Multiple postoperative complications: Making sense of the trajectories.

BACKGROUND: Many studies have evaluated predictors of postoperative complications, yet little is known about the development of multiple complications. The goal of this study was to assess complication timing in cascades of multiple complications and the risk of future complications given a patient's first complication. METHODS: This study includes 30-day, postoperative complications from the American College of Surgeons National Surgical Quality Improvement Program for all patients who underwent major inpatient and outpatient operative procedures from 2005-2013. The timing and sequencing of complications were evaluated using χ2 analysis and pairwise comparisons. RESULTS: More severe postoperative complications (cardiac arrest or myocardial infarction, renal insufficiency or failure, stroke, intubation, septic shock, coma) had the greatest impact on the risk for developing further complications, increasing the relative risk of developing future, specific, severe complications by more than 40-fold. These more severe complications occur within a few days of other complications (whether as a preceding factor or an outcome), while less severe complications, such as surgical site infection and urinary tract infection, are linked less tightly to complication cascades. CONCLUSION: This analysis highlights both the risk for secondary complications after an initial complication and when those future complications are likely to occur. Physicians can use this information to target interventions to prevent high-risk complications.

Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients.

OBJECTIVES: To evaluate the association between multiple complications and postoperative outcomes and to assess which complications occur together in patients with multiple complications. BACKGROUND: 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. METHODS: 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 χ and logistic regression analysis. Relationships among postoperative complications were explored by learning a Bayesian network model. RESULTS: 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. CONCLUSIONS: 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.

Modeling the Temporal Evolution of Postoperative Complications.

Post-operative complications have a significant impact on patient morbidity and mortality; these impacts are exacerbated when patients experience multiple complications. However, the task of modeling the temporal sequencing of complications has not been previously addressed. We present an approach based on Markov chain models for characterizing the temporal evolution of post-operative complications represented in the American College of Surgeons National Surgery Quality Improvement Program database. Our work demonstrates that the models have significant predictive value. In particular, an inhomogenous Markov chain model effectively predicts the development of serious complications (coma longer than a day, cardiac arrest, myocardial infarction, septic shock, renal failure, pneumonia) and interventional complications (unplanned re-intubation, longer than 2 days on a ventilator and bleeding transfusion).