Dengue is a common cause of acute liver failure in tropical countries. Paracetamol is the recommended antipyretic for dengue. Related observational studies in dengue have suggested that excessive paracetamol intake is related to hepatic injury. We aimed to evaluate whether standard dose paracetamol as an antipyretic in dengue infection caused transaminase elevation, and to evaluate the efficacy of paracetamol. In this randomised, double-blind, placebo-controlled trial, adult participants (aged ≥18 years) with dengue, as confirmed by either positive NS1 antigen, positive dengue IgM antigen with thrombocytopenia, or positive PCR test, were enrolled at three Royal Thai Army hospitals in Thailand. Key exclusion criteria were baseline AST or ALT concentrations of more than 3 times the upper limit of normal, cirrhosis, indication of paracetamol other than dengue infection, concurrent diagnosis of other causes of fever, or pregnancy. Patients were randomly assigned (1:1), by a computer-generated block randomisation procedure (block size of six), to receive either paracetamol (500 mg) or placebo (500 mg) every 4 h when body temperature exceeded 38°C during hospitalisation. Participants and investigators were masked to treatment assignment. The primary outcome was the proportion of participants with transaminase elevation, defined as serum aspartate transaminase (AST) and alanine transaminase (ALT) concentrations of more than 3 times the upper limit of normal on recovery day, in the intention-to-treat population. Prespecified interim analyses for safety and efficacy were performed with group sequential stopping boundaries. This trial is registered with ClinicalTrials.gov, number NCT02833584. Between Sept 1, 2016, and Dec 12, 2017, 125 participants were randomly assigned to receive either paracetamol (n=63) or placebo (n=62). 123 participants were included in the intention-to-treat population. The median daily dose of study medication was 1·5 g (IQR 0·8-2·0). The study was terminated early owing to a higher rate of transaminase elevation in the paracetamol group than in the placebo group (22% vs 10%; incidence rate ratio 3·77, 95% CI 1·36-10·46, p=0·011). The change of AST and ALT concentrations in the paracetamol group was higher than in the placebo group (mean difference 12·43 U/L per day, 7·16-17·71, p<0·0001 for AST; 7·40 U/L per day, 95% CI 3·68-11·13, p=0·0001 for ALT). Three participants in the paracetamol group had severe dengue: two had upper gastric haemorrhage and one had acute kidney injury. No patients died or had liver failure. Use of standard dose paracetamol in dengue infection increased the incidence of transaminase elevation, and also overall transaminase concentrations in the absence of a counterbalancing reduced fever or pain score. Phramongkutklao College of Medicine.

Purpose: Extensively drug-resistant Acinetobacter baumannii (XDRAB) is an important cause of nosocomial pneumonia with limited therapeutic options. Colistin-based regimen is the recommended treatment. Which drugs should be combined with colistin remains uncertain. The aim of this study was to investigate the clinical outcomes of patients with XDRAB pneumonia who were treated with colistin, combined with either 6-g sulbactam or carbapenems, in the setting of high MIC to sulbactam. Patients and methods: In this prospective cohort study, hospitalized patients diagnosed with XDRAB pneumonia in Phramongkutklao Hospital were enrolled. The primary outcome was 28-day mortality. Secondary outcomes were 7- and 14-day mortality, length of stay, ventilator days and factors associated with mortality. Results: From 1 July 2016 to 30 September 2017, 182 patients were included; 92 received colistin plus sulbactam and 90 received colistin plus carbapenems. Most of the patients were males diagnosed with ventilator-associated pneumonia in medical intensive care unit. Overall mortality rates at 7, 14, 28 days were 24.2%, 37.4%, and 53.3%, respectively. Mortality rates did not differ between sulbactam group and carbapenem groups at 7 days (19.6% vs 28.9%, p-value 0.424, adjusted HR 1.277; 95% CI = 0.702–2.322), 14 days (34.8% vs 40%, p = 0.658, adjusted HR 1.109; 95% CI = 0.703–1.749) and 28 days (51.1% vs 55.6%, p = 0.857, adjusted HR 1.038; 95% CI = 0.690–1.562). Length of stay, ICU days and ventilator days did not differ. Complications of treatment including acute kidney injury were not statistically different. Conclusion: In XDRAB pneumonia with high MIC to sulbactam, differences in mortality rates were not statistically significant between colistin plus 6-g sulbactam and colistin plus carbapenems.

Extensively drug-resistant A. baumannii (XDRAB) pneumonia has a high mortality rate in hospitalized patients. One of the recommended treatments is colistin combined with sulbactam; however, the optimal dosage of sulbactam is unclear. In an open-label, superiority, randomized controlled trial, patients diagnosed with XDRAB pneumonia were randomly assigned (1:1) to receive colistin in combination with sulbactam at either 9 g/day or 12 g/day. The primary outcome was the 28-day mortality rate in the intention-to-treat population. A total of 88 patients received colistin in combination with sulbactam at a dosage of either 12 g/day (n = 45) or 9 g/day (n = 43). Trends toward a lower mortality rate were observed in the 12 g/day group at 7 days (11.1% vs. 23.3%), 14 days (33.3% vs. 41.9%), and 28 days (46.7% vs. 58.1%). The microbiological cure rate at day 7 was significantly higher in the 12 g/day group (90.5% vs. 58.1%; p = 0.02). Factors associated with mortality at 28 days were asthma, cirrhosis, APACHEII score ≥ 28, and a dosage of sulbactam of 9 g/day for mortality at any timepoint. Treatment with colistin combined with sulbactam at 12 g/day was not superior to the combination treatment with sulbactam at 9 g/day. However, due to being an interim analysis, this trial was underpowered to detect mortality differences.

The current treatment recommendation involves administering a high dose of sulbactam alongside at least one additional agent. However, there remains a lack of data regarding the optimal dosage of sulbactam. We investigated whether administering sulbactam at a dosage of 12 g/day decreases the mortality rate among patients with CRAB pneumonia compared to 9 g/day. The study was an open-label, superiority, randomized controlled trial conducted at Phramongkutklao Hospital between September 2019 and September 2023 in patients diagnosed with CRAB. Participants were randomly assigned to receive a combination of colistin with either 9 or 12 g/day of sulbactam. The primary endpoint was the all-cause mortality rate at 28 days post-randomization. Among the 138 participants, there was a trend toward a lower mortality rate in the 12 g/day group (59.4% vs. 47.8%; p = 0.158). After adjusting for factors associated with mortality, a lower mortality was observed in the 12 g/day group (adjusted HR 0.54 [95% CI 0.33-0.87]; p = 0.0110). The microbiological cure rate at day 7 was higher in the 12 g/day group (73.2% vs. 89.4%; p = 0.02). Colistin in combination with sulbactam at a dosage of 12 g/day may improve mortality compared to 9 g/day.

Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not promptly diagnosed and treated. Throughout time, medical research has sought to identify clinical factors and strategies to improve the management of these conditions. The increasing adoption of electronic health records has led to a wealth of electronically available medical information and predictive models have emerged as invaluable tools. This manuscript offers a detailed survey of machine-learning techniques used for the diagnosis and prognosis of bacteraemia, bloodstream infections, and sepsis shedding light on their efficacy, potential limitations, and the intricacies of their integration into clinical practice. This study presents a comprehensive analysis derived from a thorough search across prominent databases, namely EMBASE, Google Scholar, PubMed, Scopus, and Web of Science, spanning from their inception dates to October 25, 2023. Eligibility assessment was conducted independently by investigators, with inclusion criteria encompassing peer-reviewed articles and pertinent non-peer-reviewed literature. Clinical and technical data were meticulously extracted and integrated into a registry, facilitating a holistic examination of the subject matter. To maintain currency and comprehensiveness, readers are encouraged to contribute manuscript suggestions and/or reports for integration into this living registry. While machine learning (ML) models exhibit promise in advanced disease stages such as sepsis, early stages remain underexplored due to data limitations. Biochemical markers emerge as pivotal predictors during early stages such as bacteraemia, or bloodstream infections, while vital signs assume significance in sepsis prognosis. Integrating temporal trend information into conventional machine learning models appears to enhance performance. Unfortunately, sequential deep learning models face challenges, showing minimal performance improvements and significant drops in external datasets, potentially due to learning missing patterns within the scarce data available rather than understanding disease dynamics. Real-life implementation receives limited attention, as meeting design requirements proves challenging within existing healthcare infrastructure. The data collected in an event-based fashion during clinical practice is insufficient to fully harness the potential of these data-hungry models. Despite limitations, opportunities abound in leveraging flexible models and exploiting real-time non-invasive data collection technologies such as wearable devices or microneedles. Addressing research gaps in early disease stages, harnessing patient history data often underused, and embracing continual diagnostics beyond treatment initiation are crucial for improving healthcare decision-making support and adoption across the entire management pathway. This comprehensive survey illuminates the landscape of ML applications in blood-related infection management, offering insights for future research and clinical practice. Implementing clinical ML-based clinical decision support systems requires balancing research with practical considerations. Current methodologies often lead to complex models lacking transparency and practical validation. Integration into healthcare systems faces regulatory, privacy, and trust challenges. Clear presentations and adherence to standards are essential to boost confidence in machine learning models for real-world healthcare applications.