Publications

All publications listed here used the CPM Registry for analysis

PCORI Research Report: Using Different Data Sets to Test How Well Clinical Prediction Models Work to Predict Patients’ Risk of Heart Disease

Kent DM, Nelson J, Upshaw JN, Gulati G, Brazil R, Venema E, Lundquist CM, Park JG, McGinnes HL, Maunder REH, Paulus JK, Van Calster B, Steyerberg EW, van Klaveren D, Wessler BS.

April 2022 doi: 10.25302/09.2021.ME.160635555

Generalizability of Cardiovascular Disease Clinical Prediction Models: 158 Independent External Validations of 104 Unique Models

Gulati G, Upshaw J, Wessler BS, Brazil RJ, Nelson J, van Klaveren D, Lundquist CM, Park JG, McGinnes H, Steyerberg EW, Van Calster B, Kent DM.

April 2022 15(4):e008487. doi: 10.1161/CIRCOUTCOMES.121.008487.PMID: 35354282; PMCID: PMC9015037.

Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination

Venema E, Wessler BS, Paulus JK, Salah R, Raman G, Leung LY, Koethe BC, Nelson J, Park JG, van Klaveren D, Steyerberg EW, Kent DM.

October 2021 138:32-39. doi: 10.1016/j.jclinepi.2021.06.017.PMID: 34175377.

External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

Wessler BS, Nelson J, Park JG, McGinnes H, Gulati G, Brazil R, Van Calster B, van Klaveren D, Venema E, Steyerberg E, Paulus JK, Kent DM.

August 2021 14(8):e007858. doi: 10.1161/CIRCOUTCOMES.121.007858.PMID: 34340529; PMCID: PMC8366535.

Clinical Predictive Models of Sudden Cardiac Arrest: A Survey of the Current Science and Analysis of Model Performances

Carrick RT, Park JG, Lundquist CM, McGinnes HL, Brown KD, Janes WA, Wessler BS, Kent DM.

August 2020 9(16):e017625. doi: 10.1161/JAHA.119.017625.PMID: 32787675.

Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities

Paulus J, Kent DM.

July 2020 3,99. doi:10.1038/s41746-020-0304-9.PMID: 32821854; PMCID: PMC7393367.

Can Clinical Predictive Models Identify Patients Who Should Not Receive TAVR? A Systematic Review

Wessler BS, Weintraub AR, Udelson JE, Kent DM.

July 2020 4(4):295-299. doi: 10.1080/24748706.2020.1782549.PMID: 32905421; PMCID: PMC7469888.

When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19

Kent DM, Paulus JK, Sharp RR, Hajizadeh N.

May 2020 4:11. doi: 10.1186/s41512-020-00079-y. PMID: 32455168; PMCID: PMC7238723.

Validation and Utility Testing of Clinical Prediction Models: Time to Change the Approach

Adibi A, Sadatsafavi M, Ioannidis JP.

March 2020 324(3):235-236. doi: 10.1001/jama.2020.1230.PMID: 32134437.

Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Luijken K, Wynants L, van Smeden M, Van Calster B, Steyerberg EW, Groenwold RHH; Collaborators. 2020 Mar;

March 2020 119:7-18. doi: 10.1016/j.jclinepi.2019.11.001.PMID: 31706963.

Clinical Prediction Models for Valvular Heart Disease

Wessler BS, Lundquist CM, Koethe B, Park JG, Brown K, Williamson T, Ajlan M, Natto Z, Lutz JS, Paulus JK, Kent DM.

October 2019 8(20):e011972. doi: 10.1161/JAHA.119.011972.PMID: 31583938; PMCID: PMC6818049.

Untapped potential of multicenter studies: a review of cardiovascular risk prediction models revealed inappropriate analyses and wide variation in reporting

Wynants L, Kent DM, Timmerman D, Lundquist CM, Van Calster B.

February 2019 3:6. doi: 10.1186/s41512-019-0046-9.PMID: 31093576; PMCID: PMC6460661.

Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database

Wessler BS, Lai YH L, Kramer W, Cangelosi M, Raman G, Lutz JS, Kent DM.

July 2015 8(4):368-375. doi: 10.1161/CIRCOUTCOMES.115.001693.PMID: 26152680; PMCID: PMC4512876.