Relationship between Oral Bacterial Count and Postoperative Complications among Patients with Cardiovascular Disease Treated by Surgery: A Retrospective Cohort Study.
Rie OsakoYuhei MatsudaChieko ItoharaYuka Sukegawa-TakahashiShintaro SukegawaSatoe OkumaYoshihiko FurukiTakahiro KannoPublished in: Healthcare (Basel, Switzerland) (2021)
In this retrospective observational study, we evaluated the relationship between perioperative oral bacterial counts and postoperative complications in cardiovascular disease (CVD) patients. From April 2012 to December 2018, all patients scheduled for surgery received perioperative oral management (POM) by oral specialists at a single center. Tongue dorsum bacterial counts were measured on the pre-hospitalization day, preoperatively, and postoperatively. Background data were collected retrospectively. Among the 470 consecutive patients, the postoperative complication incidence rate was 10.4% (pericardial fluid storage, n = 21; postoperative pneumonia, n = 13; surgical site infection, n = 9; mediastinitis, n = 2; and seroma, postoperative infective endocarditis, lung torsion, and pericardial effusion, n = 1 each). Oral bacterial counts were significantly higher in the pre-hospitalization than in the pre- and postoperative samples (p < 0.05). Sex, cerebrovascular disease, and operation time differed significantly between complications and no-complications groups (p < 0.05). Multivariate analysis with propensity score adjustment showed a significant association between postoperative oral bacterial count and postoperative complications (odds ratio 1.26; 95% confidence interval, 1.00-1.60; p = 0.05). Since the development of cardiovascular complications is a multifactorial process, the present study cannot show that POM reduces complications but indicates POM may prevent complications in CVD patients.
Keyphrases
- cardiovascular disease
- end stage renal disease
- patients undergoing
- newly diagnosed
- ejection fraction
- prognostic factors
- surgical site infection
- minimally invasive
- type diabetes
- coronary artery disease
- intensive care unit
- patient reported outcomes
- machine learning
- cardiovascular events
- big data
- cross sectional
- patient reported
- coronary artery bypass
- acute kidney injury
- extracorporeal membrane oxygenation
- data analysis
- respiratory failure