Readmission rates after abdominal surgery: can they be decreased to a minimum?

Comparison of administrative data and the American College of Surgeons National Surgical Quality Improvement Program data in a New South Wales Hospital

BACKGROUND

The National Surgical Quality Improvement Program (NSQIP) is widely used in North America for benchmarking. In 2015, NSQIP was introduced to four New South Wales public hospitals. The aim of this study is to investigate the agreement between NSQIP and administrative data in the Australian setting; to compare the performance of models derived from each data set to predict 30-day outcomes.

METHODS

The NSQIP and administrative data variables were mapped to select variables available in both data sets where coding may be influenced by interpretation of the clinical information. These were compared for agreement. Logistic regression models were fitted to estimate the probability of adverse outcomes within 30 days. Models derived from NSQIP and administrative data were compared by receiver operating characteristic curve analysis.

RESULTS

A total of 2240 procedures over 21 months had matching records. Functional status demonstrated poor agreement (kappa 0.02): administrative data recorded only one (1%) patient with partial- or total-dependence as recorded by NSQIP data. The American Society of Anesthesiologists class demonstrated excellent agreement (kappa 0.91). Other perioperative variables demonstrated poor to fair agreement (kappa 0.12-0.61). Predictive model based on NSQIP data was excellent at predicting mortality but was less accurate for complications and readmissions. The NSQIP model was better in predicting mortality and complications (receiver operating characteristic curve 0.93 versus 0.87; P = 0.029 and 0.71 versus 0.64; P = 0.027).

CONCLUSIONS

There is poor agreement between NSQIP data and administrative data. Predictive models associated with NSQIP data were more accurate at predicting surgical outcomes than those from administrative data. To drive quality improvement in surgery, high-quality clinical data are required and we believe that NSQIP fulfils this function.

Trajectory Modelling to Assess Trends in Long-Term Readmission Rate among Abdominal Aortic Aneurysm Patients

Introduction

The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate.

Results

Based on the trends in readmission rates, GBTM classified elective AAA repair (n=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (P=0.001) undergoing other vascular procedures (P=0.003), poor socioeconomic status index (P < 0.001), older age (P < 0.001), and higher comorbidity score (P < 0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (n=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure (P < 0.001), heart failure (P = 0.01), peripheral vascular disease (P < 0.001), female sex (P = 0.02), open repair (P < 0.001), and undergoing other related procedures (P=0.05). The AUC for c-statistics was 0.71.

Conclusion

Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up.

Why are patients being readmitted after surgery for esophageal cancer?

OBJECTIVE

Readmission after surgery is an unwanted adverse event that is costly to the healthcare system. We sought to evaluate factors associated with increased risk of readmission and to characterize the nature of these readmissions in patients who have esophageal cancer.

METHODS

A retrospective cohort study was performed in 306 patients with esophageal carcinoma who underwent neoadjuvant chemoradiation followed by esophagectomy at Johns Hopkins Hospital between 1993 and 2011. Logistic regression was used to identify factors associated with 30-day readmission. Readmissions were defined as inpatient admissions to our institution within 30 days of discharge.

RESULTS

The median age at surgery was 61 years; the median postoperative length of stay was 9 days; and 48% of patients had ≥1 postoperative complication (POC). The 30-day readmission rate was 13.7% (42 of 306). In univariate analysis, length of stay and having ≥1 POC were significantly associated with readmission. In multivariate analysis, having ≥1 POC was significantly associated with a >2-fold increase in risk for 30-day readmission (odds ratio 2.35, with 95% confidence interval [1.08-5.09], P = .031) when controlling for age at diagnosis and length of stay. Of the 42 patients who were readmitted, 67% experienced POCs after surgery; 50% of patients who experienced POCs were readmitted for reasons related to their postoperative complication. The most common reasons for readmission were pulmonary issues (29%), anastomotic complications (20%), gastrointestinal concerns (17%), and venous thromboembolism (14%).

CONCLUSIONS

Complications not adequately managed before discharge may lead to readmission. Quality improvement efforts surrounding venous thromboembolism prophylaxis, and discharging patients nothing-by-mouth, may be warranted.

Factors affecting readmission after pancreaticoduodenectomy

PD continues to be associated with a high rate of failed discharges, despite significant improvements in techniques and postoperative care at high-volume centers. Even in the best hands, 1 in 5 patients undergoing PD can be expected to require readmission in the early postoperative period. Efforts to minimize readmissions must be aimed at identifying high-risk patients, addressing patient expectations, establishing patient care plans, and using outpatient resources to address anticipated problems and complications.