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Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia.

ABSTRACT: Computations of placebo effects are essential in randomized controlled trials (RCTs) for separating the specific effects of treatments from unspecific effects associated with the therapeutic intervention. Thus, the identification of placebo responders is important for testing the efficacy of treatments and drugs. The present study uses data from an experimental study on placebo analgesia to suggest a statistical procedure to separate placebo responders from nonresponders and suggests cutoff values for when responses to placebo treatment are large enough to be separated from reported symptom changes in a no-treatment condition. Unsupervised cluster analysis was used to classify responders and nonresponders, and logistic regression implemented in machine learning was used to obtain cutoff values for placebo analgesic responses. The results showed that placebo responders can be statistically separated from nonresponders by cluster analysis and machine learning classification, and this procedure is potentially useful in other fields for the identification of responders to a treatment.

SUBMITTER: Aslaksen PM

PROVIDER: S-EPMC8479132 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.MethodsNeural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).Findings15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56-72) and LVEF 27% (IQR 21-33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67-1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77-1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35-0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.InterpretationAn artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.FundingMedical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.

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Cutoff criteria for the placebo response: a cluster and machine learning analysis of placebo analgesia.

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