ABSTRACT
BACKGROUND: Patients with cirrhosis and coronary artery disease (CAD) are at high risk for morbidity during surgical revascularization so they are often referred for complex percutaneous coronary intervention (PCI). Percutaneous coronary intervention in the cirrhotic population also has inherent risks; however, quantifiable data on long-term outcomes are lacking. METHODS: Patients with angiographically significant CAD and cirrhosis were identified from the catheterization lab databases of the University of Pennsylvania Health System between 2007 and 2015. Outcomes were obtained from the medical record and telephonic contact with patients/families. RESULTS: Percutaneous coronary intervention was successfully performed in 42 patients (51 PCIs). Twenty-nine patients with significant CAD were managed medically (36 angiograms). The primary outcome (a composite of mortality, subsequent revascularization, and myocardial infarction) was not significantly different between the 2 groups during a follow-up period at 1 year (PCI: 50%, Control: 40%, P = .383). In the PCI group, a composite adverse outcome rate that included acute kidney injury (AKI), severe bleed, and peri-procedural stroke was elevated (40%), with severe bleeding occurring after 23% of PCI events and post-procedural AKI occurring after 26% of events. The medical management group had significantly fewer total matched adverse outcomes (17% vs 40% in the PCI group, P = .03), with severe bleeding occurring after 11% of events and AKI occurring after 6% of events. Increased risk of adverse events following PCI was associated with severity of liver disease by Child-Pugh class. CONCLUSIONS: Percutaneous coronary intervention in patients with cirrhosis is associated with an elevated risk of adverse events, including severe bleeding and AKI.
Subject(s)
Aortic Valve Stenosis , Aortic Valve/pathology , Calcinosis , Vascular Endothelial Growth Factor Receptor-1/blood , Aged , Aortic Valve/physiopathology , Aortic Valve Stenosis/blood , Aortic Valve Stenosis/diagnosis , Aortic Valve Stenosis/physiopathology , Biomarkers/blood , Calcinosis/blood , Calcinosis/diagnosis , Calcinosis/physiopathology , Disease Progression , Female , Hemodynamics , Humans , Male , Reproducibility of Results , Sensitivity and Specificity , Severity of Illness Index , Stroke Volume , Ventricular Function, LeftABSTRACT
BACKGROUND: Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. METHODS: We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. RESULTS: Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. CONCLUSION: These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research.
