Validation of APACHE IV score in postoperative liver transplantation in southern Brazil: a cohort study

Rodrigues-Filho, Edison Moraes; Garcez, Anderson; Nedel, Wagner Luís

doi:10.1016/j.bjane.2018.12.009

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…For example, the APACHE IV score is well calibrated on average [7] and performs better than several other scores in patients with sepsis [8], but its performance suffers among cancer patients with sepsis [9]. Similarly, APACHE-IV underestimates risk for postoperative liver transplant patients [10] but overestimates risk for patients in a cardiac ICU [11]. Average performance over large populations is insufficient for individual patients who exhibit a complex array of acute and chronic disease states.…”

Section: What We Need To Make It Personalmentioning

confidence: 99%

Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions

Weissman

Vincent

2021

Current Opinion in Critical Care

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Purpose of reviewPatients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be aided by risk prediction. Although emerging artificial intelligence/machine learning (AI/ML) algorithms could reduce uncertainty surrounding these life and death decisions, certain criteria must be met to ensure their bedside value.Recent findingsAlthough ICU severity of illness scores have existed for decades, these tools have not been shown to predict well or to improve outcomes for individual patients. Novel AI/ML tools offer the promise of personalized ICU care but remain untested in clinical trials. Ensuring that these predictive models account for heterogeneity in patient characteristics and treatments, are not only specific to a clinical action but also consider the longitudinal course of critical illness, and address patient-centered outcomes related to equity, transparency, and shared decision-making will increase the likelihood that these tools improve outcomes. Improved clarity around standards and contributions from institutions and critical care departments will be essential.SummaryImproved ICU prognostication, enabled by advanced ML/AI methods, offer a promising approach to inform difficult and urgent decisions under uncertainty. However, critical knowledge gaps around performance, equity, safety, and effectiveness must be filled and prospective, randomized testing of predictive interventions are still needed.

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