Higher Readmission Research Articles

Point-of-care screening for heart failure with reduced ejection fraction using simple pulse oximetry

Abstract Background More than 64 million patients with heart failure (HF) are burdened with poor quality of life, frequent hospitalizations, and high readmission rates. While HF with reduced ejection fraction (HFrEF) accounts for approximately half of these patients, there is currently no effective tool for performing cost-effective and reliable early-stage screening. For this purpose, we derived and cross-validated a machine learning (ML) based system capable of identifying HFrEF based on analysis of the photoplethysmographic (PPG) signal recorded by a simple pulse-oximeter. Methods Patients attending a routine ambulatory check-up at an outpatient clinic were included in the study. Left ventricular ejection fraction was determined by standard transthoracic echocardiography. Diagnosis of HF was based on the latest ESC recommendations for HF. PPG was measured using a standard oximeter modified to record raw PPG signal. Before processing, the quality of the measured PPG signal was assessed by signal quality indices to determine whether the quality of signal was sufficient for further analysis. In brief, the signal processing involved noise removal, filtering for specific frequencies, automatic artifact detection and identification of individual heartbeats, on which specific parts of the signal and signal characteristics, necessary for further analysis, were identified. Four different ML processing pipelines with different approaches to feature selection and class balancing were deployed, validated using the K-fold cross-validation and compared. Results A total of 371 patients were included in the study: 54 patients with HFrEF (mean age 66.87 ± 14.35, 74.07% male) and 317 patients with HFmrEF (4), HFpEF (42) or no history of HF (271) (mean age 54.12 ± 17.79, 51.73% male). From the 4 ML pipelines, LightGBM with SMOTE + Edited Nearest Neighbors balancing had the highest cross-validated predictive performance for detection of HFrEF (c-statistics 0.8, CI 0.69 – 0.89). Conclusions Our results suggest that HFrEF can be effectively screened using a simple pulse oximeter, thanks to ML signal analysis. This solution could be adapted to a simple, non-invasive, readily available, time-saving, and inexpensive point-of-care diagnostic tool for primary care. Early disease detection and referral to a cardiologist (or HF specialist) would have a positive impact on patient morbidity and mortality.Receiver Operating Characteristics CurveModel Performance Metrics

Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals

Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.

Closing the loop: Utilizing advanced practice providers for a 7-day post hospital discharge follow-up of GI oncology patients at an academic cancer center.

304 Background: Acute care visits for oncology patients may be preventable, especially for high-risk patients with a recent hospitalization. During our 2023 Fiscal Year, we observed an approximate 22% rate for 30 day readmissions for oncology patients, with our gastrointestinal (GI) oncology patients having the most readmissions, at Stanford Cancer Center. Readmission review through clinician surveys suggested that about 28% of unplanned admissions may have been preventable, with root cause analysis identifying timely access to ambulatory symptom management as a major driver. Methods: We established a cancer transitional care (CTC) clinic to provide consistent and timely post-discharge follow-up for oncology patients. We initiated a pilot program for GI oncology patients (chosen due to high acuity and readmission rate) who had an unplanned admission to Stanford Hospital. We aimed to schedule a visit with an advanced practice provider (APPs) within 7 days of discharge. The A3 problem solving process was implemented weekly by a multidisciplinary team. We created a referral in our electronic medical record. Inpatient oncology teams were instructed to place this referral within 3 days before discharge, allowing for adequate patient and family education regarding the goals of CTC visits and ample time for the post-discharge CTC visit to be scheduled prior to patient discharge. Prior to CTC visits, pharmacists performed outreach to reconcile medications to reduce medication errors. Eight oncology APPs were trained regarding the focused nature of a CTC visit, including symptom management, medication reconciliation, and goals of care discussions. Results: At baseline, from June 1 and November 30th, 2023, 61% of patients had follow-up within 7 days of discharge and 12% of patients with none. Between December 13, 2023 and April 19, 2024, CTC implementation resulted in a total of 220 referrals for GI oncology discharges, yielding 144 CTC clinic visits. Resulting in 66.1% of patients having follow up within 7 days of discharge and 9% of patients had no follow up scheduled. 79% of visits were within 7 days of discharge, representing a 29% increase in our goal scheduling timeline. The average time between discharge date and CTC visit was 6.2 days. Sixty six patients also received detailed pharmacy reviews with medication reconciliation and addressing medication access issues. Conclusions: Through our CTC clinic, we standardized post-discharge follow-up access for GI oncology patients, reducing variability in follow up. We plan to disseminate this model across medical oncology subspecialty clinics. Barriers included standardization of follow up with clinical team and expectations for the visit for both patients and clinicians. Future directions include integrating same day symptom management and urgent care access.

Determining Inclusion Criteria For Post-Acute Care Rehabilitation – Home Alone Is Not A Barrier To Success

Abstract Background Older adults discharged after acute hospitalisation are at risk for functional decline and adverse health outcomes. With population ageing, traditional approaches to rehabilitation with extended acute care stay are not sustainable. Successful outcomes in home-based rehabilitation relies on appropriate patient selection criteria. Some evidence suggests that older adults living alone are at highest risk for poor health-related outcomes including prolonged hospital admission and high readmission rates due to lack of cohabitants to support their needs. Methods Retrospective case analysis of consecutive participants in an older persons home based rehabilitation programme were included. Demographics including age, gender, frailty status, living arrangements, length of inpatient stay savings (days) and functional improvement (FIM) were measured. Logistical regression was used to estimate the relationship between living alone and functional gains. Results Eighty-three consecutive cases were reviewed, 10 cases excluded as readmitted (n=8), or clinically deteriorated and unable to complete the programme (n=2). Readmission rate (30 day) was 9.6%. Of 73 cases, 42.5%(n=31) lived alone, and 57.5%(n=42) co-habited during the programme. Mean (SD) clinical frailty score for those living alone was 5.26(1.03) and 4.98(1.07) for those not living alone. Improvements in FIM were similar in both groups, mean (SD) 11.4(7.2) versus 9.0(6.0). There was no statistically significant difference in change in FIM score between groups (-2.40(-5.56 to 0.77), p=0.136). Of 18 items in FIM, greatest benefit was seen in locomotion, bed/chair/toilet transfers, grooming, dressing and bathing skills. Mean bed-day savings was 6.45(2.07) acute inpatient bed-days per participant. Conclusion Our findings demonstrate that older adults who live alone benefit equally well from home based rehabilitation programme. Therefore, living alone should not limit enrolment on such programmes. Rehabilitation gains in the home environment were greatest in parameters that are challenging to target in acute care settings.

External validation and comparative analysis of the HOSPITAL score and LACE index for predicting readmissions among patients hospitalised with community-acquired pneumonia in Australia.

ObjectiveCommunity-acquired pneumonia (CAP) is a leading cause of emergency hospitalisations globally and is associated with high readmission rates. Specific score systems developed for all medical conditions such as the HOSPITAL score and the LACE index can also usefully predict CAP readmissions. However, there is limited evidence regarding their performance in the Australian healthcare settings.MethodsThis multicentre retrospective study analysed adult CAP discharges from two metropolitan hospitals in South Australia between 1 January 2018 and 31 December 2023. Data for determining the HOSPITAL score and the LACE index were derived from electronic medical records. Demographic characteristics of patients readmitted within 30 days were compared with those who were not readmitted. The scores were evaluated for overall performance, discriminatory power and calibration, with discriminatory power assessed using the concordance statistic (C-statistic).ResultsOver 6years, 7245 CAP discharges were recorded, with 1329 (18.3%) readmissions within 30days. The mean (s.d.) age of the cohort was 74.4 (17.8) years. Readmitted patients were more likely to have multiple morbidities and frailty than those not readmitted (P<0.05). They also had a higher mean number of emergency department presentations and hospital admissions in the previous year and a longer initial hospital stay (P<0.05). Overall, the mean (s.d.) HOSPITAL score and LACE index were 3.4 (2.1) and 9.3 (3.6), respectively. Among readmissions, 28.4% occurred in patients with a HOSPITAL score >4 (intermediate and high-risk group), while 25.8% occurred in patients in the high-risk LACE category (LACE index>10). The C-statistic for the HOSPITAL score and LACE index was 0.62 (95% CI 0.61-0.64) and 0.63 (95% CI 0.61-0.65), respectively, with no significant difference in the area under the receiver operating characteristic curves (P>0.05).ConclusionsThe predictive abilities of the HOSPITAL score and the LACE index for CAP readmissions are modest and comparable in an Australian setting.

Patients with dementia or frailty undergoing major limb amputation have poor outcomes

ObjectiveMajor lower limb amputation is a disfiguring operation associated with impaired mobility and high near-term mortality. Informed decision-making regarding amputation requires outcomes data. Despite the co-occurrence of both chronic limb-threatening ischemia (CLTI) and Alzheimer’s disease and related dementias (ADRD), there is sparse data on the outcomes of major limb amputation in this population and the impact of frailty. We sought to determine mortality, complications, readmissions, revisions, intensive interventions (eg, cardiopulmonary resuscitation), and other outcomes after amputation for CLTI in patients living with ADRD looking at the modifying effects of frailty. MethodsWe examined Medicare fee-for-service claims data from January 1, 2016, to December 31, 2020. Patients with CLTI undergoing amputation at or proximal to the ankle were included. Along with demographic information, dementia status, and comorbid conditions, we measured frailty using a claims-based frailty index. We dichotomized dementia and frailty (pre-frail/robust = “non-frail” vs moderate/severe frailty = “frail”) to create four groups: non-frail/non-ADRD, frail/non-ADRD, non-frail/ADRD, and frail/ADRD. We used linear and logistic regression via generalized estimating equations in addition to performing selected outcomes analyses with death as a competing risk to understand the association between dementia status, frailty status, and 1-year mortality as our primary outcome in addition to the postoperative outcomes outlined above. ResultsAmong 46,930 patients undergoing major limb amputation, 11,465 (24.4%) had ADRD and 24,790 (52.8%) had frailty. Overall, 55.9% of amputations were below-knee. Selected outcomes among frail/ADRD patients undergoing amputation (n = 10,153) were: 55.3% 1-year mortality 29.6% readmissions at 30 days, and 32.3% amputation revision/reoperation within 1 year. Of all four groups, those in the frail/ADRD had the worst outcomes only for 1-year mortality. ConclusionsFirst, patients with ADRD or moderate/severe frailty suffer an array of very poor outcomes after major limb amputation for CLTI, including high mortality, readmissions, revision, and risks of discharge to higher levels of care. Second, there is a complex relationship between outcome severity and ADRD/frailty status. Specifically, frailty is more often than ADRD associated with the poorest results for any given outcome. These data provide important outcomes data to help align decision-making with health care values and goals.

Propelling Nurse-Led Structured Intervention to Enhance Self-Care among Patients with Chronic Heart Failure (PROACT-HF): A Cluster Randomized Controlled Trial Study Protocol.

Heart Failure (HF) is a common chronic disease that has a high readmission rate and is associated with worsening symptoms and major financial impacts. Disease management implemented during or after an HF hospitalization has been shown to reduce hospitalization and mortality rates. Particularly for outpatients, it is necessary to provide self-care interventions. Structured nurse-led support such as timely follow-ups, including phone calls, is beneficial for improving self-care assessments. Evidence for nurse-led support has been investigated but is less than conclusive. The aim of this study is to compare the effectiveness of a nurse-led structured intervention for outpatients with chronic HF against the usual medical care in terms of self-care behaviors and occurrence of symptom exacerbation or rehospitalization. This is a cluster-randomized controlled trial. A total of 40 facilities with certified HF nurses will be allocated to two-arm clusters at a 1:1 ratio, randomly to the intervention or usual care arms. A total of 210 participants will be assigned from the hospital. Participants will be adults aged 18 years or older diagnosed with chronic HF who are classified as Stage C according to the ACCF/AHA Heart Failure staging system. In the intervention group, patients will receive structured nursing support. This begins with weekly support, including phone calls, for the first month, then transitions to monthly support thereafter. The aim is to ensure the stability of their living conditions, promote medication adherence, and encourage self-management. In the control group, patients will receive the usual care. Primary outcomes will assess the improvement or continuation of self-care behavior as measured by changes in EHFScBS (European Heart Failure Self-Care Behavior Scale) scores. Secondary outcomes include occurrence of readmission within 30 days, 3 months, 6 months, and 1 year after discharge, duration of home care until readmission, and blood levels of BNP and NT-proBNP.

Digital Interventions to reduce hospitalization and hospital readmission for chronic obstructive pulmonary disease (COPD) patient: systematic review

BackgroundThe high readmission rate following chronic obstructive pulmonary disease (COPD) has created a significant global health challenge, with high healthcare costs and a growing burden of care. Digital interventions, including telemonitoring, Telehealth, web, or mobile-based, can address these challenges, but there has yet to be a systematic review of the impact of digital interventions on reducing hospital readmission for patients with COPD. The aim of this systematic review was to assess the effectiveness of digital interventions in lowering hospitalization and rehospitalization for patients with COPD.MethodsWe conducted a systematic literature search from PubMed and Scopus to identify randomized controlled trials (RCTs) published in English with outcomes related to hospital admission or readmission within 30 days of hospital discharge.ResultsThe search and selection process followed PRISMA guidelines and resulted in a final sample of 12 RCTs. We found mixed results. Of the 12 included studies, only four studies [mobile-based (n=1), Telehealth (n=1), Telemonitoring (n=2)] found positive effect of a digital intervention on hospital readmission. Seven studies [mobile-based (n=1), Telehealth (n=1), Telemonitoring (n=3), Web-based (n=2)] did not demonstrate clear evidence of a significant reduction in hospitalization rate. Based on these findings, the primary factors contributing to the outcome variation were differences in intervention components used and their operator, COPD severity, patient age, and sample size.ConclusionsSystematic review provides the first insight into the impact of digital intervention on hospital readmission among patients with COPD . While some studies showed positive results, the evidence is mixed, and further research is needed. COVID-19 has accelerated the use of digital intervention and created the opportunity for comprehensive research and investigation with more updated information and further impact on readmission.

Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.

In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Discharge Outcomes of Hospitalized Patients with New Onset Decompensated Cirrhosis

IntroductionThere are a lack of data describing outcomes and follow-up after hospital discharge for patients with newly diagnosed cirrhosis with complication on index admission. This study examines factors that influence outcomes such as readmission, follow-up, and mortality for patients with newly diagnosed cirrhosis.MethodsWe conducted a single-center retrospective chart review study of 230 patients with newly diagnosed cirrhosis from January 1st, 2020 through December 31st, 2021. We obtained demographics, clinical diagnoses, admission, and discharge MELD-Na, disposition, mortality, appointment requests rate, appointment show rate, and readmission.ResultsThe primary complications on admission were GI bleed (27%), ascites (25.7%), and hepatic encephalopathy (HE) (10.4%). Overall, the median length of stay (LOS) was 6 days, and the readmission rate was 27%. Out of 230 patients, 25 (10.9%) patients died while hospitalized while another 43 (18.6%) died after initial discharge within the two-year study period. Although there was a significant reduction of the MELD-Na from admission to discharge (p < 0.05), admission MELD-Na did not correlate with LOS and discharge MELD-Na did not predict readmission. Patients with HE had the highest median LOS, while patients with ascites had the highest readmission rate. The median time to an appointment was 32 days. When comparing discharge destinations, most patients were discharged to home (63%), to facilities (13.9%), or expired (10.9%). The average appointment show rate was 38.5%, although 70% of patients had appointment requests. Readmission rate and mortality did not differ based on appointment requests. No significant differences in outcomes were observed based on race, sex, or insurance status.ConclusionNew diagnosis of decompensated was found to have high mortality and high readmission rates. Higher MELD-Na score was seen in patients who died within 30 days. Routine appointment requests did not significantly improve readmission, mortality, increase appointment show rate, or decrease time to appointment. A comprehensive and specialized hepatology-specific program may have great benefits after cirrhotic decompensation, especially for those with newly diagnosed cirrhosis.

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Impact of frailty in hospitalized patients undergoing catheter ablation for atrial fibrillation.

Catheter Ablation (CA) is an effective treatment for atrial fibrillation (AF). However, frail elderly patients have been understudied due to their exclusion from landmark trials. Our study aims to evaluate outcomes in this population. The national readmission database (2016-2020) was queried, and frailty categories were defined based on hospital risk frailty scores ≦5 as low while >5 as intermediate/high frailty (IHF). We used multivariate regression and propensity-matched analysis to compare outcomes in patients undergoing CA for atrial fibrillation based on frailty index. Among 55 936 CAs for AF, 33,248 patients had low frailty, while 22 688 had intermediate/high frailty (IHF). After propensity matching (N 12 448), IHF patients were found to have higher adverse events, including mortality (3% vs. 0.3%, p < .001), stroke (1.9% vs. 0.2%, p < .001), acute heart failure (53.8% vs. 42.2%, p < .001), AKI (42.5% vs. 6.8%, p < .001), pericardial complications (2.8 vs. 1.6%, p < .001), respiratory complications (27.8 vs. 7.2%, p < .001), major adverse cardiovascular events (21.2 vs. 9.4%, p < .001) and net adverse events (76.7 vs. 55%, p < .001). IHF patients had higher readmissions at 30 (15.5 vs. 12.6%, p < .001), 90 (31.9 vs. 25.1%, p < .001), and 180-day (41 vs. 34.7%, p < .001) intervals. A higher median length of stay (LOS) (7 vs. 3 days, p < .001) and cost ($44 287 vs. $27 517, p < .001) at index admission and subsequent readmissions were also observed (p < .001). Intermediate/high frailty patients undergoing catheter ablation had worse clinical outcomes, higher healthcare burden, and readmission rates. LOS has decreased in both groups from 2016 to 2020; however, total cost has increased.

Influence of Comprehensive Nursing Care on Heart Failure Patient Management: A Systematic Review and Meta-Analysis.

Heart failure is a common chronic illness associated with high readmission rates and death. Comprehensive nursing care, management of symptoms, and psychological support are increasingly seen as critical components of successful heart failure therapy. This systematic review and meta-analysis aimed to determine the effect of comprehensive nursing care on clinical outcomes and quality of life in heart failure patients. We searched electronic databases (PubMed, PROSPERO, and Web of Science) for randomised controlled trials and observational studies on comprehensive nursing care treatments for heart failure patients. Data on readmission rates, mortality rates, and quality of life were obtained and examined. A total of 693 studies satisfied the inclusion criteria. A meta-analysis found that comprehensive nursing care reduced heart failure-related readmissions considerably when compared to conventional therapy (odds ratio [OR]: 0.77; 95% CI: 0.66-0.88, p = 0.0002). There was a significant difference in all-cause mortality (OR: 0.76; 95% CI: 0.60-0.97, p = 0.03), but comprehensive treatment enhanced quality of life and functional status (standardised mean difference -0.05, 95% CI: -0.21 to 0.10, p = 0.49). Comprehensive nursing care improves clinical outcomes and quality of life for heart failure patients. This study stresses the need to add comprehensive nurse interventions in normal heart failure treatment programmes.

Clinical and economic burden of suspected thrombotic thrombocytopenic purpura in US hospitals.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Severe ADAMTS13 deficiency (activity <10%) is the diagnostic threshold for thrombotic thrombocytopenic purpura (TTP) and is associated with various clinical symptoms, abnormal laboratory results, and long-term complications. This retrospective, noninterventional cohort study used the Premier Healthcare Database to identify patients with ADAMTS13 activity of <10% in US hospitals from January 1, 2016, through March 31, 2020. The objective was to describe patient characteristics, laboratory results, comorbidities (as measured by the Elixhauser comorbidity index), symptoms, length of stay, treatment patterns, mortality, inpatient costs, and readmission rates (summarized descriptively). Inpatient costs were calculated as total cost to the hospital. There were 211 patients with severe ADAMTS13 deficiency; 89% of patients had a TTP-related diagnosis, of whom 62% had a primary diagnosis of thrombotic microangiopathy. Over 80% of patients with available data had a decreased platelet count and elevated lactate dehydrogenase; schistocytes were detected in 99%. The most prevalent symptoms/complications were neurological, bleeding, and pain. Most patients (86%) had 2 or more Elixhauser comorbidities. Over 80% of patients received 1 or more TTP-related treatments, mostly plasma exchange. The mean length of stay was 11.5 days; 5% of patients died during their stay. Readmission rates at 30, 60, and 90 days were 20%, 26%, and 28%, respectively. The median (interquartile range) total inpatient cost to the hospital throughout the index admission was $33,221 ($19,431-$64,901). Patients with severe ADAMTS13 deficiency have substantial clinical burden, have high mortality and readmission rates, and generate high costs for hospitals. There is a high need for a therapy that replaces ADAMTS13, thus addressing the root cause of the symptoms and complications caused by this deficiency.

Prognostic Impact of Anemia in Patients Undergoing Aortic Valve Replacement: A Nationwide Study.

Patients undergoing aortic valve replacement (AVR) have high readmission rates. Several risk factors have been proposed as potential modifiable targets, including anemia. We examined the association between anemia at discharge and subsequent outcomes in these patients. Using Danish nationwide registries, we identified all patients who underwent AVR between 2015-2021, were alive at discharge (index date), and had an available hemoglobin (Hb) measurement taken between procedure and discharge. Patients were categorized as having i) moderate/severe anemia (Hb<6.2 mmol/L) or ii) no/mild anemia (Hb≥6.2 mmol). The one-year rates of all-cause mortality, all-cause hospital admission, heart failure (HF) admission, and atrial fibrillation (AF) admission were compared using multivariable Cox regression models. 8,614 patients were identified; 2,847 (33.1%) had moderate/severe anemia (60.2% male, median age 74) and 5,767 (66.9%) had no/mild anemia (68.0% male, median age 76). For these two groups, respectively, the cumulative one-year incidences of the outcomes were: i) all-cause mortality: 5.1% vs. 4.3%; ii) all-cause admission: 53.8% vs. 47.5%; iii) AF admission: 14.0% vs. 11.6%); iv) HF admission: 6.8% vs. 6.2%. In adjusted analysis, moderate/severe anemia, compared with no/mild anemia, was associated with higher rates of all-cause mortality (hazard ratio (HR) 1.27 [95%CI 1.02-1.58]), all-cause admission (HR 1.22 [95%CI 1.14-1.30]), and AF admission (HR 1.23 [95%CI 1.08-1.40]), but not HF admission (HR 1.09 [95%CI 0.91-1.31]). In patients undergoing AVR, moderate/severe anemia at discharge, compared with no/mild anemia, was associated with increased all-cause mortality, all-cause hospital admission, and AF admission, but not HF admission, at one-year post-discharge.

Evaluation of Long-Coronavirus Disease 2019 Cases Readmitted to Intensive Care Units Due to Acute Respiratory Failure: Point Prevalence Study.

Coronavirus disease 2019 (COVID-19) caused morbidity and mortality worldwide. Besides the acute effects, subacute and long-term effects are defined as long-COVID causing morbidity. The intensive care unit (ICU) data of long-COVID-19 cases were evaluated with the participation of 11 centers. Study was designed by Turkish Thoracic Society Respiratory Failure and Intensive Care Working Group to evaluate long COVID-19 patients. All patients followed up in the ICU with long-COVID diagnosis were included in point prevelance study. A total of 41 long COVID-19 patients from 11 centers were included in the study. Half of the patients were male, mean age was 66 ± 14, body mass index was 27 ± 5. Hypertension, diabetes mellitus, lung cancer, malignancy, and heart failure rates were 27%, 51%, 34%, 34%, and 27%, respectively. Eighty percent had received COVID vaccine. Patients had moderate hypoxemic respiratory failure. APACHE II, SOFA score was 18 (14-26), 6 (3-8), respectively. Forty-six percent received invasive mechanical ventilator support, 42% were sepsis, 17% were septic shock. Bilateral (67%), interstitial involvement (37%) were most common in chest x-ray. Fibrosis (27%) was detected in thorax tomography. Seventy-one percent of patients received antibiotherapy (42% carbapenem, 22% linezolid). Sixty-one percent of the patients received corticosteroid treatment. More than half of the patients had pneumonia and the majority of them used broad-spectrum antibiotics. Presence of comorbidities and malignancies, intensive care severity scores, intubation, and sepsis rates were high. Receiving corticosteroid treatment and extensive bilateral radiologic involvement due to COVID-19 might be the reasons for the high re-admission rate for the ICUs.

252 Analysis of the High Readmission Rate Following Elective Ureteroscopy – the Need for Improved Patient Information on Stent-Related Symptoms

252 Analysis of the High Readmission Rate Following Elective Ureteroscopy – the Need for Improved Patient Information on Stent-Related Symptoms

Development of a Complex Care Transition Team to Improve the Transition of Patients With Complex Care Needs to the Community.

Health care systems have historically struggled to provide adequate care for patients with complex care needs that often result in overuse of hospital and emergency department resources. Patients with complex care needs generally have increased expenses, longer length of hospital stays, an increased need for care management resources during hospitalization, and high readmission rates. Mayo Clinic in Arizona aimed to ensure successful transitions for hospitalized patients with complex care needs to the community by developing a complex care transition team (CCTT) program. With typical care management models, patients are assigned to registered nurse case managers and social workers according to the inpatient nursing unit rather than patient care complexity. Patients with complex care needs may not receive the amount of time needed to ensure an efficient and effective transition to the community setting. Furthermore, after transitioning to the community, patients with complex care needs often do not have access to care management resources if further care coordination needs arise. Acute care hospital in the US Southwest. The CCTT was composed of a registered nurse case manager, social worker, and care management assistant, with physician advisor support. The CCTT followed patients with complex care needs during their hospitalization and transition to the community for 90days after discharge. The number of inpatient admissions and hospital readmission rates were compared between 6months before and after enrollment in the CCTT program. Cost savings for decreased hospital length of stay, emergency department visits, and hospital readmissions were also determined. The CCTT selected patients according to a complex care algorithm , which identified patients who required high use of the health care system. The CCTT then followed this cohort of patients for an average of 90days after discharge. A total of 123 patients were enrolled in the CCTT program from July 1, 2019, to April 30, 2021, and 80 patients successfully graduated from the program. Readmission rates decreased from 51.2% at 6months before the intervention to 22.0% at 6months after the intervention. This reduced readmission rate resulted in a cost savings of more than $1 million. The outcomes resulting from implementation of the multidisciplinary CCTT highlight the need for a patient-specific approach to transitioning care to the outpatient setting. The patient social determinants of health that often contributed to overuse of health care resources included poor access to outpatient specialists, difficulty navigating the health care system due to illness or poor health literacy, and limited social support. The success of the CCTT program prompted the implementation of other specialty-specific pilot programs at Mayo Clinic in Arizona. The investment of time and resources, including dedicated personnel to follow patients with high hospital service usage, allows health care systems to reduce emergency department visits and hospital admissions and to provide patients with the best opportunity for success as they transition from the inpatient to outpatient setting.

Discharge to Skilled Nursing Facility Is a Risk Factor for Readmission: A Nationwide Propensity-Matched Study

IntroductionGeneral surgery procedures place stress on geriatric patients, and postdischarge care options should be evaluated. We compared the association of discharge to a skilled nursing facility (SNF) versus home on patient readmission. MethodsWe retrospectively reviewed the Nationwide Readmission Database (2016-2019) and included patients ≥65 y who underwent a general surgery procedure between January and September. Our primary outcome was 30-d readmissions. Our secondary outcome was predictors of readmission after discharge to an SNF. We performed a 1:1 propensity-matched analysis adjusting for patient demographics and hospital course to compare patients discharged to an SNF with patients discharged home. We performed a sensitivity analysis on patients undergoing emergency procedures and a stepwise regression to identify predictors of readmission. ResultsAmong 140,056 included patients, 33,916 (24.2%) were discharged to an SNF. In the matched population of 19,763 pairs, 30-d readmission was higher in patients discharged to an SNF. The most common diagnosis at readmission was sepsis, and a greater proportion of patients discharged to an SNF were readmitted for sepsis. In the sensitivity analysis, emergency surgery patients discharged to an SNF had higher 30-d readmission. Higher illness severity during the index admission and living in a small or fringe county of a large metropolitan area were among the predictors of readmission in patients discharged to an SNF, while high household income was protective. ConclusionsDischarge to an SNF compared to patients discharged home was associated with a higher readmission. Future studies need to identify the patient and facility factors responsible for this disparity.

Relationship between Baseline Serum Potassium and 1-Year Readmission in Pediatric Patients with Heart Failure: A Retrospective Cohort Study.

Pediatric heart failure (HF) is associated with high readmission rates, but the optimal serum potassium range for this population remains unclear. In this single-center retrospective cohort study, 180 pediatric patients hospitalized for HF between January 2016 and January 2022 were stratified into low-potassium (<3.7 mmol/L), middle-potassium (3.7-4.7 mmol/L), and high-potassium (≥4.7 mmol/L) groups based on the distribution of potassium levels in the study population. The primary outcome was readmission for HF within 1 year of discharge. Cox regression and restricted cubic spline models were used to assess the association between potassium levels and 1-year HF readmission rates. Notably, 38.9% of patients underwent 1 or more 1-year readmissions for HF within 1 year. The high-potassium group had a significantly higher readmission frequency than the middle-potassium group. In multivariate Cox regression models, potassium levels of ≥4.7 mmol/L were independently associated with increased 1-year readmission risk. A J-shaped relationship was observed between baseline potassium levels and 1-year readmission risk, with the lowest risk at 4.1 mmol/L. In pediatric patients with HF, a serum potassium level ≥ 4.7 mmol/L was independently associated with increased 1-year readmission risk. Maintaining potassium levels within a narrow range may improve outcomes in this population.