Patient selection for cardiac surgery: Time to consider subgroups within risk categories?

Giessen, Anoukh van; Wit, G. Ardine de; Smit, Henriëtte A.; Ruijter, Hester M. den; Nierich, Arno P.; Klomp, Wouter W. Jansen; Moons, Karel G.M.; Koffijberg, Hendrik

doi:10.1016/j.ijcard.2015.11.034

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…The cluster analysis methods used in combination in our study have numerous successful reports that are more and more often appearing in clinical studies. [23][24][25][26] Interested reader can look for principles and algorithms of cluster analysis elsewhere. 27 It is also well established that accumulated oxygen debt due to a disproportion between the oxidative requirement and the level of oxygen delivery without timely repayment leads to multiple organ failure and mortality.…”

Section: Discussionmentioning

confidence: 99%

Patterns of oxygen debt repayment in cardiogenic shock patients sustained with extracorporeal life support: a retrospective study

Ganushchak

Kurniawati

Horst

et al. 2021

Preprint

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Cardiogenic shock is the most frequent shock in cardiac intensive care, and cardiac dysfunction and sever hypoxia are often seen in critically ill patients. Inadequate organ and tissue perfusion and hypoxia result in anaerobic metabolism with hyperlactatemia and oxygen debt accumulation. However, the role of accumulated oxygen debt in course of cardiogenic shock and hypoxia is not clearly described. Here we first described the existence of several patterns of oxygen debt repayment in cardiogenic shock patients maintained by extracorporeal life support system. Oxygen debt was computed from the lactate concentration at five time points, covering the first 26 hours of ECLS. The patterns as the basic pathophysiological processes are independent from the cause of primary insult. The groups of patients classified to the specific patterns differed by survival rate from 51.5% to only 4.6%. It is very important that initial class does not predetermine the fate and can change in the course of treatment due to ‘between clusters migration’. We believe that our finding of patterns of oxygen debt repayment in cardiogenic shock patients may offer a new insights for a more rational, goal-directed treatment of such highly morbid conditions as hypoxia and cardiogenic shock.

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Section: Discussionmentioning

confidence: 99%

Patterns of oxygen debt repayment in cardiogenic shock patients sustained with extracorporeal life support: a retrospective study

Ganushchak

Kurniawati

Horst

et al. 2021

Preprint

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“…A second more general comment is that all the discussed multivariable approaches only allow for individualized treatment effect estimates in so far as variables are related to the outcome. A strategy to include variables unrelated with the outcome in a multivariable interaction test is to use unsupervised cluster analysis to identify multivariable patient clusters, and test if treatment effectiveness differs across cluster memberships …”

Section: How To Personalize Treatment Effectsmentioning

confidence: 99%

Tailoring treatments using treatment effect modification

Schmidt

Klungel

Nielen

et al. 2016

Pharmacoepidemiol Drug Saf

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Running title: Tailoring treatment. Word count text: 3336 Word count abstract: 172 Number of references: 69Number of tables: 1 Number of figures: 2Keywords: Randomized controlled trial; nonrandomized study design; observational study design; statistics, effect modification, interaction, generalizability. 2 Conflict of interest statementNone of the authors of this paper have a financial or personal relationship with other people or organisations that could inappropriately influence or bias the content of the paper. Author contributionsAFS drafted the manuscript. OHK, MN, AB, AWH and RHHG provided guidance during initial planning of the paper and during critical revision. AcknowledgementsThis work was supported by Research Focus Areas funding of the Utrecht University, which is a collaboration between the faculties of medicine, science, and veterinary medicine. The funding body had no role in decisions on the design, writing or submission of the manuscript. We want to acknowledge and thank the two anonymous reviewers for their helpful suggestions which markedly improved the manuscript. Prior postings and presentationsThis study and its results have not been previously published, neither has it been presented at conferences. 3 Key Points Clinical studies are designed to provide evidence on average treatment effects. To tailor treatment towards individual patients, the presence or absence of treatment effect modification needs to be systematically elucidated. Generalizability of treatment effects can be tested within the framework of equivalence testing. The type of patient, the presence, the magnitude, and the number of effect modifiers determines whether no further analyses, univariable subgroup analyses, or multivariable subgroup analyses may need to be performed. 4 AbstractApplying results from clinical studies to individual patients can be a difficult process.Using the concept of treatment effect modification (also referred to as interaction), defined as a difference in treatment response between patient groups, we discuss whether and how treatment effects can be tailored to better meet patients' needs. First we argue that, contrary to how most studies are designed, treatment effect modification should be expected. Second, given this expected heterogeneity, a small number of clinically relevant subgroups should be a priori selected, depending on the expected magnitude of effect modification, and prevalence of the patient type. Third, by defining generalizability as the absence of treatment effect modification we show that generalizability can be evaluated within the usual statistical framework of equivalence testing. Fourth, when equivalence cannot be confirmed, we address the need for further analyses, and studies tailoring treatment towards groups of patients with similar response to treatment. Fifth, we argue that to properly frame, the entire body of evidence on effect modification should be quantified in a prior probability. 5

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“…The existing risk prediction models stratify individuals based on their predicted risk and tailor treatment to categories of individuals in which the highest benefit is expected to be achieved [ 1 ]. Such risk stratification simply recounts the standard procedural risks and assume that individuals classified into the same risk category form a fairly homogeneous group.…”

Section: Introductionmentioning

confidence: 99%

Social and emotional factors as predictors of poor outcomes following cardiac surgery

Cromhout

Thygesen

Moons

et al. 2021

Interactive CardioVascular and Thoracic Surgery

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OBJECTIVES Existing risk prediction models in cardiac surgery stratify individuals based on their predicted risk, including only medical and physiological factors. However, the complex nature of risk assessment and the lack of parameters representing non-medical aspects of patients’ lives point towards the need for a broader paradigm in cardiac surgery. Objectives were to evaluate the predictive value of emotional and social factors on 4 outcomes; death within 90 days, prolonged stay in intensive care (≥72 h), prolonged hospital admission (≥10 days) and readmission within 90 days following cardiac surgery, as a supplement to traditional risk assessment by European System for Cardiac Operative Risk Evaluation (EuroSCORE). METHODS The study included adults undergoing cardiac surgery in Denmark 2014–2017 including information on register-based socio-economic factors, and, in a nested subsample, self-reported symptoms of anxiety and depression. Logistic regression analyses were conducted, adjusted for EuroSCORE, of variables reflecting social and emotional factors. RESULTS Amongst 7874 included patients, lower educational level (odds ratio 1.33; 95% confidence interval 1.17–1.51) and living alone (1.25; 1.14–1.38) were associated with prolonged hospital admission after adjustment for EuroSCORE. Lower educational level was also associated with prolonged intensive care unit stay (1.27; 1.00–1.63). Having a high income was associated with decreased odds of prolonged hospital admission (0.78; 0.70–0.87). No associations or predictive value for symptoms of anxiety or depression were found on any outcomes. CONCLUSIONS Social disparity is predictive of poor outcomes following cardiac surgery. Symptoms of anxiety and depression are frequent especially amongst patients with a high-risk profile according to EuroSCORE. Subj collection 105, 123

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Patient selection for cardiac surgery: Time to consider subgroups within risk categories?

Cited by 10 publications

References 27 publications

Patterns of oxygen debt repayment in cardiogenic shock patients sustained with extracorporeal life support: a retrospective study

Patterns of oxygen debt repayment in cardiogenic shock patients sustained with extracorporeal life support: a retrospective study

Tailoring treatments using treatment effect modification

Social and emotional factors as predictors of poor outcomes following cardiac surgery

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