Introduction: Intracytoplasmic sperm injection (ICSI), originally introduced as add-on to in vitro fertilisation (IVF) for couples with severe male infertility, is in current clinical practice also used in couples with mild male or even unexplained infertility. However, ICSI has involved unresolved concerns regarding the selection and damage to gametes and the health conditions of the offspring, and it is also labour intensive and therefore more expensive than conventional IVF. High-quality well-powered randomised clinical trials (RCTs) comparing ICSI and IVF are lacking. Methods and analysis: We propose a multicentre, open-label RCT in 10 reproductive medical centres across China. We will study couples with non-severe male infertility (defined as a semen concentrate 5–15×10 ⁶ /mL or sperm with a progressive motility 10%–32%) scheduled for their first or second ICSI or IVF cycle, as low fertility rate after fertilisation are more frequent in this population, which could lead to controversy about ICSI or conventional IVF for fertilisation. On the day of oocyte retrieval, eligible participants are after informed consent be randomised to undergo either ICSI or conventional IVF in a 1:1 treatment ratio. Other standard assisted reproductive treatments are similar and parallel between two groups. Our primary outcome is ongoing pregnancy leading to live birth after the first cycle with embryo transfer. To demonstrate or refute a difference of 7% between ICSI and conventional IVF, we need to include 2346 women (1173 in each intervention arm). In addition, we will follow-up neonatal outcomes after delivery to identify the influence of ICSI on offspring. Ethics and dissemination: Ethical approval was obtained from Peking University Third Hospital medical science research ethics committee. The findings will be disseminated to the public through conference presentations and peer-reviewed scientific journals. Trial registration number: ClinicalTrials.gov registry ( NCT03298633 ).

Objectives There is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals’ experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context. Setting Four tertiary hospitals in Beijing, China. Participants Nineteen healthcare professionals with experience in using EHR data for clinical research participated in the study. Methods A qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach. Results The main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation. Conclusions These results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.

Background: Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. Objective: The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. Methods: This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. Results: The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis. Conclusions: The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

Introduction Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine. Brace treatment is effective for eligible patients with AIS and the effectiveness is significantly correlated with the average brace-wear time per day. Three-dimensional (3D) printing technology is a recent advancement that offers unique opportunities for biomedical applications, and customisation of scoliosis braces might lead to greater patient satisfaction and improved compliance. We present here the design of a randomised controlled trial on the clinical effectiveness of 3D-printed braces versus thoracolumbosacral orthoses (TLSO) for patients with AIS. Methods and analysis Patients with AIS (age 10–16 years) with Risser sign 0-II, Cobb angle of main curve of 20°−40°, premenarchal or no more than 1-year postmenarchal (for women), and no history of treatment are eligible, unless they are unable to tolerate the treatment or refuse participation. A total of 88 patients will be randomised into either the 3D group or TLSO group on a 1:1 basis. Participants in the 3D group will choose between a 3D-printed brace and TLSO, according to the Zelen’s design of the trial. Primary outcome measures will include the average brace-wear time per day, health-related quality of life and Cobb angle progression of the primary curve. Secondary outcome measures will include immediate in-brace correction of Cobb angle of the primary curve, rate of conversion to surgery and incidence of any adverse events. This study is designed as a single-centre, two-arm, superiority and open-label randomised controlled trial. The sample size is calculated with reference to the preliminary study and based on the sample size calculation formula. Ethics and dissemination This study was approved by the Peking University Third Hospital Medicine Science Research Ethics Committee (No: 2019-017-02). Results of the trial will be submitted for publication in a peer-reviewed journal and as conference presentations. Trial registration number ChiCTR1900027379, pre-results.