Quality and efficiency of a standardized e-handover system for pediatric nursing: A prospective interventional study.

AIM: This study examined the effect on pediatric nursing handover quality and efficiency when a standardized e-handover system was implemented. BACKGROUND: Handover quality is an important aspect of nursing quality management; however, handover quality among nursing staff is poor. METHODS: A prospective interventional study was carried out in a general pediatrics ward from December 2019 to November 2020. The tools included a standardized e-handover system. The intervention strategies included workflow remodeling and employee training on oral handover using the standardized e-handover system. RESULTS: The omission frequency of critical handover elements decreased from 47.32% to 2.94% (p < .01), among which the omission frequencies of nine out of 16 key elements significantly decreased. Integrity also showed improvement. Specifically, the integrity of five types of critical information was significantly improved, including vital signs, signs and symptoms, laboratory test results, radiologic examination results, and treatment regimen (2.00 vs. 5.00, p < .01; 3.00 vs. 5.00, p < .01; 3.00 vs. 5.00, p < .01; 5.00 vs. 5.00, p = .009; 3.00 vs. 4.00, p < .01, respectively). Information accuracy was 100%. Workflow and efficiency significantly improved, communication duration with patient/family during work hours significantly increased (24.00 vs. 56.00, p < .01), and prehandover preparation duration significantly decreased (32.00 vs. 2.50, p < .01). Nurse handover satisfaction showed improvement (56.88 ± 15.08 vs. 74.31 ± 9.22, p < .01). CONCLUSION: The standardized e-handover system effectively improved nurse handover quality, optimized workflow, increased work efficiency, and promoted teamwork. IMPLICATIONS FOR NURSING MANAGEMENT: Standardized e-handover systems have great potential for ensuring the safety of pediatric patients and improving the quality of handover.

Artificial intelligence-assisted reduction in patients' waiting time for outpatient process: a retrospective cohort study.

BACKGROUND: Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less. METHODS: We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides. RESULTS: Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05). The total cost was 335.97 (interquartile range: 244.80, 437.60) CNY (Chinese Yuan) for the AI-assisted group and 364.58 (249.70, 497.76) CNY for the conventional group (p < 0.05). CONCLUSIONS: Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.

Physical Extraction and Feature Fusion for Multi-Mode Signals in a Measurement System for Patients in Rehabilitation Exoskeleton.

Measurement system of exoskeleton robots can reflect the state of the patient. In this study, we combined an inertial measurement unit and a visual measurement unit to obtain a repeatable fusion measurement system to compensate for the deficiencies of the single data acquisition mode used by exoskeletons. Inertial measurement unit is comprised four distributed angle sensors. Triaxial acceleration and angular velocity information were transmitted to an upper computer by Bluetooth. The data sent to the control center were processed by a Kalman filter to eliminate any noise. Visual measurement unit uses camera to acquire real time images and related data information. The two data acquisition methods were fused and have its weight. Comparisons of the fusion results with individual measurement results demonstrated that the data fusion method could effectively improve the accuracy of system. It provides a set of accurate real-time measurements for patients in rehabilitation exoskeleton and data support for effective control of exoskeleton robot.

Novel colon cancer susceptibility variants identified from a genome-wide association study in African Americans.

Genome-wide association studies (GWAS) in ethnic/racial minority populations can help to fine-map previously identified risk regions or discover new risk loci because of the genetic diversity in these populations. We conducted a GWAS of colorectal cancer (CRC) in 6,597 African Americans (1,894 cases and 4,703 controls) (Stage 1) and followed up the most promising markers in a replication set of 2,041 participants of African descent (891 cases and 1,150 controls) (Stage 2). We identified a novel variant, rs56848936 in the gene SYMPK at 19q13.3, associated with colon cancer risk (odds ratio 0.61 for the risk allele G, p = 2.4 × 10-8 ). The frequency of the G allele was 0.06 in African Americans, compared to <0.01 in Europeans, Asians and Amerindians in the 1000 Genomes project. In addition, a variant previously identified through fine-mapping in this GWAS in the region 19q13.1, rs7252505, was confirmed to be more strongly associated with CRC in the African American replication set than the variant originally reported in Europeans (rs10411210). The association between rs7252505 and CRC was of borderline significance (p = 0.05) in a Hispanic population GWAS with 1,611 CRC cases and 4,330 controls. With the three datasets combined, the odds ratio was 0.84 for the risk allele A (95% confidence interval 0.79-0.89, p = 3.7 × 10-8 ). This study further highlights the importance of conducting GWAS studies in diverse ancestry populations.

Associations Between Genetic Ancestries and Nicotine Metabolism Biomarkers in the Multiethnic Cohort Study.

Differences in internal dose of nicotine and tobacco-derived carcinogens among ethnic/racial groups have been observed. In this study, we explicitly examined the relationships between genetic ancestries (genome-wide average) and 19 tobacco-derived biomarkers in smokers from 3 admixed groups in the Multiethnic Cohort Study (1993-present), namely, African ancestry in African Americans (n = 362), Amerindian ancestry in Latinos (n = 437), and Asian and Native Hawaiian ancestries in Native Hawaiians (n = 300). After multiple comparison adjustment, both African and Asian ancestries were significantly related to a greater level of free cotinine; African ancestry was also significantly related to lower cotinine glucuronidation (P's < 0.00156). The predicted decrease in cotinine glucuronidation was 8.6% (P = 4.5 × 10(-6)) per a 20% increase in African ancestry. Follow-up admixture mapping revealed that African ancestry in a 12-Mb region on chromosome 4q was related to lower cotinine glucuronidation (P's < 2.7 × 10(-7), smallest P = 1.5 × 10(-9)), although this is the same region reported in our previous genome-wide association study. Our results implicate a genetic ancestral component in the observed ethnic/racial variation in nicotine metabolism. Further studies are needed to identify the underlying genetic variation that could potentially be ethnic/racial specific.

Fine-mapping of genome-wide association study-identified risk loci for colorectal cancer in African Americans.

Genome-wide association studies of colorectal cancer (CRC) in Europeans and Asians have identified 21 risk susceptibility regions [29 index single-nucleotide polymorphisms (SNPs)]. Characterizing these risk regions in diverse racial groups with different linkage disequilibrium (LD) structure can help localize causal variants. We examined associations between CRC and all 29 index SNPs in 6597 African Americans (1894 cases and 4703 controls). Nine SNPs in eight regions (5q31.1, 6q26-q27, 8q23.3, 8q24.21, 11q13.4, 15q13.3, 18q21.1 and 20p12.3) formally replicated in our data with one-sided P-values <0.05 and the same risk directions as reported previously. We performed fine-mapping of the 21 risk regions (including 250 kb on both sides of the index SNPs) using genotyped and imputed markers at the density of the 1000 Genomes Project to search for additional or more predictive risk markers. Among the SNPs correlated with the index variants, two markers, rs12759486 (or rs7547751, a putative functional variant in perfect LD with it) in 1q41 and rs7252505 in 19q13.1, were more strongly and statistically significantly associated with CRC (P < 0.0006). The average per allele risk was improved using the replicated index variants and the two new markers (odds ratio = 1.14, P = 6.5 × 10(-16)) in African Americans, compared with using all index SNPs (odds ratio = 1.07, P = 3.4 × 10(-10)). The contribution of the two new risk SNPs to CRC heritability was estimated to be 1.5% in African Americans. This study highlights the importance of fine-mapping in diverse populations.

A genome-wide association study for colorectal cancer identifies a risk locus in 14q23.1.

Over 50 loci associated with colorectal cancer (CRC) have been uncovered by genome-wide association studies (GWAS). Identifying additional loci has the potential to help elucidate aspects of the underlying biological processes leading to better understanding of the pathogenesis of the disease. We re-evaluated a GWAS by excluding controls that have family history of CRC or personal history of colorectal polyps, as we hypothesized that their inclusion reduces power to detect associations. This is supported empirically and through simulations. Two-phase GWAS analysis was performed in a total of 16,517 cases and 14,487 controls. We identified rs17094983, a SNP associated with risk of CRC [p = 2.5 × 10(-10); odds ratio estimated by re-including all controls (OR) = 0.87, 95% confidence interval (CI) 0.83-0.91; minor allele frequency (MAF) = 13%]. Results were replicated in samples of African descent (1894 cases and 4703 controls; p = 0.01; OR = 0.86, 95% CI 0.77-0.97; MAF = 16 %). Gene expression data in 195 colon adenocarcinomas and 59 normal colon tissues from two different studies revealed that this locus has genotypes that are associated with RTN1 (Reticulon 1) expression (p = 0.001), a protein-coding gene involved in survival and proliferation of cancer cells which is highly expressed in normal colon tissues but has significantly reduced expression in tumor cells (p = 1.3 × 10(-8)).

Genetic variation in prostaglandin synthesis and related pathways, NSAID use and colorectal cancer risk in the Colon Cancer Family Registry.

Although use of non-steroidal anti-inflammatory drugs (NSAIDs) generally decreases colorectal cancer (CRC) risk, inherited genetic variation in inflammatory pathways may alter their potential as preventive agents. We investigated whether variation in prostaglandin synthesis and related pathways influences CRC risk in the Colon Cancer Family Registry by examining associations between 192 single nucleotide polymorphisms (SNPs) and two variable nucleotide tandem repeats (VNTRs) within 17 candidate genes and CRC risk. We further assessed interactions between these polymorphisms and NSAID use on CRC risk. Using a case-unaffected-sibling-control design, this study included 1621 primary invasive CRC cases and 2592 sibling controls among Caucasian men and women aged 18-90. After adjustment for multiple comparisons, two intronic SNPs were associated with rectal cancer risk: rs11571364 in ALOX12 [OR(het/hzv) = 1.87, 95% confidence interval (CI) = 1.19-2.95, P = 0.03] and rs45525634 in PTGER2 (OR(het/hzv) = 0.49, 95% CI = 0.29-0.82, P = 0.03). Additionally, there was an interaction between NSAID use and the intronic SNP rs2920421 in ALOX12 on risk of CRC (P = 0.03); among those with heterozygous genotypes, risk was reduced for current NSAID users compared with never or former users (OR(het) = 0.60, 95% CI = 0.45-0.80), though not among those with homozygous wild-type or variant genotypes. The results of this study suggest that genetic variation in ALOX12 and PTGER2 may affect the risk of rectal cancer. In addition, this study suggests plausible interactions between NSAID use and variants in ALOX12 on CRC risk. These results may aid in the development of genetically targeted cancer prevention strategies with NSAIDs.

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.

Background: Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission. Methods: First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. Results: The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Conclusions: Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients.

BACKGROUND: To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever. METHODS: We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic. We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations. The patients were divided into the rejection and acceptance groups. Then we analyzed the rejected examination items in order to obtain the corresponding reasons. RESULTS: We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics. The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified. The average F1 score reached 0.94. A total of 58.4% of the patients accepted Xiaoyi's suggestions (acceptance group), and 41.6% refused (rejection group). Imaging examinations were rejected by most patients (46.7%). The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%). The difficulty of sampling was the main reason for rejecting routine tests (41.9%). CONCLUSION: Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.

Prediction of osteoporosis and osteopenia by routine computed tomography of the lumbar spine in different regions of interest.

BACKGROUND: We aimed to investigate the utility of Hounsfield units (HU) obtained from different regions of interest in opportunistic lumbar computed tomography (CT) to predict osteoporosis coupling with data of dual-energy X-ray absorptiometry (DXA). METHODS: A total of 100 patients who attended a university hospital in Shanghai, China, and had undergone CT and DXA tests of the lumbar spine within 3 months were included in this retrospective review. Images were reviewed on axial sections, and regions of interest (ROI) markers were placed on the round, oval, anterior, left, and right of the L1-L4 vertebra to measure the HU. The mean values of CT HU were then compared to the bone mineral density (BMD) measured by DXA. Receiver operator characteristic curves were generated to determine the threshold for diagnosis and its sensitivity and specificity values. RESULTS: The differences in CT HU of different ROI based on DXA definitions of osteoporosis, osteopenia, and normal individuals were statistically significant (p < 0.01). The HU values of the different ROI correlated well with the BMD values (Spearman coefficient all > 0.75, p < 0.01). The threshold for diagnosing osteoporosis varies from 87 to 111 HU in different ROIs, and the threshold for excluding osteoporosis or osteopenia is 99-125 HU. CONCLUSION: This is the first study on osteoporosis diagnosis of different ROI with routine CT lumbar scans. There is a strong correlation between CT HU of different ROI in the lumbar spine and BMD, and HU measurements can be used to predict osteoporosis.

Automatic Detection of Secundum Atrial Septal Defect in Children Based on Color Doppler Echocardiographic Images Using Convolutional Neural Networks.

Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

Establishment of a Simple Pediatric Lower Respiratory Tract Infections Database Based on the Structured Electronic Medical Records.

Objective: This study aimed to establish a pediatric lower respiratory tract infections (PLRTIs) database based on the structured electronic medical records (SEMRs), to provide a brief overview and the usage process of the SEMRs and the database. Methods: All the medical information is recorded by a clinical information system developed by Eureka Systems Company. A plugin of the software was used to set the properties of items of the SEMR. Children with lower respiratory tract infections (LRTIs) who were admitted to the department of respiratory medicine of our hospital from May 2020 were included. PostgreSQL 13.1 software was used to construct the PLRTIs database. Results: Seven kinds of SEMRs were established, and the admission record was the most important and complex among them. It was mainly composed of 10 parts, i.e., basic information, history of present illness, past history (without respiratory disease), past history of respiratory diseases, personal history, family history, physical examination, the score of LRTIs, auxiliary examination, and diagnosis. With the three-level doctor ward round, the recorded information of the SEMR would be checked repeatedly, thus guaranteeing the correctness of the information. The data of the SEMR and laboratory tests could be extracted directly from the hospital information system (HIS) by PostgreSQL 13.1 software with the specific structured query language (SQL) code. After manually checking the original records, the datasets were imported into PostgreSQL 13.1 software, and a simple PLRTIs database was constructed. According to the inclusion criteria of this study, a total of 1,184 children with LRTIs were included in this database from 1 May 2020 to 30 April 2021. Conclusion: A series of SEMRs for PLRTIs were designed and used during the hospitalization. A simple PLRTIs database was established based on the SEMR. The SEMRs will provide complete and high-quality data on LRTIs in children.

Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial.

Introduction: Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction. Methods: We developed an artificial intelligence (AI)-assisted program named Smart-doctor. A randomized controlled trial was conducted at Shanghai Children's Medical Center. Participants were randomly divided into an AI-assisted and conventional group. Smart-doctor was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used. Results: We enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group (p < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, p < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, p < 0.01). The overall satisfaction score was increased by 17.53% (p < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group (p < 0.01), and missing the turn (p < 0.01). Conclusions: Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

Genetic variation in the bioactivation pathway for polycyclic hydrocarbons and heterocyclic amines in relation to risk of colorectal neoplasia.

Animal work implicates chemical carcinogens, such as polycyclic aromatic hydrocarbons (PAHs) and heterocyclic aromatic amines (HAAs) as contributing to the development of colorectal cancer (CRC). The epidemiologic evidence, however, remains inconsistent possibly due to intra-individual variation in bioactivation of these compounds. We conducted a case-control study of colorectal adenoma (914 cases, 1185 controls) and CRC (496 cases, 607 controls) among Japanese Americans, European Americans and Native Hawaiians to investigate the association of genetic variation in the PAH and HAA bioactivation pathway (CYP1A1, CYP1A2, CYP1B1, AHR and ARNT) identified through sequencing with risk of colorectal neoplasia, as well as their interactions with smoking and intakes of red meat and HAAs. The A allele for ARNT rs12410394 was significantly inversely associated with CRC [odds ratios (ORs) and 95% confidence intervals (CIs) for GG, AG and AA genotypes: 1.00, 0.66 (0.48-0.89), 0.54 (0.37-0.78), P(trend) = 0.0008] after multiple comparison adjustment. CYP1A2 rs11072508 was marginally significantly associated with CRC, where each copy of the T allele was associated with reduced risk (OR: 0.72, 95% CI: 0.58-0.88, P(trend) = 0.0017). No heterogeneity of genetic effects across racial/ethnic groups was detected. In addition, no significant interaction was observed after adjusting for multiple testing between genetic variants and pack-years of smoking, intake of red meat or HAAs (PhIP, MeIQx, Di-MeIQx or total HAAs) or NAT2 genotype (Rapid versus Slow or Intermediate). This study suggests that the genomic region around ARNT rs12410394 may harbor variants associated with CRC.

Association of plasma vitamin B6 with risk of colorectal adenoma in a multiethnic case-control study.

Circulating level of vitamin B6 has been inversely associated with colorectal cancer (CRC) risk but, unlike for folate, few studies have examined the relationship of vitamin B6 to colorectal adenoma, the precursor lesion to most CRCs. We measured plasma levels of folate, vitamin B6, and vitamin B12 in 241 patients with pathologically confirmed first occurrence of colorectal adenoma and 280 controls among Caucasians, Japanese Americans, and Native Hawaiians undergoing flexible sigmoidoscopy screening in Hawaii. High plasma level of vitamin B6 was independently inversely associated with risk of colorectal adenoma [multivariate odds ratios (95% confidence intervals): 1.0, 0.71 (0.45-1.13) and 0.44 (0.26-0.74) from the lowest to the highest tertile, respectively, p (trend) = 0.002]. Plasma folate was not associated with adenoma after adjustment for plasma vitamin B6 (p (trend) > 0.3). No association was observed with plasma vitamin B12. No significant interaction was detected between the three B vitamins and alcohol intake, multivitamin use or MTHFR C677T. The results provide evidence for an inverse association of plasma vitamin B6 levels with risk of colorectal adenoma. This study expands previous findings and suggests that vitamin B6 may be protective against the early stages of colorectal carcinogenesis.

Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study.

OBJECTIVES: The purpose of this article was to establish and validate clinically applicable septic shock early warning model (SSEW model) that can identify septic shock in hospitalized children with onco-hematological malignancies accompanied with fever or neutropenia. METHODS: Data from EMRs were collected from hospitalized pediatric patients with hematological and oncological disease at Shanghai Children's Medical Center. Medical records of patients (>30 days and <19 years old) with fever (≥38°C) or absolute neutrophil count (ANC) below 1.0 × 109/L hospitalized with hematological or oncological disease between January 1, 2017 and August 1, 2019 were considered. Patients in whom septic shock was diagnosed during the observation period formed the septic shock group, whereas non-septic-shock group was the control group. In the septic shock group, the time points at 4, 8, 12, and 24 hours prior to septic shock were taken as observation points, and corresponding observation points were obtained in the control group after matching. We employed machine learning artificial intelligence (AI) to filter features and used XGBoost algorithm to build SSEW model. Area under the ROC curve (AU-ROC) was used to compare the effectiveness among the SSEW Model, logistic regression model, and pediatric sequential organ failure score (pSOFA) for early warning of septic shock. MAIN RESULTS: A total of 64 observation periods in the septic shock group and 2191 in the control group were included. AU-ROC of the SSEW model had higher predictive value for septic shock compared with the pSOFA score (0.93 vs. 0.76, Z = -2.73, P = 0.006). Further analysis showed that the AU-ROC of the SSEW model was superior to the pSOFA score at the observation points 4, 8, 12, and 24 h before septic shock. At the 24 h observation point, the SSEW model incorporated 14 module root features and 23 derived features. CONCLUSION: The SSEW model for hematological or oncological pediatric patients could help clinicians to predict the risk of septic shock in patients with fever or neutropenia 24 h in advance. Further prospective studies on clinical application scenarios are needed to determine the clinical utility of this AI model.

LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram.

Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunableQ-factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem.Main results. Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved theSensitivity, Se,Specificity, SpandScoreof 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved theScoreby 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method.Significance. Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice.

Development and Evaluation of a Leukemia Diagnosis System Using Deep Learning in Real Clinical Scenarios.

Leukemia is the most common malignancy affecting children. The morphologic analysis of bone marrow smears is an important initial step for diagnosis. Recent publications demonstrated that artificial intelligence is able to classify blood cells but a long way from clinical use. A total of 1,732 bone marrow images were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated and an end-to-end leukemia diagnosis system was developed by using raw images without pre-processing. The system creatively imitated the workflow of a hematologist by detecting and excluding uncountable and crushed cells, then classifying and counting the remain cells to make a diagnosis. The performance of the CNN in classifying WBCs achieved an accuracy of 82.93%, precision of 86.07% and F1 score of 82.02%. And the performance in diagnosing acute lymphoid leukemia achieved an accuracy of 89%, sensitivity of 86% and specificity of 95%. The system also performs well at detecting the bone marrow metastasis of lymphoma and neuroblastoma, achieving an average accuracy of 82.93%. This is the first study which included a wider variety of cell types in leukemia diagnosis, and achieved a relatively high performance in real clinical scenarios.

The Associations of Caesarean Delivery With Risk of Wheezing Diseases and Changes of T Cells in Children.

Objectives: This study aimed to assess the associations of caesarean delivery (CD) with risk of wheezing diseases and changes of immune cells in children. Design: The cross-sectional study was conducted between May, 2020 and April, 2021. Setting and participants: The study was conducted in Shanghai Children's Medical Center, Shanghai, China. A total of 2079 children with a mean age of 36.97 ± 40.27 months and their guardians were included in the present study via face-to-face inquiry and physical examination by clinicians. Methods: Logistic regression was applied to estimate odds ratio (ORs) and 95% confidence intervals (CIs) for the association between CD and first episode of wheezing (FEW) or asthma. Models were adjusted for premature or full-term delivery, exclusive breastfeeding (at least 4 months) or not. Results: Among the 2079 children, 987 children (47.47%) were born by CD and 1092 (52.53%) by vaginal delivery (VD). Children delivered by caesarean had significantly lower gestational age (P<0.01) compared with those who delivered vaginally. Our results also showed that CD was related to increased risk of FEW by the age of 3(adjusted OR 1.50, 95%CI 1.06, 2.12) and increased tendency to develop asthma by the age of 4 (adjusted OR 3.16, 95%CI 1.25, 9.01). The subgroup analysis revealed that the negative effects of CD on asthma were more obvious in children without exclusive breastfeeding (adjusted OR 4.93, 95%CI 1.53, 21.96) or without postnatal smoking exposure (adjusted OR 3.58, 95%CI 1.20, 13.13). Furthermore, compared with children born through VD, a significant change of the T cells (increased proportion of CD4+ T cells and decreased number and proportion of CD8+ T cells) were observed before the age of one in the CD group. However, the changes were insignificant in children over 1 year old. Conclusions: This study showed age-dependent associations of CD with asthma and FEW in offspring. Moreover, CD appeared to have an effect on the cellular immunity in infants, the disorder of which may contribute to the development of asthma in children.