Michael Pine

Female Gender Is an Independent Predictor of Operative Mortality After Coronary Artery Bypass Graft Surgery Contemporary Analysis of 31 Midwestern Hospitals

Background—Women have a higher operative mortality (OM) after coronary artery bypass graft (CABG) surgery than men. Suggested contributing factors have included women’s increased age, advanced disease, comorbidities, and smaller body surface area (BSA). It is unclear whether women’s increased risk factors fully account for this difference or whether female gender within itself is associated with increased OM. We attempted to determine whether, all other factors being equal, there is a significant difference in OM between men and women undergoing CABG. Methods and Results—We retrospectively reviewed a clinical database of 15,440 patients who underwent CABG at 31 Midwestern hospitals in 1999–2000. Each patient record consisted of >400 data elements. Risk-adjusted mortality rates were computed using a predictive equation derived by stepwise logistic regression. Overall, women were older, had a higher incidence of diabetes and valvular disease, and were more likely to be presenting in shock. The OM for the entire population was 2.88% (women 4.24% versus men 2.23%, P<0.0001). Lower BSA was found to be an independent predictor of increased mortality, and a direct inverse relationship between BSA and OM was noted. After adjusting for all comorbidities including BSA, female gender remained an independent predictor of increased mortality (risk-adjusted OM was 3.81% for women and 2.43% for men). Thus, whereas risk adjustment reduced women’s OM from 90% higher than men’s to 22% higher, a significant difference remained. Conclusions—In this contemporary data set from 31 Midwestern hospitals, female gender was an independent predictor of perioperative mortality, even after accounting for all comorbidities, including low BSA.

Combining administrative and clinical data to stratify surgical risk.

Objective:To evaluate whether administrative claims data (ADM) from hospital discharges can be transformed by present-on-admission (POA) codes and readily available clinical data into a refined database that can support valid risk stratification (RS) of surgical outcomes. Summary Background Data:ADM from hospital discharges have been used for RS of medical and surgical outcomes, but results generally have been viewed with skepticism because of limited clinical information and questionable predictive accuracy. Methods:We used logistic regression analysis to choose predictor variables for RS of mortality in abdominal aortic aneurysm repair, coronary artery bypass graft surgery, and craniotomy, and for RS of 4 postoperative complications (ie, physiologic/metabolic derangement, respiratory failure, pulmonary embolism/deep vein thrombosis, and sepsis) after selected operations. RS models were developed for age only (Age model), ADM only (ADM model), ADM enhanced with POA codes for secondary diagnoses (POA-ADM model), POA-ADM supplemented with admission laboratory data (Laboratory model), Laboratory model supplemented with admission vital signs and additional laboratory data (VS model), VS model supplemented with key clinical findings abstracted from medical records (KCF model), and KCF model supplemented with composite clinical scores (Full model). Models were evaluated using c-statistics, case-based errors in predictions, and measures of hospital-based systematic bias. Results:The addition of POA codes and numerical laboratory results to ADM was associated with substantial improvements in all measures of analytic performance. In contrast, the addition of difficult-to-obtain key clinical findings resulted in only small improvements in predictions. Conclusions:Enhancement of ADM with POA codes and readily available laboratory data can efficiently support accurate risk-stratified measurements of clinical outcomes in surgical patients.

Adverse outcomes in surgery: redefinition of postoperative complications

BACKGROUND We propose that excess risk-adjusted, postoperative length of stay (poLOS) is a valid indicator of an adverse outcome. METHODS Hospital administrative claims data for elective colon resection, coronary bypass graft surgery, and total hip replacement were used from the 100 largest-volume hospitals in the Health Care Cost and Utilization Project for 2005. Risk-adjusted poLOS linear models were designed and outliers were determined using control charts. Costs of hospital care were examined by the presence of coded complications (CCs) and/or being a poLOS outlier. RESULTS Patterns of CCs and risk-adjusted poLOS outliers were significantly different (P < .0001, chi-square test). For all procedures, costs of care were similar with or without CCs if the patients were not poLOS outliers. For patients who were poLOS outliers, costs were significantly different (Tukey-Kramer test) independent of whether CCs were present or not. CONCLUSIONS Adverse surgical outcomes are better defined by risk-adjusted poLOS and cost criteria rather than coded or surveillance observations.

Surgical Warranties to Improve Quality and Efficiency in Elective Colon Surgery

BACKGROUND Uncomplicated surgical care has highly variable costs. High costs of complications have led payers to deny additional payments even for predictable complications. HYPOTHESIS A payment warranty indexed to effective and efficient hospitals can promote quality and economic stewardship in surgical care. DESIGN Analysis of hospital costs for elective colon surgery in the Healthcare Cost and Utilization Projects National Inpatient Sample from 2002 through 2005. SETTING A 20% sample of acute care hospitals in the United States. PATIENTS AND METHODS Data for elective colon resections were used to create predictive models for adverse outcomes (AOs) and costs. Total hospital costs were determined using cost-to-charge ratios. Costs of AOs were computed as total costs minus predicted costs of uncomplicated care. Surgical warranties were computed as the probability of AOs times per-case predicted costs of AOs. Final predictive models were calibrated using data only from effective and efficient hospitals. RESULTS We studied 51 602 cases from 632 hospitals. There were 4048 (7.8%) AOs with 505 deaths (1.0%); 19 hospitals had excessive AOs and 95 hospitals had excessive costs. For 518 effective and efficient hospitals, total per-case costs for routine care were

Control charts to identify adverse outcomes in elective colon resection

9843 with an average warranty of

Medicare post-discharge deaths and readmissions following elective surgery.

1294 and a

Modifying ICD-9-CM Coding of Secondary Diagnoses to Improve Risk-Adjustment of Inpatient Mortality Rates

276 stop-loss allocation. This cost model would reduce national expenditures for colon surgery by 6%. CONCLUSIONS Complications and costs of care can be indexed to quality performing hospitals. Warranties for surgical care can reward effective and efficient care and preclude the need for additional payments for complications.

Prediction models of Medicare 90-day postdischarge deaths, readmissions, and costs in bowel operations

BACKGROUND Control charts have been proposed for the measurement of quality in surgical care. METHODS For each of 181 study hospitals in the 2005 National Inpatient Sample of the Healthcare Cost and Utilization Project database, an average moving range control chart for risk-adjusted postoperative length of stay (RApoLOS) was created for patients discharged alive after elective colectomy. RApoLOS outliers using upper control limits of 2.0σ, 2.5σ, and 3.0σ were correlated to coded complications (CCs). Hospital costs were correlated to RApoLOS outliers and CCs. RESULTS Of 13,118 live discharges, 902 (6.9%) were outliers using a 3.0σ upper control limit, 1,350 (10.3%) were 2.5σ outliers, and 2,053 (15.7%) were 2.0σ outliers. CCs were identified in 92.7% of 3.0σ outliers, in 81.3% of 2.5σ outliers, and 70.6% of 2.0σ outliers. Increased costs were associated with RApoLOS outliers and poorly with CCs. CONCLUSIONS Average moving range control charts for RApoLOS outliers are valid tools for measurement of surgical quality and costs.

Controlling costs without compromising quality: paying hospitals for total knee replacement.

BACKGROUND The frequency of 90-day, postdischarge deaths and readmissions in Medicare patients undergoing elective surgical procedures has not been well studied. METHODS The Medicare MedPar database for 2009 to 2010 was used to develop inpatient risk-adjusted, postoperative length-of-stay (RApoLOS) prediction models for live discharges in 21 categories of elective operations. Moving average control charts were used in each category to define RApoLOS outliers (>3σ). The relationships between RApoLOS outliers and all postdischarge deaths and readmissions within 90 days of discharge were assessed. RESULTS The inpatient mortality rate was .5%. Of 2,054,189 live discharges, 147,292 (7%) were RApoLOS outliers. There were 14,657 postdischarge deaths (.7%) and 187,566 readmissions (9%). RApoLOS outliers had a 3.5% death rate and a 17% rate of readmission, while those found not to be RApoLOS outliers had a .5% death rate and a 9% readmission rate (P < .0001). CONCLUSIONS RApoLOS outliers have increased rates of postdischarge deaths and readmissions.

Comparative effectiveness and efficiency in peripheral vascular surgery.

Objective . To assess the effect on risk-adjustment of inpatient mortality rates of progressively enhancing administrative claims data with clinical data that are increasingly expensive to obtain. Data Sources . Claims and abstracted clinical data on patients hospitalized for 5 medical conditions and 3 surgical procedures at 188 Pennsylvania hospitals from July 2000 through June 2003. Methods . Risk-adjustment models for inpatient mortality were derived using claims data with secondary diagnoses limited to conditions unlikely to be hospital-acquired complications. Models were enhanced with one or more of 1) secondary diagnoses inferred from clinical data to have been present-on-admission (POA), 2) secondary diagnoses not coded on claims but documented in medical records as POA, 3) numerical laboratory results from the first hospital day, and 4) all available clinical data from the first hospital day. Alternative models were compared using c-statistics, the magnitude of errors in prediction for individual cases, and the percentage of hospitals with aggregate errors in prediction exceeding specified thresholds. Results . More complete coding of a few under-reported secondary diagnoses and adding numerical laboratory results to claims data substantially improved predictions of inpatient mortality. Little improvement resulted from increasing the maximum number of available secondary diagnoses or adding additional clinical data. Conclusions . Increasing the completeness and consistency of reporting a few secondary diagnosis codes for findings POA and merging claims data with numerical laboratory values improved risk adjustment of inpatient mortality rates. Expensive abstraction of additional clinical information from medical records resulted in little further improvement.