Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.


Journal

Intensive care medicine
ISSN: 1432-1238
Titre abrégé: Intensive Care Med
Pays: United States
ID NLM: 7704851

Informations de publication

Date de publication:
12 2022
Historique:
received: 05 05 2022
accepted: 11 08 2022
pubmed: 3 9 2022
medline: 1 12 2022
entrez: 2 9 2022
Statut: ppublish

Résumé

The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction. Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs. In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280). Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

Identifiants

pubmed: 36056194
doi: 10.1007/s00134-022-06868-x
pii: 10.1007/s00134-022-06868-x
pmc: PMC9439274
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1726-1735

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.

Références

Beard JR, Jotheeswaran AT, Cesari M, Araujo de Carvalho I (2019) The structure and predictive value of intrinsic capacity in a longitudinal study of ageing. BMJ Open 9(11):e026119. https://doi.org/10.1136/bmjopen-2018-026119
doi: 10.1136/bmjopen-2018-026119 pubmed: 31678933 pmcid: 6830681
Flaatten H, de Lange DW, Artigas A, Bin D, Moreno R, Christensen S, Joynt GM et al (2017) The status of intensive care medicine research and a future agenda for very old patients in the ICU. Intensive Care Med 43(9):1319–1328. https://doi.org/10.1007/s00134-017-4718-z
doi: 10.1007/s00134-017-4718-z pubmed: 28238055
Theou O, Brothers TD, Mitnitski A, Rockwood K (2013) Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc 61(9):1537–1551. https://doi.org/10.1111/jgs.12420
doi: 10.1111/jgs.12420 pubmed: 24028357
Ferrante LE, Pisani MA, Murphy TE, Gahbauer EA, Leo-Summers LS, Gill TM (2019) Functional trajectories among older persons before and after critical illness. JAMA Intern Med 175(4):523–529. https://doi.org/10.1001/jamainternmed.2014.7889
doi: 10.1001/jamainternmed.2014.7889
Flaatten H, De Lange DW, Morandi A, Andersen FH, Artigas A, Bertolini G, Boumendil A et al (2017) The impact of frailty on ICU and 30-day mortality and the level of care in very elderly patients (≥ 80 years). Intensive Care Med 43(12):1820–1828. https://doi.org/10.1007/s00134-017-4940-8
doi: 10.1007/s00134-017-4940-8 pubmed: 28936626
Stein D, Sviri S, Beil M, Stav I, Marcus EL (2022) Prognosis of chronically ventilated patients in a long-term ventilation facility: association with age, consciousness and cognitive state. J Intensive Care Med 1:1. https://doi.org/10.1177/08850666221088800
doi: 10.1177/08850666221088800
Chong E, Chan M, Tan HN, Lim WS (2020) COVID-19: use of the clinical frailty scale for critical care decisions. J Am Geriatr Soc 68(6):E30–E32. https://doi.org/10.1111/jgs.16528
doi: 10.1111/jgs.16528 pubmed: 32359076 pmcid: 7267651
The National Institute for Health and Care Excellence 2020 (2020) https://www.nice.org.uk/news/article/nice-updates-rapid-covid-19-guideline-on-critical-care . Accessed 4 May 2022
Ntani G, Inskip H, Osmond C, Coggon D (2021) Consequences of ignoring clustering in linear regression. BMC Med Res Methodo 21(1):139. https://doi.org/10.1186/s12874-021-01333-7
doi: 10.1186/s12874-021-01333-7
Fronczek J, Polok K, de Lange DW, Jung C, Beil M, Rhodes A, Fjølner et al (2021) Relationship between the Clinical Frailty Scale and short-term mortality in patients ≥ 80 years old acutely admitted to the ICU. Crit Care 25(1):231. https://doi.org/10.1186/s13054-021-03632-3
doi: 10.1186/s13054-021-03632-3 pubmed: 34210358 pmcid: 8247215
Castela Forte J, Perner A, van der Horst ICC (2019) The use of clustering algorithms in critical care research to unravel patient heterogeneity. Intensive Care Med 45(7):1025–1028. https://doi.org/10.1007/s00134-019-05631-z
doi: 10.1007/s00134-019-05631-z pubmed: 31062051
Whitty CJM, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, Atherton F et al (2020) Rising to the challenge of multimorbidity. BMJ 368:l6964. https://doi.org/10.1136/bmj.l6964
doi: 10.1136/bmj.l6964 pubmed: 31907164 pmcid: 7190283
Marcucci M, Franchi C, Nobili A, Mannucci PM, Ardoino I, REPOSI Investigators (2017) Defining aging phenotypes and related outcomes: clues to recognize frailty in hospitalized older patients. J Gerontol A Biol Sci Med Sci 72(3):395–402. https://doi.org/10.1093/gerona/glw188
doi: 10.1093/gerona/glw188 pubmed: 28364542
Castela Forte J, Yeshmagambetova G, van der Grinten ML, Hiemstra B, Kaufmann T, Eck RJ, Keus F et al (2021) Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering. Sci Rep 11(1):12109. https://doi.org/10.1038/s41598-021-91297-x
doi: 10.1038/s41598-021-91297-x pubmed: 34103544 pmcid: 8187398
Knaus WA, Marks RD (2019) New phenotypes for sepsis: the promise and problem of applying machine learning and artificial intelligence in clinical research. JAMA 321(20):1981–1982. https://doi.org/10.1001/jama.2019.5794
doi: 10.1001/jama.2019.5794 pubmed: 31104067
Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, Dahlström U et al (2019) Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 7(8):e008081. https://doi.org/10.1161/JAHA.117.008081
doi: 10.1161/JAHA.117.008081
Bian J, Lyu T, Loiacono A, Viramontes TM, Lipori G, Guo Y, Wu Y et al (2020) Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data. J Am Med Inform Assoc 27(12):1999–2010. https://doi.org/10.1093/jamia/ocaa245
doi: 10.1093/jamia/ocaa245 pubmed: 33166397 pmcid: 7727392
Reddy K, Sinha P, O’Kane CM, Gordon AC, Calfee CS, McAuley DF (2020) Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med 8(6):631–643. https://doi.org/10.1016/S2213-2600(20)30124-7
doi: 10.1016/S2213-2600(20)30124-7 pubmed: 32526190
Geri G, Vignon P, Aubry A, Fedou AL, Charron C, Silva S, Repessé X, Vieillard-Baron A (2019) Cardiovascular clusters in septic shock combining clinical and echocardiographic parameters: a post hoc analysis. Intensive Care Med 45(5):657–667. https://doi.org/10.1007/s00134-019-05596-z
doi: 10.1007/s00134-019-05596-z pubmed: 30888443
Azoulay E, Zafrani L, Mirouse A, Lengliné E, Darmon M, Chevret S (2020) Clinical phenotypes of critically ill COVID-19 patients. Intensive Care Med 46(8):1651–1652. https://doi.org/10.1007/s00134-020-06120-4
doi: 10.1007/s00134-020-06120-4 pubmed: 32468086 pmcid: 8830032
Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, Berry S et al (2019) Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 321(20):2003–2017. https://doi.org/10.1001/jama.2019.5791
doi: 10.1001/jama.2019.5791 pubmed: 31104070 pmcid: 6537818
Rodríguez A, Ruiz-Botella M, Martín-Loeches I, Jimenez Herrera M, Solé-Violan J, Gómez J, Bodí M et al (2021) Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain. Crit Care 25(1):63. https://doi.org/10.1186/s13054-021-03487-8
doi: 10.1186/s13054-021-03487-8 pubmed: 33588914 pmcid: 7883885
Guidet B, de Lange DW, Boumendil A, Leaver S, Watson X, Boulanger C, Szczeklik W et al (2020) The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study. Intensive Care Med 46(1):57–69. https://doi.org/10.1007/s00134-019-05853-1
doi: 10.1007/s00134-019-05853-1 pubmed: 31784798
Guidet B, Flaatten H, Boumendil A, Morandi A, Andersen FH, Artigas A, Bertolini G et al (2018) Withholding or withdrawing of life-sustaining therapy in older adults (≥ 80 years) admitted to the intensive care unit. Intensive Care Med 44(7):1027–1038. https://doi.org/10.1007/s00134-018-5196-7
doi: 10.1007/s00134-018-5196-7 pubmed: 29774388
Jung C, Flaatten H, Fjølner J, Bruno RR, Wernly B, Artigas A, Bollen et al (2021) The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. Crit Care 25(1):149. https://doi.org/10.1186/s13054-021-03551-3
doi: 10.1186/s13054-021-03551-3 pubmed: 33874987 pmcid: 8054503
Huang ZX (1997) Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the First Pacific Asia knowledge discovery and data mining conference, Singapore. 1997, pp 21–34
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc (Ser B) 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
doi: 10.1111/j.2517-6161.1995.tb02031.x
Tenny S, Hoffman MR (2022) Odds ratio. StatPearls. StatPearls Publishing, Treasure Island
Guidet B, Jung C, Flaatten H, Fjølner J, Artigas A, Pinto BB, Schefold JC et al (2022) Increased 30-day mortality in very old ICU patients with COVID-19 compared to patients with respiratory failure without COVID-19. Intensive Care Med 48(4):435–447. https://doi.org/10.1007/s00134-022-06642-z
doi: 10.1007/s00134-022-06642-z pubmed: 35218366 pmcid: 8881896
Vink EE, Azoulay E, Caplan A, Kompanje EJO, Bakker J (2018) Time-limited trial of intensive care treatment: an overview of current literature. Intensive Care Med 44(9):1369–1377. https://doi.org/10.1007/s00134-018-5339-x
doi: 10.1007/s00134-018-5339-x pubmed: 30136140
Beil M, Sviri S, Flaatten H, De Lange DW, Jung C, Szczeklik W, Leaver S et al (2021) On predictions in critical care: the individual prognostication fallacy in elderly patients. J Crit Care 61:34–38. https://doi.org/10.1016/j.jcrc.2020.10.006
doi: 10.1016/j.jcrc.2020.10.006 pubmed: 33075607
De Biasio JC, Mittel AM, Mueller AL, Ferrante LE, Kim DH, Shaefi S (2020) Frailty in critical care medicine. Anesth Analg 130(6):1462–1473
doi: 10.1213/ANE.0000000000004665 pubmed: 32384336 pmcid: 7426653
Mart MF, Pun BT, Pandharipande P, Jackson JC, Ely EW (2021) ICU survivorship-the relationship of delirium, sedation, dementia, and acquired weakness. Crit Care Med 49(8):1227–1240. https://doi.org/10.1097/CCM.0000000000005125
doi: 10.1097/CCM.0000000000005125 pubmed: 34115639 pmcid: 8282752
Elbeddini A, Sawhney M, Tayefehchamani Y, Yilmaz Z, Elshahawi A, Josh Villegas J, Dela Cruz J (2021) Deprescribing for all: a narrative review identifying inappropriate polypharmacy for all ages in hospital settings. BMJ Open Qual 10(3):e001509. https://doi.org/10.1136/bmjoq-2021-001509
doi: 10.1136/bmjoq-2021-001509 pubmed: 34230053 pmcid: 8261885
Parry SM, Nydahl P, Needham DM (2018) Implementing early physical rehabilitation and mobilisation in the ICU: institutional, clinician, and patient considerations. Intensive Care Med 44(4):470–473. https://doi.org/10.1007/s00134-017-4908-8
doi: 10.1007/s00134-017-4908-8 pubmed: 28842731
Pollack LR, Goldstein NE, Gonzalez WC, Blinderman CD, Maurer MS, Lederer DJ, Baldwin MR (2017) The frailty phenotype and palliative care needs of older survivors of critical illness. J Am Geriatr Soc 65(6):1168–1175. https://doi.org/10.1111/jgs.14799
doi: 10.1111/jgs.14799 pubmed: 28263377 pmcid: 5478496
Shankar-Hari M, Rubenfeld GD (2019) Population enrichment for critical care trials: phenotypes and differential outcomes. Curr Opin Crit Care 25(5):489–497. https://doi.org/10.1097/MCC.0000000000000641
doi: 10.1097/MCC.0000000000000641 pubmed: 31335383
Vallet H, Schwarz GL, Flaatten H, de Lange DW, Guidet B, Dechartres A (2021) Mortality of older patients admitted to an ICU: a systematic review. Crit Care Med 49(2):324–334. https://doi.org/10.1097/CCM.0000000000004772
doi: 10.1097/CCM.0000000000004772 pubmed: 33332816
Joebges S, Biller-Andorno N (2020) Ethics guidelines on COVID-19 triage-an emerging international consensus. Crit Care 24(1):201
doi: 10.1186/s13054-020-02927-1 pubmed: 32375855 pmcid: 7202791
Wunsch H, Hill AD, Bosch N, Adhikari NKJ, Rubenfeld G, Walkey A, Ferreyro BL et al (2020) Comparison of 2 triage scoring guidelines for allocation of mechanical ventilators. JAMA Netw Open 3(12):e2029250. https://doi.org/10.1001/jamanetworkopen.2020.29250
doi: 10.1001/jamanetworkopen.2020.29250 pubmed: 33315112 pmcid: 7737087

Auteurs

Oded Mousai (O)

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel.

Lola Tafoureau (L)

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel.

Tamar Yovell (T)

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel.

Hans Flaatten (H)

Department of Anaesthesia and Intensive Care, Haukeland University Hospital, Bergen, Norway.

Bertrand Guidet (B)

Assistance Publique-Hôpitaux de Paris, Hôpital Saint-Antoine, Service de Réanimation Médicale, Paris, France.

Christian Jung (C)

Department of Cardiology, Pulmonology and Vascular Medicine, Faculty of Medicine, Heinrich-Heine-University, Dusseldorf, Germany.

Dylan de Lange (D)

Department of Intensive Care Medicine, University Medical Center, University Utrecht, Utrecht, The Netherlands.

Susannah Leaver (S)

General Intensive Care, St George's University Hospitals NHS Foundation Trust, London, UK.

Wojciech Szczeklik (W)

Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Krakow, Poland.

Jesper Fjolner (J)

Department of Anaesthesia and Intensive Care, Viborg Regional Hospital, Viborg, Denmark.

Peter Vernon van Heerden (PV)

General Intensive Care Unit, Department of Anaesthesiology, Critical Care and Pain Medicine, Faculty of Medicine, Hebrew University and Hadassah University Medical Center, Jerusalem, Israel.

Leo Joskowicz (L)

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel.

Michael Beil (M)

Department of Medical Intensive Care, Faculty of Medicine, Hebrew University and Hadassah University Medical Center, Jerusalem, Israel.

Gal Hyams (G)

School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, Israel.

Sigal Sviri (S)

Department of Medical Intensive Care, Faculty of Medicine, Hebrew University and Hadassah University Medical Center, Jerusalem, Israel. sigals@hadassah.org.il.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH