TY - JOUR
T1 - A Validated Register-Based Algorithm to Identify Patients Diagnosed with Recurrence of Surgically Treated Stage I Lung Cancer in Denmark
AU - Rasmussen, Linda Aagaard
AU - Christensen, Niels Lyhne
AU - Winther-Larsen, Anne
AU - Dalton, Susanne Oksbjerg
AU - Virgilsen, Line Flytkjær
AU - Jensen, Henry
AU - Vedsted, Peter
N1 - © 2023 Rasmussen et al.
PY - 2023
Y1 - 2023
N2 - INTRODUCTION: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.MATERIAL AND METHODS: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.RESULTS: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18-46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7-91.1), a specificity of 93.8% (95% CI: 88.5-97.1), and a positive predictive value of 87.0% (95% CI: 76.7-93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%.CONCLUSION: The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.
AB - INTRODUCTION: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.MATERIAL AND METHODS: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.RESULTS: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18-46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7-91.1), a specificity of 93.8% (95% CI: 88.5-97.1), and a positive predictive value of 87.0% (95% CI: 76.7-93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%.CONCLUSION: The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.
U2 - 10.2147/CLEP.S396738
DO - 10.2147/CLEP.S396738
M3 - Article
C2 - 36890800
SN - 1179-1349
VL - 15
SP - 251
EP - 261
JO - Clinical Epidemiology
JF - Clinical Epidemiology
ER -