Tanısal ve Prognostik Çalışmalar (TRIPOD)
Özet
TRIPOD rehberi, sağlık araştırmalarında hastalık tanısı ve gidişatını öngören çok değişkenli tahmin modellerinin şeffaf ve standart bir şekilde raporlanmasını sağlayan 22 maddelik bir kılavuzdur. Bu rehber, model geliştirme ve doğrulama süreçlerinde kullanılan veri kaynakları, katılımcı özellikleri, belirleyiciler ve istatistiksel analiz yöntemlerinin detaylıca sunulmasını hedefleyerek çalışmaların bilimsel niteliğini ve yayımlanma şansını artırır. Özellikle klinik tahmin modellerine odaklanan bu araç, araştırmacıların metodolojik hataları minimize etmelerine ve okuyucuların elde edilen risk skorlarını doğru değerlendirmelerine yardımcı olur.
The TRIPOD statement is a 22-item checklist designed to ensure the transparent and standardized reporting of multivariable prediction models used for diagnosis and prognosis in health research. It provides guidance on documenting data sources, participant characteristics, predictors, and statistical analysis methods during both model development and validation phases to enhance scientific quality and publication potential. By focusing on clinical prediction rules, this tool helps researchers minimize methodological biases and allows readers to accurately evaluate the resulting risk scores and probabilities.
Referanslar
World Health Organization (WHO). Good clinical diagnostic practice Good clinical diagnostic practice. Cairo; 2005.
Hendriksen JMT, J GG, M MKG, De Groot JAH. Diagnostic and prognostic prediction models. J Thromb Haemost. 2013;11:129–41.
Mathes T, Pieper D. An algorithm for the classification of study designs to assess diagnostic , prognostic and predictive test accuracy in systematic reviews. BMC Syst Rev. 2019;8(226):1–8.
Equator Network [Internet]. Available from: https://www.equator-network.org/
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 2015;13(1):1–10.
Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162(1):W1–73.
Heus P, Reitsma JB, Collins GS, Damen JAAG, Scholten RJPM, Altman DG, et al. Transparent Reporting of Multivariable Prediction Models in Journal and Conference Abstracts : TRIPOD for Abstracts. Ann Intern Med. 2020;173(1):42–7.
Glass TA, Goodman SN, Hernan MA, Samet JM. Causal Inference in Public Health. Annu Rev Public Heal. 2013;34:61–75.
Wynants L, Calster B Van, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19 : systematic review and critical appraisal. Br Med J. 2020;369:1–16.
Cook NR. Statistical Evaluation of Prognostic versus Diagnostic Models : Beyond the ROC Curve. Clin Chem. 2008;54(1):17–23.
Smeden M Van, Reitsma JB, Riley RD, Collins GS, Gm K, Biomedical O, et al. Clinical prediction models : diagnosis versus prognosis. J Clin Epidemiol [Internet]. 2021;132:142–5. Available from: http://dx.doi.org/10.1016/j.jclinepi.2021.01.009
The Glasgow Structured Approach to Aassessment of the Glasgow Coma Scale [Internet]. Available from: https://www.glasgowcomascale.org/
The Apgar Score [Internet]. Available from: https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2015/10/the-apgar-score
Beck Depression Inventory (BDI) [Internet]. Available from: https://www.apa.org/pi/about/publications/caregivers/practice-settings/assessment/tools/beck-depression
Bircan H. Lojistik Regresyon Analizi : Tıp Verileri Üzerine Bir Uygulama. Kocaeli Üniversitesi Sos Bilim Enstitüsü Derg [Internet]. 2004;2:185–208. Available from: https://dergipark.org.tr/tr/download/article-file/252030
Sertkaya D, Ata N, Sözer MT. Yaşam çözümlemesinde zamana bağlı açıklayıcı değişkenli Cox regresyon modeli. Ankara Üniversitesi Tıp Fakültesi Mecmuası [Internet]. 2005;58:153–8. Available from: http://dergiler.ankara.edu.tr/dergiler/36/204/1671.pdf
Hayran M, Hayran M. Sağlık Araştırmaları İçin Temel İstatistik. Birinci. Ankara: Med-Litera; 2011.
Çapan BE, Arıcıoğlu A. Psikolojik Sağlamlığın Yordayıcısı Olarak Affedicilik. e-Internationa J Educ Res. 2014;5(4):70–82.
Eskiocak M, Akbaşak D. Edirne’de Romanların sağlığı: Sağlığı sosyal belirleyicileri ve sağlık durumlarına yönelik bulgular. Turkish J Public Heal. 2017;15(2):2017.
Fusar-Poli P, Rutigliano G, Stahl D, Davies C, Bonoldi I, Reilly T, et al. Development and Validation of a Clinically Based Risk Calculator for the Transdiagnostic Prediction of Psychosis. JAMA Psychiatry. 2017;74(5):493–500.
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease : prospective cohort study. Br Med J [Internet]. 2017;2099(May):1–21. Available from: http://dx.doi.org/doi:10.1136/bmj.j2099
de Vin T, Engels B, Gevaert T, Storme G, De Ridder M. Stereotactic radiotherapy for oligometastatic cancer: A prognostic model for survival. Ann Oncol [Internet]. 2014;25(2):467–71. Available from: https://doi.org/10.1093/annonc/mdt537
Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: Independent and external validation of an updated version of QRISK2. BMJ. 2012;345(7867):1–12.
Hak E, Wei F, Nordin J, Mullooly J, Poblete S, Nichol KL. Development and Validation of a Clinical Prediction Rule for Hospitalization Due to Pneumonia or Influenza or Death during Influenza Epidemics among Community-Dwelling Elderly Persons. J Infect Dis. 2004;189(3):450–8.
Schmidt M, Burrell A, Roberts L, Bailey M, Sheldrake J, Rycus PT, et al. Predicting survival after ECMO for refractory cardiogenic shock: The survival after veno-arterial-ECMO (SAVE)-score. Eur Heart J. 2015;36(33):2246–56.
Harrison DA, Brady AR, Parry GJ, Carpenter JR, Rowan K. Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom. Crit Care Med. 2006;34(5):1378–88.
Tangri N, Stevens LA, Griffith J, Tighiouart H, Djurdjev O, Naimark D, et al. A predictive model for progression of chronic kidney disease to kidney failure. J Am Med Assoc. 2011;305(15):1553–9.