Photo Credit: ArLawKa AungTun

The following is a summary of “Refining chronic pain phenotypes: A comparative analysis of sociodemographic and disease-related determinants using electronic health records,” published in the March 2025 issue of Journal of Pain by Begum et al. 

Electronic health records (EHRs) have become a key tool for chronic pain phenotyping, with structured data fields and algorithms improving accuracy.  

Researchers conducted a retrospective study to assess the accuracy of chronic pain case definitions derived from structured data elements and examined how validation metrics varied by sociodemographic and disease-related factors.  

They analyzed EHR data from 802 randomly selected adults diagnosed with autoimmune rheumatic diseases at a large academic center in 2019. Structured data elements were extracted to develop multiple phenotyping algorithms. Chronic pain case definitions were validated through manual chart review of clinical notes, and the performance of the derived algorithms was evaluated using sensitivity/recall, specificity, and positive predictive value (PPV).  

The results showed that the highest sensitivity of 67% was achieved using ICD codes alone, while specificity reached 96% with a quadrimodal algorithm integrating pain scores, ICD codes, prescriptions, and interventions. Specificity was generally greater among males and younger individuals, particularly those aged 18–40 years, and highest in Asian/Pacific Islander and privately insured individuals. The PPV was highest among females, younger individuals, and those with private insurance. The lowest PPV and sensitivity were observed in males, Asian/Pacific Islander, and older individuals.  

Investigators concluded that the inconsistency in chronic pain phenotyping results highlighted the necessity for improved algorithms within EHRs to achieve more accurate and generalizable patient identification. 

Source: jpain.org/article/S1526-5900(25)00001-X/abstract