The burden and predictors of 30-day unplanned readmission in patients with acute liver failure: a national representative database study.

BACKGROUND: Liver diseases were significant source of early readmission burden. This study aimed to evaluate the 30-day unplanned readmission rates, causes of readmissions, readmission costs, and predictors of readmission in patients with acute liver failure (ALF). METHODS: Patients admitted for ALF from 2019 National Readmission Database were enrolled. Weighted multivariable logistic regression models were applied and based on Directed Acyclic Graphs. Incidence, causes, cost, and predictors of 30-day unplanned readmissions were identified. RESULTS: A total of 3,281 patients with ALF were enrolled, of whom 600 (18.3%) were readmitted within 30 days. The mean time from discharge to early readmission was 12.6 days. The average hospital cost and charge of readmission were $19,629 and $86,228, respectively. The readmissions were mainly due to liver-related events (26.6%), followed by infection (20.9%). The predictive factors independently associated with readmissions were age, male sex (OR 1.227, 95% CI 1.023-1.472; P = 0.028), renal failure (OR 1.401, 95% CI 1.139-1.723; P = 0.001), diabetes with chronic complications (OR 1.327, 95% CI 1.053-1.672; P = 0.017), complicated hypertension (OR 1.436, 95% CI 1.111-1.857; P = 0.006), peritoneal drainage (OR 1.600, 95% CI 1.092-2.345; P = 0.016), etc. CONCLUSIONS: Patients with ALF are at relatively high risk of early readmission, which imposes a heavy medical and economic burden on society. We need to increase the emphasis placed on early readmission of patients with ALF and establish clinical strategies for their management.

Profile of and risk factors for early unplanned readmissions in patients with acute necrotizing pancreatitis.

INTRODUCTION: Acute necrotizing pancreatitis (ANP) complicates up to 15% of acute pancreatitis cases. ANP has historically been associated with a significant risk for readmission, but there are currently no studies exploring factors that associate with risk for unplanned, early (<30-day) readmissions in this patient population. METHODS: We performed a retrospective review of all consecutive patients presenting to hospitals in the Indiana University (IU) Health system with pancreatic necrosis between December 2016 and June 2020. Patients younger than 18 years of age, without confirmed pancreatic necrosis and those that suffered in-hospital mortality were excluded. Logistic regression was performed to identify potential predictors of early readmission in this group of patients. RESULTS: One hundred and sixty-two patients met study criteria. 27.7% of the cohort was readmitted within 30-days of index discharge. The median time to readmission was 10 days (IQR 5-17 days). The most frequent reason for readmission was abdominal pain (75.6%), followed by nausea and vomiting in (35.6%). Discharge to home was associated with 93% lower odds of readmission. We found no additional clinical factors that predicted early readmission. CONCLUSION: Patients with ANP have a significant risk for early (<30 days) readmission. Direct discharge to home, rather than short or long-term rehabilitation facilities, is associated with lower odds of early readmission. Analysis was otherwise negative for independent, clinical predictors of early unplanned readmissions in ANP.

Impact of post-discharge phone calls on non-urgent hospital returns < 90 days following primary bariatric surgery.

INTRODUCTION: Quality of care delivery may improve patient outcomes post-bariatric surgery. We examined the quality of post-discharge phone calls (PhDC) to determine the impact on early (< 90 day) non-urgent hospital returns (NUHR) following primary bariatric surgery. METHODS: A retrospective review was performed on patients who underwent Roux-en-Y-gastric bypass (RYGB) or sleeve gastrectomy (SG) in 2019. Patients were compared between presence of care coaching (Jan-June 2019) versus no care coaching (July-Dec 2019). Baseline demographics, comorbidities, psychiatric history, and PhDC were collected. Index PhDCs were coded for completeness using a scoring system and rated by call quality. Patients were stratified into NUHR versus control group (Never returns [NR]). Primary analysis examined the impact of PhDC on NUHR. Sub-analysis examined the impact of call quality. Univariate analysis was performed using Chi-square or Fisher's exact tests. Multivariate analysis (MVA) was used to determine predictors of NUHR. A p-value of ≤ 0.05 was statistically significant. RESULTS: A total of 359 patients were included. Compared to the NR group (n = 294), NUHRs (n = 65) were more likely to be younger (41.3 + 12.1 versus 45.0 + 10.8 years, p = 0.024), with baseline anxiety (41.5% versus 23.5%, p = 0.003), and undergo RYGB (73.3% versus 57.8%, p = 0.031). There was a significant difference in number of PhDC in the NUHR and NR groups (p = 0.0206). Care-coached patients had significantly higher rates of high-quality phone calls (p < 0.0001) compared to non-care-coached patients. MVA demonstrated younger age (OR = 0.97, CI: 0.95-1.00; p = 0.023), anxiety (OR = 2.09, CI: 1.17-3.73; p = 0.012), RYGB (OR = 1.88, CI: 1.02-3.45; p = 0.042), and > 50% call quality versus no PhDC (OR = 0.45, CI: 0.25-0.83; p = 0.010) were independently associated with NUHRs. CONCLUSION: High-quality PhDCs may play a role in mitigating NUHRs. Care coaching represents a potential intervention to decrease high rates of NUHR in primary bariatric surgery patients.

Early unplanned readmissions following same-admission cholecystectomy for acute biliary pancreatitis.

BACKGROUND: Same-admission cholecystectomy (CCY) is recommended for mild acute biliary pancreatitis (biliary-AP). However, there is a paucity of research investigating reasons for early (30-day) unplanned readmissions in patients who undergo CCY for biliary-AP. Hence, we sought to investigate this gap using a large population database. METHODS: Using the Nationwide Readmission Database (2010-2014), we identified all adults (age ≥ 18 years) with a principal diagnosis of biliary-AP who had undergone CCY during the index hospitalization. Multivariable logistic regression models were obtained to assess independent predictors for 30-day readmission. Principal diagnosis for all readmissions was collected to ascertain the indications for early readmission. RESULTS: During the study period, 118,224 patients underwent same-admission CCY for biliary-AP. Three-fourths of all patients underwent invasive cholangiography during the hospitalization (intraoperative cholangiogram (IOC) = 57,038, ERCP = 31,500). The rate of early (30-day) readmission was 7.25% (n = 8574). Exacerbation of prior medical conditions (42.2%), sequelae of biliary-AP (resolving and recurrent pancreatitis, pseudocysts) (27.6%), surgical site and other postoperative complications (16%), choledocholithiasis and/or bile leak (9.6%), and preventable hospital-acquired conditions (4.6%) accounted for early readmissions. On multivariable analysis, predictors for readmission included male sex (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.08-1.28), insurance type (Medicare insurance [OR 1.26, 95% CI 1.13-1.40]; Medicaid [OR 1.22, 95% CI 1.09-1.38]), outside-facility discharge (OR 1.35, 95% CI 1.16-1.57), severe AP (OR 1.35, 95% CI 1.21-1.50), and ≥ 3 Elixhauser comorbidities (OR 1.55, 95% CI 1.41-1.69). Performance of IOC (OR 0.90, 95% CI 0.82-0.97) and ERCP (OR 0.81, 95% CI 0.73-0.89) were associated with decreased risk of early readmission. CONCLUSION: In this study, using a national population database evaluating patients who underwent same-admission CCY after biliary-AP, we identified potentially modifiable risk factors and causes for early readmission as well as opportunities to improve clinical care.

Use of the MMPI-2 personality profile in predicting 30-day ED-visits and readmissions following primary bariatric surgery.

BACKGROUND: Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is used for psychological screening of bariatric surgery (BS) candidates. To date, no studies have analyzed the relationship between MMPI-2 and early returns to hospital. The aim of this study was to determine whether high T scores on the MMPI-2 clinical scales were associated with early return to hospital after primary bariatric surgery. METHODS: Patients who completed an MMPI-2 evaluation, undergoing primary BS from 2014 to 2016 were evaluated. T score for the tested scales were collected and stratified into a high T score (T > 65) vs not (T < 65). The optimal 'cut-point' (specific number of high T scores predicting likelihood for 30-day ED-visit/hospital readmission) was calculated using Youden's Index (J) = Max(c) [sensitivity (c) + specificity (c) - 1], where c = number of scales with a T score > 65. Patients were stratified based on the optimal cut-point which was determined to be ≥ 4 high T scores. Univariate and multivariate logistic regression analyses were used to identify differences between groups and predictors for early ED-visits and hospital readmissions. RESULTS: 375 patients had psychological evaluations available for review. Patients were divided into those with ≥ 4 high T scores (Scr(≥4); n = 86) versus not (Scr (<4); n = 289). Multivariate analysis showed Scr(≥ 4) (aOR 2.99, CI 1.20-7.47; p = 0.019), bipolar disorder (aOR 4.82, CI 1.25-18.83; p = 0.022), and urgent hospital complications (aOR 6.81, CI 2.02-22.91; p = 0.002), were significant independent predictors of 30-day readmissions. Early ED-visits were significantly predicted by public insurance (aOR 3.30, CI 1.22-8.91; p = 0.019), but the effect of the Scr(≥4) profile (aOR 2.42, CI 0.97-6.09; p = 0.06), while influential, did not reach significance. CONCLUSION: Differences in personality traits may be associated increased 30-day readmissions following primary bariatric surgery. Our study represents a novel application of the MMPI-2.

Factors Related to 30-day Readmission following Hospitalization for Any Medical Reason among Patients with Mental Disorders: Facteurs liés à la réhospitalisation à 30 jours suivant une hospitalisation pour une raison médicale chez des patients souffrant de troubles mentaux.

OBJECTIVE: This study evaluated the contributions of clinical, sociodemographic, and service use variables to the risk of early readmission, defined as readmission within 30 days of discharge following hospitalization for any medical reason (mental or physical illnesses), among patients with mental disorders in Quebec (Canada). METHODS: In this longitudinal study, 2,954 hospitalized patients who had visited 1 of 6 Quebec emergency departments (ED) in 2014 to 2015 (index year) were identified through clinical administrative databanks. The first hospitalization was considered that may have occurred at any Quebec hospital. Data collected between 2012 and 2013 and 2013 and 2014 on clinical, sociodemographic, and service use variables were assessed as related to readmission/no readmission within 30 days of discharge using hierarchical binary logistic regression. RESULTS: Patients with co-occurring substance-related disorders/chronic physical illnesses, serious mental disorders, or adjustment disorders (clinical variables); 4+ outpatient psychiatric consultations with the same psychiatrist; and patients hospitalized for any medical reason within 12 months prior to index hospitalization (service use variables) were more likely to be readmitted within 30 days of discharge. Patients who made 1 to 3 ED visits within 1 year prior to the index hospitalization, had their index hospitalization stay of 16 to 29 days, or consulted a physician for any medical reason within 30 days after discharge or prior to the readmission (service use variables) were less likely to be rehospitalized. CONCLUSIONS: Early hospital readmission was more strongly associated with clinical variables, followed by service use variables, both playing a key role in preventing early readmission. Results suggest the importance of developing specific interventions for patients at high risk of readmission such as better discharge planning, integrated and collaborative care, and case management. Overall, better access to services and continuity of care before and after hospital discharge should be provided to prevent early hospital readmission.

Comparison of inpatients who were readmitted within 28 days of discharge with those not readmitted: an audit at an Australian private psychiatric hospital.

OBJECTIVE: To compare inpatients who had been readmitted within 28 days of discharge with patients not readmitted within the same period in a private psychiatric hospital. METHOD: Of 118 readmissions within 28 days in 2017 (7% of admissions), 50 were randomly selected and matched by age and gender with control patients who had not been readmitted within 28 days. Differences in demographics, diagnosis, length of stay and number of admissions in the previous 12 months were examined. RESULTS: Readmitted cases were 64% female, were aged 49.8 ± 18.2 years (range 19-89), 40% were in relationships and 24% were receiving disability support. Most patients were suffering an episode of depression. Cases had higher rates of multiple psychiatric diagnoses (p < .001) and physical disorders (p < .05). There were no significant differences between cases and controls on psychiatric diagnoses. Cases had a longer length of stay in their previous admission (p < .01) and a higher number of admissions in the preceding 12 months (p < .05) compared to controls. CONCLUSION: This study indicates that inpatients readmitted within 28 days of discharge were more likely to have multiple diagnoses, physical co-morbidity and relapsing conditions than patients who were not readmitted.

Characteristics, diagnoses and risk profiles of inpatients readmitted within 28 days of discharge to an Australian private psychiatric hospital.

OBJECTIVE: The objective of this study was to perform a clinical and risk audit of private hospital inpatients who had been readmitted within 28 days of a preceding admission. METHOD: Of 118 readmissions within 28 days in 2017 (7% of all admissions), 50 were randomly selected for audit. Characteristics, illness severity and clinical risk profiles were ascertained at discharge from the index admission and at readmission. RESULTS: Cases were 64% female, age 49.9 ± 18.2 years (range 19-89), 40% in relationships and 24% on disability support. At readmission, 88% posed danger to self due to effects of illness, 46% had high suicide risk and 40% had high physical risk. Illness was rated as severe in 58%, while 40% were rated markedly ill. Relapse or exacerbation of major depression was a cause of readmission in 78%, relapse of alcohol/substance use requiring readmission in 22% and relapse of psychosis in 20%. Index admission length of stay of cases did not differ from that of all hospital admissions. CONCLUSION: Most readmitted patients were suffering severe exacerbation of depression, were acutely suicidal and were otherwise at high risk of harm. If these patients had been denied readmission on the basis of insurer funding disincentives, catastrophic outcomes may well have occurred.

Early hospital readmission after kidney transplantation under a public health care system.

Early hospital readmission (EHR) is associated with increased mortality after kidney transplantation. This is influenced by population demographics and the comprehensiveness of the healthcare system. We investigated the incidence and risk factors associated with EHR and 1-year patient and graft survivals. METHODS: We included all recipients of kidney transplant between 2011 and 2012. We excluded recipients younger than 18 years, retransplants and who died or lost the graft during the index hospital admission. RESULTS: Among 1175 recipients, the incidence of EHR was 26.6%. The main reasons for EHR were infection (67%), surgical complications (14%), and metabolic disturbances (11%). Independent risk factors associated with EHR were recipient age (OR = 1.95, 95% CI 1.46-2.63, P < 0.001), CMV serology negative (OR = 2.2, 95% CI 1.31-3.65, P = 0.003), use of rabbit anti-thymocyte globulin (OR = 2.06, 95% CI 1.33-3.13, P < 0.001), treatment for acute rejection during index hospitalization (OR = 1.68, 95% CI 1.15-2.47, P = 0.008), and length of stay (OR = 1.72, 95% CI 1.18-2.5, P = 0.005). Patient (88.8% vs 97.6%, P < 0.001) and death-censored graft (97.4% vs 99.0%, P < 0.001) survivals were inferior comparing patients with and without EHR. Conclusion EHR was independently associated with mortality (OR 4.01, 95% CI 2.13-7.54, P < 0.001), but its incidence and causes are directly related to the local characteristics of the population and healthcare system.

Baseline psychiatric diagnoses are associated with early readmissions and long hospital length of stay after bariatric surgery.

BACKGROUND: The impact of well-controlled or historical psychiatric diagnoses in patients seeking bariatric surgery (BS) on perioperative outcomes is unclear. The primary objective of this study was to determine the impact of psychiatric diagnoses on hospital length of stay (LOS), 30-day readmission rates after BS, and post-operative weight loss outcomes. METHODS: Patients who underwent laparoscopic sleeve gastrectomy (LSG) or Roux-en-Y gastric bypass (LRNYGB) from 2014 to 2016 at a single academic institution were retrospectively reviewed. Baseline demographic data and psychiatric history including depression, anxiety, and/or bipolar disorder (DAB) were obtained from the electronic medical record. Hospital LOS, 30-day readmissions, and % excess body weight loss (%EBWL) were obtained on all patients and compared between DAB patients and those without any psychiatric history. RESULTS: During the study period, 354 patients were reviewed, of which 78% were female; 60% underwent LSG. The mean preoperative BMI was 48.9 ± 8.4 m/kg2. Major depression was the leading diagnosis (42%), and 13% had both depression and anxiety. The 30-day readmission rate was significantly higher than the control (10.5% vs. 3.7%, p = 0.02). Mean hospital LOS and the incidence of long hospital LOS (≥ 4 days) was not different between the groups, although within LSG patients, the incidence of long hospital LOS trended towards being higher for DAB patients (9.2% vs. 4%, p = 0.10). Patients with depression and anxiety had a higher incidence of long LOS (23.4% vs. 9.2%, p < 0.005). While 6-month %EBWL was significantly lower for DAB patients (41% vs. 46%, p = 0.004), 1-year weight loss outcomes were not different, even when adjusting for surgical procedure. CONCLUSION: Patients with baseline or historical DAB had significantly higher early readmission rates, and those with multiple diagnoses were associated with a hospital LOS ≥ 4 days. Future studies should focus on elucidating the impact of psychiatric diagnoses on these quality metrics.

72-hour hospital readmission of older people after hospital discharge with home care services.

In this retrospective cohort study in Argentina, risk factors for hospital readmission of older adults, within 72 hours after hospital discharge with home care services, were analyzed. Fifty-three percent of unplanned emergency room visits within 72 hours after hospital discharge resulted in hospital readmissions, 65% of which were potentially avoidable. By multivariate logistic regression, low functionality, pressure ulcers, and age over 83 years predicted hospital readmission among emergency room attendees. It is important to identify and analyze barriers in current home care services and the high-risk population of hospital readmission to improve the strategies to avoid adverse outcomes.

Very Early Versus Early Readmissions in General Surgery Patients.

BACKGROUND: Hospital readmission rates are an important quality metric. A readmission very soon after discharge may be related to a different cause than readmissions that occur later in the first 30 d. MATERIALS AND METHODS: The National Surgical Quality Improvement Program data sets from 2014 to 2015 were used to identify patients undergoing general surgery procedures. Demographics, comorbidities, and morbidity were analyzed. Stepwise regression was used to determine statistical predictors for any readmission. The final model variables were a combination of selected clinical variables and statistically significant variables. Multinomial logistic regression was then used with these variables to develop models for "very early" (days 0-3 after discharge) and "early" (days 4-30) readmissions. RESULTS: A total of 744,492 patients were included with 5.9% readmitted within 30 d and 1.5% readmitted within 3 d of discharge (26.1% of all readmissions). Significant risk factors for any readmission included ≥3 comorbidities, major surgery (operative time >1 h, length of stay greater >2 d), and American Society of Anesthesiologists class ≥3. When examining "very early" readmissions, the greatest risk factor was experiencing a severe complication (≥Grade III) before discharge. CONCLUSIONS: Readmissions within 3 d of discharge constitute a large portion of all 30 d readmissions. The greatest risk factor for "very early" readmission was a severe complication before discharge. Better understanding of the reason for this association is needed to develop effective prevention strategies.

Rehospitalization risk factors for psychiatric treatment among elderly Medicaid beneficiaries following hospitalization for a physical health condition.

OBJECTIVES: Hospitalizations for physical health problems can cause great mental health challenges. We examined risk factors and expenditures for early readmission for inpatient psychiatric treatment following a hospitalization for a non-psychiatric condition. METHOD: We used eight years of Florida Medicaid enrollment and claims data for (FY 2003-2011) as well as data obtained from the Florida Center for Health Information and Policy Analysis, Inpatient Hospital Database, to identify episodes of hospitalization for all Medicaid-enrolled older adults. There were a total of 781,660 index hospitalizations for non-behavioral health-related hospitalizations, 2690 (0.3%) of which resulted in a behavioral health-related rehospitalization within 30 days after discharge. We estimated the relative risk of early rehospitalization using Cox proportional hazards regression. RESULTS: Participants (n = 276,254) were 76.8 ± 8.3 years old; 68.4% female and 43.5% White. As expected, we found that having a preexisting behavioral health disorder (including severe mental illness, substance use disorder, or dementia) greatly increased the risk of readmission. We also found that male gender increased the risk of early readmission. Older age, being Black and having one or more comorbid medical conditions as identified by the Charlson Index were associated with substantially reduced risk. CONCLUSION: Identifying hospitalized patients with psychiatric conditions, as well as those who are relatively young within this age group, male, White rather than Black or of other race, alcohol abusers, and those without comorbid conditions, and providing them with care to address their behavioral needs may help in efforts to reduce early rehospitalizations for psychiatric conditions.

Early versus late readmission of subarachnoid haemorrhage patients into neurocritical care.

INTRODUCTION: Subarachnoid haemorrhage (SAH) patients will typically require monitoring in a specialised Neurocritical Care Unit (NCCU) regardless of the primary treatment modality. Once discharged from NCCU, readmission within 48 h is regarded as a "failed" discharge. The aims of this study are to (1) Evaluate the readmission rate of SAH patients into NCCU, (2) Identify the indications for readmission, (3) Analyse clinical parameters on discharge between patients readmitted early and late. MATERIALS AND METHODS: Retrospective observational study of the Intensive Care National Audit and Research Centre (ICNARC) database of patients from our unit diagnosed with SAH from January 2009-December 2014, who were readmitted into NCCU. Demographic data, World Federation of Neurosurgical Societies (WFNS) grade, Fisher grade, length of initial and subsequent NCCU stay, time of readmission, indication for readmission, and mortality rate data were collected. Patients were categorised by early (<48 h) and late (>48 h) readmission, and their clinical parameters on NCCU discharge were statistically analysed. RESULTS: Five hundred and seventy-five SAH patients were admitted into NCCU, of which 49 patients (9%) were readmitted after discharge to ward-level care. The mean age of readmitted patients was 64.1 ± 11.6 years old. The most common indications were delayed cerebral ischaemia (DCI) (50%) and infection (19%). Readmitted SAH patients were typically WFNS grade I-II (n = 22) and Fisher grade III-IV (n = 44). 17 (35%) patients were readmitted early, and were older (p = 0.0049) with a lower GCS (p = 0.0077) compared to patients readmitted later. White cell count and C-reactive protein were higher in patients readmitted early, but did not reach statistical significance (p = 0.09, p = 0.07). CONCLUSION: DCI and infection were the most common indications for NCCU readmission in SAH patients. "Failed" discharged patients from NCCU are typically older with a lower GCS than patients readmitted after 48 h, and therefore clinicians should be more cautious in discharging these patients prematurely.

A comparison of models for predicting early hospital readmissions.

Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.

Risk Factors for Early Hospital Readmission in Geriatric Patients: A Systematic Review.

The number of older patients is constantly growing, and early hospital readmissions in this population represent a major problem from a health, social and economic point of view. Furthermore, the early readmission rate is often used as an indicator of the quality of care. We performed a systematic review of the literature to better understand the risk factors of early readmission (30 and 90 days) in the geriatric population and to update the existing evidence on this subject. The search was carried out on the MEDLINE, EMBASE and PsycINFO databases. Three independent reviewers assessed the potential inclusion of the studies, and then each study was independently assessed by two reviewers using Joanna Briggs Institute critical appraisal tools; any discrepancies were resolved by the third reviewer. Studies that included inpatients in surgical wards were excluded. Twenty-nine studies were included in the review. Risk factors of early readmission can be classified into socio-economic factors, factors relating to the patient's health characteristics, factors related to the use of the healthcare system and clinical factors. Among these risk factors, those linked to patient frailty play an important role, in particular malnutrition, reduced mobility, risk of falls, fatigue and functional dependence. The early identification of patients at higher risk of early readmission may allow for targeted interventions in view of discharge.

Incidence and Causes of Early Hospital Readmissions After Living-Donor Renal Transplant at King Abdulaziz Medical City, Riyadh.

BACKGROUND:  Living-donor organ transplant has a higher long-term survival rate compared to deceased-donor organ transplant, with kidney transplantation being the optimal treatment for most kidney failure patients. However, early hospital readmission within 30 days of discharge can occur due to various factors and can negatively affect long-term outcomes. Effective communication with patients pre-and post-transplant is crucial for a better quality of life and for reducing readmissions. Chronic kidney disease and co-morbid conditions must also be addressed for better long-term outcomes. The incidence and causes of early hospital readmission vary depending on local characteristics and other factors. METHODS:  A retrospective cohort study of outcomes in patients who underwent living-donor renal transplantation at King Abdulaziz Medical City (KAMC) between 2015 and 2022. Data were collected by chart review using the BestCare system. The data collected included patients' demographics, comorbidities, surgery-related data, and the outcome of transplantation. The categorical data were presented using percentages and frequencies, while the numerical data were presented as mean and standard deviation. The Chi-square test was used for inferential statistics to find the association between categorical variables. RESULTS:  Regarding sociodemographic characteristics, the majority of patients were male, aged 19-50 years, and either overweight or had obesity class 1. The incidence of complications, graft failure, and mortality after renal transplant was low, with only a small percentage of patients experiencing these outcomes within one year of transplant. There is no significant association between gender, age, BMI, and the likelihood of readmission after renal transplantation. Patients with comorbidities such as hypertension, diabetes, and coronary artery disease had a higher likelihood of readmission after renal transplantation. The study provides an association between readmission after renal transplantation and various factors such as surgical complications, previous transplant, age at transplant, graft failure, and mortality. Out of the 107 readmitted patients, 2.8% had surgical complications, and 5.6% had a previous transplant, but the association was not statistically significant. CONCLUSION:  Early hospital readmission within 30 days of discharge can be a concern for patients undergoing renal transplants. While the incidence of complications, graft failure, and mortality after renal transplant was low, patients with comorbidities such as hypertension, diabetes, and coronary artery disease had a higher likelihood of readmission after renal transplantation. Although the association between surgical complications and readmission was not statistically significant, it is important to continue monitoring this factor in future studies. Effective communication with patients pre-and post-transplant can play a crucial role in reducing readmissions and improving long-term outcomes.

Unplanned 30-Day Readmission in Glioblastoma Patients: Implications for the Extent of Resection and Adjuvant Therapy.

BACKGROUND: Unplanned early readmission (UER) within 30 days after hospital release is a negative prognostic marker for patients diagnosed with glioblastoma (GBM). This work analyzes the impact of UER on the effects of standard therapy modalities for GBM patients, including the extent of resection (EOR) and adjuvant therapy regimen. METHODS: Records were searched for patients with newly diagnosed GBM between 2014 and 2020 who were treated at our facility. Exclusion criteria were being aged below 18 years or missing data. An overall survival (OS) analysis (Kaplan-Meier estimate; Cox regression) was performed on various GBM patient sub-cohorts. RESULTS: A total of 276 patients were included in the study. UER occurred in 13.4% (n = 37) of all cases, significantly reduced median OS (5.7 vs. 14.5 months, p < 0.001 by logrank), and was associated with an increased hazard of mortality (hazard ratio 3.875, p < 0.001) in multivariate Cox regression when other clinical parameters were applied as confounders. The Kaplan-Meier analysis also showed that patients experiencing UER still benefitted from adjuvant radio-chemotherapy when compared to radiotherapy or no adjuvant therapy (p < 0.001 by logrank). A higher EOR did not improve OS in GBM patients with UER (p = 0.659). CONCLUSION: UER is negatively associated with survival in GBM patients. In contrast to EOR, adjuvant radio-chemotherapy was beneficial, even after UER.

Low haemoglobin level predicts early hospital readmission in patients with cirrhosis and acute decompensation.

Background & Aims: Patients with decompensated cirrhosis present frequent hospitalisations with a relevant clinical and socio-economic impact. This study aims to characterise unscheduled readmissions up to 1-year follow-up and identify predictors of 30-day readmission after an index hospitalisation for acute decompensation (AD). Methods: We performed a secondary analysis of a prospectively collected cohort of patients admitted for AD. Laboratory and clinical data at admission and at discharge were collected. Timing and causes of unscheduled readmissions and mortality were recorded up to 1 year. Results: A total of 329 patients with AD were included in the analysis. Acute-on-chronic liver failure was diagnosed in 19% of patients at admission or developed in an additional 9% of patients during the index hospitalisation. During the 1-year follow-up, 182 patients (55%) were rehospitalised and 98 (30%) more than once. The most frequent causes of readmission were hepatic encephalopathy (36%), ascites (22%), and infection (21%). Cumulative incidence of readmission was 20% at 30 days, 39% at 90 days, and 63% at 1 year. Fifty-four patients were readmitted for emergent liver-related causes within 30 days. Early readmission was associated with a higher 1-year mortality (47 vs. 32%, p = 0.037). Multivariable Cox regression analysis showed that haemoglobin (Hb) ≤8.7 g/dl (hazard ratio 2.63 [95% CI 1.38-5.02], p = 0.003) and model for end-stage liver disease-sodium score (MELD-Na) >16 at discharge (hazard ratio 2.23 [95% CI 1.27-3.93], p = 0.005), were independent predictors of early readmission. In patients with MELD-Na >16 at discharge, the presence of Hb ≤8.7 g/dl doubles the risk of early rehospitalisation (44% vs. 22%, p = 0.02). Conclusion: Besides MELD-Na, a low Hb level (Hb ≤8.7 g/dl) at discharge emerged as a new risk factor for early readmission, contributing to identification of patients who require closer surveillance after discharge. Impact and Implications: Patients with decompensated cirrhosis face frequent hospitalisations. In the present study, type and causes of readmissions were analysed during 1-year follow-up in patients discharged after the index hospitalisation for an acute decompensation of the disease. Early (30-day) liver-related readmission was associated with higher 1-year mortality. The model for end-stage liver disease-sodium score and low haemoglobin at discharge were identified as independent risk factors for early readmissions. Haemoglobin emerged as a new easy-to-use parameter associated with early readmission warranting further investigation.

Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data.

Introduction: Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization. Methods: In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005-2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance. Results: Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105). Conclusions: Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.