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Lancet Neurol. 2018 May;17(5):423-433. doi: 10.1016/S1474-4422(18)30089-9. Epub 2018 Mar 26.

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

Author information

1
Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, Netherlands.
2
Department of Epidemiology, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands; Cochrane Netherlands, University Medical Centre Utrecht, Utrecht, Netherlands.
3
Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College, Dublin, Ireland.
4
'Rita Levi Montalcini' Department of Neuroscience, ALS Centre, University of Torino, Torino, Italy.
5
Maurice Wohl Clinical Neuroscience Institute, and United Kingdom Dementia Research Institute at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
6
Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK.
7
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
8
Institute of Physiology-Instituto de Medicina Molecular, Faculty of Medicine, University of Lisbon, Lisbon, Portugal.
9
Department of Neurology, Hannover Medical School, Hannover, Germany.
10
Department of Neurology, University of Ulm, Ulm, Germany.
11
Hans-Berger Department of Neurology, Jena University Hospital, Jena, Germany.
12
Neuromuscular Diseases Centre/ALS Clinic, Kantonsspital St Gallen, St Gallen, Switzerland.
13
Department of Epidemiology, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, Netherlands.
14
Centre de compétence SLA-fédération Tours-Limoges, CHU de Tours, Tours, France.
15
Centre de compétence SLA-fédération Tours-Limoges, CHU de Limoges, Limoges, France.
16
Department of Neurology, University Hospital Leuven, Leuven, Belgium; Department of Neurosciences, Katholieke Universiteit, Leuven (University of Leuven) and Centre for Brain and Disease Research, VIB, Leuven, Belgium.
17
Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College, Dublin, Ireland; Department of Neurology, Beaumont Hospital, Beaumont, Ireland.
18
Department of Neurology, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, Netherlands. Electronic address: l.h.vandenberg@umcutrecht.nl.

Abstract

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.

FUNDING:

Netherlands ALS Foundation.

PMID:
29598923
DOI:
10.1016/S1474-4422(18)30089-9
[Indexed for MEDLINE]
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