| Dokumentenart: | Artikel | ||||
|---|---|---|---|---|---|
| Titel eines Journals oder einer Zeitschrift: | BMC Medical Research Methodology | ||||
| Verlag: | BMC | ||||
| Ort der Veröffentlichung: | LONDON | ||||
| Band: | 22 | ||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 1 | ||||
| Datum: | 2022 | ||||
| Institutionen: | Medizin > Zentren des Universitätsklinikums Regensburg > Tumorzentrum e.V. | ||||
| Identifikationsnummer: |
| ||||
| Stichwörter / Keywords: | EARLY BREAST-CANCER; FOLLOW-UP; SURVEILLANCE; SURVIVAL; PROGRAM; IMPACT; Personalised screening; Risk prediction; Simulation; Healthcare efficiency; Medical decision making; cancer registries | ||||
| Dewey-Dezimal-Klassifikation: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin | ||||
| Status: | Veröffentlicht | ||||
| Begutachtet: | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden: | Ja | ||||
| Dokumenten-ID: | 57270 |
Web of ScienceZusammenfassung
Background Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual's risk development over time would allow assessing not just who to screen but also when to screen. This ...
Zusammenfassung
Background Risk-prediction tools allow classifying individuals into risk groups based on risk thresholds. Such risk categorization is often used to inform screening schemes by offering screening only to individuals at increased risk of harmful events. Adding information concerning an individual's risk development over time would allow assessing not just who to screen but also when to screen. This paper illustrates the value of personalised, time-dependent risk predictions to optimize risk-based screening schemes. Methods In a simulation analysis, two different time-dependent risk-based screening approaches are compared to another risk-based, but time-independent approach regarding their impact on screening efficiency. For this purpose, 81 scenarios featuring 5000 patients with five consecutive annual risk estimations for a hypothetical disease D are simulated, using different parameters to model disease progression and risk distribution. This simulation analysis is validated using a real-world clinical case study based on German breast cancer patients and the INFLUENCE-nomogram for locoregional breast cancer recurrence. Results If individual risk estimations were used to personalise screening for a disease D aiming at detecting a 90% of curable cases, more than 20% of screening examinations could be avoided relative to a conventional uninformed approach, depending on the simulated scenario. Whereas an individual but time-independent approach is associated with acceptable saving potentials in case of a relatively homogenous risk distribution, the time-dependent approaches are superior when the complexity of a scenario increases. With slowly progressing diseases, risk-accumulation over time needs to be considered to achieve the highest screening efficiency on population level, for rapidly progressing diseases, an interval-specific approach is superior. The possible benefits of time-dependent risk-based screening were confirmed in the real-world clinical case study. Conclusions Appropriate approaches to use time-dependent risk predictions may considerably enhance screening efficiency on individual and population level. Therefore, predicting risk development over time should be supported by future prediction tools and be incorporated in decision algorithms.
Metadaten zuletzt geändert: 29 Feb 2024 12:53
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