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      The novel cystatin C, lactate, interleukin-6, and N-terminal pro-B-type natriuretic peptide (CLIP)-based mortality risk score in cardiogenic shock after acute myocardial infarction

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          Abstract

          Background

          Cardiogenic shock (CS) complicating acute myocardial infarction (AMI) still reaches excessively high mortality rates. This analysis is aimed to develop a new easily applicable biomarker-based risk score.

          Methods and results

          A biomarker-based risk score for 30-day mortality was developed from 458 patients with CS complicating AMI included in the randomized CULPRIT-SHOCK trial. The selection of relevant predictors and the coefficient estimation for the prognostic model were performed by a penalized multivariate logistic regression analysis. Validation was performed internally, internally externally as well as externally in 163 patients with CS included in the randomized IABP-SHOCK II trial. Blood samples were obtained at randomization. The two trials are registered with ClinicalTrials.gov (NCT01927549 and NCT00491036), are closed to new participants, and follow-up is completed. Out of 58 candidate variables, the four strongest predictors for 30-day mortality were included in the CLIP score (cystatin C, lactate, interleukin-6, and N-terminal pro-B-type natriuretic peptide). The score was well calibrated and yielded high c-statistics of 0.82 [95% confidence interval (CI) 0.78–0.86] in internal validation, 0.82 (95% CI 0.75–0.89) in internal-external (temporal) validation, and 0.73 (95% CI 0.65–0.81) in external validation. Notably, it outperformed the Simplified Acute Physiology Score II and IABP-SHOCK II risk score in prognostication (0.83 vs 0.62; P < 0.001 and 0.83 vs. 0.76; P = 0.03, respectively).

          Conclusions

          A biomarker-only score for 30-day mortality risk stratification in infarct-related CS was developed, extensively validated and calibrated in a prospective cohort of contemporary patients with CS after AMI. The CLIP score outperformed other clinical scores and may be useful as an early decision tool in CS.

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          Most cited references29

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          Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.

          Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
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            Towards better clinical prediction models: seven steps for development and an ABCD for validation.

            Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.
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              Intraaortic balloon support for myocardial infarction with cardiogenic shock.

              In current international guidelines, intraaortic balloon counterpulsation is considered to be a class I treatment for cardiogenic shock complicating acute myocardial infarction. However, evidence is based mainly on registry data, and there is a paucity of randomized clinical trials. In this randomized, prospective, open-label, multicenter trial, we randomly assigned 600 patients with cardiogenic shock complicating acute myocardial infarction to intraaortic balloon counterpulsation (IABP group, 301 patients) or no intraaortic balloon counterpulsation (control group, 299 patients). All patients were expected to undergo early revascularization (by means of percutaneous coronary intervention or bypass surgery) and to receive the best available medical therapy. The primary efficacy end point was 30-day all-cause mortality. Safety assessments included major bleeding, peripheral ischemic complications, sepsis, and stroke. A total of 300 patients in the IABP group and 298 in the control group were included in the analysis of the primary end point. At 30 days, 119 patients in the IABP group (39.7%) and 123 patients in the control group (41.3%) had died (relative risk with IABP, 0.96; 95% confidence interval, 0.79 to 1.17; P=0.69). There were no significant differences in secondary end points or in process-of-care measures, including the time to hemodynamic stabilization, the length of stay in the intensive care unit, serum lactate levels, the dose and duration of catecholamine therapy, and renal function. The IABP group and the control group did not differ significantly with respect to the rates of major bleeding (3.3% and 4.4%, respectively; P=0.51), peripheral ischemic complications (4.3% and 3.4%, P=0.53), sepsis (15.7% and 20.5%, P=0.15), and stroke (0.7% and 1.7%, P=0.28). The use of intraaortic balloon counterpulsation did not significantly reduce 30-day mortality in patients with cardiogenic shock complicating acute myocardial infarction for whom an early revascularization strategy was planned. (Funded by the German Research Foundation and others; IABP-SHOCK II ClinicalTrials.gov number, NCT00491036.).
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                Author and article information

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                Journal
                European Heart Journal
                Oxford University Press (OUP)
                0195-668X
                1522-9645
                February 27 2021
                February 27 2021
                Affiliations
                [1 ]Institute of Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics, University Hospital, P.-List-Str. 13, 04103 Leipzig, Germany
                [2 ]Institute of Medical Informatics, Statistics and Epidemiology, University Leipzig, Germany
                [3 ]Klinikum Ludwigshafen und Institut für Herzinfarktforschung, Ludwigshafen, Germany
                [4 ]University Heart Center Lübeck, Lübeck, Germany
                [5 ]German Center for Cardiovascular Research (DZHK), Lübeck, Germany
                [6 ]Heart Center Leipzig at University of Leipzig, Department of Internal Medicine/Cardiology, Strümpellstr.. 39, 04289 Leipzig, and Leipzig Heart Institute, Leipzig, Germany
                Article
                10.1093/eurheartj/ehab110
                33647946
                f9ec26a6-216f-4aa1-88f4-4c6f55f402dc
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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