ObjectiveTo analyze the early results and risk factors of surgical revascularization for patients with ischemic heart disease and left ventricular dysfunction. Methodsclinical data of 318 patients with ischemic heart disease and left ventricular dysfunction with left ventricular ejection fraction (LVEF)≤50% who underwent coronary artery bypass grafting (cABG) from January 2003 to July 2013 was retrospectively reviewed. There were 266 males and 52 females with a mean age of 62.6±9.2 years (range 36 to 83). seventy-six patients underwent off-pump cABG (oPcAB) and 242 patients underwent conventional cABG. Fifteen patients underwent concomitant mitral valve repair or replacement. The patients who underwent left ventricular aneurysmectomy (LVA) were excluded from this study. Perioperative data were collected including the risk factors, echocardiographic results, morbidities and mortalities. The risk factors were analyzed with the endpoints of adverse events and mortalities to find the elements that influence the early results of the procedure. ResultsThe EuroscorE Ⅱ predicted operative mortality rate was 2.78±4.02% (range 1.00% to 45.00%) and actual mortality rate was 1.9% (6/318). Three of 6 patients died from low cardiac output syndrome. Totaladverse events rate was 47.2% (150/318) including prolonged ventilation (25.2%), low cardiac output syndrome (6.3%),ventricular arrhythmia (4.4%), acute renal dysfunction (4.1%), myocardial infarction (3.8%), cerebralvascular accident(2.8%), and re-exploration for bleeding (0.6%). Compared with those preoperatively, the LVEF was significantly improvedfrom 42.14%±5.94% to 45.64%±8.33% (t=6.084, P=0.000), and the left ventricular end diastolic dimension (LVEDD) wassignificantly reduced from 53.96±6.28 mm to 48.64±7.50 mm (t=-9.681, P=0.000) postoperatively. The logistic multiplevariables regression analysis showed perioperative intra-aortic balloon pump (IABP) implantation was mutual risk factorof prolonged ventilation and low cardiac output syndrome. ConclusionSurgical revascularization is an effective optionfor patients with ischemic heart disease and left ventricular dysfunction, demonstrated by improved LVEF and reducedLVEDD. Low cardiac output syndrome is the main cause of operative death. Perioperative IABP implantation is mutualrisk factor of prolonged ventilation and low cardiac output syndrome. Meticulous perioperative management plays a keyrole in satisfactory early results.
Objective To construct the prediction model of hospitalization expenses for ischemic heart disease, reveal the key factors affecting hospitalization expenses, and analyze the interaction between variables. Methods Patients from Sichuan medical insurance comprehensive service platform from January 2020 to December 2021 were extracted. The training set and test set were divided according to the ratio of 7∶3. Six machine learning models were constructed and trained by ten-fold cross validation, and was explained by SHAP theory. Results XGBoost model had the best performance among these models, with a R2 of 0.60, RMSE of 9 969.71 yuan, and MAE of 5 242.90 yuan in the test set. SHAP results showed that the five variables with the greatest impact on hospitalization expenses were surgery, length of stay, hospital grade, disease type and DRG. Hospitalization costs were higher when grade 3 or 4 procedures were performed, the length of stay was prolonged, the hospitalization was in a tertiary hospital, and payments were made for acute myocardial infarction and non-DRG. With the prolongation of hospital stay, the hospitalization expenses increased faster when the patient had grade 4 surgery and was in a tertiary hospital. In addition, DRG payment will reduce the length of hospital stay and the hospitalization expenses of patients with different disease types. Conclusion The interpretable XGBoost model constructed in this study has a good predictive performance for the hospitalization expenses of patients with ischemic heart disease. Combined with SHAP theory, it can effectively identify the key factors affecting the hospitalization expenses and analyze their interactions.