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Women with gestational diabetes mellitus (GDM) are at seven-fold to ten-fold increased risk of type 2 diabetes mellitus (T2DM) when compared with those who experience a normoglycaemic pregnancy, and the cumulative incidence increases with the time of follow-up post birth. This protocol outlines the development and validation of a risk prediction model assessing the 5-year and 10-year risk of T2DM in women with a prior GDM diagnosis.
Data from all birth mothers and registered births in Victoria and South Australia, retrospectively linked to national diabetes data and pathology laboratory data from 2008 to 2021, will be used for model development and validation of GDM to T2DM conversion. Candidate predictors will be selected considering existing literature, clinical significance and statistical association, including age, body mass index, parity, ethnicity, history of recurrent GDM, family history of T2DM and antenatal and postnatal glucose levels. Traditional statistical methods and machine learning algorithms will explore the best-performing and easily applicable prediction models. We will consider bootstrapping or K-fold cross-validation for internal model validation. If computationally difficult due to the expected large sample size, we will consider developing the model using 80% of available data and evaluating using a 20% random subset. We will consider external or temporal validation of the prediction model based on the availability of data. The prediction model’s performance will be assessed by using discrimination (area under the receiver operating characteristic curve, calibration (calibration slope, calibration intercept, calibration-in-the-large and observed-to-expected ratio), model overall fit (Brier score and Cox-Snell R2) and net benefit (decision curve analysis). To examine algorithm equity, the model’s predictive performance across ethnic groups and parity will be analysed. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence (TRIPOD+AI) statements will be followed.
Ethics approvals have been received from Deakin University Human Research Ethics Committee (2021–179); Monash Health Human Research Ethics Committee (RES-22-0000-048A); the Australian Institute of Health and Welfare (EO2022/5/1369); the Aboriginal Health Research Ethics Committee of South Australia (SA) (04-23-1056); in addition to a Site-Specific Assessment to cover the involvement of the Preventative Health SA (formerly Wellbeing SA) (2023/SSA00065). Project findings will be disseminated in peer-reviewed journals and at scientific conferences and provided to relevant stakeholders to enable the translation of research findings into population health programmes and health policy.
by Biniyam Tedla Mamo, Zelalem Tazu Bonger, Feyissa Regassa Senbato, Tadesse Eguale, Kibrewossen Kiflu Akililu, Samuel Muluye Welelaw, Eden Dagnachew Zeleke, Asrat Demtse, Turegne Assefa, Ruth Woldeyohannes Yirgu, Zelalem Mekuria, Joan-Miquel Balada-Llasat, Shu-Hua Wang
BackgroundNeonatal sepsis is a leading cause of mortality and morbidity. To improve the clinical outcomes of neonates with sepsis, treatment should be based on bacteriological identification and antibiotic susceptibility. This study aims to assess the proportion of culture-positive gram-negative bacteria (GNB), the antibiotic susceptibility patterns, and treatment outcomes of neonatal sepsis at two neonatal intensive care units (NICUs) in Addis Ababa.
MethodsA retrospective observational study was conducted among gram-negative sepsis suspected neonates admitted at Zewditu Memorial Hospital and Tikur Anbessa Specialized Hospital NICUs from January to December 2023. All neonates who were suspected of having sepsis were included in this study. Standard microbiological culture and biochemical tests were used to identify bacterial species and the Kirby-Bauer disc diffusion assay using Mueller-Hinton agar was employed to test the antimicrobial susceptibility of bacterial isolates as per Clinical Laboratory Standard Institute guidelines. Descriptive statistics were used to describe the study variables. Bivariable and multivariable logistic regression analyses were used to identify the factors associated with the treatment outcomes of neonatal sepsis. A p-value Results
A total of 933 neonates were diagnosed with sepsis during the study period, of which 166 neonates were enrolled in the study for gram-negative sepsis: 84 (51%) were female and 97 (58%) had early onset sepsis. The median length of hospital stay was nine days with interquartile range of 16 days. The predominant GNB identified was Klebsiella spp. (n = 89; 49%), followed by Acinetobacter spp. (n = 38; 21%) and Escherichia coli (n = 19; 11%). In both hospitals, Klebsiella spp. was resistant to most of the routinely prescribed antibiotics: (n = 68; 89%) were resistant to ceftriaxone, (n = 56, 89%) cefepime and (n = 60; 75%) to gentamicin. Lower rates of resistance were recorded for other antibiotics such as ciprofloxacin (n = 12; 18%), ertapenem (n = 11; 16%), meropenem (n = 9; 13%), and amikacin (n = 3; 4%). A total of 92 (55%) neonates with the GNB isolated in the current study had multidrug-resistant (MDR) organisms. The study found that newborns with MDR infections were five times more likely to experience poor treatment outcomes compared to those with non-resistant strains (AOR, 5.23 95% CI [2.59, 11.11]). In addition, newborns who stayed less than seven days, compared to those who spent seven or more days in the hospital was four times (AOR: 4.16, 95% CI (2.0–9.01) more likely to experience poor health outcomes.
ConclusionKlebsiella spp. was the most common GNB isolated from the NICUs. More than half neonatal sepsis was caused by MDR organisms and associated with significant poor treatment outcomes. high prevalence of MDR-gram-negative bacteremia is alarming and highlights the need for the implementation of routine surveillance and infection control measures to decrease morbidity and mortality and to combat the development of antimicrobial resistance.
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