Predictors and associations with outcomes of length of hospital stay in patients with acute heart failure: results from VERITAS

Background: The length of hospital stay (LOS) is important in patients admitted for acute heart failure (AHF) because it prolongs an unpleasant experience for the patients and adds substantially to health care costs. Methods and Results: We examined the association between LOS and baseline characteristics, 10-day post-discharge HF readmission, and 90-day post-discharge mortality in 1347 patients with AHF enrolled in the VERITAS program. Longer LOS was associated with greater HF severity and disease burden at baseline; however, most of the variability of LOS could not be explained by these factors. LOS was associated with a higher HF risk of both HF readmission (odds ratio for 1-day increase: 1.08; 95% confidence interval [CI] 1.01–1.16; P = .019) and 90-day mortality (hazard ratio for 1-day increase: 1.05; 95% CI 1.02–1.07; P < .001), although these associations are partially explained by concurrent end-organ damage and worsening heart failure during the first days of admission. Conclusions: In patients who have been admitted for AHF, longer length of hospital stay is associated with a higher rate of short-term mortality. Clinical Trial Registration: VERITAS-1 and -2: Clinicaltrials.gov identifiers NCT00525707 and NCT00524433

Quantifying the Risk of Hospital Readmissions: A Data Analysis and Predictive Modeling Approach

Despite several improvements in various domains of healthcare systems, the inability to reduce patients’ readmission rates is still a major problem faced by healthcare providers. It is extremely important to reduce the probability of readmissions because these not only increase the burden of healthcare costs on the patient but also expose them to prolonged psychological stress (e.g., trauma, pain, and discomfort due to altered physical functions) and healthcare-associated infections. Readmissions basically lead to the use of healthcare resources by the same person twice instead of being utilized by another patient. Furthermore, the readmission rate is used as a potential measure of healthcare quality. The high readmission rates may be due to a poor quality of care provided and could tarnish the reputation of the healthcare facilities. It could also reduce hospitals’ reimbursements from the insurance companies. The goal of this research is to quantify the risk of hospital readmissions by analyzing significant factors in patients undergoing skin procedures and to also identify the best time-frame and the corresponding predictors that could be used for predicting future readmissions related to skin procedures. Specifically, data analysis and predictive modeling approach will be adopted to identify the predictors of readmissions using a dataset of over 22,000 hospitalizations. The proposed the methodology will concentrate on patients’ demographics such as their age and gender along with the type of service, place of service, and others in order to predict if readmissions could be explained by these factors. Our study will analyze the significance of the above-mentioned factors for readmissions occurring over six different time intervals. The time-intervals being considered under this study are within 7 days, 15 days, 30 days, 45 days, 90 days and 1 year of initial admission. After analyzing the predictors over different time intervals, we found that although the significant factors differ for different time intervals, readmissions for the selected group of patients can be correlated to specific predicting variables for each of the time-interval. One of the predictors that seemed to be consistent over five different time intervals is the patient’s age. The care provider is another predictor, which was identified as statistically significant for more than one of the time intervals. Utilizing the training, validation, and testing data split, we were able to predict a probable outcome of whether or not it would be a case of readmission. By employing the confusion matrix to compare this predicted outcome against the actual outcome, the study checked the authenticity and accuracy of the models developed for each of the time intervals. Based on the performance measures developed using the confusion matrix, the best time-intervals to predict the readmissions are 7 days and 90 days with the F-1 score of 0.53, whereas the worst-time interval is 15 days

Variables to Predict Risk of Hospital Readmission

As the healthcare industry transitions toward accountable care and payment reform, creative approaches to healthcare is imperative. Poorly coordinated care and shorter hospital stays have resulted in higher rates of readmissions. This has large implications for hospitals and health systems

Assessing Prevalence of Known Risk Factors in a Regional Central Kentucky Medical Center Heart Failure Population as an Approach to Assessment of Needs for Development of a Program to Provide Targeted Services to Reduce 30 Day Readmissions

Abstract Objectives: Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records. Determine which HF population characteristics are significantly associated with readmissions to the hospital. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument. Design: Retrospective chart review. Setting: Regional acute care facility in Central Kentucky. Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code. Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently. Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions. Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions

The Effect of Patient and Hospital-level Factors on 30-Day Readmission After Initial Hospitalization Due to Stroke

Background: Hospital readmissions account for a large part of healthcare costs, especially among stroke patients. Readmission is common among disabled stroke survivors because they often suffer some neurological deficits, functional impairment, and other preexisting cardiovascular conditions. Although previous studies have explored the relationship between hospital readmissions after initial hospitalization due to stroke and a set of predictors using various analytical models, it often remains uncertain which predictors are most influential or essential. This study aimed to assess the effect of patient and hospital-levels factors on 30-day readmission after initial hospitalization due to stroke using the Anderson model of healthcare utilization as a guide. Methods: Data for this study was the 2014 National Readmissions Database. A generalized mixed-effect linear regression using a hierarchical modeling approach was run based on the Andersen model\u27s main block to assess the predictive capabilities of both individual and hospital-level factors on 30-day readmission. Models also assessed geographic differences that may exist among stroke patients. Results: Overall, the addition of variables blocks corresponding to the Anderson model of health utilization accounted for only a small variance in 30-day readmission. However, the addition of the enabling and need factors resulted in the most significant R2 change for hospitals in rural areas and urban areas, respectively. Conclusion: The predictive powers of individual and hospital factors on readmission within 30 days of initial stroke-caused hospitalization is weak. The results of this study suggest a holistic approach should be the goal for policymakers and legislators when developing policies to reduce readmissions