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Article Dans Une Revue The Lancet Neurology Année : 2018

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model

Jan Westeneng
  • Fonction : Auteur
Thomas Debray
  • Fonction : Auteur
Anne Visser
  • Fonction : Auteur
Ruben van Eijk
  • Fonction : Auteur
James Rooney
  • Fonction : Auteur
Martin Kauffmann
Christopher Mcdermott
Alexander Thompson
  • Fonction : Auteur
Xenia Kobeleva
  • Fonction : Auteur
Angela Rosenbohm
  • Fonction : Auteur
Beatrice Stubendorff
  • Fonction : Auteur
Helma Sommer
  • Fonction : Auteur
Bas Middelkoop
  • Fonction : Auteur
Anne Dekker
  • Fonction : Auteur
Joke van Vugt
  • Fonction : Auteur
Alice Vajda
  • Fonction : Auteur
Mark Heverin
  • Fonction : Auteur
Mbombe Kazoka
  • Fonction : Auteur
Hannah Hollinger
  • Fonction : Auteur
Marta Gromicho
Sonja Körner
  • Fonction : Auteur
Thomas Ringer
  • Fonction : Auteur
Anne Rödiger
  • Fonction : Auteur
Anne Gunkel
  • Fonction : Auteur
Christopher Shaw
  • Fonction : Auteur
Annelien Bredenoord
  • Fonction : Auteur
Michael van Es
  • Fonction : Auteur
Philip Corcia
  • Fonction : Auteur
Julian Grosskreutz
Albert Ludolph
  • Fonction : Auteur
  • PersonId : 889934
Mamede de Carvalho
Kevin Talbot
  • Fonction : Auteur
Martin Turner
Pamela Shaw
  • Fonction : Auteur
Ammar Al-Chalabi
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  • PersonId : 896934
Orla Hardiman
  • Fonction : Auteur
  • PersonId : 900590
Karel Moons
  • Fonction : Auteur
Jan Veldink
  • Fonction : Auteur
  • PersonId : 911612

Résumé

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive, fatal motor neuron disease with a variable natural history. There are no accurate models that predict the disease course and outcomes, which complicates risk assessment and counselling for individual patients, stratification of patients for trials, and timing of interventions. We therefore aimed to develop and validate a model for predicting a composite survival endpoint for individual patients with ALS. METHODS: We obtained data for patients from 14 specialised ALS centres (each one designated as a cohort) in Belgium, France, the Netherlands, Germany, Ireland, Italy, Portugal, Switzerland, and the UK. All patients were diagnosed in the centres after excluding other diagnoses and classified according to revised El Escorial criteria. We assessed 16 patient characteristics as potential predictors of a composite survival outcome (time between onset of symptoms and non-invasive ventilation for more than 23 h per day, tracheostomy, or death) and applied backward elimination with bootstrapping in the largest population-based dataset for predictor selection. Data were gathered on the day of diagnosis or as soon as possible thereafter. Predictors that were selected in more than 70% of the bootstrap resamples were used to develop a multivariable Royston-Parmar model for predicting the composite survival outcome in individual patients. We assessed the generalisability of the model by estimating heterogeneity of predictive accuracy across external populations (ie, populations not used to develop the model) using internal-external cross-validation, and quantified the discrimination using the concordance (c) statistic (area under the receiver operator characteristic curve) and calibration using a calibration slope. FINDINGS: Data were collected between Jan 1, 1992, and Sept 22, 2016 (the largest data-set included data from 1936 patients). The median follow-up time was 97·5 months (IQR 52·9-168·5). Eight candidate predictors entered the prediction model: bulbar versus non-bulbar onset (univariable hazard ratio [HR] 1·71, 95% CI 1·63-1·79), age at onset (1·03, 1·03-1·03), definite versus probable or possible ALS (1·47, 1·39-1·55), diagnostic delay (0·52, 0·51-0·53), forced vital capacity (HR 0·99, 0·99-0·99), progression rate (6·33, 5·92-6·76), frontotemporal dementia (1·34, 1·20-1·50), and presence of a C9orf72 repeat expansion (1·45, 1·31-1·61), all p<0·0001. The c statistic for external predictive accuracy of the model was 0·78 (95% CI 0·77-0·80; 95% prediction interval [PI] 0·74-0·82) and the calibration slope was 1·01 (95% CI 0·95-1·07; 95% PI 0·83-1·18). The model was used to define five groups with distinct median predicted (SE) and observed (SE) times in months from symptom onset to the composite survival outcome: very short 17·7 (0·20), 16·5 (0·23); short 25·3 (0·06), 25·2 (0·35); intermediate 32·2 (0·09), 32·8 (0·46); long 43·7 (0·21), 44·6 (0·74); and very long 91·0 (1·84), 85·6 (1·96). INTERPRETATION: We have developed an externally validated model to predict survival without tracheostomy and non-invasive ventilation for more than 23 h per day in European patients with ALS. This model could be applied to individualised patient management, counselling, and future trial design, but to maximise the benefit and prevent harm it is intended to be used by medical doctors only.

Dates et versions

hal-01784633 , version 1 (03-05-2018)

Identifiants

Citer

Jan Westeneng, Thomas Debray, Anne Visser, Ruben van Eijk, James Rooney, et al.. Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model. The Lancet Neurology, 2018, 17 (5), pp.423 - 433. ⟨10.1016/S1474-4422(18)30089-9⟩. ⟨hal-01784633⟩
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