ObjectiveTo investigate the effect of CYP2C9 and APOE on the dose of stable warfarin and model prediction in Hainan population.MethodsFrom August 2016 to July 2018, 368 patients who required heart valve replacement and agreed to take warfarin anticoagulation at the second department of cardiothoracic surgery in our hospital were enrolled, including 152 males aged 48.5–70.5 (60.03±10.18) years and 216 females aged 43.5–65.6 (54.24±11.35) years. CYP2C9 and APOE were amplified by polymerase chain reaction. The gene fragment was sequenced by the Single Nucleotide Polymorphisms (SNP) site. The patients' age, sex, weight, history of smoking and drinking, and the dose of stable warfarin were recorded. Regression analysis of these clinical data was made to construct a dose prediction model.ResultsAmong 368 patients, CYP2C9 genotype test results showed 301 patients (81.8%) with *1*1 genotype, and 67 patients (18.2%) with *1*3 type. For different CYP2C9 genotype patients, the difference was statistically significant in the dose of stable warfarin (P<0.05). The results of APOE genotype showed 93 patients (25.3%) with E2 genotype, 221 patients (60.1%) with E3 genotype, and 54 patients (14.7%) with E4 genotype; the dose of stable warfarin in patients with different APOE genotypes was statistically significant (P<0.05). Multiple regression analysis showed that patients' age, body weight, and CYP2C9 and APOE genotypes were correlated with the dose of stable warfarin. The correlation coefficient R2 was 0.572, and the prediction model was statistically significant (P<0.05).ConclusionCYP2C9 and APOE gene polymorphisms exist in Hainan population. There is significant difference in the dose of stable warfarin among different genotypes of patients. The model to predict stable warfarin can partly explain the difference of warfarin among different patients.
In order to realize the quantitative assessment of muscle strength in hand function rehabilitation and then formulate scientific and effective rehabilitation training strategies, this paper constructs a multi-scale convolutional neural network (MSCNN) - convolutional block attention module (CBAM) - bidirectional long short-term memory network (BiLSTM) muscle strength prediction model to fully explore the spatial and temporal features of the data and simultaneously suppress useless features, and finally achieve the improvement of the accuracy of the muscle strength prediction model. To verify the effectiveness of the model proposed in this paper, the model in this paper is compared with traditional models such as support vector machine (SVM), random forest (RF), convolutional neural network (CNN), CNN - squeeze excitation network (SENet), MSCNN-CBAM and MSCNN-BiLSTM, and the effect of muscle strength prediction by each model is investigated when the hand force application changes from 40% of the maximum voluntary contraction force (MVC) to 60% of the MVC. The research results show that as the hand force application increases, the effect of the muscle strength prediction model becomes worse. Then the ablation experiment is used to analyze the influence degree of each module on the muscle strength prediction result, and it is found that the CBAM module plays a key role in the model. Therefore, by using the model in this article, the accuracy of muscle strength prediction can be effectively improved, and the characteristics and laws of hand muscle activities can be deeply understood, providing assistance for further exploring the mechanism of hand functions.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
ObjectiveTo investigate the predictive value of volatile organic compounds (VOCs) on pulmonary nodules in people aged less than 50 years.MethodsThe 147 patients with pulmonary nodules and aged less than 50 years who were treated in the Department of Thoracic Surgery of Sichuan Cancer Hospital from August 1, 2019 to January 15, 2020 were divided into a lung cancer group and a lung benign disease group. The lung cancer group included 36 males and 68 females, with the age of 27-49 (43.54±5.73) years. The benign lung disease group included 23 males and 20 females, with the age of 22-49 (42.49±6.83) years. Clinical data and exhaled breath samples were collected prospectively from the two groups. Exhaled breath VOCs were analyzed by gas chromatography mass spectrometry. Binary logistic regression analysis was used to select variables and establish a prediction model. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the prediction model were calculated.ResultsThere were statistically significant differences in sex (P=0.034), smoking history (P=0.047), cyclopentane (P=0.002), 3-methyl pentane (P=0.043) and ethylbenzene (P=0.009) between the two groups. The sensitivity, specificity and area under the ROC curve of the prediction model with gender, cyclopentane, 3-methyl pentane, ethylbenzene and N,N-dimethylformamide as variables were 80.8%, 60.5% and 0.781, respectively.ConclusionThe combination of VOCs and clinical characteristics has a certain predictive value for the benign and malignant pulmonary nodules in people aged less than 50 years.
ObjectiveTo explore the independent risk factors for benign and malignant subsolid pulmonary nodules and establish a malignant probability prediction model.MethodsA retrospective analysis was performed in 443 patients with subsolid pulmonary nodules admitted to Subei People's Hospital of Jiangsu Province from 2014 to 2018 with definite pathological findings. The patients were randomly divided into a modeling group and a validation group. There were 296 patients in the modeling group, including 125 males and 171 females, with an average age of 55.9±11.1 years. There were 147 patients in the verification group, including 68 males and 79 females, with an average age of 56.9±11.6 years. Univariate and multivariate analysis was used to screen the independent risk factors for benign and malignant lesions of subsolid pulmonary nodules, and then a prediction model was established. Based on the validation data, the model of this study was compared and validated with Mayo, VA, Brock and PKUPH models.ResultsUnivariate and multivariate analysis showed that gender, consolidation/tumor ratio (CTR), boundary, spiculation, lobulation and carcinoembryonic antigen (CEA) were independent risk factors for the diagnosis of benign and malignant subsolid pulmonary nodules. The prediction model formula for malignant probability was: P=ex/(1+ex). X=0.018+(1.436×gender)+(2.068×CTR)+(?1.976×boundary)+ (2.082×spiculation)+(1.277×lobulation)+(2.296×CEA). In this study, the area under the curve was 0.856, the sensitivity was 81.6%, the specificity was 75.6%, the positive predictive value was 95.4%, and the negative predictive value was 39.8%. Compared with the traditional model, the predictive value of this model was significantly better than that of Mayo, VA, Brock and PKUPH models.ConclusionCompared with Mayo, VA, Brock and PKUPH models, the predictive value of the model is more ideal and has greater clinical application value, which can be used for early screening of subsolid nodules.
ObjectiveTo systematically review the prediction models of blood-based biomarkers for non-small cell lung cancer (NSCLC). MethodsThe PubMed, Embase, Cochrane Library, Web of Science, VIP, WanFang Data and CNKI databases were electronically searched to collect studies related to the objectives from inception to June, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4.1 software. ResultsA total of 8 studies were included and all of them were retrospective cohort studies. The models were internally validated in 2 studies and externally validated in 4 studies. The performances of the eight predictive models were stable, which was measured by the area under the curve of receiver operating characteristic curve lying between 0.664 and 0.783. However, the risk of bias was high, which may mainly be reflected in data processing, model validation and performance adjustment. Meta-analysis showed that LDH (HR=1.86, 95%CI 41.32 to 2.63, P<0.01), dNLR (HR=2.15, 95%CI 1.56 to 2.96, P<0.01) and NLR (HR=1.71, 95%CI 1.08 to 2.69, P=0.02) were independent factors of prognosis for NSCLC patients. Conclusion?Current evidence shows that the NSCLC prediction models based on peripheral blood biomarkers are still in the development stage, and the models have a high risk of bias.
Risk prediction models for postoperative pulmonary complications (PPCs) can assist healthcare professionals in assessing the likelihood of PPCs occurring after surgery, thereby supporting rapid decision-making. This study evaluated the merits, limitations, and challenges of these models, focusing on model types, construction methods, performance, and clinical applications. The findings indicate that current risk prediction models for PPCs following lung cancer surgery demonstrate a certain level of predictive effectiveness. However, there are notable deficiencies in study design, clinical implementation, and reporting transparency. Future research should prioritize large-scale, prospective, multi-center studies that utilize multiomics approaches to ensure robust data for accurate predictions, ultimately facilitating clinical translation, adoption, and promotion.
ObjectiveTo establish a predictive model for neck lateral lymph node metastasis (LLNM) in unilateral papillary thyroid cancer (uni-PTC) with central lymph node metastasis (CLNM). MethodsThe uni-PTC patients with CLNM were included in this study. The patients underwent thyroid surgery in the 960th Hospital of the PLA Joint Logistics Support Force from May 2018 to December 2021, who were randomly divided into the modeling group and the validation group according to the ratio of 7∶3. The risk factors of neck LLNM were analyzed by univariate and multivariate logistic regression and the nomogram of prediction model was constructed. The receiver operating characteristic (ROC) curve and calibration curve were used to validate the prediction model. ResultsA total of 237 patients were included in this study, including 158 patients in the modeling group and 79 patients in the validation group. The LLNM occurred in the 84 patients of the modeling group and 43 patients of the validation group. The multivariate logistic regression analysis was performed according to the statistical indicators in the univariate analysis results of the modeling group and the risk factors considered in the previous studies. The results showed that the patients with maximum diameter of the lesions >1 cm, multiple lesions, extraglandular invasion, the rate of CLNM ≥0.414, and lesions located at the upper portion had higher probability of LLNM (OR>1, P<0.05). The area under ROC curve of the nomogram in predicting LLNM in the modeling group was 0.834 [95%CI (0.771, 0.896)], which in the validation group was 0.761 [95%CI (0.651, 0.871)]. The calibration curve showed a good calibration degree in the prediction model. ConclusionThe clinical risk prediction model established based on the risk factors can better predict the probability of LLNM.
ObjectiveTo explore the risk factors affecting operation treatment selection of acute adhesive small bowel obstruction (ASBO), and establish a prediction model of surgical treatment selection to provide a guidance for clinical decision-making. MethodsThe patients with acute ASBO admitted to this hospital and met the inclusion and exclusion criteria, from January 2019 to December 2022, were retrospectively collected, and the patients were assigned into the surgical treatment and conservative treatment according to the treatment selection. The differences in the clinicopathologic factors between the patients with surgical treatment and conservative treatment were compared. Meanwhile, the factors with statistical differences (P<0.05) or the factors with clinical significance judged based on professional knowledge were included to screen the influencing factors of surgical treatment selection using the multivariate logistic regression analysis, and the selected influencing factors were used to construct the logistic regression prediction model equation. The area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (95%CI) was used to evaluate the prediction efficiency of the prediction model equation. ResultsA total of 231 patients with acute ASBO were included, 117 (50.6%) of whom underwent surgical treatment and 114 (49.4%) underwent conservative treatment. In all 16 clinicopathologic factors between the patients with surgical treatment and conservative treatment had statistical differences (P<0.05) including the body mass index (BMI), preopeative high fever, intestinal type, sign of peritonitis, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score excluded age scoring, abdominal surgery history and times of abdominal surgery history, times of pre-admission seek medical advice and preoperative conservative treatment time, the air-liquid level by X-ray plain film, and severe small bowel obstruction and adhesive bands by CT examination, as well as the white blood cell count (WBC), neutrophil percentage, albumin (ALB), and urea nitrogen. The multivariate logistic regression analysis showed that the acute ASBO accompanied by sign of peritonitis (β=1.778, P=0.028), history of abdominal surgery (β=1.394, P=0.022), and adhesive bands (β=1.321, P=0.010) and severe small bowel obstruction (β=1.183, P=0.018) by CT examination, WBC (β=0.524, P＜0.001), APACHEⅡ score excluded age scoring (β=0.291, P＜0.001), and BMI (β=0.191, P=0.011) had positive impacts on adopting surgical treatment, while preoperative ALB (β=–0.101, P=0.023) and conservative treatment time (β=–0.391, P＜0.001) had negative impacts on adopting surgical treatment. The accuracy, specificity, and sensitivity of the logistic regression prediction model equation constructed according to these 9 influencing factors were 84.8%, 71.1%, and 77.7%, respectively. The AUC (95%CI) of the prediction model equation to distinguish selection of surgical treatment from conservative treatment was 0.942 (0.914, 0.970). ConclusionsAccording to the preliminary results of this study, surgical treatment is recommended for patients with acute ASBO accompanied by signs of peritonitis, history of abdominal surgery, adhesive bands and severe small bowel obstruction by CT, increased preoperative WBC, high APACHEⅡ score excluded age scoring, high BMI, preoperative low ALB level, and shorter preoperative conservative treatment time. And the logistic prediction model equation constructed according to these characteristics in this study has a good discrimination for patients with surgical treatment or conservative treatment selection.
Epilepsy is a clinical syndrome characterized by recurrent epileptic seizures caused by various etiologies. Etiological diagnosis and localization of the epileptogenic focus are of great importance in the treatment of epilepsy. Positron emission tomography-computed tomography (PET-CT) technology plays a significant role in the etiological diagnosis and localization of the epileptogenic focus in epilepsy. It also guides the treatment of epilepsy, predicts the prognosis, and helps physicians intervene earlier and improve the quality of life of patients. With the continuous development of PET-CT technology, more hope and better treatment options will be provided for epilepsy patients. This article will review the guiding role of PET-CT technology in the diagnosis and treatment of epilepsy, providing insights into its application in etiological diagnosis, preoperative assessment of the condition, selection of treatment plans, and prognosis of epilepsy.
