Using Indirect Evidence to Determine the Comparative Effectiveness of Prescription Drugs: Do Benefits Outweigh Risks?

Effects of anesthetic adjunctive agents on postoperative cognitive dysfunction in elderly patients undergoing noncardiac surgery: A Bayesian network meta-analysis

BACKGROUND

Elderly patients are prone to postoperative cognitive dysfunction (POCD). The comparison of the effects of anesthetic adjuvant drugs on POCD in elderly patients undergoing noncardiac surgery remains controversial.

METHODS

The final search took place on June 10, 2023. Randomized controlled trials including ketamine, ulinastatin, dexmedetomidine, parecoxib, and midazolam on the prevention and treatment of POCD in elderly undergoing noncardiac surgery were collected. A Bayesian network meta-analysis was performed to quantitatively combine the evidence.

RESULTS

A total of 35 randomized trials were finally included in this systematic review, and the overall risk of bias is Allocation concealment. These anesthetic adjuvant drugs did not show significant differences in preventing POCD on postoperative days 1 and 7 compared with each other, but ulinastatin may be more effective in preventing POCD than dexmedetomidine [odds ratio (OR) = 0.28, 95% confidence interval (CI) = (0.10, 0.71)] and parecoxib [OR = 0.3, 95% CI = (0.10, 0.82 on postoperative day 3. The efficiency ranking results also find that ulinastatin and ketamine might provide better effects regarding POCD prevention.

CONCLUSIONS

Ketamine and ulinastatin might have better effects in preventing POCD in elderly patients undergoing noncardiac surgery. Our meta-analysis provided evidence for the use of ulinastatin and ketamine in the prevention of POCD in elderly patients undergoing noncardiac surgery.

Decision Curve Analysis for Personalized Treatment Choice between Multiple Options

BACKGROUND

Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial.

OBJECTIVES

Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA).

METHODS

We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo.

RESULTS

We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness.

CONCLUSIONS

This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making.

HIGHLIGHTS

Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.

Pharmacological Agents That Prevent Postoperative Cognitive Dysfunction in Patients With General Anesthesia: A Network Meta-analysis

BACKGROUND

Postoperative cognitive dysfunction (POCD) is associated with prolonged hospital stays, increased mortality, and negative socioeconomic consequences. Dexmedetomidine, ketamine, dexamethasone, and lidocaine have previously been reported to be effective for preventing POCD.

STUDY QUESTION

In this network meta-analysis, we apply direct and indirect comparisons to rank these pharmacological agents in terms of their effect on POCD, through which we seek to provide evidence for future clinical medication.

DATA SOURCES

A comprehensive literature search of PubMed, EMBASE, the Cochrane Library, and Web of Science was conducted to identify randomized controlled trials that examined the effects of dexmedetomidine, ketamine, dexamethasone, or lidocaine on POCD induced by general anesthesia.

STUDY DESIGN

For eligible studies, 2 reviewers independently extracted data and assessed the respective risk of bias. Bayesian network meta-analysis was conducted using R-3.4.1 software. A total of 30 articles were included in this meta-analysis.

RESULTS

Direct comparison showed that dexmedetomidine [odds ratio (OR) = 0.42, 95% confidence interval (CI): 0.32-0.55] may decrease the incidence of POCD for noncardiac surgery; dexmedetomidine (OR = 0.08, 95% CI: 0.01-0.63) and ketamine (OR = 0.09, 95% CI: 0.02-0.32) were found to decrease POCD for cardiac surgery. The R-software ranking result for prevention of POCD was dexmedetomidine, lidocaine, ketamine, placebo, and dexamethasone, respectively, in noncardiac surgery, and dexmedetomidine, ketamine, lidocaine, placebo, and dexamethasone in cardiac surgery.

CONCLUSION

Dexmedetomidine exhibited obvious superiority to other agents for noncardiac surgery; dexmedetomidine and ketamine exhibited a significantly better performance than other agents for cardiac surgery. Dexamethasone did not show better efficacy than the placebo. However, more rigorously designed studies comprising larger sample sizes are needed to confirm our findings.

GetReal in network meta-analysis: a review of the methodology

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.

Assessing key assumptions of network meta-analysis: a review of methods

BACKGROUND

Homogeneity and consistency assumptions underlie network meta-analysis (NMA). Methods exist to assess the assumptions but they are rarely and poorly applied. We review and illustrate methods to assess homogeneity and consistency.

METHODS

Eligible articles focussed on indirect comparison or NMA methodology. Articles were sought by hand-searching and scanning references (March 2013). Assumption assessment methods described in the articles were reviewed, and applied to compare anti-malarial drugs.

RESULTS

116 articles were included. Methods to assess homogeneity were: comparing characteristics across trials; comparing trial-specific treatment effects; using hypothesis tests or statistical measures; applying fixed-effect and random-effects pair-wise meta-analysis; and investigating treatment effect-modifiers. Methods to assess consistency were: comparing characteristics; investigating treatment effect-modifiers; comparing outcome measurements in the referent group; node-splitting; inconsistency modelling; hypothesis tests; back transformation; multidimensional scaling; a two-stage approach; and a graph-theoretical method. For the malaria example, heterogeneity existed for some comparisons that was unexplained by investigating treatment effect-modifiers. Inconsistency was detected using node-splitting and inconsistency modelling. It was unclear whether the covariates explained the inconsistency.

CONCLUSIONS

Presently, we advocate applying existing assessment methods collectively to gain the best understanding possible regarding whether assumptions are reasonable. In our example, consistency was questionable; therefore the NMA results may be unreliable.

Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers

BACKGROUND

In the last decade, network meta-analysis of randomized controlled trials has been introduced as an extension of pairwise meta-analysis. The advantage of network meta-analysis over standard pairwise meta-analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise meta-analyses are well understood, those concerning network meta-analyses are perceived to be more complex and prone to misinterpretation.

DISCUSSION

In this paper, we aim to provide a basic explanation when network meta-analysis is as valid as pairwise meta-analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network meta-analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise meta-analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network meta-analysis is as valid as pairwise meta-analysis.

SUMMARY

The validity of network meta-analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.