TY - JOUR
T1 - Development and validation of algorithms for identifying lines of therapy in multiple myeloma using real-world data
AU - Ailawadhi, Sikander
AU - Romanus, Dorothy
AU - Shah, Surbhi
AU - Fraeman, Kathy
AU - Saragoussi, Delphine
AU - Buus, Rebecca Morris
AU - Nguyen, Binh
AU - Cherepanov, Dasha
AU - Lamerato, Lois
AU - Berger, Ariel
PY - 2024/5/1
Y1 - 2024/5/1
N2 - Aim: To validate algorithms based on electronic health data to identify composition of lines of therapy (LOT) in multiple myeloma (MM). Materials & methods: This study used available electronic health data for selected adults within Henry Ford Health (Michigan, USA) newly diagnosed with MM in 2006-2017. Algorithm performance in this population was verified via chart review. As with prior oncology studies, good performance was defined as positive predictive value (PPV) ≥75%. Results: Accuracy for identifying LOT1 (N = 133) was 85.0%. For the most frequent regimens, accuracy was 92.5-97.7%, PPV 80.6-93.8%, sensitivity 88.2-89.3% and specificity 94.3-99.1%. Algorithm performance decreased in subsequent LOTs, with decreasing sample sizes. Only 19.5% of patients received maintenance therapy during LOT1. Accuracy for identifying maintenance therapy was 85.7%; PPV for the most common maintenance therapy was 73.3%. Conclusion: Algorithms performed well in identifying LOT1 - especially more commonly used regimens - and slightly less well in identifying maintenance therapy therein.
AB - Aim: To validate algorithms based on electronic health data to identify composition of lines of therapy (LOT) in multiple myeloma (MM). Materials & methods: This study used available electronic health data for selected adults within Henry Ford Health (Michigan, USA) newly diagnosed with MM in 2006-2017. Algorithm performance in this population was verified via chart review. As with prior oncology studies, good performance was defined as positive predictive value (PPV) ≥75%. Results: Accuracy for identifying LOT1 (N = 133) was 85.0%. For the most frequent regimens, accuracy was 92.5-97.7%, PPV 80.6-93.8%, sensitivity 88.2-89.3% and specificity 94.3-99.1%. Algorithm performance decreased in subsequent LOTs, with decreasing sample sizes. Only 19.5% of patients received maintenance therapy during LOT1. Accuracy for identifying maintenance therapy was 85.7%; PPV for the most common maintenance therapy was 73.3%. Conclusion: Algorithms performed well in identifying LOT1 - especially more commonly used regimens - and slightly less well in identifying maintenance therapy therein.
KW - algorithms
KW - electronic health databases
KW - lines of therapy
KW - multiple myeloma
KW - validation
UR - http://www.scopus.com/inward/record.url?scp=85188988546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85188988546&partnerID=8YFLogxK
U2 - 10.2217/fon-2023-0696
DO - 10.2217/fon-2023-0696
M3 - Article
C2 - 38231002
AN - SCOPUS:85188988546
SN - 1479-6694
VL - 20
SP - 981
EP - 995
JO - Future oncology (London, England)
JF - Future oncology (London, England)
IS - 15
ER -