultsThere were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.DiscussionWe developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Project description:BackgroundThe probability of weaning and of long-term survival of chronically mechanically ventilated cancer patients is unknown, with incomplete information available to guide therapeutic decisions. We sought to determine the probability of weaning and overall survival of cancer patients requiring long-term mechanical ventilation in a specialized weaning unit.MethodsA single-institution retrospective review of patients requiring mechanical ventilation outside of a critical care setting from 2008 to 2012 and from January 1 to December 31, 2018, was performed. Demographic and clinical data were recorded, including cancer specifics, comorbidities, treatments, and outcomes. Overall survival was determined using the Kaplan-Meier approach. Time to weaning was analyzed using the cumulative incidence function, with death considered a competing risk. Prognostic factors were evaluated for use in prospective evaluations of weaning protocols.ResultsBetween 2008 and 2012, 122 patients required mechanical ventilation outside of a critical care setting with weaning as a goal of care. The cumulative incidence of weaning after discharge from the intensive care unit was 42% at 21 days, 49% at 30 days, 58% at 60 days, 61% at 90 days, and 61% at 120 days. The median survival was 0.16 years (95% CI, 0.12 to 0.33) for those not weaned and 1.05 years (95% CI, 0.60 to 1.34) for those weaned. Overall survival at 1 year and 2 years was 52 and 32% among those weaned and 16 and 9% among those not weaned. During 2018, 36 patients at our institution required mechanical ventilation outside of a critical care setting, with weaning as a goal of care. Overall, with a median follow-up of 140 days (range, 0-425 days; average, 141 days), 25% of patients requiring long-term mechanical ventilation (9 of 36) are alive.ConclusionsCancer patients can be weaned from long-term mechanical ventilation, even after prolonged periods of support. Implementation of a resource-intensive weaning program did not improve rates of successful weaning. No clear time on mechanical ventilation could be identified beyond which weaning was unprecedented. Short-term overall survival for these patients is poor.

Dataset Information

Health-related quality of life as predictor for mortality in patients treated with long-term mechanical ventilation.

Background

Methods

Results

Publications

Health-related quality of life as predictor for mortality in patients treated with long-term mechanical ventilation.

Similar Datasets

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