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Jannello LMI, Incesu RB, Morra S, Scheipner L, Baudo A, de Angelis M, Siech C, Tian Z, Goyal JA, Luzzago S, Mistretta FA, Ferro M, Saad F, Shariat SF, Chun FKH, Briganti A, Tilki D, Ahyai S, Carmignani L, Longo N, de Cobelli O, Musi G, Karakiewicz PI. The European Network for the Study of Adrenal Tumors staging system (2015): a United States validation. J Clin Endocrinol Metab 2024:dgae047. [PMID: 38266758 DOI: 10.1210/clinem/dgae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/05/2023] [Accepted: 01/22/2024] [Indexed: 01/26/2024]
Abstract
OBJECTIVE To test the ability of the 2015 modified version of the European Network for the Study of Adrenal Tumors-staging system (mENSAT) in predicting cancer specific-mortality (CSM), as well as overall mortality (OM) in adrenocortical carcinoma (ACC) patients of all stages, in a large scale, and contemporary United States cohort. METHODS We relied on the Surveillance, Epidemiology, and End Results (SEER) database (2004-2020) to test the accuracy and calibration of the mENSAT and subsequently compared it to the 8th edition of the American Joint Committee on Cancer-staging system (AJCC). RESULTS In 858 ACC patients, mENSAT accuracy was 74.7% for three-year CSM predictions and 73.8% for three-year OM predictions. The maximum departures from ideal predictions in mENSAT were +17.2% for CSM and +11.8% for OM. Conversely, AJCC accuracy was 74.5% for three-year CSM predictions and 73.5% for three-year OM predictions. The maximum departures from ideal predictions in AJCC were -6.7% for CSM and -7.1% for OM. CONCLUSION The accuracy of mENSAT is virtually the same as that of AJCC in predicting CSM (74.7 vs. 74.5%) and OM (73.7 vs. 73.5%). However, calibration is lower for mENSAT than for AJCC. In consequence, no obvious benefit appears to be associated with the use of mENSAT relative to AJCC in United States ACC patients.
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Affiliation(s)
- Letizia Maria Ippolita Jannello
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Reha-Baris Incesu
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Morra
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Department of Neurosciences, Science of Reproduction and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Lukas Scheipner
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Department of Urology, Medical University of Graz, Graz, Austria
| | - Andrea Baudo
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Università degli Studi di Milano, Milan, Italy
- Department of Urology, IRCCS Policlinico San Donato, Milan, Italy
| | - Mario de Angelis
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Division of Experimental Oncology/Unit of Urology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Carolin Siech
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
- Department of Urology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
| | - Zhe Tian
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
| | - Jordan A Goyal
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
| | - Stefano Luzzago
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco A Mistretta
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Matteo Ferro
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Fred Saad
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
| | - Shahrokh F Shariat
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
- Department of Urology, Weill Cornell Medical College, New York, New York, USA
- Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Hourani Center of Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
| | - Felix K H Chun
- Department of Urology, University Hospital Frankfurt, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
| | - Alberto Briganti
- Division of Experimental Oncology/Unit of Urology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Derya Tilki
- Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- Department of Urology, Koc University Hospital, Istanbul, Turkey
| | - Sascha Ahyai
- Department of Urology, Medical University of Graz, Graz, Austria
| | - Luca Carmignani
- Department of Urology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Urology, IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy
| | - Nicola Longo
- Department of Neurosciences, Science of Reproduction and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Ottavio de Cobelli
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology; IEO European Institute of Oncology, IRCCS, Via Ripamonti 435, Milan, Italy
- Department of Oncology and Haemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Pierre I Karakiewicz
- Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montréal Health Center, Montréal, Québec, Canada
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Archer L, Snell KIE, Stynes S, Axén I, Dunn KM, Foster NE, Wynne-Jones G, van der Windt DA, Hill JC. Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings. Phys Ther 2023; 103:pzad128. [PMID: 37756617 PMCID: PMC10682973 DOI: 10.1093/ptj/pzad128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 05/05/2023] [Accepted: 07/14/2023] [Indexed: 09/29/2023]
Abstract
OBJECTIVE The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings. METHODS Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance. RESULTS Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]). CONCLUSION Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance. IMPACT External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.
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Affiliation(s)
- Lucinda Archer
- School of Medicine, Keele University, Keele, Staffordshire, UK
- Institute for Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Kym I E Snell
- School of Medicine, Keele University, Keele, Staffordshire, UK
- Institute for Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK
| | - Siobhán Stynes
- School of Medicine, Keele University, Keele, Staffordshire, UK
- Midlands Partnership Foundation NHS Trust, North Staffordshire Musculoskeletal Interface Service, Haywood Hospital, Staffordshire, UK
| | - Iben Axén
- Unit of Intervention and Implementation Research for Worker Health, Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Stockholm, Sweden
| | - Kate M Dunn
- School of Medicine, Keele University, Keele, Staffordshire, UK
| | - Nadine E Foster
- School of Medicine, Keele University, Keele, Staffordshire, UK
- Surgical Treatment and Rehabilitation Service (STARS) Education and Research Alliance, The University of Queensland and Metro North Hospital and Health Service, Queensland, Australia
| | | | | | - Jonathan C Hill
- School of Medicine, Keele University, Keele, Staffordshire, UK
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Lotfaliany M, Hadaegh F, Mansournia MA, Azizi F, Oldenburg B, Khalili D. Performance of Stepwise Screening Methods in Identifying Individuals at High Risk of Type 2 Diabetes in an Iranian Population. Int J Health Policy Manag 2022; 11:1391-1400. [PMID: 34060272 PMCID: PMC9808334 DOI: 10.34172/ijhpm.2021.22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 03/10/2021] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Recent evidence recommended stepwise screening methods for identifying individuals at high risk of type 2 diabetes to be recruited in the lifestyle intervention programs for the prevention of the disease. This study aims to assess the performance of different stepwise screening methods that combine non-invasive measurements with lab-based measurements for identifying those with 5-years incident type 2 diabetes. METHODS 3037 participants aged ≥30 years without diabetes at baseline in the Tehran Lipid and Glucose Study (TLGS) were followed. Thirty-two stepwise screening methods were developed by combining a non-invasive measurement (an anthropometric measurement (waist-to-height ratio, WtHR) or a score based on a non-invasive risk score [Australian Type 2 Diabetes Risk Assessment Tool, AUSDRISK]) with a lab-based measurement (different cut-offs of fasting plasma glucose [FPG] or predicted risk based on three lab-based prediction models [Saint Antonio, SA; Framingham Offspring Study, FOS; and the Atherosclerosis Risk in Communities, ARIC]). The validation, calibration, and usefulness of lab-based prediction models were assessed before developing the stepwise screening methods. Cut-offs were derived either based on previous studies or decision-curve analyses. RESULTS 203 participants developed diabetes in 5 years. Lab-based risk prediction models had good discrimination power (area under the curves [AUCs]: 0.80-0.83), achieved acceptable calibration and net benefits after recalibration for population's characteristics and were useful in a wide range of risk thresholds (5%-21%). Different stepwise methods had sensitivity ranged 20%-68%, specificity 70%-98%, and positive predictive value (PPV) 14%-46%; they identified 3%-33% of the screened population eligible for preventive interventions. CONCLUSION Stepwise methods have acceptable performance in identifying those at high risk of incident type 2 diabetes.
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Affiliation(s)
- Mojtaba Lotfaliany
- Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Barwon Health, Geelong, VIC, Australia
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Brian Oldenburg
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- WHO Collaborating Centre on Implementation Research for Prevention & Control of NCDs, University of Melbourne, Melbourne, VIC, Australia
| | - Davood Khalili
- Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Wu S, Zeng N, Sun F, Zhou J, Wu X, Sun Y, Wang B, Zhan S, Kong Y, Jia J, You H, Yang HI. Hepatocellular Carcinoma Prediction Models in Chronic Hepatitis B: A Systematic Review of 14 Models and External Validation. Clin Gastroenterol Hepatol 2021; 19:2499-2513. [PMID: 33667678 DOI: 10.1016/j.cgh.2021.02.040] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS The aim of our study was to characterize the performance of hepatocellular carcinoma (HCC) prediction models in chronic hepatitis B (CHB) patients through meta-analysis followed by external validation. METHODS We performed a systematic review and meta-analysis of current literature, followed by external validation in independent multi-center cohort with 986 patients with CHB undergoing entecavir treatment (median follow-up: 4.7 years). Model performance to predict HCC within 3, 5, 7, and 10 years was assessed using area under receiver operating characteristic curve (AUROC) and calibration index. Subgroup analysis were conducted by treatment status, cirrhotic, race and baseline alanine aminotransferase. RESULTS We identified 14 models with 123,885 patients (5,452 HCC cases), with REACH-B, CU-HCC, GAG-HCC, PAGE-B and mPAGE-B models being broadly externally validated. Discrimination was generally acceptable for all models, with pooled AUC ranging from 0.70 (95% CI, 0.63-0.76 for REACH-B) to 0.83 (95% CI, 0.78-0.87 for REAL-B) for 3-year, 0.68 (95% CI, 0.64-0.73 for REACH-B) to 0.81 (95% CI, 0.77-0.85 for REAL-B) for 5-year and 0.70 (95% CI, 0.58-0.80 for PAGE-B) to 0.81 (95% CI, 0.78-0.84 for REAL-B and 0.77-0.86 for AASL-HCC) for 10-year prediction. However, calibration performance was poorly reported in most studies. In external validation cohort, REAL-B showed highest discrimination with 0.76 (95% CI, 0.69-0.83) and 0.75 (95% CI, 0.70-0.81) for 3 and 5-year prediction. The REAL-B model was also well calibrated in the external validation cohort (3-year Brier score 0.066). Results were consistent in subgroup analyses. CONCLUSIONS In a systematic review of available HCC models, the REAL-B model exhibited best discrimination and calibration.
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Affiliation(s)
- Shanshan Wu
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Na Zeng
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Feng Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, Mainland China
| | - Jialing Zhou
- Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Xiaoning Wu
- Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Yameng Sun
- Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Bingqiong Wang
- Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Siyan Zhan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, Mainland China
| | - Yuanyuan Kong
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Jidong Jia
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China; Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China
| | - Hong You
- National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China; Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, Beijing Friendship Hospital, Capital Medical University, Beijing, Mainland China.
| | - Hwai-I Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan; Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
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Al-Farra H, de Mol BAJM, Ravelli ACJ, Ter Burg WJPP, Houterman S, Henriques JPS, Abu-Hanna A, Vis MM, Vos J, Timmers L, Tonino WAL, Schotborgh CE, Roolvink V, Porta F, Stoel MG, Kats S, Amoroso G, van der Werf HW, Stella PR, de Jaegere P. Update and, internal and temporal-validation of the FRANCE-2 and ACC-TAVI early-mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) using data from the Netherlands heart registration (NHR). Int J Cardiol Heart Vasc 2021; 32:100716. [PMID: 33537406 PMCID: PMC7843396 DOI: 10.1016/j.ijcha.2021.100716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 12/30/2020] [Accepted: 01/04/2021] [Indexed: 01/08/2023]
Abstract
Background The predictive performance of the models FRANCE-2 and ACC-TAVI for early-mortality after Transcatheter Aortic Valve Implantation (TAVI) can decline over time and can be enhanced by updating them on new populations. We aim to update and internally and temporally validate these models using a recent TAVI-cohort from the Netherlands Heart Registration (NHR). Methods We used data of TAVI-patients treated in 2013-2017. For each original-model, the best update-method (model-intercept, model-recalibration, or model-revision) was selected by a closed-testing procedure. We internally validated both updated models with 1000 bootstrap samples. We also updated the models on the 2013-2016 dataset and temporally validated them on the 2017-dataset. Performance measures were the Area-Under ROC-curve (AU-ROC), Brier-score, and calibration graphs. Results We included 6177 TAVI-patients, with 4.5% observed early-mortality. The selected update-method for FRANCE-2 was model-intercept-update. Internal validation showed an AU-ROC of 0.63 (95%CI 0.62-0.66) and Brier-score of 0.04 (0.04-0.05). Calibration graphs show that it overestimates early-mortality. In temporal-validation, the AU-ROC was 0.61 (0.53-0.67).The selected update-method for ACC-TAVI was model-revision. In internal-validation, the AU-ROC was 0.63 (0.63-0.66) and Brier-score was 0.04 (0.04-0.05). The updated ACC-TAVI calibrates well up to a probability of 20%, and subsequently underestimates early-mortality. In temporal-validation the AU-ROC was 0.65 (0.58-0.72). Conclusion Internal-validation of the updated models FRANCE-2 and ACC-TAVI with data from the NHR demonstrated improved performance, which was better than in external-validation studies and comparable to the original studies. In temporal-validation, ACC-TAVI outperformed FRANCE-2 because it suffered less from changes over time.
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Key Words
- ACC-TAVI (ACC TVT), American College of Cardiology Transcatheter Valve Therapy
- AU-PRC, Area Under the Precision-Recall Curve
- AU-ROC, Area Under the Receiver Operating-Characteristic Curve
- Amsterdam UMC, Amsterdam University Medical Center - location AMC (Academic Medical Center)
- BSS, Brier-skill score
- Closed-testing procedure
- EuroSCORE, European System for Cardiac Operative Risk Evaluation
- External Validation
- FRANCE-2, French Aortic National CoreValve and Edwards [15]
- LVEF, Left Ventricular Ejection Fraction
- MPM, Mortality Prediction Models
- Model recalibration
- Model updating
- NHR, Netherlands Heart Registration (“Nederlandse Hart Registratie in Dutch”)
- NYHA, New York Heart Association
- Prediction models
- SAVR, Surgical Aortic Valve Replacement
- TAVI (TAVR), Transcatheter Aortic Valve Implantation (Replacement)
- Transcatheter Aortic Valve Implantation (TAVI)
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Affiliation(s)
- Hatem Al-Farra
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.,Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Bas A J M de Mol
- Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Anita C J Ravelli
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - W J P P Ter Burg
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | | | - José P S Henriques
- Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - M M Vis
- Heart Center, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - J Vos
- Amphia Hospital, the Netherlands
| | - L Timmers
- St. Antonius Hospital, the Netherlands
| | | | | | | | - F Porta
- Leeuwarden Medical Center, the Netherlands
| | - M G Stoel
- Medisch Spectrum Twente, the Netherlands
| | - S Kats
- Maastricht University Medical Center, the Netherlands
| | - G Amoroso
- Onze Lieve Vrouwe Gasthuis, the Netherlands
| | | | - P R Stella
- University Medical Center Utrecht, the Netherlands
| | - P de Jaegere
- Erasmus University Medical Center, the Netherlands
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