Factors predicting the outcomes of elderly hospitalized myasthenia gravis patients: a national database study

Tiamkao, Somsak; Pranboon, Sineenard; Thepsuthammarat, Kaewjai; Sawanyawisuth, Kittisak

doi:10.2147/ijgm.s129075

Cited by 7 publications

(14 citation statements)

References 22 publications

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“…C5.0 is a promising ML-based decision tree algorithm and has been successfully used in clinical/healthcare issues [26,53]. Previously published predictive factors of the prognosis of MG in MG deterioration, severity, and hospital stay have used multivariable logistic regression analyses [49,54,55]. This research found that, compared with LR, C5.0 and the other two ML-based decision tree algorithms can generate a better accuracy.…”

Section: Discussionmentioning

confidence: 98%

“…Other large cohort studies suggested that late-onset MG patients are prone to increased disease severity, and the mortality rate increased in the elderly [15,48]. This is because elderly patients with MG tend to have comorbidities and complications, such as sepsis, resulting in long hospital stays and high costs [49]. Old onset age of MG could be associated with an increased susceptibility to autoimmune diseases, due to immune dysregulation and increased inflammatory background that can cause a high production of autoantibodies [49].…”

Section: Discussionmentioning

confidence: 99%

“…This is because elderly patients with MG tend to have comorbidities and complications, such as sepsis, resulting in long hospital stays and high costs [49]. Old onset age of MG could be associated with an increased susceptibility to autoimmune diseases, due to immune dysregulation and increased inflammatory background that can cause a high production of autoantibodies [49]. Although the elderly have a more severe presentation than younger patients, they require a low dosage of medications, have better prognosis to management, and a short weaning time in the ICU after a myasthenic crisis [48].…”

Section: Discussionmentioning

confidence: 99%

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Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

Chang

Yeh

Chiu

et al. 2022

JPM

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Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

Chang

Yeh

Chiu

et al. 2022

JPM

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“…4 These conditions are increasingly recognized as a consequence of more availability and accessibility of biomarker testing, although there is scarce epidemiological data from this region. [5][6][7] Early treatment of acute attacks is crucial to prevent mortality and long-term disabilities, especially in neuromyelitis optica spectrum disorder (NMOSD), acute inflammatory demyelinating polyneuropathy (AIDP), and autoimmune encephalitis. [8][9][10] However, several obstacles prevent neurologists from accessing and prescribing TPE.…”

Section: Introductionmentioning

confidence: 99%

Facilities, selection, outcome measurement, and limitations of therapeutic plasma exchange for neuroimmunological disorders: The South East Asian survey study

Rattanathamsakul

Siritho

Shanthi

et al. 2023

J of Clinical Apheresis

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Introduction Therapeutic plasma exchange (TPE) for neuroimmunological disorders has played an important role in the Southeast Asian region. This study investigates the challenges of performing TPE within the region. Method A questionnaire‐based survey was conducted and launched to 15 South East Asian Therapeutic Plasma Exchange Consortium (SEATPEC) members from seven countries in January 2021. It included demographics, TPE techniques, indications, challenges, timing, outcome measurement, and access to laboratory testing in each local center. Results A total of 15 neurologists from 12 participating centers were included. They usually perform five sessions of TPE (100.0%), with 1 to 1.5 plasma volume (93.3%), and exchanges via the central catheter (100.0%). Acute relapses of neuromyelitis optica spectrum disorder and myasthenia gravis are the most common indications. They used a combination of normal saline and 5% albumin (60.0%) as replacement fluid. Most (66.7%) used TPE as an add‐on treatment in steroid‐refractory cases or as first‐line treatment for severe attacks. They suggested assessing the TPE efficacy of TPE by the interval to the next attack, post‐TPE relapse rates, and TPE‐related complications. The major challenges within our region are expense, reimbursibility, and access to TPE. Conclusion Although countrywise differences exist, all share similarities regarding methods, indications, timing, obstacles, and challenges of TPE for neuroimmunological conditions. Regional collaboration will be essential to identify strategies to reduce these barriers to access to TPE in the future.

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“…Multiple factors have been reported to be associated with myasthenia gravis (MG) prognosis. For example, in patients with late‐onset MG (>60 years of age), clinical factors such as concomitant pneumonia and sepsis, as well as introduction of mechanical ventilation, have been associated with poor MG outcomes 1 . In contrast, early onset (<40 years of age) is reportedly associated with complete stable remission (CSR) status (ie, good outcomes) on the Myasthenia Gravis Foundation of America Post‐Intervention Status (MFGA‐PIS) scale 2 .…”

Section: Introductionmentioning

confidence: 99%

Assessment of clinical factors affecting outcome of myasthenia gravis

et al. 2021

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Introduction/Aims: In myasthenia gravis (MG) therapy, achieving Myasthenia Gravis Foundation of America minimal manifestation (MM) or better status is proposed as a desirable target. However, this level of control is often not achieved and clinical factors affecting prognosis remain unclear.Methods: Participants were 104 consecutive patients with MG who visited Osaka Medical College Hospital. We retrospectively assessed the association of clinical and laboratory features at baseline with prognosis. Eighty patients who achieved MM or better status were classified as the good outcome group and the remaining 24 patients were classified as the poor outcome group. Results:The rate of dysphagia at baseline was significantly higher in the poor outcome group than in the good outcome group (P = .002). The levels of serum total protein and albumin at baseline were both significantly lower in the poor outcome group than in the good outcome group (P = .036 and P = .014, respectively). In addition, Controlling Nutritional Status scores at baseline were significantly higher in the poor outcome group than in the good outcome group (P = .043). Multivariate analysis using a Cox proportional hazards model showed that dysphagia (hazard ratio [HR], 6.92; 95% confidence interval [CI], 1.49-40.31) and hypoalbuminemia (HR, 2.57; 95% CI, 1.04-6.57) at baseline were risk factors that predicted prognosis. Discussion: These findings suggest that dysphagia and hypoalbuminemia at baseline are associated with outcomes and are predictive risk factors for poorer outcomes in patients with MG.

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Factors predicting the outcomes of elderly hospitalized myasthenia gravis patients: a national database study

Cited by 7 publications

References 22 publications

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

Facilities, selection, outcome measurement, and limitations of therapeutic plasma exchange for neuroimmunological disorders: The South East Asian survey study

Assessment of clinical factors affecting outcome of myasthenia gravis

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