Hospital variability in modifiable factors driving coronary artery bypass charges

Risk factors for sternal wound infection after median sternotomy: A nested case-control study and time-to-event analysis

Although potential risk factors for sternal wound infection (SWI) have been extensively studied, the onset time of SWI and different risk factors for superficial and deep SWI were rarely reported. This nested case-control study aims to compare the onset time and contributors between superficial and deep SWI. Consecutive adult patients who underwent cardiac surgery through median sternotomy in a single center from January 2011 to January 2021 constituted the cohort. The case group was those who developed SWI as defined by CDC and controls were matched 6:1 per case. Kaplan-Meier analysis, LASSO and univariate and multivariate Cox regressions were performed. A simple nomogram was established for clinical prediction of the risk of SWI. The incidence of SWI was 1.1% (61 out of 5471) in our cohort. Totally 366 controls were matched to 61 cases. 26.2% (16 of 61) SWI cases were deep SWI. The median onset time of SWI was 35 days. DSWI had a longer latency than SSWI (median time 46 days vs. 32 days, p = 0.032). Kaplan-Meier analyses showed different time-to-SWI between patients with and without DM (p = 0.0011) or MI (p = 0.0019). Multivariate Cox regression showed that BMI (HR = 1.083, 95% CI: 1.012-1.116, p = 0.022), DM (HR = 2.041, 95% CI: 1.094-3.805, p = 0.025) and MI (HR = 2.332, 95% CI: 1.193-4.557, p = 0.013) were independent risk factors for SWI. Superficial SWI was only associated with BMI (HR = 1.089, 95% CI: 1.01-1.175, p = 0.027), while deep SWI was associated with DM (HR = 3.271, 95% CI: 1.036-10.325, p = 0.043) and surgery time (HR = 1.004, 95% CI: 1.001-1.008, p = 0.027). The nomogram for SWI prediction had an AUC of 0.67, good fitness and clinical effectiveness as shown by the calibration curve and decision curve analyses. BMI, DM and MI were independent risk factors for SWI. DSWI had a longer latency and different risk factors compared to SSWI. The nomogram showed a fair performance and good effectiveness for the clinical prediction of SWI.

Risk factors for sternal wound infection after open-heart operations: A systematic review and meta-analysis

We aimed to quantitatively and systematically elucidate the rationality of the examined variables as independent risk factors for sternal wound infection. We searched databases to screen studies, ascertained the variables to be analysed, extracted the data and applied meta-analysis to each qualified variable. Odds ratios and mean differences were considered to be the effect sizes for binary and continuous variables, respectively. A random-effects model was used for these procedures. The source of heterogeneity was evaluated using a meta-regression. Publication bias was tested by funnel plot and Egger's test, the significant results of which were then calculated using trim and fill analysis. We used a sensitivity analysis and bubble chart to describe their robustness. After screening all variables in the eligible literature, we excluded 55 because only one or no research found them significant after multivariate analysis, leaving 33 variables for synthesis. Two binary variables (age over 65 years, NYHA class >2) and a continuous variable (preoperative stay) were not significant after the meta-analysis. The most robust independent risk factors in our study were diabetes mellitus, obesity, use of bilateral internal thoracic arteries, chronic obstructive pulmonary disease, prolonged surgery time, prolonged ventilation and critical preoperative state, followed by congestive heart failure, atrial fibrillation, renal insufficiency, stroke, peripheral vascular disease and use of an intra-aortic balloon pump. Relatively low-risk factors were emergent/urgent surgery, smoking, myocardial infarction, combined surgery and coronary artery bypass grafting. Sternal wound infection after open-heart surgery is a multifactorial disease. The detected risk factors significantly affected the wound healing process, but some were different in strength. Anything that affects wound healing and antibacterial ability, such as lack of oxygen, local haemodynamic disorders, malnutrition condition and compromised immune system will increase the risk, and this reminds us of comprehensive treatment during the perioperative period.

Socioeconomic disparities in risk of financial toxicity following elective cardiac operations in the United States

BACKGROUND

While insurance reimbursements allay a portion of costs associated with cardiac operations, uncovered and additional fees are absorbed by patients. An examination of financial toxicity (FT), defined as the burden of patient medical expenses on quality of life, is warranted. Therefore, the present study used a nationally representative database to demonstrate the association between insurance status and risk of financial toxicity (FT) among patients undergoing major cardiac operations.

METHODS

Adults admitted for elective coronary artery bypass grafting (CABG) and isolated or concomitant valve operations were assessed using the 2016-2019 National Inpatient Sample. FT risk was defined as out-of-pocket expenditure >40% of post-subsistence income. Regression models were developed to determine factors associated with FT risk in insured and uninsured populations. To demonstrate the association between insurance status and risk of FT among patients undergoing major cardiac operations.

RESULTS

Of an estimated 567,865 patients, 15.6% were at risk of FT. A greater proportion of uninsured patients were at risk of FT (81.3 vs. 14.8%, p<0.001), compared to insured. After adjustment, FT risk among insured patients was not affected by non-income factors. However, Hispanic race (Adjusted Odds Ratio [AOR] 1.60), length of stay (AOR 1.17/day), and combined CABG-valve operations (AOR 2.31, all p<0.05) were associated with increased risk of FT in the uninsured.

CONCLUSION

Uninsured patients demonstrated higher FT risk after undergoing major cardiac operation. Hispanic race, longer lengths of stay, and combined CABG-valve operations were independently associated with increased risk of FT amongst the uninsured. Conversely, non-income factors did not impact FT risk in the insured cohort. Culturally-informed reimbursement strategies are necessary to reduce disparities in already financially disadvantaged populations.

Defining value in cardiac surgery: A contemporary analysis of cost variation across the United States

Objective

Isolated coronary artery bypass grafting and aortic valve replacement are common cardiac operations performed in the United States and serve as platforms for benchmarking. The present national study characterized hospital-level variation in costs and value for coronary artery bypass grafting and aortic valve replacement.

Methods

Adults undergoing elective, isolated coronary artery bypass grafting or aortic valve replacement were identified in the 2016-2018 Nationwide Readmissions Database. Center quality was defined by the proportion of patients without an adverse outcome (death, stroke, respiratory failure, pneumonia, sepsis, acute kidney injury, and reoperation). High-value hospitals were defined as those with observed-to-expected ratios less than 1 for costs and greater than 1 for quality, whereas the converse defined low-value centers.

Results

Of 318,194 patients meeting study criteria, 71.9% underwent isolated coronary artery bypass grafting and 28.1% underwent aortic valve replacement. Variation in hospital-level costs was evident, with median center-level cost of $36,400 (interquartile range, 29,500-46,700) for isolated coronary artery bypass grafting and $38,400 (interquartile range, 32,300-47,700) for aortic valve replacement. Observed-to-expected ratios for quality ranged from 0.2 to 10.9 for isolated coronary artery bypass grafting and 0.1 to 11.7 for isolated aortic valve replacement. Hospital factors, including volume and quality, contributed to approximately 9.9% and 11.2% of initial cost variation for isolated coronary artery bypass grafting and aortic valve replacement. High-value centers had greater cardiac surgery operative volume and were more commonly teaching hospitals compared to low-value centers, but had similar patient risk profiles.

Conclusions

Significant variation in hospital costs, quality, and value exists for 2 common cardiac operations. Center volume was associated with value and partly accounts for variation in costs. Our findings suggest the need for value-based care paradigms to reduce expenditures and optimize outcomes.