1 Introduction
Gastroparesis is defined as a delay in the emptying of ingested food in the absence of obvious structural abnormalities of the stomach or duodenum.[1] [2] [3]
The DG can manifest in a variety of symptoms, including early satiety, chronic nausea and vomiting, anorexia, abdominal distention and pain, postprandial fullness.[4] [5,6]
In many patients with gastroparesis, unstable postprandial glucose concentrations result in amplitudes from hypoglycemia to severe hyperglycemia and even ketoacidosis.[7] [8]
The DG is primarily a disease of gastric enteric neurons and interstitial cells of Cajal.[9,10] [9,11,12] [13]
Stabilize diabetes control are the basal managements for DG. Meanwhile, there are several therapies have been used to improve the rate of gastric emptying for treatment of DG, they are including medicine therapies, electric therapies, endoscopic therapies, and diet therapies. Medicine therapies encompass prokinetic therapy (Macrolides, substituted Benzamides) and antinauseant therapy (Serotonin Antagonists, phenothiazines, Butyrophenones, Benzodiazepines). Prokinetic agents are applied to ameliorate the rate of gastric emptying have not improved the symptoms related to DG. Antiemetics are used as first-line therapy. Although these agents decrease symptoms and improve nutritional intake, their use is associated with adverse effects. Dietary modification is beneficial but infrequently followed.[14,15] [16,17]
Accumulating evidence shown that traditional Chinese medicine and acupuncture may be beneficial for treating DG, once the limited efficacy of the medicine therapies and when patients cannot tolerate their side effects. It has been demonstrated that traditional Chinese medicine and acupuncture can improve gastric motility and facilitate gastric emptying in human.[18–20]
Treatments in patients diagnosed as DG needed to decrease symptoms, improve quality of life, ensure enough nutritional intake, and control progression of DG. These signs and symptoms can be caused or even deteriorate by DG, but the current support therapies for DG are mainly controlling the consequences rather than tackling the causes of these problems. The multifactorial etiology of DG makes these problems are intractable by the clinicians.
2 Review aims
We plan to perform a systematic review and network meta-analysis to attempt to answer the following question: which is the best treatment for diabetic patients with gastroparesis, considering the improvement of symptoms, quality of life, nutritional intake, complications, and costs of the different therapies found. We will explore in this review as primary outcome parameter is symptom scoring and grading of severity by Patient Assessment of Upper Gastrointestinal Disorders Symptom Severity Index (PAGI-SYM) questionnaire[21] [22] [23,24] [25] [26] [27]
3 Why it is important to do this review
The DG is a common autonomic neuropathy which impacts on nutritional state and quality of life in diabetic patients, it also adversely affects glycemic control in diabetes. There still lacks an effective long-term therapy to treat DG, and a few remaining questions still need to be tackled, such as “what should be done when the side effects of current therapies are overwhelming to patients” or “what should be done when the symptoms are still unimproved after treating with current therapies” or “is there an alternative potential effective therapy to treat the causes of DG.” There still lack of evidences to answer these problems definitively. Several reviews, systematic reviews and meta-analysis are only compared 2 treatments at a time, not summarized a more comprehensive set of comparisons tackling the multiple treatments available. However, some studies described different current available interventions.[4,28,29]
4 Methods
The protocol of this systematic review and network meta-analysis will be written in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P)[30] systematic review will be written using the Preferred Reporting Items for Systematic Reviews Incorporating Network Meta-Analyses[31] https://inplasy.com/inplasy-2020-4-0176/
5 Criteria for selecting studies for this review
5.1 Types of participants
Studies that enrolled patients (from 18 years of age, regardless of gender and ethnicity) were diagnosed as diabetes with dyspeptic symptoms excluding gastric outlet obstruction or ulceration.
5.2 Interventions
According to the literature, treatments for DG are of wide variation and can be split into the following groups: medicine therapies: Macrolides, Substituted Benzamides, Serotonin Antagonists, phenothiazines, Antihistamines, Butyrophenones, Antidepressants, Benzodiazepines; endoscopic therapies: botulinum toxin A intrapyloric injections, balloon dilation of pylorus, radiofrequency ablation at lower esophageal sphincter; electric therapies: gastric electric stimulation, gastric pacing; diet therapies: gastroparesis diet, high-protein drinks, total parenteral nutrition; traditional Chinese medicine: Xiangshaliujunzi Decoction; acupuncture: electroacupuncture, scalp acupuncture, eye acupuncture, abdomen acupuncture, ear acupuncture; surgical therapies (laparoscopic pyloroplasty, gastrectomy, antrectomy, transpyloric stenting, gastric per-oral endoscopic myotomy, pancreatic transplantation); and others: alternative or support therapies. Any study evaluating any of the treatments listed above will be kept for inclusion in this review. Analysis will be performed at therapy/group level.
5.3 Types of studies
We will include RCTs and CCTs. Review papers, expert opinions, case reports, and series of case reports will be excluded. The literatures of relevant systematic reviews will be viewed to identify any studies missed by our literature search.
6 Information sources and literature search
6.1 Electronic searches
Comprehensive searches of the Cochrane Library, PubMed, Embase, Medline, Central and Web of Science, and 4 Chinese databases, including China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Chinese Biomedical Literature Database, and WanFang will be completed using the strategy described in Table 1 , adjusting different search methods according to different Chinese and English databases. Two independent authors will select the literatures. The search will include all the indexed papers, computerized literature databases supplemented by hand searching of reference lists from each relevant article identified.
Table 1: Example of PubMed search strategy.
6.2 Searching other resources
At the same time, we will retrieve other resources to complete the deficiencies of the electronic databases, mainly searching for the clinical trial registries and grey literature about treatments for DG on the corresponding website.
6.3 Study selection procedure
All titles and abstracts found will be independently read. After the searches, when found, the duplicates will be removed and the articles evaluated. The abstracts found in multiple searches to identify potentially eligible papers for containing will be also studied. Inconsistencies will be addressed by discussion among independent researchers. In case of missing data or information, authors will be contacted. The reviewers that will be enrolled in the searches are experienced in diabetes management, specialist clinicians, or methodologists in evidence-based medicine. PRISMA flow diagram (Fig. 1 ) will be used to show the screening process of the study.
Figure 1: Flow chart of the study selection. RCT = randomized controlled trial.
6.4 Data collection process
A standardized, electronic data collection form implemented in Microsoft Excel will be applied to extract the following data: study design, diagnosis, number of participants, types of therapies compared, patient demographics, outcome measures, results, risk of bias assessment, and study authors’ main conclusions. Two investigators will conduct data extraction independently.
6.5 Outcomes
Different outcomes will be considered in this review whenever available:
Primary effects: reduction of symptoms (through self-reported or detected by PAGI-SYM or GCSI), improvement of gastric emptying (through scintigraphy or radio-opaque markers or wireless capsule motility test or electrogastrography)
Secondary effects: the quality of life (through PAGI-QOL), the psychological measurements (through BDI and STAI)
Comorbidity (side effects or adverse events), costs, time span of the intervention (short-span and long-span outcomes can be evaluated separately)
Compliance (adherence to the intervention) with the different therapies
6.6 Heterogeneity assessment
The different manifestations of DG may be treated separately in the outcome analysis, considering the subgroup analyses and/or meta-regression.
6.7 Assessment of effectiveness
The tools used to verify the effectiveness of DG therapy are mostly PAGI-SYM or GCSI, scintigraphy or radio-opaque markers or wireless capsule motility test or electrogastrography, PAGI-QOL. Different evaluation methods are selected according to the different efficacy indicators.
6.8 GRADE assessment
The evidence will be interpreted according to the GRADE Working Group method for rating the quality of intervention effect evaluates from network meta-analysis. This approach is based on 4 steps considering direct and indirect intervention evaluations for each comparison of the evidence network, rating the quality of each direct and indirect effect assessment, rating the network meta-analysis evaluate for each comparison of the evidence network and quality of each network meta-analysis effect assessment.[32]
6.9 Risk of bias assessment
Studies will be evaluated for bias using the Cochrane risk of bias tool considering the judgment of the random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome estimation, incomplete outcome data, selective reporting, and other sources of bias as “Low risk” of bias, “High risk” of bias, or “Unclear risk” of bias.
6.10 Data synthesis
An overview of all selected researches will be narratively exhibited. Once data are obtained, a sheet will be made to tabulate data for the different outcomes. Classification according to the population and study characteristics and nature of the therapy will be made. Both traditional pairwise meta-analyses and network meta-analyses will be performed.
6.11 Standard pairwise meta-analysis
A random effects model will be used for all pairwise analyses when data are available. The heterogeneity will be assessed through the evaluation of the variance between researches (Chi-squared test and I 2 statistic). We will divide the treatment efficacy into significantly effective, effective, and ineffective, and the number of people who were in ineffective group will be used to calculate the odds ratios and corresponding 95% confidence intervals. Mean differences or standard mean difference between treatments may also be considered.
6.12 Network geometry
The network of therapies will be judged based on the available study data presented and assessed graphically. We will estimate if there is a sufficient number of comparisons in network with no available data, if there is a high number of comparisons based on single studies, if there are any “closed loops” which allow testing agreement between direct and indirect assessments for comparison on network, if any key interventions are missing, and if the possible lumping of therapies is minimizing the clinical relevance of the review. Next, the feasibility of a network meta-analysis will be evaluated. Interventions will possibly be lumped into eight groups a priori considering the type of therapy: medicine therapies, endoscopic therapies, electric therapies, diet therapies, traditional Chinese medicine, acupuncture, surgical treatments, and other alternative or support therapies.
6.13 Network meta-analysis
Network meta-analysis will be conducted using a Bayesian framework through the Winbugs software considering the random effects models, which use vague prior distributions for all therapy effects as well as the between-study variance parameter. The results of all pairwise comparisons will be reported as odds ratio and corresponding 95% credibility intervals. The median interventions rankings and the surface under the cumulative ranking curve will be displayed as well. Analyses will be conducted using Markov chain Monte Carlo approaches.
We will evaluate the convergence based on the Gelman Rubin diagnostics and inspection of Monte Carlo errors.[33] [34]
6.14 Sensitivity analysis
Sensitivity analysis is mainly used to assess the robustness of the primary outcome measures. The approach is that removing the low-level quality study one by one and then merging the data to evaluate the impact of sample size, study quality, statistical method, and missing data on results of network meta-analysis and traditional meta-analysis.
7 Discussion
The DG is an intractable complication of diabetes and increasingly being recognized as a significant health problem. The treatment of DG is difficult because of its multifactorial etiology and fluctuating symptoms. The efficacy of current treatments is limited. This network meta-analysis is performed to identify and evaluate therapies to treat DG. Although the comparison among therapies in a network meta-analysis is conducted based on direct comparisons of treatments and indirect comparisons based on a common measurement, some factors may affect the outcomes obtained from the analysis as number of articles included in the network; heterogeneity and inconsistency and several approaches have been applied to tackle those factors.[35,36]
In conclusion, this systematic review will compare current therapies for DG, and find the best treatment of DG. We hope this study may lead to some recommendations, for both patients and investigators, as which is the best treatment for a specific patient case and how future researches need to be designed, considering what is available now and what is the reality of the patient.
Author contributions
Conceptualization: Shengju Wang, Ruili Wang.
Data curation: Shengju Wang, Ruili Wang, Xu Zhang.
Formal analysis: Yanli Zhang, Baochao Cai, Yan Lu.
Funding acquisition: Yuguo Xia.
Investigation: Shengju Wang, Ruili Wang, Yuguo Xia, Qiu Chen.
Methodology: Shengju Wang, Ruili Wang, Xu Zhang.
Project administration: Yan Lu.
Resources: Shengju Wang, Ruili Wang, Yanli Zhang.
Software: Shengju Wang, Xu Zhang, Baochao Cai.
Supervision: Yuguo Xia, Qiu Chen.
Validation: Qiu Chen.
Writing – original draft: Shengju Wang, Ruili Wang.
Writing – review & editing: Yuguo Xia, Qiu Chen.
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