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@#Objective To systematicly evaluate the constituent factors and accuracy of prediction models of outcome for patients with prolonged disorders of consciousness. Methods Articles about prediction models of outcome for patients with prolonged disorders of consciousness were retrieved from PubMed, Web of Science, CNKI and Wanfang Data until September 30th, 2021. The authors, publishing times, subjects, predictive indicators, outcomes and conclusions were extracted. Results A total of 4 313 articles were returned and 37 included, comprising randomized controlled trials and cohort studies, which published mainly from 2012 to 2021. The subjects were patients with prolonged disorders of consciousness, and their predictions consisted of clinical assessment scales, neuroimaging, neuroelectrophysiology and laboratory indicators. Conclusion Prediction models may be valuable for the long-term outcomes of patients with prolonged disorders of consciousness. Most of the current prediction models are composed of only a limited number of technical means, and the accuracy is uneven. Coma Recovery Scale-revised, default mode network and multiple evoked potentials-related prediction models are accurate, but lack a unified adaptation standard.
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Objective To analyze the effects of anesthesia-induced thermoregulatory system impairment and low temperature environment of the operating room on the perioperative thermoregulation of individualized patients by constructing a computer simulation model. Methods A simple anesthesia model was proposed and then incorporated into the self-developed individualized thermoregulatory model, in which human body was represented as a cylinder with two layers of the core and the skin. The integrated model could be used to assess the effects of individualized characteristics such as age, obesity, and cardiovascular diseases on thermoregulation by modifying different physiological parameters involving sweating, shivering and cutaneous vasomotion. Simulation of the general anesthesia effects on human thermoregulation could be achieved by reducing basal metabolic rate and thresholds for vasoconstriction and shivering. Results The elderly people showed lower core temperature but higher skin temperature, compared with the young people. In a low temperature environment, an increase in fat thickness or an increase in severity degree of the left ventricular failure (LVF) might alleviate the decrease in core temperature, while an increase in wind speed or relative humidity could result in a decrease in core temperature. When the threshold setting of vasoconstriction was reduced by 0-5-3 ℃, the core temperature showed a significant decrease. Conclusions By comparing model simulations with experimental measurements, the reliability and validity of the model in predicting human transient thermal responses during varying external thermal environment was verified. The individualized characteristics of human body had an important influence on human body temperature in a low temperature environment. Moreover, the combination of individualized characteristics of human body and general anesthesia further complicated the body′s thermoregulation and posed significant challenges for clinicians.
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OBJECTIVE: To explore the predictive value of preoperative enhanced CT combined with des-γ-carboxy-prothrombin(DCP) level for microvascular invasion(MVI) of hepatocellular carcinoma(HCC).METHODS: The clinicaldata of 105 cases of HCC diagnosed by pathology after surgical resection from January 1,2016 to March 1,2019 in thethe Affiliated Hospital of Southwest Medical University were analyzed retrospectively. According to the pathologicalresults,they were divided into two groups: experimental group(with MVI): 30 cases,control group(without MVI): 75 cases,using univariate and multivariate logistic regression analysis to predict influencing factors of MVI in HCC. Thereceiver working curve(ROC curve)was used to evaluate the predictive value of DCP combined with enhanced CT forMVI in HCC.RESULTS: Univariate analysis showed that serum DCP level(t=4.667,P<0.001),tumor size(t=9.427,P<0.001),capsule type(χ~2=18.456,P<0.001) and pathological type(χ~2=11.914,P=0.003) were risk factors for MVI in HCC,with statistically significant differences(P<0.05). Multivariate logistic regression analysis showed that tumor size(OR=12.610,P=0.001),capsule type(OR=14.363,P=0.008) and serum DCP level(OR=19.254,P=0.014)were independentrisk factors of MVI in HCC,with statistically significant differences(P<0.05). ROC curve analysis showed that thesensitivity,specificity and area under the curve(AUC) of serum DCP and enhanced CT model for predicting MVI in HCCwere 90.0%,74.7% and 0.856,respectively.CONCLUSION: Preoperative serum DCP level combined with enhanced CThelps predict MVI in HCC.