Significant Predictors Of Readmission Research Articles

0432: Six minute walk test: predictor of hospital readmission in patients with chronic heart failure

Patients with chronic heart failure are frequently readmitted to the hospital due to disease progression. Although a shorter 6-minute walk distance (6MWD) is correlated with poor prognosis, 6MWD is not considered a clinical indicator for predicting hospital readmission. We investigated whether 6MWD predicted readmission due to heart failure in CHF patients. We enrolled 814 patients admitted to the therapeutic unit of chronic heart failure from 2006 to 2013 as follow: 6MWD <300 m (n=740), 300<6MWD<450 m (n=54) and 6MWD >450m (n=20). Clinical characteristics, 6MWD and readmission due to heart failure were evaluated. The mean of age was 63 years old, 65% males. The mean 6MWD in groups were 131.4, 349 and 512m respectively. 6MWD was shorter in read-mitted patients than non-readmitted patients and was a significant predictor of readmission (P<0.0001). 6MWD measured is a predictor of hospital readmission in CHF patients for clinical or echocardiographic cardiac decompensation (p=0.01). In systolic heart failure outpatients, 6MWD demonstrated utility as predictors for hospitalization for cardiac decompensation.

Predictors of 30-day readmission following hysterectomy for benign and malignant indications at a tertiary care academic medical center

Hospital readmissions are costly, frequent, and increasingly under public scrutiny. With increased financial constraints on the medical environment, understanding the drivers of unscheduled readmissions following gynecologic surgery will become increasingly important to value-driven care. The current study was conducted to identify risk factors for 30-day readmission following hysterectomy for benign and malignant indications. A retrospective cohort study was conducted from 2008 through 2010 of all nongravid hysterectomies at a single tertiary care academic medical center. Clinical, perioperative, and physician characteristics were collected. Multivariable logistic regression models were used to identify predictors of 30-day readmission, stratified by malignant and benign indications for hysterectomy. Among 1649 women who underwent a hysterectomy (1009 for benign indications and 640 for malignancy), 6% were subsequently readmitted within 30 days (8.9% for malignancy vs 4.2% for benign; P < .0001). The mean time to readmission was 13 days (15 days for malignancy vs 10 days for benign; P= .004). The most common reasons for readmission were gastrointestinal (38%) and infectious (34%) etiologies, and 11.6% of readmitted patients experienced a perioperative complication. Among women undergoing hysterectomy for benign indications, a history of a laparotomy, including cesarean delivery (adjusted odds ratio [AOR], 2.12; 95% confidence interval [CI], 1.06-4.25; P= .03), as well as a perioperative complication (AOR, 2.41; 95% CI, 1.00-6.04; P= .05) were both associated with a >2-fold increased odds of readmission. Among women undergoing hysterectomy for malignancy, an American Society of Anesthesiologists Physical Status Classification of III or IV (AOR, 1.92; 95% CI, 1.05-3.50; P= .03), a longer length of initial hospitalization (3 days AOR, 7.83; 95% CI, 1.33-45.99; P= .02), and an estimated blood loss >500 mL (AOR, 3.29; 95% CI, 1.28-8.45; P= .01) were associated with a higher odds of readmission; however, women who underwent a laparoscopic hysterectomy (AOR, 0.32; 95% CI, 0.12-0.86; P= .02) and who were discharged on postoperative day 1 (AOR, 0.16; 95% CI, 0.03-0.82; P= .02) were at a decreased risk of readmission. Physician and operative characteristics were not significant predictors of readmission. This study found that malignancy, perioperative complications, and prior open abdominal surgery, including cesarean delivery, are significant risk factors for consequent 30-day readmission following index hysterectomy. It may be possible to identify patients at highest risk for readmission at the time of hysterectomy, which can assist in developing interventions to reduce such events.

Utility of Readmission Rates as a Quality of Care Measure and Predictors of Readmission Within 30 Days After Spinal Surgery: a Single-Center, Multivariate Analysis.

A retrospective review. The aim of this study was to establish clinically relevant readmission rates that permit accurate comparisons, improve risk-stratification, and direct efforts to minimize readmissions. The 30-day hospital readmission rate is a quality of care measure that is now being used to compare hospitals in a publicly available manner. Records from 1187 consecutive spinal surgeries at Northwestern Memorial Hospital in 2010 were retrospectively reviewed and data were collected that described the patient, surgical procedure, hospital course, complications, and readmissions. The primary outcome of interest was readmission to the hospital within 30 days. Potential risk factors were examined for association with the outcome first via bivariate analysis, with significant predictors further examined by a multivariable model. Identified readmissions were independently reviewed by attending spinal neurosurgeons not involved with the cases to determine whether the readmissions were procedure related or procedure unrelated with respect to accepted criteria. The overall readmission rate was 6.1%. Of these readmissions, 37.5% were deemed procedure related upon attending review, leading to a procedure-related readmission rate of 2.3%. Upon multivariate analysis, only 3 variables were found to be significant predictors of readmission: 2 or more patient comorbidities [odds ratio (OR) 3.72, 95% confidence interval (95% CI) 1.62-8.56], an admission to the ICU (OR 2.68, 95% CI 1.45-4.95), and each additional spinal level involved (OR 1.11, 95% CI 1.02-1.21). Our study suggests that predictors for all-cause 30-day readmission following spinal procedures include number of spinal levels performed during the surgery, number of patient comorbidities present at the time of surgery, and whether the admission required an ICU stay. Future work should focus on developing best practices to modify medical risk factors and comorbidities that have the potential to decrease 30-day readmission rates. 3.

Risk Factors Predict Increased Length of Stay and Readmission Rates in Revision Joint Arthroplasty

BackgroundThis study aimed to identify risk factors for 30-day readmission and extended length of stay (LOS) in revision total knee (RKA) and hip (RHA) arthroplasty patients. MethodsPatients who underwent RKA or RHA from 2011 to 2013 were identified in the National Surgical Quality Improvement Program database. Bivariate and multivariate analyses of risk factors for 30-day readmission and extended LOS (>75th percentile) were assessed using preoperative and intraoperative variables. ResultsA total of 4977 RKA and 5135 RHA patients were reviewed. The most common causes for revision were mechanical (52% RKA, 52% RHA), infection (13% RKA, 8% RHA), dislocation (6% RKA, 13% RHA), and fracture (1% RKA, 4% RHA). Rate of readmission for RKA patients (6.4%; 318 patients) was lower than for RHA patients (8.0%; 409 patients) (P = .002). Multivariate analysis identified severe adverse event before discharge, male sex, pulmonary disease, stroke, cardiac disease, and American Society of Anesthesiologists class 3 or 4 as significant predictors of readmission (all P ≤ .03). Surgical complications were the more common cause of readmission for both groups. Multivariate analysis of extended LOS identified infection or fracture etiology relative to mechanical loosening etiology, functional status, body mass index greater than 40 kg/m2, history of smoking, diabetes, cardiac disease, stroke, bleeding-causing disorders, wound class 3 or 4, and American Society of Anesthesiologists class 3 or 4 (all P ≤ .05) as independent predictors. ConclusionModifiable risk factors should be addressed prior to revision total joint arthroplasty to reduce 30-day readmissions and LOS. Future P4P revision arthroplasty models should incorporate procedural diagnosis as rates of readmission and extended LOS significantly differ across procedural etiologies.

Cost-Effectiveness in Hepatic Lobectomy: the Effect of Case Volume on Mortality, Readmission, and Cost of Care

Objective(s)Higher-volume centers demonstrate better perioperative outcomes for complex surgical interventions, though resource utilization implications of this hospital-level variation are unclear. We hypothesized that for hepatic lobectomy, higher operative volume correlates with better outcomes and lower costs. MethodsFrom 2009 to 2011, 4163 patients undergoing hepatic lobectomy were identified from the University HealthSystems Consortium database. Univariate, multivariate logistic regression, and decision analytic models were constructed to identify differences in hospital utilization and cost. Cost included both index and readmission hospitalizations, when applicable. ResultsThe annual number of hepatic lobectomies performed by the institutions within the study ranged from 1 to 86. The median age of the 4163 patients was 58 years with a roughly equal gender split (M/F 49 %:51 %) and a racial breakdown which reflected that of the general US population. For all patients, the overall perioperative mortality rate was 2.3 % and the 30-day readmission rate was 13.4 %. Hospitals performing >30 hepatic lobectomies per year had significantly lower mortality and readmission rates than those hospitals performing ≤15 lobectomies annually (both p < 0.05). On multivariate analysis, higher severity of illness (odd ratio (OR) 2.13, 95 % confidence interval (CI) [1.48–3.07], p < 0.001), discharge to rehab (OR 1.84, [1.28–2.64], p < 0.001), home with home health care (OR 1.38, [1.08–1.76], p = 0.01), and surgery at a low-volume hospital (OR 1.49, [1.18–1.88], p < 0.001) were significant predictors of readmission. Conversely, surgical intervention at high-volume centers was associated with decreased risk of readmission (OR 0.67, [0.53–0.85], p < 0.001). When both index and readmission costs were considered, per-patient cost at low-volume centers was 21.9 % higher than at high-volume centers ($19,669 vs. $16,137). Sensitivity analyses adjusting for perioperative mortality and readmission at all centers did not significantly change the analysis. ConclusionsThese data, for the first time, demonstrate that hospital volume in hepatic lobectomy is an important, modifiable risk factor for readmission and cost. To optimize resource utilization, patients undergoing complex hepatic surgery should be directed to higher-volume surgical institutions.

Present-Day Hospital Readmissions after Left Ventricular Assist Device Implantation: A Large Single-Center Study.

Left ventricular assist device (LVAD) therapy improves survival, hemodynamic status, and end-organ perfusion in patients with refractory advanced heart failure. Hospital readmission is an important measure of the intensity of LVAD support care. We analyzed readmissions of 148 patients (mean age, 53.6 ± 12.7 yr; 83% male) who received a HeartMate II LVAD from April 2008 through June 2012. The patients had severe heart failure; 60.1% were in Interagency Registry for Mechanically Assisted Circulatory Support class 1 or 2. All patients were observed for at least 12 months, and readmissions were classified as planned or unplanned. Descriptive and multivariate regression analyses were used to identify predictors of unplanned readmission. Twenty-seven patients (18.2%) had no readmissions or 69 planned readmissions, and 121 patients (81.8%) had 460 unplanned readmissions. The LVAD-related readmissions were for bleeding, thrombosis, and anticoagulation (n=103; 49.1%), pump-related infections (n=60; 28.6%), and neurologic events (n=28; 13.3%). The readmission rate was 2.1 per patient-year. Unplanned readmissions were for comorbidities and underlying cardiac disease (54.3%) or LVAD-related causes (45.7%). In the unplanned-readmission rate, there was no significant difference between bridge-to-transplantation and destination-therapy patients. Unplanned readmissions were associated with diabetes mellitus (odds ratio [OR]=3.3; P=0.04) and with shorter mileage from residence to hospital (OR=0.998; P=0.046). Unplanned admissions for LVAD-related sequelae and ongoing comorbidities were common. Diabetes mellitus and shorter distance from residence to hospital were significant predictors of readmission. We project that improved management of comorbidities and of anticoagulation therapy will reduce unplanned readmissions of LVAD patients in the future.

Surgery for Ulcerative Colitis Is Associated with a High Rate of Readmissions at 30 Days.

Currently, the predictors of readmission after colectomy specifically for ulcerative colitis (UC) are poorly investigated. We sought to determine the rates and predictors of 30-day readmissions after colectomy for UC. Patients undergoing total proctocolectomy and end ileostomy, abdominal colectomy with end ileostomy, proctocolectomy with ileoanal pouch anastomosis (IPAA) formation and diverting ileostomy, one stage IPAA, or abdominal colectomy with ileorectal anastomosis at a tertiary care center between January 2002 and January 2012 for UC were included. Patients were identified using ICD-9 code 556.x. Each record was manually reviewed. The electronic record system was reviewed for demographic information, medical histories, UC history, medications, and data regarding the admission and discharge. Charts were reviewed for readmissions within 30 days of surgery. Univariate and multivariate analyses were performed using Stata v.13. Two hundred nine patients with UC underwent a colectomy. Forty-three percent had a proctocolectomy with IPAA and diverting ileostomy and 32% had abdominal colectomy with end ileostomy. Seventy-six percent of surgeries were due to failure of medical therapy and 68% of patients were electively admitted for surgery. Thirty-two percent (n = 67/209) of the cohort was unexpectedly readmitted within 30 days. In multivariate model, proctocolectomy with IPAA and diverting ileostomy (odds ratio [OR] = 2.11; 95% CI, 1.06-4.19; P = 0.033) was the only significant predictor of readmission. Hospital length of stay >7 days (OR = 1.82; 95% CI, 0.98-3.41; P = 0.060), presence of limited UC (OR = 2.10; 95% CI, 0.93-4.74; P = 0.074), and steroid before admission (OR = 1.69; 95% CI, 0.90-3.2; P = 0.100) trended toward significance. Surgery for UC is associated with a high rate of readmission. Further prospective studies are necessary to determine the means to reduce these readmissions.

Drivers of Readmissions in Vascular Surgery Patients

Readmissions are frequent in vascular surgery patients, but it is not clear which factors are the drivers of readmissions. Specifically, the relative contributions of patient comorbidities vs postoperative complications are unknown. We sought to study the potential drivers of readmission and create a model for predicting the risk of readmission in vascular patients. The 2012 to 2013 American College of Surgeons National Quality Improvement Program data set was queried for unplanned readmissions in vascular patients. Bivariable and multivariable risk adjustment analyses were used to model the relative contributions of patient comorbidities, operative factors, and postoperative complications for readmission. The unplanned readmission rate was 9.3%. The preoperative model based on patient comorbidities predicted readmission risk with a low C index of 0.666; the top five predictors were American Society of Anesthesiologists class, preoperative open wound, inpatient operation, dialysis dependence, and diabetes mellitus. The postoperative model that included postoperative complications predicted readmission risk better (C index, 0.778); postoperative complications were the most significant predictor of readmission, overpowering patient comorbidities. Importantly, postoperative complications identified prior to discharge from the hospital were not a strong predictor of readmission because the model using predischarge postoperative complications had a similar C index to our preoperative model (0.681). However, the inclusion of complications identified after discharge appreciably improved the predictive power of the model (C index, 0.778). The top five predictors of readmission in the final model based on comorbidities and postoperative complications were postdischarge deep space infection, superficial wound infection, pneumonia, myocardial infection, and sepsis. Readmissions in vascular surgery patients are mainly driven by postoperative complications identified after discharge. Thus, efforts to reduce vascular readmissions based on inpatient data may prove ineffective. Our study suggests interventions to reduce vascular readmissions should focus on prompt identification of modifiable postdischarge complications.

Do Collaborative Case Management Models Decrease Hospital Readmission Rates Among High-Risk Patients?

Case management provides a process and structure in health care systems that influence and control quality of care while reducing costs. A quality indicator of widespread concern is 30-day readmission of patients. There is significant initiative to drive down hospital readmission rates through development and/or redesign of case management models. To examine the relationship of a collaborative case management model on hospital readmission rates among patients aged 65 years and older. A retrospective chart review of patients discharged alive (n = 978) was conducted to evaluate and compare 2 care management models on hospital readmission rates. Demographic data, diagnosis, insurance carrier, admission source, discharge disposition, and incidence of readmission were collected using a structured data extraction tool. Logistic regression was used to identify predictors of readmission within 30 days of hospital discharge. The sample was elderly (mean age = 79.5 years), White (88.8%), and primarily female (60%). Mean length of stay between pre- and postmodel groups was not statistically different (p = .2). The model contained 6 independent variables (gender, payer, admission source, discharge disposition, diagnosis, and length of stay) and none were statistically significant, χ2 (1, n = 978) = 1.97, p = .58. The analysis indicates that group characteristics did not distinguish who would get readmitted on the basis of independent variables measured. Age, gender, admit source, diagnosis, length of stay, and discharge disposition are not significant predictors of readmissions. Hospital case management programs may want to consider structuring processes to support patient adherence. Additional research is needed in this area.

Factors predicting 30-day readmission after laparoscopic colorectal cancer surgery within an enhanced recovery programme.

Hospital readmission within 30 days of surgery has become a marker of poor quality patient care. This study aimed to investigate factors predictive of 30-day readmission after laparoscopic colorectal cancer surgery within an enhanced recovery after surgery (ERAS) programme. Consecutive patients undergoing laparoscopic surgery for colorectal cancer within an ERAS programme between 2002 and 2009 were included. Data were collected relating to patient demographics, neoadjuvant chemoradiotherapy, ERAS compliance, and operative and postoperative outcomes. A logistic regression model was used to identify factors associated with readmissions after adjusting for the potential effect of covariables simultaneously. In all, 268 cancer patients underwent laparoscopic colorectal surgery (108 rectal resections), of whom 34 (12.7%) were readmitted due most commonly to bowel obstruction (29%) and surgical site infection (18%). The use of neoadjuvant therapy (odds ratio 4.49, 95% CI 1.41-14.35; P = 0.011) and ERAS compliance above 93% (odds ratio 0.38, 95% CI 0.18-0.84; P = 0.016) were independent predictors of readmission. Poor ERAS compliance and preoperative chemoradiotherapy were significant predictors of readmission following laparoscopic colorectal cancer surgery. Further research is required to expand the scope of ERAS beyond hospital discharge.

Predictors of Hospital Readmission after Bariatric Surgery

Identification of factors that might predict readmission after bariatric surgery could help surgeons target high-risk patients. The purpose of this study was to identify comorbidities, surgical variables, and postoperative complications associated with readmission. Patients with bariatric surgery as their primary procedure were identified from the 2012 American College of Surgeons (ACS) NSQIP database. Patient variables, operative times, and major postoperative complications were analyzed for predictors of readmission. The ACS NSQIP estimated probability of morbidity (MORBPROB) was also considered. Chi-square tests and Poisson regression were used for statistical analysis to identify significant predictors. There were 18,186 patients who met inclusion criteria. There were 1,819 who had a laparoscopic gastric band, 9,613 who had laparoscopic Roux-en-Y gastric bypass (RYGB), 6,439 who had gastroplasties (vertical banded gastroplasty and sleeve), and 315 who had open RYGB. Age, sex, BMI, American Society of Anesthesiologists (ASA) class, diabetes, hypertension, steroid use, type of procedure, and operative time all were significantly associated with readmission within 30 days of operation. All major postoperative complications were significant predictors of readmission. Patients expected to be at high risk based on the ACS NSQIP MORBPROB had a significantly higher rate of readmissions. The overall readmission rate for patients undergoing bariatric surgery was 5%. The readmission rate among patients with any major complication was 31%. Bariatric surgery is a low-risk procedure. Complexity of operation, ASA class, prolonged operative time, and major postoperative complications are important determinants of high risk for readmission. The ACS NSQIP MORBPROB may be a useful tool to identify and target patients at risk for readmission.

Emergency readmissions are substantially determined by acute illness severity and chronic debilitating illness: A single centre cohort study

BackgroundThe factors influencing hospital readmissions are debated. We assessed whether readmissions could be predicted using routinely collected hospital data. MethodsAll emergency admissions to a single institution over 12years (2002–2013) were included. The predictor variables, of acute illness severity, Manchester Triage Category, chronic disabling disease and Charlson co-morbidity scores, were studied univariably and entered into a multivariable logistic regression model to predict the bivariate of any readmission or none. A zero truncated Poisson regression model assessed the predictors against the readmission count and incidence rate ratios were calculated. Factors reflecting the clinical load on the emergency department were examined. Results66,933 admissions were recorded in 36,271 patients. The readmission rates at 1, 3, 6 and 9years were 29.5%, 38.9%, 42.9% and 44.1%. Early readmissions represented 14.1%. In the multivariable model, an admission in the previous 6months was the strongest predictor of readmission, OR of 5.02 (95% CI: 4.86, 5.18). Acute illness severity — OR of 2.68 (95% CI: 2.33, 3.09) for group VI vs group I, and chronic disabling score — OR of 2.08 (95% CI: 1.87, 2.32) for a score of 4+ vs 0 were significant predictors of readmission in the multivariable model. Both of these predictors demonstrated a linear relationship. Illness severity was the strongest predictor of an early readmission within 4weeks. ConclusionReadmissions increase as a function of time; illness severity, chronic disabling disease score and a recent admission are the strongest predictors of readmission.

Readmission rates and related factors in heart failure patients: A study in Lebanon

Heart failure is the leading cause of hospitalization among older adults in the United States and other developed countries. Readmission rates of heart failure patients is one of the key outcome performance measures used in evaluating the quality of care of these patients. In Lebanon, there are no published data on readmission of heart failure patients. The aim of the study was to examine the readmission rates of heart failure patients within 30, 60 and 90 days of discharge from the hospital, and factors associated with readmission. The medical records of all 187 patients admitted with heart failure to Rafic Hariri University Hospital in Beirut between January 1, 2010 and December 31, 2010 were reviewed. Data on demographic and relevant clinical variables were retrieved. Readmission rates were 15%, 22.2%, and 27.8% at 30, 60 and 90 days following discharge, respectively. The majority of readmissions (73.61%) were due to heart failure exacerbations. Significant predictors of readmission were: history of diabetes mellitus, coronary artery disease, length of stay at the index admission and gamma glutamyl transpeptidase levels. Management of the patients did not always conform to the evidence based guidelines. The findings suggest the need for better adherence to clinical guidelines in caring for heart failure patients and improved documentation of discharge instructions.

Perioperative Patient-Reported Outcomes Predict Serious Postoperative Complications: a Secondary Analysis of the COST Trial

BackgroundDecreased survival after colon cancer surgery has been reported in patients with deficient preoperative quality of life. We hypothesized that deficits in preoperative quality of life are associated with postoperative complications. Patient and MethodsA secondary analysis of the Clinical Outcomes Surgical Therapy trial NCCTG 93-46-53 (INT 0146, Alliance) was performed. Quality of life deficit was defined as overall quality of life score <50 on a 100-point scale and used for univariate and multivariate analysis. ResultsOf 431 patients enrolled in the quality of life portion of the trial, 81 patients (19 %) experienced complications including two deaths (0.5 %). Fifty-five patients (13 %) had a preoperative quality of life score <50. Patients with a preoperative deficit were more likely to have a serious early complication (16 vs 6 %, p = 0.023). Using stepwise logistic model, the variables significantly associated with having any early complications (yes/no) were age, ASA III and change in “activity” from baseline to day 14. Patients with an early complication experienced a 3.5-day longer hospital stay (p = 0.0001). Gender, race, tumor stage, and laparoscopic or open approach were not associated with an increased frequency of complications. After adjusting for demographics, tumor stage, ASA, and operative approach, significant predictors for readmission were preoperative pain (odds ratio (OR) 1.61, confidence interval (CI) 1.11–2.34, p = 0.0125), and changes from baseline to day 2 in fatigue (OR 1.34, CI 1.03–1.74, p = 0.032). ConclusionsThis study suggests that quality of life can provide an early indicator for patients at risk of complications. Further studies should evaluate how perioperative quality of life assessment may assist to improve outcomes.

Factors determining post-operative readmissions after left ventricular assist device implantation

In the current era of extensive healthcare reform, there has been a heightened focus on the frequency and cause for readmissions within 30 days of discharge given that readmissions are not reimbursed by most providers. The aim of our study was to determine the frequency, etiology and patterns of 30-day readmissions among recipients of continuous-flow left ventricular assist devices (LVADs) at our institution as well to determine whether there were any significant predictors of readmission. From March 2006 through June 2013, 150 patients underwent implantation of a continuous-flow LVAD at our institution. Patients were stratified into two groups based on their 30-day readmission status. A total of 12 patients died before discharge and were excluded from our analysis. Causes for 30-day readmissions and duration of hospital stay for the readmissions were recorded. Numerous pre-operative variables and post-operative complications were compared using 2-sided t-tests and chi-square tests between patients who were and were not readmitted within 30 days of their discharge after their LVAD implant. The 30-day readmission rate was 26.1% (36 of 138), with approximately 70% of post-operative readmissions occurring within 10 days of the patient's initial hospital discharge. Recurrent heart failure (12 of 36, 33.3%) and gastrointestinal bleeding (8 of 36, 22.2%) were the most common causes for 30-day readmission. The median length of stay (LOS) for readmission was 11.7 days. Thirty-day readmission did not affect short- or long-term survival. On univariate analysis, post-operative gastrointestinal bleeding (GIB) was a significant risk factor for 30-day readmissions (HR 1.4, 95% CI 0.19 to 0.99, p = 0.05), and overall length of stay was a significant factor in reducing 30-day readmission rates (HR 0.91, 95% CI 0.85 to 0.99, p = 0.02). Our experience indicates that 30-day readmission rates after LVAD implantation remain relatively high, with most occurring within 10 days of discharge. Recurrent heart failure and GIB were the most common causes of post-operative rehospitalization. In addition, GIB during the index hospitalization was a significant predictor of 30-day readmission.

The impact of insurance and socioeconomic status on outcomes for patients with left ventricular assist devices

BackgroundThere has been a steady increase of patients living in the community with Left Ventricular Assist Devices (LVADs). There is a significant gap in our fund of knowledge with respect to the impact that insurance and socioeconomic status has on outcomes for LVAD patients. We thus hypothesize that low neighborhood socioeconomic status and receipt of Medicaid, respectively, lead to earlier readmissions, earlier death, as well as longer time to transplantation among LVAD patients. MethodsThis was a retrospective review of 101 patients using existing data in the medical information warehouse database at The Ohio State University Medical Center. Primary outcomes measured included time to first event (first readmission or death), death, and time to rehospitalization. Our secondary outcome of interest included time from LVAD implantation to cardiac transplantation. ResultsRecipients of Medicaid did not have an increased risk of adverse events compared with patients without Medicaid coverage. Low Median Household Income (MHI) was associated with an increased risk of readmission (log-rank P = 0.0069) and time to first event (log-rank P = 0.0088). Bridge to transplantation was the only independent predictor of time to death (Hazard Ratio 2.1, [95% confidence interval = 1.03–4.37]). Low MHI and a history of atherosclerosis were both significant predictors for readmission and time to first event. Aldosterone antagonist use decreased the risk of readmission or time to first event by 46%. ConclusionsLVAD recipients with a low MHI were more likely to be readmitted to the hospital after LVAD implantation. Whether these patients are adequately monitored on an outpatient basis remains unclear.

Su1103 30 Day Readmissions Rate and Predictors of Readmission in Hospitalized Patients With Ulcerative Colitis

Background: 30 day readmission rates have been evaluated in many diseases and are a measure of quality of care. Currently the Affordable Care Act mandates the Centers for Medicare and Medicaid Services to penalize hospitals with high 30 day readmissions. To date, no study has systematically examined the rates or predictors of readmission in ulcerative colitis (UC). Our goal was to evaluate the overall 30 day rate of readmission and predictors of readmission in patients hospitalized with severe UC. Methods: A total of 720 admissions for severe UC were reviewed from January 2002 to January 2012 at a tertiary care medical center. Charts were reviewed to confirm the diagnosis of UC. Demographic information, admission and discharge date, past medical history, UC related history, medication history prior to admission, during admission, and on discharge, laboratory testing on admission, procedures and surgeries during admission were all assessed. All records were evaluated for readmission within 30 days of the initial hospitalization and the reason for readmission. Readmissions were further evaluated as to whether they were related to UC and if they were pre-planned. Univariate and multivariate analysis was performed using SAS v9.3. Variables with p value <0.1 were included inmultivariate analysis Results: See table 1 for demographics. 15% (109/720) of patients were readmitted within 30 days of their initial admission for a UC. Median length of stay (LOS) of this admit was 4 days. 38% (41/109) were readmitted for a recurrent UC flare or related symptoms, 45% (49/109) were related to UC surgery on the initial admission, 3% (3/109) were planned readmissions. 2% (2/109) of the readmitted patients died. An additional 26% (28/109) were readmitted a second time. The median LOS for this admission was 4 days. Surgery related issues accounted for 54% (15/28) of these readmissions; 18% (5/28) were planned readmissions. On univariate analysis extent of disease involving proctosigmoiditis, or left sided colitis, or extensive colitis, weekend discharge, abdominal x-ray during admission, CT abdomen/pelvis during admission, higher creatinine level on day of admission, use of systemic steroids prior to admission, and surgery during admission all had p values < 0.1. On multivariate analysis, systemic steroids prior to admission (P 0.049), surgery during hospitalization (P <0.005), CT abdomen/pelvis during hospitalization (P 0.012), and higher levels of creatinine on day of admission (P 0.0086) were all significant predictors of readmission (Table 2). Conclusion: UC has a high 30 day rate of readmission. Patients admitted on systemic steroids, have elevated creatinine on admission or undergo surgery during admission should be closely monitored to minimize readmissions. Future studies are necessary to validate predictors of readmission and ways to reduce readmission.

MP60-10 PREDICTORS OF EARLY POST-OPERATIVE READMISSION AFTER OPEN AND ROBOTIC RADICAL CYSTECTOMY FOR PATIENTS WITH BLADDER CANCER

You have accessJournal of UrologyBladder Cancer: Invasive III1 Apr 2014MP60-10 PREDICTORS OF EARLY POST-OPERATIVE READMISSION AFTER OPEN AND ROBOTIC RADICAL CYSTECTOMY FOR PATIENTS WITH BLADDER CANCER Jordan Allen, Ahmed Sarhan, and Ahmad Shabsigh Jordan AllenJordan Allen More articles by this author , Ahmed SarhanAhmed Sarhan More articles by this author , and Ahmad ShabsighAhmad Shabsigh More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1764AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives Robotic radical cystectomy (RRC) is a minimally-invasive technique with reported lower low grade complications rate compared to the Open radical cystectomy (ORC). Regardless of surgical technique; cystectomy remains a complex procedure with many potential complications and high readmission rate. We analyze the incidence, cause and predictors of readmission after ORC and RRC. Methods We retrospectively reviewed a total of 337 patients who underwent radical cystectomy at our institution from April 2008 through April 2013. Pre-operative patient characteristics and perioperative outcomes were analyzed. All perioperative complications within 30 days of surgery were analyzed and graded according to the Clavien complication grading system and correlated to post-operative readmission. Univariate and multivariate regression models were used to identify the predictors of post-operative readmission. Results A total of 115 and 225 patients underwent robotic and open radical cystectomy with urinary diversion for bladder cancer, respectively. The groups had similar pre-operative characteristics including age, BMI, Charlson-Romano scores, prior abdominal surgery or pelvic radiation, renal function, presence of hydronephrosis, and ASA scores. Significantly more patients undergoing an ORC had a continent urinary diversion. Overall, 26 (23.2%) RRC patients and 57 (25.3%) ORC patients had at least one readmission within 30 days of surgery (p = 0.7). Infections and gastrointestinal complications were the most common causes 27% and 25% respectively. On multivariate regression analysis weight, was the only significant predictor of readmission (p<0.05). Both history of active smoking and chemotherapy were strong predictors (p=0.09) but not significant. Factors such as prior history of dyspnea, pelvic radiation therapy, ability to walk, diabetes, renal function, surgical technique, and urinary diversions were not significant predictors of post-operative readmission. Conclusions Whether performed open or robotically, radical cystectomy is a complex procedure with a high readmission rate. The minimally-invasive approach did reduce the number of readmission, predicting readmission is a difficult task, and obesity is a major risk factor. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e636 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Jordan Allen More articles by this author Ahmed Sarhan More articles by this author Ahmad Shabsigh More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

MP6-11 PREDICTORS OF READMISSION FOLLOWING OUTPATIENT UROLOGICAL SURGERY

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Practice Patterns, Cost Effectiveness I1 Apr 2014MP6-11 PREDICTORS OF READMISSION FOLLOWING OUTPATIENT UROLOGICAL SURGERY Aksharananda Rambachan, Richard S. Matulewicz, Matthew Pilecki, John YS. Kim, and Shilajit Kundu Aksharananda RambachanAksharananda Rambachan More articles by this author , Richard S. MatulewiczRichard S. Matulewicz More articles by this author , Matthew PileckiMatthew Pilecki More articles by this author , John YS. KimJohn YS. Kim More articles by this author , and Shilajit KunduShilajit Kundu More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.245AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The Patient Protection and Affordable Care Act increases oversight of surgical outcomes and ties readmissions to Medicare reimbursement. Given the increasing volume of outpatient urological procedures, we provide the first multi-institutional, multivariate analysis of the patient factors that contribute to readmission. METHODS Using the National Surgical Quality Improvement Program database from 2011, 7,795 patients were isolated. Multiple logistic regression was utilized to predict 30 day unplanned readmissions controlling for demographics, clinical characteristics and comorbidities. The readmission rates for the 5 most common procedures were calculated along with postoperative complications associated with readmission. RESULTS Outpatient urological surgery had an overall 3.7% readmission rate. The five most common procedures and their readmission rates were cystourethroscopy and resection of bladder tumor (4.97%), laser prostatectomy (4.27%), TURP (4.24%), excision of hydrocele (1.92%) and sling operation for urinary incontinence (0.85%). The most common comorbidities in the readmitted patients included hypertension, diabetes and smoking. Risk adjusted multiple regression indicated that cancer history (OR 3.48), bleeding disorder (OR 2.03), male gender (OR 1.38), ASA level 3 or 4 (OR 1.34) and age (OR 1.01) were significant predictors for readmission. Readmitted patients also had higher rates of 30-day complications. CONCLUSIONS Readmission after outpatient urological surgery occurs at a rate of 3.7%. A history of cancer, bleeding disorder, male gender, ASA level 3 or 4 and age were associated with readmission along with greater rates of medical and surgical complications. Our results may help guide risk reduction initiatives and prevent costly readmissions. Table 1. Predictors of readmission from regression analysis Factor OR p-value 95% C.I. Alcohol use 1.860 .056 .984 3.514 History of COPD 1.323 .197 .865 2.023 Previous percutaneous coronary intervention 1.218 .395 .773 1.917 Previous percutaneous coronary surgery 1.164 .538 .718 1.886 Hypertension .850 .236 .650 1.112 Disseminated Cancer∗ 3.484 .000 1.893 6.410 Steroid Use 1.700 .074 .951 3.042 Bleeding Disorder∗ 2.034 .006 1.227 3.373 Diabetes 1.328 .053 .996 1.771 Dyspnea 1.295 .201 .871 1.926 ASA Level 3 or 4∗ 1.335 .046 1.005 1.774 Function Status prior to Surgery 1.610 .128 .872 2.972 Male gender∗ 1.384 .038 1.018 1.882 Age (year)∗ 1.016 .001 1.006 1.026 ∗ p<.05 considered to be significant. Model: forced entry (one step), C-stat: .656 © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e60-e61 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Aksharananda Rambachan More articles by this author Richard S. Matulewicz More articles by this author Matthew Pilecki More articles by this author John YS. Kim More articles by this author Shilajit Kundu More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Predictors of Readmission following Outpatient Urological Surgery

The Patient Protection and Affordable Care Act increases oversight of surgical outcomes and ties hospital readmissions to Medicare reimbursement. Given the increasing volume of outpatient urological procedures, to our knowledge this study provides the first multi-institutional multivariate analysis of patient factors that contribute to readmission. Using the 2011 National Surgical Quality Improvement Program database we identified 7,795 patients. Multiple logistic regression was used to predict 30-day unplanned hospital readmissions controlling for demographics, clinical characteristics and comorbidities. Readmission rates of the 5 most common procedures were calculated along with the rate of postoperative complications associated with readmission. Outpatient urological surgery had an overall 3.7% readmission rate. The 5 most common procedures were cystourethroscopy and resection of bladder tumor (readmission rate 4.97%), laser prostatectomy (4.27%), transurethral resection of prostate (4.24%), hydrocele excision (1.92%) and sling surgery for urinary incontinence (0.85%). The most common comorbidities in readmitted patients were hypertension, diabetes and smoking. Risk adjusted multiple regression indicated that cancer history (OR 3.48), bleeding disorder (OR 2.03), male gender (OR 1.38), ASA(®) level 3 or 4 (OR 1.34) and age (OR 1.01) were significant predictors of readmission. Readmitted patients also had a higher 30-day complication rate. Readmission after outpatient urological surgery occurs at a rate of 3.7%. A history of cancer, bleeding disorder, male gender, ASA level 3 or 4 and age were associated with readmission along with greater rates of medical and surgical complications. Our results may help guide risk reduction initiatives and prevent costly readmissions.