Frontiers in Medicine (Mar 2021)

The PREdictor of MAlnutrition in Systemic Sclerosis (PREMASS) Score: A Combined Index to Predict 12 Months Onset of Malnutrition in Systemic Sclerosis

  • Gianluca Bagnato,
  • Gianluca Bagnato,
  • Erika Pigatto,
  • Alessandra Bitto,
  • Gabriele Pizzino,
  • Natasha Irrera,
  • Giuseppina Abignano,
  • Giuseppina Abignano,
  • Antonino Ferrera,
  • Davide Sciortino,
  • Michelle Wilson,
  • Francesco Squadrito,
  • Maya H. Buch,
  • Paul Emery,
  • Elisabetta Zanatta,
  • Sebastiano Gangemi,
  • Antonino Saitta,
  • Franco Cozzi,
  • William Neal Roberts,
  • Francesco Del Galdo

DOI
https://doi.org/10.3389/fmed.2021.651748
Journal volume & issue
Vol. 8

Abstract

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Objective: Malnutrition is a severe complication in Systemic Sclerosis (SSc) and it is associated with significant mortality. Notwithstanding, there is no defined screening or clinical pathway for patients, which is hampering effective management and limiting the opportunity for early intervention. Here we aim to identify a combined index predictive of malnutrition at 12 months using clinical data and specific serum adipokines.Methods: This was an international, multicentre observational study involving 159 SSc patients in two independent discovery (n = 98) and validation (n = 61) cohorts. Besides routine clinical and serum data at baseline and 12 months, Malnutrition Universal Screening Tool (MUST) score and serum concentration of leptin and adiponectin were measured for each participant at baseline. The endpoint of malnutrition was defined according to European Society of Clinical Nutrition and Metabolism (ESPEN) recommendation. Significant parameters from univariate analysis were tested in logistic regression analysis to identify the predictive index of malnutrition in the derivation cohort.Results: The onset of malnutrition at 12 months correlated with adiponectin, leptin and their ratio (A/L), MUST, clinical subset, disease duration, Scl70 and Forced Vital Capaciy (FVC). Logistic regression analysis defined the formula: −2.13 + (A/L*0.45) + (Scl70*0.28) as the best PREdictor of MAlnutrition in SSc (PREMASS) (AUC = 0.96; 95% CI 0.93, 0.99). PREMASS < −1.46 had a positive predictive value (PPV) > 62% and negative predictive value (NPV) > 97% for malnutrition at 12 months.Conclusion: PREMASS is a feasible index which has shown very good performance in two independent cohorts for predicting malnutrition at 12 months in SSc. The implementation of PREMASS could aid both in clinical management and clinical trial stratification/enrichment to target malnutrition in SSc.

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