Dataset Information

Effect of holiday admission for acute aortic dissection on in-hospital mortality in Japan: A nationwide study

ABSTRACT:

Background

Patients admitted on weekends have higher mortality than those admitted on weekdays. However, whether the “weekend effect” results in a higher mortality after admission for acute aortic dissection (AAD),—classified according to Stanford types—remains unclear. This study aimed to examine the association between admission day and in-hospital mortality in AAD Type A and B.

Methods

We used data from the Japanese registry of all Cardiac and Vascular Diseases Diagnostic Procedure Combination, a nationwide claim-based database with data from 953 certified hospitals, and enrolled in-patients with AAD admitted between April 1, 2012, and March 31, 2016. Based on the admission day, we stratified patients into groups (Weekdays, Saturdays, and Sundays/holidays). The influence of the admission day on in-hospital mortality was assessed via multi-level logistic regression analysis. We also performed a Stanford type-based stratified analysis.

Results

Among the included 25,641 patients, in-hospital mortality was 16.0%. The prevalence of patients admitted with AAD was relatively higher on weekdays. After adjustment for covariates, patients admitted on a Sunday/holiday showed an increased risk of in-hospital mortality (odds ratio [OR] 1.20; 95% confidence interval [CI] 1.07–1.33, p<0.001) than patients admitted on weekdays. Among patients admitted on a Sunday/holiday, only the subgroup of Stanford Type A showed a significantly increased risk of in-hospital mortality. (Stanford Type A, non-surgery vs. surgery groups: 95% CI 1.06–1.48 vs. 1.17–1.68, p<0.001 for both groups, OR 1.25 vs. 1.41, respectively, Stanford Type B, non-surgery vs. surgery groups: 95% CI 0.64–1.09 vs. 0.40–2.10; p = 0.182 vs. 0.846; OR 0.84 vs. 0.92).

Conclusions

In conclusion, patients with AAD Type A admitted on a Sunday/holiday may have an increased in-hospital mortality risk.

SUBMITTER: Kato K

PROVIDER: S-EPMC8601417 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection. Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model. Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860-0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.