Objective To research the key technique in the virtual surgery, and to build a 3D virtual surgical system for medical imaging. Methods In VC++ platform, the volume data were segmented by using a 3D segmentation algorithm. The 3D reconstruction and virtual cutting were achieved with the help of VTK, and the performance of the varied cutting methods was analyzed. Results The interactive 3D reconstruction, virtual scalpel cutting and virtual surgical planning of the medical CT/MR images were realized. Conclusion This system offers great help for doctor to simulate the operative process, to observe the 3D models of human organs and to carry out the computer-aided surgery.

OBJECTIVE: To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model. METHODS: The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score. RESULTS: Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)]. CONCLUSIONS The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.
Female ; Male ; Humans ; Aged ; Adult ; Middle Aged ; Adolescent ; Critical Illness ; Hospitalization ; Length of Stay ; APACHE ; Hospital Information Systems

To evaluate the safety and efficiency of citrate anticoagulant-based continuous blood purification in patients at high risk of bleeding. 
 Methods: One hundred and fifty-two patients at high risk of bleeding were divided into local citrate group (group A, n=68) and heparin group (group B, n=84). Clotting function, change of pH, ionized sodium, bicarbonate ion, ionized calcium, activated clotting time (ACT) and complications were monitored before and during treatment. 
 Results: Compared to the group A, the incidence of clotting in filter and chamber, the degree of bleeding or fresh bleeding were significantly reduced in the group B (P<0.05). ACT of post-filter at 4, 8 and 12 h during the treatment in the group A was significantly extended compared with that without treatment (P<0.05), while there was no significant change in group B (P>0.05). The pH value, the levels of ionized sodium, bicarbonate ion and ionized calcium during the treatment were maintained in normal range in both group A and group B.
 Conclusion: Local citrate-based continuous blood purification can achieve effective anticoagulation and decrease the incidence of bleeding. It is an ideal choice for patients at high risk of bleeding.
Anticoagulants ; pharmacology ; Bicarbonates ; blood ; Blood Coagulation ; drug effects ; Blood Coagulation Tests ; Calcium ; blood ; Citrates ; Citric Acid ; therapeutic use ; Female ; Hemodiafiltration ; adverse effects ; methods ; Hemofiltration ; Hemorrhage ; etiology ; prevention & control ; Heparin ; therapeutic use ; Humans ; Intensive Care Units ; Male ; Reference Values ; Renal Dialysis ; Sodium ; blood ; Treatment Outcome