Consistency analysis of perioperative self-reported pain scores and clinical records in patients with lung cancer / 中国胸心血管外科临床杂志

ABSTRACT

@#Objective     To analyse the consistency of perioperative self-reported pain scores of lung cancer patients with clinical records to provide a basis for optimal pain management. Methods    The patients with lung cancer who underwent surgical treatment in the Department of Thoracic Surgery, Sichuan Cancer Hospital from November 2017 to January 2020 were selected. They were divided into two groups based on the source of pain data. The self-report group used a questionnaire in which patients self-reported their pain scores, and the pain scores for the clinical record group were extracted from the electronic medical record system. Kappa test was used to compare the concordance of pain scores between the two groups preoperatively, on postoperative 1-6 days and on the day of discharge. McNemar's paired χ2 test was used to compare the differences in pain intensity levels between the two groups. Binary logistic multi-factor regression was used to analyse the factors influencing the concordance of severe pain (7-10 points) between the two groups. Results     Totally 354 patients were collected, including 191 males and 163 females, with an average age of 55.64± 10.34 years. The median postoperative hospital stay was 6 days. The consistency of pain scores between the two groups was poor (Kappa=–0.035 to 0.262, P<0.05), and the distribution of pain levels at each time point was inconsistent and statistically significant (P<0.001). The percentage of inconsistent severe pain assessment ranged from 0.28% to 35.56%, with the highest percentage of inconsistent severe pain assessment on postoperative day 1 (35.56%). Single-port thoracoscopic surgical access was an influencing factor for inconsistent assessment of severe pain on postoperative day 3 (OR=2.571, P=0.005). Conclusion     Self-reported perioperative pain scores of lung cancer patients are poorly aligned with clinical records. Clinical measures are needed to improve the accuracy of patient pain data reporting by choosing the correct assessment method, increasing education, and developing effective quality control measures.