RESUMO
Methanol is highly toxic to humans. Although methanol poisoning is not uncommon in developing countries, poisoning caused by ingestion of commercial products containing undeclared methanol has rarely been reported. Herein, we describe two patients who experienced methanol poisoning after ingestion of liquid rodenticides. A 39-year-old woman attempted suicide by ingesting liquid rodenticide which contained bromadiolone. She developed high anion gap metabolic acidosis and coagulopathy. Methanol poisoning was confirmed 20 hours later. She received oral ethanol therapy and hemodialysis. Vitamin K1 was also administered. She did not develop any hemorrhage or visual impairment and was discharged after 11 days. The rodenticide sample was tested and found to have a methanol concentration of 324 g/L. In another case, a 62-year-old man ingested the same brand of rodenticide. Laboratory data showed mild metabolic acidosis with an increased osmol gap, suggestive of methanol poisoning. He received hemodialysis and eventually recovered without sequelae. Liquid rodenticide may contain methanol as a solvent. Ingestion of a methanol-containing commercial product without a clear label can result in a considerable delay in diagnosis and management. Methanol poisoning should be considered for patients who present with unexplained metabolic acidosis following exposure to liquid rodenticides or other liquid commercial products.
RESUMO
BACKGROUND: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. METHODS: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. RESULTS: In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model's S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. CONCLUSIONS: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.
