Failure to Rescue - a Closer Look at Mortality Rates Has No Added Value for Hospital Comparisons but Is Useful for Team Quality Assessment in Abdominal Aortic Aneurysm Surgery in The Netherlands.

OBJECTIVES: Failure to rescue (FTR) is a composite quality indicator, defined as the proportion of deceased patients following major complications. The aims of this study were to compare FTR with mortality for hospital comparisons in abdominal aortic aneurysm (AAA) surgery in The Netherlands and investigate hospital volume and associated factors. METHODS: Patients prospectively registered between 2013 and 2015 in the Dutch Surgical Aneurysm Audit (DSAA) were analysed. FTR was analysed for AAA patients and subgroups elective (EAAA) and acute (AAAA; symptomatic or ruptured) aneurysms. Variables and hospital volume were analysed by uni- and multivariable regression analysis. Adjusted hospital comparisons for mortality, major complications, and FTR were presented in funnel plots. Isomortality lines were constructed when presenting FTR and major complication rates. RESULTS: A total of 9258 patients were analysed in 61 hospitals: 7149 EAAA patients (77.2%) and 2109 AAAA patients (22.8%). There were 2785 (30.1%) patients with complications (unadjusted range 5-65% per hospital): 2161 (77.6%) with major and 624 (28.4%) patients with minor complications. Overall mortality was 6.6% (adjusted range 0-16% per hospital) and FTR was 28.4% (n = 613) (adjusted range 0-60% per hospital). Glasgow Coma Scale, age, pulse, creatinine, electrocardiography, and operative setting were independently associated with FTR. Hospital volume was not associated with FTR. In AAAA patients hospital volume was significantly associated with a lower adjusted major complication and mortality rate (OR 0.62, 95% CI 0.49-0.78; and 0.64, 95% CI 0.48-0.87). Four hospitals had a significant lower adjusted FTR with different major complication rates on different isomortality lines. CONCLUSIONS: There was more variation in FTR than in mortality between hospitals. FTR identified the same best performing hospitals as for mortality and therefore was of limited additional value in measuring quality of care for AAA surgery. FTR can be used for internal quality improvement with major complications in funnel plots and diagrams with isomortality lines.

Genome-scale discovery of DNA-methylation biomarkers for blood-based detection of colorectal cancer.

BACKGROUND: There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer. METHODOLOGY/PRINCIPAL FINDINGS: We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection. CONCLUSIONS/SIGNIFICANCE: Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.