Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care : cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England
OBJECTIVE: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting.
METHODS: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER).
RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY.
CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
|---|---|
| Enthalten in: |
Journal of medical economics - 25(2022), 1 vom: 15. Jan., Seite 974-983 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hill, Nathan R [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Atrial fibrillation |
|---|
| Anmerkungen: |
Date Completed 05.08.2022 Date Revised 05.08.2022 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1080/13696998.2022.2102355 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM343546973 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM343546973 | ||
| 003 | DE-627 | ||
| 005 | 20231226020714.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1080/13696998.2022.2102355 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1145.xml |
| 035 | |a (DE-627)NLM343546973 | ||
| 035 | |a (NLM)35834373 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hill, Nathan R |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care |b cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 05.08.2022 | ||
| 500 | |a Date Revised 05.08.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting | ||
| 520 | |a METHODS: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER) | ||
| 520 | |a RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY | ||
| 520 | |a CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Randomized Controlled Trial | |
| 650 | 4 | |a Atrial fibrillation | |
| 650 | 4 | |a H | |
| 650 | 4 | |a H5 | |
| 650 | 4 | |a H51 | |
| 650 | 4 | |a I | |
| 650 | 4 | |a I00 | |
| 650 | 4 | |a cost-effectiveness | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a neural network | |
| 650 | 4 | |a risk prediction | |
| 650 | 4 | |a screening | |
| 700 | 1 | |a Groves, Lara |e verfasserin |4 aut | |
| 700 | 1 | |a Dickerson, Carissa |e verfasserin |4 aut | |
| 700 | 1 | |a Boyce, Rebecca |e verfasserin |4 aut | |
| 700 | 1 | |a Lawton, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Hurst, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Pollock, Kevin G |e verfasserin |4 aut | |
| 700 | 1 | |a Sugrue, Daniel M |e verfasserin |4 aut | |
| 700 | 1 | |a Lister, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a Arden, Chris |e verfasserin |4 aut | |
| 700 | 1 | |a Davies, D Wyn |e verfasserin |4 aut | |
| 700 | 1 | |a Martin, Anne-Celine |e verfasserin |4 aut | |
| 700 | 1 | |a Sandler, Belinda |e verfasserin |4 aut | |
| 700 | 1 | |a Gordon, Jason |e verfasserin |4 aut | |
| 700 | 1 | |a Farooqui, Usman |e verfasserin |4 aut | |
| 700 | 1 | |a Clifton, David |e verfasserin |4 aut | |
| 700 | 1 | |a Mallen, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Rogers, Jennifer |e verfasserin |4 aut | |
| 700 | 1 | |a Camm, A John |e verfasserin |4 aut | |
| 700 | 1 | |a Cohen, Alexander T |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of medical economics |d 1999 |g 25(2022), 1 vom: 15. Jan., Seite 974-983 |w (DE-627)NLM098274902 |x 1941-837X |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2022 |g number:1 |g day:15 |g month:01 |g pages:974-983 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1080/13696998.2022.2102355 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 2022 |e 1 |b 15 |c 01 |h 974-983 | ||