Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Standard

Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level. / Nielsen, Mia-Louise; Petersen, Troels Christian; Maul, Julia-Tatjana; Wu, Jashin J; Rasmussen, Mads Kirchheiner; Bertelsen, Trine; Ajgeiy, Kawa Khaled; Skov, Lone; Thomsen, Simon Francis; Thyssen, Jacob Pontoppidan; Egeberg, Alexander.

I: JAMA Dermatology, Bind 158, Nr. 10, 2022, s. 1149-1156.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Nielsen, M-L, Petersen, TC, Maul, J-T, Wu, JJ, Rasmussen, MK, Bertelsen, T, Ajgeiy, KK, Skov, L, Thomsen, SF, Thyssen, JP & Egeberg, A 2022, 'Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level', JAMA Dermatology, bind 158, nr. 10, s. 1149-1156. https://doi.org/10.1001/jamadermatol.2022.3171

APA

Nielsen, M-L., Petersen, T. C., Maul, J-T., Wu, J. J., Rasmussen, M. K., Bertelsen, T., Ajgeiy, K. K., Skov, L., Thomsen, S. F., Thyssen, J. P., & Egeberg, A. (2022). Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level. JAMA Dermatology, 158(10), 1149-1156. https://doi.org/10.1001/jamadermatol.2022.3171

Vancouver

Nielsen M-L, Petersen TC, Maul J-T, Wu JJ, Rasmussen MK, Bertelsen T o.a. Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level. JAMA Dermatology. 2022;158(10):1149-1156. https://doi.org/10.1001/jamadermatol.2022.3171

Author

Nielsen, Mia-Louise ; Petersen, Troels Christian ; Maul, Julia-Tatjana ; Wu, Jashin J ; Rasmussen, Mads Kirchheiner ; Bertelsen, Trine ; Ajgeiy, Kawa Khaled ; Skov, Lone ; Thomsen, Simon Francis ; Thyssen, Jacob Pontoppidan ; Egeberg, Alexander. / Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level. I: JAMA Dermatology. 2022 ; Bind 158, Nr. 10. s. 1149-1156.

Bibtex

@article{88eb5575149c4fa880f6afb45d33dfbe,
title = "Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level",
abstract = "IMPORTANCE Identifying the optimal long-term biologic therapy for patients with psoriasisis often done through trial and error.OBJECTIVE To identify the optimal biologic therapy for individual patients with psoriasisusing predictive statistical and machine learning models.DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study used data fromDanish nationwide registries, primarily DERMBIO, and included adult patients treated formoderate-to-severe psoriasis with biologics. Data were processed and analyzed betweenspring 2021 and spring 2022.MAIN OUTCOMES AND MEASURES Patient clusters of clinical relevance were identified andtheir success rates estimated for each drug. Furthermore, predictive prognostic models toidentify optimal biologic treatment at the individual level based on data from nationwideregistries were evaluated.RESULTS Assuming a success criterion of 3 years of sustained treatment, this study included2034 patients with a total of 3452 treatment series. Most treatment series involved malepatients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) hadfinished an education longer than primary school. The average ages were 24.9 years atpsoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decisiontrees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%,and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting insuccess, gradient boost and logistic regression had accuracies of 48.5% and 44.4%,top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%.CONCLUSIONS AND RELEVANCE Of the treatment prediction models used in this cohort studyof patients with psoriasis, gradient-boosted decision trees performed significantly betterthan logistic regression when predicting specific biologic therapy (by drug as well as target)leading to a treatment duration of at least 3 years without discontinuation. Predicting theoptimal biologic could benefit patients and clinicians by minimizing the number of failedtreatment attempts.",
author = "Mia-Louise Nielsen and Petersen, {Troels Christian} and Julia-Tatjana Maul and Wu, {Jashin J} and Rasmussen, {Mads Kirchheiner} and Trine Bertelsen and Ajgeiy, {Kawa Khaled} and Lone Skov and Thomsen, {Simon Francis} and Thyssen, {Jacob Pontoppidan} and Alexander Egeberg",
year = "2022",
doi = "10.1001/jamadermatol.2022.3171",
language = "English",
volume = "158",
pages = "1149--1156",
journal = "JAMA Dermatology",
issn = "2168-6068",
publisher = "The JAMA Network",
number = "10",

}

RIS

TY - JOUR

T1 - Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level

AU - Nielsen, Mia-Louise

AU - Petersen, Troels Christian

AU - Maul, Julia-Tatjana

AU - Wu, Jashin J

AU - Rasmussen, Mads Kirchheiner

AU - Bertelsen, Trine

AU - Ajgeiy, Kawa Khaled

AU - Skov, Lone

AU - Thomsen, Simon Francis

AU - Thyssen, Jacob Pontoppidan

AU - Egeberg, Alexander

PY - 2022

Y1 - 2022

N2 - IMPORTANCE Identifying the optimal long-term biologic therapy for patients with psoriasisis often done through trial and error.OBJECTIVE To identify the optimal biologic therapy for individual patients with psoriasisusing predictive statistical and machine learning models.DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study used data fromDanish nationwide registries, primarily DERMBIO, and included adult patients treated formoderate-to-severe psoriasis with biologics. Data were processed and analyzed betweenspring 2021 and spring 2022.MAIN OUTCOMES AND MEASURES Patient clusters of clinical relevance were identified andtheir success rates estimated for each drug. Furthermore, predictive prognostic models toidentify optimal biologic treatment at the individual level based on data from nationwideregistries were evaluated.RESULTS Assuming a success criterion of 3 years of sustained treatment, this study included2034 patients with a total of 3452 treatment series. Most treatment series involved malepatients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) hadfinished an education longer than primary school. The average ages were 24.9 years atpsoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decisiontrees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%,and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting insuccess, gradient boost and logistic regression had accuracies of 48.5% and 44.4%,top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%.CONCLUSIONS AND RELEVANCE Of the treatment prediction models used in this cohort studyof patients with psoriasis, gradient-boosted decision trees performed significantly betterthan logistic regression when predicting specific biologic therapy (by drug as well as target)leading to a treatment duration of at least 3 years without discontinuation. Predicting theoptimal biologic could benefit patients and clinicians by minimizing the number of failedtreatment attempts.

AB - IMPORTANCE Identifying the optimal long-term biologic therapy for patients with psoriasisis often done through trial and error.OBJECTIVE To identify the optimal biologic therapy for individual patients with psoriasisusing predictive statistical and machine learning models.DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study used data fromDanish nationwide registries, primarily DERMBIO, and included adult patients treated formoderate-to-severe psoriasis with biologics. Data were processed and analyzed betweenspring 2021 and spring 2022.MAIN OUTCOMES AND MEASURES Patient clusters of clinical relevance were identified andtheir success rates estimated for each drug. Furthermore, predictive prognostic models toidentify optimal biologic treatment at the individual level based on data from nationwideregistries were evaluated.RESULTS Assuming a success criterion of 3 years of sustained treatment, this study included2034 patients with a total of 3452 treatment series. Most treatment series involved malepatients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) hadfinished an education longer than primary school. The average ages were 24.9 years atpsoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decisiontrees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%,and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting insuccess, gradient boost and logistic regression had accuracies of 48.5% and 44.4%,top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%.CONCLUSIONS AND RELEVANCE Of the treatment prediction models used in this cohort studyof patients with psoriasis, gradient-boosted decision trees performed significantly betterthan logistic regression when predicting specific biologic therapy (by drug as well as target)leading to a treatment duration of at least 3 years without discontinuation. Predicting theoptimal biologic could benefit patients and clinicians by minimizing the number of failedtreatment attempts.

U2 - 10.1001/jamadermatol.2022.3171

DO - 10.1001/jamadermatol.2022.3171

M3 - Journal article

C2 - 35976663

VL - 158

SP - 1149

EP - 1156

JO - JAMA Dermatology

JF - JAMA Dermatology

SN - 2168-6068

IS - 10

ER -

ID: 316751840