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New prognostic model enhances methotrexate monitoring for immune-mediated inflammatory diseases.
A newly developed prognostic model could revolutionize the monitoring of long-term methotrexate treatment, a commonly prescribed medication for immune-mediated inflammatory diseases.
The model, developed through a retrospective cohort study, has demonstrated impressive accuracy in predicting the risk of discontinuing methotrexate due to abnormal monitoring of blood test results. By providing valuable insights into patient outcomes, these data have the potential to assist physicians in making informed decisions on the frequency of blood tests, optimizing resource utilization, and ultimately enhancing care for this patient population.
Methotrexate is commonly prescribed for immune-mediated inflammatory diseases (IMIDs) such as rheumatoid arthritis (RA), psoriasis (PsO), and inflammatory bowel disease (IBD). Regular monitoring of a patient's blood is crucial during long-term methotrexate treatment in order to detect potential abnormalities and ensure patient safety.
However, investigators noted the optimal frequency of monitoring remains uncertain. To address this issue, a retrospective cohort study was conducted to develop and validate a prognostic model that can inform risk-stratified decisions on the frequency of monitoring blood tests during long-term methotrexate treatment.
Georgina Nakafero, PhD, MPH, Academic Rheumatology, University of Nottingham, and a team of investigators utilized electronic health records from the UK's Clinical Practice Research Datalink (CPRD) Gold and CPRD Aurum databases.
Included patients were adults (≥18 years) with a diagnosis of IMID who had been prescribed methotrexate for 6 months or more from 2007-2019. The main outcome of the investigation was the measure of discontinuation of methotrexate due to abnormal monitoring of blood test results.
The cohort was followed up from 6 months after the first prescription of methotrexate in primary care until the earliest of several endpoints, including discontinuation of methotrexate, leaving the practice, death, 5 years, or December 2019.
Investigators performed Cox regression analysis to develop the risk equation, and from there they adjusted for optimism in predictor effects. They also employed multiple imputation techniques to handle missing predictor data.
The performance of the model was evaluated in terms of calibration and discrimination.
The analysis included a total of 13,110 participants with 854 events and 23,999 participants with 1,486 events, which led investigators to the development and validation cohorts, respectively. The prognostic model incorporated 11 candidate predictors which comprised of 17 parameters.
The development dataset revealed an optimism-adjusted R2 at 0.13, and the optimism-adjusted Royston D statistic (a measure of discrimination) was 0.79. Results also showed the calibration slope and Royston D statistic in the validation dataset were 0.94 (95% confidence interval [CI] 0.85 - 1.02) and 0.75 (95% CI 0.67 to 0.83).
Ultimately these findings demonstrated good performance in predicting outcomes in clinically relevant subgroups defined by age group, type of IMID, and methotrexate dose.
Based on the results of this retrospective cohort study, investigators were able to successfully develop and validate a prognostic model that utilized the collected information during routine clinical care. The team's goal is that the model can be used to stratify the risk of discontinuing methotrexate due to abnormal blood test monitoring during long-term treatment.
Additionally, they noted the model could guide in informing decisions related to the frequency of blood tests, optimizing resource utilization while improving patient care, with the identification of higher-risk patients. Though, the study acknowledged that further research and validation in diverse clinical settings are warranted to establish the generalizability and utility of this model.