ObjectiveTo systematically review the effectiveness of breastfeeding duration and intensity in reducing the risk of overweight or obesity among offspring exposed to intrauterine hyperglycemia. MethodsThe PubMed, EMbase, Web of Science, CBM, WanFang Data, CNKI and VIP databases were electronically searched to collect observational studies on the associations of breastfeeding with the risk of overweight or obesity among offspring exposed to intrauterine hyperglycemia from inception to September 25th, 2021. Two reviewers independently screened the literature, extracted data and assessed the risk of bias of the included studies. Stata 16.0 software was used for the meta-analysis. ResultsA total of 12 657 participants from 13 observational studies were included. The results of meta-analysis showed that breastfeeding could reduce the risk of overweight or obesity among offspring exposed to intrauterine hyperglycemia (OR=0.67, 95%CI 0.53 to 0.84, P=0.001). Subgroup analysis revealed a protective effect of breastfeeding for both 1-6 months (OR=0.53, 95%CI 0.37 to 0.75, P<0.001) and ≥6 months (OR=0.56, 95%CI 0.46 to 0.69, P<0.001); however, breastfeeding shorter than one month was suggested to increase the risk of overweight or obesity (OR=2.15, 95%CI 1.41 to 3.27, P<0.001). ConclusionAvailable evidence suggests that breastfeeding for more than one month is effective in reducing the risk of overweight or obesity in offspring exposed to intrauterine hyperglycemia, and women with hyperglycemia should be encouraged to breastfeed their offspring for at least 1 month to achieve the effect. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
Objective To systematically review the association between periodontal disease during pregnancy and the risk of gestational diabetes mellitus (GDM). Methods PubMed, Web of Science, CBM and CNKI databases were electronically searched to collect studies on periodontal disease and GDM from inception to October 23, 2021. Two researchers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 11 studies were included, involving 2 910 pregnant women. The results of meta-analysis showed that pregnant women with periodontal disease during pregnancy reported more GDM than normal pregnant women (OR=1.81, 95%CI 1.31 to 2.50, P=0.000 3). Conclusion The current evidence suggests that there is a positive association between periodontal disease during pregnancy and the risk of GDM. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
The level of evidence in randomized controlled studies is high. However, it cannot be widely applied due to its high cost, external authenticity, ethics and other reasons. The traditional observational studies reduce the internal authenticity due to various confounding factors, and the level of evidence is low. Regression discontinuity design (RDD) is a design that observes and compares outcome of object around the threshold under practical clinical conditions. Its capability to adjust confounding factors is second only to that of randomized control studies. It can be used in cases where the intervention (or exposure) is directly related to the value of a continuous variable. For instance, whether an HIV patient needs antiretroviral treatment mainly depends on whether the CD4 cell count is lower than 200/μL. Because the measurement of continuous variables has random error, whether intervention is given near the threshold or is close to random, the baseline of patients in the intervention group and non-intervention group near the threshold should be balanced and comparable. Based on this assumption, the causal effect of intervention (or exposure) and outcome can be estimated by comparing the outcomes of populations near the threshold. RDD is mainly applicable to the study of classification outcomes in medicine, among which two-stage least square method, likelihood ratio based estimation method and Bayesian method are more commonly used model estimation methods. However, the application conditions of RDD and the requirement of sample size limit its extensive application in medicine. With the improvement of data accessibility and the development of real world research, RDD will be more widely used in clinical research.
Compared with traditional clinical trials, the real-world studies set higher requirements on the authenticity (reality), applicability, and timeliness of the evidence obtained. In this paper, we brought up a hypothesis that creating synergies between observational and experimental studies may meet these requirements. And then it was discussed in three aspects including providing evidence, research design and execution. In addition, data analysis facilitated generating efficient and robust evidence which was in support of decision making. Finally, some enlightenment may be offered for Traditional Chinese Medicine evaluation methods based on the synergies of both study types.
Observational studies based on real-world data are providing increasing amount of evidence for evaluating therapeutic outcomes, which is important for timely decision-making. Although time and costs for data collection could be saved using real-world data, it is significantly more complex to design real world researches with lower risk of bias. In order to enhance the validity of causal inference and to reduce potential risk of bias in real world studies, the Working Group of China Real world data and studies Alliance (China REAL) has formulated recommendations for designing observational studies to evaluate therapeutic outcomes based on real-world data. This guidance introduces design types commonly used in real world research; recommends key elements to consider in observational studies, including sample selection, specifying and allocating exposures, defining study entry and endpoints, and pre-designing statistical analysis protocols; and summarizes potential biases and corresponding control measures in real-world studies. These recommendations introduces key elements in designing observational studies using real-world data, for the purpose of improving the validity of causal inference. However, the application scope of these recommendations may be limited and warrant constant improvement.
Propensity score methods belong to an analytical approach by incorporating the measured covariates and mimicking randomization to enhance the comparability between groups, hence reducing the impact of potential confounding in observational studies. Propensity score methods have been increasingly used in observational studies. This paper illustrates the principle and the methods based on the propensity score, in combination with its application in observational studies. It also compares results from propensity score methods with those from multivariable regression and randomized controlled trials. It was found that currently there has been a lack of recommendations for the selection of propensity score methods. Differences may exist when comparing results from propensity score methods with findings from typical regression analyses and randomized controlled trials.
ObjectiveTo assess whether pre-operative use of infliximab (IFX) will increase the risk of post-operative infectious complications in patients with inflammatory bowel disease (IBD). MethodsPubmed, Web of Science, CBM, CNKI and Wanfang database were searched for all the trials that investigated the effects of infliximab on postoperative infectious complication rates in patients with IBD between January 1990 and April 2013. Two reviewers independently screened the literature according to the inclusion and exclusion criteria, extracted data and assessed the quality of the included studies. Then, meta-analysis was performed using RevMan 5.1 software. ResultsTotally, 14 cohort studies were finally included in the review. There was no significant difference on infectious complications [RR=0.99, 95%CI (0.47, 2.07), P=0.97] between IFX groups and control groups with ulcerative colitis. The same results were found in patients with Crohn's disease on infectious complications [RR=1.32, 95%CI (0.87, 1.98), P=0.19]. ConclusionPre-operative infliximab use is safe and does not increase the risk of post-operative infectious complications in patients with IBD.
Randomized controlled trial (RCT) are considered the "gold standard" for evaluating the causal effects of interventions on outcome measures. However, due to high research costs and ethical constraints, conducting RCT in clinical practice, especially in the surgical field, faces numerous challenges such as difficulties in subject recruitment, implementation of blinding, and standardization of interventions. In such cases, using real-world data to perform causal inference under the framework of target trial emulation (TTE), based on the principles of RCT design, helps to identify and reduce biases arising from design flaws in traditional observational studies, such as immortal time bias, confounding, selection bias, or collider bias. This approach can produce high-quality evidence comparable to that of RCT, thereby enhancing the clinical guidance value of real-world data studies. However, TTE has limitations, such as the inability to completely eliminate confounding, high quality requirements for source data, and the current lack of reporting standards. Therefore, researchers should be fully aware of these limitations to avoid making incorrect causal inferences. This article intends to provide an overview of the TTE framework, implementation points, application scope, application cases, and advantages and disadvantages of the framework.
证据质量升级的最常见原因是效应量大。当方法学严谨的观察性研究表明风险至少降低或增加2倍时，GRADE建议考虑将证据质量升高1级；当风险至少降低或增加5倍时，考虑将证据质量升高2级。当存在剂量-反应关系，或所有合理的混杂、偏倚会降低明显的治疗效应，或混杂、偏倚使得结果无效为假效应时，系统评价作者和指南制定者也可考虑升高证据质量。其他考虑因素包括起效迅速、潜在的疾病（状态）趋势以及间接证据。
Randomized controlled trials are considered as the gold standard for determining the causality, and are usually used to evaluate the efficacy and safety of medical interventions. However, in some cases it is not feasible to conduct a randomized controlled trial. In recent years, a framework called “target trial emulation study” has been formally established to guide the design and analysis of observational studies based on real-world data. This framework provides an effective method for causal inference based on observational studies. In order to facilitate domestic scholars to understand and apply the framework to solve related clinical problems, this article introduces it from the basic concept, framework structure and implementation steps, development status, and prospects.