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)

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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)

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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.

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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)

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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

Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge

Yin, L., Cui, J., Lin, X., Li, N., Fan, Y., Zhang, L., Liu, J., Chong, F., Wang, C., Liang, T., et al. (2022) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqac251 · Cited by 26

Risk Prediction for Atherosclerotic Cardiovascular Disease With and Without Race Stratification

Ghosh, A. K., Venkatraman, S., Nanna, M. G., Safford, M. M., Colantonio, L. D., Brown, T. M., Pinheiro, L. C., Peterson, E. D., Návar, A. M., Sterling, M. R., et al. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.4520 · Cited by 26

A high abdominal aortic calcification score by dual X-ray absorptiometry is associated with cardiovascular events after kidney transplantation

Benjamens, S., Pol, R. A., Glaudemans, A. W. J. M., Wieringa, I., Berger, S. P., Bakker, S. J. L., & Slart, R. H. J. A. (2018) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfy158 · Cited by 26

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

Nguyen, O. K., Makam, A. N., Clark, C., Zhang, S., Das, S. R., & Halm, E. A. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.118.008882 · Cited by 25

The reliability of motor evoked potentials to predict dorsiflexion injuries during lumbosacral deformity surgery: importance of multiple myotomal monitoring

Lieberman, J. A., Lyon, R., Jasiukaitis, P., Berven, S., Burch, S., & Feiner, J. (2018) · The Spine Journal · doi:10.1016/j.spinee.2018.07.006 · Cited by 25

HFA-PEFF scores: prognostic value in heart failure with preserved left ventricular ejection fraction

Egashira, K., Sueta, D., Komorita, T., Yamamoto, E., Usuku, H., Tokitsu, T., Fujisue, K., Nishihara, T., Oike, F., Takae, M., et al. (2021) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2021.272 · Cited by 25

Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease

King, A., Wu, L., Deng, H., Shen, H., & Wu, C. (2022) · BMC Medicine · doi:10.1186/s12916-022-02583-y · Cited by 26

Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study

Okiyama, S., Fukuda, M., Sode, M., Takahashi, W., Ikeda, M., Kato, H., Tsugawa, Y., & Iwagami, M. (2022) · Journal of Medical Internet Research · doi:10.2196/38751 · Cited by 25

Airflow limitation by the Global Lungs Initiative equations in a cohort of very old adults

Hegendörfer, E., Vaes, B., Andreeva, E., Matheï, C., Adriaensen, W. J., Pottelbergh, G. V., & Degryse, J. (2015) · European Respiratory Journal · doi:10.1183/09031936.00217214 · Cited by 25

The Diagnostic and Prognostic Value of Neuropsychological Assessment in Memory Clinic Patients

Jansen, W. J., Handels, R. L. H., Visser, P. J., Aalten, P., Bouwman, F. H., Claassen, J. A. H. R., van Domburg, P., Hoff, E. I., Hoogmoed, J., Leentjens, A. F. G., et al. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160126 · Cited by 25

Perioperative Doppler ultrasound assessment of portal vein flow pulsatility in high-risk cardiac surgery patients: a multicentre prospective cohort study

Denault, A. Y., Couture, É. J., de Médicis, É., Shim, J., Mazzeffi, M. A., Henderson, R. A., Langevin, S., Dhawan, R., Michaud, M., Guensch, D. P., et al. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2022.07.053 · Cited by 24

Net Reclassification Index Statistics Do Not Help Assess New Risk Models

Kerr, K. F. (2022) · Radiology · doi:10.1148/radiol.222343 · Cited by 24

Microbiome-derived cobalamin and succinyl-CoA as biomarkers for improved screening of anal cancer

Villar, S. S., Tincati, C., Raju, S. C., Sáenz, J. S., Moreno, E., Bargiela, R., Cabello, A., Sendagorta, E., Kurz, A., Pérez-Molina, J. A., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02407-3 · Cited by 24

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit

Zhuang, J., Huang, H., Jiang, S., Liang, J., Liu, Y., & Yu, X. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02279-0 · Cited by 24

The PRIMARY Score

Guo, S., Kang, F., Ma, S., Jiao, J., Ren, J., Wang, J., Zhang, J., & Qin, W. (2023) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000004951 · Cited by 25

Will the future lie in multitude? A critical appraisal of biomarker panel studies on prediction of diabetic kidney disease progression

Schutte, E., Gansevoort, R. T., Benner, J., Lutgers, H. L., & Heerspink, H. J. L. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv119 · Cited by 24

Plasma biomarkers improve prediction of diabetic kidney disease in adults with type 1 diabetes over a 12-year follow-up: CACTI study

Bjornstad, P., Pyle, L., Cherney, D. Z. I., Johnson, R. J., Sippl, R., Wong, R., Rewers, M., & Snell‐Bergeon, J. K. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx255 · Cited by 24

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

Solikhah, S., & Nurdjannah, S. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03794 · Cited by 24

Methylation pattern of urinary DNA as a marker of kidney function decline in diabetes

Marumo, T., Hoshino, J., Kawarazaki, W., Nishimoto, M., Ayuzawa, N., Hirohama, D., Yamanouchi, M., Ubara, Y., Okaneya, T., Fujii, T., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001501 · Cited by 24

Anthropometric measures of obesity and associated cardiovascular disease risk in the Eastern Caribbean Health Outcomes Research Network (ECHORN) Cohort Study

on behalf of the ECHORN Writing Group, Hassan, S., Oladele, C., Galusha, D. H., Adams, O. P., Maharaj, R. G., Nazario, C. M., Nunez, M. A., & Nuñez-Smith, M. (2021) · BMC Public Health · doi:10.1186/s12889-021-10399-3 · Cited by 27

Urinary Haptoglobin Predicts Rapid Renal Function Decline in Asians With Type 2 Diabetes and Early Kidney Disease

Liu, J., Liu, S., Wong, M. D. S., Gurung, R. L., & Lim, S. C. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-2094 · Cited by 23

A disease-specific comorbidity index for predicting mortality in patients admitted to hospital with a cardiac condition

Azzalini, L., Chabot-Blanchet, M., Southern, D. A., Nozza, A., Wilton, S. B., Graham, M. M., Marquis‐Gravel, G., Bluteau, J.-P., Rouleau, J. L., Guertin, M., et al. (2019) · Canadian Medical Association Journal · doi:10.1503/cmaj.181186 · Cited by 23

Direct Versus Calculated LDL Cholesterol and C-Reactive Protein in Cardiovascular Disease Risk Assessment in the Framingham Offspring Study

Ikezaki, H., Fisher, V. A., Lim, E., Ai, M., Liu, C., Cupples, L. A., Nakajima, K., Asztalos, B. F., Furusyo, N., & Schaefer, E. J. (2019) · Clinical Chemistry · doi:10.1373/clinchem.2019.304600 · Cited by 23

Measuring resilience by cognitive diagnosis models and its prediction of 6-month quality of life in Be Resilient to Breast Cancer (BRBC)

Liang, M. Z., Chen, P., Knobf, M. T., Molassiotis, A., Tang, Y. C., Hu, G. Y., Sun, Z., Yu, Y. L., & Ye, Z. J. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1102258 · Cited by 23

Should persons with acute myeloid leukemia have a transplant in first remission?

Gale, R. P., Wiernik, P. H., & Lazarus, H. M. (2014) · Leukemia · doi:10.1038/leu.2014.129 · Cited by 24

Direct comparison of FRAXR and a simplified fracture risk assessment tool in routine clinical practice: a registry-based cohort study

Leslie, W. D., Majumdar, S. R., Lix, L. M., Josse, R. G., Johansson, H., Odén, A., McCloskey, E. V., & Kanis, J. A. (2016) · Osteoporosis International · doi:10.1007/s00198-016-3613-8 · Cited by 23

Similar performance of liver stiffness measurement and liver surface nodularity for the detection of portal hypertension in patients with hepatocellular carcinoma

Souhami, A., Sartoris, R., Rautou, P., Cauchy, F., Bouattour, M., Durand, F., Giannelli, V., Gigante, E., Castéra, L., Valla, D. C., et al. (2020) · JHEP Reports · doi:10.1016/j.jhepr.2020.100147 · Cited by 22

Increased Nighttime Pulse Pressure Variability but Not Ambulatory Blood Pressure Levels Predicts 14-Year All-Cause Mortality in Patients on Hemodialysis

Huang, J.-T., Cheng, H., Yu, W., Lin, Y., Sung, S., & Chen, C.-H. (2019) · Hypertension · doi:10.1161/hypertensionaha.119.13204 · Cited by 22

Circulating biomarkers for long-term cardiovascular risk stratification in apparently healthy individuals from the MONICA 10 cohort

Frary, C. E., Blicher, M. K., Olesen, T. B., Stidsen, J. V., Greve, S. V., Vishram‐Nielsen, J. K. K., Rasmussen, S. L., Olsen, M. H., & Pareek, M. (2019) · European Journal of Preventive Cardiology · doi:10.1177/2047487319885457 · Cited by 22

Individual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany

Yu, H., Raut, J. R., Schöttker, B., Holleczek, B., Zhang, Y., & Brenner, H. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00872-y · Cited by 22

Utility of risk prediction models to detect atrial fibrillation in screened participants

Poorthuis, M. H. F., Jones, N. R., Sherliker, P., Clack, R. M., de Borst, G. J., Clarke, R., Lewington, S., Halliday, A. W., & Bulbulia, R. A. (2020) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaa082 · Cited by 22

Role of Inferior Vena Cava Dynamics for Estimating Right Atrial Pressure in Congenital Heart Disease

Egbe, A. C., Connolly, H. M., Pellikka, P. A., Anderson, J. H., & Miranda, W. R. (2022) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.122.014308 · Cited by 21

Application of Artificial Neural Network Models to Differentiate Between Complicated and Uncomplicated Acute Appendicitis

Lin, H.-A., Lin, L.-T., & Lin, S. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01932-5 · Cited by 21

Prognostic Value of Angiopoietin-2 for Death Risk Stratification in Patients with Metastatic Colorectal Carcinoma

Jary, M., Vernerey, D., Lecomte, T., Dobi, E., Ghiringhelli, F., Monnien, F., Godet, Y., Kim, S. C. H., Bouché, O., Fratté, S., et al. (2015) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-14-1059 · Cited by 21

A multimethod screening approach for pediatric depression onset: An incremental validity study.

Cohen, J. R., Thakur, H., Burkhouse, K. L., & Gibb, B. E. (2018) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000364 · Cited by 21

A new population-based risk stratification tool was developed and validated for predicting mortality, hospital admissions, and health care costs

Rea, F., Corrao, G., Ludergnani, M., Cajazzo, L., & Merlino, L. G. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.08.009 · Cited by 21

Predicting hospitalization following psychiatric crisis care using machine learning

Blankers, M., van der Post, L. F. M., & Dekker, J. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01361-1 · Cited by 22

Exploring pathological signatures for predicting the recurrence of early-stage hepatocellular carcinoma based on deep learning

Qu, W., Tian, M., Qiu, J.-T., Guo, Y., Tao, C., Liu, W., Tang, Z., Qian, K., Wang, Z.-X., Li, X., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.968202 · Cited by 21

Right atrial volume by cardiovascular magnetic resonance predicts mortality in patients with heart failure with reduced ejection fraction

Ivanov, A. V., Mohamed, A., Asfour, A. M. S., Ho, J., Khan, S., Chenn, O., Klem, I., Ramasubbu, K., Brener, S. J., & Heitner, J. F. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173245 · Cited by 21

Developing an Internally Validated Veterans Affairs Women Cardiovascular Disease Risk Score Using Veterans Affairs National Electronic Health Records

Jeon‐Slaughter, H., Chen, X., Tsai, S. X., Ramanan, B., & Ebrahimi, R. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.019217 · Cited by 20

CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19

McRae, A. D., Hohl, C. M., Rosychuk, R. J., Vatanpour, S., Ghaderi, G., Archambault, P. M., Brooks, S. C., Cheng, I., Davis, P. J., Hayward, J., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-055832 · Cited by 20

Relation of Combined Non–High-Density Lipoprotein Cholesterol and Apolipoprotein B With Atherosclerosis in Adults With Type 1 Diabetes Mellitus

Bjornstad, P., Eckel, R. H., Pyle, L., Rewers, M., Maahs, D. M., & Snell‐Bergeon, J. K. (2015) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2015.07.020 · Cited by 21

Development and Validation of the Modified Charlson Comorbidity Index in Incident Peritoneal Dialysis Patients: A National Population-Based Approach

Cho, H., Kim, M.-H., Kim, H. J., Park, J. Y., Ryu, D., Lee, H., Lee, J. P., Lim, C.-S., Kim, K. H., Oh, K., et al. (2016) · Peritoneal Dialysis International · doi:10.3747/pdi.2015.00201 · Cited by 20

The prognostic value of automated coronary calcium derived by a deep learning approach on non-ECG gated CT images from 82Rb-PET/CT myocardial perfusion imaging

Dekker, M., Waissi, F., Bank, I. E. M., Išgum, I., Scholtens, A. M., Velthuis, B. K., Pasterkamp, G., de Winter, R. J., Mosterd, A., Timmers, L., et al. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.12.079 · Cited by 20

Measured glomerular filtration rate does not improve prediction of mortality by cystatin C and creatinine

Sundin, P., Sjöström, P. A., Jones, I., Olsson, L. A., Udumyan, R., GRUBB, A. O., Lindström, V., & Montgomery, S. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx004 · Cited by 20

External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

Hwang, A. B., Schuepfer, G., Pietrini, M., & Boes, S. (2021) · PLOS ONE · doi:10.1371/journal.pone.0258338 · Cited by 19

Serious Falls in Middle‐Aged Veterans: Development and Validation of a Predictive Risk Model

Womack, J. A., Murphy, T. E., Bathulapalli, H., Smith, A. C., Bates, J., Jarad, S., Redeker, N. S., Luther, S. L., Gill, T. M., Brandt, C., et al. (2020) · Journal of the American Geriatrics Society · doi:10.1111/jgs.16773 · Cited by 19

Serum matrix metalloproteinase 7 and accelerated glomerular filtration rate decline in a general non-diabetic population

Enoksen, I. T., Svistounov, D., Norvik, J. V., Stefansson, V. T. N., Solbu, M. D., Eriksen, B. O., & Melsom, T. (2021) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfab251 · Cited by 19

The Use of Prognostic Models in Allogeneic Transplants: A Perspective Guide for Clinicians and Investigators

Sorror, M. L. (2023) · Blood · doi:10.1182/blood.2022017999 · Cited by 19

Heart rate dynamics preceding hemorrhage in the intensive care unit

Moss, T. J., Clark, M., Lake, D. E., Moorman, J. R., & Calland, J. F. (2015) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2015.08.007 · Cited by 19

Low cardiorespiratory fitness and coronary artery calcification: Complementary cardiovascular risk predictors in asymptomatic type 2 diabetics

Zafrir, B., Azaiza, M., Gaspar, T., Dobrecky‐Mery, I., Azencot, M., Lewis, B. S., Rubinshtein, R., & Halon, D. A. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.06.020 · Cited by 19

A Non-Targeted Liquid Chromatographic-Mass Spectrometric Metabolomics Approach for Association with Coronary Artery Disease: An Identification of Biomarkers for Depiction of Underlying Biological Mechanisms

Zhang, X., Zheng, S., & Hou, Y.-M. (2017) · Medical Science Monitor · doi:10.12659/msm.896298 · Cited by 19

Validation of a Model Predicting De Novo Stress Urinary Incontinence in Women Undergoing Pelvic Organ Prolapse Surgery

Jelovsek, J. E., van der Ploeg, J. M., Roovers, J., & Barber, M. D. (2019) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000003158 · Cited by 19

Long forms of cardiac troponin T for myocardial infarction diagnosis: the SuperTROPO study

Teppo, K., Airaksinen, J. K. E., Vasankari, T., Linko-Parvinen, A., Pallari, H., Paana, T., Jaakkola, S., Junes, H., Salonen, S. M., Tuominen, T., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf975 · Cited by 18

Event rate net reclassification index and the integrated discrimination improvement for studying incremental value of risk markers

van Smeden, M., & Moons, K. G. M. (2017) · Statistics in Medicine · doi:10.1002/sim.7286 · Cited by 18

Associations of cardiac stress biomarkers with incident type 2 diabetes and changes in glucose metabolism: KORA F4/FF4 study

Sujana, C., Seißler, J., Jordan, J., Rathmann, W., Köenig, W., Roden, M., Mansmann, U., Herder, C., Peters, A., Thorand, B., et al. (2020) · Cardiovascular Diabetology · doi:10.1186/s12933-020-01117-1 · Cited by 18

Measurement invariance of the 10-item resilience scale specific to cancer in Americans and Chinese: A propensity score–based multidimensional item response theory analysis

Liang, M. Z., Chen, P., Molassiotis, A., Jeon, S., Tang, Y. C., Hu, G., Zhu, Y., Sun, Z., Yu, Y., Knobf, M. T., et al. (2022) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2022.100171 · Cited by 18

Assessment of Cardiovascular Risk in Women: Progress so Far and Progress to Come

Tschiderer, L., Seekircher, L., Willeit, P., & Peters, S. A. E. (2023) · International Journal of Women s Health · doi:10.2147/ijwh.s364012 · Cited by 20

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study

Sheng, W., Wang, X., Xu, W., Hao, Z., Ma, H., & Zhang, S. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1198526 · Cited by 18

Brain connectomics improve prediction of 1-year decreased quality of life in breast cancer: A multi-voxel pattern analysis

Liang, M. Z., Tang, Y. C., Chen, P., Tang, X. N., Knobf, M. T., Hu, G. Y., Sun, Z., Liu, M. L., Yu, Y. L., & Ye, Z. (2023) · European Journal of Oncology Nursing · doi:10.1016/j.ejon.2023.102499 · Cited by 18

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model

Wang, Y., Zhou, C., Ying, L., Chan, H., Lee, E., Chughtai, A. R., Hadjiiski, L. M., & Kazerooni, E. A. (2024) · Cancers · doi:10.3390/cancers16122229 · Cited by 21

Assessment of a University of California, Los Angeles 4‐Variable Risk Score for Advanced Heart Failure

Sartipy, U., Goda, A., MANCINI, D. M., & Lund, L. H. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.114.000998 · Cited by 18

A Preoperative Multimarker Approach to Evaluate Acute Kidney Injury After Cardiac Surgery

Enger, T. B., Pleym, H., Stenseth, R., Greiff, G., Wahba, A., & Videm, V. (2016) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2016.10.005 · Cited by 18

Childhood obesity: how long should we wait to predict weight?

Butler, É. M., Derraik, J. G. B., Taylor, R. W., & Cutfield, W. S. (2018) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2018-0110 · Cited by 18

Cardiopulmonary Exercise Testing, Impedance Cardiography, and Reclassification of Risk in Patients Referred for Heart Failure Evaluation

MYERS, J. N., Christle, J. W., Tun, A., Yilmaz, B. Ö., Moneghetti, K. J., Yuen, E., Soofi, M. A., & Ashley, E. A. (2019) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2019.08.013 · Cited by 18

Choice of CTO scores to predict procedural success in clinical practice. A comparison of 4 different CTO PCI scores in a comprehensive national registry including expert and learning CTO operators

Salinas, P., Gonzalo, N., Moreno, V. H., Fuentes, M. E., Santos‐Martínez, S., Díaz, J. A. F., Amat-Santos, I. J., Ojeda, F. B., Borrego, J. C., Cuesta, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0245898 · Cited by 18

Validation of the ORIGIN Cardiovascular Biomarker Panel and the Value of Adding Troponin I in Dysglycemic People

Gerstein, H. C., Paré, G., McQueen, M., Lee, S. F., Hess, S., & for ORIGIN Trial Investigators (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-00273 · Cited by 18

Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events

Shin, Y., Cho, K.-J., Lee, Y., Choi, Y. H., Jung, J. H., Kim, S. Y., Kim, Y. H., Kim, Y. A., Cho, J., Park, S. J., et al. (2022) · Acute and Critical Care · doi:10.4266/acc.2022.00976 · Cited by 17

Ability of prehospital NEWS to predict 1-day and 7-day mortality is reduced in older adult patients

Pirneskoski, J., Lääperi, M., Kuisma, M., Olkkola, K. T., & Nurmi, J. O. (2021) · Emergency Medicine Journal · doi:10.1136/emermed-2019-209400 · Cited by 17

Subclinical cardiovascular disease and utility of coronary artery calcium score

Saydam, C. D. (2021) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2021.100909 · Cited by 20

Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach

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Latin American Clinical Epidemiology Network Series – Paper 7: Central obesity measurements better identified risk factors for coronary heart disease risk in the Chilean National Health Survey (2009–2010)

Lanas, F. T., Pamela Serón, S., Muñoz, S., Margozzini, P., & Puig, T. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.04.018 · Cited by 16

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Development of Predictive Equations for Nocturnal Hypertension and Nondipping Systolic Blood Pressure

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Semiquantitative assessed proteinuria and risk of heart failure: analysis of a nationwide epidemiological database

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Value of Excess Pressure Integral for Predicting 15‐Year All‐Cause and Cardiovascular Mortalities in End‐Stage Renal Disease Patients

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Association of Urine Haptoglobin With Risk of All-Cause and Cause-Specific Mortality in Individuals With Type 2 Diabetes: A Transethnic Collaborative Work

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Evaluation of the Wii Balance Board for Walking Aids Prediction: Proof-of-Concept Study in Total Knee Arthroplasty

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Brain Connectomics Improve the Prediction of High-Risk Depression Profiles in the First Year following Breast Cancer Diagnosis

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Optimisation of detecting chronic thromboembolic pulmonary hypertension in acute pulmonary embolism survivors: the InShape IV study

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Development of comorbidity score for patients undergoing major surgery

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Modified Pitt bacteremia score for predicting mortality in patients with candidaemia: A multicentre seven‐year retrospective study conducted in Japan

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Addition of Plasma Myeloperoxidase and Trimethylamine N-Oxide to the GRACE Score Improves Prediction of Near-Term Major Adverse Cardiovascular Events in Patients With ST-Segment Elevation Myocardial Infarction

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Commentary: Reporting standards are needed for evaluations of risk reclassification

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