All-cause unplanned readmissions in the United States: insights from the Nationwide Readmission Database.

BACKGROUND: There are few studies looking into adult, all-cause and age-group-specific unplanned readmissions. The predictors of such unplanned readmissions for all inpatient encounters remain obscure. AIMS: To describe the incidence and factors associated with unplanned readmissions in all inpatient encounters in the United States. METHODS: The US Nationwide Readmission Database (NRD) is a representative sample of hospitalisations in the United States (from approximately 28 states) accounting for approximately 60% of the US population. All inpatient encounters during January-November 2017 in the NRD were evaluated for the rates, predictors and costs of unplanned 30 days readmissions for age groups 18-44 years, 45-64 years, 65-75 years and ≥75 years. Elective readmissions and those patients who died on their index hospitalisations were excluded. Weighted analysis was performed to obtain nationally representative data. RESULTS: We identified 28 942 224 inpatient encounters with a total of 3 051 189 (10.5%) unplanned readmissions within 30 days. The age groups 18-44 years, 45-64 years, 65-74 years and ≥75 years had 7.0%, 12.0%, 11.7% and 12.3% readmissions respectively. Female gender, private insurance and elective admissions were negative predictors for readmissions. For the group aged 18-44 years, schizophrenia and diabetes mellitus complications were the most frequent primary diagnosis for readmissions, while in all older age groups septicaemia and heart failure were the most frequent primary diagnosis for readmissions. CONCLUSIONS: Thirty-day unplanned readmissions are common in patients over age 45 years, leading to significant morbidity. Effective strategies for reducing unplanned readmission may help to improve quality of care, outcomes and higher value care.

Demographics and Risk Factors of Pediatric Pulmonary Hypertension Readmissions.

BACKGROUND AND OBJECTIVES: Pulmonary hypertension (PH) leads to significant morbidity and mortality in pediatric patients and increases the readmission rates for hospitalizations. This study evaluates the risk factors and comorbidities associated with an increase in 30-day readmissions among pediatric PH patients. METHODS: National Readmission Database (NRD) 2017 was searched for patients less than 18 years of age who were diagnosed with PH based on the International Classification of Diseases, 10th Revision (ICD-10). Statistical Package for the Social Sciences (SPSS) software v25.0 (IBM Corp., Armonk, NY) was used for statistical analysis. RESULTS: Of 5.52 million pediatric encounters, 10,501 patients met the selection criteria. The 30-day readmission rate of 14.43% (p < 0.001) was higher than hospitalizations from other causes {Odds Ratio (OR) 4.02 (3.84-4.20), p < 0.001}. The comorbidities of sepsis {OR 0.75 (0.64-0.89), p < 0.02} and respiratory infections {OR 0.75 (0.67-0.85), p < 0.001} were observed to be associated with lower 30-day readmissions. Patients who required invasive mechanical ventilation via endotracheal tube {OR 1.66 (1.4-1.96), p < 0.001} or tracheostomy tube {OR 1.35 (1.15-1.6), p < 0.001} had increased unplanned readmissions. Patients with higher severity of illness based on All Patients Refined Diagnosis Related Groups (APR-DRG) were more likely to get readmitted {OR 7.66 (3.13-18.76), p < 0.001}. CONCLUSION: PH was associated with increased readmission rates compared to the other pediatric diagnoses, but the readmission rate in this study was lower than one previous pediatric study. Invasive mechanical ventilation, Medicaid insurance, higher severity of illness, and female gender were associated with a higher likelihood of readmission within 30 days.

Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.

INTRODUCTION: This study aimed to describe the rates and causes of unplanned readmissions within 30 days following carotid artery stenting (CAS) and to use artificial intelligence machine learning analysis for creating a prediction model for short-term readmissions. The prediction of unplanned readmissions after index CAS remains challenging. There is a need to leverage deep machine learning algorithms in order to develop robust prediction tools for early readmissions. METHODS: Patients undergoing inpatient CAS during the year 2017 in the US Nationwide Readmission Database (NRD) were evaluated for the rates, predictors, and costs of unplanned 30-day readmission. Logistic regression, support vector machine (SVM), deep neural network (DNN), random forest, and decision tree models were evaluated to generate a robust prediction model. RESULTS: We identified 16,745 patients who underwent CAS, of whom 7.4% were readmitted within 30 days. Depression [p < 0.001, OR 1.461 (95% CI 1.231-1.735)], heart failure [p < 0.001, OR 1.619 (95% CI 1.363-1.922)], cancer [p < 0.001, OR 1.631 (95% CI 1.286-2.068)], in-hospital bleeding [p = 0.039, OR 1.641 (95% CI 1.026-2.626)], and coagulation disorders [p = 0.007, OR 1.412 (95% CI 1.100-1.813)] were the strongest predictors of readmission. The artificial intelligence machine learning DNN prediction model has a C-statistic value of 0.79 (validation 0.73) in predicting the patients who might have all-cause unplanned readmission within 30 days of the index CAS discharge. CONCLUSIONS: Machine learning derived models may effectively identify high-risk patients for intervention strategies that may reduce unplanned readmissions post carotid artery stenting. CENTRAL ILLUSTRATION: Figure 2: ROC and AUPRC analysis of DNN prediction model with other classification models on 30-day readmission data for CAS subjects.

We present a novel deep neural network-based artificial intelligence prediction model to help identify a subgroup of patients undergoing carotid artery stenting who are at risk for short-term unplanned readmissions. Prior studies have attempted to develop prediction models but have used mainly logistic regression models and have low prediction ability. The novel model presented in this study boasts 79% capability to accurately predict individuals for unplanned readmissions post carotid artery stenting within 30 days of discharge.

Age- and Diagnosis-Based Trends for Unplanned Pediatric Rehospitalizations in the United States.

BACKGROUND AND OBJECTIVES: Hospital readmission rate helps to highlight the effectiveness of post-discharge care. There remains a paucity of plausible age-based categorization especially for ages below one year for hospital readmission rates. METHODS: Data from the 2017 Healthcare Cost and Utilization Project National Readmissions Database was analyzed for ages 0-18 years. Logistic regression analysis was performed to identify predictors for unplanned early readmissions.  Results: We identified 5,529,389 inpatient pediatric encounters which were further divided into age group cohorts. The overall rate of readmissions was identified at 3.2%. Beyond infancy, the readmission rate was found to be 6.7%. Across all age groups, the major predictors of unplanned readmission were cancers, diseases affecting transplant recipients and sickle cell patients. It was determined that reflux, milk protein allergy, hepatitis and inflammatory bowel diseases were significant diagnoses leading to readmission. Anxiety, depression and suicidal ideation depicted higher readmission rates in those older than 13 years. Across ages one to four years, dehydration, asthma and bronchiolitis were negative predictors of unplanned readmission.  Conclusions: Thirty-day unplanned readmissions remain a problem leading to billions of taxpayer dollars lost per annum. Effective strategies for mandatory outpatient follow-up may help the financial aspect of care while also enhancing the quality of care.

Refractory Angina: the Current State of Mechanical Therapies.

PURPOSE OF REVIEW: Refractory angina (RA), which is characterized by tissue ischemia along with neurological, mitochondrial, and psychogenic dysfunction, is becoming a major cause of morbidity in patients with advanced coronary artery disease. In this review, we discuss in detail the invasive mechanical non-cell therapy-based options, the evidence behind these therapies, and future trends. RECENT FINDINGS: There is extensive ongoing research in the areas of spinal-cord stimulation, transmyocardial laser revascularization, sympathectomy, angiogenesis, and other non-cell-based therapies to explore the best therapy for refractory angina. There is conflicting data in the literature suggesting subjective improvement in angina, but very few studies boast improvement in core objective parameters such as myocardial blood flow, survival, or rehospitalizations. Patients with refractory angina are a complex group of patients that need novel approaches to help alleviate their symptoms and reduce mortality. A carefully selected sequence of therapies may provide the best results in this patient population.

Smart phone applications as a source of information on stroke.

BACKGROUND AND PURPOSE: Smartphone applications have been increasingly identified as a novel platform for dissemination of healthcare related information. However, there have been no studies done to evaluate the availability and content of stroke related apps. PURPOSE: This study aims to identify and analyze stroke-related applications available on the Apple iTunes and Android Google Play Store. METHODS: The Apple iTunes store and Android Google Play Store were searched for stroke-related applications on July 27, 2013 using keywords: stroke, brain attack, intracranial hemorrhage, subarachnoid hemorrhage, cerebral infarction. The content of the applications was analyzed by two independent investigators. RESULTS: A total of 93 relevant applications (46.2% android and 53.8% iPhone) were identified of which 47.3% were available free of cost. 92% of apps were identified as useful by users and over 60% had scientifically valid information. There is a significant participation of healthcare agencies in dissemination of stroke related information through apps with 47.3% apps being uploaded by them. Over half of all stroke related apps were aimed towards health care workers (51.6%), 75% of which could be utilized as bedside tools for patient care and remainder had information related to recent research advances. The difference in scientific validity between the apps aimed at general population versus healthcare professionals was statistically significant (P<0.01). There was no statistical association between cost of app and scientific validity or usefulness. CONCLUSIONS: Smartphone apps are a significant source of information related to stroke. An increasing participation of healthcare agencies should be encouraged to promote dissemination of scientifically valid information.