Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort

Abstract

Background: Late mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis.

Methods: In the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655). Among them, 467 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct sepsis-survivors clinical classes using readily available data at ICU discharge. The primary endpoint was one-year mortality after ICU discharge.

Results: At ICU discharge, two distinct subtypes were identified (A and B) using 15 readily available clinical and biological variables. Patients assigned to subtype B (48% of the studied population) had more impaired cardiovascular and kidney functions, hematological disorders and inflammation at ICU discharge than subtype A. Sepsis-survivors in subtype B had significantly higher one-year mortality compared to subtype A (respectively, 34% vs 16%, p < 0.001). When adjusted for standard long-term risk factors (e.g., age, comorbidities, severity of illness, renal function and duration of ICU stay), subtype B was independently associated with increased one-year mortality (adjusted hazard ratio (HR) = 1.74 (95% CI 1.16-2.60); p = 0.006).

Conclusions: A subtype with sustained organ failure and inflammation at ICU discharge can be identified from routine clinical and laboratory data and is independently associated with poor long-term outcome in sepsis-survivors. Trial registration NCT01367093; https://ichgcp.net/clinical-trials-registry/NCT01367093 .

Keywords: Biomarkers; Latent profile analysis; Mixture modeling; Personalized medicine; Post-intensive care syndrome (PICS); Prognostic enrichment; Sepsis.

Conflict of interest statement

None of the authors of this paper has a financial or personal relationship with other persons or organizations that could inappropriately influence or bias the content of the paper. Dr. P.J has received honoraria to the institution for participation in advisory boards from Amgen; has received research grants to the institution from AstraZeneca (Cambridge, United Kingdom), Biotronik (Berlin, Germany), Biosensors International (Singapore), Eli Lilly (Indianapolis, Indiana) and the Medicines Company (Parsippany-Troy Hills, New Jersey); and serves as an unpaid member of the steering groups of trials funded by AstraZeneca (Cambridge, United Kingdom), Biotronik (Berlin, Germany), Biosensors (Singapore), St. Jude Medical (St. Paul, Minnesota) and the Medicines Company. Dr. L.B. laboratory reports grants from Medtronic Covidien, Draeger, non-financial support from Fisher Paykel, non-financial support from Philips, non-financial support from Sentec, non-financial support from Air Liquide, a patent with General Electric, outside the submitted work. Dr. P.R.L. has received unrelated research funding from the Canadian Institutes of Health Research, the U.S. National Institutes of Health (National Heart, Lung, and Blood Institute), the Peter Munk Cardiac Centre, the LifeArc Foundation, the Thistledown Foundation, the Ted Rogers Centre for Heart Research, the Medicine by Design Fund, the University of Toronto and the Government of Ontario. He also received unrelated consulting honoraria from Novartis, Corrona and Brigham and Women’s Hospital, as well as unrelated royalties from McGraw-Hill Publishing. Dr. A.M. has received speaker’s honoraria from Abbott (Chicago, Illinois), Orion (Auckland, New Zealand), Roche (Basel, Switzerland) and Servier (Suresnes, France); and fees as a member of the advisory boards and/or steering committees and/or research grants from BMS (New York, New York), Adrenomed (Hennigsdorf, Germany), Neurotronik (Durham, North Carolina), Roche (Basel, Switzerland), Sanofi (Paris, France), Sphyngotec (Hennigsdorf, Germany), Novartis (Basel, Switzerland), Otsuka (Chiyoda City, Tokyo, Japan), Philips (Amsterdam, Netherlands) and 4TEEN4 (Hennigsdorf, Germany). Dr. E.G. received fees as a member of the advisory boards and/or steering committees and/or from research grants from Magnisense (Paris, France), Adrenomed (Hennigsdorf, Germany) and Deltex Medical (Chichester, United Kingdom). The remaining authors declare no competing interests.

© 2022. The Author(s).

Figures

Fig. 1

Study flowchart. Abbreviation: ICU intensive care unit

Fig. 2

Comparison of class-defining variables using latent class analysis and consensus k means clustering. Description: continuous variables were plotted after natural log transformation. Every normalized variable was standardized such that all means are scaled to 0 and SDs to 1. Group means of standardized values are shown by subtype classes (A and B). A value of + 1 for the standardized variable (y-axis) indicates that the mean value for a given subtype was one SD higher than the mean value in the whole sepsis-survivors cohort (N = 467). Subtype classes sizes (n): Latent class analysis: subtype A N = 244, subtype B N = 223; consensus k means clustering: subtype A N = 255, subtype B N = 212 (concordance rate (accuracy) = 81%). The mean (± SD) of percent missingness of the 15 class-defining variables was 12% (± 6). No significant difference in missing information for class-defining variables was found between subtypes A and B at ICU discharge (Chi square test). Abbreviations: SD standard deviations, BUN blood urea nitrogen, CRP C-reactive protein, SBP systolic blood pressure, WBC white blood cell, ICU intensive care unit

Fig. 3

Comparison of host response biomarkers levels at ICU discharge between subtypes. Biomarkers data at ICU discharge were available for 350 patients (subtype A N = 191, subtype B N = 159). No significant difference in missing information for biomarkers at ICU discharge was found between subtypes A and B (Chi square test). Comparison for each biomarker was performed using the Mann–Whitney U test. Data are shown as median (IQR). Abbreviations: ICU intensive care unit, PCT procalcitonin, IL6 interleukin-6, DPP3 circulating dipeptidyl peptidase 3, Bio-ADM bio-adrenomedullin, BNP brain natriuretic peptide

Fig. 4

One-year post-ICU survival curves according to subtype membership. The log-rank test between the survival curves of the two subtypes at ICU discharge showed a p < 0.001