Asthma: Hospitalization Trends and Predictors of In-Hospital Mortality and Hospitalization Costs in the USA (2001-2010)

Kaur, Bani Preet; Lahewala, Sopan; Arora, Shilpkumar; Agnihotri, Kanishk; Panaich, Sidakpal; Secord, Elizabeth; Levine, Diane

doi:10.1159/000441687

Cited by 59 publications

(86 citation statements)

References 12 publications

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“…Asthma is among the most common and costly chronic diseases in the United States. 26 Annual U.S. healthcare expenditures for asthma are $56 billion, with over 50% of direct costs attributable to hospitalizations. 27 About 25 million or 8% of the U.S. population had asthma in 2009 and this number continues to grow.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

142

110

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Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training data sets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

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Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

142

110

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“…Poor control of symptoms and acute exacerbations requiring emergency room admittance and hospitalization expose patients to a poor quality of life, high mobility and a significant economic burden on the healthcare system and society [7]. Although to a minor extent compared with chronic obstructive pulmonary disease (COPD) [8, 9], severe and poorly controlled asthma still accounts for a significant in-hospital mortality, especially in children, elderly and mechanically ventilated patients [10, 11], or patients from low income countries [2]. For many years, important efforts have been spent to optimize asthma management; however, despite the availability of new treatment options [12], international surveys continue to provide evidence for suboptimal asthma control in many countries [13], with poor control of symptoms and exposure to risk of exacerbation in some cases affecting more than 50% of patients [14, 15].…”

Section: Introductionmentioning

confidence: 99%

The evidence on tiotropium bromide in asthma: from the rationale to the bedside

Radovanovic

Santus

Blasi

et al. 2017

Multidiscip Respir Med

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Severe and poorly controlled asthma still accounts for a great portion of the patients affected. Disease control and future risk management have been identified by international guidelines as the main goals in patients with asthma. The need for new treatment approaches has led to reconsider anticholinergic drugs as an option for asthma treatment. Tiotropium is the first anticholinergic drug that has been approved for children and adults with poorly controlled asthma and is currently considered as an option for steps 4 and 5 of the Global Initiative for Asthma. In large randomized clinical trials enrolling patients with moderate to severe asthma, add-on therapy with tiotropium has demonstrated to be efficacious in improving lung function, decreasing risk of exacerbation and slowing the worsening of disease; accordingly, tiotropium demonstrated to be non inferior compared to long acting beta-agonists in the maintenance treatment along with medium to high inhaled corticosteroids. In view of the numerous ancillary effects acting on inflammation, airway remodeling, mucus production and cough reflex, along with the good safety profile and the broad spectrum of efficacy demonstrated in different disease phenotypes, tiotropium can represent a beneficial alternative in the therapeutic management of poorly controlled asthma. The present extensive narrative review presents the pharmacological and pathophysiological basis that guided the rationale for the introduction of tiotropium in asthma treatment algorithm, with a particular focus on its conventional and unconventional effects; finally, data on tiotropium efficacy and safety. from recent randomized clinical trials performed in all age categories will be extensively discussed.

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“…Based on a study in Finland, ischemic heart disease, heart failure and lung cancer were the other causes of death [21]. All of those conditions also have been reported to have higher mortality in winter time in the general population [12,22], which may explain the higher mortality in IPF and non IPF ILD patients in winter time Of note, the in-hospital mortality of interstitial lung disease was noted to be significantly higher than the similar study in chronic obstructive lung disease (COPD) and asthma patients using the same national database ,8% vs 2%, 8% vs 1% respectively [23,24]. interestingly enough, the mortality rate was 14% higher in the winter compared to the summer, which was less pronounced than the seasonal variations of all cause of deaths of COPD patients (25% to 50% higher in the winter) [12,25].…”

Section: Discussionmentioning

confidence: 88%

Trends and seasonal variation of hospitalization and mortality of interstitial lung disease in the United States from 2006 to 2016

Shmelev

Charbek

2020

Preprint

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BackgroundIn the recent years, the overall trends in hospital admission and mortality of interstitial lung disease (ILD) are unknown. In addition, there was some evidence that interstitial lung disease death rate highest in the winter but this finding was only available in one study. This study will investigate the trend and seasonal variations in hospital admission and mortality rates of ILD from 2006 to 2016.MethodFrom the Nationwide Inpatient Sample database, we collected all cases with the International Classification of Diseases (ICD)-9 or ICD-10 codes of ILD excluding identifiable external causes (drug, organic or inorganic dusts) from 2006 to 2016. Hospitalization rates of each year were calculated based on U.S Census population data. Monthly hospitalization and in-hospital mortality rates were analyzed by seasonal and trend decomposition.ResultsFrom 2006 to 2016, there was a downtrend in all cause hospital admissions but in-hospital mortality rate did not change, with or without the presence of pneumonia. Highest hospital admission rates of ILD per 100000 of population were from January to May. The average number of hospitalizations in spring, summer, fall and winter months were 7447.9 ± 932.0, 6643.0 ± 840.5, 6551.3 ± 922.6 and 7110.3 ± 866.1 respectively. All-cause in-hospital mortality ranges from 7.13% ± 0.79% in the summer to 8.13% ± 0.60% in the winter with winter months having the highest mortality rate (p=0.018). The seasonal variations of hospital admission and mortality was not changed when infectious pneumonia cases were ruled out.ConclusionFrom 2006 to 2016, admission rates of ILD declined but in-hospital mortality remained unchanged. All-cause hospital admissions and mortality of ILD have a strong seasonal variation. Hospital admissions are highest in the period from January to May, in-hospital death was highest in the winter.

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Asthma: Hospitalization Trends and Predictors of In-Hospital Mortality and Hospitalization Costs in the USA (2001-2010)

Cited by 59 publications

References 12 publications

Machine learning approaches to personalize early prediction of asthma exacerbations

Machine learning approaches to personalize early prediction of asthma exacerbations

The evidence on tiotropium bromide in asthma: from the rationale to the bedside

Trends and seasonal variation of hospitalization and mortality of interstitial lung disease in the United States from 2006 to 2016

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