Acute respiratory distress syndrome; COVID-19; Mechanical ventilation; Outcome; Predictive survival model; Critical Care and Intensive Care Medicine
Abstract :
[en] [en] BACKGROUND: Predicting outcomes of critically ill intensive care unit (ICU) patients with coronavirus-19 disease (COVID-19) is a major challenge to avoid futile, and prolonged ICU stays.
METHODS: The objective was to develop predictive survival models for patients with COVID-19 after 1-to-2 weeks in ICU. Based on the COVID-ICU cohort, which prospectively collected characteristics, management, and outcomes of critically ill patients with COVID-19. Machine learning was used to develop dynamic, clinically useful models able to predict 90-day mortality using ICU data collected on day (D) 1, D7 or D14.
RESULTS: Survival of Severely Ill COVID (SOSIC)-1, SOSIC-7, and SOSIC-14 scores were constructed with 4244, 2877, and 1349 patients, respectively, randomly assigned to development or test datasets. The three models selected 15 ICU-entry variables recorded on D1, D7, or D14. Cardiovascular, renal, and pulmonary functions on prediction D7 or D14 were among the most heavily weighted inputs for both models. For the test dataset, SOSIC-7's area under the ROC curve was slightly higher (0.80 [0.74-0.86]) than those for SOSIC-1 (0.76 [0.71-0.81]) and SOSIC-14 (0.76 [0.68-0.83]). Similarly, SOSIC-1 and SOSIC-7 had excellent calibration curves, with similar Brier scores for the three models.
CONCLUSION: The SOSIC scores showed that entering 15 to 27 baseline and dynamic clinical parameters into an automatable XGBoost algorithm can potentially accurately predict the likely 90-day mortality post-ICU admission (sosic.shinyapps.io/shiny). Although external SOSIC-score validation is still needed, it is an additional tool to strengthen decisions about life-sustaining treatments and informing family members of likely prognosis.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Schmidt, Matthieu ; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France. matthieu.schmidt@aphp.fr ; Service de Médecine Intensive-Réanimation, Institut de Cardiologie, iCAN, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (APHP), 47, bd de l'Hôpital, 75651, Paris Cedex 13, France. matthieu.schmidt@aphp.fr ; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France. matthieu.schmidt@aphp.fr
Guidet, Bertrand; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Institut Pierre-Louis d'Epidémiologie et de Santé Publique, APHP, Hôpital Saint-Antoine, INSERM, Service de Réanimation, Sorbonne Université, Paris, France
Demoule, Alexandre; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Service de Pneumologie, Médecine Intensive-Réanimation (Département R3S), Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Paris, France ; Sorbonne Université, INSERM UMRS_1158, Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, France
Ponnaiah, Maharajah; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France
Fartoukh, Muriel; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France ; Service de Médecine Intensive-Réanimation, Hôpital Tenon, Département Médico-Universitaire APPROCHES, APHP, Paris, France ; Groupe de Recherche Clinique CARMAS, Université Paris-Est Créteil, Créteil, France
Puybasset, Louis; CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Sorbonne Université, Paris, France ; Department of Anesthesiology & Critical Care, APHP, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
Combes, Alain; Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMRS) 1166, Institute of Cardiometabolism and Nutrition, Paris, France ; Service de Médecine Intensive-Réanimation, Institut de Cardiologie, iCAN, Institute of Cardiometabolism and Nutrition, Hôpital de la Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (APHP), 47, bd de l'Hôpital, 75651, Paris Cedex 13, France ; Sorbonne Université, GRC 30, RESPIRE, APHP, Hôpital Pitié-Salpêtrière, Paris, France
Hajage, David; Département de Santé Publique, Centre de Pharmacoépidémiologie (Cephepi), INSER, Institut Pierre-Louis d'Epidémiologie et de Santé Publique, APHP, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
COVID-ICU Investigators
Other collaborator :
Lambermont, Bernard ; Centre Hospitalier Universitaire de Liège - CHU > > Service des soins intensifs
Language :
English
Title :
Predicting 90-day survival of patients with COVID-19: Survival of Severely Ill COVID (SOSIC) scores.
Publication date :
11 December 2021
Journal title :
Annals of Intensive Care
eISSN :
2110-5820
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
AP-HP - Fondation de l’AP-HP [FR] French Ministry of Social Affairs and Health [FR]
Funding text :
This study was funded by the Fondation APHP and its donators though the program “Alliance Tous Unis Contre le Virus”, the Direction de la Recherche Clinique et du Développement, and the French Ministry of Health. The Reseau European de recherche en Ventilation Artificielle (REVA) network received a 75,000 € research grant from Air Liquide Healthcare. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The sponsor was Assistance Publique Hôpitaux de Paris (APHP).
Karagiannidis C, Windisch W, McAuley DF, Welte T, Busse R. Major differences in ICU admissions during the first and second COVID-19 wave in Germany. Lancet Respir Med. 2021;9(5):e47–8. DOI: 10.1016/S2213-2600(21)00101-6
Karagiannidis C, Mostert C, Hentschker C, Voshaar T, Malzahn J, Schillinger G, et al. Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. Lancet Respir Med. 2020;8:853–62. DOI: 10.1016/S2213-2600(20)30316-7
COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators. Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: A prospective cohort study. Intensive Care Med. 2021;47:60–73. DOI: 10.1007/s00134-020-06294-x
RECOVERY Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, et al. Dexamethasone in hospitalized patients with Covid-19. N Engl J Med. 2021;384(8):693–704. DOI: 10.1056/NEJMoa2021436
Gupta RK, Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, et al. Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study. Lancet Respir Med. 2021;9:349–59. DOI: 10.1016/S2213-2600(20)30559-2
Pimentel MA, Redfern OC, Malycha J, Meredith P, Prytherch D, Briggs J, et al. Detecting deteriorating patients in hospital: development and validation of a novel scoring system. Am J Respir Crit Care Med. 2021;204(1):44–52. DOI: 10.1164/rccm.202007-2700OC
Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, et al. The National COVID Cohort Collaborative: clinical characterization and early severity prediction. JAMA Netw Open. 2021;4(7)
Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA. 1993;270:2957–63. DOI: 10.1001/jama.1993.03510240069035
Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–10. DOI: 10.1007/BF01709751
Juma S, Taabazuing M-M, Montero-Odasso M. Clinical frailty scale in an acute medicine unit: a simple tool that predicts length of stay. Can Geriatr J. 2016;19:34–9. DOI: 10.5770/cgj.19.196
Rouzé A, Martin-Loeches I, Povoa P, Makris D, Artigas A, Bouchereau M, et al. Relationship between SARS-CoV-2 infection and the incidence of ventilator-associated lower respiratory tract infections: a European multicenter cohort study. Intensive Care Med. 2021;47:188–98. DOI: 10.1007/s00134-020-06323-9
Erben Y, Franco-Mesa C, Gloviczki P, Stone W, Quinones-Hinojosa A, Meltzer AJ, et al. Deep venous thrombosis and pulmonary embolism among hospitalized coronavirus disease 2019 (COVID-19) positive patients predict higher mortality, prolonged intensive care unit and hospital stays in a multi-site healthcare system. J Vasc Surg Venous Lymphat Disord. 2021;9(6):1361–70. DOI: 10.1016/j.jvsv.2021.03.009
XGBoost|Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. https://doi.org/10.1145/2939672.2939785 Accessed 7 May 2021.
Lundberg SM, Lee S-I. A Unified approach to interpreting model predictions. advances in neural information processing systems. Curran Associates, Inc.; 2017. https://papers.nips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html. Accessed 30 Sept 2021.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21:128–38. DOI: 10.1097/EDE.0b013e3181c30fb2
Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54:774–81. DOI: 10.1016/S0895-4356(01)00341-9
Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. New York: Association for Computing Machinery; 2016. p. 785–94. https://doi.org/10.1145/2939672.2939785 Accessed 30 Sept 2021.
Gutiérrez-Gutiérrez B, Del Toro MD, Borobia AM, Carcas A, Jarrín I, Yllescas M, et al. Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study. Lancet Infect Dis. 2021;21(6):783–92. DOI: 10.1016/S1473-3099(21)00019-0
Gupta S, Hayek SS, Wang W, Chan L, Mathews KS, Melamed ML, et al. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med. 2020;180:1436–47. DOI: 10.1001/jamainternmed.2020.3596
Grasselli G, Greco M, Zanella A, Albano G, Antonelli M, Bellani G, et al. Risk factors associated with mortality among patients with COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern Med. 2020;180(10):1345–55. DOI: 10.1001/jamainternmed.2020.3539
Jung C, Flaatten H, Fjølner J, Bruno RR, Wernly B, Artigas A, et al. The impact of frailty on survival in elderly intensive care patients with COVID-19: the COVIP study. Crit Care. 2021;25:149. DOI: 10.1186/s13054-021-03551-3
Azoulay E, Fartoukh M, Darmon M, Géri G, Voiriot G, Dupont T, et al. Increased mortality in patients with severe SARS-CoV-2 infection admitted within seven days of disease onset. Intensive Care Med. 2020;46(9):1714–22. DOI: 10.1007/s00134-020-06202-3
Vandenbunder B, Ehrmann S, Piagnerelli M, Sauneuf B, Serck N, Soumagne T, et al. Static compliance of the respiratory system in COVID-19 related ARDS: an international multicenter study. Crit Care. 2021;25:52. DOI: 10.1186/s13054-020-03433-0
Doyen D, Dupland P, Morand L, Fourrier E, Saccheri C, Buscot M, et al. Characteristics of cardiac injury in critically ill patients with coronavirus disease 2019. Chest. 2021;159:1974–85. DOI: 10.1016/j.chest.2020.10.056
Raschke RA, Agarwal S, Rangan P, Heise CW, Curry SC. Discriminant accuracy of the SOFA score for determining the probable mortality of patients with COVID-19 pneumonia requiring mechanical ventilation. JAMA. 2021;325:1469–70. DOI: 10.1001/jama.2021.1545
Yadaw AS, Li Y-C, Bose S, Iyengar R, Bunyavanich S, Pandey G. Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. Lancet Digit Health. 2020;2:e516–25. DOI: 10.1016/S2589-7500(20)30217-X
Kalil AC, Patterson TF, Mehta AK, Tomashek KM, Wolfe CR, Ghazaryan V, et al. Baricitinib plus remdesivir for hospitalized adults with Covid-19. N Engl J Med. 2021;384(9):795–807. DOI: 10.1056/NEJMoa2031994
Auld SC, Caridi-Scheible M, Robichaux C, Coopersmith CM, Murphy DJ, Emory COVID-19 Quality and Clinical Research Collaborative. Declines in mortality over time for critically ill adults with coronavirus disease 2019. Crit Care Med. 2020;48(12):e1382–4. DOI: 10.1097/CCM.0000000000004687
Ursino M, Dupuis C, Buetti N, de Montmollin E, Bouadma L, Golgran-Toledano D, et al. Multistate modeling of COVID-19 patients using a large multicentric prospective cohort of critically ill patients. J Clin Med. 2021;10:544. DOI: 10.3390/jcm10030544
Demoule A, Vieillard Baron A, Darmon M, Beurton A, Géri G, Voiriot G, et al. High-flow nasal cannula in critically iii patients with severe COVID-19. Am J Respir Crit Care Med. 2020;202:1039–42. DOI: 10.1164/rccm.202005-2007LE
Dres M, Hajage D, Lebbah S, Kimmoun A, Pham T, Béduneau G, et al. Characteristics, management, and prognosis of elderly patients with COVID-19 admitted in the ICU during the first wave: insights from the COVID-ICU study: prognosis of COVID-19 elderly critically ill patients in the ICU. Ann Intensive Care. 2021;11:77. DOI: 10.1186/s13613-021-00861-1
Paul E, Bailey M, Van Lint A, Pilcher V. Performance of APACHE III over time in Australia and New Zealand: a retrospective cohort study. Anaesth Intensive Care. 2012;40:980–94. DOI: 10.1177/0310057X1204000609
Grasselli G, Tonetti T, Protti A, Langer T, Girardis M, Bellani G, et al. Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study. Lancet Respir Med. 2020;8(12):1201–8. DOI: 10.1016/S2213-2600(20)30370-2
Le Morvan M, Josse J, Scornet E, Varoquaux G. What’s a good imputation to predict with missing values? 2021. https://hal.archives-ouvertes.fr/hal-03243931 Accessed 8 Nov 2021.
