Anxiety and Depression in Cardiovascular Surgery

Although anxiety and depression are psychological risk factors for coronary artery disease (CAD), psychological aspect in patients with cardiovascular surgery has been less considered. Cognitive and psychological deficit has been still concerning in spite of notable improvement of cardiovascular surgery using cardiopulmonary bypass perfusion. The purpose of this chapter review is to discuss recent data concerning the prevalence and trend of anxiety and depression of patients with cardiovascular surgery and to introduce the nonpharmacological intervention studies. The prevalence of anxiety and depression of patients after cardiovascular surgeries has varied from 10 to 60% and has been likely higher than that of general people. From the limited studies about patients over 6 months after surgery, we guessed the followings about the trends of anxiety and depression of patients with CABG without any other additional intervention programs before/after surgery: (1) patients improved scores of anxiety and depression 3–6 months after surgery, (2) anxiety decreased considerably for about 6 months after CABG and then leveled out for some time, (3) depression remained a bit higher 6 months and more after CABG. Patients’ longitudinal psychological conditions would have been influenced by not only invasive cardiovascular surgery but also life events. The nonpharmacology intervention would have improved patients’ psychological conditions. Further research is needed to clarify the long-term psychological outcome and to develop the effective intervention programs toward patients with cardiovascular surgery

Annual report by The Japanese Association for Thoracic Surgery

All data regarding cardiovascular surgery and thoracic surgery were obtained from NCD, whereas data regarding esophageal surgery were collected from survey questionnaire by The Japanese Association for Thoracic Surgery forms because NCD of esophageal surgery does not include non-surgical cases (i.e., patients with adjuvant chemotherapy or radiation alone). Based on the change in data aggregation, there are several differences between this 2015 annual report and previous annual reports: the number of institutions decreased in each category from 578 (2014) to 568 (2015) in cardiovascular, from 762 to 714 in general thoracic and from 626 to 571 in esophageal surgery. Because more than two departments in the same institute registered their data to NCD individually, we cannot calculate correct number of institutes in this survey. Then, the response rate is not indicated in the category of cardiovascular surgery (Table 1), and the number of institutions classified by the operation number is also not calculated in the category of cardiovascular surgery (Table 2)

Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan

<p>Abstract</p> <p>Objective</p> <p>In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan.</p> <p>Methods</p> <p>Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances.</p> <p>Results</p> <p>In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay.</p> <p>Conclusion</p> <p>Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.</p