Objective To scoping review the risk prediction models for sarcopenia in China was conducted, and provide reference for scientific prevention and treatment of the disease and related research. Methods We systematically searched PubMed, Web of Science, Cochrane Library, Embase, China Knowledge Network, China Biomedical Literature Database, Wanfang Database, and Weipu Database for literature related to myasthenia gravis prediction models in China, with a time frame from the construction of the database to April 30, 2024 for the search. The risk of bias and applicability of the included literature were assessed, and information on the construction of myasthenia gravis risk prediction models, model predictors, model presentation form and performance were extracted. Results A total of 25 literatures were included, the prevalence of sarcopenia ranged from 12.16% to 54.17%, and the study population mainly included the elderly, the model construction methods were categorized into two types: logistic regression model and machine learning, and age, body mass index, and nutritional status were the three predictors that appeared most frequently. Conclusion Clinical caregivers should pay attention to the high-risk factors for the occurrence of sarcopenia, construct models with accurate predictive performance and high clinical utility with the help of visual model presentation, and design prospective, multicenter internal and external validation methods to continuously improve and optimize the models to achieve the best predictive effect.
ObjectiveTo systematically review the predictive factors of new-onset conduction abnormalities(NOCAs) after transcatheter aortic valve replacement (TAVR) in bicuspid aortic valve (BAV) patients. MethodsThe CNKI, VIP, WanFang Data, PubMed, Cochrane Library and EMbase databases were electronically searched to collect the relevant studies on NOCAs after TAVR in patients with BAV from inception to December 5, 2022. Two researchers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4 software. ResultsSix studies involving 758 patients with BAV were included. The results of the meta-analysis showed that age (MD=−1.48, 95%CI −2.73 to −0.23, P=0.02), chronic kidney disease (OR=0.14, 95%CI 0.06 to 0.34, P<0.01), preoperative left bundle branch block (LBBB) (OR=2.84, 95%CI 1.11 to 7.23, P=0.03), membranous septum length (MSL) (MD=0.93, 95%CI 0.05 to 1.80, P=0.04), implantation depth (ID) (MD=−2.06, 95%CI −2.96 to −1.16, P<0.01), the difference between MSL and ID (MD=3.05, 95%CI 1.92 to 4.18, P<0.01), and ID>MSL (OR=0.27, 95%CI 0.15 to 0.49, P<0.01) could be used as predictors of NOCAs. ConclusionCurrent evidence shows that age, chronic kidney disease, LBBB, MS, ID, the difference between MSL and ID, and ID>MSL could be used as predictors of NOCAs. Due to the limited quantity and quality of included studies, more high-quality studies are required to verify the above conclusion.
Objective To analyze the major complications and predictive factors of amputees during postoperative hospitalization, and provide a reference for amputees nursing and early rehabilitation. Methods Using the bibliometric method, we searched Embase, Ovid, Medline, PubMed, CINAHL, China National Knowledge Infrastructure, Wanfang and CQVIP databases for the data of postoperative hospitalization of amputees published from January 1st, 2008 to April 5th, 2022. Statistical description and analysis of article types, sample size, reasons for amputation, amputation sites, complications, influencing factors, predictive factors, and treatment recommendations were performed.Results Finally, 19 articles were included, including 16 in English and 3 in Chinese, all of which were quantitative studies. The literature quality scores were greater than or equal to 7 points, which were all good or excellent. The type of articles were mainly retrospective research (n=15), and the research contents were mainly lower limb amputation. The main reasons for amputation were peripheral vascular disease and diabetes mellitus (n=11). Wound infection, anemia, phantom limb pain, and psychological problems were common complications after amputation. Predictors of complications, secondary operations, and death included age, gender, smoking, drinking, obesity, preoperative comorbidities, level of amputation, anesthesia methods and other factors. Conclusions The focus of acute care after amputation should be wound healing, pain control, proximal physical movement and emotional support, especially for amputees who have prominent postoperative psychological problems. These patients need early psychological disease screening and mental support. After amputation, multi-disciplinary and multi-team coordinated care are needed to achieve both physical and psychological healing of the patient and promote early recovery.
Objective To investigate the risk factors for Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, and construct a clinical model for predicting the risk of CRKP infections. Methods A retrospective analysis was performed on Klebsiella pneumoniae infection patients hospitalized in the Third Hospital of Hebei Medical University from May 2020 to May 2021. The patients were divided into a CRKP group (117 cases) and a Carbapenem-sensitive Klebsiella pneumoniae (CSKP) group (191 cases). The predictors were screened by full subset regression using R software (version 4.3.1). The truncation values of continuous data were determined by Youden index. Nomogram and score table model for CRKP infections risk prediction was constructed based on binary logistic regression. The receiver operator characteristic (ROC) curve and area under curve (AUC) were used to evaluate the accuracy of models. Calibration curve and decision curve were used to evaluate the performance of models. Results308 patients with Klebsiella pneumoniae infections were included. A total of 8 predictors were selected by using full subset regression and truncation values were determined according to Youden index: intensive care unit (ICU) stay at time of infection>2 days, male, acute physiology and chronic health evaluation Ⅱ (APACHEⅡ) score>15 points, hospitalization stay at time of infection>10 days, any history of Gram-negative bacteria infection in the last 6 months, heart disease, lung infection, antibiotic exposure history in the last 6 months. The AUC of CRKP prediction risk curve model was 0.811 (95%CI 0.761 - 0.860). When the optimal cut-off value of the constructed CRKP prediction risk rating table was 6 points, the AUC was 0.723 (95%CI 0.672 - 0.774). The Bootstrap method was used for internal repeated sampling for 1000 times for verification. The model calibration curve and Hosmer-Lemeshow test (P=0.618) showed that these models have good calibration degree. The decision curve showed that these models have good clinical effectiveness. Conclusion The prediction model of CRKP infections based on the above 8 risk factors can be used as a risk prediction tool for clinical identification of CRKP infections.
Objective To systematically review prediction models of small for gestational age (SGA) based on machine learning and provide references for the construction and optimization of such a prediction model. Methods The PubMed, EMbase, Web of Science, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect studies on SGA prediction models from database inception to August 10, 2022. Two researchers independently screened the literature, extracted data, evaluated the risk of bias of the included studies, and conducted a systematic review. Results A total of 14 studies, comprising 40 prediction models constructed using 19 methods, such as logical regression and random forest, were included. The results of the risk of bias assessment from 13 studies were high; the area under the curve of the prediction models ranged from 0.561 to 0.953. Conclusion The overall risk of bias in the prediction models for SGA was high, and the predictive performance was average. Models built using extreme gradient boosting (XGBoost) demonstrated the best predictive performance across different studies. The stacking method can improve predictive performance by integrating different models. Finally, maternal blood pressure, fetal abdominal circumference, head circumference, and estimated fetal weight were important predictors of SGA.
Objective To investigate the influencing factors for the clinical remission of advanced esophageal squamous cell carcinoma (ESCC) after neoadjuvant chemotherapy, establish an individualized nomogram model to predict the clinical remission of advanced ESCC with neoadjuvant chemotherapy and evaluate its efficacy, providing serve for the preoperative adjuvant treatment of ESCC.Methods The clinical data of patients with esophageal cancer who underwent neoadjuvant chemotherapy (nedaplatin 80 mg/m2, day 3+docetaxel 75 mg/m2, day 1, 2 cycles, 21 days per cycle interval) in the Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College from February 2016 to August 2020 were analyzed retrospectively. According to the WHO criteria for efficacy assessment of solid tumors, tumors were divided into complete remission (CR), partial remission (PR), stable disease (SD) and progressive disease (PD). CR and PR were defined as effective neoadjuvant chemotherapy, and SD and PD were defined as ineffective neoadjuvant chemotherapy. Univariate and multivariate analyses were used to analyze the influencing factors for the short-term efficacy of neoadjuvant chemotherapy. The R software was used to establish a nomogram model for predicting the clinical remission of advanced ESCC with neoadjuvant chemotherapy, and Bootstrap method for internal verification of the model. C-index, calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance of the nomogram.Results Finally 115 patients were enrolled, including 93 males and 22 females, aged 40-75 (64.0±8.0) years. After receiving docetaxel+nedaplatin neoadjuvant chemotherapy for 2 cycles, there were 9 patients with CR, 56 patients with PR, 43 patients with SD and 7 patients with PD. Among them, chemotherapy was effective (CR+PR) in 65 patients and ineffective (SD+PD) in 50 patients, with the clinical effective rate of about 56.5% (65/115). Univariate analysis showed that there were statistical differences in smoking history, alcoholism history, tumor location, tumor differentiation degree, and cN stage before chemotherapy between the effective neoadjuvant chemotherapy group and the ineffective neoadjuvant chemotherapy group (P<0.05). Logistic regression analysis showed that low-differentiation advanced ESCC had the worst clinical response to neoadjuvant chemotherapy, moderately-highly differentiated ESCC responded better (P<0.05). Stage cN0 advanced ESCC responded better to neoadjuvant chemotherapy than stage cN1 and cN2 (P<0.05). The C-index and the area under the ROC curve of the nomogram were both 0.763 (95%CI 0.676-0.850), the calibration curve fit well, the best critical value of the nomogram calculated by the Youden index was 70.04 points, and the sensitivity and specificity of the critical value were 80.0% and 58.0%, respectively.ConclusionThe established clinical prediction model has good discrimination and accuracy, and can provide a reference for individualized analysis of the clinical remission of advanced ESCC with neoadjuvant chemotherapy and the screening of new adjuvant treatment subjects.
ObjectiveTo discuss the risk factors of acute respiratory distress syndrome (ARDS) in patients with severe pneumonia.MethodsData of 80 patients with severe pneumonia admitted in our ICU were analyzed retrospectively, and they were divided into two groups according to development of ARDS, which was defined according to the Berlin new definition. The age, gender, weight, Acute Physiology and Chronic Health EvaluationⅡscore, lactate, PSI score and LIPS score, etc. were collected. Statistical significance results were evaluated by multivariate logistic regression analysis after univariate analysis. Receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the parameter for ARDS after severe pneumonia.ResultsForty patients with severe pneumonia progressed to ARDS, there were 4 moderate cases and 36 severe cases according to diagnostic criteria. Univariate analysis showed that procalcitonin (t=4.08, P<0.001), PSI score (t=10.67, P<0.001), LIPS score (t=5.14, P<0.001), shock (χ2=11.11, P<0.001), albumin level (t=3.34, P=0.001) were related to ARDS. Multivariate logistic regression analysis showed that LIPS ［odds ratio (OR) 0.226, 95%CI=4.62-5.53, P=0.013］ and PSI (OR=0.854, 95%CI=132.2-145.5, P=0.014) were independent risk factors for ARDS. The predictive value of LIPS and PSI in ARDS occurrence was significant. The area under ROC curve (AUC) of LIPS was 0.901, the cut-off value was 7.2, when LIPS ≥7.2, the sensitivity and specificity were both 85.0%. AUC of PSI was 0.947, the cut-off value was 150.5, when PSI score ≥150.5, the sensitivity and specificity were 87.5% and 90.0% respectively.ConclusionsPSI and LIPS are independent risk factors of ARDS in patients with severe pneumonia, which may be references for guiding clinicians to make an early diagnosis and treatment plan.
ObjectiveTo predict as well as bioinformatically analyze the target genes of has-miR-451. MethodsmiRBase, miRanda, TargetScan and PicTar were used to predict the target genes of hsa-miRNA-451. The functions of the target genes were demonstrated by Gene Ontology and pathway enrichment analysis. P < 0.05 was set as statistically significant. Results18 target spots of hsa-miRNA-451 were predicted by 3 databases or prediction software at least. The functions of the target genes were enriched in proliferation and development of epithelial cells and regulation of kinase activity (P < 0.05). Pathway analysis showed that transforming growth factor-beta signaling pathway, mitogen-activated protein kinase signaling pathway, epidermal growth factor signaling pathway, Wnt signaling pathway and mammalian target of rapamycin signaling pathway were significantly enriched (P < 0.05). Conclusionhsa-miRNA-451 might be involved in various signaling pathways related to proliferation and development of epithelial cells.
Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.
ObjectiveTo explore the risk factors for accompanying depression in patients with community type Ⅱ diabetes and to construct their risk prediction model. MethodsA total of 269 patients with type Ⅱ diabetes accompanied with depression and 217 patients with simple type Ⅱ diabetes from three community health service centers in two streets of Pingshan District, Shenzhen from October 2021 to April 2022 were included. The risk factors were analyzed and screened out, and a logistic regression risk prediction model was constructed. The goodness of fit and prediction ability of the model were tested by the Hosmer-Lemeshow test and the receiver operating characteristic (ROC) curve. Finally, the model was verified. ResultsLogistic regression analysis showed that smoking, diabetes complications, physical function, psychological dimension, medical coping for face, and medical coping for avoidance were independent risk factors for depressive disorder in patients with type Ⅱ diabetes. Modeling group Hosmer-Lemeshow test P=0.345, the area under the ROC curve was 0.987, sensitivity was 95.2% and specificity was 98.6%. The area under the ROC curve was 0.945, sensitivity was 89.8%, specificity was 84.8%, and accuracy was 86.8%, showing the model predictive value. ConclusionThe risk prediction model of type Ⅱ diabetes patients with depressive disorder constructed in this study has good predictive and discriminating ability.