Probability Of Readmission Research Articles

Association between postoperative atrial fibrillation after coronary artery bypass grafting and short-term clinical outcomes

BackgroundDespite advances in prevention and treatment, postoperative atrial fibrillation (POAF) is the most common type of complication undergoing cardiac surgery. This study aimed to identify the relationship between POAF and clinical outcomes after coronary artery bypass graft.MethodsIn this cross-sectional study, we retrospectively reviewed the medical records of 324 patients who had undergone coronary artery bypass grafting in an intensive care unit between 2010 and 2019 at a tertiary hospital in Korea. Propensity score matching was used to estimate a 1:1 match (without: with POAF) using seven covariates to overcome selection bias. Kaplan–Meier survival analysis and Cox proportional hazards modeling were performed to determine the effect on intensive care unit readmission and length of hospital stay.ResultsAfter controlling for covariates, 1:1 matching was performed for 91 patients in each group. The occurrence of postoperative atrial fibrillation was found to increase the probability of readmission to the intensive care unit, with a 23% reduced probability of readmission for every 10% increase in left ventricular ejection fraction. Multivariate analysis indicated that postoperative atrial fibrillation, chronic obstructive pulmonary disease as a comorbidity, and preoperative hemoglobin were factors affecting the length of hospitalization after surgery. The Kaplan–Meier survival analysis results indicated that the without POAF group had a higher survival rate than the with POAF group.ConclusionsHealthcare professionals should recognize negative factors such as postoperative atrial fibrillation and abnormal hematologic parameters that impact major clinical outcomes in patients and may require closer monitoring before and after coronary artery bypass grafting.

Estimation of the maximum potential cost saving from reducing serious adverse events in hospitalized patients.

The increasing use of advanced medical technologies to detect adverse events, for instance, artificial intelligence-assisted technologies, has shown promise in improving various aspects within health care but may also come with substantial expenses. Therefore, understanding the potential economic benefits can guide decision-making processes regarding implementation. We aimed to estimate the potential cost savings associated with reducing length of stay and avoiding readmissions within the framework of an artificial intelligence-assisted vital signs monitoring system. We used data from Danish national registries and coarsened exact matching to estimate the difference in length of stay and probability of readmission among adult in-hospital patients exposed to and not exposed to serious adverse events. We used these estimates to calculate the maximum potential savings that could be achieved by early detection of adverse events to reduce length of stay and avoid readmissions. Patients exposed to serious adverse events during admission had 2.4 (95% CI: 2.4-2.5) additional hospital bed days and had 14% (95% CI 11%-17%) higher odds of readmissions compared with patients not exposed to such events. A base case scenario yielded maximum potential savings if one patient avoided a serious adverse event of EUR 2040 due to reduced length of stay and EUR 43 due to avoidance of readmissions caused by serious adverse events. Reductions in serious adverse events are associated with decreased healthcare costs due to reduced length of stay and avoided readmissions. Artificial intelligence-assisted vital signs monitoring systems are one potential approach to reduce serious adverse events, however, the ability of this technology to reduce adverse events remains unclear. Comprehensive prospective analyses of such systems including the intervention and implementation costs are necessary to understand their full economic impact.

Association Between Inability to Stand at ICU Discharge and Readmission: A Historical Cohort Study

Objectives: The aim of this study was to determine if being unable to stand at ICU discharge was associated with an increased probability of ICU readmission. Design: A multicenter retrospective cohort study was conducted using the Toronto Intensive Care Observational Registry (iCORE) project. Setting: Nine tertiary academic ICUs in Toronto, Canada, affiliated with the University of Toronto. Patients: All patients admitted to ICUs participating in iCORE from September 2014 to January 2020 were included. Patients had to be mechanically ventilated for more than 4 hours to be included in iCORE. Exclusion criteria were death during the initial ICU stay, transfer to another institution not included in iCORE at ICU discharge, and a short ICU stay defined as less than 2 days. Interventions: None. Measurements and Main Results: The main exposure in this study was the inability of the patient to stand at ICU discharge, documented daily in the database within the ICU Mobility Scale. The primary outcome of this study was readmission to the ICU. After adjusting for potential confounders, being unable to stand at ICU discharge was associated with increased odds of readmission (odds ratio, 1.85; 95% CI, 1.31–2.62; p < 0.001). Conclusions: In patients with an ICU stay of 2 days or more, being unable to stand at ICU discharge is associated with increased odds of readmission to the ICU.

Factors influencing hospital readmission rates in alcohol use disorder

According to data from the Federal Statistical Office, the diagnosis of alcohol use disorder (AUD) (F10) is the second most common main diagnosis for hospital treatment. Those affected by this disorder are often repeatedly hospitalized at short intervals due to relapses; however, little is known about the factors that influence readmission rates after initial treatment. The aim of this retrospective analysis is to analyze the effects of treatment type (qualified withdrawal treatment(QE) versus physical detoxification) and discharge mode on the probability of readmission in alcohol-dependent patients after inpatient treatment. Data from 981 male and female alcohol-dependent patients who completed either qualified withdrawal treatment (QE) (68% men; mean age47.6 years) or inpatient detoxification (74% men; mean age48.0 years) were analyzed. Predictors of regular discharge were determined separately for both types of treatment using stepwise logistic regression. Patients who had completed aqualified withdrawal treatment were significantly more likely to be regularly discharged. Regular completion of the qualified withdrawal treatment (QE) led to arelative reduction in the readmission rate of 25.64% within 1 year compared to aphysical detoxification. In order to prevent readmission and chronic courses of alcohol use disorder (AUD), qualified withdrawal treatment should always be recommended to affected patients instead of physical detoxification. Aktuelle Daten des Statistischen Bundesamtes für das Jahr 2022 zeigen, dass die Diagnose "Psychische und Verhaltensstörungen durch Alkohol (F10.X)" die zweithäufigste Hauptdiagnose bei Krankenhausbehandlungen darstellt [13]. Im Gesundheitssystem entstehen durch dieses Erkrankungsbild und seine somatischen und psychischen Folgeerkrankungen jährlich ca.10 Mrd. € direkte Kosten [13]. Dieser Sachverhalt wird dadurch kontrastiert, dass die Krankenkassen die qualifizierte Entzugsbehandlung (QE) als leitliniengerechte Goldstandardtherapie [4] wiederholt infrage stellen [10].

Suicide ideation and intentional self-harm as a risk factor for readmission within one year following a trauma alert: A 10 year retrospective analysis

The objective of this analysis is to study physical traumatic injury as a risk factor for suicide ideation and attempts. The analysis is retrospective, focusing on inpatient readmission with suicide ideation or intentional self-harm within one year of hospitalization as a trauma alert (TA). Injured patients were divided into TA and emergencies without activation (EMWA) of trauma services. We estimated the probability of readmission with suicide ideation or intentional self-harm using a logistic regression. Rates of readmission with intentional self-harm and suicide ideation were on average 60 and 45% higher for the TA group (p < 0.01). Over 80% of patients readmitted with intentional self-harm or suicide ideation had a preexisting mental health condition, compared to approximately 45% in the overall sample. Logistic regression results indicate these patients had an odds-ratio of near six, predicting the readmission. When patients with a preexisting mental health condition were excluded from the analysis, TA patients were significantly more likely to experience a readmission with intentional self-harm and suicide ideation. Demographic and socioeconomic characteristics (gender, age, race, insurance status, and median incomes) as well as injury type (traumatic brain injury or TBI) also help predict readmission with suicide ideation and intentional self-harm. The main risk factors for reengagement with self-harm and suicidal ideation/attempt include preexisting mental health conditions, TA status, and socioeconomic status. Targeted, sustained, and prolonged rehabilitation and trauma-informed therapy for this high-risk group of severely injured patients may yield significant health and social benefits.

Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs

Dynamic Control of Service Systems with Returns Postdischarge interventions (e.g., follow-up phone calls, outpatient appointments) are commonly used to reduce unplanned hospital readmissions. Such interventions have been shown to be effective (to varying degrees) in reducing the probability of readmission (or return). The interventions are, however, costly, and hence, their benefits need to be carefully balanced with the costs. In “Dynamic Control of Service Systems with Returns: Application to Design of Postdischarge Hospital Readmission Prevention Programs,” Chan, Huang, and Sarhangian investigate how postservice interventions should be dynamically allocated, accounting for their costs as well as their benefits in terms of reducing returns and congestion costs. To this end, they study a transient queueing control problem and examine the structure of the optimal policy by analyzing associated fluid-control problems. The structural results motivate the design of intuitive surge protocols whereby different intensities of interventions are provided based on the congestion level of the system.

Characterizing research domain criteria symptoms among psychiatric inpatients using large language models

We sought to characterize the ability of large language models to estimate NIMH Research Domain Criteria dimensions from narrative clinical notes of adult psychiatric inpatients, deriving estimate of overall burden of symptoms in each domain. We extracted consecutive admissions to a psychiatric inpatient unit between December 23, 2009 and September 27, 2015 from the electronic health records of a large academic medical center. Admission and discharge notes were scored with a HIPAA-compliant instance of a large language model (gpt-4–1106-preview). To examine convergent validity, the resulting estimates were correlated with those derived using an earlier method; for predictive validity, they were examined for association with length of hospitalization and probability of readmission. The cohort included 3619 individuals, 1779 female (49 %), 1840 male (51 %) with mean age 44 (SD=16.6). We identified modest correlations between LLM-derived RDoC scores and a previously validated scoring method, with Kendall’s tau between from.07 for arousal and 0.27 for positive and cognitive domains (p < .001 for all of these). For admission notes, greater scores on cognitive, sensorimotor, negative, and social domains were significantly associated with longer length of hospitalization in linear regression models including sociodemographic features (p < .01 for all of these); positive valence was associated with shorter hospitalization (p < .001). For discharge notes, social, arousal, and positive valence were associated with likelihood of readmission within 180 days in adjusted logistic regression models (p < .05 for social and arousal, p < .001 for positive valence). Overall, LLM-derived estimates of RDoC psychopathology demonstrated promising convergent and predictive validity, suggesting this approach may make real-world application of the RDoC framework more feasible.

Using social risks to predict unplanned hospital readmission and emergency care among hospitalized Veterans.

(1) To estimate the association of social risk factors with unplanned readmission and emergency care after a hospital stay. (2) To create a social risk scoring index. We analyzed administrative data from the Department of Veterans Affairs (VA) Corporate Data Warehouse. Settings were VA medical centers that participated in a national social work staffing program. We grouped socially relevant diagnoses, screenings, assessments, and procedure codes into nine social risk domains. We used logistic regression to examine the extent to which domains predicted unplanned hospital readmission and emergency department (ED) use in 30 days after hospital discharge. Covariates were age, sex, and medical readmission risk score. We used model estimates to create a percentile score signaling Veterans' health-related social risk. We included 156,690 Veterans' admissions to a VA hospital with discharged to home from 1 October, 2016 to 30 September, 2022. The 30-day rate of unplanned readmission was 0.074 and of ED use was 0.240. After adjustment, the social risks with greatest probability of readmission were food insecurity (adjusted probability = 0.091 [95% confidence interval: 0.082, 0.101]), legal need (0.090 [0.079, 0.102]), and neighborhood deprivation (0.081 [0.081, 0.108]); versus no social risk (0.052). The greatest adjusted probabilities of ED use were among those who had experienced food insecurity (adjusted probability 0.28 [0.26, 0.30]), legal problems (0.28 [0.26, 0.30]), and violence (0.27 [0.25, 0.29]), versus no social risk (0.21). Veterans with social risk scores in the 95th percentile had greater rates of unplanned care than those with 95th percentile Care Assessment Needs score, a clinical prediction tool used in the VA. Veterans with social risks may need specialized interventions and targeted resources after a hospital stay. We propose a scoring method to rate social risk for use in clinical practice and future research.

Impact of a prospective feedback loop aimed at reducing non-beneficial treatments in older people admitted to hospital and potentially nearing the end of life. A cluster stepped-wedge randomised controlled trial.

To investigate if a prospective feedback loop that flags older patients at risk of death can reduce non-beneficial treatment at end of life. Prospective stepped-wedge cluster randomised trial with usual care and intervention phases. Three large tertiary public hospitals in south-east Queensland, Australia. 14 clinical teams were recruited across the three hospitals. Teams were recruited based on a consistent history of admitting patients aged 75+ years, and needed a nominated lead specialist consultant. Under the care of these teams, there were 4,268 patients (median age 84years) who were potentially near the end of life and flagged at risk of non-beneficial treatment. The intervention notified clinicians of patients under their care determined as at-risk of non-beneficial treatment. There were two notification flags: a real-time notification and an email sent to clinicians about the at-risk patients at the end of each screening day. The nudge intervention ran for 16-35weeks across the three hospitals. The primary outcome was the proportion of patients with one or more intensive care unit (ICU) admissions. The secondary outcomes examined times from patients being flagged at-risk. There was no improvement in the primary outcome of reduced ICU admissions (mean probability difference [intervention minus usual care] = -0.01, 95% confidence interval -0.08 to 0.01). There were no differences for the times to death, discharge, or medical emergency call. There was a reduction in the probability of re-admission to hospital during the intervention phase (mean probability difference -0.08, 95% confidence interval -0.13 to -0.03). This nudge intervention was not sufficient to reduce the trial's non-beneficial treatment outcomes in older hospital patients. Australia New Zealand Clinical Trial Registry, ACTRN12619000675123 (registered 6 May 2019).

Predicting Unplanned Readmissions to the Intensive Care Unit in the Trauma Population.

Background: Unplanned readmission to intensive care units (UR-ICU) in trauma is associated with increased hospital length of stay and significant morbidity and mortality. We identify independent predictors of UR-ICU and construct a nomogram to estimate readmission probability. Materials and Methods: We performed an IRB-approved retrospective case-control study at a Level I trauma center between January 2019 and December 2021. Patients with UR-ICU (n = 175) were matched with patients who were not readmitted (NR-ICU) (n = 175). Univariate and multivariable binary linear regressionanalyses were performed (SPSS Version 28, IBM Corp), and a nomogram was created (Stata 18.0, StataCorp LLC). Results: Demographics, comorbidities, and injury- and hospital course-related factors were examined as potential prognostic indicators of UR-ICU. The mortality rate of UR-ICU was 22.29% vs 6.29% for NR-ICU (P < .001). Binary linear regression identified seven independent predictors that contributed to UR-ICU: shock (P < .001) or intracranial surgery (P = .015) during ICU admission, low hematocrit (P = .001) or sedation administration in the 24hours before ICU discharge (P < .001), active infection treatment (P = .192) or leukocytosis on ICU discharge (P = .01), and chronic obstructive pulmonary disease (COPD) (P = .002). A nomogram was generated to estimate the probability of UR-ICU and guide decisions on ICU discharge appropriateness. Discussion: In trauma, UR-ICU is often accompanied by poor outcomes and death. Shock, intracranial surgery, anemia, sedative administration, ongoing infection treatment, leukocytosis, and COPD are significant risk factors for UR-ICU. A predictive nomogram may help better assess readiness for ICU discharge.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population

BackgroundReadmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments. MethodsThis was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision. ResultsThis study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors. ConclusionsLeveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Racial Bias in Pain Measurement Frequency in ICU and its Impact on Early Readmission

A significant portion of hospitalizations result in readmissions, many of which are preventable. Additionally, over a quarter of admissions necessitate intensive care unit (ICU) stays. These trends impose significant strain on healthcare systems, leading to penalties and reduced reimbursements for healthcare facilities. Consequently, reducing readmissions stands as a critical priority for healthcare institutions, with a specific focus on ICU processes and procedures. While some healthcare processes, such as vital sign monitoring, are consistent across patient cohorts, disparities in pain management have been well-documented. Existing research predominantly explores biases in outpatient settings, particularly in the underestimation and undertreatment of pain for Black patients. However, these biases can extend to the frequency of pain measurements in ICUs, where continuous monitoring plays a pivotal role in patient care. This study investigates disparities in pain measurement frequencies in ICUs for patients of different races and their impact on early readmission rates. Utilizing a dataset of ICU stays from a major U.S. healthcare institution, we employ mediation analysis with instrumental variables by performing a two-step mediation analysis using an IV-Probit estimator to answer these research questions. Our findings reveal that the frequency of pain measurements in the ICU stays partially mediates the relationship between a patient's race and the probability of early readmission. Specifically, Black patients receive fewer pain measurements during their ICU stays compared to White patients, increasing their likelihood of 30-day readmission. This study represents the first to demonstrate that bias in pain management extends to the frequency of pain measurements in intensive care settings, shedding light on a critical aspect of healthcare disparities and their impact on readmission rates. The implications of our findings span healthcare practice, operations, and the growing body of literature on healthcare equity, offering valuable insights into addressing these disparities.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Elective Joint Replacement Among Medicaid Beneficiaries: Utilization and Postoperative Adverse Events by Racial and Ethnic Groups.

Hip and knee replacement have been marked by racial and ethnic disparities in both utilization and postoperative adverse events among Medicare beneficiaries, but limited knowledge exists regarding racial and ethnic differences in joint replacement care among Medicaid beneficiaries. To close this gap, this study used Medicaid claims in 2018 and described racial and ethnic differences in the utilization and postoperative adverse events of elective joint replacements among Medicaid beneficiaries. Among the 2,260,272 Medicaid beneficiaries, 5987 had an elective joint replacement in 2018. Asian (0.05%, 95% confidence interval [CI]: 0.03%-0.07%) and Hispanic beneficiaries (0.12%, 95% CI: 0.07%-0.18%) received joint replacements less frequently than American Indian and Alaska Native (0.41%, 95% CI: 0.27%-0.55%), Black (0.33%, 95% CI: 0.19%-0.48%), and White (0.37%, 95% CI: 0.25%-0.50%) beneficiaries. Black patients demonstrated the highest probability of 90-day emergency department visits (34.8%, 95% CI: 32.7%-37.0%) among all racial and ethnic groups and a higher probability of 90-day readmission (8.0%, 95% CI: 6.9%-9.0%) than Asian (3.4%, 95% CI: 0.7%-6.0%) and Hispanic patients (4.4%, 95% CI: 3.4%-5.3%). These findings indicate evident disparities in postoperative adverse events across racial and ethnic groups, with Black patients demonstrating the highest probability of 90-day emergency department visits. This study represents an initial exploration of the racial and ethnic differences in joint replacement care among Medicaid beneficiaries and lay the groundwork for further investigation into contributing factors of the observed disparities.

The correlation between admission hyperglycemia and 30-day readmission after hip fracture surgery in geriatric patients: a propensity score-matched study.

This study aimed to investigate the association between admission hyperglycemia and 30-day readmission after hip fracture surgery in geriatric patients. This retrospective study included 1253 geriatric hip fracture patients. Patients were categorized into normoglycemia(<6.10 mmol/L) and hyperglycemia groups(≥6.10 mmol/L) based on admission blood glucose. We performed multivariable logistic regression analyses and propensity score matching (PSM) to estimate adjusted odds ratios and 95% confidence intervals for 30-day readmission, controlling for potential confounding factors. An analysis of the dose-dependent association between admission blood glucose and the probability of 30-day readmission was performed. Additional subgroup analysis was conducted to examine the impact of other factors on the relationship between admission blood glucose and 30-day readmission. Patients with hyperglycemia had higher 30-day readmission rates than normoglycemic patients before (19.1% vs 9.7%, p<0.001) and after PSM (18.1% vs 12.3%, p=0.035). Admission hyperglycemia was an independent predictor of increased 30-day readmission risk, with an adjusted odds ratio of 1.57 (95% CI 1.08-2.29, p=0.019) after multivariable regression and 1.57 (95% CI 1.03-2.39, p=0.036) after PSM. A dose-response relationship was observed between higher glucose levels and increased readmission risk. Admission hyperglycemia is an independent risk factor for 30-day readmission after hip fracture surgery in the elderly. Routine glucose testing upon admission and perioperative glycemic control may help reduce short-term readmissions in this vulnerable population.

The Association of High-Quality Hospital Use on Health Care Outcomes for Pediatric Congenital Heart Defects in a Universal Health Care System.

Congenital heart disease (CHD) has an incidence of 0.8% to 1.2% worldwide, making it the most common birth defect. Researchers have compared high-volume to low-volume hospitals and found significant hospital-level variation in major complications, health resource utilization, and mortality after CHD surgery. In addition, researchers found critical CHD patients at low-volume/non-teaching facilities to be associated with higher odds of inpatient mortality when compared to CHD patients at high-volume/teaching hospitals (odds ratio 1.76). We examined the effects of high-quality hospital (HQH) use on health care outcomes and health care costs in pediatric CHD care using an instrumental variable (IV) approach. Using nationwide representative claim data from the United States Military Health System from 2016 to 2020, TRICARE beneficiaries with a diagnosis of CHD were tabulated based on relevant ICD-10 (International Classification of Diseases, 10th revision) codes. We examined the relationships between annual readmissions, annual emergency room (ER) use, and mortality and HQH use. We applied both the naive linear probability model (LPM), controlling for the observed patient and hospital characteristics, and the two-stage least squares (2SLS) model, accounting for the unobserved confounding factors. The differential distance between the patient and the closest HQH at the index date and the patient and nearest non-HQH was used as the IV. This protocol was approved by the Institutional Review Board at the University of Maryland, College Park (Approval Number: 1576246-2). The naive LPM indicated that HQH use was associated with a higher probability of annual readmissions (marginal effect, 18%; 95% CI, 0.12 to 0.23). The naive LPM indicated that HQH use was associated with a higher probability of mortality (marginal effect, 2.2%; 95% CI, 0.01 to 0.03). Using the differential distance of closest HQH and non-HQH, we identified a significant association between HQH use and annual ER use (marginal effect, -14%; 95% CI, -0.24 to -0.03). After controlling for patient-level and facility-level covariates and adjusting for endogeneity, (1) HQH use did not increase the probability of more than one admission post 1-year CHD diagnosis, (2) HQH use lowered the probability of annual ER use post 1-year CHD diagnosis, and (3) HQH use did not increase the probability of mortality post 1-year CHD diagnosis. Patients who may have benefited from utilizing HQH for CHD care did not, alluding to potential barriers to access, such as health insurance restrictions or lack of patient awareness. Although we used hospital quality rating for congenital cardiac surgery as reported by the Society of Thoracic Surgeons, the contributing data span a 4-year period and may not reflect real-time changes in center performance. Since this study focused on inpatient care within the first-year post-initial CHD diagnosis, it may not reflect the full range of health system utilization. It is necessary for clinicians and patient advocacy groups to collaborate with policymakers to promote the development of an overarching HQH designation authority for CHD care. Such establishment will facilitate access to HQH for military beneficiary populations suffering from CHD.

Reexamining Differences Between Black and White Veterans in Hospital Mortality and Other Outcomes in Veterans Affairs and Other Hospitals.

To examine Black-White patient differences in mortality and other hospital outcomes among Veterans treated in Veterans Affairs (VA) and non-VA hospitals. Lower hospital mortality has been documented in older Black patients relative to White patients, yet the mechanisms have not been determined. Comparing other hospital outcomes and multiple hospital systems may help inform the reasons for these differences. Repeated cross-sectional analysis of hospitalization records was conducted for Veterans discharged in VA and non-VA hospitals from January 1, 2013 to December 31, 2017 in 11 states. Hospital outcomes included 30-day mortality, 30-day readmissions, inpatient costs, and length of stay. Hospitalizations were for acute myocardial infarction, coronary artery bypass graft surgery, gastrointestinal bleeding, heart failure, pneumonia, and stroke. Differences in outcomes were estimated between Black and White patients for VA and non-VA hospitals and age groups younger than 65 years or 65 years and older in regression models adjusting for patient and hospital factors. There were a total of 459,574 study patients. Older Black patients had lower adjusted mortality for acute myocardial infarction, gastrointestinal bleeding, heart failure, and pneumonia. Adjusted probability of readmission was higher and adjusted mean length of stay and costs were greater for older Black patients relative to White patients in non-VA hospitals for several conditions. Fewer differences were observed in younger patients and in VA hospitals. While older Black patients had lower mortality, other outcomes compared poorly with White patients. Differences were not fully explained by observable patient and hospital factors although social determinants may contribute to these differences.

Impact of mental disorders on unplanned readmissions for congestive heart failure patients: a population-level study.

Reducing preventable hospitalization for congestive heart failure (CHF) patients is a challenge for health systems worldwide. CHF patients who also have a recent or ongoing mental disorder may have worse health outcomes compared with CHF patients with no mental disorders. This study examined the impact of mental disorders on 28day unplanned readmissions of CHF patients. This retrospective cohort study used population-level linked public and private hospitalization and death data of adults aged ≥18years who had a CHF admission in New South Wales, Australia, between 1 January 2014 and 31 December 2020. Individuals' mental disorder diagnosis and Charlson comorbidity and hospital frailty index scores were derived from admission records. Competing risk and cause-specific risk analyses were conducted to examine the impact of having a mental disorder diagnosis on all-cause hospital readmission. Of the 65861 adults with index CHF admission discharged alive (mean age: 78.6±12.1; 48% female), 19.2% (12675) had at least one unplanned readmission within 28days following discharge. Adults with CHF with a mental disorder diagnosis within 12months had a higher risk of 28day all-cause unplanned readmission [hazard ratio (HR): 1.21, 95% confidence interval (CI): 1.15-1.27, P-value<0.001], particularly those with anxiety disorder (HR: 1.49, 95% CI: 1.35-1.65, P-value<0.001). CHF patients aged ≥85years (HR: 1.19, 95% CI: 1.11-1.28), having ≥3 other comorbidities (HR: 1.35, 95% CI: 1.25-1.46), and having an intermediate (HR: 1.34, 95% CI: 1.28-1.40) or high (HR: 1.37, 95% CI: 1.27-1.47) frailty score on admission had a higher risk of unplanned readmission. CHF patients with a mental disorder who have ≥3 other comorbidities and an intermediate frailty score had the highest probability of unplanned readmission (29.84%, 95% CI: 24.68-35.73%) after considering other patient-level factors and competing events. CHF patients who had a mental disorder diagnosis in the past 12months are more likely to be readmitted compared with those without a mental disorder diagnosis. CHF patients with frailty and a mental disorder have the highest probability of readmission. Addressing mental health care services in CHF patient's discharge plan could potentially assist reduce unplanned readmissions.

Risk of 30-Day All-Cause Readmission in Interstitial Lung Disease Patients after COVID-19: National-Level Data.

Rationale: Hospital readmission within 30 days poses challenges for healthcare providers, policymakers, and patients because of its impact on care quality, costs, and outcomes. Patients with interstitial lung disease (ILD) are particularly affected by readmission, which is associated with increased morbidity and mortality and reduced quality of life. Because small sample sizes have hindered previous studies, this study seeks to address this gap in knowledge by examining a large-scale dataset. Objective: To determine the rate and probability of 30-day all-cause readmission and secondary outcomes in patients with coronavirus disease (COVID-19) or ILD admitted to the hospital. Methods: This study is a nested cohort study that used the PearlDiver patient records database. Adult patients (age ⩾18 yr) who were admitted to hospitals in 28 states in the United States with COVID-19 or ILD diagnoses were included. We defined and analyzed two separate cohorts in this study. The first cohort consisted of patients with COVID-19 and was later divided into two groups with or without a history of ILD. The second cohort consisted of patients with ILD and was later divided into groups with COVID-19 or with a non-COVID-19 pneumonia diagnosis at admission. We also studied two other subcohorts of patients with and without idiopathic pulmonary fibrosis within the second cohort. Propensity score matching was employed to match confounders between groups. The Kaplan-Meier log rank test was applied to compare the probabilities of outcomes. Results: We assessed the data of 2,286,775 patients with COVID-19 and 118,892 patients with ILD. We found that patients with COVID-19 with preexisting ILD had an odds ratio of 1.6 for 30-day all-cause readmission. Similarly, an odds ratio of 2.42 in readmission rates was observed among hospitalized individuals with ILD who contracted COVID-19 compared with those who were hospitalized for non-COVID-19 pneumonia. Our study also found a significantly higher probability of intensive care admission among patients in both cohorts. Conclusions: Patients with ILD face heightened rates of hospital readmissions, particularly when ILD is combined with COVID-19, resulting in adverse outcomes such as decreased quality of life and increased healthcare expenses. It is imperative to prioritize preventive measures against COVID-19 and establish effective postdischarge care strategies for patients with ILD.

An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients

Reducing hospital readmission rate is a significant challenge in the healthcare industry for managers and policymakers seeking to improve healthcare and lower costs. This study integrates data mining and meta-heuristic techniques to predict the early readmission probability of diabetic patients within 30 days of discharge. The research dataset was obtained from the UC Irvine Machine Learning Repository, including 101765 instances with 50 features representing patient and hospital outcomes, collected from 130 US hospitals. After data preprocessing, including cleansing, sampling, and normalization, a Chi-square analysis is done to confirm and rank the 20 identified factors affecting the readmission risk. As the algorithms' performance could vary based on the features’ characteristics, several classification algorithms, including a Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM), are employed. Moreover, the Genetic Algorithm (GA) is integrated into the SVM algorithm, called GA-SVM, for hyper-parameter tuning and increasing the prediction accuracy. The performance of the models was evaluated using accuracy, recall, precision, and f-measure metrics. The results indicate that the accuracy of RF, GA-SVM, SVM, and NN are calculated respectively as 74.04 %, 73.52 %, 72.40 %, and 70.44 %. Using GA to adjust c and gamma hyper-parameters led to a 1.12 % increase in SVM prediction accuracy. In response to increasing demand and considering poor hospital conditions, particularly during epidemics, these findings point out the potential benefits of a more tailored methodology in managing diabetic patients.