To evaluate the association between multiple complications and postoperative outcomes and to assess which complications occur together in patients with multiple complications.
Patients who suffer multiple complications have increased risk of prolonged hospital stay and mortality. However, little is known about what places patients at risk for multiple complications or which complications tend to occur in these patients.
Surgical patients were identified from the American College of Surgeons National Quality Improvement Program (ACS NSQIP) database from 2005 to 2011. The frequency of postoperative complications was assessed. Patients with less than two complications were compared with patients who had multiple complications using χ2 and logistic regression analysis. Relationships among postoperative complications were explored by learning a Bayesian network model.
The study population consisted of 470,108 general surgery patients. The overall complication rate was 15% with multiple complications in 27,032 (6%) patients. Patients with multiple complications had worse postoperative outcomes (P < 0.001). The strongest predictors for developing multiple complications were admission from chronic care facility or nursing home, dependent functional status, and higher American Society of Anesthesiologist Physical Status classification. In patients with multiple complications, the most common complication was sepsis (42%), followed by failure to wean ventilator (31%), and organ space surgical site infection (27%). We found that severe complications were most strongly associated with development of multiple complications. Using a Bayesian network, we were able to identify how strongly associated specific complications were in patients who developed multiple complications.
Almost half (40%) of patients with complications suffer multiple complications. Patient factors such as frailty and comorbidity strongly predict the development of multiple complications. The results of our Bayesian analysis identify targets for interventions aimed at disrupting the cascade of multiple complications in high-risk patients.
*Department of Surgery, University of Wisconsin, Madison, WI
†Departments of Computer Sciences and Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI
‡Department of Surgery, University of California, Berkeley, CA.
Reprints: Gregory D. Kennedy, MD, PhD, Department of Surgery, University of Wisconsin, 650 Highland Avenue, Madison, WI 53792. E-mail: email@example.com.
This work was presented at the 2014 American College of Surgeons Clinical Congress.
Disclosure: Supported by the NIH training grant T32 CA090217, NIH/NLM training grant T15 LM07359, and NIH/NCATS grant UL1 TR000427. The authors declare no conflicts of interest.