Not Only about the Drugs: Improved Survival with Noninvasive Ventilation in Amyotrophic Lateral Sclerosis.
Ontology highlight
ABSTRACT:
SUBMITTER: Berlowitz DJ
PROVIDER: S-EPMC7919150 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:To evaluate the effects of noninvasive ventilation (NIV) on sleep in patients with amyotrophic lateral sclerosis (ALS) after meticulous titration with polysomnography (PSG).In this prospective observational study, 24 ALS patients were admitted to the sleep laboratory during 4 nights for in-hospital NIV titration with PSG and nocturnal capnography. Questionnaires were used to assess subjective sleep quality and quality of life (QoL). Patients were readmitted after one month.In the total group, slow wave sleep and REM sleep increased and the arousal-awakening index improved. The group without bulbar involvement (non-bulbar) showed the same improvements, together with an increase in sleep efficiency. Nocturnal oxygen and carbon dioxide levels improved in the total and non-bulbar group. Except for oxygen saturation during REM sleep, no improvement in respiratory function or sleep structure was found in bulbar patients. However, these patients showed less room for improvement. Patient-reported outcomes showed improvement in sleep quality and QoL for the total and non-bulbar group, while bulbar patients only reported improvements in very few subscores.This study shows an improvement of sleep architecture, carbon dioxide, and nocturnal oxygen saturation at the end of NIV titration and after one month of NIV in ALS patients. More studies are needed to identify the appropriate time to start NIV in bulbar patients. Our results suggest that accurate titration of NIV by PSG improves sleep quality.A commentary on this article appears in this issue on page 511.
| S-EPMC4410930 | biostudies-literature
Project description:Amyotrophic lateral sclerosis (ALS), also referred to as Lou Gehrig's disease, is characterized by the progressive loss of cells in the brain and spinal cord that leads to debilitation and death in 3 - 5 years. Only one therapeutic drug, riluzole, has been approved for ALS and this drug improves survival by 2 - 3 months. The need for new therapeutics that can postpone or slow the progression of the motor deficits and prolong survival is still a strong unmet medical need.Although there are a number of drugs currently in clinical trials for ALS, this review provides an overview of the most promising biological targets and preclinical strategies that are currently being developed and deployed. The list of targets for ALS was compiled from a variety of websites including individual companies that have ALS programs and include those from the author's experience.Progress is being made in the identification of possible new therapeutics for ALS with recent efforts in understanding the genetic causes of the disease, susceptibility factors and the development of additional preclinical animal models. However, many challenges remain in the identification of new ALS therapeutics including: the use of relevant biomarkers, the need for an earlier diagnosis of the disease and additional animal models. Multiple strategies need to be tested in the clinic in order to determine what will be effective in patients.
| S-EPMC3367799 | biostudies-other
Project description: Not available
| S-EPMC6485636 | biostudies-literature
Project description:This SuperSeries is composed of the following subset Series: GSE39642: NanoString nCounter immune-related gene expression in blood sorted CD14+CD16- monocytes from sALS, fALS and HC subjects GSE39643: NanoString miRNA profiling of peripheral blood sorted CD14+CD16- monocytes from amyotrophic lateral sclerosis, multiple sclerosis and healthy control subjects Refer to individual Series
2012-07-25 | E-GEOD-39644 | biostudies-arrayexpress
Project description:Identification of amyotrophic lateral sclerosis (ALS) associated genes. Post mortem spinal cord grey matter from sporadic and familial ALS patients compared with controls. Keywords: other
2003-11-14 | GSE833 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2012-07-26 | GSE39644 | GEO
Project description:amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that leads to death within a few years after diagnosis. Malnutrition and weight loss are frequent and are indexes of poor prognosis. Total body fat and fat distribution have not been studied in ALS patients.Our aim was to describe adipose tissue content and distribution in ALS patients.We performed a cross-sectional study in a group of ALS patients (n?=?62, mean disease duration 22 months) along with age and gender matched healthy controls (n?=?62) using a MRI-based method to study quantitatively the fat distribution.Total body fat of ALS patients was not changed as compared with controls. However, ALS patients displayed increased visceral fat and an increased ratio of visceral to subcutaneous fat. Visceral fat was not correlated with clinical severity as judged using the ALS functional rating scale (ALS-FRS-R), while subcutaneous fat in ALS patients correlated positively with ALS-FRS-R and disease progression. Multiple regression analysis showed that gender and ALS-FRS-R, but not site of onset, were significant predictors of total and subcutaneous fat. Increased subcutaneous fat predicted survival in male patients but not in female patients (p<0.05).Fat distribution is altered in ALS patients, with increased visceral fat as compared with healthy controls. Subcutaneous fat content is a predictor of survival of ALS patients.
| S-EPMC3694869 | biostudies-other
Project description:Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment.
| S-EPMC10673879 | biostudies-literature
Project description:BackgroundBetter predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database.MethodsIn this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors.ResultsA model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class.ConclusionsUsing combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.
| S-EPMC5390993 | biostudies-literature
Project description:Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the "best" survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Results: Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. Conclusion: The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.
| S-EPMC6010549 | biostudies-literature