Tenecteplase versus alteplase for acute ischemic stroke: a systematic review and meta-analysis of randomized and non-randomized studies

OBJECTIVE

Alteplase is the current standard of care for acute ischemic stroke. Tenecteplase is a newer fibrinolytic agent with preferable administration and lower costs; however, its comparative effectiveness to alteplase remains uncertain. We set out to perform a systematic review and meta-analysis to establish the benefits and harms of tenecteplase versus alteplase for acute ischemic stroke.

METHODS

We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov from inception to April 2023 for randomized and non-randomized studies that compared tenecteplase versus alteplase for acute ischemic stroke. Paired reviewers independently assessed risk of bias and extracted data. We performed both conventional meta-analyses and Bayesian network meta-analyses (NMA) with random-effects models and used the GRADE approach to evaluate the certainty of evidence. Our primary efficacy outcome was excellent functional outcome at 3 months, defined as a score of 0-1 on the modified Rankin Scale. Our primary safety outcomes were symptomatic intracranial hemorrhage and all-cause mortality.

RESULTS

Thirty-six studies were eligible for review, including 12 randomized (n = 5533) and 24 non-randomized studies (n = 44,956). Moderate certainty evidence showed that there was no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months (odds ratio [OR], 1.10; 95% CI 0.98-1.23; risk difference [RD] 2.4%, 95% CI - 0.5 to 5.2), while moderate certainty evidence from NMA suggested that 0.25 mg/kg tenecteplase significantly improved excellent functional outcome at 3 months (OR, 1.16; 95% credible interval 1.02-1.32). Moderate certainty evidence showed that, compared to alteplase, tenecteplase may make little to no difference in the prevalence of symptomatic intracranial hemorrhage (OR, 1.12; 95% CI 0.79-1.59; RD 0.3%, 95% CI - 0.5 to 1.4), and probably reduces all-cause mortality (adjusted odds ratio [aOR], 0.44; 95% CI 0.30-0.64; RD - 4.6%; 95% CI - 5.8 to - 2.9).

CONCLUSIONS

Moderate certainty evidence suggested that there was little to no difference between tenecteplase and alteplase in increasing the proportion of patients achieving excellent functional outcome at 3 months and the risk of symptomatic intracranial hemorrhage, while compared to alteplase, tenecteplase probably reduce all-cause mortality. Administration of 0.25 mg/kg tenecteplase after acute ischemic stroke is suggestive of increasing the proportion of patients that achieve excellent functional outcome at 3 months.

Handling time-varying treatments in observational studies: A scoping review and recommendations

OBJECTIVE

Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations.

METHODS

We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts.

RESULTS

Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings.

CONCLUSIONS

There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

Comparison of clinical characteristics and disease burden between early- and late-onset type 2 diabetes patients: a population-based cohort study

BACKGROUND

The clinical characteristics of early-onset type 2 diabetes (T2D) patients are not fully understood. To address this gap, we conducted a cohort study to evaluate clinical characteristics and disease burden in the new-onset T2D population, especially regarding the progression of diseases.

METHODS

This cohort study was conducted using a population-based database. Patients who were diagnosed with T2D were identified from the database and were classified into early- (age < 40) and late-onset (age ≥ 40) groups. A descriptive analysis was performed to compare clinical characteristics and disease burden between early- and late-onset T2D patients. The progression of disease was compared using Kaplan‒Meier analysis.

RESULTS

A total of 652,290 type 2 diabetic patients were included. Of those, 21,347 were early-onset patients, and 300,676 were late-onset patients. Early-onset T2D patients had poorer glycemic control than late-onset T2D patients, especially at the onset of T2D (HbA1c: 9.3 [7.5, 10.9] for early-onset vs. 7.7 [6.8, 9.2] for late-onset, P < 0.001; random blood glucose: 10.9 [8.0, 14.3] for early-onset vs. 8.8 [6.9, 11.8] for late-onset, P < 0.001). Insulin was more often prescribed for early-onset patients (15.2%) than for late-onset patients (14.8%). Hypertension (163.0 [28.0, 611.0] days) and hyperlipidemia (114.0 [19.0, 537.0] days) progressed more rapidly among early-onset patients, while more late-onset patients developed hypertension (72.7% vs. 60.1%, P < 0.001), hyperlipidemia (65.4% vs. 51.0%, P < 0.001), cardiovascular diseases (66.0% vs. 26.7%, P < 0.001) and chronic kidney diseases (5.5% vs. 2.1%, P < 0.001) than early-onset patients.

CONCLUSIONS

Our study results indicate that patients with newly diagnosed early-onset T2D had earlier comorbidities of hypertension and hyperlipidemia. Both clinical characteristics and treatment patterns suggest that the degree of metabolic disturbance is more severe in patients with early-onset type 2 diabetes. This highlights the importance of promoting healthy diets or lifestyles to prevent T2D onset in young adults.

Reporting, handling, and interpretation of time-varying drug treatments in observational studies using routinely collected healthcare data

BACKGROUND

Time-varying drug treatments are common in studies using routinely collected health data (RCD) for assessing treatment effects. This study aimed to examine how these studies reported, handled, and interpreted time-varying drug treatments.

METHODS

A systematic search was conducted on PubMed from 2018 to 2020. Eligible studies were those used RCD to explore drug treatment effects. We summarized the reporting characteristics and methods employed for handling time-varying treatments. Logistic regressions were performed to investigate the association between study characteristics and the reporting of time-varying treatments.

RESULTS

Two hundred and fifty-six studies were included, and 225 (87.9%) studies involved time-varying treatments. Of these, 24 (10.7%) reported the proportion of time-varying treatments and 105 (46.7%) reported methods used to handle time-varying treatments. Multivariable logistic regression showed that medical studies, prespecified protocol, and involvement of methodologists were associated with a higher likelihood of reporting the methods applied to handle time-varying treatments. Among the 105 studies that reported methods, as-treated analyses were the most commonly used analysis sets, which were employed in 73.9%, 75.3% and 88.2% of studies that reported approaches for treatment discontinuation, treatment switching and treatment add-on. Among the 225 studies involved time-varying treatments, 27 (12.0%) acknowledged the potential bias introduced by treatment change, of which 14 (51.9%) suggested that potential biases may impact acceptance or rejection of the null hypothesis.

CONCLUSIONS

Among observational studies using RCD, the underreporting about the presence and methods for handling time-varying treatments was largely common. The potential biases due to time-varying treatments have frequently been disregarded. Collaborative endeavors are strongly needed to enhance the prevailing practices.

Comparison of statistical methods for integrating real-world evidence in a rare events meta-analysis of randomized controlled trials

Rare events meta-analyses of randomized controlled trials (RCTs) are often underpowered because the outcomes are infrequent. Real-world evidence (RWE) from non-randomized studies may provide valuable complementary evidence about the effects of rare events, and there is growing interest in including such evidence in the decision-making process. Several methods for combining RCTs and RWE studies have been proposed, but the comparative performance of these methods is not well understood. We describe a simulation study that aims to evaluate an array of alternative Bayesian methods for including RWE in rare events meta-analysis of RCTs: the naïve data synthesis, the design-adjusted synthesis, the use of RWE as prior information, the three-level hierarchical models, and the bias-corrected meta-analysis model. The percentage bias, root-mean-square-error, mean 95% credible interval width, coverage probability, and power are used to measure performance. The various methods are illustrated using a systematic review to evaluate the risk of diabetic ketoacidosis among patients using sodium/glucose co-transporter 2 inhibitors as compared with active-comparators. Our simulations show that the bias-corrected meta-analysis model is comparable to or better than the other methods in terms of all evaluated performance measures and simulation scenarios. Our results also demonstrate that data solely from RCTs may not be sufficiently reliable for assessing the effects of rare events. In summary, the inclusion of RWE could increase the certainty and comprehensiveness of the body of evidence of rare events from RCTs, and the bias-corrected meta-analysis model may be preferable.

Acupuncture for treatment of knee osteoarthritis: A clinical practice guideline

CLINICAL QUESTION

Is acupuncture effective in treating knee osteoarthritis (KOA)?

CURRENT PRACTICE

Although increasingly used in the clinical setting, acupuncture is not mentioned or weakly recommended in guidelines for the treatment of KOA.

RECOMMENDATIONS

We suggest acupuncture rather than no treatment in adult KOA (weak recommendation, moderate certainty evidence), and acupuncture combined with nonsteroidal anti-inflammatory drugs (NSAIDs) rather than acupuncture alone when KOA symptoms are severe (weak recommendation, moderate certainty evidence), with duration of acupuncture for 4-8 weeks depending on KOA severity and treatment response (weak recommendation, moderate certainty evidence), and discussing with patients in shared decision-making.

HOW THIS GUIDELINE WAS CREATED

This rapid recommendation was developed following the Making GRADE the Irresistible Choice (MAGIC) methodological framework. First, the clinical specialist identified the topic of recommendation and demand for evidence. Then the independent evidence synthesis group performed a systematic review to summarize available evidence and evaluate the evidence using the GRADE approach. Finally, the clinical specialist group produced recommendations for practice through a consensus procedure.

THE EVIDENCE

The linked systematic review and meta-analysis included 9422 KOA patients, 61.1% of whom were women. The median mean age was 61.8 years. Compared with no treatment, acupuncture had beneficial effect on KOA in improving the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score (moderate certainty evidence), and WOMAC pain (very low certainty evidence), WOMAC stiffness (low certainty evidence), and WOMAC function (low certainty evidence) subscale scores. Compared with usual care, acupuncture improved WOMAC stiffness subscale score (moderate certainty evidence). Subgroup analyses showed different effects in the improvement of WOMAC total scores by different durations of acupuncture and whether acupuncture combined with NSAIDs, but no difference between manual acupuncture and electroacupuncture was found.

UNDERSTANDING THE RECOMMENDATIONS

Compared with no treatment, acupuncture is suggested to reduce pain, stiffness, and disfunction in KOA patients, ultimately improving the patient's health status. Acupuncture can be used as an alternative therapy when usual care is ineffective or there are adverse reactions so that patients can no longer continue the treatment. Manual acupuncture or electroacupuncture is suggested for 4-8 weeks to improve the health status of KOA. The patient's values and preferences should be considered when selecting acupuncture for KOA treatment.

Data source profile reporting by studies that use routinely collected health data to explore the effects of drug treatment

BACKGROUND

Routinely collected health data (RCD) are important resource for exploring drug treatment effects. Adequate reporting of data source profiles may increase the credibility of evidence generated from these data. This study conducted a systematic literature review to evaluate the reporting characteristics of databases used by RCD studies to explore the effects of drug treatment.

METHODS

Observational studies published in 2018 that used RCD to explore the effects of drug treatment were identified by searching PubMed. We categorized eligible reports into two groups by journal impact factor (IF), including the top 5 general medical journals (NEJM, Lancet, JAMA, BMJ and JAMA Internal Medicine) and the other journals. The reporting characteristics of the databases used were described and compared between the two groups and between studies citing and not citing database references.

RESULTS

A total of 222 studies were included, of which 53 (23.9%) reported that they applied data linkage, 202 (91.0%) reported the type of database, and 211 (95.0%) reported the coverage of the data source. Only 81 (36.5%) studies reported the timeframe of the database. Studies in high-impact journals were more likely to report that they applied data linkage (65.1% vs. 20.2%) and used electronic medical records (EMR) (73.7% vs. 30.0%) and national data sources (77.8% vs. 51.3%) than those published in other medical journals. There were 137/222 (61.7%) cited database references. Studies with database-specific citations had better reporting of the data sources and were more likely to publish in high-impact journals than those without (mean IF, 6.08 vs. 4.09).

CONCLUSIONS

Some deficits were found in the reporting quality of databases in studies that used RCD to explore the effects of drug treatment. Studies citing database-specific references may provide detailed information regarding data source characteristics. The adoption of reporting guidelines and education on their use is urgently needed to promote transparency by research groups.