Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia

Protocol development and feasibility of the PEACH in Asia study: A pilot study on PEri-anesthetic morbidity in CHildren in Asia

BACKGROUND

Comprehensive data on pediatric anesthesia outcomes, particularly severe critical events (SCEs), are scarce in Asia. This highlights the need for standardized research to assess anesthesia safety and quality in the diverse settings.

AIMS

The PEACH in Asia pilot study aimed to test the feasibility of a standardized protocol for investigating SCEs in anesthesia practices across Asia, evaluate the data acquisition processes, and determine the sample size for a main study.

METHODS

This multicenter pilot study involved ten institutions across nine Asian countries, including children from birth to 15 years undergoing diagnostic or surgical procedures. Data on SCEs were collected using standardized definitions. The study assessed the feasibility and estimated the sample size needed for the main study.

RESULTS

The pilot study enrolled 330 patients, with a SCE incidence of 12.4% (95% CI: 9.2-16.4%). Respiratory events were observed in 7.0% of cases, cardiovascular instability in 4.9%, and drug errors in 0.6%. Based on the SCE incidence observed in the pilot study, the estimated sample size required for the main study is at least 10 958 patients. The pilot study demonstrated the feasibility of the study protocol but identified several challenges, particularly in resource-limited settings. These challenges included a significant burden associated with data collection, technical issues with electronic case report forms (e-CRFs), variability in patient enrollment across institutions (ranging from 4 to 86 patients per site), and incomplete data acquisition (24.8% of height data and 9.7% of disposition data were missing).

CONCLUSIONS

The PEACH in Asia pilot study successfully validated a protocol for investigating SCEs in pediatric anesthesia across Asia. Addressing the challenges identified in the pilot study will be crucial for generating robust data to improve pediatric anesthesia safety in the region. Key issues to address include improving data collection methods, resolving e-CRF technical difficulties, and ensuring consistent institutional support.

Using multi-level regression to determine associations and estimate causes and effects in clinical anesthesia due to patient, practitioner and hospital or health system practice variability

In this research methods tutorial of clinical anesthesia, we will explore techniques to estimate the influence of a myriad of factors on patient outcomes. Big data that contain information on patients, treated by individual anesthesiologists and surgical teams, at different hospitals, have an inherent multi-level data structure (Fig. 1). While researchers often attempt to determine the association between patient factors and outcomes, that does not provide clinicians with the whole story. Patient care is clustered together according to clinicians and hospitals where they receive treatment. Therefore, multi-level regression models are needed to validly estimate the influence of each factor at each level. In addition, we will explore how to estimate the influence that variability-for example, one anesthesiologist deciding to do one thing, while another takes a different approach-has on outcomes for patients, using the intra-class correlation coefficient for continuous outcomes and the median odds ratio for binary outcomes. From this tutorial, you should acquire a clearer understanding of how to perform and interpret multi-level regression modeling and estimate the influence of variable clinical practices on patient outcomes in order to answer common but complex clinical questions. Fig. 1 Infographics.

Overview of statistical methods usage in Indian anaesthesia publications

Background and Aims

Despite the importance of statistics being well established for medical research, it remains a neglected area of understanding and learning. The present survey aimed to examine the use of various statistical methods in a two-year sample (2019-2020) of representative Indian anaesthesia journals and compare it with an international top-ranked journal.

Methods

The literature survey included analysis of 748 original articles from 'Indian Journal of Anaesthesia' (179), 'Journal of Anaesthesiology Clinical Pharmacology' (125) and 'Anesthesia & Analgesia' (444) published over the period. Original research articles were identified from the table of contents of each issue. Articles were assessed for statistical methods, categorised as being descriptive, elementary, multivariable, advanced multivariate or diagnostic/classification.

Results

Compared to Anesthesia & Analgesia, the Indian journals (considered together) had a significantly greater use of mean (standard deviation) (91.2% versus 70%) and percentages (79.5% versus 67.6%) (P = 0.000 each); and lesser for Wilcoxon (5.4% versus 14.6%) and Pearson/Spearman (5.1% versus 13.5%) correlation tests (P = 0.000 each), multivariable tests including various regression methods (P < 0.001), classification/diagnostic tests [Receiver operating characteristic (ROC) curve analysis, P = 0.022; sensitivity/specificity, P = 0.000; precision, P = 0.006; and relative risk/risk ratio, P = 0.010] and a virtual absence of complex multivariate tests.

Conclusion

The findings show limited use of advanced complex statistical methods in Indian anaesthesia journals, usually being restricted to descriptive or elementary. There was a strong bias towards using randomised controlled designs. The findings suggest an urgent and focussed need on training in research methodology, including statistical methods, during postgraduation and continued medical training.

Interpreting and assessing confidence in network meta-analysis results: an introduction for clinicians

Purpose

We aimed to provide clinicians with introductory guidance for interpreting and assessing confidence in on Network meta-analysis (NMA) results.

Methods

We reviewed current literature on NMA and summarized key points.

Results

Network meta-analysis (NMA) is a statistical method for comparing the efficacy of three or more interventions simultaneously in a single analysis by synthesizing both direct and indirect evidence across a network of randomized clinical trials. It has become increasingly popular in healthcare, since direct evidence (head-to-head randomized clinical trials) are not always available. NMA methods are categorized as either Bayesian or frequentist, and while the two mostly provide similar results, the two approaches are theoretically different and require different interpretations of the results.

Conclusions

We recommend a careful approach to interpreting NMA results and the validity of an NMA depends on its underlying statistical assumptions and the quality of the evidence used in the NMA.

Clinical Characteristics, Treatment Effectiveness, and Predictors of Response to Pharmacotherapeutic Interventions Among Patients with Herpetic-Related Neuralgia: A Retrospective Analysis

BACKGROUND

The treatment for herpetic-related neuralgia focuses on symptom control by use of antiviral drugs, anticonvulsants, and tricyclic antidepressants. We aimed to explore the clinical characteristics associated with medication responsiveness, and to build a classifier for identification of patients who have risk of inadequate pain management.

METHODS

We recruited herpetic-related neuralgia patients during a 3-year period. Patients were stratified into a medication-resistant pain (MRP) group when the pain decrease in the visual analogue scale (VAS) is < 3 points, and otherwise a medication-sensitive pain (MSP) group. Multivariate logistic regression was performed to determine the factors associated with MRP. We fitted four machine learning (ML) models, namely logistic regression, random forest, supporting vector machines (SVM), and naïve Bayes with clinical characteristics gathered at admission to identify patients with MRP.

RESULTS

A total of 213 patients were recruited, and 132 (61.97%) patients were diagnosed with MRP. Subacute herpes zoster (HZ) (vs. acute, OR 8.95, 95% CI 3.15-29.48, p = 0.0001), severe lesion (vs. mild lesion, OR 3.84, 95% CI 1.44-10.81, p = 0.0084), depressed mood (unit increase OR 1.10, 95% CI 1.00-1.20, p = 0.0447), and hypertension (hypertension, vs. no hypertension, OR 0.36, 95% CI 0.14-0.87, p = 0.0266) were significantly associated with MRP. Among four ML models, SVM had the highest accuracy (0.917) and receiver operating characteristic-area under the curve (0.918) to discriminate MRP from MSP. Phase of disease is the most important feature when fitting ML models.

CONCLUSIONS

Clinical characteristics collected before treatment could be adopted to identify patients with MRP.

Big Data in Studying Acute Pain and Regional Anesthesia

The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyses have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, the utilization of big data in acute pain and regional anesthesia research has considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.