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

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Untitled work

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Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

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Towards better clinical prediction models: seven steps for development and an ABCD for validation

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Gut microbiota-dependent trimethylamine N-oxide in acute coronary syndromes: a prognostic marker for incident cardiovascular events beyond traditional risk factors

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Development and validation of a scoring system to predict outcomes of patients with primary biliary cirrhosis receiving ursodeoxycholic acid therapy

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Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score

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Principles and methods for transferring polygenic risk scores across global populations

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Left Atrial Strain and Compliance in the Diagnostic Evaluation of Heart Failure with Preserved Ejection Fraction

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In-depth mining of clinical data: the construction of clinical prediction model with R

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An algorithm based on deep learning for predicting in-hospital cardiac arrest

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Large-scale Metabolomic Profiling Identifies Novel Biomarkers for Incident Coronary Heart Disease

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Troponin I and cardiovascular risk prediction in the general population: the BiomarCaRE consortium

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Cystatin C as a biomarker for estimating glomerular filtration rate

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Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

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Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients

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Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule

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Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review

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Lipoprotein(a) and the risk of cardiovascular disease in the European population: results from the BiomarCaRE consortium

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Coronary Functional Abnormalities in Patients With Angina and Nonobstructive Coronary Artery Disease

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Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

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Quantifying the added value of new biomarkers: how and how not

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Carotid Plaque Characteristics Predict Recurrent Ischemic Stroke and TIA

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Lipid Management in Patients with Endocrine Disorders: An Endocrine Society Clinical Practice Guideline

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Risk prediction models for contrast induced nephropathy: systematic review

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Comparison of Methods for Improving Fracture Risk Assessment in Diabetes: The Manitoba BMD Registry

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Plasma mSEPT9: A Novel Circulating Cell-free DNA-Based Epigenetic Biomarker to Diagnose Hepatocellular Carcinoma

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How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods

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Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

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Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care

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Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes

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Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

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Neurofilament light as an outcome predictor after cardiac arrest: a post hoc analysis of the COMACARE trial

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Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

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Combination of 68 Ga-PSMA PET/CT and Multiparametric MRI Improves the Detection of Clinically Significant Prostate Cancer: A Lesion-by-Lesion Analysis

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Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer

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Assessing Risk for Adverse Outcomes in Older Adults: The Need to Include Both Physical Frailty and Cognition

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Comparison of Atherosclerotic Calcification in Major Vessel Beds on the Risk of All-Cause and Cause-Specific Mortality

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Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure.

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Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

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Evaluation of the Usefulness of Consensus Definitions of Sarcopenia in Older Men: Results from the Observational Osteoporotic Fractures in Men Cohort Study

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Endothelial Glycocalyx as Biomarker for Cardiovascular Diseases: Mechanistic and Clinical Implications

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Recurrence of Pancreatic Neuroendocrine Tumors and Survival Predicted by Ki67

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Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes

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Addition of tumor multiplicity improves the prognostic performance of the hepatoma arterial‐embolization prognostic score

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Beyond discrimination: A comparison of calibration methods and clinical usefulness of predictive models of readmission risk

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C 2 HEST Score and Prediction of Incident Atrial Fibrillation in Poststroke Patients: A French Nationwide Study

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Development and Validation of a Pediatric Comorbidity Index

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Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study

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Serum Acylcarnitines and Risk of Cardiovascular Death and Acute Myocardial Infarction in Patients With Stable Angina Pectoris

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Key steps and common pitfalls in developing and validating risk models

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Prediction of Incident Major Osteoporotic and Hip Fractures by Trabecular Bone Score (TBS) and Prevalent Radiographic Vertebral Fracture in Older Men

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Development and Validation of the Summary Elixhauser Comorbidity Score for Use With ICD-10-CM–Coded Data Among Older Adults

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Prospective Associations of Systemic and Urinary Choline Metabolites with Incident Type 2 Diabetes

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Net reclassification index at event rate: properties and relationships

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Independent Associations Between Metabolic Syndrome Severity and Future Coronary Heart Disease by Sex and Race

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Multidimensional Prognostic Index Predicts Mortality and Length of Stay During Hospitalization in the Older Patients: A Multicenter Prospective Study

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Comparison of the CHA 2 DS 2 -VASc, CHADS 2 , HAS-BLED, ORBIT, and ATRIA Risk Scores in Predicting Non–Vitamin K Antagonist Oral Anticoagulants-Associated Bleeding in Patients With Atrial Fibrillation

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Determining Risk of Barrett’s Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants

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Association between self-reported functional capacity and major adverse cardiac events in patients at elevated risk undergoing noncardiac surgery: a prospective diagnostic cohort study

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Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

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Vital signs and their cross-correlation in sepsis and NEC: a study of 1,065 very-low-birth-weight infants in two NICUs

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Cohen’s Kappa Coefficient as a Measure to Assess Classification Improvement following the Addition of a New Marker to a Regression Model

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Including mRECIST in the Metroticket 2.0 criteria improves prediction of hepatocellular carcinoma-related death after liver transplant

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How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through

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Clinical Utility of Using Lumbar Spine Trabecular Bone Score to Adjust Fracture Probability: The Manitoba BMD Cohort

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Predicting all‐cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison

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A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease

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Beyond Coronary Calcification, Family History, and C-Reactive Protein

Mody, P. S., Joshi, P. H., Khera, A., Ayers, C. R., & Rohatgi, A. K. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.03.538 · Cited by 76

Do air pollution and neighborhood greenness exposures improve the predicted cardiovascular risk?

Yitshak‐Sade, M., Kloog, I., & Novack, V. (2017) · Environment International · doi:10.1016/j.envint.2017.07.011 · Cited by 76

Novel Biomarkers to Improve the Prediction of Cardiovascular Event Risk in Type 2 Diabetes Mellitus

van der Leeuw, J., Beulens, J. W. J., van Dieren, S., Schalkwijk, C. G., Glatz, J. F. C., Hofker, M. H., Verschuren, W. M. M., Boer, J. M. A., van der Graaf, Y., Visseren, F. L. J., et al. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.003048 · Cited by 76

Dynamic α-Fetoprotein Response and Outcomes After Liver Transplant for Hepatocellular Carcinoma

Halazun, K. J., Rosenblatt, R. E., Mehta, N. J., Lai, Q., Hajifathalian, K., Gorgen, A., Brar, G., Sasaki, K., Doyle, M. B. M., Tabrizian, P., et al. (2021) · JAMA Surgery · doi:10.1001/jamasurg.2021.0954 · Cited by 74

Older Adults, “Malignant” Left Ventricular Hypertrophy, and Associated Cardiac-Specific Biomarker Phenotypes to Identify the Differential Risk of New-Onset Reduced Versus Preserved Ejection Fraction Heart Failure

Seliger, S. L., de Lemos, J. A., Neeland, I. J., Christenson, R. H., Gottdiener, J. S., Drazner, M. H., Berry, J. D., Sorkin, J. D., & deFilippi, C. R. (2015) · JACC Heart Failure · doi:10.1016/j.jchf.2014.12.018 · Cited by 72

Assessing the Incremental Contribution of Common Genomic Variants to Melanoma Risk Prediction in Two Population-Based Studies

Cust, A. E., Drummond, M., Kanetsky, P. A., Mann, G. J., Cust, A. E., Schmid, H., Hopper, J. L., Aitken, J. F., Armstrong, B. K., Giles, G. G., et al. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.05.023 · Cited by 72

Effective hospital readmission prediction models using machine-learned features

Davis, S., Zhang, J., Lee, I., Rezaei, M., Greiner, R., McAlister, F. A., & Padwal, R. S. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08748-y · Cited by 68

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

Wang, Y., Wang, L., Yang, Y., & Lian, T. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114090 · Cited by 67

The Choice of Normative Pediatric Reference Database Changes Spine Bone Mineral Density Z-Scores But Not the Relationship Between Bone Mineral Density and Prevalent Vertebral Fractures

Ma, J., Siminoski, K. G., Alos, N., Halton, J., Ho, J., Lentle, B. C., Matzinger, M., Shenouda, N. F., Atkinson, S. A., Barr, R. D., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-3096 · Cited by 67

Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions

Sawhney, S., Marks, A., Fluck, N., McLernon, D. J., Prescott, G. J., & Black, C. (2017) · BMC Nephrology · doi:10.1186/s12882-016-0430-4 · Cited by 67

Radiomics approach for preoperative identification of stages I−II and III−IV of esophageal cancer

Wu, L., Wang, C., Tan, X.-Z., Cheng, Z., Zhao, K., Yan, L., Liang, Y., Liu, Z., & Liang, C. (2018) · Chinese Journal of Cancer Research · doi:10.21147/j.issn.1000-9604.2018.04.02 · Cited by 67

A Comparison of the Updated Diamond-Forrester, CAD Consortium, and CONFIRM History-Based Risk Scores for Predicting Obstructive Coronary Artery Disease in Patients With Stable Chest Pain

Baskaran, L., Danad, I., Gransar, H., Hartaigh, B. Ó., Schulman‐Marcus, J., Lin, F. Y.-H., Peña, J. M., Hunter, A. L., Newby, D. E., Adamson, P. D., et al. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.02.020 · Cited by 65

Validation and comparison of imaging-based scores for prediction of early stroke risk after transient ischaemic attack: a pooled analysis of individual-patient data from cohort studies

Kelly, P. J., Albers, G. W., Chatzikonstantinou, A., Marchis, G. M. D., Ferrari, J., George, P. M., Mira, K., Knoflach, M., Kim, J. S., Li, L., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)30236-8 · Cited by 64

Sequential organ failure assessment score is an excellent operationalization of disease severity of adult patients with hospitalized community acquired pneumonia – results from the prospective observational PROGRESS study

The PROGRESS Study Group, Ahnert, P., Creutz, P., Horn, K., Schwarzenberger, F., Kiehntopf, M., Hossain, H., Bauer, M. W., Brunkhorst, F. M., Reinhart, K., et al. (2019) · Critical Care · doi:10.1186/s13054-019-2316-x · Cited by 63

Trabecular bone score may improve FRAX® prediction accuracy for major osteoporotic fractures in elderly Japanese men: the Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) Cohort Study

Iki, M., Fujita, Y., Tamaki, J., Kouda, K., Yura, A., Sato, Y., Moon, J., Winzenrieth, R., Okamoto, N., & Kurumatani, N. (2015) · Osteoporosis International · doi:10.1007/s00198-015-3092-3 · Cited by 63

Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis

Crowson, C. S., Rollefstad, S., Kitas, G. D., van Riel, P. L. C. M., Gabriel, S. E., & Semb, A. G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174656 · Cited by 65

A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards

Lee, Y. J., Cho, K.-J., Kwon, O., Park, H., Lee, Y., Kwon, J., Park, J., Kim, J. S., Lee, M., Kim, A. J., et al. (2021) · Resuscitation · doi:10.1016/j.resuscitation.2021.04.013 · Cited by 63

Childhood retinol-binding protein 4 (RBP4) levels predicting the 10-year risk of insulin resistance and metabolic syndrome: the BCAMS study

Li, G., Esangbedo, I. C., Xu, L., Fu, J., Li, L., Feng, D., Han, L., Xiao, X., Li, M., Mi, J., et al. (2018) · Cardiovascular Diabetology · doi:10.1186/s12933-018-0707-y · Cited by 63

ACC/AHA guidelines superior to ESC/EAS guidelines for primary prevention with statins in non-diabetic Europeans: the Copenhagen General Population Study

Mortensen, M. B., Nordestgaard, B. G., Afzal, S., & Falk, E. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw426 · Cited by 61

Sentinel node biopsy in patients with melanoma improves the accuracy of staging when added to clinicopathological features of the primary tumor

Sharouni, M. E., Stodell, M., Ahmed, T., Suijkerbuijk, K. P. M., Cust, A. E., Witkamp, A. J., Sigurdsson, V., van Diest, P. J., Scolyer, R. A., Thompson, J. F., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.11.015 · Cited by 60

Plasma Leucine-Rich α-2-Glycoprotein 1 Predicts Rapid eGFR Decline and Albuminuria Progression in Type 2 Diabetes Mellitus

Liu, J., Pek, S. L. T., Ang, K., Subramaniam, T., Lim, S. C., & study, F. S. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-00930 · Cited by 60

Risk assessment for major adverse cardiovascular events after noncardiac surgery using self-reported functional capacity: international prospective cohort study

Buse, G. L., Mauermann, E., Ionescu, D. C., Szczeklik, W., Hert, S. G. D., Filipovic, M., Beck‐Schimmer, B., Spadaro, S., Matute, P., Bolliger, D., et al. (2023) · British Journal of Anaesthesia · doi:10.1016/j.bja.2023.02.030 · Cited by 58

Key Concepts and Limitations of Statistical Methods for Evaluating Biomarkers of Kidney Disease

Parikh, C. R., & Thiessen‐Philbrook, H. (2014) · Journal of the American Society of Nephrology · doi:10.1681/asn.2013121300 · Cited by 58

In which patients does lumbar spine trabecular bone score (TBS) have the largest effect?

Martineau, P., Leslie, W. D., Johansson, H., Harvey, N. C., McCloskey, E. V., Hans, D., & Kanis, J. A. (2018) · Bone · doi:10.1016/j.bone.2018.05.026 · Cited by 58

Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death

Zhang, Y., Zhang, Y., Sholle, E., Abedian, S., Sharko, M., Turchioe, M. R., Wu, Y., & Ancker, J. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235064 · Cited by 57

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Xu, B., Dong, S.-Y., Bai, X., Song, T., Zhang, B., Zhou, L., Chen, Y., Zeng, Z., Wang, K., Zhao, H., et al. (2022) · Liver Cancer · doi:10.1159/000528034 · Cited by 57

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis

Paige, E., Barrett, J., Pennells, L. A. M., Sweeting, M. J., Willeit, P., Angelantonio, E. D., Gudnason, V. G., Nordestgaard, B. G., Psaty, B. M., Goldbourt, U., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx149 · Cited by 58

Serum uric acid predicts vascular complications in adults with type 1 diabetes: the coronary artery calcification in type 1 diabetes study

Bjornstad, P., Maahs, D. M., Rivard, C. J., Pyle, L., Rewers, M., Johnson, R. J., & Snell‐Bergeon, J. K. (2014) · Acta Diabetologica · doi:10.1007/s00592-014-0611-1 · Cited by 55

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

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Vanhaverbeke, M., Attard, R., Barteková, M., Ben‐Aicha, S., Brandenburger, T., de Gonzalo‐Calvo, D., Emanueli, C., Farrugia, R. C., Grillari, J., Hackl, M., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab327 · Cited by 54

Application of net reclassification index to non-nested and point-based risk prediction models: a review

Thomas, L. E., O'Brien, E. C., Piccini, J. P., D’Agostino, R. B., & Pencina, M. J. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy345 · Cited by 54

Prediction modeling—part 1: regression modeling

Au, E. H. K., Francis, A., Bernier-Jean, A., & Teixeira-Pinto, A. M. (2020) · Kidney International · doi:10.1016/j.kint.2020.02.007 · Cited by 56

A CT-based radiomics nomogram for distinguishing between benign and malignant bone tumours

Sun, W., Liu, S., Guo, J., Liu, S., Hao, D., Hou, F., Wang, H., & Xu, W. (2021) · Cancer Imaging · doi:10.1186/s40644-021-00387-6 · Cited by 53

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