Selected article for: "death endpoint and mechanical ventilation"

Author: Mussini, Cristina; Cozzi-Lepri, Alessandro; Menozzi, Marianna; Meschiari, Marianna; Franceschini, Erica; Milic, Jovana; Brugioni, Lucio; Pietrangelo, Antonello; Girardis, Massimo; Cossarizza, Andrea; Tonelli, Roberto; Clini, Enrico; Massari, Marco; Bartoletti, Michele; Ferrari, Anna; Cattelan, Anna Maria; Zuccalà, Paola; Lichtner, Miriam; Rossotti, Roberto; Girardi, Enrico; Nicastri, Emanuele; Puoti, Massimo; Antinori, Andrea; Viale, Pierluigi; Guaraldi, Giovanni
Title: Development and validation of a prediction model for tocilizumab failure in hospitalized patients with SARS-CoV-2 infection
  • Cord-id: 0vboxw8u
  • Document date: 2021_2_23
  • ID: 0vboxw8u
    Snippet: BACKGROUND: The aim of this secondary analysis of the TESEO cohort is to identify, early in the course of treatment with tocilizumab, factors associated with the risk of progressing to mechanical ventilation and death and develop a risk score to estimate the risk of this outcome according to patients’ profile. METHODS: Patients with COVID-19 severe pneumonia receiving standard of care + tocilizumab who were alive and free from mechanical ventilation at day 6 after treatment initiation were inc
    Document: BACKGROUND: The aim of this secondary analysis of the TESEO cohort is to identify, early in the course of treatment with tocilizumab, factors associated with the risk of progressing to mechanical ventilation and death and develop a risk score to estimate the risk of this outcome according to patients’ profile. METHODS: Patients with COVID-19 severe pneumonia receiving standard of care + tocilizumab who were alive and free from mechanical ventilation at day 6 after treatment initiation were included in this retrospective, multicenter cohort study. Multivariable logistic regression models were built to identify predictors of mechanical ventilation or death by day-28 from treatment initiation and β-coefficients were used to develop a risk score. Secondary outcome was mortality. Patients with the same inclusion criteria as the derivation cohort from 3 independent hospitals were used as validation cohort. RESULTS: 266 patients treated with tocilizumab were included. By day 28 of hospital follow-up post treatment initiation, 40 (15%) underwent mechanical ventilation or died [26 (10%)]. At multivariable analysis, sex, day-4 PaO(2)/FiO(2) ratio, platelets and CRP were independently associated with the risk of developing the study outcomes and were used to generate the proposed risk score. The accuracy of the score in AUC was 0.80 and 0.70 in internal validation and test for the composite endpoint and 0.92 and 0.69 for death, respectively. CONCLUSIONS: Our score could assist clinicians in identifying, early after tocilizumab administration, patients who are likely to progress to mechanical ventilation or death, so that they could be selected for eventual rescue therapies.

    Search related documents:
    Co phrase search for related documents
    • absolute value and logistic regression analysis: 1, 2, 3, 4
    • absolute value and logistic regression model: 1, 2, 3
    • absolute value and machine learning: 1, 2, 3, 4, 5
    • logistic regression analysis and low intermediate: 1, 2
    • logistic regression analysis and low molecular weight heparin: 1, 2, 3
    • logistic regression analysis and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
    • logistic regression analysis and machine learning approach: 1, 2
    • logistic regression and low intermediate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
    • logistic regression and low molecular weight heparin: 1, 2, 3, 4, 5, 6
    • logistic regression and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • logistic regression and machine learning approach: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18
    • logistic regression model and low intermediate: 1
    • logistic regression model and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • logistic regression model and machine learning approach: 1, 2, 3, 4
    • low intermediate and machine learning: 1, 2, 3, 4