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Background: Clinical trials have demonstrated that initiating oral anti-diabetic drugs (OADs) significantly reduce glycated hemoglobin (HbA1c) levels. However, variability in lifestyle modifications and OAD adherence impact on their actual effect on glycemic control. Furthermore, evidence on dose adjustments and discontinuation of OAD on HbA1c is lacking. This study aims to use real-world data to determine the effect of OAD initiation, up-titration, down-titration, and discontinuation on HbA1c levels, among Asian patients managed in primary care.Methods: A retrospective cohort study over a 5-year period, from Jan 2015 to Dec 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of type 2 diabetes mellitus (T2DM) managed by a network of primary care clinics in Singapore. Nine OADs from five different classes (biguanides, sulphonyurea, dipeptidyl peptidase-4 [DPP-4] inhibitors, sodium-glucose cotransporter-2 [SGLT-2] inhibitors, and alpha-glucosidase inhibitors) were evaluated. Patients were grouped into "No OAD", "Non-titrators," and "Titrators" cohorts based on prescribing patterns. For the "Titrators" cohort, the various OAD titrations were identified. Subsequently, a descriptive analysis of HbA1c values before and after each titration was performed to compute a mean difference for each unique titration identified.Results: Among the cohort of 57,910 patients, 43,338 of them had at least one OAD titration, with a total of 76,990 pairs of HbA1c values associated with an OAD titration. There were a total of 206 unique OAD titrations. Overall, initiation of OADs resulted in a reduction of HbA1c by 3 to 12 mmol/mol (0.3 to 1.1%), respectively. These results were slightly lower than those reported in clinical trials of 6 to 14 mmol/mol (0.5 to 1.25%). The change of HbA1c levels due to up-titration, down-titration, and discontinuation were -1 to -8 mmol/mol (-0.1 to -0.7%), +1 to 7 mmol/mol (+0.1 to +0.6%), and +2 to 11 mmol/mol (+0.2 to +1.0%), respectively. The HbA1c lowering effect of initiating newer OADs, namely DPP-4 inhibitors and SGLT-2 inhibitors was 8 to 11 mmol/mol (0.7 to 0.9%) and 7 to 11 mmol/mol (0.6 to 1.0%), respectively.Conclusion: The real-world data on Asians with T2DM in this study show that the magnitudes of OAD initiation and dose titration are marginally lower than the results from clinical trials. During shared decision-making in selecting treatment options, the results enable physicians to communicate realistic expectation of the effect of oral medications on the glycemic control of their patients in primary care. [ABSTRACT FROM AUTHOR]

Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model's prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model.Methods: The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process.Results: The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy.Conclusions: Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Higher resistance rates of > 20% have been noted in Enterobacteriaceae urinary isolates towards ciprofloxacin and co-trimoxazole (C + C) in Singapore, compared with amoxicillin-clavulanate and nitrofurantoin (AC + N). This study examined if treatment failure varied between different antibiotics, given different resistant rates, for uncomplicated urinary tract infections (UTIs) managed in primary care. We also aimed to identify gaps for improvement in diagnosis, investigations, and management. Methods: A retrospective cohort study was conducted from 2019 to 2021 on female patients aged 18–50 with uncomplicated UTIs at 6 primary care clinics in Singapore. ORENUC classification was used to exclude complicated UTIs. Patients with uncomplicated UTIs empirically treated with amoxicillin-clavulanate, nitrofurantoin, ciprofloxacin or co-trimoxazole were followed-up for 28 days. Treatment failure was defined as re-attendance for symptoms and antibiotic re-prescription, or hospitalisation for UTI complications. After 2:1 propensity score matching in each group, modified Poisson regression and Cox proportional hazard regression accounting for matched data were used to determine risk and time to treatment failure. Results: 3194 of 4253 (75.1%) UTIs seen were uncomplicated, of which only 26% were diagnosed clinically. Urine cultures were conducted for 1094 (34.3%) uncomplicated UTIs, of which only 410 (37.5%) had bacterial growth. The most common organism found to cause uncomplicated UTIs was Escherichia coli (64.6%), with 92.6% and 99.4% of isolates sensitive to amoxicillin-clavulanate and nitrofurantoin respectively. Treatment failure occurred in 146 patients (4.57%). Among 1894 patients treated with AC + N matched to 947 patients treated with C + C, patients treated with C + C were 50% more likely to fail treatment (RR 1.49, 95% CI 1.10–2.01), with significantly higher risk of experiencing shorter time to failure (HR 1.61, 95% CI 1.12–2.33), compared to patients treated with AC + N. Conclusion: Treatment failure rate was lower for antibiotics with lower reported resistance rates (AC + N). We recommend treating uncomplicated UTIs in Singapore with amoxicillin-clavulanate or nitrofurantoin, based on current local antibiograms. Diagnosis, investigations and management of UTIs remained sub-optimal. Future studies should be based on updating antibiograms, highlighting its importance in guideline development. [ABSTRACT FROM AUTHOR]

Background: Measurement of basal metabolic rate (BMR) is suggested as a tool to estimate energy requirements. Therefore, BMR prediction equations have been developed in multiple populations because indirect calorimetry is not always feasible. However, there is a paucity of data on BMR measured in overweight and obese adults living in Asia and equations developed for this group of interest. The aim of this study was to develop a new BMR prediction equation for Chinese adults applicable for a large BMI range and compare it with commonly used prediction equations.Methods: Subjects were 121 men and 111 women (age: 21-67 years, BMI: 16-41 kg/m(2)). Height, weight, and BMR were measured. Continuous open-circuit indirect calorimetry using a ventilated hood system for 30 min was used to measure BMR. A regression equation was derived using stepwise regression and accuracy was compared to 6 existing equations (Harris-Benedict, Henry, Liu, Yang, Owen and Mifflin). Additionally, the newly derived equation was cross-validated in a separate group of 70 Chinese subjects (26 men and 44 women, age: 21-69 years, BMI: 17-39 kg/m(2)).Results: The equation developed from our data was: BMR (kJ/d) = 52.6 x weight (kg) + 828 x gender + 1960 (women = 0, men = 1; R(2) = 0.81). The accuracy rate (within 10 % accurate) was 78 % which compared well to Owen (70 %), Henry (67 %), Mifflin (67 %), Liu (58 %), Harris-Benedict (45 %) and Yang (37 %) for the whole range of BMI. For a BMI greater than 23, the Singapore equation reached an accuracy rate of 76 %. Cross-validation proved an accuracy rate of 80 %.Conclusions: To date, the newly developed Singapore equation is the most accurate BMR prediction equation in Chinese and is applicable for use in a large BMI range including those overweight and obese. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic is rapidly evolving and remains a major health challenge worldwide. With an increase in pregnant women with COVID-19 infection, we recognized an urgent need to set up a multidisciplinary taskforce to provide safe and holistic care for this group of women. In this review of practice in a tertiary hospital in Singapore, we discuss the key considerations in setting up an isolation maternity unit and our strategies for peripartum and postpartum care. Through teleconsultation, we involve these women and their families in the discussion of timing and mode of birth, disposition of babies after birth and safety of breastfeeding to enable them to make informed decisions and individualize their care. [ABSTRACT FROM AUTHOR]