Objective · To evaluate the surgical outcome of RPR composite technique for complex hypertrophic obstructive cardiomyopathy (HOCM). Methods · From June 2009 to December 2015, 9 complex HOCM patients received RPR procedure. There were 6 males and 3 females with age from 22 to 63 years old and the average age of (43±19) years old. Transthorax echocardiography (TTE) showed systolic anterior motion (SAM) and at least moderate mitral valve regurgitation (MR) in all patients before operations. Transesophageal echocardiography (TEE) was used to evaluate the results of procedures during operation. All the patients had been followed up since one week after operation and examined by TTE. Results · All the patients were discharged without complications. Intraoperative TEE indicated that left ventricular outflow tract pressure gradient (LVOTPG) significantly decreased from (92±14) mmHg before operation to (9±3) mmHg after operation (P<0.01). SAM in all the patients disappeared. One week after operation, TTE demonstrated remarkable reduction in the thickness of ventricular septum, LVOTPG and MR than those before operation (P<0.01). Mean follow-up was 26 months. All the patients became asymptomatic. LVOTPG remained low and MR remained mild. There were no deaths, reoperations, or any other adverse consequences. Conclusion · RPR technique is an effective surgical method to relieve LVOTO and MR of complex HOCM to lead a better life.

Objective:To construct a chronic obstructive pulmonary disease (COPD) assessment test (CAT) score prediction model based on a deep network fused with air data, and to explore its significance.Methods:From February 2015 to December 2017, the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital, Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated. The daily CAT scores were recorded continuously. The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model, and the prediction accuracy of the model was compared with that of the long short-term memory model (LSTM), the LSTM-attention model and the autoregressive integrated moving average model (ARIMA).Results:A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days. The LSTM-convolutional neural networks (CNN)-autoregression (AR) model was constructed by using the collected air data and CAT score, and the root mean square error of the model was 0.85, and the mean absolute error was 0.71. Compared with LSTM, LSTM-attention and ARIMA, the average prediction accuracy was improved by 21.69%.Conclusion:Based on the air data in the environment of COPD patients, the fusion deep network model can predict the CAT score of COPD patients more accurately.

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