Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia

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.