In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.
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.
Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.
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.
The clinical performance and failure issues are significantly influenced by prosthetic malposition in unicompartmental knee arthroplasty (UKA). Uncertainty exists about the impact of the prosthetic joint line height in UKA on tibial insert wear. In this study, we combined the UKA musculoskeletal multibody dynamics model, finite element model and wear model to investigate the effects of seven joint line height cases of fixed UKA implant on postoperative insert contact mechanics, cumulative sliding distance, linear wear depth and volumetric wear. As the elevation of the joint line height in UKA, the medial contact force and the joint anterior-posterior translation during swing phase were increased, and further the maximum von Mises stress, contact stress, linear wear depth, cumulative sliding distance, and the volumetric wear also were increased. Furthermore, the wear area of the insert gradually shifted from the middle region to the rear. Compared to 0 mm joint line height, the maximum linear wear depth and volumetric wear were decreased by 7.9% and 6.8% at –2 mm joint line height, and by 23.7% and 20.6% at –6 mm joint line height, the maximum linear wear depth and volumetric wear increased by 10.7% and 5.9% at +2 mm joint line height, and by 24.1% and 35.7% at +6 mm joint line height, respectively. UKA prosthetic joint line installation errors can significantly affect the wear life of the polyethylene inserted articular surfaces. Therefore, it is conservatively recommended that clinicians limit intraoperative UKA joint line height errors to –2−+2 mm.
Objective To analyze the value of serum levels of miR-141-3p, miR-130a, miR-29a-3p, and miR-210 in predicting early neurological deterioration (END) in non-traumatic intracerebral hemorrhage. Methods The patients with non-traumatic cerebral hemorrhage who met the selection criteria and were admitted to Chengde Central Hospital between February 2021 and October 2022 were prospectively selected by convenience sampling method. The serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels upon admission and the occurrence of neurological deterioration within 24 h were collected, and the patients were divided into a deterioration group and a non-deterioration group according to whether neurological deterioration occurred. The correlation of serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels with the END of non-traumatic intracerebral hemorrhage and their predictive value to the END of non-traumatic intracerebral hemorrhage were analyzed. Results A total of 235 patient were enrolled. Of the 235 patients, 45 (19.1%) showed neurological deterioration and 190 (80.9%) showed no neurological deterioration. The levels of miR-141-3p and miR-29a-3p in the deteriorating group were significantly lower than those in the non-deteriorating group [(1.11±0.32) vs. (1.76±0.51) ng/mL, P<0.001; (1.19±0.31) vs. (1.71±0.51) ng/mL, P<0.001], and the levels of miR-130a and miR-210 were significantly higher than those in the non-deteriorating group [(5.13±1.11) vs. (3.82±1.03) ng/mL, P<0.001; (3.96±0.76) vs. (2.78±0.50) ng/mL, P<0.001]. Multivariate logistic regression analysis showed that serum miR-141-3p and miR-29a-3p levels were protective factors for the occurrence of END in non-traumatic intracerebral hemorrhage patients [odds ratio (OR)=0.513, 95% confidence interval (CI) (0.330, 0.798), P=0.003; OR=0.582, 95%CI (0.380, 0.893), P=0.013], and serum miR-130a and miR-210 levels were independent risk factors for that [OR=2.046, 95%CI (1.222, 3.426), P=0.007; OR=2.377, 95%CI (1.219, 4.638), P=0.011]. The area under the receiver operating characteristic curve was 0.857 [95%CI (0.760, 0.954)] in predicting the END of non-traumatic intracerebral hemorrhage by the combined probability of the serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 levels obtained by logistic regression, and the sensitivity was 86.7%, the specificity was 94.7%, the positive predictive value was 79.6%, and the negative predictive value was 96.8% according to the cut-off value of the prediction probability of the combined test. Conclusion The combined detection of serum miR-141-3p, miR-130a, miR-29a-3p, and miR-210 has a high predictive value in the occurrence of END in non-traumatic intracerebral hemorrhage patients.
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 preliminarily explore the effect of Osteoporosis Self-assessment Tool for Asians (OSTA) and Fracture Risk Assessment Tool (FRAX) on predicting osteoporosis and osteoporosis fracture in postmenopausal patients with maintenance hemodialysis (MHD).MethodsThirty-six postmenopausal patients undergoing MHD from August 2017 to October 2018 in Hemodialysis Center of Nephrology Department, West China Hospital of Sichuan University were selected. Relevant data such as age, height, and weight were collected. OSTA index and the 10-year probability of major osteoporotic fractures and 10-year probability of hip fractures of FRAX score were calculated. Bone mineral densities (BMD) of the hip and lumbar spine were measured by dual energy X-ray absorptiometry (DXA) at the same time. The value of OSTA index and FRAX scale in evaluating the risk of osteoporosis predicated on T value ≤−2.5 determined by DXA BMD and fracture in postmenopausal patients with MHD were analyzed.ResultsThe DXA BMD of the 36 patients showed that 50.0% (18/36) had a T value≤−2.5, and 30.6% (11/36) had a fracture history. BMD in postmenopausal patients with MHD was negatively correlated with FRAX score (model without BMD values), and positively correlated with OSTA index. The sensitivity and specificity of OSTA in the prediction of osteoporosis were 94.4% and 61.1%, respectively; and the sensitivity and specificity of FRAX (the model without BMD values) in the prediction of osteoporosis were 88.9% and 50.0%, respectively. The FRAX score with or without BMD had the same clinical value in predicting osteoporosis.ConclusionsPostmenopausal MHD patients have a higher risk of osteoporosis and fracture. Both OSTA index and FRAX scale can predict osteoporosis risk among postmenopausal MHD patients, and the FRAX scale with or without BMD has the same clinical value in predicting osteoporosis risk. In clinical work, for primary hospitals and dialysis centers lacking DXA, preliminary screening of osteoporosis in MHD patients can be performed with OSTA and FRAX scales.
Objective To systematically review risk prediction models of in-hospital cardiac arrest in patients with cardiovascular disease, and to provide references for related clinical practice and scientific research for medical professionals in China. Methods Databases including CBM, CNKI, WanFang Data, PubMed, ScienceDirect, Web of Science, The Cochrane Library, Wiley Online Journals and Scopus were searched to collect studies on risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease from January 2010 to July 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Results A total of 5 studies (4 of which were retrospective studies) were included. Study populations encompassed mainly patients with acute coronary syndrome. Two models were modeled using decision trees. The area under the receiver operating characteristic curve or C statistic of the five models ranged from 0.720 to 0.896, and only one model was verified externally and for time. The most common risk factors and immediate onset factors of in-hospital cardiac arrest in patients with cardiovascular disease included in the prediction model were age, diabetes, Killip class, and cardiac troponin. There were many problems in analysis fields, such as insufficient sample size (n=4), improper handling of variables (n=4), no methodology for dealing with missing data (n=3), and incomplete evaluation of model performance (n=5). Conclusion The prediction efficiency of risk prediction models for in-hospital cardiac arrest in patients with cardiovascular disease was good; however, the model quality could be improved. Additionally, the methodology needs to be improved in terms of data sources, selection and measurement of predictors, handling of missing data, and model evaluations. External validation of existing models is required to better guide clinical practice.
Epilepsy is a complex and widespread neurological disorder that has become a global public health issue. In recent years, significant progress has been made in the use of wearable devices for seizure monitoring, prediction, and treatment. This paper reviewed the applications of invasive and non-invasive wearable devices in seizure monitoring, such as subcutaneous EEG, ear-EEG, and multimodal sensors, highlighting their advantages in improving the accuracy of seizure recording. It also discussed the latest advances in the prediction and treatment of seizure using wearable devices.