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Yoshioka H, Jin R, Hisaka A, Suzuki H. Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases. Pharmacol Ther 2024; 259:108655. [PMID: 38710372 DOI: 10.1016/j.pharmthera.2024.108655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/22/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.
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Affiliation(s)
- Hideki Yoshioka
- Office of Regulatory Science Research, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Ryota Jin
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Akihiro Hisaka
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan.
| | - Hiroshi Suzuki
- Executive Director, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
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Riva N, Domi T, Pozzi L, Lunetta C, Schito P, Spinelli EG, Cabras S, Matteoni E, Consonni M, Bella ED, Agosta F, Filippi M, Calvo A, Quattrini A. Update on recent advances in amyotrophic lateral sclerosis. J Neurol 2024; 271:4693-4723. [PMID: 38802624 PMCID: PMC11233360 DOI: 10.1007/s00415-024-12435-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
In the last few years, our understanding of disease molecular mechanisms underpinning ALS has advanced greatly, allowing the first steps in translating into clinical practice novel research findings, including gene therapy approaches. Similarly, the recent advent of assistive technologies has greatly improved the possibility of a more personalized approach to supportive and symptomatic care, in the context of an increasingly complex multidisciplinary line of actions, which remains the cornerstone of ALS management. Against this rapidly growing background, here we provide an comprehensive update on the most recent studies that have contributed towards our understanding of ALS pathogenesis, the latest results from clinical trials as well as the future directions for improving the clinical management of ALS patients.
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Affiliation(s)
- Nilo Riva
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy.
| | - Teuta Domi
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Laura Pozzi
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Christian Lunetta
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Milan Institute, 20138, Milan, Italy
| | - Paride Schito
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Sara Cabras
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Enrico Matteoni
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Monica Consonni
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy
| | - Eleonora Dalla Bella
- 3Rd Neurology Unit and Motor Neuron Disease Centre, Fondazione IRCCS "Carlo Besta" Neurological Insitute, Milan, Italy
| | - Federica Agosta
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute Huniversity, Milan, Italy
| | - Massimo Filippi
- Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neuroimaging Research Unit, Department of Neurology, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute Huniversity, Milan, Italy
| | - Andrea Calvo
- ALS Centre, 'Rita Levi Montalcini' Department of Neuroscience, University of Turin; SC Neurologia 1U, AOU città della Salute e della Scienza di Torino, Turin, Italy
| | - Angelo Quattrini
- Experimental Neuropathology Unit, Division of Neuroscience, Institute of Experimental Neurology, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Roversi C, Tavazzi E, Vettoretti M, Di Camillo B. A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population. Sci Rep 2024; 14:11514. [PMID: 38769364 PMCID: PMC11106085 DOI: 10.1038/s41598-024-61135-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
Comorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.
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Affiliation(s)
- Chiara Roversi
- Department of Information Engineering, University of Padua, Via Giovanni Gradenigo, 6/b, 35131, Padua, Italy
| | - Erica Tavazzi
- Department of Information Engineering, University of Padua, Via Giovanni Gradenigo, 6/b, 35131, Padua, Italy
| | - Martina Vettoretti
- Department of Information Engineering, University of Padua, Via Giovanni Gradenigo, 6/b, 35131, Padua, Italy
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padua, Via Giovanni Gradenigo, 6/b, 35131, Padua, Italy.
- Department of Comparative Biomedicine and Food Science, University of Padua, Agripolis, Viale dell'Università, 16, 35020, Legnaro (PD), Italy.
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Wu YS, Taniar D, Adhinugraha K, Tsai LK, Pai TW. Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics. Biomedicines 2023; 11:2629. [PMID: 37893003 PMCID: PMC10604752 DOI: 10.3390/biomedicines11102629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/11/2023] [Accepted: 09/22/2023] [Indexed: 10/29/2023] Open
Abstract
The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.
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Affiliation(s)
- Yang-Sheng Wu
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan;
| | - David Taniar
- Department of Software Systems & Cybersecurity, Monash University, Melbourne, VIC 3800, Australia;
| | - Kiki Adhinugraha
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia;
| | - Li-Kai Tsai
- Department of Neurology and Stroke Center, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei 100, Taiwan;
| | - Tun-Wen Pai
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106, Taiwan;
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Fernandes F, Barbalho I, Bispo Júnior A, Alves L, Nagem D, Lins H, Arrais Júnior E, Coutinho KD, Morais AHF, Santos JPQ, Machado GM, Henriques J, Teixeira C, Dourado Júnior MET, Lindquist ARR, Valentim RAM. Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have. J Clin Med 2023; 12:5235. [PMID: 37629277 PMCID: PMC10455505 DOI: 10.3390/jcm12165235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/05/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.
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Affiliation(s)
- Felipe Fernandes
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ingridy Barbalho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Arnaldo Bispo Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Luca Alves
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Danilo Nagem
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Hertz Lins
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ernano Arrais Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Karilany D. Coutinho
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Antônio H. F. Morais
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | - João Paulo Q. Santos
- Advanced Nucleus of Technological Innovation (NAVI), Federal Institute of Rio Grande do Norte (IFRN), Natal 59015-000, Brazil; (A.H.F.M.); (J.P.Q.S.)
| | | | - Jorge Henriques
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - César Teixeira
- Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, Universidade de Coimbra, 3030-788 Coimbra, Portugal; (J.H.); (C.T.)
| | - Mário E. T. Dourado Júnior
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
- Department of Integrated Medicine, Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil
| | - Ana R. R. Lindquist
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
| | - Ricardo A. M. Valentim
- Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal 59010-090, Brazil; (I.B.); (A.B.J.); (L.A.); (D.N.); (H.L.); (E.A.J.); (K.D.C.); (M.E.T.D.J.); (A.R.R.L.); (R.A.M.V.)
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Alkhodari M, Xiong Z, Khandoker AH, Hadjileontiadis LJ, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Rev Cardiovasc Ther 2023; 21:531-543. [PMID: 37300317 DOI: 10.1080/14779072.2023.2223978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
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Affiliation(s)
- Mohanad Alkhodari
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Zhaohan Xiong
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ahsan H Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Huang B, Geng X, Yu Z, Zhang C, Chen Z. Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease. Ann Clin Transl Neurol 2023; 10:892-903. [PMID: 37014017 PMCID: PMC10270250 DOI: 10.1002/acn3.51771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/05/2023] Open
Abstract
OBJECTIVE Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time. METHODS A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score. RESULTS Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients. INTERPRETATION We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.
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Affiliation(s)
- Baoyi Huang
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Xiang Geng
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Zhiyin Yu
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Chengfeng Zhang
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
| | - Zheng Chen
- Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research)Southern Medical UniversityGuangzhouChina
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Soares DF, Henriques R, Gromicho M, de Carvalho M, Madeira SC. Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis. Sci Rep 2023; 13:6182. [PMID: 37061549 PMCID: PMC10105751 DOI: 10.1038/s41598-023-33223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/10/2023] [Indexed: 04/17/2023] Open
Abstract
This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.
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Affiliation(s)
- Diogo F Soares
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.
| | - Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Marta Gromicho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular and Instituto de Fisiologia, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Sara C Madeira
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
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Das T, Kaur H, Gour P, Prasad K, Lynn AM, Prakash A, Kumar V. Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. Brief Bioinform 2022; 23:6780269. [PMID: 36411673 DOI: 10.1093/bib/bbac442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.
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Affiliation(s)
- Trishala Das
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Harbinder Kaur
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Pratibha Gour
- Dept. of Plant Molecular Biology, University of Delhi, South Campus, New Delhi-110021, India
| | - Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
| | - Andrew M Lynn
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon-122413, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
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Pancotti C, Birolo G, Rollo C, Sanavia T, Di Camillo B, Manera U, Chiò A, Fariselli P. Deep learning methods to predict amyotrophic lateral sclerosis disease progression. Sci Rep 2022; 12:13738. [PMID: 35962027 PMCID: PMC9374680 DOI: 10.1038/s41598-022-17805-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/01/2022] [Indexed: 11/10/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a highly complex and heterogeneous neurodegenerative disease that affects motor neurons. Since life expectancy is relatively low, it is essential to promptly understand the course of the disease to better target the patient's treatment. Predictive models for disease progression are thus of great interest. One of the most extensive and well-studied open-access data resources for ALS is the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) repository. In 2015, the DREAM-Phil Bowen ALS Prediction Prize4Life Challenge was held on PRO-ACT data, where competitors were asked to develop machine learning algorithms to predict disease progression measured through the slope of the ALSFRS score between 3 and 12 months. However, although it has already been successfully applied in several studies on ALS patients, to the best of our knowledge deep learning approaches still remain unexplored on the ALSFRS slope prediction in PRO-ACT cohort. Here, we investigate how deep learning models perform in predicting ALS progression using the PRO-ACT data. We developed three models based on different architectures that showed comparable or better performance with respect to the state-of-the-art models, thus representing a valid alternative to predict ALS disease progression.
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Affiliation(s)
- Corrado Pancotti
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Giovanni Birolo
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy.
| | - Cesare Rollo
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Tiziana Sanavia
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padua, 35131, Padua, Italy
| | - Umberto Manera
- ALS Center, "Rita Levi Montalcini" Department of Neuroscience, University of Turin, 10126, Turin, Italy
| | - Adriano Chiò
- ALS Center, "Rita Levi Montalcini" Department of Neuroscience, University of Turin, 10126, Turin, Italy
| | - Piero Fariselli
- Department of Medical Sciences, University of Turin, 10126, Turin, Italy
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