Predictive Analysis of Amyotrophic Lateral Sclerosis Progression and Mortality in a Clinic Cohort From Singapore

Abstract

Introduction: There is currently no comprehensive Amyotrophic Lateral Sclerosis (ALS) patient database in Singapore comparable to those available in Europe and the United States. We established the Singapore ALS registry (SingALS) to draw meaningful inferences about the ALS population in Singapore through developing statistical and machine learning-based predictive models.

Methods: The SingALS registry was established through the retrospective collection of demographic, clinical, and laboratory data from 72 ALS patients at Tan Tock Seng Hospital (TTSH) and combining it with demographic and clinical data from 71 patients at Singapore General Hospital (SGH). The SingALS was compared against international ALS registries. Using comparative studies including survival and temporal feature analysis, we identified key factors influencing ALS survival and developed a machine learning model to predict survival outcomes.

Results: Compared to Caucasian-dominant registries, such as the German Swabia registry, SingALS patients had longer average survival (50.51 vs. 31.0 months), younger age of onset (56.18 vs. 66.6 years), and lower bulbar onset prevalence (20.98% vs. 34.10%). Singaporean males had poorer outcomes compared to females, with a hazard ratio (HR) of 3.12 (p = 0.008). Patients who died within 24 months had an earlier need for being bedbound (p < 0.004), percutaneous endoscopic gastrostomy (PEG) insertion (p = 0.004) and non-invasive ventilation (NIV) (p < 0.001). Machine learning and statistical analysis indicated that a steeper ALSFRS-R slope, higher alkaline phosphatase (ALP), white blood cell (WBC), absolute neutrophil counts, and creatinine levels are associated with worse mortality.

Discussion: We developed a comprehensive Singaporean ALS registry and identified key factors influencing survival.

Keywords: ALS registry; machine learning; prognostic factors; survival analysis; survival prediction.

FIGURE 1

Kaplan–Meier tracheostomy‐free survival plot by gender in TTSH database; p‐value is from Log‐Rank test.

FIGURE 2

Mean and standard deviation bar plot based on the top features with temporal components selected by the machine learning model in TTSH database. Survival outcome comparison between patients who died or received tracheostomy within 24 months (red) and those who did not die or receive tracheostomy (blue). *p value < 0.050, **p value < 0.010, ***p value < 0.001. Dot: Mean value; Bar: Standard deviation.