Serum calcium level is associated with brachial-ankle pulse wave velocity in middle-aged and elderly Chinese.

OBJECTIVE: To study the relation between serum calcium level and elevated BaPWV in Chinese subjects. METHODS: The relation between serum calcium level and elevated BaPWV was studied in 9 615 subjects. The mean value of left and right BaPWV was analyzed. BaPWV was defined as high when it was ⋝1752.5 cm/s (the upper quartile) either side. RESULTS: The BaPWV and its elevated percentage progressively increased across the quartiles of the serum calcium level (P<0.05). The prevalence of elevated BaPWV was significantly higher in subjects of the second, third and highest quartiles than in those of the lowest quartile (26.9%, 28.4%, and 33.2% vs 23.7%, P=0.0116, P=0.0004, and P<0.0001). Logistic regression analysis revealed that the risk of elevated BaPWV was 1.32- fold higher in subjects of the highest quartile than in those of the lowest quartile (OR=1.32, 95% CI: 1.08-1.60). CONCLUSION: The elevated serum calcium level is related to an elevated BaPWV and a higher risk of arterial stiffness, independent of conventional risk factors, in middle-aged and elderly Chinese subjects.

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.

Pre-Consultation System Based on the Artificial Intelligence Has a Better Diagnostic Performance Than the Physicians in the Outpatient Department of Pediatrics.

Artificial intelligence (AI) has been deeply applied in the medical field and has shown broad application prospects. Pre-consultation system is an important supplement to the traditional face-to-face consultation. The combination of the AI and the pre-consultation system can help to raise the efficiency of the clinical work. However, it is still challenging for the AI to analyze and process the complicated electronic health record (EHR) data. Our pre-consultation system uses an automated natural language processing (NLP) system to communicate with the patients through the mobile terminals, applying the deep learning (DL) techniques to extract the symptomatic information, and finally outputs the structured electronic medical records. From November 2019 to May 2020, a total of 2,648 pediatric patients used our model to provide their medical history and get the primary diagnosis before visiting the physicians in the outpatient department of the Shanghai Children's Medical Center. Our task is to evaluate the ability of the AI and doctors to obtain the primary diagnosis and to analyze the effect of the consistency between the medical history described by our model and the physicians on the diagnostic performance. The results showed that if we do not consider whether the medical history recorded by the AI and doctors was consistent or not, our model performed worse compared to the physicians and had a lower average F1 score (0.825 vs. 0.912). However, when the chief complaint or the history of present illness described by the AI and doctors was consistent, our model had a higher average F1 score and was closer to the doctors. Finally, when the AI had the same diagnostic conditions with doctors, our model achieved a higher average F1 score (0.931) compared to the physicians (0.92). This study demonstrated that our model could obtain a more structured medical history and had a good diagnostic logic, which would help to improve the diagnostic accuracy of the outpatient doctors and reduce the misdiagnosis and missed diagnosis. But, our model still needs a good deal of training to obtain more accurate symptomatic information.

Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children.

Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases. Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated. Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups. Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.

Management in the paediatric wards facing novel coronavirus infection: a rapid review of guidelines and consensuses.

OBJECTIVES: Relevant guidelines and consensuses for COVID-19 contain recommendations aimed at optimising the management in paediatric wards. The goal of this study was to determine the quality of those recommendations and provide suggestions to hospital managers for the adjustment of existing hospital prevention and control strategies, and also to offer recommendations for further research. DESIGN: A rapid review of the guidelines and consensuses for the management in paediatric wards facing COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, UpToDate, China National Knowledge Infrastructure, the Wanfang database and relevant websites such as medlive.cn, dxy.cn, the National Health and Health Commission and the China Center for Disease Control and Prevention were systematically searched through late May 2020. The Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool was then used to assess the quality of the selected articles and summarise the relevant evidence concerning management in paediatric wards. RESULTS: A total of 35 articles were included, composed of 3 consensus guidelines, 25 expert consensuses and 7 expert opinions. Of the 35 papers, 24 were from China, 2 from the USA, 1 from Spain, 1 from Brazil, 1 from Saudi Arabia and 6 from multinational cooperative studies. Scores for the six domains of the AGREE II tool (scope and purpose, stakeholder involvement, rigour of development, clarity of presentation, applicability and editorial independence) were 98.57%, 53.57%, 17.92%, 69.62%, 26.96% and 50.35%, respectively. Recommendations for nosocomial infection and control, human resource management as well as management of paediatric patients and their families were summarised. CONCLUSIONS: Due to the outbreak of COVID-19, the quality of rapid guidelines and consensuses for the management in paediatric wards affected by COVID-19 is unsatisfactory. In the future, it will be necessary to develop more high-quality guidelines or consensuses for the management in paediatric wards to deal with nosocomial outbreaks in order to fully prepare for emergency medical and health problems.

A 2-way cross-over, open-labeled trial to compare efficacy and safety of insulin Aspart and Novolin R delivered with CSII in 21 Chinese diabetic patients.

BACKGROUND: Subcutaneous absorption is accelerated by the monomeric conformation of insulin Aspart, which provides good glycemic control with a lower risk of hypoglycemia and less body weight increase. In the present study we investigated the efficacy and safety of a rapid-acting human insulin analogue (insulin Aspart) delivered with continuous subcutaneous insulin infusion (CSII) into Chinese diabetic patients. METHODS: A total of 21 patients with type 1 or type 2 diabetes were recruited for the 2-way cross-over, open-labeled trial, and then randomized to Group A (n = 10, treated with insulin Aspart) or Group B (n = 11, treated with Novolin R). Insulin Aspart and Novolin R were administered by CSII. Capillary glucose concentrations were measured at 8 time points, pre-prandial and postprandial, bedtime (10 pm), midnight (2 am) every day during the treatment. RESULTS: The average capillary glucose profiles for the day were much better controlled in Group A than in Group B (P < 0.01). The blood glucose levels were particularly better controlled in Group A than in Group B at pre-breakfast ((6.72 +/- 1.24) mmol/L vs (7.84 +/- 1.58) mmol/L, P = 0.014), post-breakfast ((8.96 +/- 2.41) mmol/L vs (11.70 +/- 3.11) mmol/L, P = 0.0028), post-supper ((8.15 +/- 2.10) mmol/L vs (10.07 +/- 2.36) mmol/L, P = 0.008), bed time ((7.73 +/- 1.72) mmol/L vs (9.39 +/- 2.05) mmol/L, P = 0.007) and midnight ((6.32 +/- 1.16) mmol/L vs (7.48 +/- 1.36) mmol/L, P = 0.0049). There was no significant difference in the frequency of hypoglycemic episodes between the two groups. CONCLUSION: Insulin Aspart results in better control of blood glucose levels than regular human insulin (Novolin R) in diabetic patients during delivery by CSII.