Abstract 473: Will Obesity Bankrupt the United States? Obese Patients Who Underwent Cardiopulmonary Resuscitation Have Increased Thirty-Day Readmission and Total Hospital Cost

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Kam Ho ◽  
Bharat Narasimhan ◽  
Lingling Wu ◽  
Shabnam Nasserifar ◽  
Jacqueline Sheehan ◽  
...  
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Cardiopulmonary Resuscitation ◽  
Hospital Mortality ◽  
The United States ◽  
Total Hospital Cost ◽  
Obese Patients ◽  
Secondary Diagnosis ◽  
Total Hospital ◽  
Total Cost

Purpose: To determine the relationship between obesity and thirty-days readmission, mortality, morbidity, and health care resource utilization in patients who underwent cardiopulmonary resuscitation (CPR) during their hospitalization in the in the United States. Method: A retrospective study was conducted using the AHRQ-HCUP NRD for the year 2014. Adults (≥ 18 years) with a primary diagnosis of CPR (1), along with a secondary diagnosis of obesity were identified using ICD-9 codes as described in the literature (2). The primary outcome was the rate of all-cause readmission within 30 days of discharge. Secondary outcomes were reasons for readmission, readmission mortality rate, morbidity, and resource use. Propensity score (PS) using the 1:1 nearest neighbor matching without replacement was utilized to adjust for confounders (3). Results: 113,394 hospital admissions among adults with a primary and secondary diagnosis of CPR were identified, of which 14.8% were obese. 1:1 PS matching was performed based on demographic and clinical characteristics. The 30-day rate of readmission among obese and non-obese with CPR were 4.94% and 2.82% (p <0.001). The most common readmission for both groups was unspecified sepsis (17.3%). During the index admission for CPR, the length of stay (LOS) among obese and non-obese patients were similar (10.3 vs 9.4 days, p=0.16). However, the total cost for the obese patients was statistically different ($33,232 vs $33,692, p <0.001). Most importantly, obese patients’ in-hospital mortality rate during their index admission was significant higher (58.7% vs 6.72%, p <0.001). Amongst those readmitted, obese patients similarly had a significantly longer LOS than their non-obese counterparts (8.1 vs 4.5 days, p <0.001) and their total cost was more expensive ($19,027 vs $10,572, p <0.001). But, obese patients’ in-hospital mortality rate during their readmission was not significant different (0.34 % vs 0.08%, p =0.09). Obesity (HR 1.77, p <0.02) was an independent predictor associated with higher risks of readmission. Conclusion: In this study, obese patients admitted with CPR have a higher 30 days of readmission rate, total hospital cost, and in-hospital mortality (p <0.02) than non-obese patients.

scholarly journals MON-643 Diabetes’ Cost to Us Taxpayers: Thirty-Day Readmission and Resource Utilization Among Diabetics Patients Admitted with ST-Elevation Myocardial Infarction: A Propensity Score Match Analysis

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Shabnam Nasserifar ◽  
Kam Sing Ho
Keyword(s):  
Myocardial Infarction ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Hospital Cost ◽  
Primary Diagnosis ◽  
Total Hospital Cost ◽  
Secondary Diagnosis ◽  
Total Hospital ◽  
History Of ◽  
St Elevation

Abstract PURPOSE: To determine the relationship between diabetes and thirty-days readmission, mortality, morbidity, and health care resource utilization in patients who were admitted with ST-Elevation Myocardial Infarction (STEMI) in the United States. METHOD: A retrospective study was conducted using the AHRQ-HCUP Nationwide Readmission Database for the year 2014. Adults (≥ 18 years) with a primary diagnosis of STEMI (1), along with a secondary diagnosis of diabetes were identified using ICD-9 codes as described in the literature (2). The primary outcome was the rate of all-cause readmission within 30 days of discharge. Secondary outcomes were reasons for readmission, readmission mortality rate, morbidity, and resource use (length of stay and total hospitalization costs and charges). Propensity score (PS) using the 1:1 nearest neighbor matching without replacement was utilized to adjust for confounders (3). Independent risk factors for readmission were identified using a Cox proportional hazards model (4). RESULTS: In total, 116,124 hospital admissions among adults with a primary diagnosis of STEMI were identified, of which 18.05% were diabetics. 1:1 PS matching was performed based on demographic (age, gender, hospital status, etc.) and clinical characteristics (Charlson comorbidity score. The 30-day rate of readmission among diabetics and non-diabetics with STEMI were 9.31% vs. 6.18% (p &lt;0.001). The most common readmission for both groups was recurrent myocardial infarction. During the index admission for STEMI, the length of stay (LOS) among diabetics and non-diabetics patients were not statistically different (4.74 vs 4.58 days, p=0.12). However, the total hospital cost for the diabetic patients was statistically different ($27,027 vs $24,807, p &lt;0.001). Most importantly, diabetics patients’ in-hospital mortality rate during their index admission was significant higher (10.20% vs 5.92%, p &lt;0.001). Amongst those readmitted, the LOS, total hospital cost, or in-hospital mortality among diabetics were not statistically different when compared to their counterparts during their readmission. Diabetes (HR 1.60, CI 1.27-2.02, p &lt;0.001) was an independent predictor associated with higher risks of readmission. Other independent predictors associated with increased 30-day readmission include acute exacerbation of CHF, acute exacerbation of COPD, acute kidney injury, secondary diagnosis of pneumonia, history of COPD, history of ischemic stroke, history of atrial fibrillation & atrial flutter, history of chronic kidney disease, history of iron deficiency, and use of mechanical ventilator. CONCLUSION: In this study, diabetics patients admitted with STEMI have a higher 30 days of readmission rate, total hospital cost, and in-hospital mortality (p &lt;0.001) than their non-diabetic counterparts.

The Impact of BMI on Adult Blunt Trauma Outcomes

2019 ◽  
Vol 85 (12) ◽  
pp. 1354-1362
Author(s):  
Rahman Barry ◽  
Milad Modarresi ◽  
Rodrigo Aguilar ◽  
Jacqueline Sanabria ◽  
Thao Wolbert ◽  
...  
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Length Of Stay ◽  
Blunt Trauma ◽  
Nationwide Inpatient Sample ◽  
The United States ◽  
Mortality Rates ◽  
Hospital Length Of Stay ◽  
Obese Patients ◽  
The Impact

Traumatic injuries account for 10% of all mortalities in the United States. Globally, it is estimated that by the year 2030, 2.2 billion people will be overweight (BMI ≥ 25) and 1.1 billion people will be obese (BMI ≥ 30). Obesity is a known risk factor for suboptimal outcomes in trauma; however, the extent of this impact after blunt trauma remains to be determined. The incidence, prevalence, and mortality rates from blunt trauma by age, gender, cause, BMI, year, and geography were abstracted using datasets from 1) the Global Burden of Disease group 2) the United States Nationwide Inpatient Sample databank 3) two regional Level II trauma centers. Statistical analyses, correlations, and comparisons were made on a global, national, and state level using these databases to determine the impact of BMI on blunt trauma. The incidence of blunt trauma secondary to falls increased at global, national, and state levels during our study period from 1990 to 2015, with a corresponding increase in BMI at all levels ( P < 0.05). Mortality due to fall injuries was higher in obese patients at all levels ( P < 0.05). Analysis from Nationwide Inpatient Sample database demonstrated higher mortality rates for obese patients nationally, both after motor vehicle collisions and mechanical falls ( P < 0.05). In obese and nonobese patients, regional data demonstrated a higher blunt trauma mortality rate of 2.4% versus 1.2%, respectively ( P < 0.05) and a longer hospital length of stay of 4.13 versus 3.26 days, respectively ( P = 0.018). The obesity rate and incidence of blunt trauma secondary to falls are increasing, with a higher mortality rate and longer length of stay in obese blunt trauma patients.

CHARACTERISTICS OF NONTRAUMATIC SUBARACHNOID HEMORRHAGE IN THE UNITED STATES IN 2003

Neurosurgery ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 1131-1138 ◽  
Author(s):  
Alisa M. Shea ◽  
Shelby D. Reed ◽  
Lesley H. Curtis ◽  
Michael J. Alexander ◽  
John J. Villani ◽  
...  
Keyword(s):  
United States ◽  
Subarachnoid Hemorrhage ◽  
Mortality Rate ◽  
Confidence Interval ◽  
Hospital Mortality ◽  
Odds Ratio ◽  
The United States ◽  
National Database ◽  
Endovascular Coiling ◽  
Hospital Outcomes

Abstract OBJECTIVE Substantial progress has been made in the diagnosis and treatment of subarachnoid hemorrhage (SAH). However, studies of SAH in the United States do not include information more recent than 2001, precluding analysis of shifts in treatment methods. We examined the epidemiology and in-hospital outcomes of nontraumatic SAH in the United States. METHODS We analyzed nationally representative data from the 2003 Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project to determine demographic and hospital characteristics, treatments, and in-hospital outcomes of patients with nontraumatic SAH. RESULTS In 2003, there were an estimated 31,476 discharges for nontraumatic SAH among patients aged 17 years or older, or 14.5 discharges per 100,000 adults. The in-hospital mortality rate was 25.3%. Microvascular clipping was performed in 7513 discharges, or 23.9% of inpatients with nontraumatic SAH; endovascular coiling was performed in 2849 discharges (9.1%). Adjusted odds of treatment with either procedure were significantly higher in urban teaching hospitals compared with urban nonteaching hospitals (odds ratio, 1.62; 95% confidence interval, 1.00–2.62) or rural hospitals (odds ratio, 3.08; 95% confidence interval, 1.93–4.91). CONCLUSION The in-hospital mortality rate associated with nontraumatic SAH continues to exceed 25%. Although it is unclear how many patients with nontraumatic SAH were actually diagnosed with a cerebral aneurysm, this study suggests that less than one-third of patients hospitalized for SAH receive surgical or endovascular treatment. Prospective studies are needed to elucidate either what systematic coding error is occurring in the national database or why patients may not receive treatment to secure a ruptured aneurysm.

scholarly journals Outcomes in Hospitalizations for Patients Admitted With Hyperosmolar Hyperglycemic State and Obesity: Analysis of National Inpatient Sample

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A428-A429
Author(s):  
Hafeez Shaka ◽  
Genaro Velazquez ◽  
Hernan Gerardo Marcos-Abdala ◽  
Ehizogie Edigin ◽  
Iriagbonse Asemota ◽  
...  
Keyword(s):  
Septic Shock ◽  
Hospital Mortality ◽  
Hospital Stay ◽  
Length Of Hospital Stay ◽  
Adverse Outcomes ◽  
Hospital Charges ◽  
Obese Patients ◽  
Secondary Diagnosis ◽  
Total Hospital ◽  
Significant Difference

Abstract Introduction: Hyperglycemic Hyperosmolar Nonketotic Syndrome (HHS) is a highly lethal disease with an estimated mortality rate of up to 20%. Although mortality has decreased in recent years, its incidence has increased in the setting of a higher prevalence of underlying conditions that have been previously well described, such as uncontrolled diabetes, Obesity, and a high-carbohydrate diet. All these comorbidities usually overlap with acute complications such as infections or dehydration, which incite the onset of HHS. Currently, limited literature exists for the role of obesity in mortality, hospital stay, and other adverse outcomes in patients with HHS. It is important to know which underlying conditions truly affect outcomes for patients being treated for this condition so further studies can be done, and treatment optimized. Objective: We aim to prove if obesity plays a role in increasing mortality and secondary adverse outcomes in patients with HHS compared to non-obese patients. Methods: A retrospective cohort study was conducted using the Nationwide Inpatient Sample from 2016 and 2017. 42,740 hospitalizations who had HHS as primary diagnosis were enrolled and further stratified based on the presence or absence of Obesity as a secondary diagnosis using ICD-10 codes. The primary outcome was inpatient mortality and secondary outcomes included length of hospital stay, total hospital charges, Sepsis, Septic Shock, Acute Kidney Injury (AKI), and Acute Respiratory Failure (ARF). Multivariate regression analysis was done to adjust for confounders. Results: Out of the 42 740 hospitalizations with HHS, 9,630 had Obesity. The in-hospital mortality for patients with HHS was 45 overall, out of which 45 patients had Obesity as a secondary diagnosis. Compared with patients without Obesity, non-obese patients had similar in-hospital mortality (OR 0.77, 95% CI 0.39–1.52, p=0.45) when adjusted for patient and hospital characteristics. Patients with HHS and Obesity had similar lengths of hospital stay, total hospital charges, rate of Sepsis, Septic Shock, and ARF in comparison to patients without Obesity; however, non-obese patients had higher odds of developing AKI throughout hospitalization. Conclusion: Although it is known and described that being obese plays a significant role in the onset of diabetes, and consequently HHS, there is no statistically significant difference in mortality or most other adverse outcomes compared to patients that are not obese and develop HHS. Although being obese plays a major role in inciting HHS in the general population, there is no need for a different approach to treatment, and outcomes are similar to non-obese patients with HHS.

Warning against long-acting sulphonamides

1966 ◽  
Vol 4 (4) ◽  
pp. 13-13
Keyword(s):  
United States ◽  
Mortality Rate ◽  
Erythema Multiforme ◽  
The United States ◽  
Stevens Johnson Syndrome ◽  
Warning Letter ◽  
Johnson Syndrome ◽  
The Us ◽  
Mucous Membranes ◽  
Long Acting

Last month the US Food and Drug Administration required American manufacturers of long-acting sulphonamides (sulphamethoxypyridazine, Lederkyn - Lederle and Midicel - PD; sulphadimethoxine - Madribon - Roche) to warn prescribers that in rare cases the Stevens-Johnson syndrome may develop as a severe and sometimes fatal side effect. This syndrome is a type of erythema multiforme in which large blisters appear on the skin and especially on the mucous membranes. The manufacturers were also to advise doctors ‘to consider prescribing short-acting sulphonamides first because they are effective for most of the same conditions’. The three drug firms concerned accordingly sent a joint warning letter to all doctors, pointing out that the Stevens-Johnson syndrome is a serious complication with a mortality rate of about 25%. So far 116 cases of this syndrome have been reported in association with the use of long-acting sulphonamides, most of them in the United States. Almost two thirds of the patients were children.

scholarly journals Epidemiology and Factors Affecting Mortality of Hospitalized Patients with Disseminated Intravascular Coagulation in the United States

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2404-2404
Author(s):  
Arya Mariam Roy ◽  
Manojna Konda ◽  
Akshay Goel ◽  
Appalanaidu Sasapu
Keyword(s):  
United States ◽  
African Americans ◽  
Native Americans ◽  
Mortality Rate ◽  
Length Of Stay ◽  
Disseminated Intravascular Coagulation ◽  
The United States ◽  
Hospitalized Patients ◽  
Northeast Region ◽  
Intravascular Coagulation

Introduction Disseminated Intravascular Coagulation (DIC) is a systemic coagulopathy which leads to widespread thrombosis and hemorrhage and ultimately results in multiorgan dysfunction. DIC usually occurs as a complication of illnesses like severe sepsis, malignancies, trauma, acute pancreatitis, burns, and obstetrical complications. The prognosis and mortality of DIC depend on the etiology, however, the mortality of DIC is known to be on the higher side. The aim of the study is to analyze if gender, race, regional differences have any association with the mortality of hospitalized patients with DIC. Method The National Inpatient Sample database from the Healthcare Cost and Utilization Project (HCUP) for the year 2016 was queried for data. We identified hospital admissions for DIC with the International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code D65. The data was analyzed with STATA 16.0 version and univariate and multivariate analysis were performed. We studied the characteristics of all such hospitalizations for the year 2016 and the factors associated with the in-hospital mortality rate (MR) of DIC. We used length of stay, cost of stay as an outcome to determine if gender, race, and location play a role in the mortality. Results A total of 8704 admissions were identified with a diagnosis of DIC during the year 2016. The mean age for admission was found to be 56.48± 0.22. The percentage of admissions in females and males did not have a notable difference (50.57% vs 49.43%). The disease specific MR for DIC was 47.7%. Admission during weekend vs weekdays did not carry a statistically significant difference in terms of MR. Females with DIC were less likely to die in the hospital when compared to males with DIC (OR= 0.906, CI 0.82 - 0.99, p= 0.031). Interestingly, African Americans (AA) with DIC admissions were found to have 24% more risk of dying when compared to Caucasians admitted with DIC (OR= 1.24, CI 1.10 - 1.39, P= 0.00), Native Americans (NA) has 67% more risk of dying when compared to Caucasians (OR= 1.67, CI 1.03 - 2.69, p= 0.035). The mortality rate of NA, AA, Caucasians with DIC was found to be 57%, 52%, 47% respectively. The MR was found to be highest in hospitals of the northeast region (52%), then hospitals in the south (47%), followed by west and mid-west (46%), p= 0.000. Patients admitted to west and mid-west were 24% less likely to die when compared to patients admitted to northeast region hospitals (OR= 0.76, p= 0.001). The average length of stay and cost of stay were also less in west and mid-west regions when compared to north east. The difference in outcomes persisted after adjusting for age, gender, race, hospital division, co-morbid conditions. Conclusion Our study demonstrated that African Americans and Native Americans with DIC have high risk of dying in the hospital. Also, there exists a difference between the mortality rate, length and cost of stay among different regions in the United States. More research is needed to elucidate the factors that might be impacting the location-based variation in mortality. Disclosures No relevant conflicts of interest to declare.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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