30-day Unplanned Readmission Rate Research Articles

Association between the LACE+ index and unplanned 30-day hospital readmissions in hospitalized patients with stroke.

BackgroundThe LACE+ index is used to predict unplanned 30-day hospital readmissions, but its utility to predict 30-day readmission in hospitalized patients with stroke is unknown.MethodsWe retrospectively analyzed 1,657 consecutive patients presenting with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and August 2020. The primary outcome of interest was unplanned 30-day readmission for any reason after index hospitalization for stroke. The 30-day readmission risk was categorized by LACE+ index to high risk (≥78), medium-to-high risk (59–77), medium risk (29–58), and low risk (≤ 28). Kaplan-Meier analysis, Log rank test, and multivariable Cox regression analysis (with backward elimination) were used to determine whether the LACE+ score was an independent predictor for 30-day unplanned readmission.ResultsThe overall 30-day unplanned readmission rate was 11.7% (194/1,657). The median LACE+ score was higher in the 30-day readmission group compared to subjects that had no unplanned 30-day readmission [74 (IQR 67–79) vs. 70 (IQR 62–75); p < 0.001]. On Kaplan-Meier analysis, the high-risk group had the shortest 30-day readmission free survival time as compared to medium and medium-to-high risk groups (p < 0.01, each; statistically significant). On fully adjusted multivariable Cox-regression, the highest LACE+ risk category was independently associated with the unplanned 30-day readmission risk (per point: HR 1.67 95%CI 1.23–2.26, p = 0.001).ConclusionSubjects in the high LACE+ index category had a significantly greater unplanned 30-day readmission risk after stroke as compared to lower LACE+ risk groups. This supports the validity of the LACE+ scoring system for predicting unplanned readmission in subjects with stroke. Future studies are warranted to determine whether LACE+ score-based risk stratification can be used to devise early interventions to mitigate the risk for unplanned readmission.

Trends in outcomes, complications and readmission rates in patients undergoing transcatheter aortic valve implantation: a nationwide analysis from 2012 to 2019

Abstract Background Transcatheter aortic valve implantation (TAVI) has evolved over the years from a procedure requiring surgical vascular access and general anesthesia, to one that can be completed entirely percutaneously with conscious sedation. Advancement in procedural techniques and operator experience has resulted in better outcomes for patients undergoing TAVI. Purpose The purpose of our study was to analyze the trends over the years in outcomes, complications and unplanned readmission rate in patients undergoing TAVI. Methods The United States Nationwide Readmission Database for the years 2012 to 2019 was queried to identify all adult patients who underwent elective TAVI. The primary outcome of the study was inpatient mortality during TAVI hospitalization. Secondary outcomes included periprocedural complications (like acute kidney injury, bleeding requiring transfusion, need for permanent pacemaker implantation, stroke), length of stay, hospitalization cost, and unplanned 30-day readmission rate. Appropriate International Classification of Diseases (ICD-9 and ICD-10) codes were used to identify comorbidities and complications. Results A total of 283,409 patients who underwent TAVI were included in the study. The baseline characteristics of these patients are shown in picture 1. The inpatient mortality during TAVI admission declined steadily and significantly from 5.3% in the year 2012 to 0.0% in the year 2019 (p value for trend &amp;lt;0.001). There was a significant reduction in peri-TAVI occurrence of acute kidney injury (from 18.1% in 2012 to 8.7% in 2019), bleeding requiring transfusion (from 28.0% in 2012 to 4.3% in 2019), and stroke (from 1.8% in 2012 to 0.1% in 2019) (p value for trend &amp;lt;0.001 for all). Periprocedural conduction abnormalities requiring permanent pacemaker implantation increased from 7.4% in 2012 to 12.1% in 2015, before coming down to 8.9% in 2019 (p value for trend &amp;lt;0.001). The mean length of hospital stay and inflation-adjusted costs during admission for TAVI decreased from 9.6 days and $64,695 in the year 2012, to 3.6 days and $49,710 in the year 2019, respectively (p value for both &amp;lt;0.001). The unplanned 30-day all-cause readmission rate after TAVI also reduced steadily and significantly from 18.2% in 2012 to 11.5% in 2019 (p value &amp;lt;0.001). Conclusion Over the years, there has been a significant reduction in inpatient mortality, periprocedural complications (such as acute kidney injury, need for blood transfusion, stroke), length of hospital stay, and inflation-adjusted hospital costs in patients undergoing transcatheter aortic valve implantation. Furthermore, there has been a significant decline in the 30-day unplanned readmission rate after transcatheter aortic valve implantation. Funding Acknowledgement Type of funding sources: None.

Social disparities in unplanned 30-day readmission rates after hospital discharge in patients with chronic health conditions: A retrospective cohort study using patient level hospital administrative data linked to the population census in Switzerland.

Unplanned readmissions shortly after discharge from hospital are common in chronic diseases. The risk of readmission has been shown to be related both to hospital care, e.g., medical complications, and to patients' resources and abilities to manage the chronic disease at home and to make appropriate use of outpatient medical care. Despite a growing body of evidence on social determinants of health and health behaviour, little is known about the impact of social and contextual factors on readmission rates. The objective of this study was to analyse possible effects of educational, financial and social resources of patients with different chronic health conditions on unplanned 30 day-readmission risks. The study made use of nationwide inpatient hospital data that was linked with Swiss census data. The sample included n = 62,109 patients aged 25 and older, hospitalized between 2012 and 2016 for one of 12 selected chronic conditions. Multivariate logistic regressions analysis was performed. Our results point to a significant association between social factors and readmission rates for patients with chronic conditions. Patients with upper secondary education (OR = 1.26, 95% CI: 1.11, 1.44) and compulsory education (OR = 1.51, 95% CI: 1.31, 1.74) had higher readmission rates than those with tertiary education when taking into account demographic, social and health status factors. Having private or semi-private hospital insurance was associated with a lower risk for 30-day readmission compared to patients with mandatory insurance (OR = 0.81, 95% CI: 0.73, 0.90). We did not find a general effect of social resources, measured by living with others in a household, on readmission rates. The risk of readmission for patients with chronic conditions was also strongly predicted by type of chronic condition and by factors related to health status, such as previous hospitalizations before the index hospitalization (+77%), number of comorbidities (+15% higher probability per additional comorbidity) as well as particularly long hospitalizations (+64%). Stratified analysis by type of chronic condition revealed differential effects of social factors on readmissions risks. Compulsory education was most strongly associated with higher odds for readmission among patients with lung cancer (+142%), congestive heart failure (+63%) and back problems (+53%). We assume that low socioeconomic status among patients with chronic conditions increases the risk of unplanned 30-day readmission after hospitalisation due to factors related to their social situation (e.g., low health literacy, material deprivation, high social burden), which may negatively affect cooperation with care providers and adherence to recommended therapies as well as hamper active participation in the medical process and the development of a shared understanding of the disease and its cure. Higher levels of comorbidity in socially disadvantaged patients can also make appropriate self-management and use of outpatient care more difficult. Our findings suggest a need for increased preventive measures for disadvantaged populations groups to promote early detection of diseases and to remove financial or knowledge-based barriers to medical care. Socially disadvantaged patients should also be strengthened more in their individual and social resources for coping with illness.

Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult.

Background: Although unplanned hospital readmission is an important indicator for monitoring the perioperative quality of hospital care, few published studies of hospital readmission have focused on surgical patient populations, especially in the elderly. We aimed to investigate if machine learning approaches can be used to predict postoperative unplanned 30-day hospital readmission in old surgical patients. Methods: We extracted demographic, comorbidity, laboratory, surgical, and medication data of elderly patients older than 65 who underwent surgeries under general anesthesia in West China Hospital, Sichuan University from July 2019 to February 2021. Different machine learning approaches were performed to evaluate whether unplanned 30-day hospital readmission can be predicted. Model performance was assessed using the following metrics: AUC, accuracy, precision, recall, and F1 score. Calibration of predictions was performed using Brier Score. A feature ablation analysis was performed, and the change in AUC with the removal of each feature was then assessed to determine feature importance. Results: A total of 10,535 unique surgeries and 10,358 unique surgical elderly patients were included. The overall 30-day unplanned readmission rate was 3.36%. The AUCs of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.6865 to 0.8654. The RF + XGBoost algorithm overall performed the best with an AUC of 0.8654 (95% CI, 0.8484–0.8824), accuracy of 0.9868 (95% CI, 0.9834–0.9902), precision of 0.3960 (95% CI, 0.3854–0.4066), recall of 0.3184 (95% CI, 0.259–0.3778), and F1 score of 0.4909 (95% CI, 0.3907–0.5911). The Brier scores of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.3721 to 0.0464, with RF + XGBoost showing the best calibration capability. The most five important features of RF + XGBoost were operation duration, white blood cell count, BMI, total bilirubin concentration, and blood glucose concentration. Conclusion: Machine learning algorithms can accurately predict postoperative unplanned 30-day readmission in elderly surgical patients.

SP9.1.2 Investigating the association between trust case volume and perioperative outcomes in colorectal cancer surgery

Abstract Background Whilst current colorectal cancer care pathways operate by referral to local hospitals, there has been a recent drive to centralise cancer services to improve patient outcomes. Centralisation will inevitably increase trust case volume and the existing literature is inconclusive regarding a possible volume-outcome association. This study aims to investigate the association between case volume and unplanned readmission or unplanned return to theatre (URTT) rates in patients undergoing colorectal surgery across England and Wales. Methods A retrospective analysis was conducted of 14028 patients across 135 trusts using the 2020 NBOCA dataset. Primary outcome measures were 30-day unplanned readmission and 30-day URTT rates. Trusts were dichotomised into low or high-volume sites. Statistical analysis used Spearman's ρ correlation, Mann-Whitney U and Student T-test. Results 11.2% (2.30–24.5) and 8.31% (0.00–23.3) of 14028 patients underwent an unplanned readmission and URTT respectively. There was no significant correlation between the number of cases and 30-day unplanned readmission (r=0.0628, 95%CI: -0.112–0.234, p=0.469), with no significant difference between low and high-volume trusts (p=0.432). Similarly, no significant correlation was found between the number of cases and 30-day URTT rate (r=-0.0848, 95%CI: -0.255–0.0904, p=0.328), with no significant difference between low and high-volume trusts (p=0.285). Conclusions No association was discovered between volume and unplanned readmission or URTT in this cohort. Increasing volume among trusts who demonstrate favourable outcomes, rather than indiscriminately increasing volume may be beneficial. This study increases our understanding of volume-outcome relationships, however further prospective studies at a national level into other outcomes should be conducted to rule out centralisation.

Impact of neoadjuvant chemotherapy duration on the survival of patients with advanced stage high-grade serous or endometrioid ovarian carcinoma (359)

Impact of neoadjuvant chemotherapy duration on the survival of patients with advanced stage high-grade serous or endometrioid ovarian carcinoma (359)

Hospital procedural volume and outcomes with catheter-directed intervention for pulmonary embolism: a nationwide analysis.

There is limited data on the association between hospital catheter-directed intervention (CDI) volume and outcomes among patients with acute pulmonary embolism (PE). The Nationwide Readmissions Database years 2016-2019 was utilized to identify hospitalizations undergoing CDI for acute PE. Hospitals were divided into tertiles based on annual CDI volume; low-volume (1-3 procedures), moderate-volume (4-12 procedures) and high-volume (>12 procedures). The primary outcome was all-cause in-hospital mortality. Among 1 436 382 PE admissions, 2.6% underwent CDI; 5.6% were in low-volume, 17.3% in moderate-volume and 77.1% in high-volume hospitals. There was an inverse relationship between hospital CDI volume and in-hospital mortality (coefficient -0.344, P < 0.001). On multivariable regression analysis, hospitals with high CDI volume were associated with lower in-hospital mortality compared with hospitals with low CDI volume (adjusted odds ratio [OR] 0.71; 95% confidence interval [CI] 0.53, 0.95). Additionally, there was an inverse association between CDI volume and length of stay (LOS) (regression coefficient -0.023, 95% CI -0.027, -0.019) and cost (regression coefficient -74.6, 95% CI -98.8, -50.3). There were no differences in major bleeding and 30-day unplanned readmission rates between the three groups. In this contemporary observational analysis of PE admissions undergoing CDI, there was an inverse association between hospital CDI volume and in-hospital mortality, LOS, and cost. Major bleeding and 30-day unplanned readmission rates were similar between the three groups.

Pneumonia after hip surgery in children with neurological complex chronic conditions.

In children with neurological complex chronic conditions (CCC) undergoing hip surgery we aimed to: estimate the rate of postoperative pneumonia, determine the effect of pneumonia on postoperative hospital resource use, and identify predictors of postoperative pneumonia. A retrospective cohort study was conducted utilizing the Pediatric Health Information System database for 2609 children (1081 females, 1528 males) aged 4 years and older with a neurological CCC who underwent hip surgery (i.e. reconstruction surgery or salvage procedure) between 2016 and 2018 in 41 US children's hospitals. Multivariable, mixed-effects logistic regression was used to assess patient characteristics and risk of pneumonia. Mean age at hip surgery was 10 years 1 month (SD 4y 8mo). The postoperative pneumonia rate was 1.6% (n=42). Median length of stay (LOS) was longer for children with pneumonia and the 30-day all-cause unplanned readmission rate and costs were higher. Variability in rates of pneumonia ranged from 1.1% to 2.8% across hospitals. Significant predictors of postoperative pneumonia were osteotomy type (p=0.005) and number of chronic conditions (p≤0.001). Postoperative pneumonia after hip surgery in children with a neurological CCC is associated with longer LOS, readmissions, and higher costs. Children undergoing pelvic osteotomies and who have multimorbidity need additional clinical support to prevent postoperative pneumonia and decrease resource utilization. Pneumonia is a major postoperative complication in children with neurological complex chronic conditions (CCC). Forty-two (1.6%) children with neurological CCC developed pneumonia after hip surgery. Length of stay, readmissions, and costs were significantly higher in the group with pneumonia. Variability in pneumonia rates existed across hospitals. Predictors of developing pneumonia include osteotomy type and number of CCC.

Effects of a transsphenoidal surgery quality improvement program on patient outcomes and hospital financial performance.

A comprehensive quality improvement (QI) program aimed at all aspects of patient care after pituitary surgery was initiated at a single center. This initiative was guided by standard quality principles to improve patient outcomes and optimize healthcare value. The programmatic goal was to discharge most elective patients within 1 day after surgery, improve patient safety, and limit unplanned readmissions. The program is described, and its effect on patient outcomes and hospital financial performance over a 5-year period are investigated. Details of the patient care pathway are presented. Foundational elements of the QI program include evidence-based care pathways (e.g., for hyponatremia and pain), an in-house research program designed to fortify care pathways, patient education, expectation setting, multidisciplinary team care, standard order sets, high-touch postdischarge care, outcomes auditing, and a patient navigator, among other elements. Length of stay (LOS), outcome variability, 30-day unplanned readmissions, and hospital financial performance were identified as surrogate endpoints for healthcare value for the surgical epoch. To assess the effect of these protocols, all patients undergoing elective transsphenoidal surgery for pituitary tumors and Rathke's cleft cysts between January 2015 and December 2019 were reviewed. A total of 609 adult patients who underwent elective surgery by experienced pituitary surgeons were identified. Patient demographics, comorbidities, and payer mix did not change significantly over the study period (p ≥ 0.10). The mean LOS was significantly shorter in 2019 versus 2015 (1.6 ± 1.0 vs 2.9 ± 2.2 midnights, p < 0.001). The percentage of patients discharged after 1 midnight was significantly higher in 2019 versus 2015 (75.4% vs 15.6%, p < 0.001). The 30-day unplanned hospital readmission rate decreased to 2.8% in 2019 from 8.3% in 2015. Per-patient hospital profit increased 71.3% ($10,613 ± $19,321 in 2015; $18,180 ± $21,930 in 2019), and the contribution margin increased 42.3% ($18,925 ± $19,236 in 2015; $26,939 ± $22,057 in 2019), while costs increased by only 3.4% ($18,829 ± $6611 in 2015; $19,469 ± $4291 in 2019). After implementation of a comprehensive pituitary surgery QI program, patient outcomes significantly improved, outcome variability decreased, and hospital financial performance was enhanced. Future studies designed to evaluate disease remission, patient satisfaction, and how the surgeon learning curve may synergize with other quality efforts may provide additional context.

Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients

Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. We applied MICE (Multivariate Imputation via Chained Equations) method to obtain a balanced, fully valued dataset and LASSO (Least Absolute Shrinkage and Selection Operator) algorithm to get the most significant features. Training (80% of records) and test (20%) cohorts were randomly selected. Study population: 3079 patients, 394 (12.8%) presented at least one readmission within 30days, and 2685 (87.2%) did not. In the test cohort AUCs (IC95%) of XGBoost, Ada Boost Classifier, Random forest, and Gradient Boosting, and LACE Index were: 0.803 (0.734-0.872), 0.782 (0.711-0.854), 0.776 (0.703-0.848), 0.786 (0.715-0.857), and 0.504 (0.414-0.594), respectively, for predicting readmissions. A SHAP analysis was performed to offer a breakdown of the ML variables associated with readmission. Positive and negative predicting values estimates of the different ML models and LACE index were also provided, for several values of readmission rate prevalence. Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.

Unplanned hospital readmissions in the first month after childhood cancer diagnosis.

e18770 Background: Overall, unplanned 30-day readmission rates at children’s hospitals are 6.5% ( JAMA, 2013;309:4). However, the rate of unplanned readmissions within 30 days following a childhood cancer diagnosis is unknown. We aimed to: i) Determine the rate of unplanned readmissions in pediatric oncology patients during the first 30 days following discharge from the hospital after diagnosis; and ii) Identify risk factors associated with these unplanned readmissions. Methods: Newly diagnosed patients enrolled in a study at Children’s of Alabama were followed for 30 days after their initial discharge and data on all readmissions collected. Unplanned readmission rates were calculated using the Agency for Healthcare Research &amp; Quality All-Condition Readmission Measure. Intensity of Treatment Rating (ITR), a validated categorization of the intensity of pediatric cancer treatment (1 = least to 4 = most intense) was assigned based on each patient’s treatment plan. Demographic and clinical variables were summarized using descriptive statistics. Bivariate logistic regression analyses were performed. Patients with less than ten days at risk of readmission were excluded from the regression model. Covariates with a significant association (P &lt; 0.1) with unplanned readmission were included in the multivariable regression models. Results: There were 105 patients included in the study (mean age, 7.7±5.4 yrs; 62% male, 66% non-Hispanic white; 50% with leukemia; 50% with ITR-rating of 3; discharged with a median of 8 [range, 2-14] home medications). The unplanned readmission rate was 48.6% (51 of 105 patients had at least one unplanned readmission during the 30-day post-discharge period). Of the 3135 patient days of follow-up, 887 were inpatient (486 planned and 401 unplanned). The unplanned inpatient days were for treatment of fever (81%), gastrointestinal issues (11%), pain (2%), neurologic issues (5%) and bleeding (1%). The proportion of patients with any unplanned readmission by Intensity of Treatment Rating (ITR) were ITR-2: 39%, ITR-3: 59%, ITR-4: 71%. Multivariable regression (adjusted for sociodemographic and clinical variables) demonstrated the following associations with unplanned readmission: ITR (OR, 1.24 per level; 95% CI, 1.01-1.50); number of discharge medications (OR, 1.05 per medication; 95% CI, 1.01-1.09). Conclusions: The 30-day unplanned readmission rate in newly diagnosed pediatric oncology patients is substantially higher (48.6%) than for children’s hospitals overall (6.5%). The odds of unplanned readmission increased independently with the number of discharge medications prescribed (5% increased odds per medication) and with the Intensity of Treatment Rating (24% increased odds per rating level). These factors may be useful in tailoring anticipatory guidance for families regarding the likelihood of readmission during the first 30 days following a pediatric cancer diagnosis.

Remote symptom monitoring after hospital discharge.

1517 Background: Strategies to improve transitions from the hospital to home for patients with cancer are considered an important component of quality, patient-centered care in oncology. CMS evaluates cancer hospital performance based on the 30-day unplanned hospital readmission rate, and this measure has been endorsed by the National Quality Forum. Nationally, the 30-day readmission rate for oncology patients ranges from 19%-27%. These readmissions come at high psychosocial, physical, and financial costs for patients and caregivers. A remote monitoring intervention that includes frequent contacts with the patient is likely to be effective in improving this transition. Methods: We evaluated the feasibility, acceptability, and perceived value of a mobile health intervention to monitor and manage symptoms of adult medical and surgical oncology patients discharged from an NCI-designated cancer center to home. Patients were monitored for 10 days, which is the median time to readmission for an oncology patient. The technology supporting the program included: 1) a patient portal enabling daily electronic patient-reported outcomes assessments; 2) a pulse oximeter to provide data on blood oxygen level and heart rate; 3) alerts for concerning symptoms; 4) an application to allow staff to review and trend symptom data; 5) a secure platform to support communications and televisits between staff and patients; 6) an advanced feedback report to provide just-in-time patient symptom education. Feasibility and acceptability were evaluated through engagement (goal: &gt; 50% response rate) and symptom alerts and perceived value was measured through a patient engagement survey that included a net promoter score (how likely the patient is to recommend the program to similar patients; goal &gt; 0.7). Results: Between September 27, 2020 to December 31, 2021, the program enrolled 1,091 medical oncology (median age: 63 years, 55% female) and 4,222 surgical oncology patients (median age: 63 years,55% female). Of those enrolled, 65% of medical and 74% of surgical oncology patients participated in home remote monitoring by self-reporting symptom data. This resulted in 2,869 completed symptom assessment from medical and 16,009 completed assessments from surgical patients. Sixty-three percent of medical oncology assessments resulted in a yellow (moderate) or red (severe) symptom alert compared with 26% for surgical oncology patients. Pain was the predominant symptom generating red alerts for medical oncology patients (17%). Fifty-two percent of patients completed the engagement survey, and the net promoter score was 0.82. Conclusions: A remote monitoring program after discharge was feasible, acceptable, and perceived to be of value by oncology patients discharged from a cancer center. Surgical and medical patients have similar response rates but differ in symptom burden. Future work will evaluate the value of a remote symptom monitoring platform in decreasing readmissions.

Increased 30-Day Postoperative Readmission and Medical Complication Rates Among Patients 65 Years and Older Following Arthroscopic Rotator Cuff Repair

PurposeThe purpose of this study is to use a large national database to assess short-term adverse events following arthroscopic rotator cuff repair in patients 65 years and older.MethodsThe ACS NSQIP database was queried to identify patients that underwent arthroscopic rotator cuff repair between December 31, 2015, and January 1, 2017. Patients were split into two groups: 1) between 40-65 years old and 2) 65+ years old. Cases involving open rotator cuff repair, total shoulder arthroplasty, hemiarthroplasty, and emergency surgery were excluded. Exact matching was used to control for confounding variables, including sex, body mass index (BMI), American Society of Anesthesiologists (ASA) score, operative time, and several medical comorbidities. After matching, the incidence of several 30-day patient complication measures was compared between the groups. Binary logistic regression was used to identify covariates associated with various 30-day complications.ResultsA total of 17,880 patients were included in the study. 69.4% (n = 12,404) patients were between 40 and 65 years old and 30.6% (n = 5,476) patients were 65+ years old. After matching, 9,210 patients were included in the final analysis. After matching, patients 65 years and older were more likely to experience 30-day unplanned readmission (P = .035) and overall medical complications (P = .036). There were no significant differences in most 30-day complication measures, including mortality (P = .250), reoperation (P = .449), non-home discharge (P = .255), surgical complications (P = .146), and several medical complications, including myocardial infarction (P = .165), deep venous thromboembolism (P = .206), pulmonary embolism (P = .196), and cerebrovascular accident (P > .999) between the two age groups.ConclusionsIn this matched cohort study, patients 65 years and older experienced a higher rate of 30-day unplanned readmission and overall medical complications following elective arthroscopic rotator cuff repair relative to patients under 65. However, these older patients did not have significantly worse rates of other 30-day complication measures, including mortality, reoperation, return to the OR, and non-home discharge.

Association Between Frailty and 30-day Unplanned Readmission After Hospitalization for Heart Failure: Results from Nationwide Readmissions Database

<h3>Purpose</h3> Frailty is associated with adverse hospital outcomes in heart failure (HF) patients. This study examines the association between frailty, readmission, and other relevant hospital outcomes using a national database. <h3>Methods</h3> This is a retrospective analysis of the Nationwide Readmissions Database from 2010 to 2014. Patients ≥65 years of age admitted for HF were included in the study. We used the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator. The primary outcome of the study was 30-day unplanned readmission rate. <h3>Results</h3> A total of 819,854 patients were admitted for HF, of which 63,302 (7.7%) were frail. Frail patients, 22,382 (35.4%) experienced unplanned readmission within 30 days after index hospitalization, as compared to non-frail patients, 168,797 (22.3%, P<0.001). Trend analysis showed that the relative decrease rates of unplanned 30-day readmission decreased for both frail (7.8%) and non-frail (6.9%) patients during the study period. Among HF patients who experienced unplanned 30-day readmission, mortality rates (16.2% versus 8.2%, P<0.001) and hospitalization cost ($13,616 versus $9,876, P<0.001) were higher among frail patients, compared to non-frail patients. Besides HF, most common reasons for unplanned 30-day readmission among frail HF patients were septicemia (11.2%), and acute renal failure (5.1%). Regression analysis showed that the risk for unplanned 30-day readmission was higher in patients with frailty (OR, 1.18; 95% CI, 1.14-1.22). Other risk factors for unplanned 30-day readmission were comorbidity index >3, AMI, CAD, arrhythmia, diabetes mellitus, cancer, chronic lung disease, chronic renal disease, liver disease, fluid and electrolyte disorder, anemia, coagulation disorder, and vasopressor use. <h3>Conclusion</h3> Frailty among elderly patients hospitalized for HF is associated with increased readmission, mortality, and cost. Identifying frailty in this population could improve quality of care and decrease hospital burden.

Impact of involvement of relatives in early home visits by a hospital-led geriatric team

ObjectivesTo evaluate whether the involvement of relatives in home visits by a geriatric team post-discharge is associated with unplanned readmissions in severely frail patients living alone. MethodsA quality improvement project evaluating outcomes from routine care of patients who were severely frail, 65+ years, living alone, and visited at home by a geriatric team after discharge from acute hospital admission. We compared patients who did and did not have relatives attending the visit. Data were analyzed by logistic regression. ResultsOf 437 patients with a mean age of 85.0 (±7.8) years 73% had severe comorbidity, 68% had a low functional ability, and 100 patients (23%) had relatives attending. Attendance of relatives was associated with a lower unplanned 30-day readmission rate (10% vs. 18%, adjusted odds ratio: 0.48 (0.23-1.00) p=0.05). ConclusionResults highlights the importance of involvement of relatives in care planning in the severely frail older patients living alone.

Thirty-Day Unplanned Readmissions Following Hospitalisation for Atrial Fibrillation in Australia and New Zealand

Atrial fibrillation (AF) is a leading cause of hospitalisations, yet little is known about 30-day readmissions following discharge despite increasing policy focus on reducing readmissions. We assessed the rate, timing, causes and predictors of 30-day unplanned readmission following an acute and elective AF hospitalisation using population-wide data. We studied all patients hospitalised for AF from 2010 to 2015 at all public and most private hospitals in Australia and New Zealand. The main outcome measures were unplanned readmissions within 30 days of discharge, primary diagnosis associated with these readmissions, and their predictors as modelled by logistic regression. Among 301,654 patients hospitalised for AF (mean age 69.2±13.6 yrs, 55.6% female, 65.2% acute presentations), 29,750 (9.9%) experienced an unplanned readmission within 30 days with 62.6% occurring by 14 days. Unplanned readmissions occurred more frequently following an acute versus elective AF hospitalisations (12.5% vs 4.9%, p<0.001). The most common diagnoses associated with readmissions were recurrence of AF (n=9,890, 33.2%), and preventable conditions including heart failure (n=2,683, 9.0%), pneumonia (n=724, 2.4%) and acute myocardial infarction (n=510, 1.7%). A higher risk of 30-day readmission was associated with congenital cardiac/circulatory defect (OR 2.18, CI 1.44-3.30), congestive heart failure (OR 1.34, CI 1.30-1.39), and arrhythmia/conduction disorders (OR 1.25, CI 1.21-1.28). Almost 1 in 10 AF hospitalisations resulted in unplanned readmission within 30-days, mostly for AF recurrence. Improved clinical management of AF and transitional care planning are required to reduce unplanned readmissions following AF hospitalisations.

356 Patient-and-Hospital-Related Risk Factors for Non-Routine Discharge After Lumbar Decompression and Fusion for Spondylolisthesis

INTRODUCTION: In various spinal surgeries, non-routine discharges have been associated with inferior outcomes. However, there exists a paucity of data regarding the relationship between non-routine discharge and quality of care among patients with spondylolisthesis. METHODS: A retrospective cohort study was performed using the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database from 2010 through 2016. Adult patients (=18 years old) who underwent spinal decompression and fusion for lumbar spondylolisthesis were identified using ICD-9-CM diagnosis and CPT procedural coding systems. The study population was divided into two cohorts based on discharge disposition: routine (RD) and non-routine discharge (NRD). A multivariate logistic regression model was used to identify the independent predictors of non-routine discharge and 30-day unplanned readmission. RESULTS: A total of 5,252 patients were identified, of which of which 4,316(82.2%) had a RD and 936 (18.8%) had a NRD. The NRD cohort tended to be older (p&lt;0.001) and have a higher BMI (p&lt;0.001). Patients who experienced a NRD had a longer LOS (NRD: 4.7±3.7 days vs RD: 3.1±2.0 days, p&lt;0.001), a higher proportion of adverse events (p&lt;0.001), higher rates of reoperation (p=0.005) and unplanned 30-day readmission rates (p&lt;0.001). On multivariate regression analysis, age [OR: 1.08, 95% CI (1.06–1.10),p&lt;0.001], female sex [OR: 2.01, 95% (1.51–2.69),p&lt;0.001], non-Hispanic Black race/ethnicity [OR: 2.10, 95% CI (1.36 – 3.24), p=0.001], BMI [OR: 1.03, 95% CI (1.01–1.05), p=0.007), dependent functional status [OR: 3.33, 95% CI (1.59 – 6.99),p=0.001], malnourishment [OR: 2.14, 95% CI (1.27 – 3.62, p=0.005], and LOS [OR: 1.26, 95% CI (1.18 – 1.33),p&lt;0.001] were all independent predictors for NRD. However, NRD did not independently predict an unplanned 30-day readmission on multivariate analysis. CONCLUSION: In our study we found that on univariate analysis NRD was associated with increased adverse events, length of stay and 30-day unplanned readmission. When controlling for patient- and hospital-related factors, we found that female sex, non-Hispanic Black race, BMI, dependent functional status, malnourishment and longer LOS were independently associated with NRD.

Is Skilled Nursing Facility Financial Status Related to Readmission Rate Improvement?

We examined whether higher skilled nursing facility (SNF) lagged profitability is associated with a lower 30-day all-cause all-payer risk-adjusted hospital readmission rate. Our aim was to provide insight into whether SNFs with limited financial resources are able to respond to incentives to lower their readmission rates to hospitals. We used data from 2012-2016 to estimate a fixed effects (FE) model with a time trend. Our data included financial data from the Centers for Medicare & Medicaid Services Healthcare Cost Report Information System SNF cost reports, facility characteristics including the all-cause all-payer risk-adjusted unplanned 30-day readmission rate from the LTCFocus (Long-Term Care Focus) project at Brown University, and county-level market variables from the Area Health Resource File. We also examined the relationship for a shorter time frame (2012-2015) after stratifying the sample by system membership or ownership. SNFs with an increase in the lagged operating margin showed a statistically significant, small decrease (<.01 percentage point) in the risk-adjusted readmission rate. The results were robust for different time periods and model specifications. Fixed effects model estimates for SNFs in the highest quartile of percentage of Medicaid patients (≥73.9%) had a lagged operating margin coefficient that is almost four times as large as the coefficient of the FE model with all SNFs. SNFs have an important role in achieving the national priority of reducing hospital readmissions. The study findings suggest that managers of SNFs should not see low profitability as an obstacle to reducing readmission rates, which is good news given the low average profitability of SNFs. Further, reductions in profitability due to penalties incurred from the recently implemented Medicare Skilled Nursing Facility Value-Based Purchasing Program may not limit SNFs' ability to lower hospital readmission rates, at least initially. However, policymakers may need to determine whether additional resources to high Medicaid SNFs can lower readmission rates for these SNFs.

Addressing the Quality of Hospital Care of Colorectal Cancer Patients Undergoing Surgery: What Can We Learn From the National Bowel Cancer Audit?

IntroductionThe National Bowel Cancer Audit (NBOCA) is the largest database in the United Kingdom that audits real-world data and allows comparison of the quality of care for colorectal cancer patients. This study aimed to highlight relevant clinical factors in the NBOCA that contribute to variation in the quality of care provided in different hospitals.MethodsData from 36,116 patients with colorectal cancer who had undergone surgery were obtained from the NBOCA. These were patients from 145 and 146 hospitals from the years 2016 and 2017, respectively. A validated multiple linear regression was performed to compare the identified clinical factors with various quality outcomes. The quality outcomes defined in this study were the length of hospital stay of more than five days, two-year mortality, 30-day unplanned readmission rate, 90-day mortality, and 18-month stoma rate.ResultsFour clinical factors (laparoscopy rate, abdominal-perineal-resection-of-rectum, pre-operative radiotherapy, and patients with distant metastases) were shown to have a significant (p < 0.05) impact on the length of hospital stay of more than five days and the 18-month stoma rate. The 18-month stoma rate was also a significant predictor (p < 0.001) with two-year mortality.ConclusionThe NBOCA should consider adjusting for these factors when reporting the quality of care provided in hospitals. Hospitals should monitor the four clinical factors for colorectal cancer patients during perioperative care. When formulating a management plan for patients with colorectal cancer, clinicians should consider these factors along with the individual patient's history.

The Utilization and Safety of Same-Day Discharge After Transurethral Benign Prostatic Hyperplasia Surgery: A Case-Control, Matched Analysis of a National Cohort

ObjectiveTo analyze the utilization and safety of same-day (SDD) vs standard-length discharge (SLD) for transurethral resection (TURP), holmium laser enucleation (HoLEP), and GreenLight photovaporization (GL-PVP) of the prostate. MethodsUsing the 2015-2019 ACS-NSQIP files, the annual proportion of TURP, HoLEP, and GL-PVP performed with SDD (length of stay [LOS] = 0 days) was calculated. Patients were stratified by LOS into SDD and SLD (TURP: LOS = 1-3 days, HoLEP and GL-PVP: LOS = 1-2 days); those with longer LOS were excluded. Patients were matched 1:1 by age, body mass index, American Society of Anesthesiologists score, and modified Charlson Comorbidity Index score. We compared 30-day unplanned readmissions, reoperations, and Clavien-Dindo (CD) complications between SLD and SDD, and evaluated predictors of adverse outcomes using logistic regression. ResultsMost GL-PVP patients underwent SDD, compared to a minority of TURP and HoLEP patients. SDD utilization increased, remained stable, and decreased over time for HoLEP, TURP, and GL-PVP, respectively. For 46,898 included cases (31,872 TURP, 2,901 HoLEP, 12,125 GL-PVP), rates of reoperation, CD I/II, or CD IV complications were comparable before and after matching. Compared to SLD, 30-day unplanned readmission rates for matched SDD patients were lower following TURP (3.48% vs 4.25%, P = .013) and HoLEP (1.93% vs 4.43%, P = .003). On multivariate regression, SLD correlated with unplanned readmission after TURP and HoLEP for both unmatched and matched cohorts. ConclusionFor appropriately selected patients, SDD after TURP, HoLEP, and GL-PVP did not confer increased risk of 30-day complications, suggesting patient selection for SDD is being done with appropriate safety nationally.