Hospitalization rates from radiotherapy complications in the United States

Hospitalizations due to radiotherapy (RT) complications result in significant healthcare expenditures and adversely affect the quality of life of cancer patients. Using a nationally representative dataset, the objective of this study is to identify trends in the incidence of these hospitalizations, their causes, and the resulting financial burden. Data from the National Inpatient Sample was retrospectively analyzed from 2005 to 2016. RT complications were identified using ICD-9 and ICD-10 external cause-of-injury codes. The hospitalization rate was the primary endpoint, with cost and in-hospital death as secondary outcomes. 443,222,223 weighted hospitalizations occurred during the study period, of which 482,525 (0.11%) were attributed to RT. The 3 most common reasons for RT-related hospitalization were cystitis (4.8%, standard error [SE] = 0.09), gastroenteritis/colitis (3.7%, SE = 0.07), and esophagitis (3.5%, SE = 0.07). Aspiration pneumonitis (1.4-fold) and mucositis (1.3-fold) had the highest relative increases among these hospitalizations from 2005 to 2016, while esophagitis (0.58-fold) and disorders of the rectum and anus were the lowest (0.67-fold). The median length of stay of patient for hospitalization for RT complications was 4.1 (IQR, 2.2–7.5) days and the median charge per patient was $10,097 (IQR, 5755–18,891) and the total cost during the study period was$4.9 billion. Hospitalization for RT-related complications is relatively rare, but those that are admitted incur a substantial cost. Use of advanced RT techniques should be employed whenever possible to mitigate the risk of severe toxicity and therefore reduce the need to admit patients

Hospitalization rates for complications due to systemic therapy in the United States

The aim of this study was to estimate the trends and burdens associated with systemic therapy-related hospitalizations, using nationally representative data. National Inpatient Sample data from 2005 to 2016 was used to identify systemic therapy-related complications using ICD-9 and ICD-10 external causes-of-injury codes. The primary outcome was hospitalization rates, while secondary outcomes were cost and in-hospital mortality. Overall, there were 443,222,223 hospitalizations during the study period, of which 2,419,722 were due to complications of systemic therapy. The average annual percentage change of these hospitalizations was 8.1%, compared to − 0.5% for general hospitalizations. The three most common causes for hospitalization were anemia (12.8%), neutropenia (10.8%), and sepsis (7.8%). Hospitalization rates had the highest relative increases for sepsis (1.9-fold) and acute kidney injury (1.6-fold), and the highest relative decrease for dehydration (0.21-fold) and fever of unknown origin (0.35-fold). Complications with the highest total charges were anemia ($4.6 billion), neutropenia ($3.0 billion), and sepsis ($2.5 billion). The leading causes of in-hospital mortality associated with systemic therapy were sepsis (15.8%), pneumonia (7.6%), and acute kidney injury (7.0%). Promoting initiatives such as rule OP-35, improving access to and providing coordinated care, developing systems leading to early identification and management of symptoms, and expanding urgent care access, can decrease these hospitalizations and the burden they carry on the healthcare system

Hospitalization rates for complications due to systemic therapy in the United States

The aim of this study was to estimate the trends and burdens associated with systemic therapy-related hospitalizations, using nationally representative data. National Inpatient Sample data from 2005 to 2016 was used to identify systemic therapy-related complications using ICD-9 and ICD-10 external causes-of-injury codes. The primary outcome was hospitalization rates, while secondary outcomes were cost and in-hospital mortality. Overall, there were 443,222,223 hospitalizations during the study period, of which 2,419,722 were due to complications of systemic therapy. The average annual percentage change of these hospitalizations was 8.1%, compared to − 0.5% for general hospitalizations. The three most common causes for hospitalization were anemia (12.8%), neutropenia (10.8%), and sepsis (7.8%). Hospitalization rates had the highest relative increases for sepsis (1.9-fold) and acute kidney injury (1.6-fold), and the highest relative decrease for dehydration (0.21-fold) and fever of unknown origin (0.35-fold). Complications with the highest total charges were anemia ($4.6 billion), neutropenia ($3.0 billion), and sepsis ($2.5 billion). The leading causes of in-hospital mortality associated with systemic therapy were sepsis (15.8%), pneumonia (7.6%), and acute kidney injury (7.0%). Promoting initiatives such as rule OP-35, improving access to and providing coordinated care, developing systems leading to early identification and management of symptoms, and expanding urgent care access, can decrease these hospitalizations and the burden they carry on the healthcare system

Hospitalization rates for complications due to systemic therapy in the United States

The aim of this study was to estimate the trends and burdens associated with systemic therapy-related hospitalizations, using nationally representative data. National Inpatient Sample data from 2005 to 2016 was used to identify systemic therapy-related complications using ICD-9 and ICD-10 external causes-of-injury codes. The primary outcome was hospitalization rates, while secondary outcomes were cost and in-hospital mortality. Overall, there were 443,222,223 hospitalizations during the study period, of which 2,419,722 were due to complications of systemic therapy. The average annual percentage change of these hospitalizations was 8.1%, compared to - 0.5% for general hospitalizations. The three most common causes for hospitalization were anemia (12.8%), neutropenia (10.8%), and sepsis (7.8%). Hospitalization rates had the highest relative increases for sepsis (1.9-fold) and acute kidney injury (1.6-fold), and the highest relative decrease for dehydration (0.21-fold) and fever of unknown origin (0.35-fold). Complications with the highest total charges were anemia ($4.6 billion), neutropenia ($3.0 billion), and sepsis ($2.5 billion). The leading causes of in-hospital mortality associated with systemic therapy were sepsis (15.8%), pneumonia (7.6%), and acute kidney injury (7.0%). Promoting initiatives such as rule OP-35, improving access to and providing coordinated care, developing systems leading to early identification and management of symptoms, and expanding urgent care access, can decrease these hospitalizations and the burden they carry on the healthcare system

Prevalence and Inpatient Hospital Outcomes of Malignancy-Related Ascites in the United States

Objective: Malignancy-related ascites (MRA) is the terminal stage of many advanced cancers, and the treatment is mainly palliative. This study looked for epidemiology and inpatient hospital outcomes of patients with MRA in the United States using a national database. Methods: The current study was a cross-sectional analysis of 2015 National Inpatient Sample data and consisted of patients ≥18 years with MRA. Descriptive statistics were used for understanding demographics, clinical characteristics, and MRA hospitalization costs. Multivariate regression models were used to identify predictors of length of hospital stay and in-hospital mortality. Results: There were 123 410 MRA hospitalizations in 2015. The median length of stay was 4.7 days (interquartile range [IQR]: 2.5-8.6 days), median cost of hospitalization was US$43 543 (IQR: US$23 485-US$82 248), and in-hospital mortality rate was 8.8% (n = 10 855). Multivariate analyses showed that male sex, black race, and admission to medium and large hospitals were associated with increased hospital length of stay. Factors associated with higher in-hospital mortality rates included male sex; Asian or Pacific Islander race; beneficiaries of private insurance, Medicaid, and self-pay; patients residing in large central and small metro counties; nonelective admission type; and rural and urban nonteaching hospitals. Conclusions: Our study showed that many demographic, socioeconomic, health care, and geographic factors were associated with hospital length of stay and in-hospital mortality and may suggest disparities in quality of care. These factors could be targeted for preventing unplanned hospitalization, decreasing hospital length of stay, and lowering in-hospital mortality for this population

Relationship between insurance status and interhospital transfers among cancer patients in the United States

Background: The relationship between insurance status and interhospital transfers has not been adequately researched among cancer patients. Hence this study aimed for understanding this relationship using a nationally representative database. Methods: A retrospective analysis was conducted using National Inpatient Sample (NIS) data collected during 2010–2016 and included all cancer hospitalization between 18 and 64 years of age. Interhospital transfers were compared based on insurance status (Medicare, Medicaid, private, and uninsured). Weighted multivariable logistic regressions were used to calculate the odds of interhospital transfers based on insurance status, after adjusting for many covariates. Results: There were 3,580,908 weighted cancer hospitalizations, of which 72,353 (2.02%) had interhospital transfers. Uninsured patients had significantly higher rates of interhospital transfers, compared to those with Medicare (P = 0.005) and private insurance (P \u3c 0.001). Privately insured patients had significantly lower rates of interhospital transfers, compared to those with Medicare (P \u3c 0.001) and Medicaid (P \u3c 0.001). Logistic regression analyses showed that the odds of having interhospital transfers were significantly higher among uninsured (adjusted odds ratio [aOR], 1.57, 95% CI: 1.45–1.69), Medicare (aOR, 1.38, 95% CI: 1.32–1.45) and Medicaid (aOR, 1.23, 95% CI: 1.16–1.30) patients when compared to those with private insurance coverages. Conclusion: Among cancer patients, uninsured and Medicare and Medicaid beneficiaries were more likely to experience interhospital transfers. In addition to medical reasons, factors such as affordability and socioeconomic status are influencing interhospital transfer decisions, indicating existing healthcare disparities. Further studies should focus on identifying the causal associations between factors explored in this study as well as additional unexplored factors

Space Weather Modeling Capabilities Assessment: Auroral Precipitation and Highâ Latitude Ionospheric Electrodynamics

As part of its International Capabilities Assessment effort, the Community Coordinated Modeling Center initiated several working teams, one of which is focused on the validation of models and methods for determining auroral electrodynamic parameters, including particle precipitation, conductivities, electric fields, neutral density and winds, currents, Joule heating, auroral boundaries, and ion outflow. Auroral electrodynamic properties are needed as input to space weather models, to test and validate the accuracy of physical models, and to provide needed information for space weather customers and researchers. The working team developed a process for validating auroral electrodynamic quantities that begins with the selection of a set of events, followed by construction of ground truth databases using all available data and assimilative data analysis techniques. Using optimized, predefined metrics, the ground truth data for selected events can be used to assess model performance and improvement over time. The availability of global observations and sophisticated data assimilation techniques provides the means to create accurate ground truth databases routinely and accurately.Key PointsA working team has been established to develop a process for validation of auroral precipitation and electrodynamics modelsValidation of auroral electrodynamic parameters requires generation of ground truth data sets for selected eventsCurrent observations and data assimilation techniques continue to improve the accuracy of global auroral electrodynamic specificationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148365/1/swe20815_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148365/2/swe20815.pd

Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.Comment: 4 pages; Heliophysics 2050 White Pape

Lipid metabolite biomarkers in cardiovascular disease: discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress)

We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning tools to gain utility from those data. We have compiled, curated, analyzed, and made available a new and more capable database of particle precipitation data that includes 51 satellite years of Defense Meteorological Satellite Program (DMSP) observations temporally aligned with solar wind and geomagnetic activity data. The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by machine learning approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. With a more capable representation of the organizing parameters and the target electron energy flux observations, PrecipNet achieves a $>$50\% reduction in errors from a current state-of-the-art model (OVATION Prime), better captures the dynamic changes of the auroral flux, and provides evidence that it can capably reconstruct mesoscale phenomena. We create and apply a new framework for space weather model evaluation that culminates previous guidance from across the solar-terrestrial research community. The research approach and results are representative of the `new frontier' of space weather research at the intersection of traditional and data science-driven discovery and provides a foundation for future efforts