The use of medicinal plants in common ophthalmic disorders: A systematic review with meta-analysis

Purpose

This study aimed to assess and compile the available research articles about medicinal plants used for ocular diseases.

Principal results

A total of 2949 articles were retrieved, 35 full-text articles were assessed for eligibility, and seven studies (4 observational and three experimental) with low to moderate quality were eligible and involved in the systematic review, with a total of 600 plants from 4 countries. Among the 600 plants, only 24 (4%) were used to assess the status. Both the fixed and random models of the studies showed that the included studies tended to predict the results for the observational studies (OR = 0.062, CI = 0.043-0.090 OR = 0.039, CI = 0.012-0.122) for different plants used for ocular diseases. High heterogeneity (estimated as I² = 87.078, Tau² = 1.161 and Q-value = 23.217 with a p-value of 0.000), while for experimental studies (I² = 94.928, Tau² = 23.211 and Q-value = 39.434 with a p-value of 0.000) and publication bias were reported.

Conclusion

Few articles representing approximately 600 plants of low to moderate quality reported using medicinal plants for ocular diseases. The meta-analysis confirmed the systematic review findings regarding the plants' traditional use with high heterogeneity and publication bias. A considerable gap was proven in the use of medicinal plants in ocular diseases requiring intensive research.

Funnel plots a graphical instrument for the evaluation of population performance and quality of trauma care: a blueprint of implementation

BACKGROUND

Using patient outcomes to monitor medical centre performance has become an essential part of modern health care. However, classic league tables generally inflict stigmatization on centres rated as "poor performers", which has a negative effect on public trust and professional morale. In the present study, we aim to illustrate that funnel plots, including trends over time, can be used as a method to control the quality of data and to monitor and assure the quality of trauma care. Moreover, we aimed to present a set of regulations on how to interpret and act on underperformance or overperformance trends presented in funnel plots.

METHODS

A retrospective observational cohort study was performed using the Dutch National Trauma Registry (DNTR). Two separate datasets were created to assess the effects of healthy and multiple imputations to cope with missing values. Funnel plots displaying the performance of all trauma-receiving hospitals in 2020 were generated, and in-hospital mortality was used as the main indicator of centre performance. Indirect standardization was used to correct for differences in the types of cases. Comet plots were generated displaying the performance trends of two level-I trauma centres since 2017 and 2018.

RESULTS

Funnel plots based on data using healthy imputation for missing values can highlight centres lacking good data quality. A comet plot illustrates the performance trend over multiple years, which is more indicative of a centre's performance compared to a single measurement. Trends analysis offers the opportunity to closely monitor an individual centres' performance and direct evaluation of initiated improvement strategies.

CONCLUSION

This study describes the use of funnel and comet plots as a method to monitor and assure high-quality data and to evaluate trauma centre performance over multiple years. Moreover, this is the first study to provide a regulatory blueprint on how to interpret and act on the under- or overperformance of trauma centres. Further evaluations are needed to assess its functionality.

LEVEL OF EVIDENCE

Retrospective study, level III.

Outlier identification and monitoring of institutional or clinician performance: an overview of statistical methods and application to national audit data

BACKGROUND

Institutions or clinicians (units) are often compared according to a performance indicator such as in-hospital mortality. Several approaches have been proposed for the detection of outlying units, whose performance deviates from the overall performance.

METHODS

We provide an overview of three approaches commonly used to monitor institutional performances for outlier detection. These are the common-mean model, the 'Normal-Poisson' random effects model and the 'Logistic' random effects model. For the latter we also propose a visualisation technique. The common-mean model assumes that the underlying true performance of all units is equal and that any observed variation between units is due to chance. Even after applying case-mix adjustment, this assumption is often violated due to overdispersion and a post-hoc correction may need to be applied. The random effects models relax this assumption and explicitly allow the true performance to differ between units, thus offering a more flexible approach. We discuss the strengths and weaknesses of each approach and illustrate their application using audit data from England and Wales on Adult Cardiac Surgery (ACS) and Percutaneous Coronary Intervention (PCI).

RESULTS

In general, the overdispersion-corrected common-mean model and the random effects approaches produced similar p-values for the detection of outliers. For the ACS dataset (41 hospitals) three outliers were identified in total but only one was identified by all methods above. For the PCI dataset (88 hospitals), seven outliers were identified in total but only two were identified by all methods. The common-mean model uncorrected for overdispersion produced several more outliers. The reason for observing similar p-values for all three approaches could be attributed to the fact that the between-hospital variance was relatively small in both datasets, resulting only in a mild violation of the common-mean assumption; in this situation, the overdispersion correction worked well.

CONCLUSION

If the common-mean assumption is likely to hold, all three methods are appropriate to use for outlier detection and their results should be similar. Random effect methods may be the preferred approach when the common-mean assumption is likely to be violated.

Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients

Background

Mortality rates of coronavirus disease-2019 (COVID-19) continue to rise across the world. The impact of several risk factors on coronavirus mortality has been previously reported in several meta‐analyses limited by small sample sizes. In this systematic review, we aimed to summarize available findings on the association between comorbidities, complications, smoking status, obesity, gender, age and D-dimer, and risk of mortality from COVID-19 using a large dataset from a number of studies.

Method

Electronic databases including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till 31 August 2020. We included all human studies regardless of language, publication date or region. Forty-two studies with a total of 423,117 patients met the inclusion criteria. To pool the estimate, a mixed-effect model was used. Moreover, publication bias and sensitivity analysis were evaluated.

Results

The findings of the included studies were consistent in stating the contribution of comorbidities, gender, age, smoking status, obesity, acute kidney injury, and D-dimer as a risk factor to increase the requirement for advanced medical care. The analysis results showed that the pooled prevalence of mortality among hospitalized patients with COVID-19 was 17.62% (95% CI 14.26–21.57%, 42 studies and 423,117 patients). Older age has shown increased risk of mortality due to coronavirus and the pooled odds ratio (pOR) and hazard ratio (pHR) were 2.61 (95% CI 1.75–3.47) and 1.31 (95% CI 1.11–1.51), respectively. A significant association were found between COVID-19 mortality and male (pOR = 1.45; 95% CI 1.41–1.51; pHR = 1.24; 95% CI 1.07–1.41), and current smoker (pOR = 1.42; 95% CI 1.01–1.83). Furthermore, risk of mortality among hospitalized COVID-19 patients is highly influenced by patients with Chronic Obstructive Pulmonary Disease (COPD), Cardiovascular Disease (CVD), diabetes, hypertension, obese, cancer, acute kidney injury and increase D-dimer.

Conclusion

Chronic comorbidities, complications, and demographic variables including acute kidney injury, COPD, diabetes, hypertension, CVD, cancer, increased D-dimer, male gender, older age, current smoker, and obesity are clinical risk factors for a fatal outcome associated with coronavirus. The findings could be used for disease’s future research, control and prevention.

[Readmissions after lung resection in France: The PMSI database]

BACKGROUND

Readmission within 30 days is an indicator of the quality of care, because it often reflects post-discharge care that is not optimal. The objective of this work is to measure over time on the one hand the readmission rate and on the other hand the number of hospitals with a standardized readmission rate beyond the national average.

METHOD

All patients with major pulmonary resection for lung cancer in France were extracted from the PMSI national database. Readmission within 30 days was defined as any new hospitalization either in the same hospital or in another establishment.

RESULTS

From January 1, 2005 to December 31, 2018, 110,603 patients were included. The 30-day all-cause readmissions rate was 24.9% (n=27,540). Patients after pneumonectomy had a readmission rate of 37% (n=4918) and 23% after lobectomy (n=2684) (P<0.0001). For the first period, we counted 10 hospitals with a standardized readmissions rate above the 99.8 limit and 10 hospitals above the 95% limit. For the second period, 8 hospitals had a standardized readmission rate above the 99.8% limit and 11 hospitals above the 95% limit. For the third period, 7 hospitals had a standardized readmission rate above the 99.8% limit and 6 hospitals above the 95% limit.

CONCLUSION

Readmissions to hospital 30 days after major lung resection for cancer in France declined little during these three periods. Measures to prevent readmissions should be introduced.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

BACKGROUND

Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

METHODS

We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

RESULTS

We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

CONCLUSIONS

The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Funnel plots and choropleth maps in cancer risk communication: a Delphi study

BACKGROUND

In the last decades, the issues related to health risk communication to stakeholders and citizens involving health care practitioners and local political authorities have been increasingly debated. The study evaluated an alternative strategy to communicate cancer risk to local communities, involving an expert panel of public health operators in comparing two different graphic tools, Funnel Plot and Choropleth map.

STUDY DESIGN

A Delphi method process was implemented to achieve a unified consensus on an expert panel of public health operators with regard to weaknesses and strengths of the Funnel Plot and the Choropleth map as tools for cancer risk communication to local communities and other stakeholders.

METHODS

Participants were asked to score the efficacy of the two tools using a scale. Six properties were explored through two consecutive consensus rounds. Scales were used to calculate frequencies and the content validity ratio for each domain within the consensus rounds.

RESULTS

After the two consecutive rounds, participants expressed their preference in favour of the Choropleth map for its ability to define the spatial location of the risk and to locate any potential cluster, while reaching a consensus with regard to the Funnel Plot properties to identify hot spots, displaying the scope of the phenomenon under investigation, and to show the precision of estimates and communicating the significance of estimates.

CONCLUSIONS

The Delphi process allowed us to conclude that Funnel Plot could be used as a complement to the current and commonly used graphical and visual formats to effectively communicate cancer epidemiological data to communities and local authorities, representing a useful tool for empowering the general population.

Evaluation of the effectiveness of hip and knee implant models used in Catalonia: a protocol for a prospective registry-based study

BACKGROUND

Monitoring results regarding the effectiveness of knee and hip arthroplasties may be useful at the clinical, economic and patient level and help reduce the number of prosthesis revisions. In Spain, and specifically in Catalonia, there is currently no systematic monitoring of the different prosthesis models available on the market. Within this context, the aim of the project presented in this protocol is to evaluate the short- and medium-term effectiveness of knee and hip models implanted in Catalonia and to identify where the results could be better or worse than expected.

METHODS

A prospective observational design will be drawn up based on data from a population-based arthroplasty register for hip and knee replacements that includes data from 53 of the 61 public hospitals in Catalonia. The knee and hip prosthesis models used will be identified and classified according to the type of prosthesis, fixation and, in total hip replacements, the bearing surface. For the data analysis, two methodological approaches will be used sequentially: first, an approach based on a survival analysis, followed by an approach based on standardised revision ratios and funnel plots. Following the analyses, a panel of experts will evaluate the results to identify possible sources of bias. Lastly, those models with results better or worse than expected compared to those from the comparison group will be valued, and strengths and difficulties for routine implementation of this methodology within the Catalan Arthroplasty Register will be identified.

DISCUSSION

The study presented in this protocol will allow us to identify the hip and knee prosthesis models whose results might be better or worse than expected. This information could have a potential impact at the patient, orthopaedic surgeon, healthcare manager, decision-making and industry levels, both in the short term and in the medium and long term.

Funnel plots and choropleth maps in cancer risk communication: a comparison of tools for disseminating population-based incidence data to stakeholders

BACKGROUND

Population-based cancer registries provide epidemiological cancer information, but the indicators are often too complex to be interpreted by local authorities and communities, due to numeracy and literacy limitations. The aim of this paper is to compare the commonly used visual formats to funnel plots to enable local public health authorities and communities to access valid and understandable cancer incidence data obtained at the municipal level.

METHODS

A funnel plot representation of standardised incidence ratio (SIR) was generated for the 82 municipalities of the Palermo Province with the 2003-2011 data from the Palermo Province Cancer Registry (Sicily, Italy). The properties of the funnel plot and choropleth map methodologies were compared within the context of disseminating epidemiological data to stakeholders.

RESULTS

The SIRs of all the municipalities remained within the control limits, except for Palermo city area (SIR=1.12), which was sited outside the upper control limit line of 99.8%. The Palermo Province SIRs funnel plot representation was congruent with the choropleth map generated from the same data, but the former resulted more informative as shown by the comparisons of the weaknesses and strengths of the 2 visual formats.

CONCLUSIONS

Funnel plot should be used as a complementary valuable tool to communicate epidemiological data of cancer registries to communities and local authorities, visually conveying an efficient and simple way to interpret cancer incidence data.

Guidelines on constructing funnel plots for quality indicators: A case study on mortality in intensive care unit patients

Funnel plots are graphical tools to assess and compare clinical performance of a group of care professionals or care institutions on a quality indicator against a benchmark. Incorrect construction of funnel plots may lead to erroneous assessment and incorrect decisions potentially with severe consequences. We provide workflow-based guidance for data analysts on constructing funnel plots for the evaluation of binary quality indicators, expressed as proportions, risk-adjusted rates or standardised rates. Our guidelines assume the following steps: (1) defining policy level input; (2) checking the quality of models used for case-mix correction; (3) examining whether the number of observations per hospital is sufficient; (4) testing for overdispersion of the values of the quality indicator; (5) testing whether the values of quality indicators are associated with institutional characteristics; and (6) specifying how the funnel plot should be constructed. We illustrate our guidelines using data from the Dutch National Intensive Care Evaluation registry. We expect that our guidelines will be useful to data analysts preparing funnel plots and to registries, or other organisations publishing quality indicators. This is particularly true if these people and organisations wish to use standard operating procedures when constructing funnel plots, perhaps to comply with the demands of certification.

Weighing Evidence "Steampunk" Style via the Meta-Analyser

The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. We envisage that our application will be a useful tool for researchers when interpreting their data. First, to facilitate a simple understanding of fixed and random effects modeling approaches; second, to assess the importance of outliers; and third, to show the impact of adjusting for small study bias. This final aim is realized by introducing a novel graphical interpretation of the well-known method of Egger regression.

Contour plot assessment of existing meta-analyses confirms robust association of statin use and acute kidney injury risk

OBJECTIVES

Robustness of an existing meta-analysis can justify decisions on whether to conduct an additional study addressing the same research question. We illustrate the graphical assessment of the potential impact of an additional study on an existing meta-analysis using published data on statin use and the risk of acute kidney injury.

STUDY DESIGN AND SETTING

A previously proposed graphical augmentation approach is used to assess the sensitivity of the current test and heterogeneity statistics extracted from existing meta-analysis data. In addition, we extended the graphical augmentation approach to assess potential changes in the pooled effect estimate after updating a current meta-analysis and applied the three graphical contour definitions to data from meta-analyses on statin use and acute kidney injury risk.

RESULTS

In the considered example data, the pooled effect estimates and heterogeneity indices demonstrated to be considerably robust to the addition of a future study. Supportingly, for some previously inconclusive meta-analyses, a study update might yield statistically significant kidney injury risk increase associated with higher statin exposure.

CONCLUSIONS

The illustrated contour approach should become a standard tool for the assessment of the robustness of meta-analyses. It can guide decisions on whether to conduct additional studies addressing a relevant research question.

In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias

OBJECTIVE

To assess the utility of funnel plots in assessing publication bias (PB) in meta-analyses of proportion studies.

STUDY DESIGN AND SETTING

Meta-analysis simulation study and meta-analysis of published literature reporting peri-operative mortality after abdominal aortic aneurysm (AAA) repair. Data for the simulation study were stochastically generated. A literature search of Medline and Embase was performed to identify studies for inclusion in the published literature meta-analyses.

RESULTS

The simulation study demonstrated that conventionally constructed funnel plots (log odds vs. 1/standard error [1/SE]) for extreme proportional outcomes were asymmetric despite no PB. Alternative funnel plots constructed using study size rather than 1/SE showed no asymmetry for extreme proportional outcomes. When used in meta-analyses of the mortality of AAA repair, these alternative funnel plots highlighted the possibility for conventional funnel plots to demonstrate asymmetry when there was no evidence of PB.

CONCLUSION

Conventional funnel plots used to assess for potential PB in meta-analyses are inaccurate for meta-analyses of proportion studies with low proportion outcomes. Funnel plots of study size against log odds may be a more accurate way of assessing for PB in these studies.