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 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.
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.
ObjectivesTo evaluate the predicting value of bedside pulmonary ultrasound in bronchopulmonary dysplasia (BPD) in premature infants.MethodsPremature infants with gestational age below 28 weeks or birth weight below 1 500 g admitted to NICU of Chengdu Women and Children’s Central Hospital from June 2018 to June 2019 were included. Pulmonary bedside ultrasound monitoring was performed on the 3rd, 7th, 14th and 28th day after admission, and the characteristic ultrasound images were recorded and scored. BPD were diagnosed by NICHD standard. The clinical data and pulmonary ultrasound data were compared and analyzed. Then diagnostic value of bedside pulmonary ultrasound in BPD of premature infants were analyzed.ResultsA total of 81 children involving 32 BPD and 49 non-BPD were included. The sensitivity (Sen), specificity (Spe) and area under curve (AUC) of receiver operating characteristic (ROC) of the "alveolar-interstitial syndrome" within 3 days after birth and the "fragment sign" on 28 days after birth were 81.25%, 51.02%, 0.66 and 31.25%, 97.96%, 0.65, respectively. The lung ultrasound scores in the BPD group on the 3rd, 7th, 14th, and 28th day after birth were 71.99.%, 68.39%, 0.71; 87.50%, 57.14%, 0.72; 78.13%, 73.47%, 0.76 and 56.25 %, 75.51%, 0.66. Sen, Spe and ROC AUC of comprehensive evaluation of lung ultrasound predicted the occurrence of BPD been 81.25%, 63.27%, and 0.85.ConclusionsThe comprehensive evaluation of combination of "alveolar interstitial syndrome" image characteristics within 3 days after birth, "fragment sign" image characteristics after 28 days, and lung ultrasound score at different times after birth can predict the premature infants with bronchopulmonary dysplasia.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.
ObjectiveTo establish a predictive model of surgical site infection (SSI) following colorectal surgery using machine learning.MethodsMachine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network. The whole data set was divided into two parts, with 80% as the training data set and 20% as the testing data set. In order to improve the training effect, the whole data set was divided into two parts again, with 90% as the training data set and 10% as the testing data set. The predictive result of the model was compared with the actual infected cases, and the sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated, the area under receiver operating characteristic (ROC) curve was used to evaluate the predictive capacity of the model, odds ratio (OR) was calculated to tested the validity of evaluation with a significance level of 0.05.ResultsThere were 7 285 patients in the whole data set registered from January 15th, 2015 to June 16th, 2016, among whom 234 were SSI cases, with an incidence of SSI of 3.21%. The predictive model was established by random forest algorithm, which was trained by 90% of the whole data set and tested by 10% of that. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 76.9%, 59.2%, 3.3%, and 99.3%, respectively, and the area under ROC curve was 0.767 [OR=4.84, 95% confidence interval (1.32, 17.74), P=0.02].ConclusionThe predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs, but more data training should be needed to improve the predictive capacity of the model before clinical application.
ObjectiveTo construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. MethodsFrom the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. ResultsThe mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. ConclusionThe fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.
ObjectiveTo investigate the clinical characteristics and predicting factors for death in critically ill patients with severe community-acquired pneumonia (CAP). MethodA total of 143 hospitalized patients with severe CAP between January 2009 and December 2012 were included and their clinical data were retrospectively analyzed. According to the clinical outcome, patients were divided into survival group and death group, and their clinical features and laboratory test results were compared, and multivariate regression analysis was conducted to search for predicting factors for death. ResultsIn this study, a total of 118 patients survived and 25 patients died, and the mortality rate was 17.5%. The number of underlying diseases in the two groups were different, and death group had more patients with 3 kinds of diseases than the survival group[76.0% (19/25) vs. 22.8% (13/57), P<0.05]. The intubation rate in the death group was significantly higher than that in the survival group[84.0% (21/25) vs. 33.1% (39/118), P<0.05], and the arterial blood pH value (7.15±0.52 vs. 7.42±0.17, P<0.05), HCO3- concentration[(18.07±6.25) vs. (25.07±5.44) mmol/L, P<0.05], PaO2[(58.92±35.18) vs. (85.92±32.19) mm Hg (1 mm Hg=0.133 kPa), P<0.05] and PaO2/FiO2[(118.23±98.02) vs. (260.17±151.22) mm Hg, P<0.05)] in the death group were significantly lower than those in the survival group. And multivariate regression analysis indicated that the number of underlying diseases[OR=0.202, 95%CI (0.198, 0.421), P=0.003], PaO2[OR=1.203, 95%CI (1.193, 1.294), P=0.011] and PaO2/FiO2[OR=0.956, 95%CI (0.927, 0.971), P=0.008] were independent predictors of death in the patients with severe pneumonia. ConclusionsPatients who died of severe pneumonia often had severe illnesses before admission, and the number of underlying diseases and PaO2 have highly predictive value for death.
ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.
ObjectiveTo review individual treatment effect (ITE) models developed from randomized controlled trials, with the aim of systematically summarizing the current state of model development and assessing the risk of bias. MethodsPubMed and Embase databases were searched for studies published between 1990 and 14 June 2024. Data were extracted using the CHARMS inventory, and the PROBAST risk of bias tool was used to assess model quality. ResultsA total of 11 publications were included, containing 19 ITE models. The ITE modelling methods were regression models with interaction terms (n=8, 42.1%), dual-range models (n=5, 26.3%) and machine learning (n=6, 31.6%). The ITE models had a reporting rate of 78.9%, 73.2% and 10.5% for differentiation, calibration and clinical validity, respectively. Fourteen models were assessed as having a high risk of bias (73.7%), particularly in the area of statistical analysis, due to inappropriate handling of missing data (n=15, 78.9%), inappropriate consideration of model fit issues (n=5, 26.3%), etc. ConclusionCommon approaches to ITE model development include constructing interaction terms, dual procedure theory, and machine learning, but suffer from a low number of model developments, more complex modeling methods, and non-standardized reporting. In the future, emphasis should be placed on further exploration of ITE models, promoting diversified modeling methods and standardized reporting to improve the clinical promotion and practical application value of the models.