Risk Factors for Hospital Readmission in Patients With Interstitial Lung Disease

BACKGROUND

Little is known about the rates, causes, or risk factors for hospital readmission among patients with interstitial lung disease (ILD). We investigated the prevalence, features, and comorbidities of subjects hospitalized with ILD and their subsequent re-hospitalizations in this retrospective study.

METHODS

A retrospective analysis of subjects enrolled in the University of Chicago ILD Natural History registry was conducted. Demographic data, comorbidities, and timing and cause of subsequent hospitalizations were collected from the medical record. The primary outcome was time to first readmission via a cause-specific Cox hazards model with a sensitivity analysis with the Fine-Gray cumulative hazard model; the secondary outcome was the number of hospitalizations per subject via a Poisson multivariable model.

RESULTS

Among 1,796 patients with ILD, 443 subjects were hospitalized, with 978 total hospitalizations; 535 readmissions were studied, 282 (53%) for a respiratory indication. For the outcome of time to readmission, Black race was the only subject characteristic associated with an increased hazard of readmission in the Cox model (hazard ratio 1.50, P = .03) while Black race, hypersensitivity pneumonitis, and sarcoidosis were associated with increased hazard of readmission in the Fine-Gray model. Black race, female sex, atrial fibrillation, obstructive lung disease, and pulmonary hypertension were associated with an increased number of hospitalizations in the Poisson model.

CONCLUSIONS

We demonstrated that hospital readmission from any cause was a common occurrence in subjects with ILD. Further efforts to improve quality of life among these subjects could focus on risk scores for readmission, mitigating racial health disparities, and treatment of comorbidities.

Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis

BACKGROUND

The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure algorithmic bias, but their application to real-world tasks is limited.

OBJECTIVE

This study aims to evaluate the algorithmic bias associated with the application of common 30-day hospital readmission models and assess the usefulness and interpretability of selected fairness metrics.

METHODS

We used 10.6 million adult inpatient discharges from Maryland and Florida from 2016 to 2019 in this retrospective study. Models predicting 30-day hospital readmissions were evaluated: LACE Index, modified HOSPITAL score, and modified Centers for Medicare & Medicaid Services (CMS) readmission measure, which were applied as-is (using existing coefficients) and retrained (recalibrated with 50% of the data). Predictive performances and bias measures were evaluated for all, between Black and White populations, and between low- and other-income groups. Bias measures included the parity of false negative rate (FNR), false positive rate (FPR), 0-1 loss, and generalized entropy index. Racial bias represented by FNR and FPR differences was stratified to explore shifts in algorithmic bias in different populations.

RESULTS

The retrained CMS model demonstrated the best predictive performance (area under the curve: 0.74 in Maryland and 0.68-0.70 in Florida), and the modified HOSPITAL score demonstrated the best calibration (Brier score: 0.16-0.19 in Maryland and 0.19-0.21 in Florida). Calibration was better in White (compared to Black) populations and other-income (compared to low-income) groups, and the area under the curve was higher or similar in the Black (compared to White) populations. The retrained CMS and modified HOSPITAL score had the lowest racial and income bias in Maryland. In Florida, both of these models overall had the lowest income bias and the modified HOSPITAL score showed the lowest racial bias. In both states, the White and higher-income populations showed a higher FNR, while the Black and low-income populations resulted in a higher FPR and a higher 0-1 loss. When stratified by hospital and population composition, these models demonstrated heterogeneous algorithmic bias in different contexts and populations.

CONCLUSIONS

Caution must be taken when interpreting fairness measures' face value. A higher FNR or FPR could potentially reflect missed opportunities or wasted resources, but these measures could also reflect health care use patterns and gaps in care. Simply relying on the statistical notions of bias could obscure or underplay the causes of health disparity. The imperfect health data, analytic frameworks, and the underlying health systems must be carefully considered. Fairness measures can serve as a useful routine assessment to detect disparate model performances but are insufficient to inform mechanisms or policy changes. However, such an assessment is an important first step toward data-driven improvement to address existing health disparities.

Trends and outcomes of outpatient total shoulder arthroplasty after its removal from CMS's inpatient-only list

BACKGROUND

In January 2021, the US Medicare program approved reimbursement of outpatient total shoulder arthroplasties (TSA), including anatomic and reverse TSAs. It remains unclear whether shifting TSAs from the inpatient to outpatient setting has affected clinical outcomes. Herein, we describe the rate of outpatient TSA growth and compare inpatient and outpatient TSA complications, readmissions, and mortality.

METHODS

Medicare fee-for-service claims for 2019-2022Q1 were analyzed to identify the trends in outpatient TSAs and to compare 90-day postoperative complications, all-cause hospital readmissions, and mortality between outpatients and inpatients. Outpatient cases were defined as those discharged on the same day of the surgery. To reduce the COVID-19 pandemic's impact and selection bias, we excluded 2020Q2-Q4 data and used propensity scores to match 2021-2022Q1 outpatients with inpatients from the same period (the primary analysis) and from 2019-2020Q1 (the secondary analysis), respectively. We performed both propensity score-matched and -weighted multivariate analyses to compare outcomes between the two groups. Covariates included sociodemographics, preoperative diagnosis, comorbid conditions, the Hierarchical Condition Category risk score, prior year hospital/skilled nursing home admissions, annual surgeon volume, and hospital characteristics.

RESULTS

Nationally, the proportion of outpatient TSAs increased from 3% (619) in 2019Q1 to 22% (3456) in 2021Q1 and 38% (6778) in 2022Q1. A total of 55,166 cases were identified for the primary analysis (14,540 outpatients and 40,576 inpatients). Overall, glenohumeral osteoarthritis was the most common indication for surgery (70.8%), followed by rotator cuff pathology (14.6%). The unadjusted rates of complications (1.3 vs 2.4%, P < .001), readmissions (3.7 vs 6.1%, P < .001), and mortality (0.2 vs 0.4%, P = .024) were significantly lower among outpatient TSAs than inpatient TSAs. Using 1:1 nearest matching, 12,703 patient pairs were identified. Propensity score-matched multivariate analyses showed similar rates of postoperative complications, hospital readmissions, and mortality between outpatients and inpatients. Propensity score-weighted multivariate analyses resulted in similar conclusions. The secondary analysis showed a lower hospital readmission rate in outpatients (odds ratio: 0.8, P < .001).

CONCLUSIONS

There has been accelerated growth in outpatient TSAs since 2019. Outpatient and inpatient TSAs have similar rates of postoperative complication, hospital readmission, and mortality.

The Early HOSPITAL Score to Predict 30-Day Readmission Soon After Hospitalization: a Prospective Multicenter Study

BACKGROUND

The simplified HOSPITAL score is an easy-to-use prediction model to identify patients at high risk of 30-day readmission before hospital discharge. An earlier stratification of this risk would allow more preparation time for transitional care interventions.

OBJECTIVE

To assess whether the simplified HOSPITAL score would perform similarly by using hemoglobin and sodium level at the time of admission instead of discharge.

DESIGN

Prospective national multicentric cohort study.

PARTICIPANTS

In total, 934 consecutively discharged medical inpatients from internal general services.

MAIN MEASURES

We measured the composite of the first unplanned readmission or death within 30 days after discharge of index admission and compared the performance of the simplified score with lab at discharge (simplified HOSPITAL score) and lab at admission (early HOSPITAL score) according to their discriminatory power (Area Under the Receiver Operating characteristic Curve (AUROC)) and the Net Reclassification Improvement (NRI).

KEY RESULTS

During the study period, a total of 3239 patients were screened and 934 included. In total, 122 (13.2%) of them had a 30-day unplanned readmission or death. The simplified and the early versions of the HOSPITAL score both showed very good accuracy (Brier score 0.11, 95%CI 0.10-0.13). Their AUROC were 0.66 (95%CI 0.60-0.71), and 0.66 (95%CI 0.61-0.71), respectively, without a statistical difference (p value 0.79). Compared with the model at discharge, the model with lab at admission showed improvement in classification based on the continuous NRI (0.28; 95%CI 0.08 to 0.48; p value 0.004).

CONCLUSION

The early HOSPITAL score performs, at least similarly, in identifying patients at high risk for 30-day unplanned readmission and allows a readmission risk stratification early during the hospital stay. Therefore, this new version offers a timely preparation of transition care interventions to the patients who may benefit the most.

Predictive validity of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition indicators to diagnose malnutrition tool in hospitalized adults: a cohort study

BACKGROUND

The lack of a widely accepted, broadly validated tool for diagnosing malnutrition in hospitalized patients limits the ability to assess the integral role of nutrition as an input and outcome of health, disease, and treatment.

OBJECTIVES

This study aimed to evaluate the predictive validity of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition (ASPEN) indicators to diagnose malnutrition (AAIM) tool and determine if it can be simplified.

METHODS

A prospective cohort study was conducted from August 2019 to September 2022 with 32 hospitals in United States. At baseline, 290 adult patients were evaluated for a diagnosis of malnutrition using the AAIM tool, which assesses weight loss, inadequate energy intake, subcutaneous fat and muscle loss, edema, and hand grip strength. Healthcare outcomes were extracted from the medical record: composite incidence of emergency department (ED) visits and hospital readmissions within 90 d postdischarge; length of hospital stay (LOS); and Medicare Severity Disease Related Group (MS-DRG) relative weight (i.e., healthcare resource utilization). We used multilevel, multivariable negative binomial or generalized linear regression models to evaluate relationships between malnutrition diagnosis and healthcare outcomes.

RESULTS

After adjusting for disease severity and acuity and sociodemographic characteristics, individuals diagnosed with severe malnutrition had a higher incidence rate of ED visits and hospital readmissions (incidence rate ratio: 1.89; 95% CI: 1.14, 3.13; P = 0.01), and individuals diagnosed with moderate malnutrition had a 25.2% longer LOS (95% CI: 2.0%, 53.7%; P = 0.03) and 15.1% greater healthcare resource utilization (95% CI: 1.6%, 31.9%; P = 0.03) compared with individuals with no malnutrition diagnosis. Observed relationships remained consistent when only considering malnutrition diagnoses supported by at least 2 of these indicators: weight loss, subcutaneous fat loss, muscle wasting, and inadequate energy intake.

CONCLUSIONS

Findings from this multihospital study confirm the predictive validity of the original or simplified AAIM tool and support its routine use for hospitalized adult patients. This trial was registered at clinicaltrials.gov as NCT03928548 (https://classic.

CLINICALTRIALS

gov/ct2/show/NCT03928548).

Characterizing Current and Optimal Involvement of Hospital Pharmacists in the Discharge Process: A Survey of Pharmacists in British Columbia

Background

Transitions of care represent a vulnerable time when patients are at increased risk of medication errors. Medication-related problems constitute one of the main contributors to hospital readmissions. Discharge interventions carried out by pharmacists have been shown to reduce hospital readmissions. Although clinical pharmacists in British Columbia are involved in discharges, their degree of involvement and the interventions they prioritize in practice have not been fully elucidated.

Objectives

To characterize the current involvement of BC hospital pharmacists at the time of discharge, to identify which discharge interventions they believe should be prioritized, and who they feel should be responsible for these interventions, as well as to identify strategies to optimize the discharge process.

Methods

A survey of BC hospital pharmacists was conducted in January and February 2022. The survey included questions about pharmacists' current involvement at the time of discharge, interventions required for a successful discharge, solutions for optimizing the patient discharge process, and participants' baseline characteristics.

Results

The survey response rate was 20% (101/500). Pharmacists reported performing all interventions for less than 60% of their patients. Interventions such as medication reconciliation on discharge, medication education, and ensuring adherence were considered very important for a successful discharge and were considered to be best performed by pharmacists. Solutions for optimizing the discharge process included improved staffing, weekend coverage, timely notification of discharge, and prescribing by pharmacists.

Conclusions

Despite the belief that most interventions listed in the survey are necessary for successful discharge, various barriers prevented pharmacists from providing them to all patients. Increased resources and expanded scope of practice for pharmacists could reduce hospital readmissions and enable broader implementation of discharge interventions.

Impact of Program Changes Including Telemedicine and Telephonic Care During the COVID-19 Pandemic in Preventing 30-Day Hospital Readmission for Patients in a Care Transitions Program

INTRODUCTION/OBJECTIVES

To describe health outcomes of older adults enrolled in the Mayo Clinic Care Transitions (MCCT) program before and during the COVID-19 pandemic compared to unenrolled patients.

METHODS

We conducted a retrospective cohort study of adults (age >60 years) in the MCCT program compared to a usual care control group from January 1, 2019, to September 20, 2022. The MCCT program involved a home, telephonic, or telemedicine visit by an advanced care provider. Outcomes were 30- and 180-day hospital readmissions, emergency department (ED) visit, and mortality. We performed a subgroup analysis after March 1, 2020 (during the pandemic). We analyzed data with Cox proportional hazards regression models and hazard ratios (HRs) with 95% CIs.

RESULTS

Of the 1,012 patients total, 354 were in the MCCT program and 658 were in the usual care group with a mean (SD) age of 81.1 (9.1) years overall. Thirty-day readmission was 16.9% (60 of 354) for MCCT patients and 14.7% (97 of 658) for usual care patients (HR, 1.24; 95% CI, 0.88-1.75). During the pandemic, the 30-day readmission rate was 15.1% (28 of 186) for MCCT patients and 14.9% (68 of 455) for usual care patients (HR, 1.20; 95% CI, 0.75-1.91). There was no difference between groups for 180-day hospitalization, 30- or 180-day ED visit, and 30- or 180-day mortality.

CONCLUSIONS

Numerous factors involving patients, providers, and health care delivery systems during the pandemic most likely contributed to these findings.

Respiratory Interventions, Hospital Utilization, and Clinical Outcomes of Persons with COPD and COVID-19

Purpose

Coronavirus disease 2019 (COVID-19) impacted outcomes of persons with chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD). This study investigated the differences in respiratory interventions, hospital utilization, smoking status, and 30-day readmission in those with COPD and COVID-19 based on hospital survival status.

Methods

A retrospective cross-sectional study was conducted from February 2020 to October 2020 and included persons with COPD and COVID-19 infection. We examined respiratory interventions, hospital utilization and outcomes, and 30-day hospital readmission. Chi-square test analysis was used to assess categorical variables, and t-test or Mann-Whitney was used to analyze continuous data based on normality.

Results

Ninety persons were included in the study, 78 (87%) were survivors. The most common comorbidity was hypertension 71 (78.9%) (p = 0.003). Twenty-two (24%) persons were intubated, from whom 12 (15%) survived (p < 0.001). There were 25 (32.1%) and 12 (100%), (p < 0.001) persons who required an ICU admission from the survivor and non-survivor groups, respectively. Among the survivor group, fifteen (19%) persons required 30-day hospital readmission.

Conclusion

Persons with COPD and COVID-19 had a lower mortality rate (13%) compared to other studies in the early pandemic phase. Non-survivors had increased ICU utilization, endotracheal intubation, and more frequent application of volume control mode. Discharging survivors to long-term acute care facilities may reduce 30-day hospital readmissions.

Effectiveness of Transitional Care Program among High-Risk Discharged Patients: A Quasi-Experimental Study on Saving Costs, Post-Discharge Readmissions and Emergency Department Visits

Transitional care programs (TCPs), where hospital care team members repeatedly follow up with discharged patients, aim to reduce post-discharge hospital or emergency department (ED) utilization and healthcare costs. We examined the effectiveness of TCPs at reducing healthcare costs, hospital readmissions, and ED visits. Centers for Medicare and Medicaid Services Bundled Payments for Care Improvement (BPCI) program adjudicated claims files and electronic health records from Greenville Memorial Hospital, Greenville, SC, were accessed. Data on post-discharge 30- and 90-day ED visits and readmissions, total costs, and episodes with costs over BPCI target prices were extracted from November 2017 to July 2020 and compared between the "TCP-Graduates" (N = 85) and "Did Not Graduate" (DNG) (N = 1310) groups. As compared to the DNG group, the TCP-Graduates group had significantly fewer 30-day (7.1% vs. 14.9%, p = 0.046) and 90-day (15.5% vs. 26.3%, p = 0.025) readmissions, episodes with total costs over target prices (25.9% vs. 36.6%, p = 0.031), and lower total cost/episode (USD 22,439 vs. USD 28,633, p = 0.018), but differences in 30-day (9.4% vs. 11.2%, p = 0.607) and 90-day (20.0% vs. 21.9%, p = 0.680) ED visits were not significant. TCP was associated with reduced post-discharge hospital readmissions, total care costs, and episodes exceeding target prices. Further studies with rigorous designs and individual-level data should test these findings.

Factors associated with acute care service use after epilepsy hospitalisation in people with intellectual disability

BACKGROUND

This study aimed to identify factors associated with unplanned acute hospital readmission and emergency department (ED) presentation after hospitalisation for epilepsy in people with intellectual disability (ID).

METHODS

This study is a retrospective cohort study using linked administrative datasets. We identified 3293 people with ID aged 5-64 years with a hospitalisation for epilepsy between 2005 and 2014 in New South Wales, Australia. We examined unplanned readmission and ED presentation within 30 or 365 days and associations with demographic, socio-economic and health status variables. Modified Poisson regression with robust estimation was used to model outcomes within 30 days. Negative binomial regression was used to account for the overdispersion of the data and to model 365-day outcome rates.

RESULTS

Around half of the cohort had an unplanned readmission and ED presentation within 365 days of the index hospitalisation. In fully adjusted models, being female, being a young adult and having a longer or acute care index admission, mental and physical comorbidities and a history of incarceration were associated with an elevated risk of readmission or ED presentation. The strongest association was observed between history of self-harm and 365-day readmission (incidence rate ratio 2.15, 95% confidence interval 1.41-3.29).

CONCLUSIONS

Socio-demographic, justice and health factors are associated with unplanned readmission and ED presentation risk after hospitalisation for epilepsy in people with ID. Interventions targeting improving continuity of care should be tailored for individuals and their support workers. The findings also emphasise the importance of person-centred multidisciplinary care across different health sectors.

Ambulatory Orbital Fracture Repair: An Analysis of ER Visits After Surgery From a Multistate Study

OBJECTIVE

To determine the 30-day postoperative emergency room (ER) visit rate following ambulatory orbital fracture repair with same-day discharge, and the causes and risk factors associated with ER visit.

STUDY DESIGN

Database study.

SETTING

State Ambulatory Surgery and Services Database (SASD) and State Emergency Department Database (SEDD) for California, New York, and Florida for 2011.

METHODS

We identified orbital fracture repair procedures among adults from the SASD, which was linked to the SEDD to identify the incidence and causes of ER visits within 30 days. Univariate and multivariable logistic regression models were used to determine the factors associated with ER visit.

RESULTS

Among 762 patients, the 30-day postoperative ER visit rate was 4.5%. Most ER visits (58.9%) occurred during the first week after surgery. The most common reasons for ER visits were related to pain, swelling, headache, dizziness, and fatigue (29.4%), followed by ophthalmologic etiologies including visual disturbances and infection of the eye (14.7%). There was no case of retrobulbar hematoma. In the multivariate analysis, patients living in Florida were at a significantly higher risk for ER visit compared to those in California (odds ratio: 4.48 [1.43-14.10], p = .010).

CONCLUSION

Ambulatory orbital fracture repair appears to be safe. Common reasons for ER visit included pain, swelling, and ophthalmic symptoms. An increased risk for ER visit was seen with certain geographic regions but not with medical comorbidities or concurrent facial fractures or procedures.

Effect of the UNOS policy change on rates of rejection, infection, and hospital readmission following heart transplantation

BACKGROUND

The 2018 adult heart allocation policy sought to improve waitlist risk stratification, reduce waitlist mortality, and increase organ access. This system prioritized patients at greatest risk for waitlist mortality, especially individuals requiring temporary mechanical circulatory support (tMCS). Posttransplant complications are significantly higher in patients on tMCS before transplantation, and early posttransplant complications impact long-term mortality. We sought to determine if policy change affected early posttransplant complication rates of rejection, infection, and hospitalization.

METHODS

We included all adult, heart-only, single-organ heart transplant recipients from the UNOS registry with pre-policy (PRE) individuals transplanted between November 1, 2016, and October 31, 2017, and post-policy (POST) between November 1, 2018, and October 31, 2019. We used a multivariable logistic regression analysis to assess the effect of policy change on posttransplant rejection, infection, and hospitalization. Two COVID-19 eras (2019-2020, 2020-2021) were included in our analysis.

RESULTS

The majority of baseline characteristics were comparable between PRE and POST era recipients. The odds of treated rejection (p = 0.8), hospitalization (p = 0.69), and hospitalization due to rejection (p = 0.76) and infection (p = 0.66) were similar between PRE and POST eras; there was a trend towards reduced odds of rejection (p = 0.08). In both COVID eras, there was a clear reduction in rejection and treated rejection with no effect on hospitalization for rejection or infection. Odds of all-cause hospitalization was increased in both COVID eras.

CONCLUSIONS

The UNOS policy change improves access to heart transplantation for higher acuity patients without increasing early posttransplant rates of treated rejection or hospitalization for rejection or infection, factors which portend risk for long-term posttransplant mortality.

Outcomes of Patients With Takotsubo Syndrome Compared With Type 1 and Type 2 Myocardial Infarction

Background Takotsubo syndrome (TS) and myocardial infarction (MI) share similar clinical and laboratory characteristics but have important differences in causes, demographics, management, and outcomes. Methods and Results In this observational study, the National Inpatient Sample and National Readmission Database were used to identify patients admitted with TS, type 1 MI, or type 2 MI in the United States between October 1, 2017, and December 31, 2019. We compared patients hospitalized with TS, type 1 MI, and type 2 MI with respect to key features and outcomes. Over the 27-month study period, 2 035 055 patients with type 1 MI, 639 075 patients with type 2 MI, and 43 335 patients with TS were identified. Cardiac arrest, ventricular fibrillation, and ventricular tachycardia were more prevalent in type 1 MI (4.02%, 3.2%, and 7.2%, respectively) compared with both type 2 MI (2.8%, 0.8%, and 5.4% respectively) and TS (2.7%, 1.8%, and 5.3%, respectively). Risk of mortality was lower in TS compared with both type 1 MI (3.3% versus 7.9%; adjusted odds ratio [OR], 0.3; P<0.001) and type 2 MI (3.3% versus 8.2%; adjusted OR, 0.3; P<0.001). Mortality rate (OR, 1.2; P<0.001) and cardiac-cause 30-day readmission rate (adjusted OR, 1.7; P<0.001) were higher in type 1 MI than in type 2 MI. Conclusions Patients with type 1 MI had the highest rates of in-hospital mortality and cardiac-cause 30-day readmission. Risk of all-cause 30-day readmission was highest in patients with type 2 MI. The risk of ventricular arrhythmias in patients with TS is lower than in patients with type 1 MI but higher than in patients with type 2 MI.

Comparing Machine Learning Classifiers for Predicting Hospital Readmission of Heart Failure Patients in Rwanda

High rates of hospital readmission and the cost of treating heart failure (HF) are significant public health issues globally and in Rwanda. Using machine learning (ML) to predict which patients are at high risk for HF hospital readmission 20 days after their discharge has the potential to improve HF management by enabling early interventions and individualized treatment approaches. In this paper, we compared six different ML models for this task, including multi-layer perceptron (MLP), K-nearest neighbors (KNN), logistic regression (LR), decision trees (DT), random forests (RF), and support vector machines (SVM) with both linear and radial basis kernels. The outputs of the classifiers are compared using performance metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. We found that RF outperforms all the remaining models with an AUC of 94% while SVM, MLP, and KNN all yield 88% AUC. In contrast, DT performs poorly, with an AUC value of 57%. Hence, hospitals in Rwanda can benefit from using the RF classifier to determine which HF patients are at high risk of hospital readmission.

Routine vs. On-Demand Discharge Planning Strategy in Intermediate-Risk Patients for Complex Discharge: a Cluster-Randomized, Multiple Crossover Trial

BACKGROUND

Early hospital discharge planning can help to reduce the length of stay and unplanned readmission in high-risk patients. Therefore, it is important to select patients who can benefit from a personalized discharge planning based on validated tools. The modified Blaylock Risk Assessment Screening Score (BRASS) is routinely used in the Molinette Hospital (Turin, Italy) to screen patients at high risk for discharge, but the effectiveness of the discharge planning is uncertain in intermediate-risk patients.

OBJECTIVE

To evaluate the best strategy for discharge planning by the Continuity of Care Hospital Unit (CCHU) in intermediate-risk patients according to modified BRASS.

DESIGN

Cluster-randomized, multiple crossover trial.

PARTICIPANTS

Adult patients admitted in the Medicine and Neurology departments of the Molinette Hospital in Turin, Italy, between June 2018 and May 2019 with a BRASS intermediate risk.

INTERVENTIONS

A routine discharge planning strategy (RDP, Routine Discharge Plan), which involved the management of all intermediate-risk patients, was compared to an on-demand discharge planning strategy (DDP, on-Demand Discharge Planning), which involved only selected patients referred to the CCHU by ward staff.

MAIN MEASURES

The primary outcome was the 90-day hospital readmission for any cause (HR90). Secondary outcomes included the prolonged length of stay (pLOS).

KEY RESULTS

Eight hundred two patients (median age 79 years) were included (414 RDP and 388 DDP). Comparing RDP vs. DDP periods, HR90 was 27.6% and 27.3% (OR 1.01, 90%CI 0.76-1.33, p = 0.485); and pLOS was 47 (11.4%) and 40 (10.3%) (OR 1.24, 95%CI 0.72-2.13, p = 0.447), respectively.

CONCLUSIONS

This is one of the largest randomized study conducted to compare the effectiveness of two different hospital discharge planning strategies. In patients with intermediate risk of hospital discharge, a RDP offers no advantage over a DDP and results in an unnecessary increase in staff workload.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT03436940.

Association Between Mental Health Conditions at the Hospitalization for Birth and Postpartum Hospital Readmission

Background: The relationship between physical comorbidities and postpartum hospital readmission is well studied, with less research regarding the impact of mental health conditions on postpartum readmission. Methods: Using hospital discharge data (2016-2019) from the Hospital Cost and Utilization Project Nationwide Readmissions Database (n = 12,222,654 weighted), we evaluated the impact of mental health conditions (0, 1, 2, and ≥3), as well as five individual conditions (anxiety, depressive, bipolar, schizophrenic, and traumatic/stress-related conditions) on readmission within 42 days, 1-7 days ("early"), and 8-42 days ("late") of hospitalization for birth. Results: In adjusted analyses, the rate of 42-day readmission was 2.2 times higher for individuals with ≥3 mental health conditions compared to those with none (3.38% vs. 1.56%; p < 0.001), 50% higher among individuals with 2 mental health conditions (2.33%; p < 0.001), and 40% higher among individuals with 1 mental health condition (2.17%; p < 0.001). We found increased adjusted risk of 42-day readmission for individuals with anxiety (1.98% vs. 1.59%; p < 0.001), bipolar (2.38% vs. 1.60%; p < 0.001), depressive (1.93% vs. 1.60%; p < 0.001), schizophrenic (4.00% vs. 1.61%; p < 0.001), and traumatic/stress-related conditions (2.21% vs. 1.61%; p < 0.001), relative to individuals without the respective condition. Mental health conditions had larger impacts on late (8-42 day) relative to early (1-7 day) readmission. Conclusions: This study found strong relationships between mental health conditions during the hospitalization for birth and readmission within 42 days. Efforts to reduce the high rates of adverse perinatal outcomes in the United States should continue to address the impact of mental health conditions during pregnancy and throughout the postpartum period.

Understanding risk for early dislocation resulting in reoperation within 90 days of reverse total shoulder arthroplasty: extreme rare event detection through cost-sensitive machine learning

BACKGROUND

Reliable prediction of postoperative dislocation after reverse total shoulder arthroplasty (RSA) would inform patient counseling as well as surgical and postoperative decision making. Understanding interactions between multiple risk factors is important to identify those patients most at risk of this rare but costly complication. To better understand these interactions, a game theory-based approach was undertaken to develop machine learning models capable of predicting dislocation-related 90-day readmission following RSA.

MATERIAL & METHODS

A retrospective review of the Nationwide Readmissions Database was performed to identify patients who underwent RSA between 2016 and 2018 with a subsequent readmission for prosthetic dislocation. Of the 74,697 index procedures included in the data set, 740 (1%) experienced a dislocation resulting in hospital readmission within 90 days. Five machine learning algorithms were evaluated for their ability to predict dislocation leading to hospital readmission within 90 days of RSA. Shapley additive explanation (SHAP) values were calculated for the top-performing models to quantify the importance of features and understand variable interaction effects, with hierarchical clustering used to identify cohorts of patients with similar risk factor combinations.

RESULTS

Of the 5 models evaluated, the extreme gradient boosting algorithm was the most reliable in predicting dislocation (C statistic = 0.71, F2 score = 0.07, recall = 0.84, Brier score = 0.21). SHAP value analysis revealed multifactorial explanations for dislocation risk, with presence of a preoperative humerus fracture; disposition involving discharge or transfer to a skilled nursing facility, intermediate care facility, or other nonroutine facility; and Medicaid as the expected primary payer resulting in strong, positive, and unidirectional effects on increasing dislocation risk. In contrast, factors such as comorbidity burden, index procedure complexity and duration, age, sex, and presence or absence of preoperative glenohumeral osteoarthritis displayed bidirectional influences on risk, indicating potential protective effects for these variables and opportunities for risk mitigation. Hierarchical clustering using SHAP values identified patients with similar risk factor combinations.

CONCLUSION

Machine learning can reliably predict patients at risk for postoperative dislocation resulting in hospital readmission within 90 days of RSA. Although individual risk for dislocation varies significantly based on unique combinations of patient characteristics, SHAP analysis revealed a particularly at-risk cohort consisting of young, male patients with high comorbidity burdens who are indicated for RSA after a humerus fracture. These patients may require additional modifications in postoperative activity, physical therapy, and counseling on risk-reducing measures to prevent early dislocation after RSA.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Impact of Nursing Interventions on Hospital Readmissions in Patients With Pulmonary Tuberculosis: A Quasi-Experimental Study

PURPOSE

Our study aimed to evaluate the effectiveness of the nursing care program on the incidence and rate of 28-day hospital readmissions among pulmonary tuberculosis (TB) patients.

METHODS

We conducted a quasi-experimental study using a historical control (usual care) group. Patients diagnosed with pulmonary TB who received nursing interventions between January 28, 2021, and May 31, 2021, were categorized as an intervention group, whereas historical controls were selected from January 1, 2020, to December 31, 2020. The primary outcomes were the incidence and rates of hospital readmissions within 28 days due to TB-related complications. The secondary outcome was the change in knowledge and self-care behavior scores at discharge and 28 days postdischarge. Cox models were used to assess the intervention's impact on the incidence of hospital readmission. Rates of readmission were compared by the Poisson model. Both Cox and Poisson models were adjusted for age, sex, sputum smears at diagnosis, serum albumin level, and diabetes mellitus at baseline.

RESULTS

Among 104 pulmonary TB patients included in the analysis (68 were in a historical control group and 36 were in an intervention group), 20 patients were readmitted due to TB-related complications. We found that our nursing care program resulted in a significant reduction in the incidence (adjusted hazard ratio was 0.16 [95% CI 0.03, 0.87]) and the rate of hospital readmissions (adjusted incidence rate ratio was 0.22 [95% CI 0.06, 0.85]). Furthermore, nursing interventions significantly improved knowledge and self-care behavior scores with significant score retention at 28 days postdischarge.

CONCLUSIONS

The nursing care program can significantly decrease the incidence and rate of 28-day hospital readmission and improve knowledge and self-care behavior scores in pulmonary TB patients.

Current approach to health care transition and integration into adult care for pediatric liver transplant recipients: A call for partnership

Despite the increased risk of non-adherence, allograft rejection, and mortality following transfer from pediatric to adult care in liver transplantation (LT), there is no standardized approach to health care transition (HCT). Two electronic national surveys were developed and distributed to members of the Society for Pediatric Liver Transplantation and all adult LT programs in the United States to examine current HCT practices. Responses were received from 40 pediatric and 79 adult centers. Pediatric centers were more likely to focus on HCT noting the presence of a transition/transfer policy (60.2% vs. 39.2%), transition clinic (51.6% vs. 16.5%), and the routine use of transition readiness assessment tools (54.8% vs. 10.2%). Perceived barriers to HCT were similar among pediatric and adult respondents and included patient willingness to transfer and participate in care, failure to show for appointments, and lack of sufficient time and staffing. These results highlight the need for an increased awareness of HCT at both pediatric and adult LT centers. The path to improvement requires a partnership between pediatric and adult providers. Recognizing the importance of a comprehensive HCT program initiated in pediatrics and continued throughout young adulthood with ongoing support by the adult team is essential.

Effect of Sotagliflozin on Early Mortality and Heart Failure-Related Events: A Post Hoc Analysis of SOLOIST-WHF

BACKGROUND

Approximately 25% of patients admitted to hospitals for worsening heart failure (WHF) are readmitted within 30 days.

OBJECTIVES

The authors conducted a post hoc analysis of the SOLOIST-WHF (Effect of Sotagliflozin on Cardiovascular Events in Patients With Type 2 Diabetes Post-WHF) trial to evaluate the efficacy of sotagliflozin versus placebo to decrease mortality and HF-related events among patients who began study treatment on or before discharge from their index hospitalization.

METHODS

The main endpoint of interest was cardiovascular death or HF-related event (HF hospitalization or urgent care visit) occurring within 90 and 30 days after discharge for the index WHF hospitalization. Treatment comparisons were by proportional hazards models, generating HRs, 95% CIs, and P values.

RESULTS

Of 1,222 randomized patients, 596 received study drug on or before their date of discharge. Sotagliflozin reduced the main endpoint at 90 days after discharge (HR: 0.54 [95% CI: 0.35-0.82]; P = 0.004) and at 30 days (HR: 0.49 [95% CI: 0.27-0.91]; P = 0.023) and all-cause mortality at 90 days (HR: 0.39 [95% CI: 0.17-0.88]; P = 0.024). In subgroup analyses, sotagliflozin reduced the 90-day main endpoint regardless of sex, age, estimated glomerular filtration rate, N-terminal pro-B-type natriuretic peptide, left ventricular ejection fraction, or mineralocorticoid receptor agonist use. Sotagliflozin was well-tolerated but with slightly higher rates of diarrhea and volume-related events than placebo.

CONCLUSIONS

Starting sotagliflozin before discharge in patients with type 2 diabetes hospitalized for WHF significantly decreased cardiovascular deaths and HF events through 30 and 90 days after discharge, emphasizing the importance of beginning sodium glucose cotransporter treatment before discharge.

Annual Variation in 30-Day Risk-Adjusted Readmission Rates in U.S. Children's Hospitals

OBJECTIVE

Reducing pediatric readmissions has become a national priority; however, the use of readmission rates as a quality metric remains controversial. The goal of this study was to examine short-term stability and long-term changes in hospital readmission rates.

METHODS

Data from the Pediatric Health Information System were used to compare annual 30-day risk-adjusted readmission rates (RARRs) in 47 US children's hospitals from 2016 to 2017 (short-term) and 2016 to 2019 (long-term). Pearson correlation coefficients and weighted Cohen's Kappa statistics were used to measure correlation and agreement across years for hospital-level RARRs and performance quartiles.

RESULTS

Median (IQR) 30-day RARRs remained stable from 7.7% (7.0-8.3) in 2016 to 7.6% (7.0-8.1) in 2019. Individual hospital RARRs in 2016 were strongly correlated with the same hospital's 2017 rate (R2 = 0.89 [95% confidence interval (CI) 0.80-0.94]) and moderately correlated with those in 2019 (R2 = 0.49 [95%CI 0.23-0.68]). Short-term RARRs (2016 vs 2017) were more highly correlated for medical conditions than surgical conditions, but correlations between long-term medical and surgical RARRs (2016 vs 2019) were similar. Agreement between RARRs was higher when comparing short-term changes (0.73 [95%CI 0.59-0.86]) than long-term changes (0.45 [95%CI 0.27-0.63]). From 2016 to 2019, RARRs increased by ≥1% in 7 (15%) hospitals and decreased by ≥1% in 6 (13%) hospitals. Only 7 (15%) hospitals experienced reductions in RARRs over the short and long-term.

CONCLUSIONS

Hospital-level performance on RARRs remained stable with high agreement over the short-term suggesting stability of readmission measures. There was little evidence of sustained improvement in hospital-level performance over multiple years.

Targeted care navigation to reduce hospital readmissions in 'at-risk' patients

BACKGROUND

Care navigation is commonly used to reduce preventable hospitalisation. The use of Electronic Health Record-derived algorithms may enable better targeting of this intervention for greater impact.

AIMS

To evaluate if community-based Targeted Care Navigation, supported by an Electronic Health Record-derived readmission risk algorithm, is associated with reduced rehospitalisation.

METHODS

A propensity score matching cohort (5 comparison to 1 intervention cohort ratio) study was conducted in an 850-bed Victorian public metropolitan health service, Australia, from May to November 2017. Admitted acute care patients with a non-surgical condition, identified as at-risk of hospital readmission using an Electronic Health Record-derived readmission risk algorithm provide by the state health department, were eligible. Targeted Care Navigation involved telephone follow-up support provided for 30 days post-discharge by a registered nurse. The hazard ratio for hospital readmission was calculated at 30, 60 and 90 days post-discharge using multivariable Cox Proportional Hazards regression.

RESULTS

Sixty-five recipients received care navigation and were matched to 262 people who did not receive care navigation. Excellent matching was achieved with standardised differences between groups being <0.1 for all 11 variables included in the propensity score, including the readmission risk score. The Targeted Care Navigation group had a significantly reduced hazard of readmission at 30 days (hazard ratio 0.34; 95% confidence interval: 0.12, 0.94) compared with the comparison group. The effect size was reduced at 60 and 90 days post-discharge.

CONCLUSION

We provide preliminary evidence that Targeted Care Navigation supported by an Electronic Health Record-derived readmission risk algorithm may reduce 30-day hospital readmissions.

Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data

Sepsis is a life-threatening condition that occurs due to a dysregulated host response to infection. Recent data demonstrate that patients with sepsis have a significantly higher readmission risk than other common conditions, such as heart failure, pneumonia and myocardial infarction and associated economic burden. Prior studies have demonstrated an association between a patient's physical activity levels and readmission risk. In this study, we show that distribution of activity level prior and post-discharge among patients with sepsis are predictive of unplanned rehospitalization in 90 days (P-value<1e-3). Our preliminary results indicate that integrating Fitbit data with clinical measurements may improve model performance on predicting 90 days readmission.Clinical relevance Sepsis, Activity level, Hospital readmission, Wearable data.

The Impact of Cocaine Use and the Obesity Paradox in Patients With Heart Failure With Reduced Ejection Fraction Due to Non-ischemic Cardiomyopathy

Background Obesity and illicit drugs are independent risk factors for developing heart failure (HF). However, recent studies have suggested that patients who already have HF and are obese have better clinical outcomes. We aim to study the effect of cocaine use on this obesity paradox phenomenon as it pertains to HF readmissions. Methodology In a retrospective chart analysis, we reviewed patients with a diagnosis of HF with reduced ejection fraction (HFrEF) admitted to Metropolitan Hospital in New York. We studied the association between body mass index (BMI) categories, namely, non-obese (<30 kg/m2) and obese (≥30 kg/m2), cocaine use, and the primary outcome (time to readmission for HF within 30 days after discharge). The interaction between cocaine and obesity status and its association with the primary outcome was also assessed. Results A total of 261 patients were identified. Non-obese status and cocaine use were associated with an increased hazard of readmission in 30 days (hazard ratio (HR) = 2.28, p = 0.049 and HR = 3.12, p = 0.004, respectively). Furthermore, cocaine users who were non-obese were over six times more likely to be re-admitted in 30 days compared to non-cocaine users who were obese (HR = 6.45, p = 0.0002). Conclusions Non-obese status and continued use of cocaine have a negative additive effect in impacting HF readmissions.

Readmissions and postdischarge mortality by race and ethnicity among Medicare beneficiaries with multimorbidity

BACKGROUND

Disparities in readmission risk and reasons they might exist among diverse complex patients with multimorbidity, disability, and unmet social needs have not been clearly established. These characteristics may be underestimated in claims-based studies where individual-level data are limited. We sought to examine the risk of readmissions and postdischarge mortality by race and ethnicity after rigorous adjustment for multimorbidity, physical functioning, and sociodemographic and lifestyle characteristics.

METHODS

We used Health and Retirement Study (HRS) data linked to Medicare claims. To obtain ICD-9-CM diagnostic codes to compute the ICD-coded multimorbidity-weighted index (MWI-ICD) we used Medicare Parts A and B (inpatient, outpatient, carrier) files between 1991-2015. Participants must have had at least one hospitalization between January 1, 2000 and September 30, 2015 and continuous enrollment in fee-for-service Medicare Part A 1-year prior to hospitalization. We used multivariable logistic regression to assess the association of MWI-ICD with 30-day readmissions and mortality 1-year postdischarge. Using HRS data, we adjusted for age, sex, BMI, smoking, physical activity, education, household net worth, and living arrangement/marital status, and examined for effect modification by race and ethnicity.

RESULTS

The final sample of 10,737 participants had mean ± SD age 75.9 ± 8.7 years. Hispanic adults had the highest mean MWI-ICD (16.4 ± 10.1), followed by similar values for White (mean 14.8 ± 8.9) and Black (14.7 ± 8.9) adults. MWI-ICD was associated with a higher odds of readmission, and there was no significant effect modification by race and ethnicity. For postdischarge mortality, a 1-point increase MWI-ICD was associated with a 3% higher odds of mortality (OR = 1.03, 95% CI: 1.03-1.04), which did not significantly differ by race and ethnicity.

CONCLUSIONS

Multimorbidity was associated with a monotonic increased odds of 30-day readmission and 1-year postdischarge mortality across all race and ethnicity groups. There was no significant difference in readmission or mortality risk by race and ethnicity after robust adjustment.

Hepatic encephalopathy increases the risk for mortality and hospital readmission in decompensated cirrhotic patients: a prospective multicenter study

Introduction

Hepatic encephalopathy (HE) affects the survival and quality of life of patients with cirrhosis. However, longitudinal data on the clinical course after hospitalization for HE are lacking. The aim was to estimate mortality and risk for hospital readmission of cirrhotic patients hospitalized for HE.

Methods

We prospectively enrolled 112 consecutive cirrhotic patients hospitalized for HE (HE group) at 25 Italian referral centers. A cohort of 256 patients hospitalized for decompensated cirrhosis without HE served as controls (no HE group). After hospitalization for HE, patients were followed-up for 12 months until death or liver transplant (LT).

Results

During follow-up, 34 patients (30.4%) died and 15 patients (13.4%) underwent LT in the HE group, while 60 patients (23.4%) died and 50 patients (19.5%) underwent LT in the no HE group. In the whole cohort, age (HR 1.03, 95% CI 1.01-1.06), HE (HR 1.67, 95% CI 1.08-2.56), ascites (HR 2.56, 95% CI 1.55-4.23), and sodium levels (HR 0.94, 95% CI 0.90-0.99) were significant risk factors for mortality. In the HE group, ascites (HR 5.07, 95% CI 1.39-18.49) and BMI (HR 0.86, 95% CI 0.75-0.98) were risk factors for mortality, and HE recurrence was the first cause of hospital readmission.

Conclusion

In patients hospitalized for decompensated cirrhosis, HE is an independent risk factor for mortality and the most common cause of hospital readmission compared with other decompensation events. Patients hospitalized for HE should be evaluated as candidates for LT.

Effectiveness of a home-based telesurveillance program in reducing hospital readmissions in older patients with chronic disease: The eCOBAHLT randomized controlled trial

INTRODUCTION

Given that chronic, long-term conditions are increasingly common in older patients, the impact of telesurveillance program on clinical outcomes is uncertain. This study aimed to evaluate the feasibility and effectiveness of a 12-month remote monitoring program in preventing rehospitalizations in older patients with two or more chronic diseases returning home after hospitalization.

METHODS

We conducted a multicenter randomized controlled trial in two parallel groups to evaluate the remote monitoring system. Elderly patients with chronic diseases (at least two comorbidities) aged 65 years or older and discharged home after acute hospital care for a chronic disease were randomized to receive a home telemonitoring program (intervention group, n = 267) or conventional care (control group, n = 267). The remote home monitoring program was an online biometric home life analysis technology (e-COBAHLT) with tele-homecare/automation and biometric sensors. The eCOBALTH intervention group received the automation sensors containing chronic disease clinical factor trackers to monitor their biometric parameters and detect any abnormal prodromal disease decompensation by remote monitoring and providing geriatric expertise to general practitioners. The usual care group received no eCOBALTH program. In both groups, baseline visits were conducted at baseline and the final visit at 12 months. The primary outcome was the incidence of unplanned hospitalizations for decompensation during the 12-month period.

RESULTS

Among 534 randomized participants (mean [SD] age, 80.3 [8.1] years; 280 [52.4%] women), 492 (92.1%) completed the 12-month follow-up; 182 (34.1) had chronic heart failure, 115 (21.5%) had stroke, and 77 (14.4%) had diabetes. During the 12-month follow-up period, 238 patients had at least one unplanned hospitalization for decompensation of a chronic disease: 108 (40.4%) in the intervention group versus 130 (48.7%) in the control group (P = 0.04). The risk of rehospitalization was significantly reduced in the intervention group (age- and sex-adjusted relative risk: 0.72, 95% 95% confidence intervals 0.51-0.94).

CONCLUSION

A 12-month home telemonitoring program with online biometric analysis using Home life technology combining telecare and biometric sensors is feasible and effective in preventing unplanned hospitalizations for chronic disease decompensation in elderly patients with chronic diseases at high risk for hospitalizations.

Unplanned 30-Day ER Visit Rate and Factors Associated With ER Visits After Ambulatory Sinus Surgery

BACKGROUND

Emergency room (ER) visits after surgery can be inconvenient and costly to the patient and the healthcare system. Estimates of the 30-day ER visit rate following ambulatory sinus procedures and their risk factors are largely unknown in the literature.

OBJECTIVE

To determine the 30-day postoperative ER visit rate following ambulatory sinus procedures and the causes and risk factors associated with ER visits.

METHODS

This is a retrospective, cohort study using data from the State Ambulatory Surgery and Services Databases (SASD) and the State Emergency Department Databases (SEDD) for California, New York, and Florida in 2019. We identified adult (18 years old) patients with chronic rhinosinusitis who underwent ambulatory sinus procedures from the SASD. Cases were linked to the SEDD to identify ER visits occurring within 30 days after the procedure. Logistic regression models were used to identify patient- and procedure-related risk factors associated with the 30-day postoperative ER visit.

RESULTS

Among the 23 239 patients, the 30-day postoperative ER visit rate was 3.9%. The most common reason for ER visit was bleeding (32.7%). A total of 56.9% of the ER visits occurred within the first week. In the multivariate analysis, factors associated with ER visits included Medicare (odds ratio [OR] 1.29 [1.09-1.52], P  =  .003), Medicaid (OR 2.06 [1.69-2.51], P < .001), self-pay/no insurance (OR 1.44 [1.03-2.00], P  =  .031), chronic kidney disease/end-stage renal disease (OR 1.63 [1.06-2.51], P =  .027), chronic pain/opioid use (OR 2.70 [1.02-7.11], P  =  .045), and a disposition other than home (OR 12.61 [8.34-19.06], P < .001).

CONCLUSION

The most common reason for ER visit after ambulatory sinus procedures was bleeding. An increased ER visit rate was associated with certain demographic factors and medical comorbidities but not with procedure characteristics. This information can help us identify the patient populations who are at higher risk for ER visits to improve their postoperative recovery.

The Impact of Potentially Inappropriate Medications and Polypharmacy on 3-Month Hospital Readmission among Older Patients: A Retrospective Cohort Study from Malaysia

INTRODUCTION

Potentially inappropriate medications (PIMs) use and polypharmacy are two issues that are commonly encountered among older people. They are associated with several negative outcomes including adverse drug reactions and medication-related hospitalization. There are insufficient studies regarding the impact of both PIMs and polypharmacy on hospital readmission, especially in Malaysia.

AIM

To investigate the possible association between polypharmacy and prescribing PIMs at discharge and 3-month hospital readmission among older patients.

MATERIALS AND METHOD

A retrospective cohort study involved 600 patients ≥60 years discharged from the general medical wards in a Malaysian teaching hospital. The patients were divided into two equal groups: patients with or without PIMs. The main outcome was any readmission during the 3-month follow-up. The discharged medications were assessed for polypharmacy (≥five medications) and PIMs (using 2019 Beers' criteria). Chi-square test, Mann-Whitney test, and a multiple logistic regression were conducted to study the impact of PIMs/polypharmacy on 3-month hospital readmission.

RESULTS

The median number for discharge medications were six and five for PIMs and non-PIMs patients, respectively. The most frequently prescribed PIMs was aspirin as primary prevention of cardiovascular diseases (33.43%) followed by tramadol (13.25%). The number of medications at discharge and polypharmacy status were significantly associated with PIMs use. Overall, 152 (25.3%) patients were re-admitted. Polypharmacy and PIMs at discharge did not significantly impact the hospital readmission. After applying the logistic regression, only male gender was a predictor for 3-month hospital readmission (OR: 2.07, 95% CI: 1.022-4.225).

CONCLUSION

About one-quarter of the patients were admitted again within three months of discharge. PIMs and polypharmacy were not significantly associated with 3-month hospital readmissions while male gender was found to be an independent risk factor for readmission.

Association of Home Monitoring and Unanticipated Interstage Readmissions in Infants With Hypoplastic Left Heart Syndrome

Background The impact of home monitoring on unanticipated interstage readmissions in infants with hypoplastic left heart syndrome has not been previously studied. We sought to examine the association of our institution's Infant Single Ventricle Management and Monitoring Program (ISVMP) with readmission frequency, cumulative readmission days, and readmission illness severity and to identify patient-level risk factors for readmission. Methods and Results We performed a retrospective single-center cohort study comparing infants with hypoplastic left heart syndrome enrolled in ISVMP (December 2010-December 2019) to historical controls (January 2007-November 2010). The primary outcome was number of readmissions per interstage days. Secondary outcomes were cumulative interstage readmission days and occurrence of severe readmissions. Inverse probability weighted and multivariable generalized linear models were used to examine the association between ISVMP and the outcomes. We compared 198 infants in the ISVMP to 128 historical controls. Infants in the ISVMP had more than double the risk of interstage readmission compared with controls (adjusted incidence rate ratio, 2.38 [95% CI, 1.50-3.78]; P=0.0003). There was no difference in cumulative interstage readmission days (adjusted incidence rate ratio, 1.02 [95% CI, 0.69-1.50]; P=0.90); however, infants in the ISVMP were less likely to have severe readmissions (adjusted odds ratio, 0.28 [95% CI, 0.11-0.68]; P=0.005). Other factors independently associated with number of readmissions included residing closer to our center, younger gestational age, genetic syndrome, and discharge on exclusive enteral feeds. Conclusions Infants in the ISVMP had more frequent readmissions but comparable readmission days and fewer severe unanticipated readmissions. These findings suggest that home monitoring can reduce interstage morbidity without increasing readmission days.

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

BACKGROUND

Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization.

OBJECTIVE

Machine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs).

METHODS

We used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared.

RESULTS

A total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions.

CONCLUSIONS

We predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

Predictors of Readmission after the First Acute Coronary Syndrome and the Risk of Recurrent Cardiovascular Events-Seven Years of Patient Follow-Up

Recurrent hospitalization after acute coronary syndromes (ACS) is common. Identifying risk factors associated with subsequent cardiovascular events and hospitalization is essential for the management of these patients. Our research consisted in observing the outcomes of subjects after they suffered an acute coronary event and identifying the factors that can predict rehospitalization in the first 12 months and the recurrence of another acute coronary episode. Data from 362 patients admitted with ACS during 2013 were studied. Recurrent hospitalizations were retrospectively reviewed from medical charts and electronic hospital archives over a period of seven years. The mean age of the studied population was 64.57 ± 11.79 years, 64.36% of them being males. The diagnosis of ACS without ST elevation was registered in 53.87% of the patients at index hospitalization. More than half had recurrent hospitalization in the first year after the first ACS episode. Patients with lower ejection fraction (39.20 ± 6.85 vs. 42.24 ± 6.26, p < 0.001), acute pulmonary edema during the first hospitalization (6.47% vs. 1.24%, p = 0.022), coexistent valvular heart disease (69.15% vs. 55.90%, p = 0.017), and three-vessel disease (18.90% vs. 7.45%, p = 0.002) were more frequently readmitted in the following twelve months after their first acute coronary event, while those with complete revascularization were less frequently admitted (24.87% vs. 34.78%, p = 0.005). In multiple regression, complete revascularization during the index event (HR = 0.58, 95% CI 0.35-0.95, p = 0.03) and a higher LVEF (left ventricular ejection fraction) (HR = 0.95, 95% CI 0.92-0.988, p = 0.009) remained independent predictors of fewer early readmissions. Complete revascularization of the coronary lesions at the time of the first event and a preserved LVEF were found to be the predictors of reduced hospitalizations in the first year after an acute coronary event.

The impact of including a medication review in an integrated care pathway: A pilot study

AIM

The objective of the present study was to measure the impact of the intervention of combining a medication review with an integrated care approach on potentially inappropriate medications (PIMs) and hospital readmissions in frail older adults.

METHODS

A cohort of hospitalized older adults enrolled in the French PAERPA integrated care pathway (the exposed cohort) was matched retrospectively with hospitalized older adults not enrolled in the pathway (unexposed cohort) between January 1st, 2015, and December 31st, 2018. The study was an analysis of French health administrative database. The inclusion criteria for exposed patients were admission to an acute care department in a general hospital, age 75 years or over, at least three comorbidities or the prescription of diuretics or oral anticoagulants, discharge alive and performance of a medication review.

RESULTS

For the study population (n = 582), the mean ± standard deviation age was 82.9 ± 4.9 years, and 380 (65.3%) were women. Depending on the definition used, the overall median number of PIMs ranged from 2 [0;3] on admission to 3 [0;3] at discharge. The intervention was not associated with a significant difference in the mean number of PIMs. Patients in the exposed cohort were half as likely to be readmitted to hospital within 30 days of discharge relative to patients in the unexposed cohort.

CONCLUSION

Our results show that a medication review was not associated with a decrease in the mean number of PIMs. However, an integrated care intervention including the medication review was associated with a reduction in the number of hospital readmissions at 30 days.

Age-Stratified 30-day Rehospitalization and Mortality and Predictors of Rehospitalization Among Patients With Systemic Lupus Erythematosus: A Medicare Cohort Study

OBJECTIVE

Recent studies suggest young adults with systemic lupus erythematosus (SLE) have high 30-day readmission rates, which may necessitate tailored readmission reduction strategies. To aid in risk stratification for future strategies, we measured 30-day rehospitalization and mortality rates among Medicare beneficiaries with SLE and determined rehospitalization predictors by age.

METHODS

In a 2014 20% national Medicare sample of hospitalizations, rehospitalization risk and mortality within 30 days of discharge were calculated for young (aged 18-35 yrs), middle-aged (aged 36-64 yrs), and older (aged 65+ yrs) beneficiaries with and without SLE. Multivariable generalized estimating equation models were used to predict rehospitalization rates among patients with SLE by age group using patient, hospital, and geographic factors.

RESULTS

Among 1.39 million Medicare hospitalizations, 10,868 involved beneficiaries with SLE. Hospitalized young adult beneficiaries with SLE were more racially diverse, were living in more disadvantaged areas, and had more comorbidities than older beneficiaries with SLE and those without SLE. Thirty-day rehospitalization was 36% among young adult beneficiaries with SLE-40% higher than peers without SLE and 85% higher than older beneficiaries with SLE. Longer length of stay and higher comorbidity risk score increased odds of rehospitalization in all age groups, whereas specific comorbid condition predictors and their effect varied. Our models, which incorporated neighborhood-level socioeconomic disadvantage, had moderate-to-good predictive value (C statistics 0.67-0.77), outperforming administrative data models lacking comprehensive social determinants in other conditions.

CONCLUSION

Young adults with SLE on Medicare had very high 30-day rehospitalization at 36%. Considering socioeconomic disadvantage and comorbidities provided good prediction of rehospitalization risk, particularly in young adults. Young beneficiaries with SLE with comorbidities should be a focus of programs aimed at reducing rehospitalizations.

Low calf circumference adjusted for body mass index is associated with prolonged hospital stay

BACKGROUND

Calf circumference (CC) is of emerging importance because of its practicality, high correlation with skeletal muscle, and potential predictive value for adverse outcomes. However, the accuracy of CC is influenced by adiposity. CC adjusted for BMI (BMI-adjusted CC) has been proposed to counteract this problem. However, its accuracy to predict outcomes is unknown.

OBJECTIVES

To evaluate the predictive validity of BMI-adjusted CC in hospital settings.

METHODS

A secondary analysis of a prospective cohort study in hospitalized adult patients was conducted. The CC was adjusted for BMI by reducing 3, 7, or 12 cm for BMI (in kg/m²) of 25-29.9, 30-39.9, and ≥40, respectively. Low CC was defined as ≤34 cm for males and ≤33 cm for females. Primary outcomes included length of hospital stay (LOS) and in-hospital death, and secondary outcomes were hospital readmissions and mortality within 6 mo after discharge.

RESULTS

We included 554 patients (55.2 ± 14.9 y, 52.9% men). Among them, 25.3% presented with low CC, whereas 60.6% had BMI-adjusted low CC. In-hospital death occurred in 13 patients (2.3%), and median LOS was 10.0 (5.0-18.0) d. Within 6 mo from discharge, 43 patients (8.2%) died, and 178 (34.0%) were readmitted to the hospital. BMI-adjusted low CC was an independent predictor of LOS ≥ 10 d (odds ratio = 1.70; 95% confidence interval: 1.18, 2.43], but it was not associated with the other outcomes.

CONCLUSIONS

BMI-adjusted low CC was identified in more than 60% of hospitalized patients and was an independent predictor of longer LOS.

Low skeletal muscle mass is associated with mortality in kidney transplant recipients

Muscle wasting in chronic kidney disease is associated with increased cardiovascular events, morbidity, and mortality. However, whether pretransplantation skeletal muscle mass affects kidney transplantation (KT) outcomes has not been established. We analyzed 623 patients who underwent KT between 2004 and 2019. We measured the cross-sectional area of total skeletal muscle at the third lumbar vertebra level on pretransplantation computed tomography scan. The patients were grouped into low and normal skeletal muscle mass groups based on the sex-specific skeletal muscle mass index lowest quartile. During the entire follow-up period, 45 patients (7.2%) died and 56 patients (9.0%) experienced death-censored graft loss. Pretransplantation low skeletal muscle mass was independently associated with all-cause mortality (adjusted hazard ratio, 2.269; 95% confidence interval, 1.232-4.182). Low muscle mass was also associated with an increased risk of hospital readmission within 1 year after transplantation. Death-censored graft survival rates were comparable between the 2 groups. The low muscle group showed higher creatinine-based estimated glomerular filtration rates (eGFRs) than the normal muscle group. Although cystatin C-based eGFRs were measured in only one-third of patients, cystatin C-based eGFRs were comparable between the 2 groups. Pretransplantation low skeletal muscle mass index is associated with an increased risk of mortality and hospital readmission after KT.

Readmissions after radical nephrectomy in a national cohort

OBJECTIVE

To analyze the factors and costs associated with 30-day readmissions for patients undergoing radical nephrectomy.

MATERIALS AND METHODS

We used the 2014 Nationwide Readmission Database to identify adults who underwent radical nephrectomy for renal cancer, stratified by surgical approach. We determined patient factors associated with readmission rates, diagnoses, and costs using multivariate logistic regression.

RESULTS

Among 19,523 individuals, the 30-day readmission rate was 7.7% (n = 1,506). On multivariate regression, odds of readmission were significantly increased with age ≥75 in those who underwent open nephrectomy (OR: 1.35; 95%CI: 1.03-1.78). Subjects with a Charlson comorbidity score ≥3 had significantly higher rates of readmission regardless of surgical approach (Open RN - OR: 1.85; 95%CI: 1.33-2.56; Lap RN - OR: 1.99; 95%CI 1.10-3.59; Robotic RN - OR: 2.18; 95%CI: 1.23-3.86). Common reasons for readmission were gastrointestinal, cardiovascular, urinary tract infections, and wound complications across all surgical approaches. The mean cost per readmission was as high as 126% ($20,357) of the mean index admission cost.

CONCLUSION

One in 13 adults undergoing radical nephrectomy is readmitted within 30 days of discharge. Associated readmission cost is up to 1.26 times the cost of index admission. Our findings may inform efforts aiming to reduce hospital readmissions and curtail healthcare costs.

A Parathyroid Hormone-guided Calcium and Calcitriol Supplementation Protocol Reduces Hypocalcemia-related Readmissions Following Total Thyroidectomy

OBJECTIVE

To determine the effect of a 4 hour postoperative serum parathyroid hormone (PTH) guided calcium and calcitriol supplementation protocol on the incidence of hypocalcemia and hospital readmissions in patients undergoing total thyroidectomy.

METHODS

This was a single institution, retrospective chart review of patients who underwent total thyroidectomy, 148 had been operated on prior to the protocol implementation and 389 after. Hypocalcemia risk was stratified as low (PTH >30 pg/ml), medium (15-30 pg/ml), and high (<15 pg/ml), using serum PTH values obtained 4 hours postoperatively. Hypocalcemia was defined as a total serum calcium level <8 mg/dl. Baseline demographic and operative characteristics, and postoperative outcome were recorded for both groups. Fisher's exact test or Wilcoxon rank sum test were used to compare the characteristics of the two groups. A multivariable logistic regression model was applied to account for potentially confounding variables.

RESULTS

Postoperative hypocalcemia occurred significantly less frequently in the protocol group compared to the preprotocol group (10.3% vs. 20.9%, P=.002). The reduction in hypocalcemia in the protocol group was observed both in patients with (16.3% vs. 25.6%) and without (8.4% vs. 19.3%) cervical lymph node dissection. There was a significantly lower incidence of hospital readmission events in the protocol group compared to the preprotocol group (1.0% vs. 4.7%, P=.013).

CONCLUSIONS

Compared to a historical cohort, a PTH-guided protocol for calcium and calcitriol supplementation significantly reduces postoperative hypocalcemia and hospital readmission rates, in patients undergoing total thyroidectomy.

Discharge Planning for People with Inpatient Hyperglycaemia: A Review on Pharmacological Management

Inadequate glycaemic control post-discharge is the root cause of readmission in people with diabetes mellitus (DM) and is often linked to improper discharge planning (DP). A structured DP plays a crucial role in ensuring continuing home care and avoiding readmissions. DP should help patients in self-care and provide appropriate guidance to maintain optimal glycaemic control. There is a scarcity of reports and recommendations on the proper DP for people with DM on insulin therapy. The present review provides important consideration based on experts' opinions from the National Insulin and Incretin summit (NIIS), focusing on the effective treatment strategies at the time of discharge, especially for insulin therapy. A review of literature from PubMed and Embase was conducted. The consensus was derived, and recommendations were made on effective DP for patients with DM. Recommendations were drawn at the NIIS for post-discharge treatment for medical and surgical cases, stress-induced hyperglycaemia, elderly, pregnant women, and coronavirus disease 2019 (COVID-19) cases. The committee also recommended a comprehensive checklist to assist the physicians during discharge.

Predicting Hospital Readmissions in Patients Receiving Novel-Dose Sacubitril/Valsartan Therapy: A Competing-Risk, Causal Mediation Analysis

Backgrounds: Our study aimed to identify and predict patients with heart failure (HF) taking novel-dose Sacubitril/Valsartan (S/V) at risk for all-cause readmission, as well as investigate the possible role of left ventricular reverse remodeling (LVRR). Methods and results: There were 464 patients recruited from December 2017 to September 2021 in our hospital with a median follow-up of 660 days (range, 17-1494). Competing risk analysis with Gray's Test showed statistically significant differences in all-cause readmission (p-value< .001) across the three different dose groups. Models 1 and 2 were developed based on the results of univariable competing risk analysis, least absolute shrinkage and selection operator approach, backward stepwise regression, and multivariable competing risk analysis. The internal verification (data-splitting method) indicated that Model 1 had better discrimination, calibration, and clinical utility. The corresponding nomogram showed that patients aged 75 years and above, or taking the lowest-dose S/V (≤50 mg twice a day), or diagnosed with ventricular tachycardia, or valvular heart disease, or chronic obstructive pulmonary disease, or diabetes mellitus were at the highest risk of all-cause readmission. In the causal mediation analysis, LVRR was considered as a critical mediator that negatively affected the difference of novel-dose S/V in readmission. Conclusions: A significant association was detected between novel-dose S/V and all-cause readmission in HF patients, in part negatively mediated by LVRR. The web-based nomogram could provide individual prediction of all-cause readmission in HF patients receiving novel-dose S/V. The effects of different novel-dose S/V are still needed to be explored further in the future.

The Impact of Community Health Information Exchange Usage on Time to Reutilization of Hospital Services

PURPOSE

Few studies have determined whether clinician usage of a community health information exchange (HIE) directly improves patient care transitions. We hypothesized that lookup in the HIE by primary care physicians of patients recently released from the hospital would increase the time until hospital reuse.

METHODS

We identified a retrospective cohort of 8,216 hospital inpatients aged over 18 years that were discharged from January 1, 2021 through November 30, 2021 using the Paso del Norte Health Information Exchange, in El Paso County, Texas. All patients had a primary care physician visit within 30 days after hospital discharge, and we identified patients that were looked up in the HIE close to that visit. Of the cohort, 2,627 were rehospitalized and 3,809 visited an emergency department (ED) during the follow-up window. The remaining 1,780 patients were controls. We conducted survival analysis, censoring at the second ED or inpatient visit or end of the study window (January 31, 2022). The model was adjusted by ethnicity, gender, insurance, and age.

RESULTS

Lookup in the HIE was significantly associated with reducing the likelihood of visiting the ED by 53% and being rehospitalized by 61%. Lookup in the HIE was associated with an increased median time to use of the ED after inpatient discharge from 99 to 238 patient days. Ethnicity, insurance, gender, and age were also significant predictors of hospital reuse.

CONCLUSIONS

Increased utilization of community HIEs by primary care physicians on behalf of their recently discharged patients may dramatically increase the time until inpatient or ED reuse.

Prediction of 30-Day Readmission in Hospitalized Older Adults Using Comprehensive Geriatric Assessment and LACE Index and HOSPITAL Score

(1) Background: Elders have higher rates of rehospitalization, especially those with functional decline. We aimed to investigate potential predictors of 30-day readmission risk by comprehensive geriatric assessment (CGA) in hospitalized patients aged 65 years or older and to examine the predictive ability of the LACE index and HOSPITAL score in older patients with a combination of malnutrition and physical dysfunction. (2) Methods: We included patients admitted to a geriatric ward in a tertiary hospital from July 2012 to August 2018. CGA components including cognitive, functional, nutritional, and social parameters were assessed at admission and recorded, as well as clinical information. The association factors with 30-day hospital readmission were analyzed by multivariate logistic regression analysis. The predictive ability of the LACE and HOSPITAL score was assessed using receiver operator characteristic curve analysis. (3) Results: During the study period, 1509 patients admitted to a ward were recorded. Of these patients, 233 (15.4%) were readmitted within 30 days. Those who were readmitted presented with higher comorbidity numbers and poorer performance of CGA, including gait ability, activities of daily living (ADL), and nutritional status. Multivariate regression analysis showed that male gender and moderately impaired gait ability were independently correlated with 30-day hospital readmissions, while other components such as functional impairment (as ADL) and nutritional status were not associated with 30-day rehospitalization. The receiver operating characteristics for the LACE index and HOSPITAL score showed that both predicting scores performed poorly at predicting 30-day hospital readmission (C-statistic = 0.59) and did not perform better in any of the subgroups. (4) Conclusions: Our study showed that only some components of CGA, mobile disability, and gender were independently associated with increased risk of readmission. However, the LACE index and HOSPITAL score had a poor discriminating ability for predicting 30-day hospitalization in all and subgroup patients. Further identifiers are required to better estimate the 30-day readmission rates in this patient population.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach

BACKGROUND

A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.

OBJECTIVE

This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information.

METHODS

The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility.

RESULTS

In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement.

CONCLUSIONS

Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

The Impact of 90-day Physician Follow-up Care on the Risk of Readmission Following a Psychiatric Hospitalization

AIMS

This study measures the impact of 90-day physician follow-up care after psychiatric hospitalization among 3,311 adults and youth, with risk of subsequent readmission within six months.

METHODS

A 5-year investigation was conducted based on Quebec (Canada) medical administrative databases. Cox proportional-hazards regression was performed, with 90-day follow-up care as the main independent variable, controlling for various sociodemographic, clinical, and other service use variables.

RESULTS

Within the 90-day follow-up period after patient discharge, or in the first 30 days, receiving at least one consultation per month as opposed to no consultation was associated with a reduced risk of psychiatric readmission. Women showed an increased readmission risk compared to men, while those living in less materially deprived areas a decreased risk as opposed to more deprived areas. Patients hospitalized for suicide attempt or schizophrenia spectrum and other psychotic disorders, and those with co-occurring mental and substance-related disorders or chronic physical illnesses, especially illnesses high on the severity index, also presented a heightened risk of hospitalization. Patients hospitalized for personality disorders or receiving a high continuity of physician care showed a reduced risk of readmission.

CONCLUSION

This study demonstrates that follow-up care, if provided within the first 30 days of discharge or monthly during the 90-day follow-up period, decreased the risk of readmission, as did having a high continuity of physician care prior to and within the 90-day follow-up period. However, few patients in this study had received such high-quality care, indicating that the Quebec system needs to considerably improve its discharge planning processes.

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study

BACKGROUND

Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).

OBJECTIVE

We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Adult patients discharged from a CCHS hospital April 2017-September 2020.

MAIN MEASURES

Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.

RESULTS

The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.

CONCLUSIONS

The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Nutritional and Educational Intervention to Recover a Healthy Eating Pattern Reducing Clinical Ileostomy-Related Complications

The aim of this study was to evaluate a diet intervention implemented by our hospital in order to determinate its capacity to improve the eating pattern of patients with an ileostomy, facilitating the implementation new eating-related behaviors, reducing doubt and dissatisfaction and other complications. The study was conducted with a quasi-experimental design in a tertiary level hospital. The elaboration and implementation of a nutritional intervention consisting of a Mediterranean-diet-based set of menus duly modified that was reinforced by specific counseling at the reintroduction of oral diet, hospital discharge and first follow-up appointment. Descriptive, bivariate and multivariate analyses were performed. The protocol was approved by the competent Ethics Committee. The patients of the intervention group considered that the diet facilitated eating five or more meals a day and diminished doubt and concerns related to eating pattern. Most patients (86%) had a favorable experience regarding weight recovery and a significant reduction of all-cause readmissions and readmission with dehydration (p = 0.015 and p < 0.001, respectively). The intervention helped an effective self-management of eating pattern by patients who had a physical improvement related to hydration status, which, together with an improvement in weight regain, decreased the probability of readmissions.

The relationship of family functioning and family health with hospital readmission in patients with heart failure: insights from an international cross-sectional study

BACKGROUND

The growing hospital readmission rate among patients with heart failure (HF) has imposed a substantial economic burden on healthcare systems. Therefore, it is essential to identify readmission associating factors to reduce hospital readmission.

AIMS

This study aimed to investigate the relationship of family functioning and family health with hospital readmission rates over six months in patients with HF and identify the sociodemographic and/or clinical variables associated with hospital readmission.

METHODS

This international multicentre cross-sectional study involved a sample of 692 patients with HF from three countries (Denmark 312, Iran 288, and Iceland 92) recruited from January 2015 to May 2020. The Family Functioning, Health, and Social Support questionnaire was used to collect the data. The number of patients' hospital readmissions during the six-month period was retrieved from patients' hospital records.

RESULTS

Of the total sample, 184 (26.6%) patients were readmitted during the six-month period. Of these, 111 (16%) had one readmission, 68 (9.9%) had two readmissions, and 5 (0.7%) had three readmissions. Family functioning, family health, being unemployed, and country of residence were significant factors associated with hospital readmission for the patients.

CONCLUSION

This study highlights the critical roles of family functioning and family health in six-month hospital readmission among patients with HF. Moreover, the strategy of healthcare systems in the management of HF is a key determinant that influences hospital readmission. Our findings may assist the investigation of potential strategies to reduce hospital readmission in patients with HF.

Associations Between Implementation of the Caregiver Advise Record Enable (CARE) Act and Health Service Utilization for Older Adults with Diabetes: Retrospective Observational Study

BACKGROUND

The Caregiver Advise Record Enable (CARE) Act is a state level law that requires hospitals to identify and educate caregivers ("family members or friends") upon discharge.

OBJECTIVE

This study examined the association between the implementation of the CARE Act in a Pennsylvania health system and health service utilization (ie, reducing hospital readmission, emergency department [ED] visits, and mortality) for older adults with diabetes.

METHODS

The key elements of the CARE Act were implemented and applied to the patients discharged to home. The data between May and October 2017 were pulled from inpatient electronic health records. Likelihood-ratio chi-square tests and multivariate logistic regression models were used for statistical analysis.

RESULTS

The sample consisted of 2591 older inpatients with diabetes with a mean age of 74.6 (SD 7.1) years. Of the 2591 patients, 46.1% (n=1194) were female, 86.9% (n=2251) were White, 97.4% (n=2523) had type 2 diabetes, and 69.5% (n=1801) identified a caregiver. Of the 1801 caregivers identified, 399 (22.2%) received discharge education and training. We compared the differences in health service utilization between pre- and postimplementation of the CARE Act; however, no significance was found. No significant differences were detected from the bivariate analyses in any outcomes between individuals who identified a caregiver and those who declined to identify a caregiver. After adjusting for risk factors (multivariate analysis), those who identified a caregiver (12.2%, 219/1801) was associated with higher rates of 30-day hospital readmission than those who declined to identify a caregiver (9.9%, 78/790; odds ratio [OR] 1.38, 95% CI 1.04-1.87; P=.02). Significantly lower rates were detected in 7-day readmission (P=.02), as well as 7-day (P=.03) and 30-day (P=.01) ED visits, among patients with diabetes whose identified caregiver received education and training than those whose identified caregiver did not receive education and training in the bivariate analyses. However, after adjusting for risk factors, no significance was found in 7-day readmission (OR 0.53, 95% CI 0.27-1.05; P=.07), 7-day ED visit (OR 0.63, 95% CI 0.38-1.03; P=.07), and 30-day ED visit (OR 0.73, 95% CI 0.52-1.02; P=.07). No significant associations were found for other outcomes (ie, 30-day readmission and 7-day and 30-day mortality) in both the bivariate and multivariate analyses.

CONCLUSIONS

Our study found that the implementation of the CARE Act was associated with certain health service utilization. The identification of caregivers was associated with higher rates of 30-day hospital readmission in the multivariate analysis, whereas having identified caregivers who received discharge education was associated with lower rates of readmission and ED visit in the bivariate analysis.