Hemogram parameters can predict in-hospital mortality of patients with Myasthenic crisis

Che-Wei Hsu; Nai-Ching Chen; Wei-Chin Huang; Hui-Chen Lin; Wan-Chen Tsai; Chih-Cheng Huang; Ben-Chung Cheng; Nai-Wen Tsai

doi:10.1186/s12883-021-02412-4

Hemogram parameters can predict in-hospital mortality of patients with Myasthenic crisis

BMC Neurol. 2021 Oct 6;21(1):388. doi: 10.1186/s12883-021-02412-4.

Authors

Che-Wei Hsu¹, Nai-Ching Chen¹, Wei-Chin Huang¹, Hui-Chen Lin¹, Wan-Chen Tsai¹, Chih-Cheng Huang¹, Ben-Chung Cheng², Nai-Wen Tsai³

Affiliations

¹ Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta Pei Road, Niao Sung District, Kaohsiung, 833, Taiwan.
² Department of Nephrology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
³ Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Ta Pei Road, Niao Sung District, Kaohsiung, 833, Taiwan. tsainw@yahoo.com.tw.

Abstract

Background: Myasthenia gravis (MG) is an autoimmune disease involving the neuromuscular junction. Myasthenic crisis (MC), which is characterized by respiratory failure and the requirement of mechanical ventilation in patients with MG, is still a medical emergency despite the decrease in mortality with the advances in acute management. Hemogram is a cost-effective test for evaluating hematological complications and systemic inflammation, and hemogram data have been used to predict various clinical outcomes of several diseases. The relationship between hemogram and MG has been discussed, but the role of hemogram data in predicting the prognosis of MC patients has not been established.

Methods: To identify whether hemogram data can predict in-hospital mortality in patients with MC, we retrospectively investigated 188 myasthenic crisis events from the Chang Gung Research Database between April 2001 and March 2019. Demographic and clinical characteristics were collected, as well as hemogram data before intubation and extubation. The endpoints were mortality during mechanical ventilation and mortality after extubation.

Results: The overall in-hospital mortality rate was 22%. Multivariate logistic regression analysis for predicting mortality during mechanical ventilation showed that old age at MC onset (OR = 1.039, p = 0.022), moderate-to-severe anemia (OR = 5.851, p = 0.001), and extreme leukocytosis (OR = 5.659, p = 0.022) before intubation were strong predictors of mortality, while acute management with plasma exchange or double-filtration plasmapheresis (PE/DFPP) significantly decreased mortality (OR = 0.236, p = 0.012). For predicting mortality after extubation, moderate-to-severe anemia before extubation (OR = 8.452, p = 0.017) and non-treated with disease-modifying therapy before MC (OR = 5.459, p = 0.031) were crucial predictive factors.

Conclusion: This study demonstrated that both old age at MC onset and moderate-to-severe anemia are important predictors of in-hospital mortality in patients with MC, and extreme leukocytosis is another crucial predictor of mortality during mechanical ventilation. The suggested mechanism is that anemia-induced hypoxia may enhance the release of proinflammatory cytokines, exacerbate systemic inflammation, and lead to multiple organ dysfunction syndrome and, finally, mortality.

Keywords: Anemia; In-hospital mortality; Myasthenic crisis.

MeSH terms

Hospital Mortality
Humans
Myasthenia Gravis* / complications
Myasthenia Gravis* / therapy
Respiration, Artificial
Respiratory Insufficiency*
Retrospective Studies