Elective Surgical Procedures

The Clinformatics™ Data Mart captures administrative health claims across the United States for members of a large national managed care company affiliated with OptumInsight (Eden Prairie, MN). We examined claims from January 1, 2012 to June 30, 2015 among adults ages 18 to 64 to capture surgical procedures performed between 2013 and 2014 to account for the 12-month preoperative and 6-month postoperative study period. We included only individuals with continuous medical and prescription drug coverage to evaluate the complete health care experience. We excluded patients ages 18 and younger, as well as patients older than 64 years due to incomplete capture of Medicare Part D prescriptions claims data. The study was deemed exempt from review by the University of Michigan Institutional Review Board.
We selected 13 common elective surgical procedures, and categorized these into minor and major groups based on prior literature. Minor surgical procedures included varicose vein removal, laparoscopic cholecystectomy, laparoscopic appendectomy, hemorrhoidectomy, thyroidectomy, transurethral prostate surgeries, and parathyroidectomy. Major surgical procedures included ventral incisional hernia repair, colectomy, reflux surgery, bariatric surgery, and hysterectomy. We identified patients undergoing surgery using Current Procedural Terminology (CPT) or International Statistical Classification of Diseases and Related Health Problems (ICD9_ procedure codes (Supplemental Table 1).
We sought to determine new persistent opioid use after surgery, and included only patients who filled an opioid prescription fill either in the month prior to surgery or within two weeks after discharge. Comparable to previous studies of opioid naïve surgical populations,^{7 (link),8} patients who had filled one or more prescriptions for opioids 12 months to 31 days prior to their surgical procedure were excluded from the analysis (Figure 1). To account for prescriptions provided preoperatively for postoperative pain control, patients filling opioids in the 30 days prior to surgery were included, and prescriptions filled in this time was included as a covariate in the analyses. Lastly, we excluded patients who underwent additional surgical procedures during the study period using subsequent procedural codes for anesthesia in the 6-month postoperative period.
As a comparison cohort of patients who did not undergo surgery, we identified a random 10% sample patients ages 18 to 64 years of age who did not undergo surgery in the study period We included only patients in the nonoperative group who did not fill an opioid prescription during a 12 month period and did not have any codes for surgical procedures or anesthesia during this period. These patients were then given a random date of surgery. No patients had an opioid fill in the year prior to their fictitious surgery date nor did they have any anesthesia codes in the 6 months following their fictitious surgery date.

We first estimated risk-adjusted hospital mortality rates with all three procedures during 2003–04. We defined mortality as death within 30 days of operation or prior to hospital discharge. We use this definition because the 30-day cut-off is somewhat arbitrary, and when a death occurs in the hospital after major elective surgery it is almost certainly attributable to the operation itself or complications from the surgery. We adjusted for patient age, gender, race, urgency of operation, median ZIP-code income, and coexisting medical conditions. Coexisting medical conditions were obtained from secondary diagnoses in the claims data using the methods of Elixhauser (Southern, Quan, and Ghali 2004 (link)). Using logistic regression, we estimated the expected number of deaths in each hospital and then divided the observed deaths by this expected number of deaths to obtain the ratio of observed to expected mortality (O/E ratio). We then multiplied the O/E ratio by the average mortality rate to obtain a risk-adjusted mortality rate for each hospital.
We next used hierarchical modeling techniques to adjust these mortality estimates for reliablity (See Technical Appendix for details). Using random effects logistic regression models, we generated empirical Bayes predictions of mortality for each hospital (Morris 1983 ; Normand, Glickman, and Gatsonis 1997 ). This technique shrinks the point estimate of mortality back towards the average mortality rate, with the amount of shrinkage proportional to the reliability at each hospital. Reliability is a measure of precision and is a function of both hospital sample size (which determines “noise” variation) and the amount of true variation across hospitals (“signal”). For example, for hospitals with low caseloads of a particular procedure, mortality rates have lower reliability and are shrunk more towards the average mortality. For hospitals with high caseloads, mortality rates are more reliable and shrunk less towards the average mortality. The resulting reliability adjusted mortality is considered the best estimate of a hospital’s “true” mortality rate with each operation (Morris 1983 ).
An underlying assumption of reliability adjustment is that hospitals provide average performance until the data are sufficiently robust to prove otherwise. For example, consider a hospital performing 10 pancreatic resections in a year with 2 deaths (observed mortality rate of 20%). Because of the small number of cases, there is considerable likelihood that this estimate of 20% is the result of chance and not truly an indication of bad performance. From the empirical Bayes perspective, the true mortality rate lies somewhere between this observed rate of 20% and the population-based rate of 5% (the average mortality rate across all hospitals). Using reliability adjustment, the observed rate of 20% is “shrunk” back toward the average rate of 5%. The degree of shrinkage is proportional to the reliability with which the mortality rate is measured. The more reliable the observed mortality rate, the more weight it is afforded. Reliability is assessed on a scale of 0 to 1, with 1 representing perfect reliability. In this case, suppose the reliability based on 20 cases is 0.15, and the remaining weight (0.85) is placed on the average mortality. Thus, the reliability adjusted mortality for this hospital is (0.20)(0.15) + (0.05)(1−0.15) = 7.2%. To further illustrate the impact of reliability adjustment, Figure 1 shows mortality rates before and after reliability adjustment for 20 randomly selected hospitals for each of the 3 procedures in this study. After reliability-adjustment, there is a much less variation across hospitals, as the most extreme observations are shrunk back towards the average mortality rate.