System-Wide Prediction of General, All-Cause, Preventable Hospital Readmissions

Existing studies of hospital readmissions typically focus on specific diagnoses, age groups, discharge dispositions, payer classes, or hospitals, and often use small samples. It is not clear how predictive models generated from such studies generalize across diseases, hospitals, or time periods. In this study, a logistic regression model of readmission risk within 30 days based on hospital administrative data was constructed and validated across hospitals and time periods. The hospitals included both general and specialty hospitals such as long-term care, women’s, and children’s hospitals. The administrative data included information on patient’s demographics, diagnoses, procedures, and discharge disposition. Derivation and validation samples for the cross-hospital analysis yielded C-statistics of 0.722 and 0.706, respectively. The cross-time period analysis yielded C-statistics from 0.736 to 0.755 for five derivation samples, and from 0.681 to 0.701 for fifteen validation samples. The findings indicate that a prediction model can be used with relative success to extrapolate beyond the estimation sample both in terms of hospital and time period. Such risk estimates can be used to inform discharge intervention decisions and increase care coordination

Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets

Incidence of and causes for all-cause hospitalizations in patients with atrial fibrillation

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, and its prevalence is expected to further increase in the future. AF patients not only have a high number of comorbidities, but they also have an increased risk of hospital admissions compared to individuals without AF. Nevertheless, predicting hospital admission risk among patients with AF remains difficult, and possible preventive strategies unclear. Based on these gaps in knowledge, the overall goal of this PhD thesis was to investigate the incidence of and causes for all-cause hospital admission in patients with AF. The specific aims were (1) to perform a systematic review and meta-analysis summarizing the current evidence of the incidence of and associated risk factors for hospital admissions in AF patients; (2) to identify risk factors for hospital admissions in our own cohorts and subsequently use this knowledge to develop and validate a risk score for predicting hospital admissions; (3) to identify psychosocial factors associated with hospital admissions in patients with AF. Methods: For the meta-analysis, we performed a comprehensive literature search in PubMed, EMBASE and CENTRAL, and pooled incidence rates for hospital admissions using random-effects models. Factors associated with observed between-study heterogeneity were identified using meta-regression analysis. For the second and third study, we used data of two ongoing, prospective observational cohort studies, the Basel Atrial Fibrillation Cohort Study (BEAT-AF) and the Swiss Atrial Fibrillation Cohort Study (Swiss-AF) in which 3,968 patients with diagnosed AF were enrolled. Unplanned hospital admissions were defined as any unpredicted admission leading to at least one overnight stay. For the second study, we used the Swiss-AF data set as the derivation cohort and performed a variable selection using the least absolute shrinkage and selection operator (LASSO) method. Multivariable adjusted Cox regression analyses were performed to assess the effect of the selected variables on all-cause hospitalization. Based on regression coefficients we constructed a risk score and subsequently validated the score in the external validation cohort (BEAT-AF). For the third study, we used psychosocial factors, such as marital status, education, level of depression and health perception, and investigated their effects on risk of hospital admission. Cox regression analyses adjusted for conventional risk factors for hospital admission were performed to calculate hazard ratio (HR). Results: We included 35 studies of 311’314 AF patients in the meta-analysis. The pooled incidence of all-cause hospital admissions was 43.7 per 100 person-years. AF patients were more often admitted for cardiovascular causes (26.3 per 100 person-years), but the risk of non-cardiovascular hospitalizations was substantial (15.7 per 100 person-years). Associated factors for hospital admission were older age, longer follow-up time and prevalent chronic pulmonary disease or cancer. In the second study we found that the most important predictors for all-cause hospital admission were age (75-79 years: adjusted hazard ratio [aHR], 1.33; 95% confidence interval [95% CI], 1.00-1.77; 80-84 years: aHR, 1.51; 95% CI, 1.12-2.03; 85 years: aHR, 1.88; 95% CI, 1.35-2.61), prior pulmonary vein isolation (aHR, 0.74; 95% CI, 0.60-0.90), hypertension (aHR, 1.16; 95% CI, 0.99-1.36), diabetes (aHR, 1.38; 95% CI, 1.17-1.62), coronary heart disease (aHR, 1.18; 95% CI, 1.02-1.37), prior stroke/TIA (aHR, 1.28; 95% CI, 1.10-1.50), heart failure (aHR, 1.21; 95% CI, 1.04-1.41), peripheral artery disease (aHR, 1.31; 95% CI, 1.06-1.63), cancer (aHR, 1.33; 95% CI, 1.13-1.57), renal failure (aHR, 1.18, 95% CI, 1.01-1.38), and previous falls (aHR, 1.44; 95% CI, 1.16-1.78). A risk score with these variables was well calibrated, and achieved a C statistic of 0.64 (95% CI, 0.61-0.66) in the derivation and 0.59 (95% CI, 0.56-0.63) in the external validation cohort. In the third study including patients from Swiss-AF, 1582 (67.1%) were married, 156 (6.6%) were single, 287 (12.2%) were divorced, and 333 (14.1%) were widowed. Two hundred and seventy six patients (11.7%) had at most a primary education, 1171 (49.7) had secondary education, and 911 (38.6%) had a college or university degree. Depression or depressive symptoms was present in 99 (4.2%) patients. Median health perception was 75 (interquartile range [IQR], 60-85) on a scale ranging from 0-100, with higher scores indicated better health perception. The highest risk of all-cause hospital admission was observed in single (aHR, 1.35; 95% CI, 1.05-1.75) or divorced patients (aHR, 1.26; 95% CI, 1.03-1.54), and in those who reported low health perception (aHR for <75 points, 1.40; 95% CI, 1.21-1.61). Conclusions: The overall incidence of hospital admissions in patients with AF is high. The risk of hospital admissions is related to multiple cardiovascular and non-cardiovascular risk factors, including several psychosocial factors and subjective health perception. Outlook: Given the high risk among AF patients of being admitted to the hospital and the high burden of associated risk factors, new multidisciplinary preventive strategies are needed with the goal to reduce hospital admissions, unfavorable patient outcomes and healthcare costs

Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.

Objective: To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions. Design: Systematic review. Setting/data source: CINAHL, Embase, MEDLINE from 2011 to 2015. Participants: All studies of 28-day and 30-day readmission predictive model. Outcome measures Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models. Results: Of 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21–0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables ‘comorbidities’, ‘length of stay’ and ‘previous admissions’ were frequently cited across 73 models. The variables ‘laboratory tests’ and ‘medication’ had more weight in the models for cardiovascular disease and medical condition-related readmissions.Conclusions: The predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority

All-cause readmission and repeat revascularization after percutaneous coronary intervention

Background: Percutaneous coronary intervention (PCI) is one of the most frequently performed cardiac interventions. However, there is limited data regarding the cause of recurrent hospitalization and repeat revascularization. The aim of this study was to assess re-hospitalization and repeat revascularization within 30 days of the initial hospitalization for PCI, using data from Opolskie Voivodeship, National Health Fund (NHF) Registry. Methods: The study population consisted of all PCI patients treated in three interventional cardiology laboratories in Opolskie Voivodeship in Poland between 1 July 2008 and 30 June 2009. All PCI patients who died during the initial hospitalization or who were transferred to other units were excluded from the analysis. The study end-point comprised 30 day all-cause readmission and repeat revascularization. Results: A total of 2,039 PCI patients were included in the analysis. The all-cause 30-day readmission rate was 14.6%. The 30-day readmission rate of acute coronary syndrome (ACS) patients was significantly higher compared to the stable coronary disease patients (ACS 15.8%, non-ACS 10.7%, p = 0.008). The 30-day readmission rate did not differ between the three cardiac laboratories. Approximately half (46.2%) of all readmitted patients underwent a repeat revascularization procedure, mainly in the form of PCI. The overall all-cause 30-day mortality rate was 0.8%. Compared to the PCI patients who did not require readmission, the readmitted patients had a significantly higher all-cause 30-day mortality rate (3.6% vs 0.3%, p < 0.001). Conclusions: Almost one in seven PCI patients requires readmission within 30 days of hospital discharge. Approximately 50% of all readmitted PCI patients resulted in a repeat revascularization procedure. PCI patients who were readmitted within 30 days of an index PCI procedure had a significantly higher all-cause 30-day mortality rate. (Cardiol J 2012; 19, 2: 174&#8211;179

A 5-year retrospective cohort study of unplanned readmissions in an Australian tertiary paediatric hospital

Objective: The aim of this study was to examine the characteristics and prevalence of all-cause unplanned hospital readmissions at a tertiary paediatric hospital in Western Australia from 2010 to 2014. Methods: A retrospective cohort descriptive study was conducted. Unplanned hospital readmission was identified using both 28- and 30-day measurements from discharge date of an index hospital admission to the subsequent related unplanned admission date. This allowed international comparison. Results: In all, 73 132 patients with 134 314 discharges were identified. During the 5-year period, 4070 discharges (3.03%) and 3330 patients (4.55%) were identified as 30-day unplanned hospital readmissions. There were minimal differences in the rate of readmissions on Days 28, 29 and 30 (0.2%). More than 50% of readmissions were identified as a 5-day readmission. Nearly all readmissions for croup and epiglottitis occurred by Day 5 those for acute bronchiolitis and obstructive sleep apnoea requiring tonsillectomy and/or adenoidectomy occurred by Day 15 and those for acute appendicitis and abdominal and pelvic pain occurred by Day 30. Conclusion: This study highlights the variability in the distribution of time intervals from discharge to readmission among diagnoses, suggesting the commonly used 28- or 30-day readmission measurement requires review. It is crucial to establish an appropriate measurement for specific paediatric conditions related to readmissions for the accurate determination of the prevalence and actual costs associated with readmissions. What is known about this topic?: Unplanned hospital readmissions result in inefficient use of health resources. Australia has used 28 days to measure unplanned readmissions. However, the 30-day measurement is commonly used in the literature. Only five Australian studies were identified with a focus on readmissions associated with specific paediatric health conditions. What does this paper add?: This is the first known study examining paediatric all-cause unplanned same-hospital readmissions in Western Australia. The study used both 28- and 30-day measures from discharge to unplanned readmission to allow international comparison. More than half the unplanned hospital readmissions occurred between Day 0 and Day 5 following discharge from the index admission. Time intervals from discharge date to readmission date varied for diagnosis-specific readmissions of paediatric patients. What are the implications for practitioners?: Targeting the top principal index admission diagnoses identified for paediatric readmissions is critical for improvement in the continuity of discharge care delivery, health resource utilisation and associated costs. Because 52% of unplanned readmissions occurred in the first 5 days, urgent investigation and implementation of prevention strategies are required, especially when the readmission occurs on the date of discharge

Predictors of 30-day readmission after total knee arthroplasty: analysis of 566,323 procedures in the United Kingdom

BACKGROUND: All-cause 30-day readmission after total knee arthroplasty (TKA) is currently used as a measure of hospital performance in the United States and elsewhere. Readmissions from surgical causes may more accurately reflect preventability and costs. However, little is known about whether predictors of each type of readmission differ. METHODS: All primary TKAs recorded in England's National Health Service administrative database from 2006 to 2015 were included. Multilevel logistic regression analysis was used to describe the effects of patient-related factors on 30-day readmission risk using 3 different readmission metrics: all-cause, surgical (defined using International Classification of Disease-10 primary admission diagnoses), and those resulting in return to theater (RTT). RESULTS: In total, 566,323 procedures were recorded. The comorbidity with the highest odds ratio (OR) for all types of readmission was psychoses (RTT OR 2.52, P 2 emergency admissions, all-cause OR 2.38, P < .001). Length of stay either more than or less than 2 days was associated with an increased risk of all-cause and surgical readmission but not RTT readmission. CONCLUSION: Patient-related predictors of surgical and RTT readmission following TKA differ from those for all-cause readmission, but only the latter metric is in widespread use

Causal Impact of the Hospital Readmissions Reduction Program on Hospital Readmissions and Mortality

Estimating causal effects of the Hospital Readmissions Reduction Program (HRRP), part of the Affordable Care Act, has been very controversial. Associational studies have demonstrated decreases in hospital readmissions, consistent with the intent of the program, although analyses with different data sources and methods have differed in estimating effects on patient mortality. To address these issues, we define the estimands of interest in the context of potential outcomes, we formalize a Bayesian structural time-series model for causal inference, and discuss the necessary assumptions for estimation of effects using observed data. The method is used to estimate the effect of the passage of HRRP on both the 30-day readmissions and 30-day mortality. We show that for acute myocardial infarction and congestive heart failure, HRRP caused reduction in readmissions while it had no statistically significant effect on mortality. However, for pneumonia, HRRP had no statistically significant effect on readmissions but caused an increase in mortality.Comment: 10 pages, 1 figure, 2 table

Reducing Thirty-Day Hospital Readmissions in Drug and Medication Poisoning: An Observational Study

A common and costly occurrence in the United States is thirty-day hospital readmissions. Awareness of 30-day hospital readmissions is currently a national priority. To reduce avoidable readmissions, the Patient Protection and Affordable Care Act of 2010 established a “Hospital Readmission Reduction Program” implemented to provide possible solutions for preventable thirty-day readmissions. Part of this policy states that hospitals with higher than expected adjusted re-hospitalization rates have lower reimbursement rates. One specific area known to be a cause of thirty-day hospital readmissions is drug and medication poisoning. An observational study of data from the Nationwide Readmissions Database is being used to help identify contributing factors and provide suggestions for preventable thirty-day readmissions relative to drug and medication poisoning. Factors that include: gender; demographics; cost index; socio-economic, and hospital factors are identified to aid in the understanding of thirty-day hospital readmission of drug and medication poisoning. Finally, suggestions based on quantitative analyses contribute to the understanding of risk factors of thirty-day readmissions in drug and medication poisoning occurrences. Outcomes include statistical significance in gender and significance in the cost index of the individual patient; such as the ability to pay or not to pay for services rendered. Certain socio-economic factors whereas contributed, however, overall socioeconomic status was not significant along with hospital specific factors being insignificant. The study resulted in the identification of factors to aid in drug/medication episodic occurrences in a patient population experiencing thirty-day readmissions. Prevention strategy from both a clinical and practical application may be used to initiate cost saving applications. Future studies suggest expanding on drug and medication poisoning in certain sub-specific populations, further identifying illegal vs. legal drug/medication differentiation, and conducting international comparisons based on current findings

Systematic review of hospital readmissions in stroke patients

Background Previous evidence on factors and causes of readmissions associated with high-impact users of stroke is scanty. The aim of the study was to investigate common causes and pattern of short- and long-term readmissions stroke patients by conducting a systematic review of studies using hospital administrative data. Common risk factors associated with the change of readmission rate were also examined. Methods The literature search was conducted from 15th February to 15th March 2016 using various databases, such as Medline, Embase, and Web of Science. Results There were total of 24 studies (n=2,126,617) included in the review. Only 4 studies assessed causes of readmissions in stroke patients with the follow-up duration from 30 days to 5 years. Common causes of readmissions in majority of the studies were recurrent stroke, infections and cardiac conditions. Common patient-related risk factors associated with increased readmission rate were age and history of coronary heart disease, heart failure, renal disease, respiratory disease, peripheral arterial disease and diabetes. Among stroke-related factors, length of stay of index stroke admission was associated with increased readmission rate, followed by bowel incontinence, feeding tube and urinary catheter. Conclusion Although risk factors and common causes of readmission were identified, but none of the previous studies investigated causes and their sequence of readmissions among high-impact stroke users