Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU.

Autor:	Boldingh JHL; Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.; Department of Anaesthesiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Arbous MS; Department of Critical Care, Leiden University Medical Center, Leiden, The Netherlands., Biemond BJ; Department of Hematology, Amsterdam University Medical Center (location AMC), University of Amsterdam, Amsterdam, The Netherlands., Blijlevens NMA; Department of Hematology, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands., van Bommel J; Department of Critical Care, Erasmus Medical Center, Rotterdam, The Netherlands., Hilkens MGEC; Department of Critical Care, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands., Kusadasi N; Department of Critical Care, Erasmus Medical Center, Rotterdam, The Netherlands.; University Medical Center Utrecht, Utrecht, The Netherlands., Muller MCA; Department of Critical Care, Amsterdam University Medical Center (location AMC), University of Amsterdam, Amsterdam, The Netherlands., de Vries VA; Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands., Steyerberg EW; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands., van den Bergh WM; Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Jazyk:	angličtina
Zdroj:	Critical care explorations [Crit Care Explor] 2024 May 24; Vol. 6 (6), pp. e1093. Date of Electronic Publication: 2024 May 24 (Print Publication: 2024).
DOI:	10.1097/CCE.0000000000001093
Abstrakt:	Objectives: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. Design: A retrospective cohort study. Setting: Five university hospitals in the Netherlands between 2002 and 2015. Patients: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. Interventions: None. Measurements and Main Results: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers ( C -statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model ( C -statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. Conclusions: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations. Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest. (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)
Databáze:	MEDLINE
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