A new predictive tool that uses symptoms and lab tests could help nurses identify patients with myasthenia gravis (MG) at risk of myasthenic crisis, according to a study recently published in BMC Neurology.
In MG, autoantibodies against the neuromuscular junction cause progressive muscle weakness. In a dangerous complication called myasthenic crisis, the muscles that control breathing become weak, leading to respiratory failure.
Research shows that early risk identification significantly reduces in-hospital mortality. Therefore, accurate tools are needed to help healthcare providers recognize high-risk patients — especially nurses, who often have the most patient contact.
Most current predictive tools rely on variables available only to physicians. Because nursing staff typically interact with patients more frequently, the authors proposed a tool based on information that is accessible to nurses, including observable signs such as body position, mood changes and secretions, as well as routine measures like the oxygenation index (OI).
Researchers analyzed common characteristics in 300 patients with MG treated at a hospital in China and assessed the correlation of each characteristic with the onset of myasthenic crisis.
They found that Myasthenia Gravis Foundation of America (MGFA) class III–IV, thymoma, inability to lie flat, and an oxygenation index ≤300 mmHg were closely correlated with the onset of respiratory insufficiency.
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The predictive tool they developed is a type of diagram called a nomogram. It incorporates six bedside/laboratory inputs: MGFA class, thymoma, body position, PCO2, OI and oropharyngeal secretions. A patient’s scores for each of these inputs are added together to determine their risk of a myasthenic crisis. The researchers found the tool was reliable in identifying patients at risk of a myasthenic crisis.
The authors suggest that a standardized, nurse-deployable tool could help enable earlier, targeted escalation, especially when changes in body position signal new onset respiratory muscle weakness.
“The nurse-led nomogram developed in this study is a user-friendly prediction model for assessing MC risk in MG patients with bulbar weakness and can significantly assist clinical nurses in the early identification and implementation of targeted interventions for high-risk patients,” the authors concluded.
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