Evidence map›Paper›PMID 26152680›Full record

SynthesisCirculation. Cardiovascular quality and outcomes2015

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

Benjamin S Wessler, Lana Lai Yh, Whitney Kramer, Michael Cangelosi, Gowri Raman, Jennifer S Lutz, David M Kent

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Circulation. Cardiovascular quality and outcomes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 10 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  8. Clinical Prediction Models for Valvular Heart Disease.Journal of the American Heart Association · 2019
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Benjamin S WesslerFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
Lana Lai YhFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
Whitney KramerFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
Michael CangelosiFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
Gowri RamanFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
Jennifer S LutzFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.).
David M KentFrom the Division of Cardiology, Tufts Medical Center, Boston, MA (B.S.W.); Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center/Tufts University School of Medicine, Boston, MA (B.S.W., L.L.Y., J.S.L., D.M.K.); Business Intelligence and Analytics, Iora Health, Cambridge, MA (W.K.); Health Economics and Reimbursement, Boston Scientific, Marlborough, MA (M.C.); and Center for Clinical Evidence Synthesis, ICRHPS, Medical Center/Tufts University School of Medicine, Boston, MA (G.R.). dkent1@tuftsmedicalcenter.org.

Funding

Tufts Clinical and Translational Research InstituteUL1TR001064 · NCATS · TUFTS UNIVERSITY BOSTON · PI SELKER, HARRY P. · 2013 to 2017
$26.4M
Training Program in Cardiovascular ResearchT32HL069770 · NHLBI · TUFTS MEDICAL CENTER · PI KARAS, RICHARD H · 2003 to 2013
$4.7M
Value of Personalized Risk InformationU01NS086294 · NINDS · TUFTS MEDICAL CENTER · PI KENT, DAVID M, NEUMANN, PETER · 2013 to 2017
$2.6M
NCATS NIH HHS UL1 TR001064NHLBI NIH HHS T32 HL069770NHLBI NIH HHS T32HL069770NINDS NIH HHS U01 NS086294NINDS NIH HHS U01NS086294
6 · The paper itself

Abstract

backgroundClinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. METHODS AND

resultsWe conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration.

conclusionsThere is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood.

Indexed as

Models, TheoreticalCardiovascular DiseasesGlobal HealthHumansMorbidityRisk AssessmentRisk Factorscardiovascular diseasescerebrovascular disordersindividualized medicinelogistic modelsprognosis

Identifiers

PMID26152680
PMCPMC4512876

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.