METHODOLOGY: Randomly, we collected 436 oropharyngeal swabs from healthy children aged 2-4 years in 30 registered childcare centres in Kuala Lumpur (August 2018-May 2019). Informed consent and written questionnaires were obtained from parents. H. influenzae was identified by standard microbiological methods. Univariable analysis was carried out to describe variables associated with colonization. All variables with p
AIM: To identify and critically appraise existing clinical prediction models of extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-EKP) infection or colonization.
METHODS: Electronic databases, reference lists, and citations were searched from inception to April 2018. Papers were included in any language describing the development or validation, or both, of models and scores to predict the risk of ESBL-EKP infection or colonization.
FINDINGS: In all, 1795 references were screened, of which four articles were included in the review. The included studies were carried out in different geographical locations with differing study designs, and inclusion and exclusion criteria. Most if not all studies lacked external validation and blinding of reviewers during the evaluation of the predictor variables and outcome. All studies excluded missing data and most studies did not report the number of patients excluded due to missing data. Fifteen predictors of infection or colonization with ESBL-EKP were identified. Commonly included predictors were previous antibiotic use, previous hospitalization, transfer from another healthcare facility, and previous procedures (urinary catheterization and invasive procedures).
CONCLUSION: Due to limitations and variations in the study design, clinicians would have to take these differences into consideration when deciding on how to use these models in clinical practice. Due to lack of external validation, the generalizability of these models remains a question. Therefore, further external validation in local settings is needed to confirm the usefulness of these models in supporting decision-making.
METHODS: A prospective cohort study of preterm infants with gestational age
OBJECTIVES: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers.
METHODS: The methodology includes four phases. Firstly, eight important laboratory criteria are chosen using two feature selection approaches. Real and simulation datasets from various medical perspectives are integrated to produce a new dataset involving 56 patients with different health conditions and can be used to check asymptomatic cases that can be detected within the prioritisation configuration. The first phase aims to develop a new decision matrix depending on the intersection between 'multi-laboratory criteria' and 'COVID-19 patient list'. In the second phase, entropy is utilised to set the objective weight, and TOPSIS is adapted to prioritise patients in the third phase. Finally, objective validation is performed.
RESULTS: The patients are prioritised based on the selected criteria in descending order of health situation starting from the worst to the best. The proposed framework can discriminate among mild, serious and critical conditions and put patients in a queue while considering asymptomatic carriers. Validation findings revealed that the patients are classified into four equal groups and showed significant differences in their scores, indicating the validity of ranking.
CONCLUSIONS: This study implies and discusses the numerous benefits of the suggested framework in detecting/recognising the health condition of patients prior to discharge, supporting the hospitalisation characteristics, managing patient care and optimising clinical prediction rule.