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Background: Health care providers, hospitals, and pay-for-performance programs are focused on strategies identifying patients at highest risk for re-admission after colorectal surgery. The study objective was to determine characteristics most associated with re-admission after elective colorectal surgery using a conceptual framework approach.Methods: This is an observational study of Michigan Surgical Quality Collaborative clinical registry data for 8962 colorectal surgery cases between July-2012 and April-2015. Separate mixed models were fit using known re-admission risk factors aligned in categories that may impact re-admissions by different mechanisms. Overall model discrimination was evaluated using Area Under the Curve estimated on a hold-out data set and examining differences in predicted versus observed re-admission across risk quintiles.Results: The overall 30-day re-admission rate was 10.5%. From Model 1 to Model 6, discrimination of re-admission was poor until Model 6 (AUC, 0.56, 0.61, 0.65, 0.63, 0.72, 0.81). Differences for observed re-admission rates comparing 'very low' versus 'very high' risk strata from Model 1 to Model 6 were 6%, 11%, 15%, 14%, 20%, and 30% respectively, and all comparisons were significant (p < 0.01). Though there were significant predictors in the first five models, most were no longer significant when additional predictors were included in subsequent models. Complications identified after discharge significantly increased the likelihood of re-admission and were the strongest predictors.Conclusion: Statistical models that include complications identified after discharge predict re-admission. Strategies to reduce re-admission after colorectal surgery should emphasize prevention of complications and more effective interventions to manage and ameliorate evolving complications identified after discharge. [ABSTRACT FROM AUTHOR]

Background: Current data addressing the role of robotic surgery for the management of colorectal disease are primarily from single-institution and case-matched comparative studies as well as administrative database analyses. The purpose of this study was to compare minimally invasive surgery outcomes using a large regional protocol-driven database devoted to surgical quality, improvement in patient outcomes, and cost-effectiveness.Methods: This is a retrospective cohort study from the prospectively collected Michigan Surgical Quality Collaborative registry designed to compare outcomes of patients who underwent elective laparoscopic, hand-assisted laparoscopic, and robotic colon and rectal operations between July 1, 2012 and October 7, 2014. We adjusted for differences in baseline covariates between cases with different surgical approaches using propensity score quintiles modeled on patient demographics, general health factors, diagnosis, and preoperative co-morbidities. The primary outcomes were conversion rates and hospital length of stay. Secondary outcomes included operative time, and postoperative morbidity and mortality.Results: A total of 2735 minimally invasive colorectal operations met inclusion criteria. Conversion rates were lower with robotic as compared to laparoscopic operations, and this was statistically significant for rectal resections (colon 9.0 vs. 16.9%, p < 0.06; rectum 7.8 vs. 21.2%, p < 0.001). The adjusted length of stay for robotic colon operations (4.00 days, 95% CI 3.63-4.40) was significantly shorter compared to laparoscopic (4.41 days, 95% CI 4.17-4.66; p = 0.04) and hand-assisted laparoscopic cases (4.44 days, 95% CI 4.13-4.78; p = 0.008). There were no significant differences in overall postoperative complications among groups.Conclusions: When compared to conventional laparoscopy, the robotic platform is associated with significantly fewer conversions to open for rectal operations, and significantly shorter length of hospital stay for colon operations, without increasing overall postoperative morbidity. These findings and the recent upgrades in minimally invasive technology warrant continued evaluation of the role of the robotic platform in colorectal surgery. [ABSTRACT FROM AUTHOR]

. The proportional odds logistic regression model is widely used for relating an ordinal outcome to a set of covariates. When the number of outcome categories is relatively large, the sample size is relatively small and/or certain outcome categories are rare, maximum likelihood can yield biased estimates of the regression parameters. Firth and Kosmidis proposed a procedure to remove the leading term in the asymptotic bias of the maximum likelihood estimator. Their approach is most easily implemented for univariate outcomes. We derive a bias correction that exploits the proportionality between Poisson and multinomial likelihoods for multinomial regression models. Specifically, we describe a bias correction for the proportional odds logistic regression model, based on the likelihood from a collection of independent Poisson random variables whose means are constrained to sum to 1, that is straightforward to implement. The method proposed is motivated by a study of predictors of post-operative complications in patients undergoing colon or rectal surgery. [ABSTRACT FROM AUTHOR]

Background: Urinary tract infections (UTIs) are the most common hospital-acquired infections in the United States. We hypothesized that the risk of UTI after colorectal surgery exceeds the risk after other gastrointestinal operations. Study Design: We used National Surgical Quality Improvement Program (NSQIP) data from 2005 to 2008 to compute rates of UTI after colorectal resections and other gastrointestinal and general surgery. We used a validated multivariate UTI prediction model to compare risk-adjusted rates of UTI by type of procedure. Then we identified predictors of UTI after colorectal resection using stepwise logistic regression models. Results: Crude UTI rates were significantly greater after colorectal resection (4.1%) than after other gastrointestinal (1.8%) or nongastrointestinal procedures (1.2%; all p < 0.001). Even using standard risk-adjustment from the NSQIP, rates of UTI were significantly higher after segmental colectomy (2.8%; 95% CI 2.5% to 3.2%), total colectomy (3.5%; 95% CI 2.9% to 4.3%), proctectomy (3.5%, 95% CI 3.1% to 4.2%), proctocolectomy (4.6%, 95% CI 3.8% to 5.7%), and abdominoperineal resection (5.6%, 95% CI 4.6% to 6.8%) than after noncolorectal gastrointestinal surgery (2.6%, 95% CI 2.2% to 2.9%). Factors associated with UTI after colorectal resections include age, sex, functional status, American Society of Anesthesiologists class, and resection type. Conclusions: Colorectal resections incur substantial risk of postoperative UTI, exceeding rates predicted by the NSQIP model. Because of their patients'' unanticipated high incidence of UTI, surgeons with a specialty interest in colorectal surgery risk being flagged as “high outliers,” particularly if they perform many rectal resections. A simple set of risk factors discriminates 10-fold differences in the rate of UTI after colorectal resection. [ABSTRACT FROM AUTHOR]

Background: We previously developed an intraoperative 10-point Surgical Apgar Score—based on blood loss, lowest heart rate, and lowest mean arterial pressure—to predict major complications after colorectal resection. However, because complications often arise after uncomplicated hospitalizations, we sought to evaluate whether this intraoperative metric would predict postdischarge complications after colectomy. Methods: We linked our institution''s National Surgical Quality Improvement Program database with an Anesthesia Intraoperative Management System for all colorectal resections over 4 years. Using Chi-square trend tests and logistic regression, we evaluated the Surgical Apgar Score''s prediction for major postoperative complications before and after discharge. Results: Among 795 colectomies, there were 230 (29%) major complications within 30 days; 45 (20%) after uncomplicated discharges. Surgical Apgar Scores predicted both inpatient complications and late postdischarge complications (both P < .0001). Late complications occurred from 0 to 27 (median, 11) days after discharge; the most common were surgical site infections (42%), sepsis (24%), and venous thromboembolism (16%). In pairwise comparisons against average-scoring patients (Surgical Apgar Scores, 7–8), the relative risk of postdischarge complications trended lower, to 0.6 (95% confidence interval [CI], 0.2–1.7) for those with the best scores (9–10); and were significantly higher, at 2.6 (95% CI, 1.4–4.9) for scores 5–6, and 4.5 (95% CI, 1.8–11.0) for scores 0–4. Conclusion: The intraoperative Surgical Apgar Score remained a useful metric for predicting postcolectomy complications arising after uncomplicated discharges. Even late complications may thus be related to intraoperative condition and events. Surgeons could use this intraoperative metric to target low-scoring patients for intensive postdischarge surveillance and mitigation of postdischarge complications after colectomy. [Copyright &y& Elsevier]

Background Over 180 000 coronary artery bypass grafting (CABG) procedures are performed annually, accounting for $7 to $10 billion in episode expenditures. Assessing tradeoffs between spending and quality contributing to value during 90-day episodes has not been conducted but is essential for success in bundled reimbursement models. We, therefore, identified determinants of variability in hospital 90-day episode value for CABG. Methods Medicare and private payor admissions for isolated CABG from 2014 to 2016 were retrospectively linked to clinical registry data for 33 nonfederal hospitals in Michigan. Hospital composite risk-adjusted complication rates (≥1 National Quality Forum-endorsed, Society of Thoracic Surgeons measure: deep sternal wound infection, renal failure, prolonged ventilation >24 hours, stroke, re-exploration, and operative mortality) and 90-day risk-adjusted, price-standardized episode payments were used to categorize hospitals by value by defining the intersection between complications and spending. Results Among 2573 total patients, those at low- versus high-value hospitals had a higher percentage of prolonged length of stay >14 days (9.3% versus 2.4%, P=0.006), prolonged ventilation (17.6% versus 4.8%, P<0.001), and operative mortality (4.8% versus 0.6%, P=0.001). Mean total episode payments were $51 509 at low-compared with $45 526 at high-value hospitals (P<0.001), driven by higher readmission ($3675 versus $2177, P=0.005), professional ($7462 versus $6090, P<0.001), postacute care ($7315 versus $5947, P=0.031), and index hospitalization payments ($33 474 versus $30 800, P<0.001). Among patients not experiencing a complication or 30-day readmission (1923/2573, 74.7%), low-value hospitals had higher inpatient evaluation and management payments ($1405 versus $752, P<0.001) and higher utilization of inpatient rehabilitation (7% versus 2%, P<0.001), but lower utilization of home health (66% versus 73%, P=0.016) and emergency department services (13% versus 17%, P=0.034). Conclusions To succeed in emerging bundled reimbursement programs for CABG, hospitals and physicians should identify strategies to minimize complications while optimizing inpatient evaluation and management spending and use of inpatient rehabilitation, home health, and emergency department services. [ABSTRACT FROM AUTHOR]

Background: The Hospital Acquired Condition Reduction Program (HACRP) is a national pay-for-performance program that includes a measure of surgical site infection (SSI) after hysterectomy and colectomy. This study compares the HACRP SSI measure with other published methods.Study Design: This was a retrospective cohort study from the Michigan Surgical Quality Collaborative (MSQC). The outcome was 30-day, adjusted deep and organ space SSI ("complex SSI"). Observed-to-expected ratios of complex SSI for each hospital were calculated using HACRP, National Healthcare Safety Network (NHSN), and MSQC methodologies. C-statistics were compared between models. Hospital rankings were compared, and ladder plots show changes in hospitals' HACRP scores that derive from each algorithm.Results: Complex SSI occurred in 1.1% (190 of 16,672) of hysterectomies and 4.8% (n = 514 of 10,725) of colectomies. The HACRP risk-adjustment model for hysterectomy had a C-statistic of 0.55, significantly lower than NHSN (0.61, p = 0.0461) or MSQC models (0.77, p < 0.0001). For colectomy, C-statistics were 0.57, 0.66 (p < 0.0001) and 0.73 (p < 0.0001), respectively. For both operations, there were 5 high-outlier hospitals using HACRP, but fewer (4 or 3) using the other methods. Most hospitals in the bottom quartile were not statistical outliers, but would be flagged under HACRP. More than 50% of hospitals changed ranking position between models, which would result in different scores under HACRP.Conclusions: This study showed that the HACRP SSI measure unfairly places hospitals at risk for financial penalties that are not statistical outliers. Policy makers need to weigh the burden of data collection and the accuracy needed to identify hospitals for financial reward or penalty. [ABSTRACT FROM AUTHOR]