Evidence map›Paper›PMID 41546077›Full record

ArticleJournal of anesthesia, analgesia and critical care2026

Validity of ICU prognostic risk stratification tools in the oldest patients: a comparative analysis of clinical scores in a multicenter binational cohort.

Je Min Suh, Laurence Weinberg, Nattaya Raykateeraroj, Kaden Tran, David Pilcher, Dong-Kyu Lee

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Article in Journal of anesthesia, analgesia and critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Je Min SuhDepartment of Anaesthesia, Austin Health, Melbourne, Australia.
Laurence WeinbergDepartment of Anaesthesia, Austin Health, Melbourne, Australia. laurence.weinberg@austin.org.au.
Nattaya RaykateerarojDepartment of Medicine, The University of Melbourne, Melbourne, Australia.
Kaden TranDepartment of Anaesthesia, Austin Health, Melbourne, Australia.
David PilcherAustralian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia.
Dong-Kyu LeeDepartment of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the growing numbers of nonagenarians and centenarians admitted to intensive care, clinicians face complex decisions regarding care appropriateness and intensity. Prognostic tools such as APACHE III, SOFA, and the Australia and New Zealand Risk of Death (ANZROD) model are widely used, yet their validity in very old patients remains uncertain given the influence of frailty, multimorbidity, and age-related physiological changes. In this study, we evaluated and compared the performance of APACHE III, its Risk of Death score, SOFA, and ANZROD in a large binational cohort of ICU-admitted nonagenarians and centenarians, assessing discrimination, calibration, and predictive utility for short- and long-term mortality using AUROC, standardized mortality ratios, and decision curve analysis.

methodsWe conducted a multicenter retrospective cohort study of 40,910 patients aged 90 years and older who were admitted to ICUs across Australia and New Zealand from 2010 to 2024. Discrimination was assessed using time-dependent area under the receiver operating characteristic curve (AUROC). Calibration was evaluated with standardized mortality ratios (SMRs), and clinical utility was examined using decision curve analysis (DCA) across ICU, hospital, 1-month, 6-month, and 1-year mortality outcomes.

resultsANZROD consistently showed the highest discriminative ability, with AUROCs of 0.870 for ICU mortality and 0.768 for 1-month mortality. APACHEIII ROD and APACHE III followed, while SOFA had the lowest AUROCs across all timepoints. SMR analysis revealed overestimation of short term and underestimation of long term mortality by all models. ANZROD had the best calibration for hospital mortality. DCA showed ANZROD provided greater net benefit than the other scores at clinically relevant thresholds.

conclusionsIn this large binational cohort of over 40,000 nonagenarian and centenarian ICU patients, ANZROD outperformed APACHE III and SOFA in short-term mortality prediction, but all models showed poor calibration and declining discrimination over longer horizons. Current scores remain useful for benchmarking but are limited for individual decision-making, underscoring the need for age-specific prognostic tools that integrate frailty, function, and geriatric priorities.

trial registrationACTRN12625000775415 (23/07/2025).

Indexed as

ElderlyIntensive careMortalityNonagenarianPredictionPrognostic scoring systemsRisk stratification

Identifiers

PMID41546077
PMCPMC12896050

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.