Unplanned hospital readmissions after kidney transplantation among patients in Hefei,China: Incidence,causes and risk factors

ObjectivesUnplanned readmissions severely affect a patient’s physical and mental well-being after kidney transplantation (KT), which is also independently associated with morbidity. A retrospective study was conducted to identify the incidence, causes and risk factors for unplanned readmission after KT among Chinese patients.MethodsPatients who underwent KT were admitted to the organ transplant center of the Affiliated Hospital of University of Science and Technology of China (2017–2018). Medical records for these patients were obtained through the hospital information system (HIS).ResultsIn 518 patients, the incidence of unplanned readmissions within 30 days (n = 9) was 1.74%, and 90 days (n = 64) was 12.35%. The one-year unplanned readmission rate was 22.59% (n = 122). Overall, 122 patients were readmitted because of infection, renal events, metabolic disturbances, surgical complications, etc. Hemodialysis (OR = 10.462, 95% CI: 1.355–80.748), peritoneal dialysis (OR = 8.746, 95% CI: 1.074–71.238) and length of stay (OR = 1.023, 95% CI: 1.006–1.040) were independent risk factors for unplanned readmissions.ConclusionUnplanned readmission rates increased with time after KT. Certain risk factors related to unplanned readmissions should be deeply excavated. Targeted interventions for controllable factors to alleviate the rate of unplanned readmissions should be identified.     

Unplanned pediatric intensive care unit readmissions: A single-center experience

Purpose

The purpose of the study was to compare patients readmitted to the pediatric intensive care unit (PICU) unexpectedly within 48 hours (early), more than 48 hours from transfer (late), or not readmitted during the same hospitalization.

Materials and Methods

A retrospective study (2007-2009) was performed at a tertiary care pediatric academic hospital. Readmitted at-risk patients were grouped by timing of readmission, and a sample of nonreadmitted patients was randomly selected. Early readmissions were compared to late readmissions and to nonreadmissions.

Results

Of 3805 eligible patients, 3.9% had an unplanned PICU readmission with almost half occurring within 48 hours. Median times to readmission were 21.5 hours (early) and 7 days (late). Compared with late readmissions, early readmissions were more often admitted from and transferred to a surgical service, transferred on a weekend, and readmitted with the same primary diagnosis. Compared with nonreadmitted patients, independent risk factors for early readmission were admission source and respiratory support at PICU transfer. Readmitted patients had longer total PICU and hospital lengths of stay than nonreadmitted patients. Late readmissions had a higher mortality than early readmissions.

Conclusions

Patients requiring an unplanned PICU readmission had worse outcomes than those without a readmission. Future studies should focus on identifying modifiable risk factors for targeted interventions.     

Unplanned discharges from a surgical intensive care unit: Readmissions and mortality

Purpose

There is great patient turnover every day on surgical intensive care units (SICUs). Surgical intensive care unit beds are provided for major elective surgery. Emergency admissions trigger unplanned discharges. Those patients are at risk for a worse outcome.

Materials and Methods

We retrospectively analyzed 2558 patients discharged from a 20-bed SICU within 1 year. They were followed up whether discharged electively or not. Patients readmitted to the SICU were stratified according to reason for readmission.

Results

Readmission rate to the SICU was 8.3% (139/1675) in elective discharges, and 25.1% (110/439) in unplanned discharges (P < .001); 50% (125/249) of all readmissions were for surgical complications. Hospital mortality was 2.28% (50/2,197) in patients not readmitted to the SICU and 13.3% (33/249) for those readmitted (P < .001). The mortality rate increased by 4% in readmissions for each year of age (P < .05, OR for death 1.04 for each year of age, 95% CI 1.010-1.071). Respiratory failure as a reason for readmission implied a 44% risk of death (P < .001, OR 11.85, 95% CI 5.11-27.45).

Conclusions

Earlier-than-planned discharge from a SICU leads to a substantially higher readmission rate. Readmission correlates with an elevated risk of death. Most readmissions in a surgical clinic are due to surgical complications. Readmission for respiratory failure accounts for most of the mortality.     

A survey of the documentation of unplanned readmissions following cardiac surgery

Aims: To quantify the frequency of unplanned readmission among cardiac surgery patients at a large teaching hospital; to identify reasons for readmission and to ascertain the comprehensiveness of related hospital documentation.Method: A retrospective survey of the content of documentation concerning unplanned readmissions following cardiac surgery. A semi-structured 31 item questionnaire was designed to collect information concerning a range of discharge and readmission data to be extracted from medical and nursing notes. Four cardiac liaison nurses then extracted these data for all patients who had undergone cardiac surgery over a period of one year and had been readmitted within 3 months.Findings: Data were collected for 65 patients. The mean length of stay for those who were readmitted was 16 days compared with 11.6 for all those treated over the 12 months. One-third of patients were informed they were going home on the day of discharge; half of the patients had no record of being informed of the timing of their discharge and there was a record of 30% of carers being informed of impending discharge. Documentation of services arranged appeared low.The main reasons for readmission were cardiac complications (52%) and infection (28%). Documented reasons for readmission focused on medical issues, but cardiac liaison nurses were able to identify additional reasons concerned with inadequate discharge preparation and social support. Pain and anxiety were the most frequently documented care needs on readmission.Conclusions:Nursing and medical documentation should include all details of discharge arrangements for patients who have undergone cardiac surgery; community-based health professionals need to be more aware of the subsequent problems of pain and anxiety; and a prospective longitudinal study of this group is needed to clarify the association between discharge preparation and readmission, and to identify risk factors for readmission.     

Homeward bound: An analysis of patients discharged home from an oncologic intensive care unit

Purpose

The objectives of our study were to evaluate the characteristics and outcomes of patients discharged home directly from an oncologic intensive care unit (ICU) and their 30-day hospital readmission patterns.

Materials and Methods

We retrospectively reviewed ICU discharges over 3 years (2008-2010) and identified patients who were discharged directly home. Demographic, clinical, ICU discharge, and 30-day hospital readmission and mortality rates were analyzed.

Results

Ninety-five patients (3.6%) were discharged home directly from the ICU (average annual rate of 3.9%). ICU diagnoses primarily included respiratory insufficiency, sepsis, cardiac syndromes, and gastrointestinal bleeding. Home discharge occurred most commonly between Thursday and Saturday. Five (5.3%) patients, including 2 hospice patients, died within 30 days of ICU home discharge. Thirty (31.6%) patients were readmitted within 30 days of discharge. The unplanned 30-day readmission rate was 23.2% (22/95) with a median time to hospital readmission of 13 (8-18) days. Most (64%) of the unplanned readmissions were related to the initial ICU admission.

Conclusions

Home discharge of ICU patients at our institution is infrequent but consistent. Almost one third of these patients were readmitted to the hospital within 30 days. Enhancements to the ICU home discharge process may be required to ensure optimal post-ICU care.     

Hypertensive Diseases of Pregnancy Increase Risk of Readmission With Heart Failure: A National Readmissions Database Study

ObjectiveTo study the association between hypertensive diseases of pregnancy and immediate postpartum development of heart failure in a large national database.Patients and MethodsUsing the 2013 to 2014 National Readmissions Database, which covered admissions from January 1 through September 30 in years 2013 and 2014, we examined 90-day readmission rates in parturients with a diagnosis of hypertensive disease of pregnancy who were discharged after delivery. The primary outcome was the association between the presence of hypertensive disease of pregnancy and readmission with heart failure within 90 days of delivery discharge. Secondary outcomes included readmission mortality, time between delivery discharge and readmission, length of stay, and costs of readmission.ResultsWomen with hypertensive disease of pregnancy were more likely to be readmitted with heart failure (1809 of 25,908 readmissions (7.0%) vs 2622 of 89,660 readmissions (2.9%); P<.001). This difference persisted after adjustment for potential cofounders (6.3% vs 3.1%; odds ratio, 2.15; 95% CI, 1.92-2.40; P<.001). Women with a diagnosis of heart failure at readmission were readmitted sooner (11 days vs 23 days; P<.001) and had a longer length of stay (4 days vs 3 days; P<.001) and higher costs of readmission ($10,361 vs $6977; P<.001) than did women without a diagnosis of heart failure.ConclusionParturients with hypertensive disease of pregnancy were more likely to be readmitted with heart failure within 90 days of delivery. Most patients readmitted with heart failure were readmitted within 2 weeks of discharge after delivery. Patients readmitted with heart failure had substantial health care expenditures.     

结直肠癌术后患者3个月内非计划性再入院危险因素分析

目的探讨结直肠癌患者术后非计划性再入院危险因素。方法选取2017年9月—2019年9月在医院进行结直肠癌手术治疗的924例患者,根据患者出院后3个月内非计划入院情况,将其中术后非计划性再入院的37例患者设为病例组,未再入院的887例患者设为对照组。采用单因素和多因素Logistic回归分析结直肠癌患者术后非计划性再入院的危险因素。结果单因素分析结果显示,性别、术前糖尿病、术前低蛋白血症及术后并发症发生情况是结直肠癌患者术后非计划性再入院的影响因素(P<0.05);多因素Logistic回归分析结果显示,男性、术前糖尿病、术前低蛋白血症、术后并发症为导致结直肠癌患者术后非计划性再入院的独立危险因素(P<0.05)。结论结直肠癌患者术后非计划性再入院危险因素包括患者为男性、术前糖尿病、术前低蛋白血症、术后并发症,因此,在患者首次治疗前,应进行有效评估,改善患者血糖及营养水平,并采取快速康复护理及延续护理等措施避免患者术后再入院。     

基于Logistic回归与决策树模型的结直肠癌术后患者非计划性再入院影响因素分析

目的 采用Logistic回归模型与决策树模型分析结直肠癌术后患者非计划性再入院的影响因素。方法 回顾性分析2018年3月—2019年12月在胃肠外科接受手术治疗的1 383例结直肠癌患者的临床病例资料,分别建立Logistic回归模型与决策树模型,比较2种模型的分析结果。结果 1 383例结直肠癌术后患者71例非计划性再入院,非计划性再入院率为5.13%。Logistic回归模型分析显示,术后并发症、肿瘤TNM分期≥III期、术前合并症≥2项是结直肠癌术后患者非计划性再入院的独立危险因素。决策树模型分析显示,术后并发症是最主要的危险因素,其后依次为术前合并症≥2项、肿瘤TNM分期≥III期、肠造口、手术方式。Logistic回归模型的ROC曲线下面积为0.773,决策树模型为0.790,2个模型的ROC曲线下面积差异无统计学意义（Z=0.414,P>0.05）。结论 术后并发症、肿瘤TNM分期≥III期和术前合并症≥2项是结直肠癌术后患者非计划性再入院的重要影响因素,Logistic回归模型与决策树模型对结直肠癌术后患者非计划性再入院影响因素的分析结果有较高的一致性,临床护士需采取针对性的预防和护理干预措施,降低患者非计划性再入院率。     

Unplanned hospital readmissions: a home care perspective

BACKGROUND: The extensive literature concerning hospital readmissions is grounded in a medical or hospital perspective, and fails to address hospital readmissions during home care. OBJECTIVES: To describe clients who have unplanned returns to an inpatient setting during the first 100 days of home care service delivery. METHOD: Using the Hospital Readmission Inventory (HRI), an audit tool with previously established validity and reliability, 916 medical records for clients from 11 midwestern home care agencies were reviewed retrospectively. RESULTS: Typically, clients were referred for their first home care admission after a 9-day hospital length of stay for a cardiovascular, respiratory, or neoplastic disorder. After an average 18-day length home care stay, clients were readmitted to the hospital, usually due to the development of a new problem, or due to deterioration in health status related to the primary or to a secondary medical diagnosis. Significant respiratory, cardiovascular or GI symptoms were generally present at hospital readmission. Typically, readmitted clients were 75 year old married females, who had been able to care for themselves at home. At hospital readmission, home care nurses judged these clients to be moderately ill, and likely in need of acute care. CONCLUSIONS: Chronic illness appears to be the best indicator for hospital readmission. The crucial time period for hospital readmission during home care is the first 2-3 weeks following hospital discharge. Intensive study of home care service arrangements utilized by readmitted patients, as well as agency variations, are needed. Study findings concerning patients readmitted from home care point to similarities with rehospitalized patients generally. Findings may assist home care clinicians in targeting high risk patients who could benefit from interventions aimed at minimizing unplanned returns to the hospital.     

Chronic obstructive pulmonary disease exacerbation: A single-center perspective on hospital readmissions

Background. Chronic obstructive pulmonary disease (COPD) is the third most common cause in the United States of hospital readmission within 30 days of discharge. Readmissions, which are attributed to poor quality of care, are costly. We examined the factors associated with 30-day readmission in patients hospitalized with acute exacerbation of COPD. Our hypothesis was that early readmissions among patients with COPD are related to patient factors rather than system or provider factors. Methods. We performed a retrospective chart review of all patients discharged from our facility from June 2010 to May 2011 with a primary discharge diagnosis of COPD. Detailed patient characteristics were obtained from the electronic medical record. Patients were followed for 30 days post–discharge date. We examined the differences in baseline characteristics of patients readmitted within 30 days and those not readmitted. Results. A total of 160 patients were admitted for 192 hospitalizations during the study period; 31 patients (19.4%) were readmitted within 30 days. Patients who were readmitted did not differ from those who were not readmitted of the following factors: baseline medication use, length of stay, and outpatient follow-up postdischarge. Readmitted patients were more likely to be black, to have coronary artery disease, to have a history of alcohol abuse, and to be on supplemental oxygen. Multivariate analysis showed a 2.17 odds of 30-day readmission (95% CI, 1.16–4.09) in patients with alcohol abuse, and 2.52 (95% CI, 1.18–5.38) in those on supplemental oxygen. Conclusion. In our study population, 19.4% of acute exacerbation COPD patients were readmitted within 30 days. Patient factors (such as alcohol abuse and advanced disease) were associated with 30-day readmission.     

Factors contributing to hospital readmission in a Hong Kong regional hospital: a case-controlled study

BACKGROUND: As many as 50% of total hospital admissions are readmissions. Because the factors contributing to hospital readmission are multiple, and research findings are not conclusive, it is important for clinicians to gain an understanding of the key factors that contribute to readmission. OBJECTIVES:This study explores the factors contributing to hospital readmission and derives an explanatory model that can best identify characteristics of patients at high risk for hospital readmission. METHODS: This research was a case-controlled study with readmitted patients (n = 168) as the readmitted group and non-readmitted patients (n = 98) as the control group. The variables included demographic data, health assessment data, medical diagnosis, frequency of admissions, severity of illness, intensity of service and improvement of condition. The study sample was also interviewed to explore the patients' views on their repeated hospitalization. RESULTS: In the bivariate analysis significant differences between the study and control groups were multiple and generally consistent with findings in other studies. Using multiple logistic regression, however, the final model shows that only three factors best predict readmissions: frequency (3-4 times) of readmissions (OR = 9.96, p < .0001) and frequency (more than 5 times) of readmissions (OR = 15.73, p < .0001), financial assistance (OR = 5.03, p < .001), and severity of illness (OR = 3.12, p < .01). Our interview data suggest that the readmitted patients required assistance to accomplish daily living activities upon discharge and often returned to the hospital for the same health reason. CONCLUSION: The study findings suggest that patients who are frequently readmitted to the hospital are severely ill; are on public assistance; and may need special attention when discharged in order to attenuate repeated hospital readmission.     

A limited sampling strategy for estimating mycophenolic acid area under the curve in adult heart transplant patients treated with concomitant cyclosporine

Objective: Heart transplantation studies have shown a relationship between the mycophenolic acid area under the curve (AUC) 0–12 h (MPA AUC_0–12h) values and risk of acute rejection episodes and fewer side‐effects in patient receiving cyclosporine during the first year post‐transplant. However, measurement of full AUC is costly and time consuming and in this case it is an impractical approach to drug monitoring. Therefore, the authors describe a limited sampling strategy to estimate the MPA AUC_0–12h value in adult heart transplant recipients. Methods: Ninety MPA pharmacokinetic (PK) profiles were studied. The samples were collected immediately before and 0·5, 1, 1·5, 2, 2·5, 3, 4, 6, 9, 12 h after the morning dose of mycophenolate mofetil (MMF) following an overnight fast. PK profiles were determined at 6–8 weeks, 6, 12 months and more than 1 year after transplantation. Using stepwise multiple linear regression analysis a sampling strategy from 60 of PK profiles was obtained and next the bias and precision of the model were evaluated in another 30 PK profiles. Results: The three‐point model using C_0·5h, C_1h, C_2h was found to be superior to all other models tested (r² = 0·841). The regression equation for AUC estimation which gave the best fit to this model is: 9·69 + 0·63C_0·5 + 0·61C₁ + 2·20C₂. Using that model 63 of the 90 (70%) full AUC values were estimated within 15% of their actual value. For the best‐fit model, the mean prediction error was 3·2%, with 95% confidence intervals for prediction error to range from ?42·2% to 40·3%. All other models which use one, two or three time‐points over the first 2 h are poorer predictors of the full AUC than the model above. Conclusion: The proposed three time‐point equation to estimate AUC will be helpful in optimizing immunosuppressive therapy in heart transplantation.     

Risk factors associated with 30-day unplanned hospital readmission for patients with mental illness

Unplanned hospital readmission rate is up to 43% in mental health settings, which is higher than in general health settings. Unplanned readmissions delay the recovery of patients with mental illness and add financial burden on families and healthcare services. There have been efforts to reduce readmissions with a particular interest in identifying patients at higher readmission risk after index admission; however, the results have been inconsistent. This systematic review synthesized risk factors associated with 30-day unplanned hospital readmissions for patients with mental illness. Eleven electronic databases were searched from 2010 to 30 September 2021 using key terms of 'mental illness', 'readmission' and 'risk factors'. Sixteen studies met the selection criteria for this review. Data were synthesized using content analysis and presented in narrative and tabular form because the extracted risk factors could not be pooled statistically due to methodological heterogeneity of the included studies. Consistently cited readmission predictors were patients with lower educational background, unemployment, previous mental illness hospital admission and more than 7 days of the index hospitalization. Results revealed the complexity of identifying unplanned hospital readmission predictors for people with mental illness. Policymakers need to specify the expected standards that written discharge summary must reach general practitioners concurrently at discharge. Hospital clinicians should ensure that discharge summary summaries are distributed to general practitioners for effective ongoing patient care and management. Having an advanced mental health nurse for patients during their transition period needs to be explored to understand how this role could ensure referrals to the general practitioner are eventuated.     

Retracted: Identifying patient readmission subtypes from unplanned readmissions to hospitals in Hong Kong: A cluster analysis

It has been conjectured with regard to patient readmission patterns that there might be significant differences in patient characteristics, need factors, enabling resources, and health behavior. The aim of this study was to identify the profiles of readmitted patients in Hong Kong (n = 120) based on their predisposing characteristics, needs, health behavior, and enabling resources. All the readmitted patients were recruited to the study in three hospitals from 2003 to 2005. A cluster analysis yielded three clusters: Clusters 1, 2, and 3 constituted 27.5% (n = 33), 27.5% (n = 33), and 45.0% (n = 54) of the total sample, respectively. The study results show that community nurse services do affect the rate at which patients are admitted to hospital for a second time. The findings might help by providing important information that will enable health‐care policy‐makers to identify strategies to target a specific group of patients in the hope of reducing its readmission rate.     

Are readmissions to the intensive care unit a useful measure of hospital performance?

BACKGROUND: Although patients readmitted to intensive care units (ICUs) typically have poor outcomes, ICU readmission rates have not been studied as a measure of hospital performance. OBJECTIVES: To determine variation in ICU readmission rates across hospitals and associations of readmission rates with other ICU-based measures of hospital performance. RESEARCH DESIGN: Observational cohort study. SUBJECTS: One hundred three thousand nine hundred eighty four consecutive ICU patients who were admitted to twenty eight hospitals who were then transferred to a hospital ward in those 28 hospitals. MEASURES: Predicted risk of in-hospital death and ICU length of stay (LOS) were determined by a validated method based on age, ICU admission source, diagnosis, comorbidity, and physiologic abnormalities. Severity-adjusted mortality rates, LOS, and readmission rates were determined for each hospital. RESULTS: One or more ICU readmissions occurred in 5.8% patients who were initially classified as postoperative and in 6.4% patients who were initially classified as nonoperative. In-hospital mortality rate was 24.7% in patients who were readmitted as compared with 4.0% in other patients (P < 0.001). After adjusting for predicted risk of death, the odds of death remained 7.5 times higher (OR 7.5, 95% CI, 6.8-8.3). Readmitted patients also had longer (P < 0.001) ICU LOS (5.2 vs. 3.7 days) and hospital LOS (29.3 vs. 11.7 days). Severity-adjusted readmission rates varied across hospitals from 4.2% to 7.6%. Readmission rates were not correlated with severity-adjusted hospital mortality, ICU LOS, or hospital LOS. CONCLUSIONS: ICU patients who were subsequently readmitted have a higher risk of death and longer LOS after adjusting for severity of illness. However, readmission rates were not associated with severity-adjusted mortality or LOS. Those data indicate that ICU readmission may capture other aspects of hospital performance and may be complementary to these measures.     

Readmission to intensive care: a review of the literature.

Acutely ill patients are commonly found on general hospital wards; some of these are patients who have been recently discharged from an intensive care unit (ICU). These patients may require a higher level of care than other ward patients and, due to the acuity of their illness, are at risk of readmission to ICU. Research has indicated that patients readmitted to ICU have mortality rates up to six times higher than those not readmitted and are eleven times more likely to die in hospital. Numerous studies have retrospectively examined these readmissions but, despite this, there is still no clear indication of why ICU readmissions occur or what the common characteristics of readmitted patients are. This literature review examines the published studies on patients who have been readmitted to ICU. Further research is needed to explore why readmissions to ICU occur and the type of patient who is at greatest risk for readmission.     

Permanent pacing for late-onset atrioventricular block in patients with heart transplantation: a single center experience

Introduction: The incidence, mechanisms, clinical associations, and outcomes in patients with late‐onset (>3 months) atrioventricular (AV) block following heart transplantation are not well known. This study will characterize late‐onset AV block following cardiac transplantation. Methods: We retrospectively reviewed our databases to identify patients who required pacemakers for late‐onset AV block postheart and heart‐lung transplantation from January 1990 to December 2007. Orthotopic heart and heart‐lung transplantation were separately analyzed. Results: This study included 588 adults who received cardiac transplants over a 17‐year period at our center (519 orthotopic, 64 heart‐lung transplants, and five heterotopic heart transplants). Of the 519 patients with orthotopic heart transplant, 39 required pacing (7.5%), 17 (3.3%) within 3 months posttransplant, 11 (2.1%) for late‐onset sinus node dysfunction (SND), 11 (2.1%) for late‐onset AV block. Also, five patients (7.8%) out of 64 heart‐lung transplants required pacemakers, two (3.1%) for late‐onset SND, three (4.7%) for late‐onset AV block. None of the five patients who underwent heterotopic transplant required cardiac pacing prior to or posttransplant. Conclusions: Late‐onset AV block occurs in 2.4% of patients with orthotopic heart transplant or heart‐lung transplant. AV block is predominantly intermittent and, often, does not progress to permanent AV block. There are no predictable factors for its onset. (PACE 2011; 72–75)