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BACKGROUND@#Current guidelines recommend hepatocellular carcinoma (HCC) screening in high-risk populations. However, the ideal HCC screening interval and screening modality have not been determined. This study aimed to compare the screening efficacy among different modalities with various intervals.@*METHODS@#PubMed and other nine databases were searched through June 30, 2021. Binary outcomes were pooled using risk ratio (RR) with 95% confidence intervals (CIs). Survival rates were also pooled using RR with 95% CIs because most eligible studies only provided the number of survival patients instead of hazard ratio.@*RESULTS@#In all, 13 studies were included. Two random controlled trials (RCTs) and six cohort studies compared screening intervals for ultrasonography (US) screening and found no significant differences between shorter (3- or 4-month) and longer (6- or 12-month) screening intervals in terms of early HCC proportion, HCC significant mortality, 1-year survival rate; screening at 6-month interval significantly increased the proportion of early HCC (RR = 1.17, 95% confidence interval [CI]: 1.08-1.26) and prolonged the 5-year survival rate (RR = 1.39, 95% CI: 1.07-1.82) relative to the 12-month interval results. Three other RCTs and two cohort studies compared different screening modalities in cirrhosis or chronic hepatitis B, which indicated no statistical differences in the proportion of early HCC (RR = 0.89, 95% CI: 0.40-1.96) and HCC mortality (RR = 0.69, 95% CI: 0.23-2.09) between the biannual US and annual computed tomography (CT screening). Biannual US screening showed a lower proportion of early HCC than biannual magnetic resonance imaging (MRI) (RR = 0.60, 95% CI: 0.37-0.97) and biannual US combined with annual CT (RR = 1.31, 95% CI: 1.13-1.51) screening. The proportion of early HCC in the contrast-enhanced US group was slightly higher than that in the B-mode US (RR = 1.08, 95% CI: 1.00-1.23) group.@*CONCLUSIONS@#The evidence suggests that 6 months may be the best HCC screening interval for US screening. The effectiveness of CT and MRI is better than US during same screening intervals. However, MRI and CT are more expensive than US, and CT also can increase the risk of radiation exposure. The selection of CT or MRI instead of US should be carefully considered.@*REGISTRATION@#No. CRD42020148258 at PROSPERO website ( https://www.crd.york.ac.uk/PROSPERO/ ).
Subject(s)
Humans , Carcinoma, Hepatocellular/pathology , Liver Neoplasms/pathology , Liver Cirrhosis/complications , Risk Factors , Cohort StudiesABSTRACT
Objective:To investigate the effects of repetitive transcranial magnetic stimulation combined with paroxetine hydrochloride on executive function in depressed adolescents with non-suicidal self-injury.Methods:The clinical data of 120 depressed adolescents with depressive disorders who were admitted to The Second Hospital of Jinhua from August 2021 to July 2022 were retrospectively analyzed. They were randomly assigned to undergo treatment either with paroxetine hydrochloride (control group, n = 60) or repetitive transcranial magnetic stimulation combined with paroxetine hydrochloride (observation group, n = 60). All patients were treated for 2 months. Hamilton Depression Rating Scale-24 (HAMD-24) score, Non-suicidal Self-injury Behavior and Function Scale for Adolescents (ANSSIQ) score, executive function, brain-derived neurotrophic factor, 5-hydroxytryptamine, and clinical efficacy were determined in each group. Results:After treatment, the Hamilton Depression Rating Scale-24 score in the observation group was significantly lower than that in the control group [(15.85 ± 1.08) points) vs. (18.72±1.21) points, t = 13.71, P < 0.001). After treatment, the number of self-injury attacks, number of self-injury impulsions, and the intensity of self-injury thought within 2 weeks in the observation group were significantly lower than those in the control group ( t = 3.42, 3.03, 1.92, all P < 0.05). The scores of the Trail Making Test, Stroop Word test, Stroop Color test, and Stroop Color-Word Interference Test were significantly higher in the observation group than those in the control group ( t = 2.66, 3.33, 3.97, 4.64, all P < 0.01). Brain-derived neurotrophic factor and 5-hydroxytryptamine levels in the observation group were (11.45 ± 1.79) μg/L and (136.68 ± 11.90) μg/L, respectively, which were significantly higher than (9.06±2.21) μg/L and (124.82 ± 10.34) μg/L in the control group ( t = 6.51, 5.83, both P < 0.001). The total response rate in the observation group was significantly higher than that in the control group (91.7% vs. 78.3%, Z = 2.73, P = 0.006). Conclusion:Repetitive transcranial magnetic stimulation combined with paroxetine hydrochloride is highly effective on depressive disorders in adolescents with non-suicidal self-injury. The combined therapy can reduce symptoms, improve executive function and cognitive function, and optimize serological indicators, and thereby deserves the clinical promotion.
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ObjectivesTo examine whether and to what extent hospital strain will increase the risk of death from Covid-19. DesignRetrospective cohort study. SettingEngland. ParticipantsData on all the 147,276 Covid-19 deaths and 601,084 hospitalized Covid-19 patients in England during the period between 9 April 2020 and 11 March 2022 were extracted on a daily basis from the UK Health Security Agency. Main outcome measuresThe number of Covid-19 patients currently in hospitals was used as the measure of hospital strain. Daily case fatality was estimated as the measure of risk of death from Covid-19. The study was divided into 4 periods, which represented largely the wild, Alpha, Delta and Omicron waves. Weighted linear regression models were used to assess the association between hospital strain and Covid-19 fatality with adjustment for potential confounders including vaccination score, hospital admission rate, percentage of deaths outside hospitals, study period and interaction between patients currently in hospitals and study period. ResultsThe daily case fatality from Covid-19 increased linearly as the number of patients currently in hospitals increased in the 4 study periods except the Omicron wave. After adjusting for potential confounders, an increase in 1000 patients currently in hospitals was associated with a relative increase of 6.3% (95% CI: 5.9%~6.8%), 1.4% (95% CI: 1.3% ~ 1.5%) and 12.7% (95% CI: 10.8%~14.7%) in daily case fatality during study periods 1, 2 and 3 respectively. Compared with the lowest number of patients currently in hospitals, the highest number was associated with a relative increase of 188.0% (95% CI: 165.9%~211.6%), 69.9% (95% CI: 59.0%~81.8%) and 58.2% (95% CI: 35.4%~89.0%) in daily case fatality in the first 3 study periods respectively. Sensitivity analyses using the number of patients in ventilation beds as the measure of hospital strain showed similar results. ConclusionsThe risk of death from Covid-19 was linearly associated with the number of patients currently in hospitals, suggesting any (additional) effort to ease hospital strain or maintain care quality be beneficial during large outbreaks of Covid-19 and likely of other similar infectious diseases. Summary boxO_ST_ABSWhat is already known on this topicC_ST_ABS- During the Covid-19 pandemic, tremendous efforts have been made in many countries to suppress epidemic peaks and strengthen hospital services so as to avoid hospital strain with an ultimate aim to reduce the risk of death from Covid-19. - These efforts were made according to the widely held belief that hospital strain would increase the risk of Covid-19 death but good empirical evidence was largely lacking to support the hypothesis. - A few small studies showed that shortage in intensive care was associated with an increased Covid-19 fatality but strains may occur in many areas in the healthcare system besides intensive care and they may all increase the risk of death from Covid-19. - The totality of hospital strain can be approximated by the number of patients currently in hospitals but its effects on the risk of Covid-19 death has not been demonstrated. What this study adds- We found the risk of death from Covid-19 was linearly associated with the number of patients currently in hospitals before the Omicron period. - Compared with the lowest number of patients currently in hospitals in an outbreak, the highest number could be associated with a relative increase in the risk of death between 58.2% and 188.0%. - The number of patients currently in hospitals during the Omicron period was not found associated with the risk of death but there remains uncertainty if the number of patients currently in hospitals reached a level much higher than that actually occurred in England or in places other than England. How this study might affect research, practice, or policy- Facing the on-going Covid-19 pandemic and future outbreaks alike, the linear relation between hospital strain and fatality suggests importantly any (additional) effort to reduce hospital strain would be beneficial during a large Covid-19 outbreak.
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Comprehensive interventions have been widely used in health system, public health, education and communities and have become increasingly focus of systematic reviews. There have been many reporting guidelines about systematic reviews, but they do not take the features of comprehensive interventions in medical area into consideration. As a result, PRISMA-CI has been developed as an extension of PRISMA, which adds or modifies the essential items of PRISMA. This paper introduces the items of PRISMA-CI and explains the items with an example to help authors, publishers, and readers understand PRISMA-CI and use it in systematic reviews on comprehensive interventions. As it become more and more popular with comprehensive interventions, PRISMA-CI will provide important structure and guidance for its systematic review and Meta-analysis.
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This paper summarizes the Risk of Bias of Individual Studies in Systematic Reviews of Health Care Interventions revised by the Agency for Healthcare Research and Quality (AHRQ) and introduces how to use Revman software make risk of bias graph or risk of bias summary.AHRQ tool can be used to evaluate following study designs:RCTs,cohort study,case-control study (including nested case-control),case series study and cross-sectional study.The tool evaluates the risk of bias of individual studies from selection bias,performance bias,attrition bias,detection bias and reporting bias.Each of the bias domains contains different items,and each item is available for the assessment of one or more study designs.It is worth noting that the appropriate items should be selected for evaluation different study designs instead of using all items to directly assess the risk of bias.AHRQ tool can be used to evaluate risk of bias individual studies when systematic reviews of health care interventions is including different study designs.Moreover,the tool items are relatively easy to understand and the assessment process is not complicated.AHRQ recommends the use of high,medium and low risk classification methods to assess the overall risk of bias of individual studies.However,AHRQ gives no recommendations on how to determine the overall bias grade.It is expected that future research will give corresponding recommendations.
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This paper summarizes the Risk of Bias of Individual Studies in Systematic Reviews of Health Care Interventions revised by the Agency for Healthcare Research and Quality (AHRQ) and introduces how to use Revman software make risk of bias graph or risk of bias summary.AHRQ tool can be used to evaluate following study designs:RCTs,cohort study,case-control study (including nested case-control),case series study and cross-sectional study.The tool evaluates the risk of bias of individual studies from selection bias,performance bias,attrition bias,detection bias and reporting bias.Each of the bias domains contains different items,and each item is available for the assessment of one or more study designs.It is worth noting that the appropriate items should be selected for evaluation different study designs instead of using all items to directly assess the risk of bias.AHRQ tool can be used to evaluate risk of bias individual studies when systematic reviews of health care interventions is including different study designs.Moreover,the tool items are relatively easy to understand and the assessment process is not complicated.AHRQ recommends the use of high,medium and low risk classification methods to assess the overall risk of bias of individual studies.However,AHRQ gives no recommendations on how to determine the overall bias grade.It is expected that future research will give corresponding recommendations.
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In this last paper of the series about risk of bias assessment,we introduce the application of risk of bias assessment results.Risk of bias assessment is one of the key steps in the assessment of quality of evidence.The risk of bias assessment results could be the “diagnosis” of individual studies,which helps decision making related to the inclusion and exclusion of individual studies,as well as the data analysis in the systematic review process.This paper focuses on how to incorporate risk of bias assessment results in the GRADE assessment for quality of evidence,including the principles and the tips for the application.
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This paper introduces the tools related to Quality In Prognosis Studies (QUIPS) to assess the risk of bias in studies of prognostic factors and the relevant points of assessment and to illustrate the application of QUIPS in published prognostic research.The QUIPS tool identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors including participation,attrition,measurement on prognostic factors,outcomes,confounding factors,statistical analysis and reporting.It also provided a new method for evaluation on bias in the areas of prognostic research.
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This paper introduced the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2),including the development and comparison with the original QUADAS,and illustrated the application of QUADAS-2 in a published paper related to the study on diagnostic accuracy which was included in systematic review and Meta-analysis.QUADAS-2 presented considerable improvement over the original tool.Confused items that included in QUADAS had disappeared and the quality assessment of the original study replaced by the rating of risk on bias and applicability.This was implemented through the description on the four main domains with minimal overlapping and answering the signal questions in each domain.The risk of bias and applicability with'high','low'or'unclear'was in line with the risk of bias assessment of intervention studies in Cochrane,so to replace the total score of quality assessment in QUADAS.Meanwhile,QUADAS-2 was also applicable to assess the diagnostic accuracy studies in which follow-up without prognosis was involved in golden standard.It was useful to assess the overall methodological quality of the study despite more time consuming than the original QUADAS.However,QUADAS-2 needs to be modified to apply in comparative studies on diagnostic accuracy and we hope the users would follow the updates and give their feedbacks on line.
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This paper summaries the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I),a tool for evaluating risk of bias about Non-randomized Studies-of Interventions (NRSI),and introduces the application of ROBINS-I in a published NRSI.According to the characteristics of NRSI,evaluation field and signaling question were designed in ROBINS-I to provide essential information about risk of bias for NRSI included in systematic reviews.ROBINS-I is the tool in assessment of risk of bias in observational studies and quasi-randomised studies.Although the tool hasbeen used in practice to some extent,but it still needs further improvement.Attention should be paid to its update and progress.
ABSTRACT
In this last paper of the series about risk of bias assessment,we introduce the application of risk of bias assessment results.Risk of bias assessment is one of the key steps in the assessment of quality of evidence.The risk of bias assessment results could be the “diagnosis” of individual studies,which helps decision making related to the inclusion and exclusion of individual studies,as well as the data analysis in the systematic review process.This paper focuses on how to incorporate risk of bias assessment results in the GRADE assessment for quality of evidence,including the principles and the tips for the application.
ABSTRACT
This paper introduces the tools related to Quality In Prognosis Studies (QUIPS) to assess the risk of bias in studies of prognostic factors and the relevant points of assessment and to illustrate the application of QUIPS in published prognostic research.The QUIPS tool identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors including participation,attrition,measurement on prognostic factors,outcomes,confounding factors,statistical analysis and reporting.It also provided a new method for evaluation on bias in the areas of prognostic research.
ABSTRACT
This paper introduced the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2),including the development and comparison with the original QUADAS,and illustrated the application of QUADAS-2 in a published paper related to the study on diagnostic accuracy which was included in systematic review and Meta-analysis.QUADAS-2 presented considerable improvement over the original tool.Confused items that included in QUADAS had disappeared and the quality assessment of the original study replaced by the rating of risk on bias and applicability.This was implemented through the description on the four main domains with minimal overlapping and answering the signal questions in each domain.The risk of bias and applicability with'high','low'or'unclear'was in line with the risk of bias assessment of intervention studies in Cochrane,so to replace the total score of quality assessment in QUADAS.Meanwhile,QUADAS-2 was also applicable to assess the diagnostic accuracy studies in which follow-up without prognosis was involved in golden standard.It was useful to assess the overall methodological quality of the study despite more time consuming than the original QUADAS.However,QUADAS-2 needs to be modified to apply in comparative studies on diagnostic accuracy and we hope the users would follow the updates and give their feedbacks on line.
ABSTRACT
This paper summaries the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I),a tool for evaluating risk of bias about Non-randomized Studies-of Interventions (NRSI),and introduces the application of ROBINS-I in a published NRSI.According to the characteristics of NRSI,evaluation field and signaling question were designed in ROBINS-I to provide essential information about risk of bias for NRSI included in systematic reviews.ROBINS-I is the tool in assessment of risk of bias in observational studies and quasi-randomised studies.Although the tool hasbeen used in practice to some extent,but it still needs further improvement.Attention should be paid to its update and progress.
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Objective To explore the risk of developing metabolic syndrome (MS) by using the data from cohorts involving people having received screening programs for physical check-up,in three areas of China (Beijing,Hongkong and Taiwan).Methods A total number of 6 828 non-metabolic syndromic adults,who received physical examination for the first time and with records kept for longer than 5 years (between 2004 and 2010) at the MJ centers,were recruited.Criteria developed by the Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia in Chinese Adults in 2007 (JCDCG-2007) was used for defining the metabolic syndrome.Cox proportional hazards regression model was used to examine the difference of the risk of developing MS among the three cohorts that received the health screening programs.Results The standardized incidence densities of MS were 3.14 per 100 person-years,2.19 per 100 person-years and 2.03 per 100 person-years in the cohorts of Beijing,Hongkong and Taiwan,respectively.After adjusting for gender,age,cigarette smoking,dietary patterns at the baseline,the HRs for people in Beijing and Hongkong were 1.60 (95％CI:1.34-1.91) and 1.08 (95％CI:0.83-1.41) respectively,in developing MS,when compared with people from Taiwan.Factors as being male,elderly,cigarette smoking,meat/food intake dietary pattern and MS components at the baseline all showed significantly positive effects on the risk of developing MS.Conclusions There were significant differences regarding the risk of developing MS among health screening people from the Beijing,Hongkong and Taiwan.Factors as being male,elderly,cigarette smoking,meat/food intake,dietary pattern and MS components at the baseline appear to be the risk factors for developing the MS.
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This paper introduces the revised cochrane risk of bias tool for individually randomized,parallel group trials (RoB2.0),compates RoB2.0 and the previous version (RoB1.0).And illustrates the application of RoB2.0 for a published clinical trial.As a comprehensive tool,RoB2.0 provides more information on the risk of bias for evidence synthesis.RoB2.0 is still under development and it is suggested that the users should follow the updates of the developers in the furure.
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This paper reviews the concept of risk of bias,followed by demonstrating why assessment of risk of bias in systematic reviews should be different from that of quality of evidence,methodological quality,reporting quality,precision,and external validity.We also discuss the recent development of tools for risk of bias assessment,the problems with the tools themselves,and the challenges in using these tools.This review may help systematic reviewers understand risk of bias assessment and the use of assessment tools.
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Objective To explore the risk of developing metabolic syndrome (MS) by using the data from cohorts involving people having received screening programs for physical check-up,in three areas of China (Beijing,Hongkong and Taiwan).Methods A total number of 6 828 non-metabolic syndromic adults,who received physical examination for the first time and with records kept for longer than 5 years (between 2004 and 2010) at the MJ centers,were recruited.Criteria developed by the Joint Committee for Developing Chinese Guidelines on Prevention and Treatment of Dyslipidemia in Chinese Adults in 2007 (JCDCG-2007) was used for defining the metabolic syndrome.Cox proportional hazards regression model was used to examine the difference of the risk of developing MS among the three cohorts that received the health screening programs.Results The standardized incidence densities of MS were 3.14 per 100 person-years,2.19 per 100 person-years and 2.03 per 100 person-years in the cohorts of Beijing,Hongkong and Taiwan,respectively.After adjusting for gender,age,cigarette smoking,dietary patterns at the baseline,the HRs for people in Beijing and Hongkong were 1.60 (95％CI:1.34-1.91) and 1.08 (95％CI:0.83-1.41) respectively,in developing MS,when compared with people from Taiwan.Factors as being male,elderly,cigarette smoking,meat/food intake dietary pattern and MS components at the baseline all showed significantly positive effects on the risk of developing MS.Conclusions There were significant differences regarding the risk of developing MS among health screening people from the Beijing,Hongkong and Taiwan.Factors as being male,elderly,cigarette smoking,meat/food intake,dietary pattern and MS components at the baseline appear to be the risk factors for developing the MS.
ABSTRACT
This paper introduces the revised cochrane risk of bias tool for individually randomized,parallel group trials (RoB2.0),compates RoB2.0 and the previous version (RoB1.0).And illustrates the application of RoB2.0 for a published clinical trial.As a comprehensive tool,RoB2.0 provides more information on the risk of bias for evidence synthesis.RoB2.0 is still under development and it is suggested that the users should follow the updates of the developers in the furure.
ABSTRACT
This paper reviews the concept of risk of bias,followed by demonstrating why assessment of risk of bias in systematic reviews should be different from that of quality of evidence,methodological quality,reporting quality,precision,and external validity.We also discuss the recent development of tools for risk of bias assessment,the problems with the tools themselves,and the challenges in using these tools.This review may help systematic reviewers understand risk of bias assessment and the use of assessment tools.
