Influenza in Malaysian adult patients hospitalized with community-acquired pneumonia, acute exacerbation of chronic obstructive pulmonary disease or asthma: a multicenter, active surveillance study.

BACKGROUND: Available data on influenza burden across Southeast Asia are largely limited to pediatric populations, with inconsistent findings. METHODS: We conducted a multicenter, hospital-based active surveillance study of adults in Malaysia with community-acquired pneumonia (CAP), acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and acute exacerbation of asthma (AEBA), who had influenza-like illness ≤10 days before hospitalization. We estimated the rate of laboratory-confirmed influenza and associated complications over 13 months (July 2018-August 2019) and described the distribution of causative influenza strains. We evaluated predictors of laboratory-confirmed influenza and severe clinical outcomes using multivariate analysis. RESULTS: Of 1106 included patients, 114 (10.3%) were influenza-positive; most were influenza A (85.1%), with A/H1N1pdm09 being the predominant circulating strain during the study following a shift from A/H3N2 from January-February 2019 onwards. In multivariate analyses, an absence of comorbidities (none versus any comorbidity [OR (95%CI), 0.565 (0.329-0.970)], p = 0.038) and of dyspnea (0.544 (0.341-0.868)], p = 0.011) were associated with increased risk of influenza positivity. Overall, 184/1106 (16.6%) patients were admitted to intensive care or high-dependency units (ICU/HDU) (13.2% were influenza positive) and 26/1106 (2.4%) died (2.6% were influenza positive). Males were more likely to have a severe outcome (ICU/HDU admission or death). CONCLUSIONS: Influenza was a significant contributor to hospitalizations associated with CAP, AECOPD and AEBA. However, it was not associated with ICU/HDU admission in this population. Study registration, NMRR ID: NMRR-17-889-35,174.

Proposal of a Clinical Decision Tree Algorithm Using Factors Associated with Severe Dengue Infection.

BACKGROUND: WHO's new classification in 2009: dengue with or without warning signs and severe dengue, has necessitated large numbers of admissions to hospitals of dengue patients which in turn has been imposing a huge economical and physical burden on many hospitals around the globe, particularly South East Asia and Malaysia where the disease has seen a rapid surge in numbers in recent years. Lack of a simple tool to differentiate mild from life threatening infection has led to unnecessary hospitalization of dengue patients. METHODS: We conducted a single-centre, retrospective study involving serologically confirmed dengue fever patients, admitted in a single ward, in Hospital Kuala Lumpur, Malaysia. Data was collected for 4 months from February to May 2014. Socio demography, co-morbidity, days of illness before admission, symptoms, warning signs, vital signs and laboratory result were all recorded. Descriptive statistics was tabulated and simple and multiple logistic regression analysis was done to determine significant risk factors associated with severe dengue. RESULTS: 657 patients with confirmed dengue were analysed, of which 59 (9.0%) had severe dengue. Overall, the commonest warning sign were vomiting (36.1%) and abdominal pain (32.1%). Previous co-morbid, vomiting, diarrhoea, pleural effusion, low systolic blood pressure, high haematocrit, low albumin and high urea were found as significant risk factors for severe dengue using simple logistic regression. However the significant risk factors for severe dengue with multiple logistic regressions were only vomiting, pleural effusion, and low systolic blood pressure. Using those 3 risk factors, we plotted an algorithm for predicting severe dengue. When compared to the classification of severe dengue based on the WHO criteria, the decision tree algorithm had a sensitivity of 0.81, specificity of 0.54, positive predictive value of 0.16 and negative predictive of 0.96. CONCLUSION: The decision tree algorithm proposed in this study showed high sensitivity and NPV in predicting patients with severe dengue that may warrant admission. This tool upon further validation study can be used to help clinicians decide on further managing a patient upon first encounter. It also will have a substantial impact on health resources as low risk patients can be managed as outpatients hence reserving the scarce hospital beds and medical resources for other patients in need.