Delay in admission for elective coronary-artery bypass grafting is associated with increased in-hospital mortality

Sobolev, Boris; Fradet, Guy; Hayden, Robert; Kuramoto, Lisa; Levy, Adrian R.; FitzGerald, Mark

doi:10.1186/1472-6963-8-185

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…Cardiovascular diseases are strongly linked to waitinglists problem. [1][2][3][4] Epidemiological data on acute coronary syndrome (ACS) in Italy 5 revealed an incidence of 227.3 per 100,000 for men (aged 25-84 years) and 97.9 per 100,000 for women (aged 25-84 years), and a prevalence of myocardial infarction of 1.5% in men and 0.4% in women. These data suggest that delays in ACS treatment affect a huge number of the population, decreasing life expectancy.…”

Section: Introductionmentioning

confidence: 99%

Waiting lists in coronary artery bypass graft patients and role of coronary angiography: the Apulian experience

Ciccone¹,

Aquilino²,

Frasso³

et al. 2013

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Purpose: To evaluate waiting lists during the coronary artery bypass graft (CABG) diagnostictherapeutic pathway of patients, in order to detect the actual waiting time between diagnostic evaluation and surgery, and to explore if coronary angiography provides an option as a starting point of waiting lists for CABG patients. Methods: We analyzed 496 hospital discharge cards (year 2009) from main cardiac surgery units in Apulia, Italy. Exclusion criteria were emergency patients coming from regions other than Apulia, and CABG associated with cardiac valve surgery. Results: A total of 97.6% patients underwent CABG within 0-30 days of their reservation date, and 81.7% passed from the first diagnostic step to coronary angiography within 0-30 days. The mean time delay in the diagnostic (ie, the time elapsing from the date of first test performed to detect coronary heart disease and that of coronary angiography) and therapeutic (ie, the time elapsing from the date of waiting-list admission and that of cardiac surgery unit admission) phases was 17.3 ± 31.306 and 5.09 ± 9.375 days respectively; 27% and 19.8% underwent CABG within the same day or at least the day after reservation day, whilst 47.2% completed the diagnostic phase on the same day. Conclusion: The waiting lists for CABG surgery diagnostic-therapeutic phase in Apulia are short.

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Section: Introductionmentioning

confidence: 99%

Waiting lists in coronary artery bypass graft patients and role of coronary angiography: the Apulian experience

Ciccone¹,

Aquilino²,

Frasso³

et al. 2013

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“…1), if we take into account the population growth in the period. This might be one explanation for the high mortality in the state 21 .…”

Section: Year N Of Camentioning

confidence: 99%

Desempenho e gastos do SUS com revascularizações do miocárdio no Estado do Rio de Janeiro, de 1999 a 2008

Oliveira¹,

Klein²,

Silva³

2011

Arq. Bras. Cardiol.

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. In the analysis of decreasing mortality trends due to IHD, multiple factors must be considered, including age, sex, diagnosis, therapeutic interventions and environmental and socioeconomic conditions. The studies with data recorded from clinical practice provide a view that is closer to reality than the experimental ones 3 .In a study with samples of coronary angiography (CA) records performed in public hospitals in the city of Rio de Janeiro (CRJ), from 1999 to 2003, a mean in-hospital mortality of 1.6% was observed, ranging from 0.9 % to 6.8%. Mortality rates in primary and rescue CA were 17.4% and 13.1%, and in elective CA, it was 0.8% 4 . During the same period, in 2,692 coronary artery bypass graft (CABG) records in the CRJ, a variation between hospitals was observed, from 7.0% to 14.3% in the in-hospital period, and 8.5% to 20.2 % up to one year post-hospital release and a significant association between the occurrence of complications and postoperative death (p <0.0001) 5. Therefore, regarding myocardial revascularization procedures, the minimum required performance was not observed in the SRJ 6 , representing an important financial burden for the Brazilian Public Health System (SUS), with individual and social consequences 7 .In the middle of the last decade of the twentieth century, it was estimated that 80% of medical or hospital care was financed by the Public Health System (SUS) in Brazil

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“…The impact of delayed treatment varies by condition. For conditions such as cardiac disease, cataract surgery, and cancer, longer waiting times can often lead to more serious and sudden adverse events such as deterioration, disability or even death (Sobolev et al, 2008;Kulkarni et al, 2009;Hodge et al, 2007;Guttmann et al, 2011). For orthopaedic surgeries, increased delays may have lifelong impacts on health including mental and emotional well-being (Wright and Menaker, 2011).…”

Section: The Effects Of Waiting In Healthcarementioning

confidence: 99%

Predicting Waiting Times in Radiation Oncology Using Machine Learning

Joseph

Hijal

Kildea

et al. 2017

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)

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This thesis describes a study of machine learning and its application to waiting times in radiation oncology. Specifically, an evaluation of waiting time estimates for daily radiation treatment appointments at the McGill University Health Centre was conducted and two unique communication tools for conveying these waiting time estimates were developed. To evaluate waiting time estimates, a subset of previously-treated patient records was used for training and modelling by several off-the-shelf machine learning algorithms and tested using treatment records unseen by the algorithms. Furthermore, machine learning was explicitly used to predict treatment durations in order to calculate an overall waiting time estimate. It was found that the random forest regression algorithm provided the best model for daily radiation treatment durations. On average, the model is able to predict treatment durations to within 7.2 minutes. This is a significant improvement on the rough estimates currently given to patients, and also on the estimates one would obtain by conducting simple averages on all patients. One of the tools developed to Cependant, une recherche distincte examinant la distribution de renseignements personnels sur la santé, includant les estimations de temps d'attente via une application mobile, est présentement en cours. Ceci est discutée dans cette dissertation.ii ACKNOWLEDGEMENTS

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Delay in admission for elective coronary-artery bypass grafting is associated with increased in-hospital mortality

Cited by 22 publications

References 23 publications

Waiting lists in coronary artery bypass graft patients and role of coronary angiography: the Apulian experience

Waiting lists in coronary artery bypass graft patients and role of coronary angiography: the Apulian experience

Desempenho e gastos do SUS com revascularizações do miocárdio no Estado do Rio de Janeiro, de 1999 a 2008

Predicting Waiting Times in Radiation Oncology Using Machine Learning

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