Net Reclassification Improvement: Computation, Interpretation, and Controversies: A Literature Review and Clinician's Guide

Summary of the second report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel II)

Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944

Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III)

Expert Panel on Detection, E. (2001) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.285.19.2486 · Cited by 26,011

Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.

(2002) · Circulation · doi:10.1161/circ.106.25.3143 · Cited by 14,092

Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)

Grundy, S. M., Becker, D. M., Clark, L. T., Cooper, R., Denke, M. A., Howard, J. L., Hunninghake, D. B., Illingworth, D. R., Luepker, R. V., McBride, P. E., et al. (2002) · Civil War Book Review · Cited by 12,198

European Guidelines on cardiovascular disease prevention in clinical practice (version 2012)

Authors/Task Force Members:, Perk, J., De Backer, G., Gohlke, H., Graham, I., Reiner, Ž., Verschuren, M., Albus, C., Benlian, P., Boysen, G., et al. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs092 · Cited by 8,490

2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation

Roffi, M., Patrono, C., Collet, J.-P., Mueller, C., Valgimigli, M., Andreotti, F., Bax, J. J., Borger, M. A., Brotons, C., Chew, D. P., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv320 · Cited by 8,261

Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493

Decision curve analysis: a novel method for evaluating prediction models

Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341

Untitled work

Kearon (2012) · doi:10.1378/chest.11-2301 · Cited by 6,119

Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures

Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170

Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data

Wang, H., Wu, J., Ding, X., Ruan, Z., Zhu, S., Wang, Y., Liu, L., Lin, J., Zhu, J., & Chen, X. (2025) · Journal of International Medical Research · doi:10.1177/03000605251378951 · Cited by 5

Developing and Validating a Simple Risk Score for Patients with Acute Myocardial Infarction

Ge, Z.-Y., He, Y., Jiang, T., Tao, J., & He, Y. (2023) · Cardiology · doi:10.1159/000535370 · Cited by 5

Development and Validation of Prognostic Models for Bleeding and Ischemia in Elderly Patients With Comorbid Acute Coronary Syndrome and Atrial Fibrillation

Zhang, H., Zheng, L., Yang, Z., Zhao, H., Zhu, Y., Ma, Y., Wu, Z., Qiu, W., Zhou, Z., Liu, Y., et al. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.124.035086 · Cited by 5

Personalized fracture risk assessment: where are we at?

Nguyen, T. V. (2021) · Expert Review of Endocrinology & Metabolism · doi:10.1080/17446651.2021.1924672 · Cited by 5

The usefulness of global longitudinal peak strain and left atrial volume index in predicting atrial fibrillation in patients with ischemic stroke

Park, S.-H., Kim, Y., Lee, M., Lee, S., Bae, J. S., Lee, J.-H., Kim, T. J., Ko, S., Jeong, S., Kim, D., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2023.1287609 · Cited by 5

KDIGO Guidelines and Kidney Transplantation: Is the Cystatin-C Based Recommendation Relevant?

Masson, I., Maillard, N., Cavalier, E., Alamartine, É., Mariat, C., & Delanaye, P. (2015) · American Journal of Transplantation · doi:10.1111/ajt.13258 · Cited by 5

The role of KIAA1191 in the necroptotic pathway of multiple myeloma

Zhao, X., Sun, Y., Jiang, J., & Liu, P. (2022) · Annals of Hematology · doi:10.1007/s00277-021-04710-w · Cited by 5

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV

Baysan, M., Arbous, M. S., Steyerberg, E. W., & van der Bom, J. G. (2022) · Critical Care Explorations · doi:10.1097/cce.0000000000000750 · Cited by 5

Potassium levels as a marker of imminent acute kidney injury among patients admitted with acute myocardial infarction. Soroka Acute Myocardial Infarction II (SAMI-II) Project

Plakht, Y., Saad, S. N. G., Gilutz, H., & Shiyovich, A. (2020) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.08.044 · Cited by 5

Fracture Risk Prediction Modeling and Statistics: What Should Clinical Researchers, Journal Reviewers, and Clinicians Know?

Schousboe, J. T., Langsetmo, L., Taylor, B. C., & Ensrud, K. E. (2017) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2017.06.012 · Cited by 5

Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application

Wang, Y., Wang, J. C., & Chen, H. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07746-6 · Cited by 4

Long Troponin T to Separate Troponin Elevations Among Patients With Atrial Fibrillation Versus Myocardial Infarction

Airaksinen, K. E. J., Teppo, K., Vasankari, T., Paana, T., Junes, H., Salonen, S. M., Tuominen, T., Simonen, S., Strandberg, M., Hellman, T., et al. (2026) · Journal of the American Heart Association · doi:10.1161/jaha.125.044092 · Cited by 4

Predictability of the National Psychological Stress Screening for Subsequent Long-Term Psychiatric Sick Leave Among Employees

Kawamura, T., & Kobayashi, D. (2024) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000003078 · Cited by 4

Novel inflammatory and insulin resistance indices provide a clue in cerebral amyloid angiopathy

Zhu, H., Wang, Y., He, L.-C., Luo, H., Zong, C., Yang, Y., Wu, J.-H., Song, B., Gao, Y., Xu, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-62280-z · Cited by 4

Association of preoperative antiviral treatment with incidences of post-hepatectomy liver failure in hepatitis B virus-related hepatocellular carcinoma

Wang, X., Lin, Z.-Y., Zhou, Y., Zhong, Q., Li, Z.-R., Lin, X.-X., Hu, M., & He, K. (2024) · World Journal of Gastrointestinal Surgery · doi:10.4240/wjgs.v16.i7.2106 · Cited by 4

Incorporating clinical and demographic data into the Elixhauser Comorbidity Model: deriving and validating an enhanced model in a tertiary hospital’s internal medicine department

Leibner, G., Katz, D. E., Esayag, Y., Kaufman, N., Brammli‐Greenberg, S., & Rose, A. J. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11663-z · Cited by 4

Predicting stroke with machine learning techniques in a sub-Saharan African population

Aribisala, B. S., Edward, D., Ogbole, G. I., Akpa, O. M., Ayilara, S., Sarfo, F. S., Olabanjo, O. A., Fakunle, A. G., Macaulay, B. O., Yaria, J. O., et al. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100216 · Cited by 4

Association between pentraxin 3 levels and aortic valve calcification

Norimatsu, K., Miura, S., Suematsu, Y., Shiga, Y., Miyase, Y., Nakamura, A., Zhang, B., & Saku, K. (2015) · Journal of Cardiology · doi:10.1016/j.jjcc.2015.08.010 · Cited by 4

The Use of Genetic Information in the Prediction of Type 2 Diabetes

Drenos, F. (2015) · Personalized Medicine · doi:10.2217/pme.15.19 · Cited by 4

Graphical representations and summary indicators to assess the performance of risk predictors

Antolini, L., Tassistro, E., Valsecchi, M. G., & Bernasconi, D. P. (2018) · Biometrical Journal · doi:10.1002/bimj.201700186 · Cited by 4

Association of novel biomarkers with major clinical outcomes in a cohort of patients with atherosclerotic renovascular disease

Vassallo, D., Alderson, H. V., Vuilleumier, N., Ritchie, J. P., Green, D., Pagano, S., Virzi, J., Chrysochou, C., & Kalra, P. A. (2019) · Annals of Clinical Biochemistry International Journal of Laboratory Medicine · doi:10.1177/0004563219850850 · Cited by 4

Diagnostic and prognostic test assessment in emergency medicine: likelihood and diagnostic odds ratios

Marill, K. A. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2020-210506 · Cited by 4

Integrated Predictors by Propensity Scoring With Tumor Markers and Plasma Levels of microRNA-21-5p, IL-17, and IL-10 Complement Early Detection of Hepatocellular Carcinoma in Patients With Liver Cirrhosis

Machida, M., Ambo, M., Mishina, R., Hada, N., Tachibana, F., Yamashita, M., Konda, A., & Tsuji, K. (2023) · Technology in Cancer Research & Treatment · doi:10.1177/15330338231212084 · Cited by 4

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning

Lamp, J., Wu, Y., Lamp, S., Afriyie, P., Ashur, N., Bilchick, K. C., Breathett, K. K., Kwon, Y., Li, S., Mehta, N. M., et al. (2024) · American Heart Journal · doi:10.1016/j.ahj.2024.02.001 · Cited by 4

European and US Guideline‐Based Statin Eligibility, Genetically Predicted Coronary Artery Disease, and the Risk of Major Coronary Events

Park, H., Kim, D., You, S. C., Jang, E., Yu, H. T., Kim, T., Kim, D., Sung, J., Pak, H., Lee, M., et al. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.123.032831 · Cited by 4

Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study

Barrett, J., Sweeting, M. J., & Wood, A. (2017) · Handbook of statistics · doi:10.1016/bs.host.2017.05.004 · Cited by 4

Clinically feasible stratification of 1-year to 3-year post-myocardial infarction risk

Horne, B. D., Muhlestein, J. B., Bhandary, D., Hoetzer, G. L., Khan, N. D., Bair, T. L., & LAPPE, D. L. (2018) · Open Heart · doi:10.1136/openhrt-2017-000723 · Cited by 4

Combining Frailty and Trabecular Bone Score Did Not Improve Predictive Accuracy in Risk of Major Osteoporotic Fractures

Li, G., Leslie, W. D., Kovács, C. S., Prior, J. C., Josse, R. G., Towheed, T., Davison, K. S., Thabane, L., Παπαϊωάννου, Α., Levine, M., et al. (2020) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3971 · Cited by 4

Developing a clinical utility framework to evaluate prediction models in radiogenomics

Wu, Y., Liu, J., del Río, A. M., Page, D. C., Alagöz, O., Peissig, P. L., Onitilo, A. A., & Burnside, E. S. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081954 · Cited by 4

Ten-second central SBP variability predicts first and recurrent cardiovascular events

Sluyter, J., Camargo, C. A., & Scragg, R. K. R. (2018) · Journal of Hypertension · doi:10.1097/hjh.0000000000001930 · Cited by 4

You Have Been Reclassified! Challenges in Creating and Applying Noninvasive Risk Models for Coronary Artery Disease

Azzalini, L., & Jolicoeur, M. E. (2015) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2015.04.002 · Cited by 4

Preventing tooth loss with biannual dental visits and genetic testing

Ioannidis, J. P. A. (2015) · The Journal of the American Dental Association · doi:10.1016/j.adaj.2015.01.015 · Cited by 4

On Contrast-Induced Acute Kidney Injury, Risk Prediction, and the Future of Predictive Model Development

Azzalini, L., & García–Moll, X. (2017) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2017.03.008 · Cited by 4

Evening home pulse pressure predicted cardiovascular events and mortality in older adults with hypertension: findings based on the STEP trial

Ji, Y., Peng, X., Chen, S., Song, Q., Bai, J., & Cai, J. (2025) · Hypertension Research · doi:10.1038/s41440-025-02349-y · Cited by 3

Low-Density Lipoprotein Cholesterol Time in Target Range and Clinical Outcomes in the General Population

Huang, J., Yu, Z., Wu, Y., Chen, C., Chen, Z., Zhao, J., He, J., Dong, Y., Li, B., Liu, C., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102184 · Cited by 3

Symptomatic Carotid Atheroma Inflammation Lumen-stenosis score compared with Oxford and Essen risk scores to predict recurrent stroke in symptomatic carotid stenosis

Gorey, S., McCabe, J. J., Camps‐Renom, P., Giannotti, N., McNulty, J. P., Barry, M. C., Cassidy, T., Cronin, S., Dolan, E., Fernández‐León, A., et al. (2023) · European Stroke Journal · doi:10.1177/23969873231186911 · Cited by 3

Individualized prediction of survival benefits from perioperative chemoradiotherapy for patients with resectable gastric cancer

Che, K., Liu, F., Wu, N., Liu, Q., Yuan, L., & Wei, J. (2020) · Cancer Medicine · doi:10.1002/cam4.3350 · Cited by 3

Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables

Kuronuma, K., Miller, R. J. H., Kriekinge, S. D. V., Han, D., Singh, A., Gransar, H., Dey, D., Berman, D. S., & Slomka, P. J. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06323-z · Cited by 3

Cost-effectiveness of applying high-sensitivity troponin I to a score for cardiovascular risk prediction in asymptomatic population

Jülicher, P., Makarova, N., Ojeda, F., Giusepi, I., Peters, A., Thorand, B., Cesana, G., Jørgensen, T., Linneberg, A. R., Salomaa, V. V., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307468 · Cited by 3

A methodology review on the incremental prognostic value of computed tomography biomarkers in addition to Framingham risk score in predicting cardiovascular disease: the use of association, discrimination and reclassification

Pang, C. L., Pilkington, N., Wei, Y., Peters, J., Roobottom, C. A., & Hyde, C. (2018) · BMC Cardiovascular Disorders · doi:10.1186/s12872-018-0777-5 · Cited by 3

Should non-cardiovascular mortality be considered in the SCORE model? Findings from the Prevention of Renal and Vascular End-stage Disease (PREVEND) cohort

Demissei, B. G., Postmus, D., Valente, M. A. E., van der Harst, P., van Gilst, W. H., van den Heuvel, E. R., & Hillege, H. L. (2014) · European Journal of Epidemiology · doi:10.1007/s10654-014-9967-3 · Cited by 3

The value of B-type natriuretic peptide plasma concentrations in very old people with chronic peripheral oedema

Belmin, J., Donadio, C., Jarzebowski, W., Genranmayeh, K., Valembois, L., & Lafuente‐Lafuente, C. (2020) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2019.12.009 · Cited by 3

End-to-End Deep Learning Vector Autoregressive Prognostic Models to Predict Disease Progression with Uneven Time Intervals

Bridge, J., Harding, S. P., & Zheng, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_38 · Cited by 3

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort

Deardorff, W. J., Jing, B., Jeon, S. Y., Boscardin, W. J., Lee, A. K., Fung, K. Z., & Lee, S. J. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03126-z · Cited by 3

Net reclassification improvement with serial biomarkers and bed-sided spirometry to early predict the need of organ support during the early post-transplantation in-hospital stay in allogeneic HCT recipients

Braun, S., Kühn, M., Bergmann, S., Katzke, S., Weise, M., Alakel, N., Stölzel, F., Kroschinsky, F., Ehninger, G., Höffken, G., et al. (2018) · Bone Marrow Transplantation · doi:10.1038/s41409-018-0258-6 · Cited by 3

Prognostic Comparison of the Estimations of Renal Function in Patients With Acute Heart Failure

Cheng, Y.-L., Sung, S., Cheng, H., Huang, J.-T., Guo, C., Hsu, P., Yu, W., & Chen, C. (2019) · Circulation Journal · doi:10.1253/circj.cj-18-1013 · Cited by 3

Towards better use of the net reclassification improvement (NRI) index

Leslie, W. D., Schousboe, J. T., & Lix, L. M. (2015) · Osteoporosis International · doi:10.1007/s00198-015-3281-0 · Cited by 3

Time to move from prognostication to diagnosis and treatment of heart disease in acute exacerbation of COPD

Roversi, S., & Hawkins, N. M. (2017) · European Respiratory Journal · doi:10.1183/13993003.00912-2017 · Cited by 3

Absolute vs Additive Net Reclassification Index

Leening, M. J. G., & Pencina, M. J. (2018) · JAMA · doi:10.1001/jama.2017.20541 · Cited by 3

Predicting Mortality with Percent Predicted and z -Scores of FEV1: Not an EZ Task

Díaz, A. A. (2018) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201805-356ed · Cited by 3

Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study

Ma, L., Zhang, B., Wu, X., Li, X., Song, D., Jiang, Z., Mao, G., Zhong, H., Guan, H., Lu, W., et al. (2026) · Neurosurgical Review · doi:10.1007/s10143-025-04067-y · Cited by 2

Traditional Cox regression outperforms large language models in predicting long-term progression of intermediate to advanced hepatocellular carcinoma

Li, K., Wang, C., Xiong, Y., Song, Y., Zhang, Y., 牟丹蕾, Hu, C., Guo, D., Mei, T., Li, A., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1710529 · Cited by 2

Development and validation of a prediction model for in-hospital mortality in intensive care unit patients with cirrhosis and sepsis: a multicentre retrospective cohort study

Xu, Z., Yang, Q., Peng, D., Jiang, Y., Su, Y., Wu, B., Chen, Z., Huang, J., & Liu, X. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1759988 · Cited by 2

Metabolic status modifies the predictive value of the C-reactive protein–triglyceride–glucose Index–waist-to-height ratio for major adverse cardiovascular and cerebrovascular events: a prospective cohort study from the China Health and Retirement Longitudinal Study (CHARLS)

Li, X., Meng, Y., Zhang, L., Li, T., & Yan, G. (2026) · Cardiovascular Diabetology · doi:10.1186/s12933-026-03181-5 · Cited by 2

Prediction of distant metastasis and specific survival prediction of small intestine cancer patients with metastasis: A population‐based study

Xu, J., Yao, Z. H., Liao, G., OuYang, X., Mao, S., Cao, J., & Lai, B. (2023) · Cancer Medicine · doi:10.1002/cam4.6166 · Cited by 2

Measurement of the Immunosuppressant Possession Ratio by Transplant Clinical Pharmacists Captures a Non-Adherence Associated With Antibody-Mediated Rejection

Chambord, J., Chauveau, B., Djabarouti, S., Vignaud, J., Taton, B., Moreau, K., Visentin, J., Merville, P., Xuereb, F., & Couzi, L. (2023) · Transplant International · doi:10.3389/ti.2023.11962 · Cited by 3

External Validation of the CAST and rCAST Score in Patients With Out‐of‐Hospital Cardiac Arrest Who Underwent Extracorporeal Cardiopulmonary Resuscitation: A Secondary Analysis of the SAVE‐J II Study

Misumi, K., Hagiwara, Y., Kimura, T., Hifumi, T., Inoue, A., Sakamoto, T., Kuroda, Y., Ogura, T., Sawano, H., Egawa, Y., et al. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.123.031035 · Cited by 2

Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes

Draper, K., Li, B., Syed, M. H., Shaikh, F., Zamzam, A., AbuHalimeh, B. J., Rasheed, K., Younes, H. K., Abdin, R., & Qadura, M. (2024) · Diabetology · doi:10.3390/diabetology6010002 · Cited by 2

Enhancing Prostate Cancer Detection: Integrating Multiparametric Magnetic Resonance Imaging and 68Ga-prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography with Machine Learning

Sadeghi, M. H., Bagheri, H., Rajaeinejad, M., Ardalan, M. A., Karami, I., Sadeghi, S., Mosadeghkhah, A., Sina, S., Khajehrahimi, F., & Sheiki, M. (2025) · Journal of Medical Physics · doi:10.4103/jmp.jmp_48_25 · Cited by 2

Prognostic Significance of Peripheral Monocyte Counts in Patients With Chronic Heart Failure

Nozuhara, A., Yamamoto, E., Komorita, T., Sueta, D., Fujisue, K., Oike, F., Ishii, M., Usuku, H., Hanatani, S., Araki, S., et al. (2025) · Circulation Reports · doi:10.1253/circrep.cr-25-0102 · Cited by 2

Prediction of scleroderma renal crisis in patients of SSc: insight from the CRDC cohort study

Huang, H., Xu, S., Li, H., Duan, X., Hui, M., Zhou, J., Zhang, L. J., Li, M., Zeng, X., & Xu, D. (2025) · Rheumatology · doi:10.1093/rheumatology/keaf034 · Cited by 2

Predicting mortality in patients treated differently: updating and external validation of a prediction model for nursing home residents with dementia and lower respiratory infections

Rauh, S. P., Heymans, M. W., Mehr, D. R., Kruse, R. L., Lane, P., Kowall, N. W., Volicer, L., & van der Steen, J. T. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011380 · Cited by 2

The Interaction of a Diabetes Gene Risk Score With 3 Different Antihypertensive Medications for Incident Glucose-level Elevation

Barzilay, J. I., Lai, D., Davis, B. R., Pressel, S., Previn, H. E., & Arnett, D. K. (2018) · American Journal of Hypertension · doi:10.1093/ajh/hpy199 · Cited by 2

Imprinted gene detection effectively improves the diagnostic accuracy for papillary thyroid carcinoma

Chen, Y., Yin, M., Zhang, Y., Zhou, N., Zhao, S., Yin, H., Shao, J., Min, X., & Chen, B. (2024) · BMC Cancer · doi:10.1186/s12885-024-12032-z · Cited by 3

Pulmonary Artery Acceleration Time in Young Adulthood and Cardiovascular Outcomes Later in Life: The Coronary Artery Risk Development in Young Adults Study

Moreira, H. T., Vasconcellos, H. D. D., Ambale‐Venkatesh, B., Brittain, E. L., Nwabuo, C. C., Schmidt, A., Lloyd‐Jones, D. M., Carr, J. J., Lewis, C. E., Jacobs, D. R., et al. (2019) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2019.07.025 · Cited by 2

Development and application of statistical methods for prognosis research

Snell, K. I. (2015) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2

The value of pregnancy-related factors in the prediction of cardiovascular disease: a systematic review

Yang, Z., van Os, H. J. A., Kist, J. M., Vos, R. C., Vos, H. M. M., Chavannes, N. H., & Petrus, A. H. J. (2025) · International Journal of Cardiology Cardiovascular Risk and Prevention · doi:10.1016/j.ijcrp.2025.200483 · Cited by 1

The role of eCyPA as an inflammatory biomarker for predicting 28-day mortality in ARDS patients

Chen, J., Dai, X., Lv, J., Liu, M., Goligher, E. C., & Deng, W. (2025) · Respiratory Research · doi:10.1186/s12931-025-03406-3 · Cited by 1

The long and winding road of radiomics: learnings from two meta-analyses of the radiomics quality score

Barry, N., Kendrick, J., Molin, K., Li, S., Rowshanfarzad, P., Hassan, G. M., Dowling, J., Ong, J. S. L., Parizel, P. M., Hofman, M. S., et al. (2026) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae36e0 · Cited by 1

Construction of a Nomogram for Lymphovascular Invasion or Ductal Involvement in Node‐Negative Superficial Esophageal Squamous Cell Carcinoma

Yue, C., Liang, Y., Wei, X., Liang, Y., Bai, X., Zhu, A., Huang, S., Zhou, Y., & Feng, Y. (2026) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.70237 · Cited by 1

Experimental and computational synergy in MAPbCl3 perovskites: Structural, electrical, and optical properties from single crystals to nanofibers

Naqvi, S. M. Z. A., Qunfeng, N., Jiaqi, N., Awais, M., Hu, J., & Li, W. (2026) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2026.187161 · Cited by 1

Rethinking post-infarction remodelling: identification and validation of data-driven cardiac MRI phenotypes for risk stratification

Xiang, J.-Y., Wu, J., Zhao, Y., Yin, T., Zhu, G.-J., Cao, S.-Y., Chen, B., An, D., Wu, L., & Pu, J. (2026) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeag124 · Cited by 1

Comprehensive assessment of vasospastic angina using coronary computed tomography angiography: synergistic value of the presence of myocardial bridge, perivascular inflammation, and myocardial extracellular volume fraction

Sayama, K., Kanaji, Y., Usui, E., Ueno, H., Tahara, T., Watanabe, T., Murakami, R., Mineo, T., Hiroi, S., Hosokawa, K., et al. (2026) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeag181 · Cited by 1

Comparative validation of AHA/ACC 2026 and ESC 2019 risk stratification in hospitalized patients with acute pulmonary embolism

Li, Y. X., Feng, J., Yu, S., Sun, X., & Shi, J. (2026) · Thrombosis Research · doi:10.1016/j.thromres.2026.109794 · Cited by 1

Photoreceptor layer thinning is an early biomarker for type 2 diabetes: a cohort study in UK Biobank

Ma, Y., Wu, Y., Hu, L., Zhang, X., Zheng, D., Liu, Z., & Jin, G. (2025) · Eye · doi:10.1038/s41433-025-04068-7 · Cited by 1

Development and Validation of a Harmonization Pipeline for Multi-Site Computed Tomography Perfusion Cerebral Blood Flow Maps—An Analysis of the AcT Trial

Charatpangoon, P., Zhang, J., Singh, N., Addeh, A., Almekhlafi, M., Buck, B. H., Catanese, L., Sajobi, T. T., Swartz, R. H., Tkach, A., et al. (2026) · Clinical Neuroradiology · doi:10.1007/s00062-026-01626-8 · Cited by 1

MRI-based prediction of very early recurrence within 1 year after resection of HCC: An international cohort study

Wei, H., Heo, S., Yang, Y., Li, M., Fu, F., Xia, T., Lee, S. S., Choi, E. S., Sartoris, R., Grégory, J., et al. (2026) · Journal of Hepatology · doi:10.1016/j.jhep.2026.05.027 · Cited by 1

Lipoprotein heterogeneity may help to detect individuals with insulin resistance

Lorenzo, C., Hanley, A. J., Rewers, M., Festa, A., & Haffner, S. M. (2015) · Diabetologia · doi:10.1007/s00125-015-3743-0 · Cited by 1

A simple assessment of lung nodule location for reduction in unnecessary invasive procedures

Kinsey, C. M., Billatos, E. S., Mori, V., Tonelli, B., Cole, B. F., Duan, F., Marques, H. S., Bruere, I. D. L., Onieva, J. O., Estépar, R. S. J., et al. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-20-3093 · Cited by 1

Binge alcohol drinking before pregnancy is closely associated with the development of macrosomia: Korean pregnancy registry cohort

Koo, S., Kim, W., Park, J. H., Roh, G. S., Lim, N. K., Park, H. Y., & Kim, W. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271291 · Cited by 1

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Translation into Russian

Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2023) · Вопросы современной педиатрии · doi:10.15690/vsp.v22i2.2557 · Cited by 1

Dynamic changes in liver function tests do not correctly reclassify patients at risk of choledocholithiasis beyond ASGE 2019 criteria

Ramírez-Peña, T., Vargas, R., Moreno, C. E. L., Hortúa, L. M. R., & Múñoz, Ó. (2023) · Therapeutic Advances in Gastrointestinal Endoscopy · doi:10.1177/26317745231202869 · Cited by 1

False Complaint Report Detection Based on Contrastive Learning Model

Fan, Q., & Jie, W. (2023) · International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) · doi:10.1109/aihcir61661.2023.00099 · Cited by 1

“Trending” statistical methods

Howard, G., & Long, D. L. (2018) · Research and Practice in Thrombosis and Haemostasis · doi:10.1002/rth2.12075 · Cited by 1

On the trail of the perfect prognosticator in advanced heart failure patients

Baptista, R. (2018) · Revista Portuguesa de Cardiologia · doi:10.1016/j.repc.2018.01.005 · Cited by 1

Combination of the Centre for International Blood and Marrow Transplant Registry Risk Score and the Global Severity Score Enhances Prognostic Risk Stratification in Patients Receiving Frontline Therapy for Chronic Graft-versus-Host Disease

Shapiro, R. M., Shin, E. M., Law, A., Lam, W., Michelis, F. V., Viswabandya, A., Kumar, R., Lipton, J. H., Messner, H. A., Mattsson, J., et al. (2019) · Transplantation and Cellular Therapy · doi:10.1016/j.bbmt.2019.05.029 · Cited by 1

Postoperative Cardiac Troponin I Thresholds Associated With 1-Year Cardiac Mortality After Adult Cardiac Surgery: An Attempt to Link Risk Stratification With Management Stratification in an Observational Study

Provenchère, S., Guglielminotti, J. R., Gouél‐Cheron, A., Bresson, E., Desplanque, L., Bouleti, C., Iung, B., Montravers, P., Dehoux, M., & Longrois, D. (2019) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2019.06.039 · Cited by 1

Utility of the “omics” in kidney disease: Methods of analysis, sampling considerations, and technical approaches in renal biomarkers

Moreno, V. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815923-1.00002-x · Cited by 1

Prognostic Risks and Preoperative Assessment

Ladha, K. S., & Wijeysundera, D. N. (2020) · Springer eBooks · doi:10.1007/978-3-030-47887-2_2 · Cited by 1

The prognostic predictive value of the components of the PR interval in hospitalized patients with heart failure

Yu, Y., Huang, Y., Zhao, X., Zhao, L., Tian, P.-C., Zhou, Q., Zhai, M., Wang, Y., Zhang, Y.-H., & Zhang, J. (2023) · BMC Cardiovascular Disorders · doi:10.1186/s12872-022-03028-3 · Cited by 1

Predilection for Poor Prediction with the Surgical Apgar Score

Hyder, J. A. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001066 · Cited by 1

Thrombotic Risk Assessment in End Stage Renal Disease Patients on Renal Replacement Therapy

Sharma, S. (2016) · University of Hertfordshire Research Archive (University of Hertfordshire) · doi:10.18745/th.17114 · Cited by 1

Absolute vs Additive Net Reclassification Index—Reply

Alba, A. C., Devereaux, P. J., & Guyatt, G. H. (2018) · JAMA · doi:10.1001/jama.2017.20548 · Cited by 1

Single‐number summary and decision analytic measures can happily coexist

Pencina, M. J., Steyerberg, E. W., & D’Agostino, R. B. (2019) · Statistics in Medicine · doi:10.1002/sim.8031 · Cited by 1

Tools for Assessing Fracture Risk and for Treatment Monitoring

Leslie, W. D., Lix, L. M., & Morin, S. N. (2015) · Springer eBooks · doi:10.1007/978-3-319-23639-1_4

Home blood pressure and treatment decisions

Angeli, F., Reboldi, G., & Verdecchia, P. (2016) · Journal of Hypertension · doi:10.1097/hjh.0000000000000999

Modeling and Prediction in Neurological Disorders: The Biostatistical Perspective

Copetti, M., Fontana, A., & Pellegrini, F. (2016) · Monographs in clinical neuroscience/Frontiers of neurology and neuroscience/Monographs in neural sciences · doi:10.1159/000445412

Identifying Persons at Increased Risk of Cardiovascular Disease

Leening, M. J. G. (2016) · EUR Research Repository (Erasmus University Rotterdam)

Investigating Predictive Disease Model Transportability through Cohort Simulation and Causal Analysis

Singleton, K. (2016) · eScholarship (California Digital Library)

Complement C3a levels and misinterpretation of classifier technology

Petretta, M. (2016) · Inflammation Research · doi:10.1007/s00011-016-1016-0

Commentary: Reporting standards are needed for evaluations of risk reclassification

Pepe, M. S., & Janes, H. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr083 · Cited by 26

The Net Reclassification Index (NRI): a Misleading Measure of Prediction Improvement with Miscalibrated or Overfit Models

PEPE, M. S., Fang, J., Feng, Z., Gerds, T. A., & Hilden, J. (2013) · Collection of Biostatistics Research Archive · Cited by 13

Net reclassification improvement and integrated discrimination improvement require calibrated models: relevance from a marker and model perspective

Leening, M. J. G., Steyerberg, E. W., CALSTER, B. V., D’Agostino, R. B., & Pencina, M. J. (2014) · Statistics in Medicine · doi:10.1002/sim.6133 · Cited by 55

Performance of the Net Reclassification Improvement for Nonnested Models and a Novel Percentile-Based Alternative

McKearnan, S. B., Wolfson, J. A., Vock, D. M., Vazquez‐Benitez, G., & O’Connor, P. J. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx374 · Cited by 26

Net Reclassification Improvement

Jewell, E. S., Maile, M. D., Engoren, M., & Elliott, M. (2016) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000001141 · Cited by 40