RESUMO
Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography. We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system. In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings. Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms. Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.
RESUMO
Objective To evaluate musical therapy combined with sufentanil postoperative intravenous analgesia on hemodynamic changes in patient accepted lung cancer operation.Methods Sixty lung cancer surgery patients (ASA Ⅰ-Ⅱ grade) were selected and divided randomly into musical therapy (group M; n =30) and control (group C; n =30).In group M,patients accepted music relaxation training for fifteen minutes before surgery,and music intervention for one hour at 3,7,15,19 hour after surgery.Whereas,in Group C,patients did not listen to any music during the same period.In the intensive care unit,patients were connected to a patient controlled analgesia (PCA) device.The PCA device (sufentanil 2 μg/kg,100 ml saline) was set to deliver a bolus of 2 ml,with a lockout interval of 10 min and background infusion volume of 0.5 ml/h.Hemodynamic changes,the visual analog scale (VAS) and consumption of sufentanil were recorded at the 4th,8th,12th,16th,20th and 24th hour after operation.Results SBP,DBP,HR and VAS of group M were significantly decreased compared to the group C,respectively (P ＜0.05),and significant difference was found in the PCA delivery frequency [group C (30.96 ± 4.00),group M (19.06 ± 3.49),t =12.39,P ＜ 0.01] and postoperative sufentanil consumption[group C (82.65±6.19)μg,group M (52.68 ±7.07)μg,t =20.00,P ＜0.01].Conclusions Musical therapy combined with sufentanil postoperative intravenous analgesia was able to produce better analgesic effect in the treatment of patient accepted lung cancer operation,which decreased postoperative sufentanil consumption and effectively reduced SBP,DBP and HR,and relieved the patient's anxiety.
