Objective. To identify demographic, occupational, exposure, clinical, and laboratory factors independently associated with diagnostic confirmation of hantavirus infection by RT-PCR or IgM among patients with suspected hantavirus pulmonary syndrome evaluated in Río Negro and Neuquén, Argentina.
Methods. A retrospective case–control study using emergency department records and epidemiological notification forms from public hospitals in Río Negro and Neuquén, Argentina, during 1996–2025. Cases were patients with suspected hantavirus pulmonary syndrome confirmed by RT-PCR detection of hantavirus RNA and/or hantavirus-specific IgM ELISA through a standardized laboratory confirmation pathway verified by the reference laboratory. Controls were suspected cases in whom hantavirus infection was ruled out through the same pathway. Demographic, occupational, exposure, clinical, laboratory, and chest radiography variables were analyzed. Logistic regression models were adjusted for age and sex; symptoms were mutually adjusted in the clinical block.
Results. We included 325 suspected cases: 113 confirmed and 212 ruled out. Compared with no evident risk, the activity-based exposure scale showed strong associations for high-risk activity (aOR 156; 95% CI [10, 2 448]) and moderate-risk activity (aOR 33; 95% CI [2.4, 456]). Confirmed cases more often had elevated hematocrit (aOR 7.40; 95% CI [3.96, 13.8]), elevated lactate dehydrogenase (aOR 10.67; 95% CI [4.85, 23.3]),thrombocytopenia (aOR 11.08; 95% CI [5.59, 21.9]), and elevated creatinine (aOR 2.91; 95% CI [1.59, 5.34]). Severe respiratory failure/criteria for mechanical ventilation (aOR 5.58; 95% CI [3.16, 9.85]) and neurological symptoms (aOR 5.17; 95% CI [1.16, 28.0]) were independently associated with confirmation.
Conclusions. Among patients with suspected hantavirus pulmonary syndrome evaluated in southern Argentina, structured exposure assessment and selected laboratory abnormalities were associated with laboratory confirmation. Given the study limitations, these hypothesis-generating findings may inform future development and prospective validation of clinical prediction tools.