V65: Understanding Inpatient Mortality Following Major Surgery- whose Fault Is It Anyway?

Mortality following inpatient surgery in multi-factorial. The relative contribution of patient factors, hospital characteristics, and case volume is unknown. Our goal was to quantify variability in hospital mortality following major surgery.


Methods: The Nationwide Inpatient Sample linked to the American Hospital Association survey dataset from 2006-2011 was used to identify patients undergoing pancreatic, esophageal, lung, bladder, and rectal surgery. Relevant patient and hospital characteristics were identified using a classification and regression tree. Multilevel logistic regression models were constructed using reliability adjusted patient level in hospital mortality (IHM) as the dependent variable and patient characteristics, hospital characteristics and hospital case volume as independent variables. The percent variability in IHM attributable to each of these categories was calculated


Results:

The study population consisted of a total of 80,969 patients with IHM that ranged from 3.9% for esophageal surgery to 0.9% for rectal surgery. High mortality hospitals, defined as ≥75^th centile mean IHM, were more likely to have patients with co-morbidities and on Medicaid, lower case volumes, less patient beds, lower staffing ratios, and non-teaching status compared to low mortality hospitals. For esophagus, pancreas, lung, and rectal surgery, patient characteristics explained the largest percentage of the variability in IHM, 63%, 63%, 44% and 41% respectively. Unexplained variability in IHM was significant for lung, bladder and rectal surgery; 44%, 39%, and 34%. The contribution of hospital volume to inpatient mortality was low overall and ranged from 29% for bladder to 8% for rectal and lung surgery.


Conclusions:

Although much emphasis has been placed on the association between case volume and postoperative mortality this was not the most important factor in IHM for any of the surgery types. Quality improvement efforts to reduce IHM should focus on patient optimization and structural changes. Other efforts should be directed towards explaining the proportion of variation in IHM that remains unknown.