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

Ian Qian  Xu; Ling  Guo; Jing Xu; Stella  Setiawan; Xiao  Deng; Yew  Long Lo; Josiah Yui  Huei Chai; Zachary  Simmons; Savitha  Ramasamy; Crystal Jing Jing Yeo

doi:10.1002/mus.28416

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

Ian Qian Xu
, Ling Guo
, Jing Xu
, Stella Setiawan
, Xiao Deng
, Yew Long Lo
, Josiah Yui Huei Chai
, Zachary Simmons
, Savitha Ramasamy^* (Corresponding Author)
, Crystal Jing Jing Yeo^* (Corresponding Author)

^*Corresponding author for this work

Medical Education

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	71-81
Number of pages	11
Journal	Muscle & nerve
Volume	72
Issue number	1
Early online date	23 Apr 2025
DOIs	https://doi.org/10.1002/mus.28416
Publication status	Published - Jul 2025

Bibliographical note

Open Access via the Wiley Agreement

We would like to thank Prof. Roger Vaughan from Duke-NUS for assistance with statistics and all patients who have participated in this study. We would also like to thank Sisters Su Rong Fam and Winnie Mei Lian Goh with their help with data collection.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Funding

This study was in part supported by the A*STAR Career Development Award (CDA) grant C210112024 and National Neuroscience Institute (NNI) Pilot grant IRNMR21CPGJJ to C.J.J. Yeo.

Keywords

ALS registry
machine learning
Prognostic factors
Survival analysis
survival prediction

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Cite this

@article{ff05ab4f67934d6daafae961f278f6ae,

title = "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",

author = "Xu, \{Ian Qian\} and Ling Guo and Jing Xu and Stella Setiawan and Xiao Deng and \{Long Lo\}, Yew and \{Huei Chai\}, \{Josiah Yui\} and Zachary Simmons and Savitha Ramasamy and Yeo, \{Crystal Jing Jing\}",

note = "Open Access via the Wiley Agreement We would like to thank Prof. Roger Vaughan from Duke-NUS for assistance with statistics and all patients who have participated in this study. We would also like to thank Sisters Su Rong Fam and Winnie Mei Lian Goh with their help with data collection.",

year = "2025",

month = jul,

doi = "10.1002/mus.28416",

language = "English",

volume = "72",

pages = "71--81",

journal = "Muscle \& nerve",

issn = "0148-639X",

publisher = "John Wiley \& Sons Inc.",

number = "1",

}

TY - JOUR

T1 - Predictive Analysis of Amyotrophic Lateral Sclerosis Progression and Mortality in a Clinic Cohort from Singapore

AU - Xu, Ian Qian

AU - Guo, Ling

AU - Xu, Jing

AU - Setiawan, Stella

AU - Deng, Xiao

AU - Long Lo, Yew

AU - Huei Chai , Josiah Yui

AU - Simmons, Zachary

AU - Ramasamy, Savitha

AU - Yeo, Crystal Jing Jing

N1 - Open Access via the Wiley Agreement We would like to thank Prof. Roger Vaughan from Duke-NUS for assistance with statistics and all patients who have participated in this study. We would also like to thank Sisters Su Rong Fam and Winnie Mei Lian Goh with their help with data collection.

PY - 2025/7

Y1 - 2025/7

N2 - 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.

AB - 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.

KW - ALS registry

KW - machine learning

KW - Prognostic factors

KW - Survival analysis

KW - survival prediction

U2 - 10.1002/mus.28416

DO - 10.1002/mus.28416

M3 - Article

SN - 0148-639X

VL - 72

SP - 71

EP - 81

JO - Muscle & nerve

JF - Muscle & nerve

IS - 1

ER -

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

Abstract

Bibliographical note

Data Availability Statement

Funding

Keywords

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