BackgroundPercutaneous coronary intervention (PCI) has been widely used to treat acute coronary syndrome but is only recommended as an additional treatment to medical therapy and risk modification in patients with refractory or progressing angina. The number of PCI in this patient population is still increasing. Post-PCI chest pain (PPCP) is one of the common problems of PCI. Its presentation and causes in patients with stable angina are poorly understood.Patients and methodsThis study retrospectively collected clinical information of 167 patients who had stable angina and underwent elective PCI, including 70 patients with PPCP 24 hours after procedure and 97 patients without PPCP. The incidence and predictors of PPCP were analyzed.ResultsThe incidence of PPCP was 41.9% (70/167). Compared with non-PPCP patients, PPCP patients had more abnormal post-PCI electrocardiogram (ECG) changes (new Q-waves, ST-segment shifts, or T-waves inversion) and serum cardiac troponin I (cTnI) elevation, more PCI vessels, and stent placement (all P<0.05). More PPCP patients required repeat revascularization than non-PPCP patients after PCI (P=0.043). PPCP was correlated with abnormal post-PCI ECG changes (P<0.0001), cTnI elevation (P<0.0001), post-PCI serum level of cTnI (P<0.0001), number of stents placed (P=0.009), and pre-PCI cTnI level (P=0.049). The strongest predictors of PPCP were abnormal post-PCI ECG changes (P<0.0001), post-PCI cTnI level (P<0.0001), and cTnI elevation (P<0.0001), followed by the number of stents placed (P=0.048).ConclusionPPCP is common in patients with stable angina in our cohort. It is associated with abnormal ECG changes, cTnI elevation, and number of stents placed.
There are many medical departments in Taiwan's hospitals which make the patients not easily selecting the appropriate outpatient appointment. Patients often make their selection based on their own experience. This may lead incorrect appointment, delay of treatment, and waste of time. To help patients in making appropriate choice of outpatient appointment, this study has built a two-stage integrated model for decision support applications. artificial neural networks (ANN), support vector machine (SVM), and classification and regression tree (CART) are used in combination for modeling of the system. The first is to input the symptoms into the classifier and generate the category of outpatient department. The second stage is for continued classification of input symptoms and generates the recommended medical specialty/division for appointment. The results show ANN-SVM model has achieved the highest overall yield of 93.94%. ANN-CART and SVM-CART models achieved 80.75 and 78.57%, respectively. This two-stage model is a cost-effective tool in medical decision support and helps providing efficient medical service.
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