Risk Of Readmission Research Articles

Association of physical function with hospital readmissions among older adults: A systematic review.

Hospital readmissions pose significant burdens on healthcare systems, particularly among older adults. While efforts to reduce readmissions have historically focused on medical management, emerging evidence suggests physical function may also play a role in successful care transitions. However, there is a limited understanding of the relationship between functional measures and readmission risk. This systematic review aims to assess the association between physical function impairments and hospital readmissions. This systematic review aims to assess the association between physical function impairments and hospital readmissions. A systematic review was conducted following PRISMA guidelines, with studies identified through databases including PubMed, CINAHL, Embase, and others published January 1, 2010-December 31, 2022. Inclusion criteria encompassed observational studies of adults aged 50 and older in the United States, reporting readmissions within 90 days of discharge and assessing physical function across domains of the International Classification of Function model. Data extraction and risk of bias assessment were independently conducted by two authors using theScottish Intercollegiate Guidelines Network (SIGN) tool. Seventeen studies, representing 80,008 participants, were included in this systematic review. Patient populations included a wide array of medical populations, including general medical inpatients and those undergoing cardiac surgery. Across various functional measures assessed before or during admission, impairments were consistently associated with increased risk for hospital readmissions up to 90 days after admission. Measures of participation, including life-space mobility, were also associated with increased readmission risk. Functional impairments are robust predictors of hospital readmissions in older adults. Routine assessment of physical function during hospitalization can improve risk stratification and may support successful care transitions, particularly in older adults.

Role of Charlson comorbidity index in predicting intensive care unit readmission in patients with aortic aneurysm.

The purpose of this study was to investigate the value of the Charlson comorbidity index (CCI) in predicting intensive care unit (ICU) readmission in aortic aneurysm (AA) patients. Patient information came from the Medical Information Mart for Intensive Care- IV (MIMIC-IV) database. The relationship between CCI and ICU readmission was analyzed by restricted cubic spline, generalized linear regression, trend analysis, and hierarchical analysis. The clinical value of CCI in predicting ICU readmission was analyzed by receiver operating characteristic curve, decision curve analysis, XGBoost regression, and random forest regression. A total of 523 patients with AA were enrolled in the study. Patients with AA who were readmitted to the ICU had higher width of red blood cell distribution width (RDW) and higher CCI. CCI had better performance and clinical net benefit for predicting ICU readmission than RDW. An independent nonlinear relationship was found between CCI and ICU readmission. The trend analysis suggested that the risk of ICU readmission increased with higher CCI scores. The hierarchical analysis showed that their association was mainly found in surgery requirement populations regardless of AA types. Further, CCI was found to have better clinical value in predicting ICU readmission of thoracic aortic aneurysm (TAA) patients undergoing surgery. Age, renal disease, chronic lung disease, and dementia were important components of CCI in predicting ICU readmission of TAA patients undergoing surgery. CCI was independently associated with the ICU readmission of AA patients in a positive relationship and had more favorable prediction performance in TAA patients who underwent surgery.

The Relationship Between Surgeon Volume and Major Surgical Complications After Total Shoulder Arthroplasty: An Evaluation of 3177 US Orthopedic Surgeons.

Total shoulder arthroplasty (TSA), which includes anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA), is a technically demanding procedure and limited data exist on the relationship between case volume and complications. We sought to identify volume thresholds for TSA, aTSA, and rTSA at which risk of a major surgical complication decreased and to compare complications of patients treated by high-volume surgeons with those of patients treated by low-volume surgeons. Primary, elective TSAs (aTSA and rTSA) from January 1, 2016, to December 31, 2019, were identified in the Premier Healthcare Database. Multivariable logistic regression with restricted cubic splines modeled the relationship between annual TSA, aTSA, and rTSA surgeon volume and 90-day risk of major surgical complications. The 90-day complications of patients treated by high- and low-volume surgeons were compared. From 2016 to 2019, 3177 surgeons performed 78,639 TSAs. Increasing annual volume was associated with decreasing major surgical complication risk (thresholds: 50 TSAs, 25 aTSAs, and 36 rTSAs). High- and low-volume surgeons performed 24,595 and 54,044 TSAs, respectively. Patients of high-volume surgeons had lower risk of major surgical complications (adjusted odds ratio [aOR], 0.69; 95% CI, 0.56-0.84), myocardial infarction (aOR, 0.59; 95% CI, 0.36-0.97), and readmission (aOR, 0.71; 95% CI, 0.62-0.81). Importantly, 74.9% of high-volume and 93.0% of low-volume surgeon-year units had major surgical complication rates below the mean of all recorded surgeons. While most high- and low-volume surgeons had major surgical complication rates below the cohort average, increasing TSA volume was associated with a decreased risk of complications. [Orthopedics. 202x;4x(x):xx-xx.].

Low transthyretin concentration linked to adverse prognosis in elderly inpatients

BackgroundTo investigate the association between low transthyretin (prealbumin) concentration and mortality or readmission for all causes in elderly inpatients.MethodsThis analysis is based on a prospective cohort study conducted from September 2018 to April 2019 in ten wards of three tertiary referral hospitals in Beijing. Patients aged 65 years or older were enrolled, and their clinical data, laboratory test results, and auxiliary test results for patients were collected. A three-year follow-up was conducted with patients. Based on the 5th and 95th percentiles of transthyretin concentration, patients were split into three groups. The correlation between transthyretin concentration and the outcome of elderly hospitalized patients was investigated. The primary outcome of the research was death or readmission from all causes within three years.ResultsAmong the 636 individuals in the study, 335 (52.7%) were males, with a median age of 74.7 years (interquartile range [IQR]: 69.3–80.1). During a median follow-up period of 1,099.0 days (IQR: 1,016.3-1,135.0), 363 individuals (57.0%) experienced all-cause mortality or readmission events. Patients with transthyretin concentrations at or below the 5th percentile had a significantly increased risk of all-cause mortality or readmission compared to those with concentrations between the 5th and 95th percentiles (hazard ratio [HR]: 2.25; 95% confidence interval [CI]: 1.55–3.26). Even after adjusting for potential confounders, low transthyretin concentration remained an independent risk factor for poor prognosis in elderly inpatients (HR: 1.84; 95% CI: 1.03–3.28). Since women have consistently lower baseline transthyretin levels than men, we performed gender-specific analysis. We found that low transthyretin concentration is an independent risk factor for adverse prognosis in elderly male inpatients (HR: 2.99; 95% CI: 1.35–6.62) but not in females.ConclusionsLow transthyretin concentrations are associated with increased all-cause mortality or readmission in elderly inpatients, particularly among male patients.Graphical

Association of postoperative opioid type with mortality and readmission rates: multicentre retrospective cohort study.

Opioid treatment in postoperative pain management is crucial, but the impact of administration practices on outcomes is unclear. The hypothesis was that prescription trends remained stable over recent years, and that no difference in mortality and readmission risks is associated with prescription strategies. Electronic health records of surgical episodes in the Capital and Zealand Regions of Denmark from 2017 to 2021 were analysed. All opioids administered during postoperative admission were converted to oral morphine equivalents (OMEQs) and an average daily dose per patient was calculated. The opioid administered in the highest OMEQ dosages is considered the primary opioid strategy for the surgical case. Administration trends were analysed through linear regression, and Cox regression was used to calculate hazard ratios to assess dominant opioid strategies' association with 90-day mortality and readmission rates while controlling for confounders. A total of 183 317 patients met the inclusion criteria. Prescription trends remained steady during the study period. Multivariable analysis revealed increased readmission risk (HR 1.18, P < 0.001) of tramadol and tapentadol compared to morphine. They exhibited decreased 90-day mortality risk (HR 0.63, P < 0.001). Oxycodone had similar readmission risk (HR 1.009, P = 0.24) but lower 90-day mortality risk (HR 0.68, P < 0.001). Postoperative in-hospital opioid administration remained stable from 2017 to 2021. Tramadol/tapentadol had a higher risk of readmission but lower mortality risk. Oxycodone had comparable readmission but reduced mortality risk. This study provides a framework for future clinical trials assessing this potential impact of opioids in a targeted manner.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models

Background/Objectives: Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital. Methods: We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days. Results: Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies. Conclusions: Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate.

Prediction of Hospital Readmission Using the CORE and CORE+ Scores in Persons With COPD.

Identifying persons with COPD at high risk for hospital readmission provides opportunities for efficient and appropriate care to lower readmission risk. This study examined 30-d and 60-d hospital readmission prediction of the COPD-readmission (CORE) score and a newly developed CORE+ score. The relationship between CORE and CORE+ scores and ICU admission, endotracheal intubation, and in-hospital noninvasive ventilation (NIV) use was explored. A retrospective cohort study evaluated participants with spirometry-confirmed COPD from 2 Midwestern academic hospitals. The CORE score variables included eosinophil blood count, FEV1/FVC (<0.70) and FEV1 (≤40% of predicted), triple inhaler therapy, previous hospitalization, and presence of neuromuscular disease. Out-of-hospital NIV use and Charlson comorbidity index were added to compose the CORE+ score. Researchers assessed associations between variables and outcomes with chi-square test or Fisher exact test, compared results of CORE and CORE+ scores with Wilcoxon signed-rank test, assessed each score's 30-d and 60-d readmission predictive power with multiple logistic regression, and evaluated predictive accuracy with AUC of receiver operating characteristic using alpha < 0.05. Of 391 participants, the study found a 22% 30-d, all-cause readmission rate and a 16% 60-d, all-cause readmission rate. CORE+ score had better predictive accuracy than the CORE score for 30-d readmission (area under the curve 0.81 [95% CI 0.76-0.86]; AUC 0.73 [95% CI 0.66-0.79], P < .001) and 60-d readmission (AUC 0.77 [95% CI 0.71-0.83]; AUC 0.75 [95% CI 0.69-0.81], P < .001). Participants who used in-hospital NIV had higher median CORE+ scores (P = < .001). CORE and CORE+ scores demonstrated good to very good predictive accuracy for 30-d and 60-d readmission, respectively. Moreover, this study demonstrated a linear relationship between in-hospital NIV use and CORE+ score.

Using implementation science to encourage Serious Illness Conversations on general medicine inpatient services: An interrupted time series.

Serious Illness Conversations (SICs) are not consistently integrated into existing inpatient workflows. We assessed the implementation of multiple interventions aimed at encouraging SICs with hospitalized patients. We used the Consolidated Framework for Implementation Research to identify determinants for conducting SICs by interviewing providers and the Expert Recommendations for Implementing Change to develop a list of interventions. Adult patient encounters with a Readmission Risk Score (RRS) > 28% admitted to a general medicine service from January 2019 to October 2021 and without standardized SIC documentation in the prior year were included. A multivariable segmented logistic regression model, suitable for an interrupted time series analysis, was used to assess changes in the odds of standardized SIC documentation. Barriers included those associated with the COVID-19 pandemic, such as extreme census. Facilitators included the presence of the Speaking About Goals and Expectations program and palliative care consultations. Key interventions included patient identification via the existing Quality and Safety Dashboard (QSD), weekly emails, in-person outreach, and training for faculty and trainees. There was no significant change in the odds of standardized SIC documentation despite interventions (change in temporal trend odds ratio (OR) 1.16, 95% Confidence Interval (CI) 0.98-1.39). The lack of significant change in standardized SIC documentation may be attributed to insufficient or ineffective interventions and COVID-19-related challenges. Although patient identification is a known barrier to SICs, this issue was minimized with the use of the QSD and RRS. Further research is needed to enhance the implementation of SICs in inpatient settings.

A Readmission Risk Assessment Tool Is Not Predictive of 90-Day Readmission After Total Joint Arthroplasty at an Urban Tertiary Referral Hospital

A Readmission Risk Assessment Tool Is Not Predictive of 90-Day Readmission After Total Joint Arthroplasty at an Urban Tertiary Referral Hospital

Medicare Transitional Care Management Service Association with Readmissions and Mortality

Abstract In 2013 CMS introduced codes to reimburse outpatient providers for post-discharge Transitional Care Management (TCM). Understanding the implications of TCM reimbursement on outcomes is crucial for evaluating CMS’s investment and guiding future policy. We analyzed the association between physician organization (PO) TCM code use and post-discharge readmissions and mortality using 100% fee-for-service Medicare claims. Employing a difference-in-differences approach we compared 1131 “high-TCM” POs (top quartile of TCM code use from 2015-2017) to 1133 “low-TCM” POs (bottom quartile) from before (2012) and after (2015-2017) TCM code implementation, controlling for PO and beneficiary attributes and readmission risk. TCM code use was associated with decreased 30- and 90-day readmissions [-0.31 (95%CI -0.52, -0.09) and -0.42 (95%CI -0.71, -0.14) percentage points, respectively], but no significant difference in mortality. Year-by-year, 2017 saw greatest readmission reduction, with a slight mortality reduction in that year only. Readmission reductions were greatest in POs not affiliated with health systems, Accountable Care Organizations (ACOs), or academic medical centers, and least in those with fewer primary care physicians. Narrow, indirect interventions like fee-for-service TCM billing code reimbursement may have limited potential to improve post-discharge outcomes overall. However, small independent practices may derive somewhat greater benefit from this support for post-discharge care.

Explainable machine learning and multi-modal data for predicting readmission in patients with heart failure

Abstract Background Cardiovascular diseases (CVDs) are among the leading causes of mortality and morbidity globally, with an estimated 1 million hospital admissions for CVD in England in 2019/20, leading to 5.5 million bed days. In addition, patients with CVDs have a notably high rate of hospital readmission, with approximately one in four patients experiencing heart failure (HF) being readmitted within one month of discharge. We explored whether machine learning models trained on electrocardiogram (ECG) and patient electronic health records (EHRs) can support decision-making and risk management and ultimately improve health outcomes for HF patients. Purpose The project aims to develop a machine learning model to predict health outcomes for HF patients. To test the hypothesis, we trained and tested an explainable machine learning model to predict the risk of mortality and hospital readmission rate for HF patients. Methods We used EHR data and tabulated ECG records to train an XGBoost model to predict the risk of 30-day mortality and 30-day hospital readmission for HF patients. EHR data from a cohort of 2,868 patients with their ECG records were used for model development. We extracted a total of 78 features from the ECG data, such as heart rate variability (HRV), P-wave durations, and QT intervals. These ECG features were combined with coded and free-text EHR data (e.g., demographic information, diagnosis, medication history, and lab results) and used as tabular inputs to the XGBoost model. Of the 2,868 patients, an internal set of 574 patients stratified by age, sex, and health outcomes (30-day mortality and 30-day hospital readmission) were held out for testing. Results Among 2,868 HF patients with 10-year follow-up, 1065 patients were reported for mortality or hospital readmission within 30 days of discharge. When the machine learning model was trained on ECG data in addition to EHR data, the model was able to predict patient mortality and hospital readmission rate in the test set with an area under the curve (AUC) of 0.91 and 0.70, respectively. For mortality prediction, the model achieved a sensitivity and specificity of 0.81 and 0.82, respectively, when employing an operating point with minimum difference between the sensitivity and specificity. When the threshold was adjusted to favor sensitivity at 0.91, the model specificity decreased to 0.74. The machine learning model also identified high sensitive troponin levels, QT dispersion, and lactate dehydrogenase levels as features of high importance for predicting the outcomes for HF patients. Conclusions Our developed machine learning model was able to predict mortality and readmission risks in HF patients using multi-modal data. Upon further validation, the machine learning model has the potential to be tested for supporting clinical decision-making and risk management for HF patients.ROC CurveFeature Importance

Predicting hospital readmission in multimorbid patients with the use of AI: A systematic review

Abstract Background Multimorbid patients are at higher risk of hospital readmission due to the complex nature of their conditions. Identifying those who may be at particularly high risk would allow us to intervene early and potentially delay or prevent such readmissions occurring, thus reducing healthcare costs. We conducted a systematic review investigating the use of machine learning models in predicting 30-days unplanned hospital readmission of multimorbid patients. Methods This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines, and was registered with PROSPERO (CRD42022373937). We searched MEDLINE, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Included studies developed an AI model for prediction of 30-days hospital readmission for adult patients with two or more health conditions. The CHARM and PROBAST checklists for data extraction and bias assessment were used. The quality of included studies was assessed with the CASP checklist. Results Eighteen papers were eligible for inclusion. A total of 669 predictors were reported with an average of 37 used per model. Predictors were classified as modifiable and non-modifiable with the most common modifiable predictors being hospital length of stay, hypertension, anaemia and obesity. Average sensitivity and specificity of the models was reported to be 72% in 13 studies and 79.2 in 11 studies, respectively. Area under the curve (AUC) was reported in 13 studies, five of which were considered to have good discrimination power (AUC&amp;gt;0.8). Conclusions Machine learning models are capable of accurately predicting 30-days hospital readmission of multimorbid patients. Identifying modifiable predictors with highest weight allows for better planning and resource allocation to potentially reduce the risk of 30-days readmissions. An important area for future work would be the implementation of these high performance models in practice. Key messages • Hospital readmissions are preventable and identifying those at higher risk allows us to intervene early. • Machine learning models are capable of predicting 30-days readmission of multimorbid patients.

Sex-specific clinical outcomes and healthcare resource utilization in the 5 years following hospitalization for heart failure

Abstract Background Heart failure (HF) a leading cause of hospitalization, and sex differences in care have been described. Purpose We assessed sex-specific clinical outcomes and healthcare resource utilization following hospitalization for HF. Methods This was an exploratory analysis of patients hospitalized for HF across 10 Canadian hospitals enrolled in the Patient-Centred Care Transitions in HF pragmatic cluster-randomized trial. Primary outcome was all-cause mortality. Secondary outcomes included all-cause readmissions, HF readmissions, emergency department (ED) visits, and healthcare resource utilization. Outcomes were obtained via linkages with administrative datasets. Results The 4441 patients (50.7% female) had high event rates. At 5 years of follow-up, 63.6% male and 65.5% of male and female patients, respectively, had died (p=0.19); 85.4% and 84.4% of male and female patients, respectively, were readmitted with no sex differences in mean [SD] all-cause readmissions (males, 2.8 [7.8] and females, 3.0 [8.4], p=0.54) or HF readmissions (males, 0.9 [3.6] and females, 0.9 [4.5], p=0.80) per person. The mean (SD) annual total healthcare costs per patient was $80334 (116762) for males and $81010 (112625) for females, with no sex difference (p=0.90); however, there were sex differences in cost breakdown: males incurred greater costs from specialist, hemodialysis, and day surgical care, and females incurred greater costs from home visits and long-term care. Conclusions Males and females were at similarly high risk of mortality and readmissions, and had similar total healthcare costs following hospitalized for HF. Notwithstanding similar risks, males received relatively more specialist and invasive care, and females received relatively more supportive care.

Multimorbidity patterns in adults admitted to hospital with heart failure: an analysis of 175 million hospitalisation episodes

Abstract Background The prevalence of heart failure (HF) is increasing as a result of population ageing, improved survival following myocardial infarction and the widespread uptake of evidence-based HF treatments. Greater understanding of multimorbidity patterns in patients with HF may enable the development of treatment strategies that target multiple, related long-term conditions (LTCs), producing synergistic benefits. Purpose To characterise multimorbidity patterns in patients with HF and compare these to a matched population without HF. Methods A retrospective matched cohort study was conducted using the United States National Readmission Database (NRD), 2010–2020. International Classification of Disease (ICD) codes were used to identify HF and 92 other common LTCs. The most frequent combinations of LTCs were identified for individuals with HF and a control cohort of individuals of the same age and sex, with a non-HF-related hospital admission in the same year (matched in a 1:5 ratio). Multivariable regression analysis (adjusted for age, sex, socioeconomic status, tobacco use, year of hospitalisation, and hospital characteristics) was used to evaluate the association between multimorbidity burden, length of stay (LOS; negative binomial regression) and 30-day readmission (logistic regression). Results From a total of 175,388,410 hospitalisation episodes, 3,913,924 individuals with an HF-related hospitalisation were identified and matched to 19,552,954 controls (Figure 1a). The median (interquartile range, IQR) age of a patient admitted with HF was 74 (63–85) years; 1,856,268 (47.5%) were female. The most common comorbidities in those with HF were hypertension (87.2 vs. 72.8% in matched controls), chronic kidney disease (CKD; 55.3 vs. 34.6%), coronary artery disease (CAD; 53.7 vs. 31.0%), dyslipidaemia (46.9 vs. 39.7%), diabetes (46.6 vs. 33.9%), atrial fibrillation (AF; 43.0 vs. 23.0%) and anaemia (33.2 vs. 30.1%; p all &amp;lt; 0.001). Patients with HF had a greater number of LTCs compared with matched controls (median [IQR] 7 [5–9] vs. 6 [4–8]; p &amp;lt; 0.001). From 2010–2020, the number of LTCs in those with an HF admission increased (from 6 [4–8] in 2020, to 8 [6–10] in 2020; Figure 1b). The most common multimorbidity patterns in those with HF are presented in Figure 2. In the HF cohort, a greater multimorbidity burden was associated with a longer LOS (+6% for each additional LTC; 95% confidence interval [CI] 5–7%; p&amp;lt;0.001) and greater risk of 30-day readmission (adjusted odds ratio [aOR] for 6 vs. 0 LTCs: 1.15, 95% CI 1.08–1.22; aOR for ≥12 vs. 0 LTCs: 1.54, 95% CI 1.45–1.64). Conclusions Patients admitted to hospital with HF have a significant burden of multimorbidity, which has increased over time and is associated with increased length of stay and unplanned hospital re-admission. The high prevalence of multiple LTCs warrants the development of novel treatment strategies that target common multimorbidity patterns observed in HF.

Safe transition home after transcatheter aortic valve implantation feasibility (START-F) study: Very early self-reported function and recovery status after early discharge

Abstract Background Despite significant excellent outcomes and significant improvements in rapid recovery protocols and early discharge, rates of 30-day readmission after transcatheter aortic valve implantation (TAVI) remain stubbornly high. There can be numerous drivers for readmission in older people with valvular heart disease; the time of transition home is the period of greatest risk when patients are most vulnerable to repeat hospitalization. We sought to assess patients’ early recovery (&amp;lt; post-procedure day 3) to inform the evaluation of a nurse-led telephone intervention to mitigate the risks of 30-day readmission after early (1 day) discharge TAVI. Methods We conducted a prospective observational cohort feasibility study in five Canadian TAVI programs to ascertain acceptability, compliance, delivery of the intervention, recruitment and retention. In this preliminary report, we assessed key indicators of patients’ status at the time of discharge and functional status in their early recovery. Results We recruited 188 participants, mean age 81.6 years (SD=7.0), 41.5% women with baseline mean KKCQ 53.4 (SD=20.1). Patients were admitted on the day of the procedure, and underwent TAVI with a balloon-expandable (n=146, 77.6%) or self-expanding (n=30, 16.0%) device, with local anaesthesia +/- light sedation (n=171, 90.1%) or general anaesthesia (n=17, 9.0%). There was 1 in-hospital death (0.5%) and 182 (97.4%) were discharged home. At the time of the 3-day telephone follow-up, 168 (95.5%) reported that an individual had stayed with them during the first 24 hours [spouse: 85 (48.9%); friend: 77 (44.3%)], and 165 (93.3%; missing: 11) were able to complete all activities of daily living, including cooking their own meals (n=80, 45.5%). Self-reported energy was low/very low for 49 participants (26.1%), moderate for 93 (52.8%), and high/very high for 34 (18.1%), with 104 (55.3%) describing feeling somewhat or quite a bit better than before TAVI, and 24 (12.8%) somewhat or quite a bit worse. Bruising was present in 120 participants (68.6%) and 137 (78.8%) had removed their bandage as instructed. 18 participants were readmitted within 30 days, including 14 cardiac-related events. Conclusion This is one of the first reports of TAVI patients’ self-reported function following next-day discharge, providing novel evidence that most patients describe safe self-care and very early improved functional status. Research is needed to identify and support patients who are at higher risk of poorer trajectories of recovery and readmission, and report the feasibility of a nurse-led intervention to mitigate these risks.

Value-based versus usual care for heart failure patients leads to reduction in cost, hospital admissions and improved survival

Abstract Background OptumCare (OC) in our city, Nevada established a heart failure (HF) program comprised of a team of outpatient cardiologists including two who are board certified in advanced heart failure. Referred patients are enrolled in the HF program once HF is diagnosed by a cardiologist. Patients are managed by an interdisciplinary team of cardiologists, advanced practice clinicians (APC), registered nurse case managers (RNCM), and a social worker. Patients at higher risk for HF admissions are enrolled in remote patient telemonitoring. In contrast, OC in Arizona utilizes a "usual" care approach of fee-for-service contracted cardiology groups to manage HF patients in outpatient and in-patient settings. Purpose A head-to-head comparison was performed between the cohort of patients enrolled in HF program in OC Nevada (treatment group) and patients with HF diagnosis in OC Arizona (control group). The groups were compared on the following measures: medical cost, hospital admissions, and survival rates. Methods In this retrospective study, a 1:1 iterative propensity score (PS) matching was performed to closely approximate patient demographics and comorbidities, which resulted in 818 matched pairs (total n=1,636) of the patients with program enrollment or 1st HF diagnosis between March 1, 2019 and June 30, 2022. For Difference-in-Difference Analysis (Diff-in-Diff), generalized linear models were used on PS matched data to test the difference in difference in medical cost and utilizations changes between control and treatment group over a six month period. Log-Binomial regression was used to estimate the relative risk of post period 30-day readmission and hospice enrollment. Kaplan Meier Estimator was used to test the difference in survival rates over time. Results Average age of patients was 77.6 years old, and 49% were female. The treatment group resulted in significantly lower overall medical costs per member per month (PMPM), significantly less emergency room (ER) visits, less acute hospital and skilled nursing facility (SNF) admissions, and shorter overall hospital length of stays. The control group was twice as likely to be readmitted to the hospital. Lastly, the treatment group had a significantly better survival rate than the control group. 19% of the patients in the control group expired within 12 months of enrollment vs. 10% of the patients in the treatment group expired during the same time period. Average survival was 142 days in the control group vs. 200 days in the treatment group. Conclusions A value-based care approach involving a multidisciplinary team in an outpatient-based cardiology HF program resulted in statistically significant improvement in medical costs, acute care visits/admissions, and survival rates compared to "usual" care approach for HF patients.

Clinical evaluation and outcome in patients with heart failure receiving chemotherapy with different anti-cancer agents

Abstract Background Over the last few decades, advances in cancer therapy have prolonged the life expectancies of patients diagnosed with malignancies. Although traditional cytotoxic chemotherapy, molecularly targeted therapy, and immunotherapy have improved survival rates, off-target adverse effects such as complications of the cardiovascular system require attention. The optimal strategy for modern chemotherapy should be based on a comprehensive approach for patients with cancer and cardiovascular diseases. Therefore, cardio-oncology has received increasing attention owing to the cardiotoxic effects of anti-cancer therapies. Purpose To assess the clinical characteristics and outcomes of patients with heart failure (HF) who received chemotherapy compared with those of a matched cohort with HF who did not receive chemotherapy, using real-world HF data. Methods This study used the Diagnosis Procedure Combination database of the Japanese Registry of All Cardiac and Vascular Disease. We identified 1,328,113 patients hospitalized for HF between April 2012 and March 2021. We excluded 14,043 patients with unknown outcomes, non-emergency admissions, and unknown chemotherapy data. The primary endpoint was readmission. Thus, our study included 5,774 patients who received chemotherapy and 39,412 patients who did not. The propensity score (PS) was estimated using a logistic regression model, with chemotherapy as the dependent variable, and clinically score-matched analysis of 11,532 patients with HF with or without chemotherapy. Results Colon, lung, breast, and prostate cancers accounted for &amp;gt; 60% of all cancer types. After PS matching, 62.5% and 19.5% of patients with HF had prostate and breast cancer, respectively. After PS matching, readmission was more frequently observed in patients undergoing chemotherapy than those not undergoing chemotherapy (odds ratio [OR], 1.26; 95% confidence interval [CI] 1.17–1.36, p&amp;lt;0.0001). Treatment with genomic epidermal growth factor receptor tyrosine kinase inhibitors (OR, 1.69; 95% CI 1.39–2.07), taxane (OR, 2.95; 95% CI 2.11–4.12), anthracyclines (OR, 1.86; 95% CI 1.19–2.90), and fluorouracil agents (OR, 1.65; 95% CI 1.18–2.30) resulted in a significant risk of readmission. Moreover, patients treated with endocrine therapy before hospitalization had significantly higher readmission rates than the matched patients who did not receive chemotherapy (aromatase inhibitors: OR, 1.40; 95% CI 1.19–1.65: anti-androgens: OR, 1.39; 95% CI 1.27–1.52: luteinizing hormone-releasing hormone agonists: OR, 1.18; 95% CI 1.08–1.30 and gonadotropin-releasing hormone antagonists: OR, 2.17; 95% CI 1.68–2.79). Conclusions Medical providers need to monitor and follow-up patients with HF, depending on the characteristics of the anticancer agents and types of cancer. Furthermore, close collaboration among oncologists, cardiologists, and healthcare professionals will ensure the delivery of optimal care for patients with HF undergoing chemotherapy.

Healthcare utilization following acute myocardial infarction findings from RECORD MI registry

Abstract Background As therapeutic options and epidemiology have evolved rapidly, the contemporary healthcare utilization following acute myocardial infarction (MI) is not well known. Methods All patients admitted with MI between 01/2015 and 12/2021 in the Baylor Scott &amp; White health system were studied. Patient characteristics and outcomes including hospital readmissions, cardiovascular (CV)-related hospital readmissions, emergency department (ED) encounters, and outpatient visits were studied. Results Of 6804 patients, 6556 were discharged alive. Median age was 69 years, 60% were male, and 77% had non-ST elevation MI (NSTEMI); 1172 (22%) had an LVEF &amp;lt;40%, 493 (7.5%) were revascularized within 3 months of discharge, and 1090 (17%) had multivessel disease. The number of unique patients with first hospital readmission within 30-days, 3-months, and 12-months were 844 (13%), 1372 (21%) and 2306 (35%), respectively, with a higher risk in patients with NSTEMI, prior heart failure (HF), new HF, and LVEF &amp;lt;40%. ED visits at 12 months for any cause were 2401 (37%) and for CV cause were 1321 (20%), with a higher risk in patients with prior HF. Patients with at least 1 primary care visit within 12 months of discharge were captured for 4102 (63%), CV specialty visits for 5009 (76%), and non-CV visits for 3860 patients (59%), with similar distribution across subgroups (Table). Conclusions Post-MI patients had a high risk of readmissions, and ED and outpatient visits, especially amongst those with a prior or new HF diagnosis.

One-year prognosis difference of MINOCA versus MI-CAD in developing countries: insights from the Moroccan experience

Abstract Introduction The debate remains open as to the difference in prevalence of mortality and occurrence of acute events in patients with Myocardial infarction with non-obstructive coronary arteries (MINOCA) and others with Myocardial infarction with coronary arteries disease (MI-CAD) (1). Purpose We carried out an analysis of the clinical characteristics and one-year prognosis of MINOCA versus MI-CAD. Methods We conducted a prospective study for patients admitted for Acute coronary syndrome (ACS) during 2-years, to analyze the clinical and prognostic characteristics of patients with MINOCA versus MI-CAD. We defined 1-year all-cause mortality as the primary outcome, and the secondary outcome as a composite of 1-year readmission for myocardial infarction or acute heart failure (AHF). The primary and secondary outcomes was studied using a Cox proportional hazards regression model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for variables described in the literature. Results Our study included 1077 patients, 95.3% with MI-CAD and 4.7% with MINOCA. At admission, 71.1% patient were diagnosed STEMI and 28.9% with NSTEMI. The difference between the two groups was found on age (p&amp;lt;0.001), hypertension, diabetes with consecutive p-values of 0.007 and 0.001, as well as Ejection fraction (p&amp;lt;0.001). For the outcomes studied, we found, firstly before adjustment, that patients in the MINOCA group had a better prognosis than the MI-CAD group, for all-cause 1-year mortality (HR=0. 442; 95%CI 0.227-0.760; p=0.041) with a significant difference in the Kaplein Meier survival analysis with a log-rank test (p-value =0.02) (Figure 1), and a lower risk of readmission for ACS (HR=0.297; 95%CI 0.316-0.821; p=0.034), as well as for readmission for AHF (HR=0.555; 95%CI 0.329-0.836; p=0.045) also with a significant difference between the two groups in the Kaplein Meier analysis with consecutive log-rank test p-values of 0.033 and 0.035 (Figure 2). After adjustment for age, arterial hypertension, diabetes, active smoking, EF&amp;lt;40%, proximal LAD or LM as culprit artery, TIMI 0-I flow, CRP &amp;gt;300mg and cardiogenic shock during hospitalization, the results remained significantly in favor of a better prognosis for MINOCA: (HR=0. 672; 95%CI 0.376-0.872; p=0.003) for 1-year all-cause mortality, (HR=0.301; 95%CI 0.482-0.879; p=0.012) for readmission for ACS and finally (HR=0.604; 95%CI 0.309-0.812; p=0.039) for readmission for AHF. Conclusion Despite the ambiguity in the genesis of MINOCA, the short- and long-term prognosis of these patients remains generally favorable.Figure 1.Figure 2.

The relationship between daily blood glucose fluctuation and readmission in patients with acute coronary syndrome

Abstract Background Although the comprehensive diagnos and treatment of patients with acute coronary syndrome(ACS) has been greatly improved,the morbidity and mortality are still high. Previous literature has reported that about 70-75% of patients diagnosed with acute coronary syndrome have abnormal blood glucose metabolism1,and there existed gender differences. At present, most research results showed that elevated blood glucose is related to the prognosis of ACS. However, further intervention studies indicated that simply strengthen the reducement of blood glucose did not improve prognosis of these patients2. Glucose fluctuations, another dimension of blood glucose, reflect acute changes in blood glucose. However, there are few studies on the effect of blood glucose fluctuation on the prognosis of patients with ACS. Purpose This study aimed to explore the relationship between seven daily fluctuations of blood glucose and readmission in ACS patients. Methods In this study, a total of 664 patients admitted to our University from January 1, 2019 to December 31, 2019 were included and their blood glucose was monitored seven times a day during their hospitalization. The coefficient of variation of blood glucose (CV) was calculated from the results of seven daily blood glucose monitoring to represent the daily fluctuation of blood glucose. Univariate and multivariate regression analyses were performed to determine whether CV was an independent risk factor for re-hospitalization. In addition, the area under the curve (AUC) was used to evaluate the predictive efficacy of CV for outcome events. Finally, we performed a subgroup analysis based on gender and diabetes status to assess the association between CV and readmission in each subgroup. Results After a median follow-up of 21 months, the rate of readmission was 38.4%. Unstable angina pectoris (71.4%) and heart failure (16.4%) were the most common reasons for readmission. Patients with greater CV levels typically exhibited lower fasting blood glucose, higher post breakfast blood glucose, and higher admission blood glucose.The readmission rate increased with the increasing blood glucose variability after the cox regression analysis. Moreover, independent of glycated hemoglobin,CV could predict the risk of readmission in patients with ACS.When using 26.66% as the cutoff point of CV, the study population was divided into two groups and showed a significant differentiation for rehospitalization.In subgroup analysis, there was no significant association between seven daily blood glucose fluctuations and readmission in female and non-diabetes mellitus patients with acute coronary syndrome. Conclusions Besides some normal blood glucose index,seven daily fluctuations of blood glucose was associated with readmission in ACS patients. ACS patients who were male and combined with diabetes were more likely to be readmitted if their CV levels were greater.