Impact of length of stay after coronary bypass surgery on short-term readmission rate: an instrumental variable analysis

Case Management in Prevention of 30-Day Readmission in Post-Coronary Artery Bypass Graft Surgery

PURPOSE OF STUDY

Thirty-day readmission is associated with increased morbidity and mortality among postoperative coronary artery bypass graft (CABG) surgery patients. Interventions such as case management and follow-up care may reduce 30-day readmission. The purpose of this article is to report a study on modifiable factors that may have significant implications for case management in the prevention of readmission after CABG surgery.

PRIMARY PRACTICE SETTINGS

The study population included all the adult patients who underwent first-time CABG surgery from January 1, 2013, to January 1, 2016, from a Mid-South hospital.

METHODOLOGY AND SAMPLE

A retrospective case-control study was employed to examine 1,712 patients who underwent CABG surgery.

RESULTS

The results revealed that patients readmitted within 30 days had a significantly shorter length of stay (LOS) (6 days vs. 10 days; p < .0001), more days in intensive care unit (6 days vs. 4 days; p = .0391), and significantly higher diabetes/renal (4% vs. 1%), infection (17% vs. 2%), and respiratory-related diagnoses (10% vs. 1%; p < .0001).

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

Among these factors, hospital LOS is a major factor that can be addressed through case management in addition to other modifiable risk factors. Understanding modifiable factors associated with higher readmission risk is crucial for effective intervention and case management planning.

Barriers to cardiac rehabilitation and patient perceptions on the usage of technologies in cardiac rehabilitation: A cross-sectional study

AIMS AND OBJECTIVES

The study aimed to identify factors associated with participation in Phase II cardiac rehabilitation and to assess patient perceptions towards the usage of technologies in cardiac rehabilitation.

BACKGROUND

Despite efforts to promote utilisation of cardiac rehabilitation (CR), participation among patients remains unsatisfactory. Little is known of patient decision to participate Phase II CR in a multi-ethnic country.

DESIGN

A cross-sectional study design.

METHODS

A consecutive sampling of 240 patients with coronary heart disease completed Coronary Artery Disease Education Questionnaire (CADE-Q) II, Hospital Anxiety and Depression Scale (HADS), Multidimensional Scale of Perceived Social Support (MSPSS) and Cardiac Rehabilitation Barriers Scale (CRBS).

RESULTS

Seventy per cent of patients (mean age 60.5 [SD = 10.6] years, 80.8% male) participated in phase II cardiac rehabilitation. Self-driving to cardiac rehabilitation centres, higher barriers in perceived need/health care and logistical factors were significantly associated with decreased odds of participation. Patients with more barriers from comorbidities/functional status, higher perceived social support from friends, and anxiety were more likely to participate. Chinese and Indians were less likely to participate when compared with Malays. More than 80% of patients used both home and mobile broadband internet, and 72.9% of them would accept the usage of technologies, especially educational videos, instant messenger, and video calls to partially replace the face-to-face, centre-based cardiac rehabilitation approach.

CONCLUSION

Several barriers were associated with non-participation in phase II cardiac rehabilitation. With the high perceived acceptance of technology usage in cardiac rehabilitation, home-based and hybrid cardiac rehabilitation may represent potential solutions to improve participation.

RELEVANCE TO CLINICAL PRACTICE

By addressing the barriers to cardiac rehabilitation, patients are more likely to be ready to adopt health behaviour changes and adhere to the cardiac rehabilitation programme. The high perceived acceptance of using technologies in cardiac rehabilitation may provide insights into new delivery models that can improve and overcome barriers to participation.

Feasibility of Postoperative Day One or Day Two Discharge After Robotic Cardiac Surgery

INTRODUCTION

The robotic platform reduces the invasiveness of cardiac surgical procedures, thus facilitating earlier discharge in select patients. We sought to evaluate the characteristics, perioperative management, and early outcomes of patients who underwent postoperative day 1 or 2 (POD1-2) discharge after robotic cardiac surgery at our institution.

METHODS

Retrospective review of 169 patients who underwent robotic cardiac surgery at our facility between 2019 and 2021 identified 57 patients discharged early on POD1 (n = 19) or POD2 (n = 38) and 112 patients who underwent standard discharge (POD3 or later). Relevant data were extracted and compared.

RESULTS

In the early discharge group, median patient age was 62 [IQR: 55, 66] (IQR = interquartile range) years, and 70.2% (40/57) were male. Median Society of Thoracic Surgeons predictive risk of mortality score was 0.36 [IQR: 0.25, 0.56] %. The most common procedures performed were mitral valve repair [66.6%, (38/57)], atrial mass resection [10.5% (6/57)], and coronary artery bypass grafting [10.5% (6/57)]. The only significant differences between the POD1 and POD2 groups were shorter operative time, higher rate of in-operating room extubation, and shorter ICU length of stay in the POD1 group. Lower in-hospital morbidity and comparable 30-day mortality and readmission rates were observed between the early and standard discharge groups.

CONCLUSIONS

POD1-2 discharge after various robotic cardiac operations afforded lower morbidity and similar 30-day readmission and mortality rates compared to discharge on POD3 or later. Our findings support the feasibility of POD1-2 discharge after robotic cardiac surgery for patients with low preoperative risk, an uncomplicated postoperative course, and appropriate postoperative management protocols.

Decreased activities of daily living at discharge predict mortality and readmission in elderly patients after cardiac and aortic surgery: A retrospective cohort study

Recently, activities of daily living (ADL) were identified as a prognostic factor among elderly patients with heart disease; however, a specific association between ADL and prognosis after cardiac and aortic surgery is not well established. We aimed to clarify the impact of ADL capacity at discharge on prognosis in elderly patients after cardiac and aortic surgery.This retrospective cohort study included 171 elderly patients who underwent open operation for cardiovascular disease in a single center (median age: 74 years; men: 70%). We used the Barthel Index (BI) as an indicator for ADL. Patients were classified into 2 groups according to the BI at discharge, indicating a high (BI ≥ 85) or low (BI < 85) ADL status. All-cause mortality and unplanned readmission events were observed after discharge.Thirteen all-cause mortality and 44 all-cause unplanned readmission events occurred during the median follow-up of 365 days. Using Kaplan-Meier analysis, a low ADL status was determined to be significantly associated with all-cause mortality and unplanned readmission. In the multivariable Cox proportional hazard models, a low ADL status was an independent predictor of all-cause mortality and unplanned readmission after adjusting for age, sex, length of hospital stay, and other variables (including preoperative status, surgical parameter, and postoperative course).A low ADL status at discharge predicted all-cause mortality and unplanned readmission in elderly patients after cardiac and aortic surgery. A comprehensive approach from the time of admission to postdischarge to improve ADL capacity in elderly patients undergoing cardiac and aortic surgery may improve patient outcomes.

Predictors of Hospital Readmission within 30 Days after Coronary Artery Bypass Grafting: Data Analysis of 2,272 Brazilian Patients

Introduction

In order to reduce readmission rates after coronary artery bypass grafting (CABG), its predictors should be known in different contexts. The objective of this study was to identify predictive factors of hospital readmission within 30 days after CABG in a Brazilian center.

Methods

A secondary analysis of an electronic database of patients submitted to isolated CABG was performed. The relationship between readmission within 30 days and demographic, anthropometric, clinical, and surgery-related characteristics was investigated by univariate analyses. Predictors were identified by multiple logistic regression.

Results

Data from 2,272 patients were included, with an incidence of readmission of 8.6%. The predictors of readmission were brown skin color (Beta=1.613; 95% confidence interval [CI] 1.047-2.458; P=0.030), African-American ethnicity (Beta=0.136; 95% CI 0.019-0.988; P=0.049), chronic kidney disease (Beta=2.214; 95% CI 1.269-3.865; P=0.005), postoperative use of blood products (Beta=1.515; 95% CI 1.101-2.086; P=0.011), chronic obstructive pulmonary disease (Beta=2.095; 95% CI 1.284-3.419; P=0.003), and use of acetylsalicylic acid (Beta=1.418; 95% CI 1.000-2.011; P=0.05). Preoperative antibiotic prophylaxis (Beta=0.742; 95% CI 0.5471.007; P=0.055) was marginally significant.

Conclusion

The predictors identified may support a closer postoperative follow-up and individualized planning for a safe discharge.

Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning

Purpose

Predictive analytics (PA) is a new trending approach in the field of healthcare that uses machine learning to build a prediction model using supervised learning algorithms. Isolated coronary artery bypass grafting (iCABG), an open-heart surgery, is commonly performed in the treatment of coronary heart disease.

Aim

The aim of this study was to develop and evaluate a model to predict postoperative length of stay (PLoS) for iCABG patients using supervised machine learning techniques, and to identify the features with the highest contribution to the model.

Methods

This is a retrospective study that uses historic data of adult patients who underwent isolated CABG (iCABG). After initial data pre-processing, data imputation using the kNN method was applied. The study used five prediction models using Naïve Bayes, Decision Tree, Random Forest, Logistic Regression and k Nearest Neighbor algorithms. Data imbalance was managed using the following widely used methods: oversampling, undersampling, "Both", and random over-sampling examples (ROSE). The features selection process was conducted using the Boruta method. Two techniques were applied to examine the performance of the models, (70%, 30%) split and cross-validation, respectively. Models were evaluated by comparing their performance using AUC and other metrics.

Results

In the final dataset, six distinct features and 621 instances were used to develop the models. A total of 20 models were developed using R statistical software. The model generated using Random Forest with "Both" resampling method and cross-validation technique was deemed the best fit (AUC=0.81; F1 score=0.82; and recall=0.82). Attributes found to be highly predictive of PLoS were pulmonary artery systolic, age, height, EuroScore II, intra-aortic balloon pump used, and complications during operation.

Conclusion

This study demonstrates the significance and effectiveness of building a model that predicts PLoS for iCABG patients using patient specifications and pre-/intra-operative measures.

A risk model for prediction of 30-day readmission rates after surgical treatment for colon cancer

PURPOSE

The purpose of this study was to develop a risk model for the prediction of 30-day unplanned readmission rate after surgery for colon cancer.

METHOD

This study was a cross-sectional analysis of data from Nationwide Readmissions Database, collected during 2010-2014. Patients ≥ 18 years of age who underwent surgery for colon cancer were included in the study. The primary outcome of the study was 30-day unplanned readmission rate.

RESULTS

There were 141,231 index hospitalizations for surgical treatment of colon cancers and 16,551 had unplanned readmissions. Age, sex, primary payer, Elixhauser comorbidity index, node positive or metastatic disease, length of stay, hospital bedsize, teaching status, hospital ownership, presence of stoma, surgery types, surgery procedures, infectious complications, surgical complications, mechanical wounds, pulmonary complications, and gastrointestinal complications were selected for the risk analysis during backward regression model. Based on the estimated coefficients of selected variables, risk scores were developed and stratified as low risk (≤ 1.08), moderate risk (> 1.08 to ≤ 1.5), and high risk (> 1.5) for unplanned readmission. Validation analysis (n = 42,269) showed that 7.1% of low-risk individuals, 11.1% of moderate-risk individuals, and 17.1% of high-risk individuals experienced unplanned readmissions (P < 0.001). Pairwise comparisons also showed statistically significant differences between low-risk and moderate-risk participants (P < 0.001), between moderate-risk and high-risk participants (P < 0.001), and between low-risk and high-risk participants (P < 0.001). The area under the ROC curve was 0.622.

CONCLUSIONS

Our risk model could be helpful for risk-stratifying patients for readmission after surgical treatment for colon cancer. This model needs further validation by incorporating all possible clinical variables.

Post-acute care referral in United States of America: a multiregional study of factors associated with referral destination in a cohort of patients with coronary artery bypass graft or valve replacement

Background

The use of post-acute care (PAC) for cardiovascular conditions is highly variable across geographical regions. Although PAC benefits include lower readmission rates, better clinical outcomes, and lower mortality, referral patterns vary widely, raising concerns about substandard care and inflated costs. The objective of this study is to identify factors associated with PAC referral decisions at acute care discharge.

Methods

This study is a retrospective Electronic Health Records (EHR) based review of a cohort of patients with coronary artery bypass graft (CABG) and valve replacement (VR). EHR records were extracted from the Cerner Health-Facts Data warehouse and covered 49 hospitals in the United States of America (U.S.) from January 2010 to December 2015. Multinomial logistic regression was used to identify associations of 29 variables comprising patient characteristics, hospital profiles, and patient conditions at discharge.

Results

The cohort had 14,224 patients with mean age 63.5 years, with 10,234 (71.9%) male and 11,946 (84%) Caucasian, with 5827 (40.96%) being discharged to home without additional care (Home), 5226 (36.74%) to home health care (HHC), 1721 (12.10%) to skilled nursing facilities (SNF), 1168 (8.22%) to inpatient rehabilitation facilities (IRF), 164 (1.15%) to long term care hospitals (LTCH), and 118 (0.83%) to other locations. Census division, hospital size, teaching hospital status, gender, age, marital status, length of stay, and Charlson comorbidity index were identified as highly significant variables (p- values < 0.001) that influence the PAC referral decision. Overall model accuracy was 62.6%, and multiclass Area Under the Curve (AUC) values were for Home: 0.72; HHC: 0.72; SNF: 0.58; IRF: 0.53; LTCH: 0.52, and others: 0.46.

Conclusions

Census location of the acute care hospital was highly associated with PAC referral practices, as was hospital capacity, with larger hospitals referring patients to PAC at a greater rate than smaller hospitals. Race and gender were also statistically significant, with Asians, Hispanics, and Native Americans being less likely to be referred to PAC compared to Caucasians, and female patients being more likely to be referred than males. Additional analysis indicated that PAC referral practices are also influenced by the mix of PAC services offered in each region.

Using information and communication technology in the recovery after a coronary artery bypass graft surgery: patients' attitudes

BACKGROUND

Patients who have undergone a coronary artery bypass graft (CABG) surgery are exposed to physical and mental problems after discharge from the specialist hospital and are often in need of post-discharge support and follow-up.

AIM

This study aimed to explore the attitudes of CABG patients toward using information and communication technology (ICT) during the first year of recovery after discharge from hospital.

METHODS

A cross-sectional design utilizing an electronic survey was employed. The sample consisted of 197 patients who had undergone a CABG surgery during 2015. The questionnaire included questions about follow-up needs, contacts with health professionals, use of the Internet, and attitudes toward using ICT in the recovery phase.

RESULTS

Mean age of the participants was 67.3 years; 18.3% were women. A total of 48.2% of the patient group was satisfied with the pre-discharge information. Only 27% had contacted the hospital after discharge. Whereas 58.4% of the participants had used the Internet to acquire information, only 30.4% found this information to be useful. Many patients (40%) reported that they could benefit from online health information and Skype meetings with professionals. More than 30% reported that nutritional guidance on the Internet could be motivating for choosing healthy diets, and 42.6% reported that Internet-based illustrative videotapes could be motivating for undertaking physical training.

CONCLUSION

ICT can be useful and resource-saving for patients who have undergone a CABG surgery, as well as for the health care services. The technology must be appropriately tailored, with regard to content and design, to be helpful for patients.

Prevention of 30-Day Readmission After Coronary Artery Bypass Surgery

Although many studies have focused on risk factors for 30-day readmission after coronary artery bypass graft (CABG) surgery, there is very little known about the prevention of modifiable risk factors associated with readmission. The research questions that guided this focused literature review were (1) What are the modifiable risk factors of 30-day readmission after CABG surgery identified in recent literature? and (2) What are the clinical programs and strategies available in preventing 30-day readmission after CABG surgery? A focused literature review from 1997 to 2014 yielded 17 published reports. Findings of this review revealed a significant gap between addressing modifiable patient-specific risk factors and the current clinical program initiatives, which are focused on care processes. Clinical programs and strategies for 30-day readmission after CABG surgery are evolving. Many programs and studies have included discharge planning and education as interventions to prevent 30-day readmissions; however, there is inconsistency in the literature on the impact of early discharge on readmission. Future studies need to focus on targeting the clinical modifiable risk factors and discharge planning and education, which may help to prevent 30-day readmissions.

Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study

BACKGROUND

There is a growing concern that reduction in hospital length of stay (LOS) may raise the rate of hospital readmission. This study aims to identify the rate of avoidable 30-day readmission and find out the association between LOS and readmission.

METHODS

All consecutive patient admissions to the internal medicine services (n = 5,273) at King Abdullah University Hospital in Jordan between 1 December 2012 and 31 December 2013 were analyzed. To identify avoidable readmissions, a validated computerized algorithm called SQLape was used. The multinomial logistic regression was firstly employed. Then, detailed analysis was performed using the Decision Trees (DTs) model, one of the most widely used data mining algorithms in Clinical Decision Support Systems (CDSS).

RESULTS

The potentially avoidable 30-day readmission rate was 44%, and patients with longer LOS were more likely to be readmitted avoidably. However, LOS had a significant negative effect on unavoidable readmissions.

CONCLUSIONS

The avoidable readmission rate is still highly unacceptable. Because LOS potentially increases the likelihood of avoidable readmission, it is still possible to achieve a shorter LOS without increasing the readmission rate. Moreover, the way the DT model classified patient subgroups of readmissions based on patient characteristics and LOS is applicable in real clinical decisions.

Socioeconomic Factors Are Associated With Readmission After Lobectomy for Early Stage Lung Cancer

BACKGROUND

Data regarding risk factors for readmissions after surgical resection for lung cancer are limited and largely focus on postoperative outcomes, including complications and hospital length of stay. The current study aims to identify preoperative risk factors for postoperative readmission in early stage lung cancer patients.

METHODS

The National Cancer Data Base was queried for all early stage lung cancer patients with clinical stage T2N0M0 or less who underwent lobectomy in 2010 and 2011. Patients with unplanned readmission within 30 days of hospital discharge were identified. Univariate analysis was utilized to identify preoperative differences between readmitted and not readmitted cohorts; multivariable logistic regression was used to identify risk factors resulting in readmission.

RESULTS

In all, 840 of 19,711 patients (4.3%) were readmitted postoperatively. Male patients were more likely to be readmitted than female patients (4.9% versus 3.8%, p < 0.001), as were patients who received surgery at a nonacademic rather than an academic facility (4.6% versus 3.6%; p = 0.001) and had underlying medical comorbidities (Charlson/Deyo score 1+ versus 0; 4.8% versus 3.7%; p < 0.001). Readmitted patients had a longer median hospital length of stay (6 days versus 5; p < 0.001) and were more likely to have undergone a minimally invasive approach (5.1% video-assisted thoracic surgery versus 3.9% open; p < 0.001). In addition to those variables, multivariable logistic regression analysis identified that median household income level, insurance status (government versus private), and geographic residence (metropolitan versus urban versus rural) had significant influence on readmission.

CONCLUSIONS

The socioeconomic factors identified significantly influence hospital readmission and should be considered during preoperative and postoperative discharge planning for patients with early stage lung cancer.

Relocation of patients after cardiac surgery: is it worth the effort?

BACKGROUND

Fast-track protocols may facilitate early patient discharge from the site of surgery through the implementation of more expedient pathways. However, costs may merely be shifted towards other parts of the health care system. We aimed to investigate the consequence of patient transfers on overall hospitalisation, follow-up and readmission rate after cardiac surgery.

METHODS

A single-centre descriptive cohort study using prospectively entered registry data. The study included 4,515 patients who underwent cardiac surgery at Aarhus University Hospital during the period 1 April 2006 to 31 December 2012. Patients were grouped and analysed based on type of discharge: Directly from site of surgery or after transfer to a regional hospital. The cohort was obtained from the Western Denmark Heart Registry and matched to the Danish National Hospital Register.

RESULTS

Median overall length of stay was 9 days (7.0;14.4). Transferred patients had longer length of stay, median difference of 2.0 days, p < 0.001. Time to first outpatient consultation was 41(30;58) days in transferred patients vs. 45(29;74) days, p < 0.001. 18.6% was readmitted within 30 days. Mean time to readmission was 18.4 ± 6.4 days. Median length of readmission was 3(1,6) days. There was no difference in readmissions between groups. Leading cause of readmission was cardiovascular disease with 48%.

CONCLUSION

Transfer of patients does not overtly reduce health care costs, but overall LOS and time to first outpatient consultation are substantially longer in patients transferred to secondary hospitals than in patients discharged directly. Readmission rate is high during the month after surgery, but with no difference between groups.

Treatment Effect Estimation Using Nonlinear Two-Stage Instrumental Variable Estimators: Another Cautionary Note

OBJECTIVE

To examine the settings of simulation evidence supporting use of nonlinear two-stage residual inclusion (2SRI) instrumental variable (IV) methods for estimating average treatment effects (ATE) using observational data and investigate potential bias of 2SRI across alternative scenarios of essential heterogeneity and uniqueness of marginal patients.

STUDY DESIGN

Potential bias of linear and nonlinear IV methods for ATE and local average treatment effects (LATE) is assessed using simulation models with a binary outcome and binary endogenous treatment across settings varying by the relationship between treatment effectiveness and treatment choice.

PRINCIPAL FINDINGS

Results show that nonlinear 2SRI models produce estimates of ATE and LATE that are substantially biased when the relationships between treatment and outcome for marginal patients are unique from relationships for the full population. Bias of linear IV estimates for LATE was low across all scenarios.

CONCLUSIONS

Researchers are increasingly opting for nonlinear 2SRI to estimate treatment effects in models with binary and otherwise inherently nonlinear dependent variables, believing that it produces generally unbiased and consistent estimates. This research shows that positive properties of nonlinear 2SRI rely on assumptions about the relationships between treatment effect heterogeneity and choice.

Prehospital transportation to therapeutic hypothermia centers and survival from out-of-hospital cardiac arrest

Background

Clinical trials supporting the use of therapeutic hypothermia (TH) in the treatment of out-of-hospital cardiac arrest (OHCA) are based on small patient samples and do not reflect the wide variation in patient selection, cooling methods, and other elements of post-arrest care that are used in everyday practice. This study provides a real world evaluation of the effectiveness of post-arrest care in TH centers during a time of growing TH dissemination in the state of New Jersey (NJ).

Methods

Using a linked database of prehospital, hospital, and mortality records for NJ in 2009-2010, we compared rates of neurologically intact survival at discharge and at 30 days for OHCA patients transported to TH centers (N = 2363) versus other hospitals (N = 2479). We used logistic regression to adjust for patient and hospital covariates. To account for potential endogeneity in prehospital transportation decisions, we used an instrumental variable (IV) based on differential distance to the nearest TH and non-TH hospitals.

Results

Patients taken to TH centers were older, more likely to have a witnessed arrest, more likely to receive defibrillation, and waited a shorter amount of time for initial EMS response. Also, TH hospitals were larger, more likely to be teaching facilities, and operated in a service area with a relatively lower poverty rate compared to hospitals statewide. A Stock-Yogo test confirmed the strength of our IV (F = 2349.91, p < 0.0001). Nevertheless, the data showed no evidence of endogenous transportation to TH centers related to in-hospital survival (Z = -0.08, p = 0.934) or 30-day survival (Z = 0.94, p = 0.349). In logistic regression models, treatment at a TH center was associated with greater odds of 30-day neurologically intact survival (OR = 1.70; 95 % CI: 1.19 – 2.42) but not associated with the odds of neurologically intact survival to hospital discharge (OR = 0.90; 95 % CI: 0.61 – 1.31).

Conclusions

Post-arrest outcomes are more favorable at TH centers but these improved outcomes are not apparent until after hospital discharge. This finding may reflect superior care by TH centers in later stages of post-arrest treatment such as care provided in the intensive care unit, which has greater potential to affect longer term outcomes than initial treatment in the emergency department.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-015-1199-z) contains supplementary material, which is available to authorized users.

A validated, risk assessment tool for predicting readmission after open ventral hernia repair

BACKGROUND/PURPOSE

To present a validated model that reliably predicts unplanned readmission after open ventral hernia repair (open-VHR).

STUDY DESIGN

A total of 17,789 open-VHR patients were identified using the 2011-2012 ACS-NSQIP databases. This cohort was subdivided into 70 and 30% random testing and validation samples, respectively. Thirty-day unplanned readmission was defined as unexpected readmission for a postoperative occurrence related to the open-VHR procedure. Independent predictors of 30-day unplanned readmission were identified using multivariable logistic regression on the testing sample (n = 12,452 patients). Subsequently, the predictors were weighted according to β-coefficients to generate an integer-based Clinical Risk Score (CRS) predictive of readmission, which was validated using receiver operating characteristics (ROC) analysis of the validation sample (n = 5337 patients).

RESULTS

The rate of 30-day unplanned readmission was 4.7%. Independent risk factors included inpatient status at time of open-VHR, operation time, enterolysis, underweight, diabetes, preoperative anemia, length of stay, chronic obstructive pulmonary disease, history of bleeding disorders, hernia with gangrene, and panniculectomy (all P < 0.05). ROC analysis of the validation cohort rendered an area under the curve of 0.71, which demonstrates the accuracy of this prediction model. Predicted incidence within each 5 risk strata was statistically similar to the observed incidence in the validation sample (P = 0.18), further highlighting the accuracy of this model.

CONCLUSION

We present a validated risk stratification tool for unplanned readmissions following open-VHR. Future studies should determine if implementation of our CRS optimizes safety and reduces readmission rates in open-VHR patients.

A novel risk scoring system reliably predicts readmission after pancreatectomy

BACKGROUND

Postoperative readmissions have been proposed by Medicare as a quality metric and can impact provider reimbursement. Because readmission after pancreatectomy is common, we sought to identify factors associated with readmission to establish a predictive risk scoring system.

STUDY DESIGN

A retrospective analysis of 2,360 pancreatectomies performed at 9 high-volume pancreatic centers between 2005 and 2011 was performed. Forty-five factors strongly associated with readmission were identified. To derive and validate a risk scoring system, the population was randomly divided into 2 cohorts in a 4:1 fashion. A multivariable logistic regression model was constructed and scores were assigned based on the relative odds ratio (OR) of each independent predictor. A composite Readmission after Pancreatectomy (RAP) score was generated and then stratified to create risk groups.

RESULTS

Overall, 464 (19.7%) patients were readmitted within 90 days. Eight pre- and postoperative factors, including earlier MI (OR = 2.03), American Society of Anesthesiologists class ≥ 3 (OR = 1.34), dementia (OR = 6.22), hemorrhage (OR = 1.81), delayed gastric emptying (OR = 1.78), surgical site infection (OR = 3.31), sepsis (OR = 3.10), and short length of stay (OR = 1.51) were independently predictive of readmission. The 32-point RAP score generated from the derivation cohort was highly predictive of readmission in the validation cohort (area under the receiver operating curve = 0.72). The low-risk (0 to 3), intermediate-risk (4 to 7), and high-risk (>7) groups correlated with 11.7%, 17.5%, and 45.4% observed readmission rates, respectively (p < 0.001).

CONCLUSIONS

The RAP score is a novel and clinically useful risk scoring system for readmission after pancreatectomy. Identification of patients with increased risk of readmission using the RAP score will allow efficient resource allocation aimed to attenuate readmission rates. It also has potential to serve as a new metric for comparative research and quality assessment.

Impact of hospital-acquired infection on long-term outcomes after endovascular and open abdominal aortic aneurysm repair

BACKGROUND

We hypothesized that infectious complications after open surgery (OPEN) and endovascular repair (EVAR) of nonruptured abdominal aortic aneurysms (AAAs) negatively affected long-term outcomes.

METHODS

Elective OPEN and EVAR cases were selected from 2005-2007 Medicare databases, and rates of postoperative infection, readmission, and longitudinal mortality were compared.

RESULTS

Forty thousand eight hundred ninety-two EVARs and 16,669 OPEN AAA repairs were evaluated. Patients with OPEN developed infection during and after the index hospitalization (12.8% and 4.9%, respectively) more often than those who had undergone EVAR (3.2% and 3.9%, respectively; P < 0.0001 for both). Patients with hospital-acquired infection compared to noninfectious ones were more likely to die during the index hospitalization (odds ratio [OR]: 3.7 [95% confidence interval {CI}: 3.22-4.30]) and within 30 days after discharge (OR: 3.6 [95% CI: 2.83-4.45]). They also were more likely to be readmitted to the hospital during 30 days after index discharge (OR: 1.8 [95% CI: 1.63-1.94]). Index infections associated with the greatest readmission were urinary tract infection after OPEN and sepsis after EVAR. Hospital-acquired infection significantly increased the duration of hospital stay (14.2 ± 13.2 vs 4.0 ± 4.4 days; P < 0.0001) and total hospital charges ($133,070 ± $136,100 vs $66,359 ± $45,186; P < 0.0001). The most common infections to develop 30 days after initial discharge were surgical site infection after EVAR (1.27%) and urinary tract infection after OPEN (1.38%).

CONCLUSION

Hospital-acquired infections had a dramatic effect by increasing hospital and 30-day mortality, readmission rates, and hospital resource use after AAA repair. Programs minimizing infectious complications may decrease future readmissions and mortality after AAA repair.

Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP

OBJECTIVE

In 2012, Medicare began cutting reimbursement for hospitals with high readmission rates. We sought to define the incidence and risk factors associated with readmission after surgery.

METHODS

A total of 230,864 patients discharged after general, upper gastrointestinal (GI), small and large intestine, hepatopancreatobiliary (HPB), vascular, and thoracic surgery were identified using the 2011 American College of Surgeons National Surgical Quality Improvement Program. Readmission rates and patient characteristics were analyzed. A predictive model for readmission was developed among patients with length of stay (LOS) 10 days or fewer and then validated using separate samples.

RESULTS

Median patient age was 56 years; 43% were male, and median American Society of Anesthesiologists (ASA) class was 2 (general surgery: 2; upper GI: 3; small and large intestine: 2; HPB: 3; vascular: 3; thoracic: 3; P < 0.001). The median LOS was 1 day (general surgery: 0; upper GI: 2; small and large intestine: 5; HPB: 6; vascular: 2; thoracic: 4; P < 0.001). Overall 30-day readmission was 7.8% (general surgery: 5.0%; upper GI: 6.9%; small and large intestine: 12.6%; HPB: 15.8%; vascular: 11.9%; thoracic: 11.1%; P < 0.001). Factors strongly associated with readmission included ASA class, albumin less than 3.5, diabetes, inpatient complications, nonelective surgery, discharge to a facility, and the LOS (all P < 0.001). On multivariate analysis, ASA class and the LOS remained most strongly associated with readmission. A simple integer-based score using ASA class and the LOS predicted risk of readmission (area under the receiver operator curve 0.702).

CONCLUSIONS

Readmission among patients with the LOS 10 days or fewer occurs at an incidence of at least 5% to 16% across surgical subspecialties. A scoring system on the basis of ASA class and the LOS may help stratify readmission risk to target interventions.