Cited by 594Statistical Methods in EpidemiologyMeta-analysis and systematic reviewsAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 140 of the 141 references it cites were found in a registry or an index.

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

Open access

Leening, Maarten J.G., Vedder, Moniek M., Witteman, Jacqueline C.M., Pencina, Michael J., Steyerberg, Ewout W.·2014·Annals of Internal Medicine

DOI 10.7326/m13-1522

Open this paper in CoChat — free account

Abstract

The net reclassification improvement (NRI) is an increasingly popular measure for evaluating improvements in risk predictions. This article details a review of 67 publications in high-impact general clinical journals that considered the NRI. Incomplete reporting of NRI methods, incorrect calculation, and common misinterpretations were found. To aid improved applications of the NRI, the article elaborates on several aspects of the computation and interpretation in various settings. Limitations and controversies are discussed, including the effect of miscalibration of prediction models, the use of the continuous NRI and “clinical NRI,” and the relation with decision analytic measures. A systematic approach toward presenting NRI analysis is proposed: Detail and motivate the methods used for computation of the NRI, use clinically meaningful risk cutoffs for the category-based NRI, report both NRI components, address issues of calibration, and do not interpret the overall NRI as a percentage of the study population reclassified. Promising NRI findings need to be followed with decision analytic or formal cost-effectiveness evaluations.

Integrity signals

Registry

Crossref · 10.7326/m13-1522

Cites

140 of 140 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
Self-citation
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
Self-citation

Flagged first, then by citations

Show all 141 references

1 of the 141 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

Di Angelantonio E, 2012, JAMA, 307, 2499
Melander O, 2009, JAMA, 302, 2090
Greenland P, 2010, Circulation, 122, 584
Hilden J, 2013, Stat Med

Sorted by citations

Items 301–400 of 591

Waist Circumference and Insulin Resistance Are the Most Predictive Metabolic Factors for Steatosis and Fibrosis
Claypool, K. T., Long, M. T., & Patel, C. J. (2022) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2022.05.021 · Cited by 12
Lung Ultrasound, Echocardiography, and Fluid Challenge for the Differential Diagnosis of Pulmonary Hypertension
D’Alto, M., Liccardo, B., Maio, M. D., Giudice, C. D., Romeo, E., Argiento, P., Renon, F., Vergara, A., Vilio, A. D., Caiazza, E., et al. (2023) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2023.07.010 · Cited by 12
Comparison of scoring methods for the Brief Insomnia Questionnaire in a general population sample
Chung, K., Yeung, W., Ho, F. Y., Ho, L., Yung, K.-P., Yu, B. Y., & Kwok, C.-W. (2014) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2014.11.015 · Cited by 12
Risk Factors and Prediction of Stroke in a Population with High Prevalence of Diabetes: The Strong Heart Study
Wang, W., Zhang, Y., Lee, E. T., Howard, B. V., Devereux, R. B., Cole, S. A., Best, L. G., Welty, T. K., Rhoades, E. R., Yeh, J. L., et al. (2017) · World Journal of Cardiovascular Diseases · doi:10.4236/wjcd.2017.75014 · Cited by 12
Prognostic Relevance of a Complete Pathologic Response in Liver Transplantation for Hepatocellular Carcinoma
Grąt, M., Krawczyk, M., Stypułkowski, J., Morawski, M., Krasnodębski, M., Wasilewicz, M. P., Lewandowski, Z., Grąt, K., Patkowski, W., & Zieniewicz, K. (2019) · Annals of Surgical Oncology · doi:10.1245/s10434-019-07811-z · Cited by 12
Sex-specific added value of cardiac biomarkers for 10-year cardiovascular risk prediction
Zhu, F., Arshi, B., Leening, M. J. G., Aribas, E., Ikram, M. A., Boersma, E. H., Ikram, M. K., & Kavousi, M. (2022) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwac091 · Cited by 12
Self-citation
Control beliefs and risk for 4-year mortality in older adults: a prospective cohort study
Duan‐Porter, W., Hastings, S. N., Neelon, B., & Houtven, C. H. V. (2017) · BMC Geriatrics · doi:10.1186/s12877-016-0390-3 · Cited by 13
A Foundation Transformer Model with Self-Supervised Learning for ECG-Based Assessment of Cardiac and Coronary Function
Moody, J. B., Poitrasson-Rivière, A., Renaud, J. M., Hagio, T., Alahdab, F., Al-Mallah, M. H., Vanderver, M. D., Goonewardena, S. N., Ficaro, E. P., & Murthy, V. L. (2025) · NEJM AI · doi:10.1056/aioa2500164 · Cited by 14
Incremental value of exercise echocardiography over exercise electrocardiography in a chest pain unit
Bouzas‐Mosquera, A., Peteiro, J., Broullón, F. J., Álvarez‐García, N., Rodríguez-Garrido, J. L., Mosquera, V. X., Martínez, D., Yáñez, J. C., & Vázquez‐Rodríguez, J. M. (2015) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2015.08.002 · Cited by 11
qSOFA predicted pneumonia mortality better than minor criteria and worse than CURB-65 with robust elements and higher convergence
Guo, Q., Li, H., Song, W., Liu, H., Yu, H., Li, Y., Lü, Z., Liang, L.-H., Zhao, Q.-Z., & Jiang, M. (2021) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2021.11.029 · Cited by 11
Biventricular Cardiac Power Reserve in Heart Failure with Preserved Ejection Fraction
Alogna, A., Omar, M., Popović, D., Sorimachi, H., Omote, K., Reddy, Y. N. V., Pieske, B. M., & Borlaug, B. A. (2023) · European Journal of Heart Failure · doi:10.1002/ejhf.2867 · Cited by 11
Integrated myocardial flow reserve (iMFR) assessment: diffuse atherosclerosis and microvascular dysfunction are more strongly associated with mortality than focally impaired perfusion
Moody, J. B., Poitrasson-Rivière, A., Renaud, J. M., Hagio, T., Al-Mallah, M. H., Weinberg, R. L., Ficaro, E. P., & Murthy, V. L. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06448-1 · Cited by 11
Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning
Jeon, E., Jung, S. J., Yeo, T. Y., Seo, W., & Jung, J. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1243700 · Cited by 11
The National Early Warning Score 2 with Age and Body Mass Index (NEWS2 Plus) to Determine Patients with Severe COVID-19 Pneumonia
Trongtrakul, K., Tajarernmuang, P., Limsukon, A., Theerakittikul, T., Niyatiwatchanchai, N., Surasit, K., Glunriangsang, P., Liwsrisakun, C., Bumroongkit, C., Pothirat, C., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13010298 · Cited by 11
Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening
Wang, Y., Zhou, C., Ying, L., Lee, E., Chan, H., Chughtai, A. R., Hadjiiski, L. M., & Kazerooni, E. A. (2024) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.230196 · Cited by 12
Development of algorithms for identifying fatal cardiovascular disease in Medicare claims
Xie, F., Colantonio, L. D., Curtis, J. R., Kilgore, M. L., Levitan, E. B., Monda, K. L., Safford, M. M., Taylor, B., Woodward, M., & Muntner, P. (2018) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4421 · Cited by 11
Pulse rate variability predicts atrial fibrillation and cerebrovascular events in a large, population-based cohort
Sluyter, J., Camargo, C. A., Lowe, A., & Scragg, R. K. R. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.10.026 · Cited by 11
Validation of randomized controlled trial-derived models for the prediction of postintervention outcomes in chronic limb-threatening ischemia
Wijnand, J. G. J., van Koeverden, I. D., Teraa, M., Spreen, M. I., Mali, W. P. T. M., van Overhagen, H., Pasterkamp, G., de Borst, G. J., Conte, M. S., Gremmels, H., et al. (2019) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2019.06.195 · Cited by 11
Building clinical risk score systems for predicting the all‐cause and expanded cardiovascular‐specific mortality of patients with type 2 diabetes
Liu, C.-S., Li, C., Wang, M., Yang, S.-Y., Li, T., & Lin, C. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14240 · Cited by 12
Man vs. Machine: Comparing Physician vs. Electronic Health Record–Based Model Predictions for 30-Day Hospital Readmissions
Nguyen, O. K., Washington, C., Clark, C. R., Miller, M. E., Patel, V. A., Halm, E. A., & Makam, A. N. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06355-3 · Cited by 11
Prognostic value of three rapid scoring scales and combined predictors for the assessment of patients with coronavirus disease 2019
Hu, H., Kong, W., Yao, N., Qiu, Y., & Yao, R. (2021) · Nursing Open · doi:10.1002/nop2.934 · Cited by 11
A novel predictive model for poor in-hospital outcomes in patients with acute kidney injury after cardiac surgery
Chen, Z., Li, J., Sun, Y., Wang, C., Yang, W., Ma, M., Luo, Z., Yang, K., & Chen, L. (2021) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2021.04.085 · Cited by 11
Inflammatory Markers Showed Significant Incremental Value for Predicting Post-Hepatectomy Liver Failure in Hepatocellular Carcinoma Patients
Wang, X., Wang, W., Lin, X., Chen, X., Zhu, M., Xu, H., & He, K. (2023) · Life · doi:10.3390/life13101990 · Cited by 10
Comparison of Performance Between a Short Categorized Lifestyle Exposure-based Colon Cancer Risk Prediction Tool and a Model Using Continuous Measures
Liu, Y., Colditz, G. A., ROSNER, B. A., Dart, H., Wei, E. K., & Waters, E. A. (2018) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-18-0196 · Cited by 10
A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants
Xue, M., Su, Y., Feng, Z., Wang, S., Zhang, M., Wang, K., & Yao, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-68383-7 · Cited by 10
Predicting Bacteremia among Septic Patients Based on ED Information by Machine Learning Methods: A Comparative Study
Goh, V., Chou, Y.-J., Lee, C., Ma, M.-C., Wang, W. Y. C., Lin, C., & Hsieh, C. (2022) · Diagnostics · doi:10.3390/diagnostics12102498 · Cited by 10
Predicting in-hospital mortality in pulmonary embolism patients: development and external validation of the PATHOS score
Spampinato, M. D., Covino, M., Passaro, A., Benedetto, M., D’Angelo, L., Galizia, G., Fabbri, I. S., Pagano, T., Portoraro, A., Guarino, M., et al. (2022) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.22.369 · Cited by 11
Prediction of Mortality Among Patients With Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study
Song, J., Shin, S. D., Jamaluddin, S. F., Chiang, W., Tanaka, H., Song, K. J., Ahn, S., Park, J.-H., Kim, J. Y., Cho, H., et al. (2023) · Journal of Neurotrauma · doi:10.1089/neu.2022.0280 · Cited by 10
Prognostic significance of troponin I in assessing cardiovascular risk in the Russian population. Data from ESSE-RF1 and ESSE-RF2 multicenter studies
Drapkina, O. M., Shalnova, S. A., Васильевна, К. А., Kutsenko, V. A., Владимировна, К. А., Balanova, Y. А., Евстифеева, С. Е., Иванова, А. А., Имаева, А. Э., Владимирович, М. Н., et al. (2023) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2023-3548 · Cited by 10
Evaluating Prognostic Markers Using Relative Utility Curves and Test Tradeoffs
Baker, S. G., & Kramer, B. S. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.58.0092 · Cited by 10
Examining Parental Medication Adherence as a Predictor of Child Medication Adherence in Pediatric Anxiety Disorders
Bushnell, G., Brookhart, M. A., Gaynes, B. N., Compton, S. N., Dusetzina, S. B., & Stürmer‎, T. (2018) · Medical Care · doi:10.1097/mlr.0000000000000911 · Cited by 10
A Novel Laboratory-Based Model to Predict the Presence of Obstructive Coronary Artery Disease
Zhou, L., Yin, W., Wang, J., Hu, C., Liu, K., Wen, J., Peng, L., & Zuo, X. (2020) · International Heart Journal · doi:10.1536/ihj.19-532 · Cited by 10
Age, Serum Creatinine, and Left Ventricular Ejection Fraction Improved the Performance of the CatLet Angiographic Scoring System in Terms of Outcome Predictions for Patients with Acute Myocardial Infarction: A Median 4.3-Year Follow-Up Study
Teng, R.-L., Liu, M., Sun, B.-C., Xu, J., He, Y., & He, Y. (2021) · Cardiology · doi:10.1159/000515759 · Cited by 10
Presence of Geriatric Conditions Is Prognostic of Major Bleeding in Older Patients with Atrial Fibrillation: a Cohort Study
Wang, W., Saczynski, J. S., Lessard, D., Goldberg, R. J., Parish, D. C., Helm, R. H., Kiefe, C. I., Trymbulak, K., Mehawej, J., Abu, H. O., et al. (2022) · Journal of General Internal Medicine · doi:10.1007/s11606-022-07410-x · Cited by 10
Adiposity measures and pre‐diabetes or diabetes in adults with hypertension in Singapore polyclinics
Wang, Y., Shirore, R. M., Ramakrishnan, C., Tan, N. C., & Jafar, T. H. (2019) · Journal of Clinical Hypertension · doi:10.1111/jch.13587 · Cited by 10
Blood biomarkers for the differentiation between central and peripheral vertigo in the emergency department: a systematic review and meta‐analysis
Klokman, V. W., Koningstein, F. N., Dors, J. W. W., Sanders, M. S., Koning, S. W., de Kleijn, D. P. V., & Jie, K. E. (2024) · Academic Emergency Medicine · doi:10.1111/acem.14864 · Cited by 10
Statistically Significant Association Does not Imply Improvement in Prediction of Clinical Outcomes
Jiang, S., ROSNER, B. A., & Colditz, G. A. (2025) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-25-0056 · Cited by 9
Developing Economic Models for Assessing the Cost-Effectiveness of Multiple Diagnostic Tests: Methods and Applications
Xie, X., Tiggelaar, S., Guo, J., Wang, M., Vandersluis, S., & Ungar, W. J. (2022) · Medical Decision Making · doi:10.1177/0272989x221089268 · Cited by 9
Prediction models as gatekeepers for diagnostic testing in angina patients with suspected chronic coronary syndrome
Bjerking, L. H., Winther, S., Hansen, K. W., Galatius, S., Bøttcher, M., & Prescott, E. (2022) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcac025 · Cited by 9
Protein Biomarkers and Cardiovascular Outcomes in People With Type 2 Diabetes and Acute Coronary Syndrome: The ELIXA Biomarker Study
Gerstein, H. C., Hess, S., Claggett, B. L., Dickstein, K., Køber, L., Maggioni, A. P., McMurray, J. J. V., Probstfield, J. L., Riddle, M. C., Tardif, J., et al. (2022) · Diabetes Care · doi:10.2337/dc22-0453 · Cited by 9
Combined fine-needle aspiration and selective intraoperative frozen section to optimize prediction of malignant thyroid nodules: A retrospective cohort study of more than 3000 patients
Mao, Z., Ding, Y., Wen, L., Zhang, Y., Wu, G., You, Q., Wu, J., Luo, D., Teng, L., & Wang, W. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1091200 · Cited by 9
Updating cut-off values of severity scoring systems for community-acquired pneumonia to orchestrate more predictive accuracy
Guo, Q., Li, H., Song, W., Li, M., Chen, X., Liu, H., Peng, H.-L., Yu, H., Liu, N., Li, Y., et al. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2202414 · Cited by 9
Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network
Jia, Y., Li, Y., Luosang, G., Wang, J., Peng, G., Pu, X., Jiang, W., Li, W., Zhao, Z., Peng, Y., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae007 · Cited by 9
A confidence ellipse for the Net Reclassification Improvement
Mühlenbruch, K., Kuxhaus, O., Pencina, M. J., Boeing, H., Liero, H., & Schulze, M. B. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0001-1 · Cited by 9
Self-citation
Prognostic value of plasma high mobility group box 1 protein and histone H3 levels in patients with disseminated intravascular coagulation: a multicenter prospective cohort study
Mori, H., Kataoka, Y., Harada‐Shirado, K., Kawano, N., Hayakawa, M., Seki, Y., Uchiyama, T., Yamakawa, K., Ishikura, H., Irie, Y., et al. (2022) · Thrombosis Journal · doi:10.1186/s12959-022-00390-2 · Cited by 9
Incremental prognostic value of renal function for stroke prediction in atrial fibrillation
O'Brien, E. C., Holmes, D. N., Thomas, L. E., Singer, D. E., Fonarow, G. C., Mahaffey, K. W., Kowey, P. R., Hylek, E. M., Pokorney, S. D., Ansell, J., et al. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.07.113 · Cited by 9
Self-citation
CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death
Martineau, P., Slomka, P. J., Goertzen, A. L., & Leslie, W. D. (2018) · Journal of Nuclear Cardiology · doi:10.1007/s12350-018-01556-0 · Cited by 9
Comparison of Performance of the Pediatric Index of Mortality (PIM)-2 and PIM-3 Scores in the Pediatric Intensive Care Unit of a High Complexity Institution
Quiñonez-López, D., Patiño-Hernández, D., Zuluaga, C. A., Peña, Á. A. G., & Múñoz, Ó. (2020) · Indian Journal of Critical Care Medicine · doi:10.5005/jp-journals-10071-23659 · Cited by 9
RE: Net Risk Reclassification P Values: Valid or Misleading?
Pencina, M. J., Neely, B., & Steyerberg, E. W. (2014) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju355 · Cited by 9
Self-citation
Is It Time to Employ a Measure of Visit-to-Visit Variability in Clinical Practice?
Whelton, P. K., & Pollock, B. D. (2017) · Hypertension · doi:10.1161/hypertensionaha.117.09455 · Cited by 9
Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model
Ficorella, L., Eriksson, M., Czene, K., Leslie, G., Yang, X., Carver, T. J., Stokes, A. E., Easton, D. F., Hall, P. N., & Antoniou, A. C. (2025) · JCO Precision Oncology · doi:10.1200/po-25-00203 · Cited by 8
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Translation in to 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. (2022) · Digital Diagnostics · doi:10.17816/dd110794 · Cited by 10
Self-citation
Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia
Lee, T., Hwang, E. J., Park, C. M., & Goo, J. M. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.02.016 · Cited by 8
Statistical models for respiratory disease diagnosis and prognosis
Wolfe, R. S., & Carlin, J. B. (2015) · Respirology · doi:10.1111/resp.12519 · Cited by 8
A prognostic model for development of significant liver fibrosis in HIV-hepatitis C co-infection
Moqueet, N., Kanagaratham, C., Gill, M. J., Hull, M., Walmsley, S. L., Radzioch, D., Saeed, S., Platt, R. W., Klein, M. B., & 222), F. T. C. C.-I. C. S. (. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176282 · Cited by 9
Development and validation of a risk scoring system based on baseline angiographic results by visual estimation for risk prEdiction of side‐branch OccLusion in coronary bifurcation InterVEntion: The baseline V‐RESOLVE score
He, Y., Zhang, D., Yin, D., Zhu, C., Feng, L., Song, C., Chen, C., Feng, L., & Dou, K. (2019) · Catheterization and Cardiovascular Interventions · doi:10.1002/ccd.28068 · Cited by 8
Credibility analysis of water environment complaint report based on deep cross domain network
Fan, Q., Han, H., & Wu, S. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02842-0 · Cited by 8
Urine Protein/Creatinine Ratio in Thrombotic Microangiopathies: A Simple Test to Facilitate Thrombotic Thrombocytopenic Purpura and Hemolytic and Uremic Syndrome Diagnosis
Burguet, L., Taton, B., Prezelin‐Reydit, M., Rubin, S., Picard, W., Gruson, D., Ryman, A., Contin‐Bordes, C., Coppo, P., Combe, C., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11030648 · Cited by 8
Validation of the ARIC prediction model for sudden cardiac death in the European population: The ESCAPE-NET project
Welten, S. J. G. C., Remmelzwaal, S., Blom, M. T., van der Heijden, A. A. W. A., Nijpels, G., Tan, H. L., van Valkengoed, I. G. M., Empana, J. P., Jouven, X., Ågesen, F. N., et al. (2023) · American Heart Journal · doi:10.1016/j.ahj.2023.03.018 · Cited by 8
An assessment of the relationship between clinical utility and predictive ability measures and the impact of mean risk in the population
McGeechan, K., Macaskill, P., Irwig, L., & Bossuyt, P. M. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-86 · Cited by 8
Improvement in the predictive ability of the Intermountain Mortality Risk Score by adding routinely collected laboratory tests such as albumin, bilirubin, and white cell differential count
May, H. T., Anderson, J. L., Muhlestein, J. B., LAPPE, D. L., Ronnow, B. S., & Horne, B. D. (2016) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2015-1258 · Cited by 8
Relation of multi-marker panel to incident chronic kidney disease and rapid kidney function decline in African Americans: the Jackson Heart Study
Mwasongwe, S. E., Young, B. A., Bidulescu, A., Sims, M., Correa, A., & Musani, S. K. (2018) · BMC Nephrology · doi:10.1186/s12882-018-1026-y · Cited by 8
Multimarker Approach in Risk Stratification of Patients with Decompensated Heart Failure
Protasov, V. N., Narusov, O. Y., Skvortsov, A. A., Protasova, D. E., Kuznetsova, T. V., Анатольевна, П. А., Masenko, V. P., & Tereshchenko, S. N. (2019) · Kardiologiia · doi:10.18087/cardio.2637 · Cited by 8
Ensemble learning for poor prognosis predictions: A case study on SARS-CoV-2
Wu, H., Zhang, H., Karwath, A., Ibrahim, Z. M., Shi, T., Zhang, X., Wang, K., Sun, J., Dhaliwal, K., Bean, D. M., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa295 · Cited by 8
Bullous retinal detachment in a patient with central serous chorioretinopathy.
Yang, C., & Lin, C. (1998) · PubMed · Cited by 8
Serum Total 25‐OH Vitamin D Adds Little Prognostic Value in Patients Undergoing Coronary Catheterization
Gerling, M. E., James, M. T., Wilton, S. B., Naugler, C. T., Southern, D. A., Galbraith, P. D., Knudtson, M. L., & de Koning, L. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.004289 · Cited by 8
Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke
Jeon, E., Lee, S. H., Eun, M., & Jung, J. (2024) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2024.08.006 · Cited by 7
Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study
Chen, S., Xu, Z., Yin, J., Gu, H., Shi, Y., Guo, C., Meng, X., Li, H., Huang, X., Jiang, Y., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae487 · Cited by 7
Development and Validation of Body Mass Index–Specific Waist Circumference Thresholds in Postmenopausal Women
Aragaki, A. K., Manson, J. E., LeBlanc, E. S., Chlebowski, R. T., Tinker, L. F., Allison, M., Haring, B., Odegaard, A., Wassertheil-Smoller, S. W., Saquib, N., et al. (2025) · Annals of Internal Medicine · doi:10.7326/annals-24-00713 · Cited by 7
Clinical utility of self-reported sleep duration and insomnia symptoms in type 2 diabetes prediction
Wright, A. K., Huang, T., Carr, M., Premdayal, A. D., Saluja, S., Dashti, H. S., Anderson, S. G., Ray, D. W., Jones, S. E., Wood, A. R., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06503-6 · Cited by 7
Tailoring the Implementation of New Biomarkers Based on Their Added Predictive Value in Subgroups of Individuals
van Giessen, A., Moons, K. G. M., de Wit, G. A., Verschuren, W. M. M., Boer, J. M. A., & Koffijberg, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0114020 · Cited by 7
Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models
Harrison, T. G., Hemmelgarn, B. R., James, M. T., Sawhney, S., Manns, B., Tonelli, M. A., Ruzycki, S. M., Zarnke, K. B., Wilson, T., McCaughey, D., et al. (2023) · BMC Nephrology · doi:10.1186/s12882-023-03093-6 · Cited by 7
Growth Differentiation Factor 15 and Risk of Bleeding Events: The Atherosclerosis Risk in Communities Study
Mathews, L. M., Hu, X. F., Ding, N., Ishigami, J., Rifai, M. A., Hoogeveen, R. C., Coresh, J., Ballantyne, C. M., Selvin, E., & Matsushita, K. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.121.023847 · Cited by 7
A Novel ypTLM Staging System Based on LODDS for Gastric Cancer After Neoadjuvant Therapy: Multicenter and Large‐Sample Retrospective Study
Que, S., Zhong, Q., Chen, Q., Truty, M. J., Yan, S., Ma, Y., Ding, F., Zheng, C., Li, P., Wang, J., et al. (2023) · World Journal of Surgery · doi:10.1007/s00268-023-06994-7 · Cited by 7
Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke
Huang, S., Joshi, A., Shi, Z., Wei, J., Tran, H. M., Zheng, S. L., Duggan, D. J., Ashworth, A., Billings, L. K., Helfand, B. T., et al. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.131990 · Cited by 7
Real-time estimated Sequential Organ Failure Assessment (SOFA) score with intervals: improved risk monitoring with estimated uncertainty in health condition for patients in intensive care units
He, Y., Luo, Q., Wang, H., Zheng, Z., Luo, H., & Ooi, O. C. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00331-5 · Cited by 7
Biomarkers of RV Dysfunction in HFrEF Identified by Direct Tissue Proteomics: Extracellular Proteins Fibromodulin and Fibulin-5
Běhounek, M., Lipcseyová, D., Vít, O., Žáček, P., Talacko, P., Husková, Z., Kikerlová, S., Tykvartová, T., Wohlfahrt, P., Melenovský, V., et al. (2025) · Circulation Heart Failure · doi:10.1161/circheartfailure.124.011984 · Cited by 7
Impact of spine-hip discordance on fracture risk assessment and treatment qualification in Canada: the Manitoba BMD registry
Leslie, W. D., Morin, S. N., Lix, L. M., & Binkley, N. (2020) · Archives of Osteoporosis · doi:10.1007/s11657-020-00763-y · Cited by 7
Development and validation of a nomogram for discriminating between benign and malignant breast masses by conventional ultrasound and dual-mode elastography: a multicenter study
Yang, K., Ye, X., Tian, H., Li, Q., Liu, Q., Li, J., Guo, J., Xu, J., & Dong, F. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-237 · Cited by 7
Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease
Ishii, M., Kaikita, K., Yasuda, S., Akao, M., Ako, J., Matoba, T., Nakamura, M., Miyauchi, K., Hagiwara, N., Kimura, K., et al. (2023) · Open Heart · doi:10.1136/openhrt-2023-002292 · Cited by 7
Role of fatty liver in the epidemic of advanced chronic liver disease among people with HIV: protocol for the Canadian LIVEHIV multicentre prospective cohort
Cinque, F., Saeed, S., Kablawi, D., Ballesteros, L. R., Elgretli, W., Moodie, E. E. M., Price, C. E., Monteith, K., Cooper, C., Walmsley, S. L., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-076547 · Cited by 7
Association between subjective physical function and occurrence of new fractures in older adults: A retrospective cohort study
Sato, S., Sasabuchi, Y., Aso, S., Okada, A., & Yasunaga, H. (2024) · Geriatrics and gerontology international/Geriatrics & gerontology international · doi:10.1111/ggi.14830 · Cited by 7
Framingham score and work-related variables for predicting cardiovascular disease in the working population
van der Zwaard, A.-J., Geraedts, A. S., Norder, G., Heymans, M. W., & Roelen, C. A. M. (2019) · European Journal of Public Health · doi:10.1093/eurpub/ckz008 · Cited by 7
Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model
Lee, S., Hong, S., Cha, W. C., & Kim, K. (2019) · JMIR Medical Informatics · doi:10.2196/16117 · Cited by 7
Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation
Lin, J. C.-W., Chang, C.-M., Pan, H., Ho, Y.-L., Tu, Y., & Lai, C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02470-3 · Cited by 6
Urinary Neutrophil Gelatinase–Associated Lipocalin/Hepcidin-25 Ratio for Early Identification of Patients at Risk for Renal Replacement Therapy After Cardiac Surgery: A Substudy of the BICARBONATE Trial
Elitok, S., Kuppe, H., Devarajan, P., Bellomo, R., Isermann, B., Westphal, S., Kube, J., Albert, C., Ernst, M., Kropf, S., et al. (2021) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000005741 · Cited by 6
Interpretation of SPECT wall motion with deep learning
Zhang, Y., Bos, E., Clarkin, O. J., Wilson, T., Small, G. R., Wells, R. G., Lu, L., & Chow, B. J. W. (2024) · Journal of Nuclear Cardiology · doi:10.1016/j.nuclcard.2024.101881 · Cited by 6
CatLet score and clinical CatLet score as predictors of long-term outcomes in patients with acute myocardial infarction presenting later than 12 hours from symptom onset
He, Y., Masuda, S., Jiang, T.-B., Xu, J., Sun, B.-C., & Ge, J. (2024) · Annals of Medicine · doi:10.1080/07853890.2024.2349190 · Cited by 6
The SpO2/FiO2 Ratio Combined with Prognostic Scores for Pneumonia and COVID-19 Increases Their Accuracy in Predicting Mortality of COVID-19 Patients
Zinna, G., Pipitò, L., Colomba, C., Scichilone, N., Licata, A., Barbagallo, M., Russo, A., Coppola, N., & Cascio, A. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13195884 · Cited by 6
Assessment and integration of multiparametric MRI for liver fibrosis staging in rat non-alcoholic steatohepatitis: Evaluation of diagnostic efficiency and model interpretation
Huang, Z., Xia, X., Liang, Y., Wen, Y., Yang, M., Pan, Y., Luo, P., & Lei, P. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111821 · Cited by 6
Integrating D-Dimer Thresholds into the Revised Caprini Risk Stratification to Predict Deep Vein Thrombosis Risk in Preoperative Knee Osteoarthritis Patients
Guo, Y.-F., Zhang, D., Chen, Y., Liu, W., Gao, N., Weng, X., Jin, L., Jin, J., Qian, W., Yang, X., et al. (2025) · Clinical and Applied Thrombosis/Hemostasis · doi:10.1177/10760296241311265 · Cited by 6
Best Albuminuria Measurement to Predict Cardiovascular and Renal Events
Bauer, L., Emrich, I. E., Pickering, J. W., Untersteller, K., Sandermann, F., Rogacev, K. S., Seiler-Mußler, S., Fliser, D., & Heine, G. H. (2016) · American Journal of Nephrology · doi:10.1159/000446483 · Cited by 6
Usefulness of Coronary Artery Calcium Score to Rule Out Obstructive Coronary Artery Disease Before Transcatheter Aortic Valve Implantation
Natanzon, S. S., Fardman, A., Mazin, I., Barbash, I. M., Segev, A., Konen, E., Goitein, O., Guetta, V., Raanani, E., Maor, E., et al. (2022) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2022.07.030 · Cited by 6
Usefulness of gallium-67 scintigraphy for evaluating the histopathological activity in interstitial nephritis
Matsumura, M., Okada, A., Yokoyama, H., Sekiguchi, M., Shimizu, A., Tanaka, T., Nangaku, M., & Takano, H. (2022) · Clinical and Experimental Nephrology · doi:10.1007/s10157-022-02302-0 · Cited by 6
Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions
Roelen, C. A. M., Bültmann, U., Groothoff, J. W., Twisk, J. W. R., & Heymans, M. W. (2015) · International Archives of Occupational and Environmental Health · doi:10.1007/s00420-015-1032-3 · Cited by 6
Risk Prediction Score for Cancer Development in Patients With Acute Coronary Syndrome
Ishii, M., Marume, K., Nakai, M., Ogata, S., Kaichi, R., Ikebe, S., Mōri, T., Komaki, S., Kusaka, H., Toida, R., et al. (2021) · Circulation Journal · doi:10.1253/circj.cj-21-0071 · Cited by 6
Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation
Wu, Y., Abbey, C. K., Chen, X., Liu, J., Page, D. C., Alagöz, O., Peissig, P. L., Onitilo, A. A., & Burnside, E. S. (2015) · Journal of medical imaging · doi:10.1117/1.jmi.2.4.041005 · Cited by 6
Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study
Pollock, B. D., Hu, T., Chen, W., Harville, E. W., Li, S., Webber, L. S., Fonseca, V. A., & Bazzano, L. (2016) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2016.07.025 · Cited by 6
Development of an ensemble machine learning prognostic model to predict 60-day risk of major adverse cardiac events in adults with chest painPreprint
Kennedy, C. J., Mark, D. G., Huang, J., van der Laan, M. J., Hubbard, A., & Reed, M. E. (2021) · medRxiv · doi:10.1101/2021.03.08.21252615 · Cited by 6
Assessment of uteroplacental perfusion with 3D power Doppler for the early prediction of pre-eclampsia: a systematic review and meta-analysis
Wang, Y., Zheng, M., Bao, M., & Wang, H. (2025) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-2025-158 · Cited by 5

Authors: Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J., & Steyerberg, E. W.
Affiliations: Duke University (US)
ORCID: Leening, Maarten J.G.
Pencina, Michael J.
Steyerberg, Ewout W.
Venue: Annals of Internal Medicine
Publisher: American College of Physicians
Published: 2014-01-21
Volume: 160
Issue: 2
Pages: 122-131
ISSN: 0003-4819 (ISSN-L)
1539-3704
DOI: 10.7326/m13-1522
OpenAlex: W2103645914
Cited by: 594
References: 141
Citations by year: 2026: 38 · 2025: 30 · 2024: 41 · 2023: 59 · 2022: 59 · 2021: 53 · 2020: 56 · 2019: 39 · 2018: 49 · 2017: 44 · 2016: 55 · 2015: 47 · 2014: 23
FWCI: 7.11
Type: journal article
Language: en
Open access: green
hdl.handle.net
Licence: other-oa
www.acpjournals.org/journal/aim/text-and-data-mining (tdm)
Locations: Annals of Internal Medicine (publishedVersion)
PubMed (publishedVersion)
EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords: net reclassification improvement, risk prediction model, model calibration, cost-effectiveness, reporting quality
MeSH terms: Data Interpretation, Statistical, Decision Support Techniques, Humans, Models, Statistical, Risk Assessment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks