Comparison of risk prediction models in infarct-related cardiogenic shock

Abstract Background Several prediction models have been developed to allow accurate risk assessment and provide better treatment guidance in patients with infarct-related cardiogenic shock (CS). However, comparative data between these models are still scarce. Objectives To externally validate different risk prediction models in infarct-related CS and compare their predictive value in the early clinical course. Methods The Simplified Acute Physiology Score (SAPS)-II Score, the CardShock score, the IABP-SHOCK II score and the Society for Cardiovascular Angiography and Intervention (SCAI) classification were each externally validated in a total of 1055 patients with infarct-related CS enrolled into the randomized CULPRIT-SHOCK trial or the corresponding registry. Discriminative power was assessed by comparing area under the curves (AUC) in case of continuous scores. Results In direct comparison of the continuous scores in a total of 161 patients, the IABP-SHOCK II score revealed best discrimination (AUC=0.74), followed by the CardShock score (AUC=0.69) and the SAPS-II score, giving only moderate discrimination (AUC=0.63). All of the three scores revealed acceptable calibration by Hosmer-Lemeshow test. The SCAI classification as a categorical predictive model displayed good prognostic assessment for the highest risk group (stage E), but showed poor discrimination between stages C and D with respect to short-term-mortality. Conclusion Based on the present findings, the IABP-SHOCK II score appears to be the most suitable of the examined models for immediate risk prediction in infarct-related CS. Prospective evaluation of the models, further modification or even development of new scores might be necessary to reach higher levels of discrimination. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union, German Centre for Cardiovascular Research Survival probabilities continuous scores Survival probabilities SCAI

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COMPARATIVE PROGNOSTIC ACCURACY OF CARDIOGENIC SHOCK RISK PREDICTION MODELS

Canadian Journal of Cardiology ◽

10.1016/j.cjca.2017.07.385 ◽

2017 ◽

Vol 33 (10) ◽

pp. S196-S197

Author(s):

R. Miller ◽

S. Van Diepen ◽

G. Schnell ◽

A. Grant

Keyword(s):

Cardiogenic Shock ◽

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models ◽

Prognostic Accuracy

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Faculty Opinions recommendation of Calibration of risk prediction models: impact on decision-analytic performance.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718545520.793499575 ◽

2014 ◽

Author(s):

Ewout Steyerberg

Keyword(s):

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models

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Faculty Opinions recommendation of Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727476053.793541151 ◽

2018 ◽

Author(s):

Robert Keith ◽

Melissa New

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Risk Prediction ◽

Validation Study ◽

Prediction Models ◽

Lung Cancer Screening ◽

Risk Prediction Models ◽

Selection Of

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Patient-Reported Symptoms Improve Performance of Risk Prediction Models for Emergency Department Visits Among Patients With Cancer: A Population-Wide Study in Ontario Using Administrative Data

Journal of Pain and Symptom Management ◽

10.1016/j.jpainsymman.2019.07.007 ◽

2019 ◽

Vol 58 (5) ◽

pp. 745-755 ◽

Cited By ~ 6

Author(s):

Rinku Sutradhar ◽

Mehdi Rostami ◽

Lisa Barbera

Keyword(s):

Emergency Department ◽

Risk Prediction ◽

Administrative Data ◽

Prediction Models ◽

Emergency Department Visits ◽

Improve Performance ◽

Risk Prediction Models ◽

Patients With Cancer ◽

Patient Reported

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Rare Germline Pathogenic Variants Identified by Multigene Panel Testing and the Risk of Aggressive Prostate Cancer

Cancers ◽

10.3390/cancers13071495 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1495

Author(s):

Tú Nguyen-Dumont ◽

James G. Dowty ◽

Robert J. MacInnis ◽

Jason A. Steen ◽

Moeen Riaz ◽

...

Keyword(s):

Prostate Cancer ◽

Risk Prediction ◽

Prediction Models ◽

Risk Prediction Models ◽

Aggressive Prostate Cancer ◽

Panel Testing ◽

Pathogenic Variants ◽

Increased Risk ◽

Predisposition Genes ◽

Rare Genetic Variants

While gene panel sequencing is becoming widely used for cancer risk prediction, its clinical utility with respect to predicting aggressive prostate cancer (PrCa) is limited by our current understanding of the genetic risk factors associated with predisposition to this potentially lethal disease phenotype. This study included 837 men diagnosed with aggressive PrCa and 7261 controls (unaffected men and men who did not meet criteria for aggressive PrCa). Rare germline pathogenic variants (including likely pathogenic variants) were identified by targeted sequencing of 26 known or putative cancer predisposition genes. We found that 85 (10%) men with aggressive PrCa and 265 (4%) controls carried a pathogenic variant (p < 0.0001). Aggressive PrCa odds ratios (ORs) were estimated using unconditional logistic regression. Increased risk of aggressive PrCa (OR (95% confidence interval)) was identified for pathogenic variants in BRCA2 (5.8 (2.7–12.4)), BRCA1 (5.5 (1.8–16.6)), and ATM (3.8 (1.6–9.1)). Our study provides further evidence that rare germline pathogenic variants in these genes are associated with increased risk of this aggressive, clinically relevant subset of PrCa. These rare genetic variants could be incorporated into risk prediction models to improve their precision to identify men at highest risk of aggressive prostate cancer and be used to identify men with newly diagnosed prostate cancer who require urgent treatment.

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Neurocognitive measures of self-blame and risk prediction models of recurrence in major depressive disorder

Biological Psychiatry Cognitive Neuroscience and Neuroimaging ◽

10.1016/j.bpsc.2021.06.010 ◽

2021 ◽

Author(s):

Andrew J. Lawrence ◽

Daniel Stahl ◽

Suqian Duan ◽

Diede Fennema ◽

Tanja Jaeckle ◽

...

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Risk Prediction ◽

Prediction Models ◽

Major Depressive ◽

Risk Prediction Models ◽

Self Blame ◽

Neurocognitive Measures

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Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105332 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5332

Author(s):

Po-Hsiang Lin ◽

Jer-Guang Hsieh ◽

Hsien-Chung Yu ◽

Jyh-Horng Jeng ◽

Chiao-Lin Hsu ◽

...

Keyword(s):

Barrett’S Esophagus ◽

Risk Prediction ◽

Barrett's Esophagus ◽

Gastrointestinal Endoscopy ◽

Prediction Models ◽

Upper Gastrointestinal Endoscopy ◽

Health Examination ◽

Endoscopic Screening ◽

Risk Prediction Models ◽

Upper Gastrointestinal

Determining the target population for the screening of Barrett’s esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

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