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Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort

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Abstract

Sickle cell disease (SCD) is associated with multiple known complications and increased mortality. This study aims to further understand the profile of intensive care unit (ICU) admissions of SCD patients. In this single-center retrospective cohort (approval number 0926–11), we evaluated SCD-related ICU admissions at our hospital in São Paulo, Brazil. Admissions were clustered using clinical data and organ dysfunction at ICU admission. A hierarchical clustering method was used to distinguish phenotypes. From 140 admissions obtained, 125 were included. The mean age was 30 years, 48% were male, and SS genotype was predominant (71.2%). Non-surgical causes of admissions accounted for 85.6% (n = 107). The mean Sequential Organ Failure Assessment score (SOFA) was 4 (IQR 2–7). Vasopressors were required by 12% and mechanical ventilation by 17.6%. After analysis of the average silhouette width, the optimal number of clusters was 3: cluster 1 (n = 69), cluster 2 (n = 25), cluster 3 (n = 31). Cluster 1 had a mean age of 29 years, 87% of SS genotype, and mean SOFA of 4. Cluster 2 had a mean age of 37 years, 80% of SS genotype, and mean SOFA of 8. Cluster 3 had a mean age of 26 years, 29% of SS genotype, and mean SOFA of 3. The need for mechanical ventilation was 11.6%, 44%, and 9.7%, respectively. Mortality was significantly higher in cluster 2 (44%, p = 0.012). This cohort of critical SCD admissions suggested the presence of three different profiles. This can be informative in the ICU setting to identify SCD patients at higher risk of worse outcomes.

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Authors and Affiliations

  1. Department of Medicine, University of Connecticut, 263 Farmington Ave, Farmington, CT, 06030, USA

    Eduardo Messias Hirano Padrão, Brian Bustos & Ashwin Mahesh

  2. Department of Hematology, Universidade de São Paulo, São Paulo, SP, Brazil

    Guilherme Henrique Hencklain Fonseca

  3. Department of Internal Medicine, Universidade de São Paulo, São Paulo, SP, Brazil

    Leandro Utino Taniguchi

Authors
  1. Eduardo Messias Hirano Padrão
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  2. Brian Bustos
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  4. Guilherme Henrique Hencklain Fonseca
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  5. Leandro Utino Taniguchi
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Contributions

First draft: all authors.

Conception: E. M. H. P., L. U. T., G. H. H. F.

Statistical analysis: L. U. T.

Interpretation of results: E. M. H. P., L. U. T., B. B., A. M.

Approval of final version: all authors.

Corresponding author

Correspondence to Eduardo Messias Hirano Padrão.

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This study was carried out at Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo.

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The authors declare no competing interests.

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Padrão, E.M.H., Bustos, B., Mahesh, A. et al. Phenotypes of sickle cell intensive care admissions: an unsupervised machine learning approach in a single-center retrospective cohort. Ann Hematol 101, 1951–1957 (2022). https://doi.org/10.1007/s00277-022-04918-4

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  • DOI: https://doi.org/10.1007/s00277-022-04918-4

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