Limitations of score-based daily outcome predictions in the individual intensive care patient

Lefering, Rolf; Wolfrum, B.; Wauer, Helmar; Neugebauer, Edmund

doi:10.1007/s00011-004-0350-9

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…40 For example, meanwhile well accepted scoring systems to predict outcome, e.g. APACHE, SAPS, TRISS, and so forth, have been developed for comparison between populations.…”

Section: Discussionmentioning

confidence: 99%

Trauma Associated Severe Hemorrhage (TASH)-Score: Probability of Mass Transfusion as Surrogate for Life Threatening Hemorrhage after Multiple Trauma

Yücel

Lefering

Maegele

et al. 2006

The Journal of Trauma: Injury, Infection, and Critical Care

358

288

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“…40 For example, meanwhile well accepted scoring systems to predict outcome, e.g. APACHE, SAPS, TRISS, and so forth, have been developed for comparison between populations.…”

Section: Discussionmentioning

confidence: 99%

Trauma Associated Severe Hemorrhage (TASH)-Score: Probability of Mass Transfusion as Surrogate for Life Threatening Hemorrhage after Multiple Trauma

Yücel

Lefering

Maegele

et al. 2006

The Journal of Trauma: Injury, Infection, and Critical Care

358

288

View full text Add to dashboard Cite

show abstract

“…APACHE II is not really intended to generate predictions about an individual patient’s chance for survival. Methods have certainly been developed to support such predictions, but given the wide variety of factors that influence this type of outcome, but are not accounted for in the calculations, such methods are rather imprecise [ 29 – 31 ]. This is just one important reason that it is highly questionable to base decisions about discontinuation of therapy or withholding of interventions based on APACHE II scores and similar scoring systems.…”

Section: Ethical Aspects Of Applying Scoring Systems To Clinical Carementioning

confidence: 99%

“…The beginning of the end came when further research called the reliability of its predictions into question. The authors of Riyadh found no false predictions of death, but several other investigations identified a survival rate of up to 41 % among those predicted to die [ 31 ]. At least one German study has shown that the prognoses of experienced clinicians are at least as good as those generated by the Riyadh algorithm, and another study showed that of 53 patients with “high probability of death,” one third survived.…”

Section: Ethical Aspects Of Applying Scoring Systems To Clinical Carementioning

confidence: 99%

Clinical decision-making and secondary findings in systems medicine

Fischer

Erdmann²,

Langanke

2016

BMC Med Ethics

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BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

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Limitations of score-based daily outcome predictions in the individual intensive care patient

Abstract: Sequential assessment of scores in intensive care could identify high risk patients, but with some degree of uncertainty. Therefore, the scores should only be used by those familiar with their limitations and risks.

Cited by 2 publications

References 19 publications

Trauma Associated Severe Hemorrhage (TASH)-Score: Probability of Mass Transfusion as Surrogate for Life Threatening Hemorrhage after Multiple Trauma

Trauma Associated Severe Hemorrhage (TASH)-Score: Probability of Mass Transfusion as Surrogate for Life Threatening Hemorrhage after Multiple Trauma

Clinical decision-making and secondary findings in systems medicine

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