Higher Risk Of Readmission Research Articles

Characteristics, outcomes, and 30-day readmissions following pediatric extracorporeal membrane oxygenation in the United States: A Nationwide Readmissions Database study.

Extracorporeal membrane oxygenation (ECMO) is an increasingly used mode of critical care support for pediatric patients refractory to conventional therapy. We evaluated the characteristics, outcomes, and readmissions rates for pediatric ECMO in the United States. Data was extracted from the Nationwide Readmissions Database, a database designed to support national readmissions analyses, for patients aged 1-18years undergoing ECMO between 2012-2018. Baseline demographics, comorbidities, and characteristics were identified using International Classification of Diseases codes. Out of 897,117 index pediatric hospitalizations, 3706 patients underwent ECMO [median age 9 years (IQR 2,15); 51.6% males]. 2246 (60.6%) patients survived to hospital discharge, with a 30-day readmissions rate of 17% among survivors. Cardiac conditions associated with ECMO were congenital heart disease (25.3%), cardiogenic shock (23.6%), and congestive heart failure (16.2%). The common respiratory associations were sepsis (36.2%), pneumonia (35.6%), and asthma (15.4%). Patients who survived were more likely to have diagnoses of asthma, bronchiolitis, myocarditis, pneumonia, and sepsis. Acute kidney injury (51.5%), disseminated intravascular coagulation (22.5%), and surgical site bleeding (12.7%) were the commonly associated complications. The trend for yearly survival rates was not statistically significant (linear p-trend = 0.38). Pediatric ECMO continues to be associated with notable mortality and complication rates. We did not observe a meaningful trend for the yearly survival rates over the study period, and over one-sixth of survivors were readmitted within 30-days. More research is needed to identify patients at high risk of mortality and readmissions, to help target resources more efficiently and improve survival.

Preparing for participation in the centers for Medicare and Medicaid Services' bundle care payment initiative-advanced for major bowel surgery.

As healthcare costs rise, there is an increasing emphasis on alternative payment models to improve care efficiency. The bundled payment represents an alternative reimbursement model gaining popularity within the surgical sphere. We aimed to assess where the largest opportunities for care improvement lay and how best to identify patients at high risk of suffering costly complications. We utilized itemized CMS claims data for a retrospective cohort of patients between 2014 and 2016 who met inclusion criteria for the Major Bowel Bundled Payment Program and performed a cost analysis to identify opportunities for improved care efficiency. Based on the results of this cost analysis, we identified readmissions as a target for improvement. We then assessed whether the American College of Surgeons' National Surgical Quality Improvement Program surgical risk calculator (ACS NSQIP SRC) could accurately identify patients within our bundled payment population who were at high risk of readmission using a logistic regression model. Our study cohort included 252 patients. Readmissions accounted for 12.8% of the average total care episode cost with a coefficient of variation of 2.72, thereby representing the most substantial, non-fixed cost for our bundled payment patients. Patients readmitted within their 90-day care episode were 2.53 times more likely to be high-cost (>$60,000) than patients not readmitted. However, the ACS NSQIP SRC did not accurately predict patients at high risk of readmission within the first 30 days with an AUROC of 0.58. Our study highlights the importance of reducing readmissions as a central component of improving care for bowel surgery bundled payment patients. Preventing such readmissions requires accurate identification of patients at high risk of readmission; however, current risk prediction models lack the adaptability necessary for this task.

Validation of the Hospital Score as Predictor of 30-Day Potentially Avoidable Readmissions in a Brazilian Population: Retrospective Cohort Study

Background: Hospital readmissions are associated with several negative health outcomes and higher hospital costs. The HOSPITAL score is one of the tools developed to identify patients at high risk of hospital readmission, but its predictive capacity in more heterogeneous populations involving different diagnoses and clinical contexts is poorly understood. Objective: The aim of this study is to externally validate the HOSPITAL score in a hospitalized Brazilian population. Methods: A retrospective cohort study was carried out with patients over the age of 18 years in a tertiary university hospital. We performed a refitted HOSPITAL score with the same definitions and predictive variables included in the original HOSPITAL score and compared the predictive capacity of both. The receiver operating characteristic was constructed by comparing the performance risk forecasting tools measuring the area under the curve (AUC). Results: Of the 47,464 patients, 50.9% were over 60 years and 58.4% were male. The frequency of 30-day potentially avoidable readmission was 7.70%. The accuracy of original and refitted HOSPITAL scores was close, although statistically different ([Formula: see text]), AUC: 0.733 (CI 95%: 0.718, 0.748) and 0.7401 (CI 95%: 0.7256, 0.7547), respectively. The frequency of 60, 90, 180, and 365-days readmissions ranged from 10.60% to 18.30%. Conclusion: The original and refitted HOSPITAL score is a useful tool to identify patients at high risk of 30-day potentially avoidable readmission, in patients with different diagnoses in public tertiary hospitals. In this sense, our study expands and reinforces the usefulness of the HOSPITAL score as a tool that can be used as part of intervention strategies to reduce the rate of hospital readmission.

Risk factors associated with readmission within 30 days following stoma surgery: Development of a 'traffic light' prediction model.

Patients undergoing stoma surgery have a higher risk for early readmission. Some patients may benefit from closer postdischarge surveillance to provide early detection of complications and timely intervention. However, there is a paucity of validated tools to identify those at higher risk of readmission. Here, we aim to determine the independent risk factors associated with readmission within 30 days of discharge following stoma surgery, attempt to validate previous predictive models and develop a novel prediction tool. A retrospective review of 423 patients who underwent ileostomy or colostomy stoma formation at a UK tertiary colorectal centre between 2019 and 2021. Univariate, multivariate and logistic regression analyses were used to analyse a large number of demographics and risk factors and the association with readmission. This study cohort included 220 ileostomy and 203 colostomy patients. Of these, 87 (20.6%) were readmitted within 30 days of discharge following index surgery. A large number of demographics were evaluated for association with readmission. Readmission was associated with chronic heart failure (p< 0.05), postoperative stoma-specific complications (bleeding, p= 0.02; high-output stoma, p= 0.01) and those with a loop ileostomy (34.0% vs. 18.6%; p= 0.01). A previous predictive model was ineffective in this cohort, therefore a simplified 'traffic light' risk scoring system was developed and found to have improved discrimination. Readmission following stoma formation is associated with key variables that could provide the means to triage, risk score and potentially predict readmissions. We found that a novel and simplified scoring system may provide improved prediction.

A transition of care model from hospital to community for Hispanic/Latino adult patients with diabetes: design and rationale for a pilot study

BackgroundThe Hispanic/Latino population is disproportionately affected and has a higher risk of developing diabetes than their non-Hispanic White counterparts and worse diabetes-related outcomes. Diabetes continues to be an economic burden. This economic burden is partially due to the significantly higher rates of hospital readmission for individuals with diabetes. People with diabetes, particularly those who are members of racial/ethnic minority groups, are at higher risk for readmission and emergency department (ED) visits. Despite recommendations regarding transition of care, an optimal approach to the transition of care for ethnic/minority patients remains unclear.MethodsThe study population includes self-identified Hispanic/Latino adults with diabetes. We have two aims: (1) designed and developed a transition of care model and (2) pilot test the newly developed transition of care model. For aim 1, semi-structures interviews conducted with patients and providers. For aim 2, patients admitted to the hospital enrolled to receive the newly designed transition of care model. For aim 1, patients and providers completed a short questionnaire. For aim 2, patients completed a set of questionnaires including demographic information, medical history, sociocultural, and social support. The primary outcome for aim 2 is emergency department visit within 30 days post-discharge. The secondary outcome is 30- days unplanned readmissions. Feasibility outcomes include the number of participants identified, number of patients enrolled, number of participants who completed all the questionnaires, number of participants with a 30-day follow-up call, and number of participants who completed the 30-day post-discharge questionnaire. Due to the COVID-19 pandemic, the study design was adapted to include the Plan-Do-Study-Act framework to adjust to the ongoing changes in transition of care due to the pandemic burden on the health care systems.ConclusionTransition of care for Hispanic/Latino patients with diabetes remains a major area of interest that requires further research. The pandemic required that we adapted the study to reflect the realities of health care systems during a time of crisis. The methods share in this manuscript can potentially help other investigators as they designed their studies.Trial registrationClinicalTrials.gov identifier NCT04864639. 4/29/2021. https://clinicaltrials.gov/ct2/show/NCT04864639.

Stability of Blood Eosinophils in COPD with Multiple Acute Exacerbations Within 1 Year and Its Relationship with Prognosis.

The relationship between increased blood eosinophils (EOS) and the prognosis of patients with chronic obstructive pulmonary disease (COPD) remains controversial. We aimed to explore the stability of blood eosinophils in patients with multiple hospitalizations for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) over a 1-year period and its relationship with readmission rates and mortality. Prospectively include patients with at least 2 hospitalizations for AECOPD in 1 year between June 2019 and December 2021. Using 150 cells/ul as the cut-off value, the study population was divided into EOS, non-EOS, and fluctuating groups based on the longitudinal stability of blood EOS. The relationship between blood EOS and readmission rate and mortality was analyzed according to the 6-month follow-up after hospital discharge. A total of 202 patients were included. 48, 108, and 46 patients were in the EOS, non-EOS, and fluctuating groups, respectively. The stability of blood EOS at 1 year was 77.2%. The risk of death was lower in the EOS group compared to the non-EOS group (HR=0.323, 95% CI 0.113-0.930, P =0.036). The risk of readmission was lower in both the EOS group (HR=0.486, 95% CI 0.256-0.923, P =0.027) and the non-EOS group (HR=0.575, 95% CI 0.347-0.954, P = 0.032) than in the fluctuating group. The blood EOS of COPD patients is relatively stable over 1 year. Patients with consistently high blood EOS had a lower risk of all-cause mortality after discharge; patients with fluctuating blood EOS had a higher risk of readmission.

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients

Healthcare regulatory agencies have mandated a reduction in 30-day hospital readmission rates and have targeted COPD as a major contributor to 30-day readmissions. We aimed to develop and validate a simple tool deploying an artificial neural network (ANN) for early identification of COPD patients with high readmission risk. Using COPD patient data from eight hospitals within a large urban hospital system, four variables were identified, weighted and validated. These included the number of in-patient admissions in the previous 6 months, the number of medications administered on the first day, insurance status, and the Rothman Index on hospital day one. An ANN model was trained to provide a predictive algorithm and validated on an additional dataset from a separate time period. The model was implemented in a smartphone app (Re-Admit) incorporating four input risk factors, and a clinical care plan focused on high-risk readmission candidates was then implemented. Subsequent readmission data was analyzed to assess impact. The areas under the curve of receiver operating characteristics predicting readmission with ANN is 0.77, with sensitivity 0.75 and specificity 0.67 on the separate validation data. Readmission rates in the COPD high-risk subgroup after app and clinical intervention implementation saw a significant 48% decline. Our studies show the efficacy of ANN model on predicting readmission risks for COPD patients. The AI enabled Re-Admit smartphone app predicts readmission risk on day one of the patient’s admission, allowing for early implementation of medical, hospital, and community resources to optimize and improve clinical care pathways.

Readmission and adverse outcomes after percutaneous coronary intervention in patients with dementia.

As the population ages, clinicians increasingly encounter ischemic heart disease in patients with underlying dementia. Therefore, we quantified differences in inhospital adverse events and 30-day readmission rates among patients with and without dementia undergoing percutaneous coronary intervention (PCI). Using the National Readmissions Database 2017-2018, we identified 755,406 index hospitalizations in which PCI was performed, of which 17,309 (2.3%) had a diagnosis of dementia. After propensity score matching patients with and without dementia, we assessed 30-day readmission and inhospital adverse events by Cox proportional hazards and logistic regression modeling and compared them with those of other common cardiac (pacemaker placement [PP]) and noncardiac (hip replacement surgery [HRS]) procedures. Thirty-day readmission was significantly higher in patients with dementia than patients without dementia (hazard ratio [HR] 1.67, 95% confidence interval [CI] 1.60-1.74). Patients with dementia also experienced higher odds of delirium (odds ratio [OR] 4.37, CI 3.69-5.16), inhospital mortality (OR 1.15, CI 1.01-1.30), cardiac arrest (OR 1.19, CI 1.01-1.39), acute kidney injury (OR 1.30, CI 1.21-1.39), and fall (OR 2.51, CI 2.06-3.07). On multivariable Cox modeling, dementia independently predicted 30-day readmission (HR 1.14, CI 1.07-1.20). The higher readmission risk with PCI (11%) among those with dementia was similar to that of patients undergoing PP (10%), but lower than in those undergoing HRS (41%). Patients with dementia who undergo PCI experience significantly increased rates of inhospital delirium, mortality, kidney injury, falls, and 30-day readmission. These adverse outcomes should be considered during shared decision-making with patients and their families.

Radical prostatectomy readmissions: Causes, risk factors, national rates, & costs

IntroductionReadmissions have substantial clinical and financial impacts on the healthcare system. Radical prostatectomy (RP) is considered a standard treatment in the management of clinically localized prostate cancer. Yet, there is a paucity of research evaluating readmissions for RP in a national dataset. Patients and methodsPatients with histologically confirmed prostate cancer managed with RP were identified within the 2016 to 2018 Nationwide Readmissions Database. Patient factors, facility factors, and surgical characteristics were evaluated for associations with readmission using univariable and multivariable analyses. ResultsA total of 133,727 patients receiving RP were identified. Early (30-day) and late (31–90-day) readmission rates were 4.2% and 1.8% respectively. The most common cause of early readmission was postoperative digestive system complication (10%) and the most common cause of late readmission was septicemia (13%). On multivariable logistic regression, factors associated with both early and late readmission include nonroutine discharge at index (early: OR 1.877, 95% CI 1.667–2.113; late: OR 1.801, 95% CI 1.490–2.183), and circulatory system comorbidity (early: OR 1.29, 95% CI 1.082–1.538); late: OR 1.515, 95% CI 1.157–1.984). ConclusionsOur findings regarding factors associated with readmission provide insight for RP counseling and may inform postoperative care pathways. Elucidation of readmission trends may allow the identification and proactive management of patients at higher risk for readmission.

Characteristics of Lymphoma Patients Experiencing Unplanned Readmissions at a Large Academic Medical Center

Background: Patients with lymphoma frequently experience hospital readmissions. Yet, limited data exist describing hospital readmissions in the lymphoma population. In this study, we sought to identify additional patient-specific characteristics associated with unplanned readmissions in patients with lymphoma at our institution. Methods: We conducted a retrospective, single-center study of patients with lymphoma who were age >18 and discharged from medical oncology wards from January-December 2019 (index admission). We categorized patients as having high-risk medications per the Institute for Safe Medication Practices, potentially inappropriate medications (PIMs) for older patients (age 65+) per the American Geriatric Society Beers Criteria, and polypharmacy and major polypharmacy as 5 or more and 10 or more scheduled medications on the discharge medication list, respectively. We performed univariate logistic regression to assess the association of with having an unplanned readmission within 30 days of discharge from index admission. The following factors, selected for clinical relevance, were assessed: age; gender; race; marital status; Charlson comorbidity score; lymphoma diagnosis and related parameters (stage, LDH, prior treatment ECOG PS); index hospitalization reason, duration, discharge disposition and prior healthcare utilization; activities of daily living (ADL) status; Morse fall score; laboratory values at time of discharge; discharge medication list; 30-day readmission status and readmission reason if applicable. Results: We included 207 patients (mean age 63.9 years, 38.6% female, 83.1% white race, 56.8% partnered), 65 (31.4%) of whom experienced a 30-day unplanned readmission. Patients experiencing a 30-day unplanned readmission were more likely to have ECOG PS >2 (36.9% vs 14.2%, p<0.001), higher age-adjusted IPI score (score 0, 3.3% vs 7.8%; score 1, 23.3% vs 35.7%; score 2, 55.0% vs 47.8%; score > 3, 18.3% vs 8.7%; p=0.012), received CAR T-cell therapy (9.2% vs 2.1%, p=0.032), index length of stay >5 days (55.4% vs 33.1%, p=0.003), been discharged to a skilled nursing facility (12.3% vs 3.5%, p=0.023), albumin <3.5 g/dL (78.5% vs 52.1%, p<0.001), and sodium <135 mEq/L (18.5% vs 8.5%, p=0.041). Patients discharged home were less likely to have a 30-day unplanned readmission (56.9% vs 76.8%, p=0.004). Age, sex, marital status, Charlson comorbidity score, aggressive versus non-aggressive lymphoma diagnosis, prior lymphoma treatment, discharge home with services or to inpatient rehabilitation, Morse fall score, bowel and/or bladder continence, independence in feeding or bathing, neutropenia (ANC<1.5 k/mcL), and Hgb <10 g/dL were not significantly associated with increased likelihood for unplanned readmission. Among medication-related factors, we found that patients with major polypharmacy on the index admission discharge medication list had a higher likelihood for unplanned readmission (56.9% vs 37.3%, p=0.009). Patients with discharge medication lists with polypharmacy (90.8% vs 81.7%, 00, long-acting opiates (10.8% vs 4.2%, p=0.082), and/or anticoagulants (26.2% vs 15.5%, p=0.071) had a higher likelihood for unplanned readmissions, but these differences did not reach statistical significance. Presence of PIMs was not associated with readmission. Conclusions: In this relatively large cohort of patients with lymphoma, we identified several risk factors for unplanned hospital readmissions. We found associations among unplanned readmissions and impaired performance status, receipt of prior CAR T therapy, higher age-adjusted IPI score, discharge location (home, skilled nursing facility, etc), lower albumin and sodium levels, as well as major polypharmacy on the discharge medication list. Future directions include further efforts to identify lymphoma patients at high risk for hospital readmissions and developing readmission prevention strategies in those patients. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Abstract 12963: Increased Readmission Rates in Cardiogenic Shock Patients Who Received Temporary Mechanical Circulatory Support: A Call to Targeted Action

Introduction: Patients hospitalized for cardiogenic shock who survive to discharge are at high risk of readmission. Patients requiring temporary mechanical circulatory support (tMCS) during their index admission reflect a high risk population. Therefore, we aim to quantify and compare the 90-day readmission trends for patients who did and did not receive tMCS at their index hospitalization for cardiogenic shock. Methods: From 2016 to 2019, patients with an admission diagnosis for cardiogenic shock were identified in the Florida State Inpatient Database. Patient characteristics, management at index admission, and causes of readmission were analyzed. Results: In total, 31,986 patients had an index admission for cardiogenic shock, of which 5,229 (16.3%) were treated with tMCS. In a 90-day cohort analysis of 18,317 patients, 6,119 (33.4%) patients were readmitted. Among patients that received tMCS during their index admission, 1,583 (51.2%) were readmitted compared with 4,536 (29.8%) that did not receive tMCS. Repeat readmissions for cardiogenic shock were present in 490 (15.9%) of patients who received tMCS at index admission compared with 232 (1.5%) of patients who did not have tMCS at index hospitalization. There was no statistically significant difference in the readmission rates across the four years of the study. Conclusions: Despite significant improvements in medical therapy and mechanical support in recent years, the rate of readmission after index admission for cardiogenic shock has not decreased. Trends in 90-day readmission demonstrate that patients who received tMCS at their index hospitalization for cardiogenic shock were not only more likely to be readmitted, but also more likely to be readmitted specifically with a repeat diagnosis of cardiogenic shock. Patients who require tMCS at their index admission for cardiogenic shock should be targeted as a high risk group to prevent readmission.

Abstract 10209: The Effect of the Dual SGLT1 and 2 Inhibitor Sotagliflozin on Cardiovascular Mortality and Hospital Readmission Rates for Heart Failure at 30 and 90 Days Post Discharge in Patients With Type 2 Diabetes Hospitalized for Worsening Heart Failure in the SOLOIST-WHF Trial

Introduction: SGLT inhibitors demonstrate reductions in CV mortality and HHF in patients with diabetes and chronic HF. Despite therapy, patients hospitalized for worsening HF (WHF) are at high risk of death or readmission. Hypothesis: The SOLOIST-WHF trial admitted patients for their index WHF event and initiated randomized therapy during hospitalization. This study evaluated the effect of the SGLT1 and 2 inhibitor sotagliflozin on 30- and 90-day CV death and HF readmission rates after discharge. Methods: Of 1222 randomized patients with T2D, 86% were admitted for their index WHF event. Of them, 46% (n = 563) started study drug prior to or at discharge. In post hoc exploratory analyses, CV death and HF readmission (HHF or urgent visits for HF) rates were determined 30- or 90-days post discharge. Comparisons were done by Cochran-Mantel-Haenszel test stratified by region and baseline LVEF (&lt;50%, ≥50%). Results: Treatment with sotagliflozin showed significant relative risk reduction of &gt;50% for readmission for non-fatal HF-related events or the composite of CV death and readmission for HF-related events within 30 or 90 days of discharge vs placebo (Table 1). The sotagliflozin safety profile in those windows was generally consistent with the overall study duration. Incidence of adverse events (AEs) and serious AEs was similar between groups. Increased incidence of diarrhea and volume-related AEs was noted with sotagliflozin. No appreciable eGFR change from baseline occurred at 30 days, but there was lower incidence of AEs linked to acute kidney injury with sotagliflozin. Conclusions: Sotagliflozin, given to patients with T2D prior to or at hospital discharge after an episode of WHF, was well tolerated and resulted in a significant &gt;50% reduction in CV mortality or readmission for a HF event at 30 and 90 days post-discharge.

Abstract 10757: Machine-Learning Models Based on Real-World Data to Predict Rehospitalization or Death After Acute Myocardial Infarction

Introduction: Prospectively identifying patient characteristics associated with myocardial infarction (MI) readmission would help target patients at high risk of recurrent events for improved clinical management. This study aimed to build and evaluate machine learning models based solely on real world data to identify predictors of rehospitalization or death within one year following acute MI. Methods: The study population consisted of adult patients who experienced a first hospitalization for MI between January 1, 2013 and December 31, 2017 in the Optum® Clinformatics® Data Mart, an administrative health claims database from a large US national managed care company. Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Deep Neural Network (DNN) models were developed and tested. Model variables included patient demographics, clinical characteristics, and specific treatments. Variable importance for model prediction was ranked. Results: Among 96,244 patients who had an initial MI, 15,925 (16.5%) had rehospitalization/death within a year. The LR, XGBoost, and DNN models performed similarly (area under the curve 0.762, 0.774, and 0.785 respectively). The XGBoost was selected as the primary model to assess the relative importance of the variables in predicting MI rehospitalization/death. Of the most impactful variables, older age, higher Charlson Comorbidity Index, presence of chronic kidney disease (all stages), previous heart failure, and no previous coronary arterial bypass graft were highly predictive of MI rehospitalization/death (Figure). Conclusions: The machine learning models developed in this study can be implemented within clinical systems to identify patients at high risk for MI readmission to improve care and decision-making. However, these models require external validation in other datasets. Furthermore, performance of the models might be improved by including information such as LDL-C levels and PCSK9 inhibitor use.

Evaluation of Pharmacy Resident-Driven Medication Reconciliation on Patients at High Risk of Hospital Readmission.

Purpose: Pharmacists play a key role in preventing medication errors during transitions of care and preventing hospital readmissions through medication reconciliation (MR) programs. This study retrospectively evaluated the implementation of a standardized pharmacy residentdriven MR program for patients at high risk for readmission as defined by the Hospital Readmissions Reduction Program (HRRP). Methods: This was a single-center, retrospective cross sectional study of a pharmacy resident-driven MR program including patients at high risk of readmission defined by HRRP. The primary objective was to determine the number of inpatient regimen interventions identified during the MR. Secondary objectives include severity of interventions, number of medication discrepancies identified, types of interventions and discrepancies identified, and all-cause hospital readmission rates within 30 days of discharge.. Results: Fifty-three high-risk patients were included in the study. Pharmacy intervention recommendations were accepted by prescribers for nine patients (9/53; 17.0%) with a total of 13 accepted inpatient regimen interventions. The two most commonly identified medication classes for interventions were anticonvulsants (3/13; 23.1%) and antidepressants (6/13; 46.2%). Discrepancies on the admission MR were identified for 46 (46/53; 86.8%) patients with a median of three discrepancies per patient (interquartile range 2-4). The most common type of discrepancy was an incorrect or unnecessary drug. The 30-day all-cause readmission rate was 35.8% (19/53) for the total patient Conclusion: A pharmacy-resident driven MR program provided value in clarifying prior to admission medications and may help prevent drugrelated adverse events.

Factors associated with racial differences in all-cause 30-day readmission in adults with cardiovascular disease: an observational study of a large healthcare system

ObjectiveTo examine factors contributing to racial differences in 30-day readmission in patients with cardiovascular disease (CVD).DesignPatients were enrolled from 1 January 2015 to 31 August 2017 and data were collected...

Evaluating Social Determinants of Health in a Mobile Integrated Healthcare-Community Paramedicine Program.

In 2018, the University of Maryland Medical Center and the Baltimore City Fire Department implemented a community paramedicine program to help medically or socially complex patients transition from hospital to home and avoid hospital utilization. This study describes how patients' social determinants of health (SDoH) needs were identified, and measures the association between needs and hospital utilization.SDoH needs were categorized into ten domains. Multinomial logistic regression was used to measure association between identified SDoH domains and predicted risk of readmission. Poisson regression was used to measure association between SDoH domains and actual 30-day hospital utilization.The most frequently identified SDoH needs were in the Coordination of Healthcare (37.7%), Durable Medical Equipment (18.8%), and Medication (16.3%) domains. Compared with low-risk patients, patients with an intermediate risk of readmission were more likely to have needs within the Coordination of Healthcare (RRR [95% CI] 1.12 [1.01, 1.24], p = 0.032) and Durable Medical Equipment (RRR = 1.13 [1.00, 1.27], p = 0.046) domains. Patients with the highest risk for readmission were more likely to have needs in the Utilities domain (RRR = 1.76 [0.97, 3.19], p = 0.063). Miscellaneous domain needs, such as requiring a social security card, were associated with increased 30-day hospital utilization (IRR = 1.23 [0.96, 1.57], p = 0.095).SDoH needs within the Coordination of Healthcare, Durable Medical Equipment, and Utilities domains were associated with higher predicted 30-day readmission, while identification documentation and social services needs were associated with actual readmission. These results suggest where to allocate resources to effectively diminish hospital utilization.

Prevalence and risk factors associated with readmission with acute kidney injury in patients receiving vancomycin outpatient parenteral antimicrobial therapy.

Vancomycin is commonly used during outpatient parenteral antimicrobial therapy (OPAT). Therapeutic drug monitoring (TDM) of vancomycin is recommended to ensure effective and safe therapy, as use has been associated with acute kidney injury (AKI). The MarketScan® Commercial Database was queried from 2010 to 2016 to identify patients aged 18-64 years discharged from an inpatient hospitalization on vancomycin OPAT. The primary endpoint was hospital readmission with AKI within 6 weeks of index hospital discharge. TDM was defined as at least one vancomycin level obtained during outpatient therapy. Bivariate analysis was used to examine associations with outcomes; significant factors were incorporated into a multivariable logistic regression model. A total of 14,196 patients were included in the study; median age was 54 years and 53.8% were male. Readmission with AKI occurred in 385 (2.7%) and was independently associated with chronic kidney disease (aOR 2.63 [95%CI 1.96-3.52]), congestive heart failure (1.81 [1.34-2.44]), chronic liver disease (1.74 [1.17-2.59]), hypertension (1.73 [1.39-2.17]), septicemia (1.61 [1.30-2.00]), and concomitant OPAT with IV penicillins (1.73 [1.21-2.49]) while skin and soft tissue infection (0.67 [0.54-0.83]) and surgical site infection (0.74 [0.59-0.93]) were associated with lower risk of readmission with AKI. TDM was not associated with lower risk of readmission with AKI. Chronic kidney disease, congestive heart failure, hypertension, chronic liver disease, septicemia, and concomitant OPAT with IV penicillins were significantly associated with higher risk of readmission with AKI during vancomycin OPAT.

The 30-day hospital readmission and mortality after surgery in colorectal cancer patients

PurposeHospital readmissions in the first weeks following surgery are common, expensive, and associated with increased mortality among colorectal cancer patients. This study is designed to assess the 30-day hospital readmission after colorectal cancer surgery and evaluate the risk factors that affect hospital readmission.MethodsThe study uses data from the Ministry of National Guard-Health Affairs Cancer Registry. All colorectal cancer patients who underwent colorectal cancer surgery between January 1, 2016, and November 31, 2021, were investigated. Factors examined were age, gender, marital status, Body Mass Index, Charlson Comorbidity Index, chemotherapy, radiotherapy, tumor stage, grade, site, surgical approach, length of stay, and discharge location. Kaplan–Meier curves were constructed to assess survival rates between readmitted and non-readmitted patients, and logistic regressions were performed to assess predictors of readmission.ResultsA total of 356 patients underwent tumor resection and 49 patients were readmitted within 30-day of index discharge. The most common reasons for hospital readmissions were gastrointestinal (22.45%), urinary tract infection (16.33%), and surgical site infection (12.24%). In the multivariable analysis, females were 89% more likely to be readmitted compared to males (odds ratio 1.89, 95% confidence intervals 1.00–3.58). Patients with distant metastatic tumors have higher odds of readmission (odds ratio 4.52, 95% confidence intervals 1.39–14.71) compared to patients with localized disease.ConclusionsColorectal cancer readmission is more common in patients with metastatic disease. Strategies to reduce readmission include planned transition to outpatient care, especially among patients with a high risk of readmission.

Risk factors and prediction score model for unplanned readmission among neonates with NRDS under one year of age: A retrospective cohort study.

ObjectiveThis study aimed to analyze the risk factors and establish a prediction score model for unplanned readmission among neonates with neonatal respiratory distress syndrome (NRDS) for respiratory problems under one year of age.MethodsThis retrospective cohort study enrolled 230 neonates with NRDS who were admitted between January 2020 and December 2020. The infants were classified into two subgroups based on whether they were readmitted for respiratory problems under one year of age: readmit group and non-readmit group. Readmission risk factors for NRDS were analyzed by logistic regression and a prediction score model was generated.ResultsAmong the 230 enrolled infants, 51 (22%) were readmitted, and 179 (78%) were not readmitted. In univariate analysis, compared with non-readmit group infants, readmit group infants had a significantly younger birth gestational age (31.9 ± 2.3 vs. 32.8 ± 2.5 weeks, p = 0.012), lower birth weight (1,713.7 ± 501.3 g vs. 1,946.8 ± 634.4 g, p = 0.007), older age at discharge (41.7 vs. 31.7 days, p = 0.012), higher proportion of necrotizing enterocolitis (NEC) (31% vs. 16%, p = 0.016), higher rate of blood transfusion (39% vs. 25%, p = 0.049), higher rate of postnatal dexamethasone (DEX) administration (28% vs. 9.5%, p = 0.001), and higher rate of home oxygen therapy (HOT) (57% vs. 34%, p = 0.003). Moreover, readmit group infants had significantly longer antibiotic days usage (12.0 vs. 10.0 days, p = 0.026) and a longer duration of hospital stay (41.0 vs. 31.0 days, p = 0.012) than non-readmit group infants. The multivariate logistic regression analysis showed that taking readmission as a target variable, postnatal DEX administration (OR: 2.689, 95% CI: 1.168–6.189, p = 0.020), HOT (OR: 2.071, 95% CI: 1.060–4.046, p = 0.033), and NEC (OR: 2.088, 95% CI: 0.995–4.380, p = 0.051) could be regarded as risk factors for readmission. A scoring model predicting readmission was administered with a positive predictive value of 0.651 (95% CI: 0.557–0.745, p = 0.002), with a sensitivity of 0.412 and a specificity of 0.888 at a cut-off of 3.5 points, which were evaluated on the receiver operating characteristic curve.ConclusionsPostnatal DEX administration, HOT, and NEC were risk factors for readmission of NRDS. NRDS infants with a predictive score of 3.5 points or more were at high risk for unplanned readmission.

Multidisciplinary Postoperative Care Pathway to Reduce Readmissions following Endoscopic Transsphenoidal Pituitary Surgery: Improving Quality of Patient Care.

Background Thirty-day unplanned readmission following endoscopic transsphenoidal pituitary surgery (ETPS) occurs in up to 14% of patients. Delayed hyponatremia is one of the most common causes, accounting for 30% of readmissions and often occurs within 1 week of surgery. The authors' prior retrospective review identified endocrinology follow-up as protective factor. Objectives Implementation of a multidisciplinary postoperative care (POC) pathway: (1) to reduce 30-day hospital readmissions following ETPS and (2) improve inpatient and outpatient coordination of care with endocrinologist. Methods This study is a single institution temporal cohort study of patients prior to (control cohort) and after implementation of the POC pathway (intervention cohort). The POC pathway utilized postdischarge 1 to 1.5 L/d fluid restriction, postoperative days 5 to 7 serum sodium, and endocrinology follow-up within 1 week of discharge to stratify patients into tiered hyponatremia regimens. Results A total of 542 patients were included in the study, 409 (75%) in the control cohort and 133 (25%) in the intervention cohort. All-cause readmission was significantly reduced following implementation of the POC pathway (14 vs. 6%, p = 0.015). Coordination with endocrinologist significantly increased in the inpatient (96 vs. 83%, p < 0.001) and outpatient (77 vs. 68%, p = 0.042) settings. Patients who were not in the POC pathway had the highest risk of readmission (odds ratio: 2.5; 95% confidence interval: 1.1-5.5). Conclusion A multidisciplinary POC pathway incorporating endocrinologist in conjunction with postdischarge weight-based fluid restriction and postoperative serum sodium levels can safely be used to reduce 30-day readmissions following ETPS.