Leptospirosis in the elderly: the role of age as a predictor of poor outcomes in hospitalized patients.
Elizabeth De Francesco DaherDouglas de Sousa SoaresGabriela Studart GaldinoÊnio Simas MacedoPedro Eduardo Andrade de Carvalho GomesRoberto Da Justa Pires NetoGeraldo Bezerra da Silva JúniorPublished in: Pathogens and global health (2019)
Background: The aim of this study was to investigate factors associated with poor outcomes among elderly hospitalized patients with leptospirosis. Methods: This is a retrospective cohort study with leptospirosis patients admitted to three tertiary hospitals in Fortaleza, Brazil, from January 1985 to July 2017. Patients were divided into two groups: elderly (age ≥60 years) and young (age <60 years). A comparison of demographical, clinical and laboratory data, treatment and outcomes was executed in order to investigate differences between groups. Results: A total of 507 hospitalized patients were included, with mean age 38 ± 15 years. Elderly group presented lower incidence of myalgia, vomiting, and dyspnea, as well as, higher medium systolic blood pressure. Elderly also manifested higher frequency of AKI (85.9 vs. 74.7%, p = 0.05), hemodialysis requirement (54.7 vs. 37.0%, p = 0.007) and death (32.8 vs. 12.2%, p < 0.001). In multivariate analysis, age ≥60 years was a predictor of hemodialysis requirement (p = 0.008, OR = 2.049, 95% CI = 1.207-3.477) and death (p < 0.001, OR = 3.520, 95% CI = 1.940-6.386). Conclusion: Leptospirosis in the elderly is associated with less hemodynamic impairment and higher frequency of AKI. Advanced age was also a predictor of poor outcomes, such as hemodialysis requirement and death, mostly due to kidney involvement.
Keyphrases
- middle aged
- end stage renal disease
- blood pressure
- chronic kidney disease
- community dwelling
- peritoneal dialysis
- acute kidney injury
- heart failure
- ejection fraction
- type diabetes
- skeletal muscle
- newly diagnosed
- replacement therapy
- patient reported outcomes
- heart rate
- glycemic control
- smoking cessation
- combination therapy
- deep learning