Unplanned Hospital Readmissions Research Articles

Prediction of Readmission Following Sepsis Using Social Determinants of Health.

To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables. Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data. Thirty-five hospitals across the United States from 2017 to 2021. Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization. None. Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission. In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

Advanced practice nurse-coordinated same-day-discharge after atrial fibrillation ablation

Abstract Background Catheter ablation is an effective atrial fibrillation (AF) therapy, but a complex, resource-intensive procedure. Same-day discharge (SDD) reduces utilization of health care resources including personell and hospital beds, and may help to accommodate the increasing demand for AF ablation. However, besides structural adaptations, SDD requires more efficient logistics and coordination. Purpose Against this background, we established and validated a streamlined SDD program fully coordinated by an advanced practice nurse. Methods As dedicated SDD coordinator the advanced practice nurse was in charge of the full SDD protocol, including patient eligibility, patient flow, in-hospital logistics, patient education, discharge and short-term follow-up. For inclusion, basic home support and accessibility of the hospital within 60 minutes had to be warranted. Patients with LVEF <35% were excluded. Ablation was performed at the operator’s discretion. Ultrasound-guided femoral puncture was at the operator’s discretion for the first 150 SDD patients but became mandatory per protocol for all subsequent patients. Discharge by the SDD coordinator followed a standardized protocol including supervised ambulation, patient education, femoral inspection and echo. On post-ablation days 1 and 3, patients were called by the SDD coordinator to ensure well-being and good aspect of the femoral access site (smartphone foto control). In addition, patients could consult the SDD coordinator via mobile phone any time during working hours. In-person follow-up visits were scheduled at 2 weeks and 3, 6 and 12 months post-ablation. Results 420 consecutive patients were evaluated by the SDD coordinator of whom 331 were eligible for SDD. Reasons for exclusion were lack of home support (4.5%), living remotely (6.9%), or LVEF <35% (4.0%). 300 of the eligible patients (91%) were successfully discharged the same day (Fig. 1). Rates of unplanned medical attention (4.0%) and 30d-readmission (1.6%) were extremely low. Importantly, there was no major post-SDD complication. Unplanned medical attention and hospital readmissions were largely driven by minor femoral access site complications. Of note, those were significantly reduced upon introduction of mandatory ultrasound-guided puncture after the first 150 SDD patients (p=0.0145, Fig. 2). SDD coordination by the advanced practice nurse streamlined patient flow and in-hospital logistics and significantly reduced the total workload of nursing and medical staff. This resulted in great acceptance of the SDD program and satisfaction both among staff and patients. Conclusions Advanced practice nurse-coordinated SDD after AF ablation is safe and efficient. In fact, the position of a dedicated coordinator may be a key in the forthcoming transition of hospitals to SDD. Importantly, ultrasound-guided femoral puncture virtually eliminated relevant femoral access site complications and should be a prerequisite for SDD.Figure 1Figure 2

Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission

BackgroudAcute myocardial infarction (AMI) has a high morbidity rate, high mortality rate, high readmission rate, high health care costs, and a high symptomatic, psychological, and economic burden on patients. Patients with AMI usually present with multiple symptoms simultaneously, which are manifested as symptom clusters. Symptom clusters have a profound impact on the quality of survival and clinical outcomes of AMI patients.ObjectiveThe purpose of this study was to analyze unplanned hospital readmissions among cluster groups within a 1-year follow-up period, as well as to identify clusters of acute symptoms and the characteristics associated with them that appeared in patients with AMI.MethodsBetween October 2021 and October 2022, 261 AMI patients in China were individually questioned for symptoms using a structured questionnaire. Mplus 8.3 software was used to conduct latent class analysis in order to find symptom clusters. Univariate analysis is used to examine characteristics associated with each cluster, and multinomial logistic regression is used to analyze a cluster membership as an independent predictor of hospital readmission after 1-year.ResultsThree unique clusters were found among the 11 acute symptoms: the typical chest symptom cluster (64.4%), the multiple symptom cluster (29.5%), and the atypical symptom cluster (6.1%). The cluster of atypical symptoms was more likely to have anemia and the worse values of Killip class compared with other clusters. The results of multiple logistic regression indicated that, in comparison to the typical chest cluster, the atypical symptom cluster substantially predicted a greater probability of 1-year hospital readmission (odd ratio 8.303, 95% confidence interval 2.550–27.031, P < 0.001).ConclusionOut of the 11 acute symptoms, we have found three clusters: the typical chest symptom, multiple symptom, and atypical symptom clusters. Compared to patients in the other two clusters, those in the atypical symptom cluster—which included anemia and a large percentage of Killip class patients—had worse clinical indicators at hospital readmission during the duration of the 1-year follow-up. Both anemia and high Killip classification suggest that the patient's clinical presentation is poor and therefore the prognosis is worse. Intensive treatment should be considered for anemia and high level of Killip class patients with atypical presentation. Clinicians should focus on patients with atypical symptom clusters, enhance early recognition of symptoms, and develop targeted symptom management strategies to alleviate their discomfort in order to improve symptomatic outcomes.

Patient-Physician Sex Discordance and "Before Medically Advised" Discharge from Hospital: A Population-Based Retrospective Cohort Study.

Patient-physician sex discordance (when patient sex does not match physician sex) has been associated with reduced clinical rapport and adverse outcomes including post-operative mortality and unplanned hospital readmission. It remains unknown whether patient-physician sex discordance is associated with "before medically advised" hospital discharge (BMA discharge; commonly known as discharge "against medical advice"). To evaluate whether patient-physician sex discordance is associated with BMA discharge. Retrospective cohort study using 15years (2002-2017) of linked population-based administrative health data for all non-elective, non-obstetrical acute care hospitalizations from British Columbia, Canada. All individuals with eligible hospitalizations during study interval. Exposure: patient-physician sex discordance. BMA discharge (primary), 30-day hospital readmission or death (secondary). We identified 1,926,118 eligible index hospitalizations, 2.6% of which ended in BMA discharge. Among male patients, sex discordance was associated with BMA discharge (crude rate, 4.0% vs 2.9%; adjusted odds ratio [aOR] 1.08; 95%CI 1.03-1.14; p = 0.003). Among female patients, sex discordance was not associated with BMA discharge (crude rate, 2.0% vs 2.3%; aOR 1.02; 95%CI 0.96-1.08; p = 0.557). Compared to patient-physician sex discordance, younger patient age, prior substance use, and prior BMA discharge all had stronger associations with BMA discharge. Patient-physician sex discordance was associated with a small increase in BMA discharge among male patients. This finding may reflect communication gaps, differences in the care provided by male and female physicians, discriminatory attitudes among male patients, or residual confounding. Improved communication and better treatment of pain and opioid withdrawal may reduce BMA discharge.

Physician financial incentives to reduce unplanned hospital readmissions: A propensity score weighted cohort study

Unplanned hospital readmissions are associated with adverse patient outcomes and substantial healthcare costs. It remains unknown whether physician financial incentives for enhanced discharge planning can reduce readmission risk. In 2012, policymakers in British Columbia, Canada, introduced a $75 fee-for-service physician payment to incentivize enhanced discharge planning (the 'G78717' fee code). We used population-based administrative health data to compare outcomes among G78717-exposed and G78717-unexposed patients. We identified all non-elective hospitalizations potentially eligible for the incentive over a five-year study interval. We examined the composite risk of unplanned readmission or death and total direct healthcare costs accrued within 30 days of discharge. Propensity score overlap weights and adjustment were used to account for differences between exposed and unexposed patients. A total of 5262 of 24,787 G78717-exposed and 28,096 of 136,541 unexposed patients experienced subsequent unplanned readmission or death, suggesting exposure to the G78717 incentive did not reduce the risk of adverse outcomes after discharge (crude percent, 21.1% vs 20.6%; adjusted odds ratio, 0.97; 95%CI, 0.93-1.01; p=0.23). Mean direct healthcare costs within 30 days of discharge were $3082 and $2993, respectively (adjusted cost ratio, 1.00; 95%CI, 0.95-1.05; p=0.93). A physician financial incentive that encouraged enhanced hospital discharge planning did not reduced the risk of readmission or death, and did not significantly increase or decrease direct healthcare costs. Policymakers should consider the baseline prevalence and effectiveness of enhanced discharge planning, the magnitude and design of financial incentives, and whether auditing of incentivized activities is required when implementing similar incentives elsewhere. ClinicalTrials.gov ID, NCT03256734.

Predictive Modeling of Medical and Orthopaedic-Related 90-Day Readmissions Following Primary Total Hip Arthroplasty

The rate of unplanned hospital readmissions following total hip arthroplasty (THA) varies from 3 to 10%, representing a major economic burden. However, it is unknown if specific factors are associated with different types of complications (i.e., medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following THA. A prospective cohort of primary unilateral THAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 8,893 patients) using a validated institutional data collection system. Orthopaedic-related readmissions were specific complications affecting the prosthesis, joint, and surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between pre-specified risk factors and 90-day readmissions, as well as medical/orthopaedic-related readmissions independently. Overall, the rate of 90-day readmissions was 5.6%. Medical readmissions (4.2%) were found to be more prevalent than orthopaedic-related readmissions (1.4%). The area under curve (AUC) for the 90-day readmission model was 0.71 (95% CI [confidence interval]: 0.69 to 0.74). Factors significantly associated with medical-related readmissions were advanced age, black race, education, Charlson comorbidity index (CCI), surgical approach, opioid overdose risk (NARX) score, and nonhome discharge. In contrast, risk factors linked to orthopaedic-related readmissions encompassed body mass index (BMI), PROM phenotype, non-osteoarthritis indication, NARX, and non-home discharge. Of the overall 90-day readmissions following primary THA, 75% were due to medical-related complications. Our successful predictive model for complication-specific 90-day readmissions highlights how different risk factors may disproportionately influence medical versus orthopaedic-related readmissions, suggesting that patient-specific, tailored preventive measures could reduce postoperative readmissions in the current value-based healthcare setting.

The effect of family-centered care on unplanned emergency room visits, hospital readmissions and intensive care admissions after surgery: a root cause analysis from a prospective multicenter study in the Netherlands

BackgroundOptimizing transitional care by practicing family-centered care might reduce unplanned events for patients who undergo major abdominal cancer surgery. However, it remains unknown whether involving family caregivers in patients’ healthcare also has negative consequences for patient safety. This study assessed the safety of family involvement in patients’ healthcare by examining the cause of unplanned events in patients who participated in a family involvement program (FIP) after major abdominal cancer surgery.MethodsThis is a secondary analysis focusing on the intervention group of a prospective cohort study conducted in the Netherlands. Data were collected from April 2019 to May 2022. Participants in the intervention group were patients who engaged in a FIP. Unplanned events were analyzed, and root causes were identified using the medical version of a prevention- and recovery-information system for monitoring and analysis (PRISMA) that analyses unintended events in healthcare. Unplanned events were compared between patients who received care from family caregivers and patients who received professional at-home care after discharge. A Mann-Whitney U test was used to analyze data.ResultsOf the 152 FIP participants, 68 experienced an unplanned event and were included. 112 unplanned events occurred with 145 root causes since some unplanned events had several root causes. Most root causes of unplanned events were patient-related factors (n = 109, 75%), such as patient characteristics and disease-related factors. No root causes due to inadequate healthcare from the family caregiver were identified. Unplanned events did not differ statistically (interquartile range 1–2) (p = 0.35) between patients who received care from trained family caregivers and those who received professional at-home care after discharge.ConclusionBased on the insights from the root-cause analysis in this prospective multicenter study, it appears that unplanned emergency room visits and hospital readmissions are not related to the active involvement of family caregivers in surgical follow-up care. Moreover, surgical follow-up care by trained family caregivers during hospitalization was not associated with increased rates of unplanned adverse events. Hence, the concept of active family involvement by proficiently trained family caregivers in postoperative care appears safe and feasible for patients undergoing major abdominal surgery.

Artificial intelligence informing clinical decision making on the risk of hospital readmissions in multi-morbid patients: a systematic review

Abstract Introduction Multi-morbid patients have complex care needs and are more likely to utilise healthcare services, which represents a financial burden on healthcare organisations. 30-day readmission of patients is a method of measuring the quality of the services provided within healthcare.[1] Artificial intelligence (AI) models can flag multi-morbidity patients at increased risk of readmission.[2] However, it is unclear which common predictors have been used to develop these models. Aim To systematically review the medical literature to identify predictors that have been used to develop AI models that predicted unplanned 30-day hospital readmissions of multi-morbid patients, and whether these predictors were modifiable or non-modifiable. Methods Four large databases: Medline, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched on 15th November 2022. Only publications that developed a machine learning algorithm to predict unplanned 30-day hospital readmission for patients with multi-morbidity were included. Articles published in English language were eligible for inclusion. A narrative synthesis of all eligible studies was undertaken. Key findings were identified through inductive strategy, which includes simplifying raw data in the included articles into a comprehensive format. This systematic review was registered with the PROSPRO database (CRD42022373937) and followed PRISMA guidelines. Results There were 1,906 articles extracted from all databases, 18 of which met our inclusion criteria. A total of 669 predictors of hospital readmission were found, and were divided into 103 modifiable (i.e., action could be taken to change the outcome) and 566 non-modifiable (i.e. age, gender, number of previous emergency admissions. The average number of modifiable predictors per prediction model was six, with the most common being length of hospital stay, multi-morbidity, obesity, hypertension, diabetes, depression, and anaemia. The most commonly used AI algorithm was the gradient boosting algorithm. This algorithm generally showed a better performance than other models reported. The performance of all models in the included studies was compared; 13 studies showed an average sensitivity of 72%. The average specificity was calculated at 73% from 11 studies. The highest AUC reported was above 0.9, which demonstrates an excellent performing model, while five models did not report a value for the AUC. Conclusion We identified modifiable and non-modifiable predictors used in previously developed AI algorithms that predict 30-day hospital readmissions of multi-morbid patients. Modifiable predictors in particular can help guide clinical decision-making by allowing early actions to be taken in primary care to potentially reduce the risk of readmission. However, one limitation of this review is that some included studies used balanced (i.e. synthetic) data which could have introduced a level of bias.

Hospitalized Patients with Delirium and 28-Day Unplanned Hospital Readmissions: A Longitudinal Retrospective Cohort Study

ObjectiveTo establish the predictors of 28-day unplanned hospital readmissions (28D-UHR) in older adults (aged >65 years) with delirium during index hospital admission. DesignRetrospective longitudinal cohort study. Setting and Participants1634 patients (aged >65 years) admitted to a Melbourne quaternary hospital with delirium during index admission. MethodsDelirium during hospital admission was defined by the inclusion of one of the following International Classification of Diseases, Tenth Revision, codes F05.0, F05.1, F05.8, or F05.9 in the hospital medical discharge summary. Descriptive statistics were obtained for baseline characteristics. Multivariate logistic regression model was developed to assess predictors of 28D-UHR. ResultsA total of 1634 patients with delirium during their inpatient admission were included, with 9.8% (160 patients) incidence of 28D-UHR. For patients who were readmitted, a shorter length of stay [odds ratio (OR) 0.98, 95% CI 0.96-0.99], higher number of medications on discharge from index admission (OR 1.10, 95% CI 1.06-1.14), and residing in a nursing home preadmission (OR 1.35, 95% CI 1.04-1.75) were associated with 28D-UHR. Conclusions and ImplicationsThis study found that nursing home residence pre index admission, shorter length of stay (LOS), and polypharmacy were predictors of 28D-UHR. Further research into strategies to minimize 28D-UHR is required. Exploration of predischarge pharmacy-driven deprescribing programs and hospital-based postdischarge support for nursing home staff are important areas for future intervention.

Multi-Trajectories of Symptoms and their Associations with Unplanned 30-day Hospital Readmission among Patients with Heart Failure: A longitudinal study.

This study aimed to uncover hidden patterns and predictors of symptom multi-trajectories within 30 days after discharge in patients with heart failure and assess the risk of unplanned 30-day hospital readmission in different patterns. The study was conducted from September 2022 to September 2023 in four third-class hospitals in Tianjin, China. A total of 301 patients with heart failure were enrolled in the cohort, and 248 patients completed a 30-day follow-up after discharge. Three multi-trajectory groups were identified: mild symptom status (24.19%), moderate symptom status (57.26%), and severe symptom status (18.55%). With the mild symptom status group as a reference, physical frailty, psychological frailty, and comorbid renal dysfunction were predictors of the moderate symptom status group. Physical frailty, psychological frailty, resilience, taking diuretics, and comorbid renal dysfunction were predictors of the severe symptom status group. Compared with the mild symptom status group, the severe symptom status group was significantly associated with high unplanned 30-day hospital readmission risks. This study identified three distinct multi-trajectory groups among patients with heart failure within 30 days after discharge. The severe symptom status group was associated with a significantly increased risk of unplanned 30-day hospital readmission. Common and different factors predicted different symptom multi-trajectories. Healthcare providers should assess the physical and psychological frailty and renal dysfunction of patients with heart failure before discharge. Inpatient care aimed at alleviating physical and psychological frailty and enhancing resilience may be important to improve patients' symptom development post-discharge.

Association between patient activation and delayed discharge in elective laparoscopic cholecystectomy: A prospective cohort analysis

BackgroundImproving patient activation may be an effective way to reduce healthcare costs and improve patient outcomes after surgery. ObjectiveTo determine whether preoperative patient activation is associated with delayed discharge (i.e., length of stay >24 h) after elective laparoscopic cholecystectomy. Postoperative symptoms, unscheduled access to healthcare facilities within seven days of surgery, unplanned hospital readmissions, and postoperative complications were analyzed as secondary outcomes. DesignThis cohort study was a secondary analysis of the DeDiLaCo study (Delayed Discharge after day-surgery Laparoscopic Cholecystectomy) collecting data of patients undergoing elective laparoscopic cholecystectomy during 2021 in Italy. Data was analyzed from June 2022 to April 2023. Setting90 Italian surgical centers participating in the study. Participants4708 adult patients with an instrumental diagnosis of gallbladder disease and undergoing laparoscopic cholecystectomy. Patient activation was assessed using the Italian translation of Patient Activation Measure in the preoperative setting. ResultsOf 4532 cases analyzed the median (IQR) Patient Activation Measure score was 80.3 (71.2–92.3). Participants were on average 55.5 years of age and 58.1 % were female. Two groups based on the activation level were created: 270 (6 %) had low activation, and 4262 had high activation. The low activation level was associated with the likelihood of delayed discharge (odds ratio [OR] 1.47, 95 % CI, 1.11–1.95; P = .008), higher symptom burden (OR 1.99, 95 % CI 1.49–2.66, P < .0001), and unplanned healthcare utilization within seven days after hospital discharge (OR 1.85, 95 % CI, 1.29–2.63; P = .001). There was no difference between the high and low activation groups in the incidence of postoperative complications (OR 1.28, 95 % CI, 0.95–1.73; P = .10) and hospital readmission after discharge (OR 0.95, 95 % CI, 0.30–3.05; P = .93). ConclusionsOur results suggest that patients with low activation have 1.47 times the risk of delayed discharge compared with patients with higher activation, almost twice the risk of the onset of postoperative symptoms, and 1.85 times the risk of unscheduled use of hospital services. Screening for patient activation in the preoperative setting could not only identify patients not suitable for early discharge, but more importantly, help physicians and nurses develop tailored interventions.

Association of Malnutrition with Surgical and Hospital Outcomes after Spine Surgery for Spinal Metastases: A National Surgical Quality Improvement Program Study of 1613 Patients.

Background: Malnutrition is a common condition that may exacerbate many medical and surgical pathologies. However, few have studied the impact of malnutrition on surgical outcomes for patients undergoing surgery for metastatic disease of the spine. This study aims to evaluate the impact of malnutrition on perioperative complications and healthcare resource utilization following surgical treatment of spinal metastases. Methods: We conducted a retrospective cohort study using the 2011-2019 American College of Surgeons National Surgical Quality Improvement Program database. Adult patients with spinal metastases who underwent laminectomy, corpectomy, or posterior fusion for extradural spinal metastases were identified using the CPT, ICD-9-CM, and ICD-10-CM codes. The study population was divided into two cohorts: Nourished (preoperative serum albumin values ≥ 3.5 g/dL) and Malnourished (preoperative serum albumin values < 3.5 g/dL). We assessed patient demographics, comorbidities, intraoperative variables, postoperative adverse events (AEs), hospital LOS, discharge disposition, readmission, and reoperation. Multivariate logistic regression analyses were performed to identify the factors associated with a prolonged length of stay (LOS), AEs, non-routine discharge (NRD), and unplanned readmission. Results: Of the 1613 patients identified, 26.0% were Malnourished. Compared to Nourished patients, Malnourished patients were significantly more likely to be African American and have a lower BMI, but the age and sex were similar between the cohorts. The baseline comorbidity burden was significantly higher in the Malnourished cohort compared to the Nourished cohort. Compared to Nourished patients, Malnourished patients experienced significantly higher rates of one or more AEs (Nourished: 19.8% vs. Malnourished: 27.6%, p = 0.004) and serious AEs (Nourished: 15.2% vs. Malnourished: 22.6%, p < 0.001). Upon multivariate regression analysis, malnutrition was found to be an independent and associated with an extended LOS [aRR: 3.49, CI (1.97, 5.02), p < 0.001], NRD [saturated aOR: 1.76, CI (1.34, 2.32), p < 0.001], and unplanned readmission [saturated aOR: 1.42, CI (1.04, 1.95), p = 0.028]. Conclusions: Our study suggests that malnutrition increases the risk of postoperative complication, prolonged hospitalizations, non-routine discharges, and unplanned hospital readmissions. Further studies are necessary to identify the protocols that pre- and postoperatively optimize malnourished patients undergoing spinal surgery for metastatic spinal disease.

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.

Multicenter Registry of Adenomas of the Pituitary and Related Disorders: Initial Description of Cushing Disease Cohort, Surgical Outcomes, and Surgeon Characteristics.

To address the lack of a multicenter pituitary surgery research consortium in the United States, we established the Registry of Adenomas of the Pituitary and Related Disorders (RAPID). The goals of RAPID are to examine surgical outcomes, improve patient care, disseminate best practices, and facilitate multicenter surgery research at scale. Our initial focus is Cushing disease (CD). This study aims to describe the current RAPID patient cohort, explore surgical outcomes, and lay the foundation for future studies addressing the limitations of previous studies. Prospectively and retrospectively obtained data from participating sites were aggregated using a cloud-based registry and analyzed retrospectively. Standard preoperative variables and outcome measures included length of stay, unplanned readmission, and remission. By July 2023, 528 patients with CD had been treated by 26 neurosurgeons with varying levels of experience at 9 academic pituitary centers. No surgeon treated more than 81 of 528 (15.3%) patients. The mean ± SD patient age was 43.8 ± 13.9 years, and most patients were female (82.2%, 433/527). The mean tumor diameter was 0.8 ± 2.7 cm. Most patients (76.6%, 354/462) had no prior treatment. The most common pathology was corticotroph tumor (76.8%, 381/496). The mean length of stay was 3.8 ± 2.5 days. The most common discharge destination was home (97.2%, 513/528). Two patients (0.4%, 2/528) died perioperatively. A total of 57 patients (11.0%, 57/519) required an unplanned hospital readmission within 90 days of surgery. The median actuarial disease-free survival after index surgery was 8.5 years. This study examined an evolving multicenter collaboration on patient outcomes after surgery for CD. Our results provide novel insights on surgical outcomes not possible in prior single-center studies or with national administrative data sets. This collaboration will power future studies to better advance the standard of care for patients with CD.

Frailty-aware surgical care: Validation of Hospital Frailty Risk Score (HFRS) in older surgical patients

Introduction: Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services. Method: A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS &lt;5), intermediate (HFRS 5–15) and high (HFRS &gt;15) risk of frailty. Results: Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission. Conclusion: To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty.

Rate and Causes of Unplanned Hospital Returns within 60 Days following Head and Neck Surgery

Abstract Introduction Unplanned hospital returns are frequent and may be preventable. Objective To comprehend the reasons for unplanned hospital readmission and return to the Outpatient Department (OPD) and Emergency Department (ED) within 60 days after discharge following head and neck surgery (HNS) at a tertiary care center in Saudi Arabia. Methods In the present retrospective study, the medical records of all patients who underwent HNS for benign and malignant conditions between January 2015 and June 2022 were reviewed in terms of demographic data, comorbidities, and reasons for hospital return. Results Out of 1,030 cases, 119 (11.55%) returned to the hospital within 60 days after discharge, 19 of which (1.84%) were readmitted. In total, 90 (8.74%) patients returned to the OPD, and 29 (2.82%), to the ED. The common reasons for readmission included infections (26.32%) and neurological symptoms (21.05%). For OPD visits, the common causes were hematoma (20%) and neurological symptoms (14.44%). For ED returns, the frequent causes were neurological symptoms (20.69%) and equipment issues (17.24%). Compared with nonreadmitted patients, readmitted patients had a higher preoperative baseline health burden when examined using the American Society of Anesthesiologists (ASA) score (p = 0.004) and the Cumulative Illness Rating Scale (CIRS; p = 0.002). Conclusion The 60-day rates of unplanned hospital return to the OPD and ED were of 8.74% and 2.82% respectively, and 1.84% of the patients were readmitted. Hematoma, infections, and neurological symptoms were common causes. Addressing the common reasons may be beneficial to decrease postoperative hospital visits.

Community health navigator-assisted transition of care from hospital to community: protocol for a randomised controlled trial

IntroductionThe objective of this parallel group, randomised controlled trial is to evaluate a community health navigator (CHN) intervention provided to patients aged over 40 years and living with chronic health...

Multifactorial risk factors for hospital readmissions among patients with symptoms of advanced heart failure.

Economic burden of heart failure is attributed to hospital readmissions. Previous studies assessing risk factors for readmissions have focused on single group of risk factors, were limited to 30-day readmissions, or did not account for competing risk of mortality. This study investigates the biological, socio-economic, and behavioural risk factors predicting hospital readmissions while accounting for the competing risk of mortality. In this prospective cohort study, we recruited 250 patients hospitalized with symptoms of advanced heart failure [New York Heart Association (NYHA) Class III and IV] between July 2017 and April 2019. We analysed their baseline survey data and their hospitalization records over the next 4.5years (July 2017 to January 2022). We used a joint-frailty model to determine the multifactorial risk factors for all-cause and unplanned hospital readmissions and mortality. At the time of recruitment, patients' mean (SD) age was 66 (12) years, majority being male (72%) and NYHA class IV (68%) with reduced ejection fraction (72%). 87% of the patients had poor self-care behaviours, 51% had diabetes and 56% had weak grip strength. Within 2years of a hospital admission, 74% of the patients had at least one readmission. Among all readmissions during follow-up, 68% were unplanned. Results from the multivariable regression analysis shows that the independent risk factors for hospital readmissions were biologic-weak grip strength [hazard ratio (95% CI): 1.59 (1.06, 2.13)], poor functional status [1.79 (0.98, 2.61)], diabetes [1.42 (0.97, 1.86)]; behavioural-poor self-care [1.66 (0.84, 2.49)], and socio-economic-preference for maximal life extension at high cost for those with high education [1.98 (1.17, 2.80)]. Risk factors for unplanned hospital readmissions were similar. A higher hospital readmission rate increased the risk of mortality [1.86 (1.23, 2.50)]. Other risk factors for mortality were biologic-weak grip strength [3.65 (0.57, 6.73)], diabetes [2.52 (0.62, 4.42)], socio-economic-lower education [2.45 (0.37, 4.53)], and being married [2.53 (0.37, 4.69)]. Having a private health insurance [0.40 (0.08, 0.73)] lowered the risk for mortality. Risk factors for hospital readmissions and mortality are multifactorial. Many of these factors, such as weak grip strength, diabetes, poor self-care behaviours, are potentially modifiable and should be routinely assessed and managed in cardiac clinics and hospital admissions.

Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data

Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, nearly 20% of Medicare patients were readmitted after discharge, where the average cost of readmission was approximately USD 15,000, as reported by the Agency for Healthcare Research and Quality (AHQR) in 2018. To tackle this issue, we first conducted a descriptive analysis study to understand the risk factors associated with hospital readmission, and then we applied machine learning approaches to predict hospital readmission by using patients’ demographic and clinical data extracted from the Electronic Health Record of the MIMIC-III clinical database. The results showed that the number of previous admissions during the last 12 months, hyperosmolar imbalance and comorbidity index were the top three significant factors for hospital readmission. The predictive model achieved a performance of 95.6% AP and an AUC = 97.3% using the Gradient Boosting algorithm trained on all features.

The Extent of Nurses' Discharge Education to the Patient's Preparedness for Self-Management

Background:Nurses are known for providing their patients with comprehensive care; patient education through discharge instructions typically falls short of expectations due to understaffing and work overload. Patient self-management of their health conditions at home demands adequate discharge education. Insufficient discharge education can compromise patients' ability to manage their health conditions at home, causing unplanned hospital readmissions. When the patients feel that they are being cared for through the provision of discharge education, they are likely to comply with the instructions given, which may contribute to the patient's optimal well-being and may prevent hospital readmission. Objective: This study aimed to determine the correlation between nurses' extentofdischargeeducationandpatients'preparednessforself-management.Methods:Adescriptive-correlational research design was used to determine the correlation between nurses' extent of discharge education and patients' preparedness for self-management. One hundred eight (108) respondents were selected through consecutive sampling. They answered the Medication, Exercise, Health Teaching, Outpatient Patient Department Visit, Referral, Diet, and Spirituality (MEHORDS) discharge assessment tool. MEHORDS is a researcher-made questionnaire with 0.756 and 0.789 reliability coefficients for assessing nurses' extent of discharge education and patients' preparedness for self-management, respectively. Results:The study shows that most of the respondents are middle-aged adults (22.22%), male (57%), married (63.89%), high school graduates (69.44%), unemployed (61.11%), with cerebrovascular disease (35.91%), and have experienced at least one hospital readmission (37.96%). The study revealed that respondents received a poor level of discharge education (x=1.49) during their discharge process and had no overall preparedness (x = 1.75) for self-management. Moreover, there is a significant positive correlation between the nurses' discharge education and patients' preparedness for self-management (p=0.001). Conclusion:This study highlighted the importance of providing nurses comprehensive discharge education in promoting patients' self-management. Incorporating the MEHORDS contents may not only assist the nurses in managing their time in the provision of discharge education to patients but also serve as a guide to nurses in delivering a thorough discharge education