Introduction: Methanol poisoning usually occurs in a cluster and initial diagnosis can be challenging. Mortality is high without immediate interventions. This paper describes a methanol poisoning outbreak and difficulties in managing a large number of patients with limited resources. Methodology: A retrospective analysis of a methanol poisoning outbreak in September 2018 was performed, describing patients who presented to a major tertiary referral centre. Result: A total of 31 patients were received over the period of 9 days. Thirty of them were males with a mean age of 32 years old. They were mostly foreigners. From the 31 patients, 19.3% were dead on arrival, 3.2% died in the emergency department and 38.7% survived and discharged. The overall mortality rate was 61.3%. Out of the 12 patients who survived, two patients had toxic optic neuropathy, and one patient had uveitis. The rest of the survivors did not have any long-term complications. Osmolar gap and lactate had strong correlations with patient's mortality. Serum pH, bicarbonate, lactate, potassium, anion gap, osmolar gap and measured serum osmolarity between the alive and dead patients were significant. Post-mortem findings of the brain were unremarkable. Conclusion: The mortality rate was higher, and the morbidity includes permanent visual impairment and severe neurological sequelae. Language barrier, severity of illness, late presentation, unavailability of intravenous ethanol and fomipezole and delayed dialysis may have been the contributing factors. Patient was managed based on clinical presentation. Laboratory parameters showed difference in median between group that survived and succumbed for pH, serum bicarbonate, lactate, potassium and osmolar and anion gap. Management of methanol toxicity outbreak in resource-limited area will benefit from a well-designed guideline that is adaptable to the locality.
Background. Many predictive models have been developed to predict an outbreak, identify and stratify dengue but none has predicted death in severe dengue cases. To build a predictive model for deaths in severe dengue, a multicentre retrospective cohort study was conducted. Methods. Patients with severe dengue based on the World Health Organisation (WHO) 2009 classification were studied. Demographic, clinical and laboratory data were collected at diagnosis of severe dengue. Penalised regression was used for variable selection and model-building. Ten-fold cross-validation with 1000 repeats were performed for internal validation. Results. A cohort of 786 severe dengue cases, including 35 deaths, was analysed. Our model that predicts death in severe dengue cases comprises eight independent predictors: persistent diarrhoea, body mass index, respiratory rate, platelet count, aspartate transaminase, serum bicarbonate, serum lactate and serum albumin. The area under the receiver operating characteristic curve is 89·6% with a sensitivity of 99·6%, specificity of 23·6%, positive predictive value of 96·6%, negative predictive value of 71·1%, positive likelihood ratio 1·45 and negative likelihood ratio 0·01. We also found that the proportion of patients that were in the febrile phase at diagnosis of severe dengue for the overall cohort, decompensated and compensated shock were 74·3%, 73% and 75·4%, respectively. Conclusions. We developed a high-performance dengue death prediction model comprising clinical and laboratory data, and deployed an open-access web-based tool (www.saifulsafuan.com/REPROSED2017E2) for any centre to utilise for local validation. We additionally found that a large majority of patients developed severe dengue during the febrile phase. Keywords: Dengue; severe; model; predict; deaths; phase.
Background Many predictive models have been developed to predict an outbreak, identify and stratify dengue but none in predicting mortality in severe dengue cases. To build a predictive model for deaths in severe dengue, a multicentre retrospective cohort study was conducted. Methods Patients with severe dengue based on WHO 2009 classification were studied. Demographic, clinical and laboratory data were collected at diagnosis of severe dengue. Penalised regression was used for variable selection and model-building. Ten-fold cross-validation with 1000 repeats was performed for internal validation. Results A cohort of 786 severe dengue cases including 35 deaths was analysed. Our model that predicts death in severe dengue cases comprises eight independent predictors: persistent diarrhoea, BMI, respiratory rate, platelet count, AST, serum bicarbonate, serum lactate and serum albumin. The AUROC is 89·6% with a sensitivity of 99·6%, specificity of 23·6%, positive predictive value of 96·6%, negative predictive value of 71·1%, positive likelihood ratio 1·45 and negative likelihood ratio 0·01. We also found that the proportion of patients that were in the febrile phase at diagnosis of severe dengue for the overall cohort, decompensated and compensated shock were 74·3%, 73% and 75·4%, respectively. Conclusions We developed a high performance dengue mortality prediction model comprising clinical and laboratory data and deployed an open access web-based tool (www.saifulsafuan.com/REPROSED2017E2) for any centre to utilise for local validation and found that a large majority of patients developed severe dengue during febrile phase.
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