All-cause 30-day Hospital Readmission Research Articles

Pharmacist-led heart failure patient focus group using mobile apps-based intervention mix

AbstractObjectivesThe impact of clinical pharmacists on inpatient heart failure (HF) care has been proven in many studies. Evidence regarding the use of mobile apps by pharmacists to achieve positive clinical outcomes in HF is limited. The purpose of this study is to examine the impact of a pharmacist-led HF patient focus group using mobile apps-based intervention mix.MethodsIn this pre–post-exploratory study, using an intervention mix of hospital discharge counselling, MyTherapy app, WhatsApp and occasional phone calls to improve clinical outcomes in HF. Patient medication adherence, health-related quality of life (HRQoL) and 30-day all-cause hospital readmission rate were assessed. The Wilcoxon Signed-Rank Test was used to find statistical significance of improvements in medication adherence and HRQoL using SPSS software version 26.Key findingsThe intervention mix of initial hospital-discharge counselling, MyTherapy app, follow-up with the WhatsApp group and occasional phone calls established an effective communication with patients. All 10 patients had improvements in their clinical outcomes. Significant improvements in medication adherence and HRQoL were found (Z-value −2.810 and Z-value −2.805, respectively; 95% confidence interval). No patients were readmitted in 30 days or within 3 months of study.ConclusionsA pharmacist-led HF patient focus group using mobile apps-based intervention mix was found to be effective in improving clinical outcomes in HF patients. Further research should be performed on long-term effects of our approach.

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction

BackgroundAcute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources.MethodsIn this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly,we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.ResultsThe results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713).ConclusionIt is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.

Type 2 Diabetes and HbA1c Predict All-Cause Post-Metabolic and Bariatric Surgery Hospital Readmission.

The main goal of this analysis was to determine whether type 2 diabetes and hemoglobin A1c (HbA1c) predict all-cause 30-day hospital readmission after metabolic and bariatric surgery (MBS). It was hypothesized that a diagnosis of type 2 diabetes or high HbA1c values would predict all-cause hospital readmission rates post MBS. A retrospective analysis from the 2015-2018 Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) cohort was completed (N = 744,776); 30,972 participants were readmitted during the 30 days post MBS. Mean age of the MBSAQIP sample was 45.1 (11.5) years, and the majority were female (80.7%) and non-Hispanic White (59.4%). The all-cause hospital readmission rate was 4.2% and increased by 10% in those with uncontrolled type 2 diabetes (HbA1c > 7.5% [> 58 mmol/mol]); after adjustment, diabetes was not associated with increased readmission. Uncontrolled type 2 diabetes, type 2 diabetes, and prediabetes resulted in less weight loss 30 days post MBS. These results based on a national MBS cohort showed that uncontrolled type 2 diabetes is associated with a greater likelihood of all-cause hospital readmission and reduced weight loss 30 days post MBS. Both type 2 diabetes and prediabetes were also associated with decreased weight loss 30 days post MBS. These findings highlight the need to classify and optimize glycemic control prior to MBS.

Hospital Readmission Rates Following Outpatient Versus Inpatient Shoulder Arthroplasty.

Outpatient total shoulder arthroplasty (TSA) is an alternative to surgery with inpatient admission for appropriate patients. Controlled studies assessing differences in perioperative outcomes between inpatient and outpatient TSA are lacking. In this study, the primary outcome was 30-day all-cause hospital readmission following inpatient vs outpatient TSA. The National Surgical Quality Improvement Program (NSQIP) database was used to identify patients undergoing both primary and revision TSA from 2010 to 2017. Patients were identified using Current Procedural Terminology codes. A 1:1 propensity score matching was used to create two groups of patients, those who underwent outpatient surgery and those who underwent inpatient surgery, while matching for age, sex, American Society of Anesthesiologists classification, primary vs revision surgery, smoking, diabetes, chronic obstructive pulmonary disease, and congestive heart failure. This study had a power of 85% to detect a difference of 1% in 30-day readmission. Following 1:1 propensity score matching, 1714 patients who underwent inpatient TSA and 1714 patients who underwent outpatient TSA were analyzed. All-cause 30-day readmission rates were 3.4% in the outpatient group and 1.7% in the inpatient group (P<.01). A total of 1.9% of patients who underwent outpatient surgery had a 30-day readmission for a surgical complication compared with 1.4% of patients who underwent inpatient surgery (P=.32). Although patients who underwent outpatient TSA had an increased risk of all-cause 30-day readmission compared with equally matched controls who underwent inpatient TSA, readmission for surgical complications was equivalent between the two groups. Careful patient selection for outpatient TSA should be emphasized to minimize the potential for postoperative hospital admission. [Orthopedics. 2021;44(2):e173-e178.].

Does a brief functional assessment in the emergency department predict outcomes of patients admitted with heart failure? The FASTER-HF study

Evaluation of patients with acute decompensated heart failure includes symptom review, biomarker measurement and comorbidity assessment. Early objective evaluation of functional status is generally not performed. To investigate whether a simple low-impact functional assessment and measurement of sarcopenia would be safe, feasible and predictive of hospital length of stay and all-cause 30-day hospital readmission. We administered 3-minute bicycle ergometry and hand grip strength tests at admission and discharge to patients for whom a decision to admit for heart failure management was made in the emergency department. Associations were examined between test results and length of stay and 30-day readmission. Exclusion criteria included acute coronary syndrome, hypoxia, end-stage renal disease, dementia/delirium and inability to sit at bedside. The Kansas City Cardiomyopathy Questionnaire-12, the Patient Health Questionnaire-2 and the visual analogue scale for dyspnoea were administered at admission, the visual analogue scale at discharge and the Kansas City Cardiomyopathy Questionnaire-12 at 30 days. Fifty patients were enrolled: 58% were female; the mean age was 66.2±12.5 years; 24% had heart failure with preserved ejection fraction. Bicycle ergometry variables did not correlate with outcomes. Change in handgrip strength correlated with readmission, but not after adjustment (rpartial=0.14; P=0.35). Total diuretic dose correlated with length of stay; only discharge visual analogue scale and baseline lung disease had significant adjusted correlations with readmission. Functional assessment in the emergency department of patients admitted for heart failure did not predict outcomes. However, the prognostic value of these assessments for decision-making about disposition (admission or discharge) may still be warranted.

How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data.

Hospital readmission is a main cost driver for healthcare systems, but existing works often had poor or moderate predictive results. Although the available information differs in different studies, improving prediction is different from the search for important explanatory variables. With large sample size and abundant information, this study explores state-of-the-art machine-learning algorithms and shows their performance in prediction. Using administrative data on 1 631 611 hospital stays from Quebec between 1995 and 2012, we predict the probability of 30-day readmission at hospital admission and discharge. We compare the performance between traditional logistic regression, logistic regression with penalization, and more recent machine-learning algorithms such as random forest, deep learning, and extreme gradient boosting. After a 10-fold cross-validation on the training set (80% of the data), machine learning produced very good results on a separate hold-out test set (20% of the data). The importance of explanatory variables is not the same for different algorithms. The area under receiver operating characteristic curve (AUC) reached above 0.79 at hospital admission and above 0.88 at hospital discharge. Diagnostic codes, which include many different categories, are among the most predictive variables. Logistic regression with penalization also produced good results, but a standard logistic regression failed without penalization. The good results are confirmed by calibration curves. Although the identification of those at highest risk of readmission is just 1 step to preventing hospital readmissions, 30-day readmission is highly predictable with machine learning.

Risk of Adverse Clinical Outcomes in Hyponatremic Adult Patients Hospitalized for Acute Medical Conditions: A Population-Based Cohort Study.

ContextHyponatremia has been associated with excess long-term morbidity and mortality. However, effects during hospitalization are poorly studied.ObjectiveThe objective of this work is to examine the association of hyponatremia with the risk of in-hospital mortality, 30-day readmission, and other short-term adverse events among medical inpatients.Design and SettingA population-based cohort study was conducted using a Swiss claims database of medical inpatients from January 2012 to December 2017PatientsHyponatremic patients were 1:1 propensity-score matched with normonatremic medical inpatients.Main Outcome MeasureThe primary outcome was a composite of all-cause in-hospital mortality and 30-day hospital readmission. Secondary outcomes were intensive care unit (ICU) admission, intubation rate, length-of-hospital stay (LOS), and patient disposition after discharge.ResultsAfter matching, 94 352 patients were included in the cohort. Among 47 176 patients with hyponatremia, 8383 (17.8%) reached the primary outcome compared with 7994 (17.0%) in the matched control group (odds ratio [OR] 1.06 [95% CI, 1.02-1.10], P = .001). Hyponatremic patients were more likely to be admitted to the ICU (OR 1.43 [95% CI, 1.37-1.50], P < .001), faced a 56% increase in prolonged LOS (95% CI, 1.52-1.60, P < .001), and were admitted more often to a postacute care facility (OR 1.38 [95% CI 1.34-1.42, P < .001). Of note, patients with the syndrome of inappropriate antidiuresis (SIAD) had lower in-hospital mortality (OR 0.67 [95% CI, 0.56-0.80], P < .001) as compared with matched normonatremic controls.ConclusionIn this study, hyponatremia was associated with increased risk of short-term adverse events, primarily driven by higher readmission rates, which was consistent among all outcomes except for decreased in-hospital mortality in SIAD patients.

Readmission Outcomes of Sliding Scale Insulin Compared to Basal-Bolus Insulin Prescribed at Discharge in an Insulin-Naive Patient Population

Background: Limited data are available that examine hospital readmission outcomes of sliding scale compared to basal-bolus insulin in indigent and insulin-naive patients. Objective: To evaluate hospital readmission outcomes in patients who are insulin naive with type 2 diabetes mellitus who are initiated on either sliding scale or basal-bolus insulin upon hospital discharge. Methods: A retrospective chart review was conducted of adult patients with a history of type 2 diabetes mellitus, who were insulin naive, had a hemoglobin A1c (HbA1c) 10% or greater, and were discharged with a prescription for sliding scale or basal-bolus insulin from January 2015 to July 2018. The primary objective measured all-cause 30-day hospital readmissions. The secondary objectives measured diabetes-related 30-day hospital readmissions and HbA1c change after 3 months of initial hospital admission. Data were analyzed using descriptive statistics, χ2 test, paired sample t test, and logistic regression. Results: Forty-one patients were prescribed sliding scale insulin and 105 patients were prescribed basal-bolus insulin. The majority were male (60%), spoke English (84%), were self-pay (39%), and had a mean age of 51 ± 10.2 years, initial HbA1c of 13% ± 1.9%, and LACE+ score of 51 ± 15.6 upon discharge. All-cause 30-day hospital readmissions occurred in 14.6% of sliding scale and 6.7% of basal-bolus insulin groups (odds ratio [OR]: 2.40, 95% confidence interval [CI]: 0.75-7.63). Hyperglycemia occurred in 7.3% of sliding scale and 0.9% of basal-bolus insulin groups. Mean HbA1c difference for basal-bolus and sliding scale insulin was 3.3 ± 3.1 and 2.9 ± 2.7, respectively (P = 0.459). Conclusion: There was no significant difference in all-cause 30-day hospital readmissions comparing sliding scale to basal-bolus insulin.

Ability of the LACE index to predict 30-day hospital readmissions in patients with community-acquired pneumonia

Background and objectiveHospital readmissions within 30 days are used as an indicator of quality of hospital care. We aimed to evaluate the ability of the LACE (Length of stay, Acuity of admission, Comorbidities based on Charlson comorbidity score and number of Emergency visits in the last 6 months) index to predict the risk of 30-day readmissions in patients hospitalised for community-acquired pneumonia (CAP).MethodsIn this retrospective cohort study a LACE index score was calculated for patients with a principal diagnosis of CAP admitted to a tertiary hospital in Sydney, Australia. The predictive ability of the LACE score for 30-day readmissions was assessed using receiver operator characteristic curves with C-statistic.ResultsOf 3996 patients admitted to hospital for CAP at least once, 8.0% (n=327) died in hospital and 14.6% (n=584) were readmitted within 30 days. 17.8% (113 of 636) of all 30-day readmissions were again due to CAP, followed by readmissions for chronic obstructive pulmonary disease, heart failure and chest pain. The LACE index had moderate discriminative ability to predict 30-day readmission (C-statistic=0.6395) but performed poorly for the prediction of 30-day readmissions due to CAP (C-statistic=0.5760).ConclusionsThe ability of the LACE index to predict all-cause 30-day hospital readmissions is comparable to more complex pneumonia-specific indices with moderate discrimination. For the prediction of 30-day readmissions due to CAP, the performance of the LACE index and modified risk prediction models using readily available variables (sex, age, specific comorbidities, after-hours, weekend, winter or summer admission) is insufficient.

Reducing All-cause 30-day Hospital Readmissions for Patients Presenting with Acute Heart Failure Exacerbations: A Quality Improvement Initiative.

BackgroundCongestive heart failure (CHF) is the most common cause of hospitalization in the US for people older than 65 years of age. It has the highest 30-day re-hospitalization rate among medical and surgical conditions, accounting for up to 26.9% of the total readmission rates. We conducted a quality improvement project at our hospital with the objective to reduce the 30-day all-cause readmissions of patients with CHF by improving the transition of care and setting up scheduled follow-up appointments within two weeks of patient discharge.MethodRetrospective data were collected to understand the pattern of admission for CHF during November 2017. Data on 30-day readmission post-discharge was also collected to understand readmission rates. Similarly, all patients who were admitted with acute CHF exacerbation to our hospital during the month of November 2018 were included in our intervention cohort. The 30-day readmission rates of these patients post-intervention were calculated and compared to the initial cohort.ResultsAs part of our study, we ensured that 58% of the enrolled patients had a follow-up appointment scheduled within two weeks of discharge compared to only 30% in 2017. Also, 56% of the enrolled patients kept their follow-up appointments compared to 37% in 2017. The 30-day readmission rate of CHF patients was reduced in half after the implementation of our project, with a 14% readmission rate for our study patients compared to 28% in 2017.ConclusionPatient education and measures to augment post-discharge follow-up appointments can lead to substantial reductions in the readmission rates of heart failure (HF) patients.

Advancing Pharmacy Practice through an Innovative Ambulatory Care Transitions Program at an Academic Medical Center

Hospital readmissions are common and often preventable, leading to unnecessary burden on patients, families, and the health care system. The purpose of this descriptive communication is to share the impact of an interdisciplinary, outpatient clinic-based care transition intervention on clinical, organizational, and financial outcomes. Compared to usual care, the care transition intervention decreased the median time to Internal Medicine Clinic (IMC) or any clinic follow-up visit by 5 and 4 days, respectively. By including a pharmacist in the hospital follow-up visit, the program significantly reduced all-cause 30-day hospital readmission rates (9% versus 26% in usual care) and the composite endpoint of 30-day health care utilization, which is defined as readmission and emergency department (ED) rates (19% versus 44% usual care). Over the course of one year, this program can prevent 102 30-day hospital readmissions with an estimated cost reduction of $1,113,000 per year. The pharmacist at the IMC collaborated with the Family Medicine Clinic (FMC) pharmacist to standardize practices. In the FMC, the hospital readmission rate was 6.5% for patients seen by a clinic-based pharmacist within 30 days of discharge compared to 20% for those not seen by a pharmacist. This transitions intervention demonstrated a consistent and recognizable contribution from pharmacists providing direct patient care and practicing in the ambulatory care primary care settings that has been replicated across clinics at our academic medical center.

The Bundled Hospital Elder Life Program—HELP and HELP in Home Care—and Its Association With Clinical Outcomes Among Older Adults Discharged to Home Healthcare

To describe the Bundled Hospital Elder Life Program (HELP and HELP in Home Care), an adaptation of HELP, and examine the association of 30-day all-cause unplanned hospital readmission risk among older adults discharged to home care with and without Bundled HELP. Matched case-control study. Two medical-surgical units within two midwestern rural hospitals and patient homes (home health). Hospitalized patients, aged 65 years and older, discharged to home healthcare with and without Bundled HELP exposure between January 1, 2015, and September 30, 2017. Each case (Bundled HELP, n = 148) was matched to a control (non-Bundled HELP, n = 148) on Charlson Comorbidity Index, primary hospital diagnosis of orthopedic condition or injury, and cardiovascular disease using propensity score matching. The primary study outcome was 30-day all-cause unplanned hospital readmission. Additional outcomes measured were 30-day emergency department (ED) visit, hospital length of stay (LOS), and total number of skilled home care visits. Fewer cases (16.8%) than controls (28.4%) had a 30-day all-cause unplanned hospital readmission. The fully adjusted model showed significantly lower risk of 30-day hospital readmission for case (Bundled HELP) patients (0.41; 95% confidence interval = 0.22-0.77; P < .01). The difference between case (10.8%) and control (15.5%) 30-day ED visit was not significant (P = .23). A lower LOS for the case group was shown (P < .01), while the number of skilled home care visits was not significantly different between groups (P = .28). HELP protocol implementation during a patient's hospital stay and as a continued component of home care among older adults at risk for cognitive and/or functional decline appears to be associated with favorable outcomes. Our initial evaluation supports continued study of the Bundled HELP. Further research is needed to confirm the initial findings and to evaluate the impact of the adapted model on functional outcomes and delirium incidence in the home. J Am Geriatr Soc 67:1730-1736, 2019.

A Comparison of 30-Day Hospital Readmission and Complication Rates After Outpatient Versus Inpatient 1 and 2 Level Anterior Cervical Discectomy and Fusion Surgery: An Analysis of a Medicare Patient Sample

Same-day surgery has been demonstrated to be a safe and cost-effective alternative to traditional inpatient surgery. Several studies have demonstrated no differences in the postoperative complication profile or 30-day hospital readmission rates with outpatient versus inpatient anterior cervical discectomy and fusion (ACDF). However, none of these studies compared the outcomes in elderly patients (aged >65 years) undergoing ACDF. Whether the results from previous studies can be applied to this subgroup pf patients remains unknown. The aim of the present study was to compare the 30-day hospital readmission rates for Medicare patients (aged >65 years) undergoing outpatient versus inpatient ACDF. We performed a retrospective analysis of a Medicare database, including data from 17,421 patients. Of the 17,421 patients, 16,386 had undergone inpatient ACDF and 1035, outpatient ACDF. Age, sex, comorbidities, postoperative complications, readmission rates, and overall financial costs were compared between the 2 cohorts. In a Medicare sample (aged >65 years), inpatient ACDF was associated with a greater incidence of postoperative complications compared with outpatient ACDF. Outpatient surgery was associated with significantly lower rates of postoperative complications (urinary tract infection, surgical site infection, deep vein thrombosis, pulmonary embolism, and myocardial infarction) and significantly lower treatment costs (P ≤ 0.001). All-cause 30-day hospital readmission rates were also greater for inpatients (10.1% vs. 4%; P= 0.17). The results from the present study suggest that outpatient ACDF appears to be safe and effective with low complication and readmission rates in a Medicare patient sample.

Risk factors associated with 30-day all-cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia.

AimTo identify risk factors associated with 30‐day all‐cause unplanned hospital readmission at a tertiary children's hospital in Western Australia.MethodsAn administrative paediatric inpatient dataset was analysed retrospectively. Patients of all ages discharged between 1 January 2010 and 31 December 2014 were included. Demographic and clinical information at the index admission was examined using multivariate logistic regression analysis.ResultsA total of 3330 patients (4.55%) experienced at least one unplanned readmission after discharge. Readmission was more likely to occur in patients who were either older than 16 years (odds ratio (OR) = 1.46; 95% confidence interval (CI) 1.07–1.98), utilising private insurance as an inpatient (OR = 1.16; 95% CI 1.00–1.34), with greater socio‐economic advantage (OR = 1.20; 95% CI 1.02–1.41), admitted on Friday (OR = 1.21; 95% CI 1.05–1.39), discharged on Friday/Saturday/Sunday (OR = 1.26, 95% CI 1.10–1.44; OR = 1.34, 95% CI 1.15–1.57; OR = 1.24, 95% CI 1.05–1.47, respectively), with four or more diagnoses at the index admission (OR = 2.41; 95% CI 2.08–2.80) or hospitalised for 15 days or longer (OR = 2.39; 95% CI 1.88–2.98). Area under receiver operating characteristic curve of the predictive model is 0.645.ConclusionsA moderate discriminative ability predictive model for 30‐day all‐cause same hospital readmission was developed. A structured discharge plan is suggested to be commenced from admission to ensure continuity of care for patients identified as being at higher risk of readmission. A recommendation is made that a designated staff member be assigned to co‐ordinate the plan, including assessment of patients' and primary carers' readiness for discharge. Further research is required to establish comprehensive paediatric readmission rates by accessing linkage data to capture different hospital readmissions.

Derivation and validation of a hospital all-cause 30-day readmission index

The study derives and validates a 30-day hospital readmission risk index to predict a patient's likelihood of readmission, utilizing a health systems electronic medical record. A retrospective data extraction and analysis was conducted using data from the electronic medical record to identify risks of 30-day all-cause hospital readmission on adult patients admitted to a large multi-site health system. Univariate and multivariable logistic regression was performed on a derivation cohort of hospital admissions (n=40,668) and analyzed 91 variables associated with 30-day hospital readmission. A10-variable risk prediction equation was generated and validated in a second patient cohort (n=7,820). The prediction index's discriminative ability was determined using the c-statistic, and calibration of the prediction index was assessed with the use of the Hosmer-Lemeshow test. The hospital all-cause thirty-day readmission index (HATRIX) identified 10 variables to be highly associated with 30-day readmission. The discriminative ability of the derived prediction equation was determined using the c-statistic and was calculated to be 0.73 (95% confidence interval [CI] 0.72-0.73) for the derivation cohort. The prediction equation was validated using a second cohort of patients and resulted with an area under the curve (AUC) of 0.72 (95% CI 0.70-0.73), indicating modest discrimination. An original risk prediction index for 30-day hospital readmission was derived and validated using 2 cohorts of patients. Identifying patients who have an increased risk of 30-day hospital readmission with the use of the electronic medical record is an ideal method for targeting interventions and improving transitions-of-care to reduce hospital readmissions.

Pericardial closure with extracellular matrix scaffold following cardiac surgery associated with a reduction of postoperative complications and 30-day hospital readmissions

BackgroundA prospective, multi-center study (RECON) was conducted to evaluate the clinical outcomes of pericardial closure using a decellularized extracellular matrix (ECM) graft derived from porcine small intestinal submucosa.MethodsPatients indicated for open cardiac surgery with pericardial closure using ECM were eligible for the RECON study cohort. Postoperative complications and readmission of the RECON patients were compared to the patient cohort in the Nationwide Readmissions Database (NRD). Inverse probability of treatment weighting was used to control the differences in patient demographics, comorbidities, and risk factors.ResultsA total of 1420 patients at 42 centers were enrolled, including 923 coronary artery bypass grafting (CABG) surgeries and 436 valve surgeries. Significantly fewer valve surgery patients in the RECON cohort experienced pleural effusion (3.1% vs. 13.0%; p < 0.05) and pericardial effusion (1.5% vs. 2.6%; p < 0.05) than in the NRD cohort. CABG patients in the RECON cohort were less likely to suffer bleeding (1.2% vs. 2.9%; p < 0.05) and pericardial effusion (0.2% vs. 2.2%, p < 0.05) than those in the NRD cohort. The 30-day all-cause hospital readmission rate was significantly lower among RECON patients than NRD patients following both valve surgery (HR: 0.34; p < 0.05) and CABG surgery (HR: 0.42; p < 0.05). In the RECON study, 14.4% of CABG patients and 27.0% of valve patients had postoperative atrial fibrillation as compared to previously reported risks, which generally ranges from 20 to 30% after CABG and from 35 to 50% after valve surgery.ConclusionsPericardial closure with ECM following cardiac surgery is associated with a reduction in the proportion of patients with pleural effusion, pericardial effusion, and 30-day readmission compared to a nationwide database.Trial registrationNCT02073331, Registered on February 27, 2014.

Risk of 30-Day Hospital Readmission Among Patients Discharged to Skilled Nursing Facilities: Development and Validation of a Risk-Prediction Model

ObjectivesPatients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF. DesignRetrospective cohort study. SettingTen independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system. ParticipantsWe evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization. MeasurementsThe primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation. ResultsAmong 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months. Conclusions and implicationsWe developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.

Abstract 72: Early Outpatient Followup With Primary Care is Associated With Lower Readmission at 30 and 90 days in Patients With Ischemic Stroke

Background: Readmissions after ischemic strokes are common and associated with high morbidity and cost. Whether early follow-up after stroke can reduce readmissions remains unclear. We examined the relationship between follow-up after stroke hospitalization with primary care or neurology and readmissions at 30 and 90 days. Methods: A retrospective cohort study was performed using PharMetrics claims database of US commercial insurers from 2009 to 2015. Inclusion: (1) inpatient admission with primary diagnosis of ischemic stroke, (2) discharge home, (3) age 18-89, (4) 1 year of enrollment prior to stroke and 3 months after discharge. Exclusion: (1) stroke transfers, (2) repeat admission for stroke. The primary outcome was all-cause 30-day and 90-day hospital readmission. Multivariable Cox models were used with primary care and neurology visits specified as time-dependent covariates, adjusting for patient demographics, comorbidities (calculated for one year prior to stroke) and stroke severity measures. Results: The cohort consisted of 16,965 patients (table). Readmissions at 30 days occurred in 6.56% of patients at a median (interquartile range) of 12 days (5-20). Readmissions at 90 days occurred in 13.2% of patients at a median of 31 days (12-58). By 30 days, 50.2% had a primary care visit and 17.1% had a neurology visit. Patients with a primary care visit within 30 days had a 23% lower rate of readmission than those who did not (hazard ratio [HR], 0.77; 95% confidence interval, 0.66-0.89). The association is less strong for primary care visits within 90 days and readmissions (HR 0.88; 0.80-0.98). Neurology follow-up did produce benefit but did not reach significance. Conclusion: Early outpatient follow-up with primary care is associated with a significant reduction in hospital readmission. Even though this was an insured population, almost half of all patients did not receive primary care follow-up at 30 days, which represents an opportunity for intervention.

Use of Hospital-Based Rehabilitation Services and Hospital Readmission Following Ischemic Stroke in the United States

ObjectiveTo examine the association between hospital-based rehabilitation service use and all-cause 30-day hospital readmission among patients with ischemic stroke. DesignSecondary analysis of inpatient Medicare claims data using Standard Analytical Files. SettingAcute hospitals across the United States. ParticipantsFrom nationwide data, Medicare fee-for-service beneficiaries (N=88,826) aged 66 years or older hospitalized for ischemic stroke between January to November 2010. InterventionsHospital-based rehabilitation services were quantified using Medicare inpatient claims revenue center codes for evaluation (occupational therapy [OT] and physical therapy [PT]), as well as the number of therapy units delivered. Therapy minutes for both OT and PT services were categorized into none, low, medium, and high. Main Outcome MeasuresAll-cause 30-day hospital readmission. A generalized linear mixed model was used to examine the effect of hospital-based rehabilitation services on 30-day hospital readmission, after adjusting for patient and hospital characteristics. ResultsIn fully adjusted models, compared to patients who received no PT, we observed a monotonic inverse relationship between the amount of PT and hospital readmission. For low PT (30 minutes), the odds ratio (OR) was 0.90 (95% confidence interval [CI], 0.83-0.96). For medium PT (>30 to ≤75 minutes), the OR was 0.89 (95% CI, 0.82-0.95). For high PT (>75 minutes), the OR was 0.86 (95% CI, 0.80-0.93). ConclusionHospital-based PT services were associated with lower risk of 30-day hospital readmission in patients with ischemic stroke.

Impact of a community pharmacy transitions-of-care program on 30-day readmission

ObjectivesThe primary objective of this study was to evaluate the impact of a transitions-of-care (TOC) program on both all-cause and related 30-day hospital readmission. The secondary objective was to evaluate which patient-specific factors, if any, are predictive of 30-day hospital readmissions. Design, setting, and participantsA TOC program in an outpatient pharmacy, driven primarily by student pharmacists, provided telephone-based counseling to recently discharged patients. The calls were conducted within 2 to 7 days after discharge and focused on medication counseling and reconciliation, as well as promotion of a physician follow-up visit. The goal of this program was to decrease hospital readmissions among patients discharged with a cardiovascular-related diagnosis. Patient-specific information was recorded in a spreadsheet, including discharge diagnosis, and readmission diagnosis for those who returned to an inpatient facility within 30 days. This study was a retrospective chart review. Data were manually extracted from the program’s data spreadsheet and the institution’s electronic medical record for patients referred to the TOC program from June through November 2017. Patients discharged to hospice, prison, or a long-term care facility were excluded from analysis. Researchers collected information on patient demographics, diagnoses, and readmissions. Data analyses were performed with the use of SAS 9.4. Outcome measuresThe primary outcome measure was 30-day all-cause readmission, and the secondary measure was 30-day related readmission. ResultsA total of 1219 encounters were examined. Compared with those patients without TOC participation, those who used the TOC program had a 67% decreased odds of all-cause 30-day readmission (odds ratio [OR] 0.33, 95% confidence interval [CI] 0.22–0.48; P < 0.0001) and a 62% decreased odds of a related readmission (OR 0.38, 95% CI 0.18–0.82; P = 0.008). ConclusionCommunity pharmacists and Advanced Pharmacy Practice Experience–level student pharmacists have the potential to make a significant impact on reducing hospital readmission rates.