Propensity score method for partially matched omics studies

PsmPy: A Package for Retrospective Cohort Matching in Python

Propensity score matching (PSM) is a technique used in retrospective investigation of cohort matching as an alternative approach to the prospective matching that is typically used by a randomized control trial (RCT). The process of selecting untreated cases that are the best match to the treated cases is the focus of this research. We created a PSM package for the python environment, termed PsmPy, to carry out this task. The PsmPy package debuted and proposed here is based on a logistic regression logit score where a match is selected using k-nearest neighbors (k-NN). Additional plotting and arguments are available to the user and are also described. To benchmark our method, we compared it with the existing R package, MatchIt, and evaluated our covariates' residual effect sizes with respect to the treatment condition before and after matching. Using a Mann-Whitney statistical test, we showed that our method significantly outperformed MatchIt in cohort matching (U=49, p<0.0001) when comparing residual effect sizes of the covariates. The PsmPy demonstrated a 10-fold average improvement in residual effect sizes amongst covariates when compared with the package MatchIt, suggesting that it is a viable alternative for use in propensity matching studies.

Circulating Plasma Biomarkers of Survival in Antifibrotic-Treated Patients With Idiopathic Pulmonary Fibrosis

BACKGROUND

A number of circulating plasma biomarkers have been shown to predict survival in patients with idiopathic pulmonary fibrosis (IPF), but most were identified before the use of antifibrotic (AF) therapy in this population. Because pirfenidone and nintedanib have been shown to slow IPF progression and may prolong survival, the role of such biomarkers in AF-treated patients is unclear.

RESEARCH QUESTION

To determine whether plasma concentration of cancer antigen 125 (CA-125), C-X-C motif chemokine 13 (CXCL13), matrix metalloproteinase 7 (MMP7), surfactant protein D (SP-D), chitinase-3-like protein-1 (YKL-40), vascular cell adhesion protein-1 (VCAM-1), and osteopontin (OPN) is associated with differential transplant-free survival (TFS) in AF-exposed and nonexposed patients with IPF.

STUDY DESIGN AND METHODS

A pooled, multicenter, propensity-matched analysis of IPF patients with and without AF exposure was performed. Optimal thresholds for biomarker dichotomization were identified in each group using iterative Cox regression. Longitudinal biomarker change was assessed in a subset of patients using linear mixed regression modeling. A clinical-molecular signature of IPF TFS was then derived and validated in an independent IPF cohort.

RESULTS

Three hundred twenty-five patients were assessed, of which 68 AF-exposed and 172 nonexposed patients were included after propensity matching. CA-125, CXCL13, MMP7, YKL-40, and OPN predicted differential TFS in AF-exposed patients but at higher thresholds than in AF-nonexposed individuals. Plasma biomarker level generally increased over time in nonexposed patients but remained unchanged in AF-exposed patients. A clinical-molecular signature predicted decreased TFS in AF-exposed patients (hazard ratio [HR], 5.91; 95% CI, 2.25-15.5; P < .001) and maintained this association in an independent AF-exposed cohort (HR, 3.97; 95% CI, 1.62-9.72; P = .003).

INTERPRETATION

Most plasma biomarkers assessed predicted differential TFS in AF-exposed patients with IPF, but at higher thresholds than in nonexposed patients. A clinical-molecular signature of IPF TFS may provide a reliable predictor of outcome risk in AF-treated patients but requires additional research for optimization and validation.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.