The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

The Nationwide Readmissions Database (NRD) is database under the Healthcare Cost and Utilization Project (HCUP) which contains nationally representative information on hospital readmissions for all ages, including all payers and the uninsured. The NRD contains data from approximately 18 million discharges per year (35 million weighted discharges) across most of the United States.

Data elements include:

• Discharge month, quarter, and year
• Verified patient linkage number
• Timing between admissions for a patient
• Length of inpatient stay (days)
• Transfers, same-day stays, and combined transfer records
• Identification of patient residency in the state in which he or she received hospital care
• International Classification of Diseases (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (prior to October 1, 2015)
• ICD-10-CM/PCS diagnosis, procedures, and external cause of morbidity codes (beginning October 1, 2015)
• Patient demographics (e.g., sex, age, income quartile, rural/urban residency)
• Expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay, those billed as 'no charge', and other insurance types)
• Total charges and hospital cost (calculated using the "Cost-to-Charge Ratio" file)

The NRD consists of four data files:

• Core File: Available for all years of the NRD and contains commonly used data elements (e.g., age, expected primary payer, discharge status, ICD-10-CM/PCS codes, total charges)
• Severity File: Available for all years of the NRD and contains additional data elements related to identifying health conditions at discharge.
• Diagnosis and Procedure Groups File: Contains additional information on ICD-10-CM/PCS; available beginning in 2018.
• Hospital File: Available for all years of the NRD and contains additional information on participating hospital characteristics.

The Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages.

A recent study found that approximately 1 in every 6 patients hospitalized for the 1st episode of syncope had an underlying pulmonary embolism (PE). As current guidelines do not strongly emphasize evaluation for PE in the workup of syncope, we hypothesize that there might be a higher rate of 30-day readmission due to untreated venous thromboembolism (VTE). The objective of this study is to measure the 30-day readmission rate due to VTE and identify predictors of 30-day readmission with VTE among syncope patients. We identified patients admitted with syncope with ICD9 diagnoses code 780.2 in the Nationwide Readmission Database (NRD-2013), Healthcare Cost and Utilization Project (HCUP). The 30-day readmission rate was calculated using methods described by HCUP. Logistic-regression was used to identify predictors of 30-day readmission with VTE. Discharge weights provided by HCUP were used to generate national estimates. In 2013, NRD included 207,339 eligible patients admitted with syncope. The prevalence rates of PE and DVT were 1.1% and 1.4%, respectively. At least one syncope associated condition was present in 60.9% of the patients. Among the patients who were not diagnosed with VTE during index admission for syncope (N = 188,015), 30-day readmission rate with VTE was 0.5% (0.2% with PE and 0.4% with DVT). In conclusion, low prevalence of VTE in patients with syncope and extremely low 30-day readmission rate with VTE argues against missed diagnoses of VTE in index admission for syncope. These results warrant further studies to determine clinical impact of work up for PE in syncope patients without risk factors.

This dataset tracks the updates made on the dataset "Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD)" as a repository for previous versions of the data and metadata.

Baseline characteristics among readmitted and non-readmitted patients by age categorya.

Adjusted odds of 30-day readmission by age category and payer groupa.

BackgroundMedical management is recommended for uncomplicated type B aortic dissection (TBAD). However, data focused on sex differences in outcomes in TBAD patients managed medically are limited.MethodsHospitalizations of adults with TBAD were identified using the 2016–2019 Nationwide Readmissions Database. TBAD diagnosis was deduced by inclusion of thoracic or thoracoabdominal aorta dissection and exclusion of presumed type A aortic dissection. Hospitalizations associated with intervention were excluded. Multivariable logistic regression modeling was used to investigate the association of sex with in-hospital mortality. A Cox proportional hazards model was used to assess the association between sex and readmission rates.ResultsThere were 52,269 TBAD hospitalizations (58% male). Compared to men, women were older (72 vs. 65 years), had higher in-hospital mortality (11.5% vs. 8.5%), shorter median length of stay (3.95 vs. 4.23 days), and lower rates of elective admissions (6.4% vs. 8.2%) (all p 

Baseline characteristics of 30-day readmissions in syncope patients with Venous Thromboembolism (VTE) (N = 188,015).

Multivariate predictors of 30-days unplanned readmission with Venous thromboembolism in syncope patients (N = 188,015, weighted N = 419,276).

IntroductionCardiac sarcoidosis (CS) is an increasingly recognized cause of cardiac disease. Because the clinical presentation of CS is non-specific, the diagnosis is often delayed. Early detection is essential to initiate treatments that reduce the risk of heart failure (HF) and arrhythmic death. We therefore aimed to describe the features of CS hospitalizations during which the initial diagnosis of CS is made.MethodsWe performed a retrospective analysis of hospitalizations from 2016 to 2019 in the Nationwide Readmission Database (NRD). Hospitalizations with a primary diagnosis suggestive of CS (HF/cardiomyopathy, cardiac arrest, arrhythmias, or heart block) were categorized into cases with and without CS as a secondary diagnosis (CS+ and CS−, respectively). One-to-one propensity score matching (PSM) was performed.ResultsThe CS+ cohort comprised 1,146 hospitalizations and the CS− cohort 3,250,696 hospitalizations. The CS+ cohort included patients who were younger and more often male. PSM resulted in highly matched cohorts (absolute standardized mean difference

Examine predictors of clinical and resource utilization outcomes associated with Alzheimer’s disease and related dementias (ADRD), stratified by patient severity profiles. Cross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010–2015 Nationwide Readmissions Database (NRD) using ICD 9th-10th codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles. Of 164,598 ADRD patients, 3,848 were mild, 68803 were moderate, 72428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value

Background & purposeObesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM.Patients & methodsWe analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk.ResultsThere were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively.ConclusionMetabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.

Unadjusted and adjusted hazard ratios for predictors of readmission among acute heart failure with breast cancer patients.