Xinhua Hospital Research Articles

Development of a Nomogram and Risk Grouping System for Predicting 1-Year Overall Survival of Patients With Atypical Teratoid/Rhabdoid Tumors.

Atypical teratoid/rhabdoid tumor (AT/RT) is a kind of central nervous system malignant tumor in children. In this study, we aimed to develop a practically clinical nomogram and risk grouping system to predict 1-year overall survival for patients with atypical teratoid/rhabdoid tumor. The nomogram was constructed based on the pediatric tumor registry of Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. Fifty-four information-integrated patients with atypical teratoid/rhabdoid tumor were included from the database. Cox regression analyses were used to select independent prognostic factors. Based on the fitted multivariate Cox regression model, a nomogram of 1-year overall survival for atypical teratoid/rhabdoid tumor patients was generated. Moreover, the nomogram was validated by assessing its discrimination and calibration. In these patients, age at diagnosis, the extent of tumor resection, radiotherapy, and chemotherapy were included in the multivariate Cox regression model. Based on this multivariate Cox regression model, a nomogram of 1-year overall survival for atypical teratoid/rhabdoid tumor patients was generated. The nomogram had good discrimination (the concordance index was 0.781) and calibration curves showed no deviation from reference lines. Decision curve analysis demonstrated this nomogram was useful for clinical practice. The risk grouping system was built based on nomogram-derived risk scores, which could classify patients into 3 risk groups. Compared with the low-risk group, the risk of 1-year death was significantly higher in the intermediate-risk group (hazard ratio = 1.42, 95%, confidence intervals = 0.49-4.11) and high-risk group (hazard ratio = 9.78, 95% confidence intervals = 3.53-27.1). A nomogram and risk grouping system were built to predict for the 1-year overall survival of atypical teratoid/rhabdoid tumor patients. The nomogram could facilitate a personalized prognostic evaluation for atypical teratoid/rhabdoid tumor patients and help medical practitioners make better treatment.

Micra Leadless and Transvenous Pacemaker: A Single-Center Comparative Study of QRS Wave Duration Resulting From Different Pacing Sites.

To compare the paced QRS duration on different sites in age-, gender-, and indication-matched patients implanted with Micra leadless pacemakers and conventional transvenous pacemakers (TV-PM). A total of 82 patients from Xinhua Hospital, Shanghai Jiaotong University, were enrolled, including two groups of 41 patients matched according to gender, age, and pacemaker indications, who underwent Micra and TV-PM implantations, respectively. The baseline data of the patients, the pacing site described using three- and nine-partition methods, and the paced QRS duration on 12-lead electrocardiogram were then analyzed. Overall, patients in our population were on average 79.2 years of age and mostly male (75.6%). Atrioventricular node dysfunction was the most common indication (56.1%) for pacemaker therapy. Mid-septum, especially Site 5, is the implantation site for most patients in both groups. Micra (145 ms) and TV-PM (133 ms) both had the narrowest-paced QRS at high septum, but Micra may exhibit significantly more reduced QRS duration than TV-PM at low septum (Micra vs. TV-PM: 143.0 [142.8-156.5] ms vs. 163.5 ± 17.5 ms, p = 0.044). The narrowest-paced QRS complex for Micra leadless pacemakers is achieved at high septum, and pacing at low septum by Micra may acquire shorter QRS duration than conventional TV-PM.

Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China.

Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echocardiographic parameters or restricted data. This study introduces an artificial intelligence (AI)-based model, which uses natural language processing (NLP) and machine learning with the aim of improving spontaneous closure predictability in PMVSD. We did a multicentre, retrospective analysis using data from 29 142 PMVSD patients across six tertiary centres in China from May, 2004, to September, 2022, for training (70%) and validation (30%; dataset 1, 27 269 patients), and from September, 2001, to December, 2009 for testing (dataset 2, 1873 patients). NLP extracted structured data from echocardiography reports and medical records, which were used to develop machine learning models. Models were evaluated for spontaneous closure occurrence and timing by use of area under the receiver operating characteristic curve (AUC), decision curve analysis, and calibration index. Spontaneous closure occurred in 3520 patients (12·1%) at a median of 31 months (IQR 16-56). Eleven NLP-derived predictors, identified via least absolute shrinkage and selection operator, highlighted the importance of defect morphology and patient age. The random survival forest algorithm, selected for its superior concordance indexes, showed excellent predictive performance with validation set AUCs (95% CI) of 0·95 (0·94-0·96) for 1-year and 3-year predictions, and 0·95 (0·95-0·96) for 5-year predictions; testing set AUCs were 0·95 (0·94-0·97) for 1-year predictions, 0·97 (0·96-0·98) for 3-year predictions, and 0·98 (0·97-0·99) for 5-year predictions. The model showed high clinical utility through decision curve analysis, calibration, and risk stratification, maintaining consistent accuracy across centres and subgroups. This AI-based model for predicting spontaneous closure in PMVSD patients represents a substantial advancement, potentially improving patient management, reducing risks of delayed or inappropriate treatment, and enhancing clinical outcomes. National Natural Science Foundation of China, Shanghai Municipal Hospital Clinical Technology Project, Shanghai Municipal Health Commission, and Clinical Research Unit of XinHua Hospital.

Analysis of risk factors for post-prematurity respiratory disease in very preterm infants

Objective: To investigate the risk factors associated with post-prematurity respiratory disease (PPRD) in very preterm infants. Methods: A prospective cohort study was conducted, enrolling 369 very preterm infants who were admitted to the neonatal intensive care unit of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, within one week of birth from January 2019 to June 2023. Data on maternal and infant clinical characteristics, neonatal morbidities, and treatments during hospitalization were collected. The very preterm infants were divided into 2 groups based on whether they developed PPRD. Continuous variables were compared using Mann-Whitney U test, while categorical variables were compared using χ² tests or continuity correction χ2 test. Multivariate Logistic regression analysis was used to identify the independent risk factors for PPRD in very preterm infants. Results: Among the 369 very preterm infants, 217 cases(58.8%) were male, with a gestational age of 30 (28, 31) weeks at birth and a birth weight of 1 320 (1 085, 1 590) g. Of these, 116 cases (31.4%) developed PPRD, while 253 cases (68.6%) did not. The very preterm infants in the PPRD group had a lower gestational age and lower birth weight (both, P<0.001). The PPRD group also had a higher proportion of males, lower Apgar scores at the 1th minute after birth and the 5th minutes after birth, a higher rate of born via cesarean delivery, and a higher incidence of bronchopulmonary dysplasia, more pulmonary surfactant treatment, longer durations of mechanical ventilation, longer total oxygen therapy, and lower Z-score for weight at discharge (all P<0.05). Multivariate Logistic regression analysis showed that gestational age (OR=0.85, 95%CI 0.73-0.99, P=0.037), born via cesarean delivery (OR=2.23, 95%CI 1.21-4.10, P=0.010), a duration of mechanical ventilation ≥7 days (OR=2.51, 95%CI 1.43-4.39, P=0.001), and a Z-score for weight at discharge (OR=0.82, 95%CI 0.67-0.99, P=0.040) were all independent risk factors for PPRD in very preterm infants. Conclusion: Very preterm infants with a small gestational age, born via cesarean section, mechanical ventilation ≥7 days, and a low Z-score for weight at discharge should be closely monitored for PPRD, and provided with standardized respiratory management after discharge.

Comprehensive spinal correction rehabilitation (CSCR) study: a randomised controlled trial to investigate the effectiveness of CSCR in children with early-onset idiopathic scoliosis on spinal deformity, somatic appearance, functional status and quality of life in Shanghai, China

IntroductionPresently, the physiotherapy scoliosis-specific exercise recommended by the International Scientific Society on Scoliosis Orthopaedic and Rehabilitation Treatment guidelines is primarily advised for adolescents with scoliosis, and there is a lack...

The effect of chelation on bone Pb stores in Pb poisoned children.

Lead exposure remains a key problem for children during development. One treatment for lead poisoning is chelation - a topic that remains controversial with varied results. Bone lead serves as a marker of total body burden of lead and encompasses between 60-90% of lead storage in children. In this study, we aimed to identify the change in bone lead as a result of chelation therapy in a group of lead poisoned children (blood lead >25 µg/dL). Upon diagnosis with lead poisoning at Xinhua Hospital in Shanghai, China, children were recruited to our study, had their bone lead levels measured, and underwent one-week of intravenous, in-patient ethylenediaminetetraacetic acid chelation treatment. Up to three clinical visits with the same treatment protocol and bone lead measurements were completed over the two-year study. We measured biomarkers of lead exposure for children exposed via various potential sources, including home contaminants, local industrial emissions, traditional medicines, or lead cookware. We observed significant differences with bone lead after chelation therapy (p < 0.0001), even after calculating a conservative model for theoretical decay from known bone turnover (p = 0.01). The difference identified between our observed change in bone lead and literature established bone lead change significantly increased with more chelation treatments. A significant reduction in bone lead was observed following chelation treatment of children with lead poisoning - a difference that increased more with more chelation. Study results indicate that chelation treatment is effective in reducing bone lead stores in children with initial blood lead levels greater than 25 µg/dL. Lead exposure in children is a consistent problem - drastically impacting health across the life span. After exposure, lead stores in the bone of children serving as a potential endogenous source of exposure for years to decades. Our study demonstrated that chelation therapy, while reducing blood lead levels, additionally reduced bone lead levels. A reduction in bone lead would effectively reduce the potential for endogenous release of lead and restrict the damage done by lead exposure.

Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia.

There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). This retrospective study included 413 patients hospitalized at Xinhua hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections (P <0.001) than patients with subphenotype-2 and had lower 30-day (P <0.001) and 90-day (P <0.001) mortality, and lower in-hospital (P=0.001) and 2-year (P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes (P<0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.

Evolution of the "Internet Plus Health Care" Mode Enabled by Artificial Intelligence: Development and Application of an Outpatient Triage System.

Although new technologies have increased the efficiency and convenience of medical care, patients still struggle to identify specialized outpatient departments in Chinese tertiary hospitals due to a lack of medical knowledge. The objective of our study was to develop a precise and subdividable outpatient triage system to improve the experiences and convenience of patient care. We collected 395,790 electronic medical records (EMRs) and 500 medical dialogue groups. The EMRs were divided into 3 data sets to design and train the triage model (n=387,876, 98%) and test (n=3957, 1%) and validate (n=3957, 1%) it. The triage system was altered based on the current BERT (Bidirectional Encoder Representations from Transformers) framework and evaluated by recommendation accuracies in Xinhua Hospital using the cancellation rates in 2021 and 2022, from October 29 to December 5. Finally, a prospective observational study containing 306 samples was conducted to compare the system's performance with that of triage nurses, which was evaluated by calculating precision, accuracy, recall of the top 3 recommended departments (recall@3), and time consumption. With 3957 (1%) records each, the testing and validation data sets achieved an accuracy of 0.8945 and 0.8941, respectively. Implemented in Xinhua Hospital, our triage system could accurately recommend 79 subspecialty departments and reduce the number of registration cancellations from 16,037 (3.83%) of the total 418,714 to 15,338 (3.53%) of the total 434200 (P<.05). In comparison to the triage system, the performance of the triage nurses was more accurate (0.9803 vs 0.9153) and precise (0.9213 vs 0.9049) since the system could identify subspecialty departments, whereas triage nurses or even general physicians can only recommend main departments. In addition, our triage system significantly outperformed triage nurses in recall@3 (0.6230 vs 0.5266; P<.001) and time consumption (10.11 vs 14.33 seconds; P<.001). The triage system demonstrates high accuracy in outpatient triage of all departments and excels in subspecialty department recommendations, which could decrease the cancellation rate and time consumption. It also improves the efficiency and convenience of clinical care to fulfill better the usage of medical resources, expand hospital effectiveness, and improve patient satisfaction in Chinese tertiary hospitals.

Circadian variation pattern of sudden cardiac arrest occurred in Chinese community

BackgroundThe circadian variation pattern of sudden cardiac arrest (SCA) occurred in Chinese community including both community healthcare centres and primary hospitals remains unknown. This study analysed the circadian variation of...

Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.

The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators. A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients. A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively. CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.

Myeloperoxidase and its derivative hypochlorous acid combined clinical indicators predict new-onset atrial fibrillation in sepsis: a case-control study

BackgroudNew-onset atrial fibrillation (NOAF) is a common complication of sepsis and linked to higher death rates in affected patients. The lack of effective predictive tools hampers early risk assessment for the development of NOAF. This study aims to develop practical and effective predictive tools for identifying the risk of NOAF.MethodsThis case-control study retrospectively analyzed patients with sepsis admitted to the emergency department of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from September 2017 to January 2023. Based on electrocardiographic reports and electrocardiogram monitoring records, patients were categorized into NOAF and non-NOAF groups. Laboratory tests, including myeloperoxidase (MPO) and hypochlorous acid (HOCl), were collected, along with demographic data and comorbidities. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were employed to identify predictors. The area under the curve (AUC) was used to evaluate the predictive model’s performance in identifying NOAF.ResultsA total of 389 patients with sepsis were included in the study, of which 63 developed NOAF. MPO and HOCl levels were significantly higher in the NOAF group compared to the non-NOAF group. Multivariate logistic regression analysis identified MPO, HOCl, tumor necrosis factor-α (TNF-α), white blood cells (WBC), and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score as independent risk factors for NOAF in sepsis. Additionally, a nomogram model developed using these independent risk factors achieved an AUC of 0.897.ConclusionThe combination of MPO and its derivative HOCl with clinical indicators improves the prediction of NOAF in sepsis. The nomogram model can serve as a practical predictive tool for the early identification of NOAF in patients with sepsis.

Endoscopic variceal ligation combined with sclerotherapy for management of gastroesophageal variceal bleeding in pediatric patients: a single-center retrospective study.

Portal hypertension (PH) frequently gives rise to severe and life-threatening complications, including hemorrhage accompanied by the rupture of esophageal and gastric varices. In contrast to the guidelines for the management of PH in adults, the optimal endoscopic management of variceal bleeding for secondary prophylaxis in children remains unclear. The present study evaluated the efficacy and safety of endoscopic variceal ligation (EVL) and endoscopic sclerotherapy (EST) to control gastroesophageal variceal bleeding in children. This retrospective study included children with gastroesophageal variceal bleeding who underwent EST or EVL at Xinhua Hospital, Shanghai Jiaotong University School of Medicine, between February 2013 and March 2020. Short-term hemostasis rate and long-term rebleeding rate were evaluated. Adverse events related to the procedures, such as esophageal ulcer, esophageal stricture, abnormal embolization, pneumonia and perforation, were also recorded. EVL (n = 8) and EST (n = 13) were performed successfully in all pediatric patients diagnosed with moderate to severe esophageal varices concurrent with gastric varices. Hemostasis was achieved during episodes of upper gastrointestinal bleeding. The mean volume of each single aliquot of cyanoacrylate injected was 0.3 ± 0.1 ml (range: 0.1-0.5 ml). Varices were eradicated in six (75%) of the eight patients who underwent EVL after a median 2 (range: 1-4) procedures and a median time of 3.40 months (range: 1.10-13.33 months). Eleven (52.4%) of the 21 patients developed rebleeding events, with the mean duration of hemostasis being 11.1 ± 11.6 months (range 1.0-39.2 months). No treatment-related complications, for example, distal embolism, occurred except for abdominal pain in one patient (4.8%). EST, alone or in combination with EVL, is an effective and safe method of managing gastroesophageal variceal hemorrhage in children undergoing secondary prophylaxis.

Analysis of MTHFR gene variants in fifteen children with hyperhomocysteinemia

The clinical manifestations, biochemical and metabolic data, genetic variations and treatment data of children with MTHFR gene variant induced hyperhomocysteinemia admitted to Hangzhou Children's Hospital and Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from November 2015 to September 2021 were analysed retrospectively. A total of 15 pediatric patients were included, including 10 males and 5 females, with onset ages ranging from 6 days to 18 years old and confirmed ages ranging from 40 days to 18 years old. One confirmed case was detected through neonatal screening, and the remaining 14 cases were all diagnosed through genetic diagnosis after onset. The main clinical manifestations were feeding difficulties, hypotonia, epilepsy, developmental delay. All patients had elevated levels of blood homocysteine, with blood homocysteine levels before and after treatment being (151.46±57.44) μmol/L and (69.96±32.88) μmol/L, significantly decreased after treatment compared with before treatment, with a statistically significant difference (P<0.001). The blood methionine level before the treatment was 9.40 (6.20, 11.96) μmol/L, normal or slightly decreased compared to the reference range. The methionine level returned to normal after treatment. A total of 19 MTHFR gene variants were detected, with 6 being unreported variants and 13 being known variants. c.1316C>T (p.L439P) was the most common variant(16.6%,5/30). All the patients had varied neurological damages, with 7 patients improved after metabolic therapy by carnitine and folinic acid, 8 patients experiencing developmental delay, and 1 patient experiencing frequent epilepsy. The clinical manifestations of MTHFR gene variation-related hyperhomocysteinemia are complex and variable. Early-onset and homozygous variants often have a poor prognosis. Blood homocysteine, blood amino acid analysis, serum total homocysteine assay and gene testing are helpful for early diagnosis.

Analysis of clinical and genetic characteristics of the severe liver disease phenotype in patients with hepatolenticular degeneration

Objective: To investigate the clinical and genetic characteristics and predictive role of the severe liver disease phenotype in patients with hepatolenticular degeneration (HLD). Methods: Inpatients with HLD confirmed at Xinhua Hospital affiliated with Shanghai Jiao Tong University School of Medicine from January 1989 to December 2022 were selected as the research subjects. Clinical classification was performed according to the affected organs. Patients with liver disease phenotypes were classified into the liver disease group and further divided into the severe liver disease group and the ordinary liver disease group. The clinical characteristics and genetic variations were compared in each group of patients. The predictive indicators of patients with severe liver disease were analyzed by multiple regression. Statistical analysis was performed using the t-test, Mann-Whitney U test, or χ(2) test according to different data. Results: Of the 159 HLD cases, 142 were in the liver disease group (34 in the severe liver disease group and 108 in the ordinary liver disease group), and 17 were in the encephalopathy group. The median age of onset was statistically significantly different between the liver disease group and the encephalopathy group [12.6 (7.0, 13.3) years versus 16.9 (11.0, 21.5) years, P<0.01]. 156 ATP7B gene mutation sites were found in 83 cases with genetic testing results, of which 54 cases carried the p.Arg778Leu gene mutation (allele frequency 46.2%). Compared with patients with other types of gene mutations (n=65), patients with homozygous p.Arg778Leu mutations (n=18) had lower blood ceruloplasmin and albumin levels, a higher prognostic index, Child-Pugh score, an international normalized ratio, and prothrombin time (P<0.05). Hemolytic anemia, corneal K-F ring, homozygous p.Arg778Leu mutation, and multiple laboratory indexes in the severe liver disease group were statistically significantly different from those in the ordinary liver disease group (P<0.05). Multivariate logistic regression analysis showed that the predictive factors for severe liver disease were homozygous p.Arg778Leu mutation, total bilirubin, and bile acids (ORs=16.512, 1.022, 1.021, 95% CI: 1.204-226.425, 1.005-1.039, and 1.006-1.037, respectively, P<0.05). The drawn ROC curve demonstrated a cutoff value of 0.215 3, an AUC of 0.953 2, and sensitivity and specificity of 90.91% and 92.42%, respectively. Conclusion: Liver disease phenotypes are common in HLD patients and have an early onset. Total bilirubin, bile acids, and the homozygous p.Arg778Leu mutation of ATP7B is related to the severity of liver disease in HLD patients, which aids in predicting the occurrence and risk of severe liver disease.

A deep learning algorithm to identify carotid plaques and assess their stability.

Carotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning. A total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People's Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices. Modeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%. Deep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Multiple primary tumors in children: a clinicopathological analysis of four cases

Objective: To investigate the clinicopathological features of children with metachronous or synchronous primary tumors and to identify related genetic tumor syndromes. Methods: The clinicopathological data of 4 children with multiple primary tumors diagnosed in the Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China from 2011 to 2023 were collected. The histological, immunophenotypic and molecular characteristics were examined using H&E staining, immunohistochemical staining, PCR, Sanger sequencing and next-generation sequencing (NGS). The patients were followed up. Results: Case 1 was an 8-year-old boy with the adrenal cortical carcinoma, and 5 years later a poorly differentiated gastric adenocarcinoma was detected. Case 2 was a 2-year-old boy, presented with a left ventricular choroid plexus carcinoma, and a hepatoblastoma was detected 8 months later. Case 3 was a 9-month-old girl, diagnosed with renal rhabdoid tumor first and intracranial atypical teratoid/rhabdoid tumor (AT/RT) 3 months later. Case 4 was a 7-year-old boy and had a sigmoid colon adenocarcinoma 3 years after the diagnosis of a glioblastoma. The morphology and immunohistochemical features of the metachronous or synchronous primary tumors in the 4 cases were similar to the corresponding symptom-presenting/first-diagnosed tumors. No characteristic germ line mutations were detected in cases 1 and 2 by relevant molecular detection, and the rhabdoid tumor predisposition syndrome was confirmed in case 3 using NGS. Case 4 was clearly related to constitutional mismatch repair deficiency as shown by the molecular testing and clinical features. Conclusions: Childhood multiple primary tumors are a rare disease with histological morphology and immunophenotype similar to the symptom-presenting tumors. They are either sporadic or associated with a genetic (tumor) syndrome. The development of both tumors can occur simultaneously (synchronously) or at different times (metachronously). Early identification of the children associated with genetic tumor syndromes can facilitate routine tumor screening and early treatment.

Early predictive value of lipocalin-type prostaglandin D synthase for 28-day mortality in cardiac arrest patients: study protocol for a prospective study

IntroductionPublic training in cardiopulmonary resuscitation and treatment in emergency and intensive care unit have made tremendous progress. However, cardiac arrest remains a major health burden worldwide, with brain damage being...

Clinical outcome of combined over-the-top reconstruction with modified Lemaire procedure in one-stage revision anterior cruciate ligament reconstruction

Objective: To investigate the clinical effect of over-the-top (OTT) reconstruction of the anterior cruciate ligament (ACL) combined with the modified Lemaire technique in one-stage ACL revision. Methods: It's a retrospective study. The clinical data of 37 patients who underwent one-stage revision of ACL using OTT combined with modified Lemaire technique from April 2020 to May 2023 in the Department of Sports Medicine of Xinhua Hospital Affiliated to Dalian University were retrospectively analyzed. There were 28 males and 9 females with a mean age of (32.7±5.7) years. The postoperative knee function and surgical revision effect were evaluated by subjective and objective evaluation indexes of the knee joint before and 3 months and 24 months after surgery. The subjective evaluation indexes included the International Knee Documentation Committee (IKDC) score, Lysholm score, and Tegner score; and the objective evaluation indexes included the anterior-posterior static displacement distance of the tibia, tibia-femur relative rotation angle, the Lachman test, the axial shift test, and the ratio of the loss of muscle strength of the lower limb. Results: At the final follow-up, all patients returned to pre-injury motor level, with significant improvements in IKDC scores (45.3±6.8 preoperatively, 67.5±4.7 and 93.2±2.3 at 3 months and 2 years after the operation, respectively), Lysholm scores (57.2±2.6 preoperatively, 72.6±2.9 and 89.7±3.7 at 3 months and 2 years after the operation, respectively), and Tegner scores (3.1±0.7 preoperatively, 4.9±0.6 and 5.8±1.3 at 3 months and 2 years after the operation, respectively) (all P<0.001). The anterior-posterior static displacement distance of the tibia reduced significantly [from (5.2±0.5) mm before the operation to (1.4±0.5) mm at 2 years postoperatively) (P<0.001)]; and the relative rotation angle of the tibia-femur restored to the normal physiological range, it was reduced from 6.2°±1.2° before the operation to 1.7°±0.3° 2 years after (P<0.001). The Lachman test and axial shift test at 2 years postoperatively were all negative. The muscle strength loss ratio of the affected lower limb was significantly better than that before the operation (P<0.001). Conclusion: The use of OTT reconstruction combined with modified Lemaire technique for revision of ACL in patients with ACL reconstruction failure can circumvent the tunnel problem for one-stage revision and better restore the stability and function of the knee joint, with ideal clinical results.

Changing clinical characteristics of pediatric inpatients with pneumonia during COVID-19 pandamic: a retrospective study

BackgroundThe COVID-19 pandemic have impacts on the prevalence of other pathogens and people’s social lifestyle. This study aimed to compare the pathogen, allergen and micronutrient characteristics of pediatric inpatients with pneumonia prior to and during the COVID-19 pandemic in a large tertiary hospital in Shanghai, China.MethodsPatients with pneumonia admitted to the Department of Pediatric Pulmonology of Xinhua Hospital between March-August 2019 and March-August 2020 were recruited. And clinical characteristics of the patients in 2019 were compared with those in 2020.ResultsHospitalizations for pneumonia decreased by 74% after the COVID-19 pandemic. For pathogens, virus, mycoplasma pneumoniae (MP) and mixed infection rates were all much lower in 2020 than those in 2019 (P < 0.01). Regarding allergens, compared with 2019, the positive rates of house dust mite, shrimp and crab were significantly higher in 2020 (P < 0.01). And for micronutrients, the levels of vitamin B2, B6, C and 25-hydroxyvitamin D (25(OH)D) in 2020 were observed to be significantly lower than those in 2019 (P < 0.05). For all the study participants, longer hospital stay (OR = 1.521, P = 0.000), milk allergy (OR = 6.552, P = 0.033) and calcium (Ca) insufficiency (OR = 12.048, P = 0.019) were identified as high-risk factors for severe pneumonia by multivariate analysis.ConclusionsThe number of children hospitalized with pneumonia and incidence of common pathogen infections were both reduced, and that allergy and micronutrient status in children were also changed after the outbreak of the COVID-19 pandemic.

Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study

Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-biliary atresia in these young patients presenting with cholestasis poses a formidable challenge, given the similarity in their clinical manifestations. To this end, our study endeavors to construct a screening model aimed at prognosticating outcomes in cases of BA. Within this study, we introduce a wrapper feature selection model denoted as bWFMVO-SVM-FS, which amalgamates the water flow-based multi-verse optimizer (WFMVO) and support vector machine (SVM) technology. Initially, WFMVO is benchmarked against eleven state-of-the-art algorithms, with its efficiency in searching for optimized feature subsets within the model validated on IEEE CEC 2017 and IEEE CEC 2022 benchmark functions. Subsequently, the developed bWFMVO-SVM-FS model is employed to analyze a cohort of 870 consecutively registered cases of neonates and infants with cholestasis (diagnosed as either BA or non-BA) from Xinhua Hospital and Shanghai Children's Hospital, both affiliated with Shanghai Jiao Tong University. The results underscore the remarkable predictive capacity of the model, achieving an accuracy of 92.639 % and specificity of 88.865 %. Gamma-glutamyl transferase, triangular cord sign, weight, abnormal gallbladder, and stool color emerge as highly correlated with early symptoms in BA infants. Furthermore, leveraging these five significant features enhances the interpretability of the machine learning model's performance outcomes for medical professionals, thereby facilitating more effective clinical decision-making.