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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background The objective of this study was to develop multiple prediction tools that calculate the risk of developing dengue haemorrhagic fever. Methods Training data consisted of 1771 individuals from 2006–2008 admitted with dengue fever whereby 304 developed dengue haemorrhagic fever during hospitalisation. Least absolute shrinkage and selection operator regression was used to construct three types of calculators, static admission calculators and dynamic calculators that predict the risk of developing dengue haemorrhagic fever for a subsequent day (DAily Risk Tomorrow [DART]) or for any future point after a specific day since fever onset (DAily Risk Ever [DARE]). Results From 119 admission covariates, 35 were in at least one of the calculators, which reported area under the curve (AUC) values of at least 0.72. Addition of person-time data for DART improved AUC to 0.76. DARE calculators displayed a large increase in AUC to 0.79 past day 7 with the inclusion of a strong predictor, maximum temperature on day 6 since onset, indicative of a saddleback fever. Conclusions All calculators performed well when validated with 2005 data. Addition of daily variables further improved the accuracy. These calculators can be used in tandem to assess the risk of dengue haemorrhagic fever upon admission and updated daily to obtain more precise risk estimates. [ABSTRACT FROM AUTHOR]

Abstract Background Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data. Methods Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore’s busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed. Results The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67 [95% CI: 0.59–0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics. Conclusion The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.

Abstract Background The anti-retroviral combination of abacavir/lamivudine plus rilpivirine (ABC/3TC/RPV) is not recommended by international guidelines as the first-line regimen. However, it is potent, well-tolerated, and affordable, especially in resource-limited settings. This study evaluates the efficacy and safety of ABC/3TC/RPV as an initial regimen for treatment-naïve HIV-1 infected patients. Methods A retrospective study was conducted in the largest HIV care centre in Singapore, with data collected June 2011 to September 2017. All treatment-naïve HIV-1 infected adults prescribed ABC/3TC as part of their initial anti-retroviral therapy regimen were included. The third drug was a non-nucleoside reverse-transcriptase inhibitor (NNRTI) such as RPV or efavirenz (EFV), or boosted protease-inhibitor (PI). Patients were followed up for 48 weeks. The primary end-point was the percentage of patients achieving virologic suppression, analysed using on-treatment analysis. Secondary outcomes included CD4-count change, treatment discontinuation and treatment-related adverse events. Results 170 patients were included in the study, 66 patients in the RPV group, 104 patients in the comparator group (EFV or boosted PI). 96% (n = 24) in the RPV group and 87% (n = 26) in the comparator group achieved viral suppression at 48 weeks (p = 0.28). Median (interquartile range) time to viral suppression was similar: 17 (14–24) weeks in the RPV group, and 21 (13–26) weeks in the comparator group. There were no statistically significant differences in the CD4 count between the two groups. 14% (n = 9) of patients on RPV discontinued treatment before 48 weeks, compared to 30% (n = 31) from the comparator group (p = 0.053). Of these, 23 discontinuations were due to drug adverse effects, and only 1 attributed to RPV (p