biostudies-literatureUnknown12(9)Ortega-Marquez J<h4>Background</h4>Phantom limb pain (PLP) is highly prevalent after amputation. However, the influence of non-painful sensations (PLS) remains unclear. This study examines the PLP-PLS index as a novel tool to differentiate PLP from PLS and explores the association of clinical factors with the index.<h4>Methods</h4>We conducted a cross-sectional analysis of baseline data from 112 participants in a previous factorial trial in patients with unilateral traumatic lower limb amputation. Linear regression models were used to examine the associations between the index and various demographic, psychological and clinical factors. Logistic and Poisson regression, and e-value calculation were utilized for sensitivity analyses.<h4>Results</h4>Adjusted multivariable linear regression models demonstrateBiomedicines2035https://www.ebi.ac.uk/biostudies/studies/S-EPMC11429012biostudies-literatureClinical Characteristics Associated with the PLP-PLS Index, a New Potential Metric to Phenotype Phantom Limb Pain.PMC11429012Grutzmacher EBPacheco-Barrios KPalagi Vigano AVVallejos-Penaloza GMartinez-Magallanes DMena Lde Paula-Garcia WNGarnier JOrtega-Marquez JVaz de Macedo ACosta VSchwartz DSFregni FfalseClinical Characteristics Associated with the PLP-PLS Index, a New Potential Metric to Phenotype Phantom Limb Pain.<h4>Background</h4>Phantom limb pain (PLP) is highly prevalent after amputation. However, the influence of non-painful sensations (PLS) remains unclear. This study examines the PLP-PLS index as a novel tool to differentiate PLP from PLS and explores the association of clinical factors with the index.<h4>Methods</h4>We conducted a cross-sectional analysis of baseline data from 112 participants in a previous factorial trial in patients with unilateral traumatic lower limb amputation. Linear regression models were used to examine the associations between the index and various demographic, psychological and clinical factors. Logistic and Poisson regression, and e-value calculation were utilized for sensitivity analyses.<h4>Results</h4>Adjusted multivariable linear regression models demonstrate2024-01-01T00:00:00Z2024 Sep2025-04-04T00:51:47.102Z2025-04-04T00:51:47.102ZS-EPMC114290123933554810.3390/biomedicines12092035