Trends in fluid overload-related hospitalisations among patients with diabetes mellitus The impact of chronic kidney disease.

Introduction: Fluid overload is a known complication in patients with diabetes mellitus, particularly those with cardiovascular and/or chronic kidney disease (CKD). This study investigates the impact of fluid overload on healthcare utilisation and its association with diabetes-related complications. Method: Electronic medical records from the SingHealth Diabetes Registry (2013-2022) were analysed. Hospitalisations due to fluid overload were identified using International Classification of Diseases, 10th Revision (ICD-10) discharge codes. Trends were examined using Joinpoint regression, and associations were assessed with generalised estimating equation models. Results: Over a period of 10 years, 259,607 individuals treated at primary care clinics and tertiary hospitals were studied. The incidence of fluid overload-related hospitalisations decreased from 2.99% (n=2778) in 2013 to 2.18% (n=2617) in 2017. However, this incidence increased from 2.42% (n=3091) in 2018 to 3.71% (n=5103) in 2022. The strongest associations for fluid overload-related hospitalisation were found with CKD stages G5 (odds ratio [OR] 6.61, 95% confidence interval [CI] 6.26-6.99), G4 (OR 5.55, 95% CI 5.26-5.86) and G3b (OR 3.18, 95% CI 3.02-3.35), as well as with ischaemic heart disease (OR 3.97, 95% CI 3.84-4.11), acute myocardial infarction (OR 3.07, 95% CI 2.97-3.18) and hypertension (OR 3.90, 95% CI 3.45-4.41). Additionally, the prevalence of stage G5 CKD among patients with fluid overload increased between 2018 and 2022. Conclusion: Our study revealed a significant increase in fluid overload-related hospitalisations and extended lengths of stay, likely driven by severe CKD. This underscores an urgent need for initiatives aimed at slowing CKD progression and reducing fluid overload-related hospitalisations in diabetes patients.

Sodium-Glucose Cotransporter-2 Inhibitors Use in Patients with Reduced Kidney Function Hospitalized for Fluid Overload and Heart Failure: An Observational Study.

INTRODUCTION: Sodium-glucose cotransporter-2 inhibitors (SGLT2is) are recommended in kidney disease and heart failure to reduce adverse clinical outcomes, but utilization can vary. To understand potential gaps in clinical practice and identify opportunities for improvement, we aimed to describe the prevalence and factors associated with SGLT2i prescription in patients with reduced kidney function hospitalized for fluid overload and/or heart failure. METHODS: Single-center observational study of patients with reduced kidney function (eGFR 20-59 mL/min/1.73 m2) hospitalized for fluid overload or heart failure between January 2022 and December 2023. Data were retrieved from electronic medical records. The outcome was SGLT2i prescription at discharge. Potential variables affecting SGLT2i prescription were identified during stakeholder engagement and evaluated using multivariable logistic regression. RESULTS: Among 2,543 patients, the median age was 79 (71, 86) years and admission eGFR was 38.7 (28.4, 49.4) mL/min/1.73 m2. SGLT2i was prescribed to 630 (24.8%) patients at discharge. SGLT2i prescription at discharge was independently associated with cardiovascular disease (OR 1.76, 95% CI: 1.31-2.35), diabetes (OR 1.59, 95% CI: 1.19-2.14), fluid overload or heart failure as the primary discharge diagnosis (OR 1.71, 95% CI: 1.29-2.28), SGLT2i pre-hospitalization (OR 104.91, 95% CI: 63.22-174.08), RAS blocker (OR 2.1, 95% CI: 1.65-2.89), and higher eGFR (OR 1.01, 95% CI: 1.003-1.02) at discharge; but inversely associated with older age (OR 0.97, 95% CI: 0.96-0.98). CONCLUSION: SGLT2i prescription at discharge was suboptimal among patients with reduced kidney function hospitalized for fluid overload and/or heart failure, especially in older age and more severe kidney disease. Additionally, cardiovascular disease, diabetes, primary discharge diagnosis of fluid overload or heart failure, prior SGLT2i use, and concurrent RAS blocker at discharge were independently associated with SGLT2i prescription at discharge. Interventions are needed to increase clinicians' knowledge and overcome clinical inertia to increase SGLT2i use in patients with fluid overload and heart failure.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Hospital Readmissions for Fluid Overload among Individuals with Diabetes and Diabetic Kidney Disease: Risk Factors and Multivariable Prediction Models.

AIMS: Hospital readmissions due to recurrent fluid overload in diabetes and diabetic kidney disease can be avoided with evidence-based interventions. We aimed to identify at-risk patients who can benefit from these interventions by developing risk prediction models for readmissions for fluid overload in people living with diabetes and diabetic kidney disease. METHODS: This was a single-center retrospective cohort study of 1,531 adults with diabetes and diabetic kidney disease hospitalized for fluid overload, congestive heart failure, pulmonary edema, and generalized edema between 2015 and 2017. The multivariable regression models for 30-day and 90-day readmission for fluid overload were compared with the LACE score for discrimination, calibration, sensitivity, specificity, and net reclassification index (NRI). RESULTS: Readmissions for fluid overload within 30 days and 90 days occurred in 8.6% and 17.2% of patients with diabetes, and 8.2% and 18.3% of patients with diabetic kidney disease, respectively. After adjusting for demographics, comorbidities, clinical parameters, and medications, a history of alcoholism (HR 3.85, 95% CI: 1.41-10.55) and prior hospitalization for fluid overload (HR 2.50, 95% CI: 1.26-4.96) were independently associated with 30-day readmission in patients with diabetic kidney disease, as well as in individuals with diabetes. Additionally, current smoking, absence of hypertension, and high-dose intravenous furosemide were also associated with 30-day readmission in individuals with diabetes. Prior hospitalization for fluid overload (HR 2.43, 95% CI: 1.50-3.94), cardiovascular disease (HR 1.44, 95% CI: 1.03-2.02), eGFR ≤45 mL/min/1.73 m2 (HR 1.39, 95% CI: 1.003-1.93) was independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide prescription at discharge reduced 90-day readmission in diabetic kidney disease, while the need for high-dose intravenous furosemide predicted 90-day readmission in diabetes. The clinical and clinico-psychological models for 90-day readmission in individuals with diabetes and diabetic kidney disease had better discrimination and calibration than the LACE score. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetes was 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease was 5.6% and 38.9%, respectively. CONCLUSION: The risk models can potentially be used to identify patients at risk of readmission for fluid overload for evidence-based interventions, such as patient education or transitional care programs to reduce preventable hospitalizations.

Early repeat hospitalization for fluid overload in individuals with cardiovascular disease and risks: a retrospective cohort study.

AIMS: Fluid overload is a common manifestation of cardiovascular and kidney disease and a leading cause of hospitalizations. To identify patients at risk of recurrent severe fluid overload, we evaluated the incidence and risk factors associated with early repeat hospitalization for fluid overload among individuals with cardiovascular disease and risks. METHODS: Single-center retrospective cohort study of 3423 consecutive adults with an index hospitalization for fluid overload between January 2015 and December 2017 and had cardiovascular risks (older age, diabetes mellitus, hypertension, dyslipidemia, kidney disease, known cardiovascular disease), but excluded if lost to follow-up or eGFR < 15 ml/min/1.73 m2. The outcome was early repeat hospitalization for fluid overload within 30 days of discharge. RESULTS: The mean age was 73.9 ± 11.6 years and eGFR was 54.1 ± 24.6 ml/min/1.73 m2 at index hospitalization. Early repeat hospitalization for fluid overload occurred in 291 patients (8.5%). After adjusting for demographics, comorbidities, clinical parameters during index hospitalization and medications at discharge, cardiovascular disease (adjusted odds ratio, OR 1.66, 95% CI 1.27-2.17), prior hospitalization for fluid overload within 3 months (OR 2.52, 95% CI 1.17-5.44), prior hospitalization for any cause in within 6 months (OR 1.33, 95% CI 1.02-1.73) and intravenous furosemide use (OR 1.58, 95% CI 1.10-2.28) were associated with early repeat hospitalization for fluid overload. Higher systolic BP on admission (OR 0.992, 95% 0.986-0.998) and diuretic at discharge (OR 0.50, 95% CI 0.26-0.98) reduced early hospitalization for fluid overload. CONCLUSION: Patients at-risk of early repeat hospitalization for fluid overload may be identified using these risk factors for targeted interventions.

A deep learning system for retinal vessel calibre improves cardiovascular risk prediction in Asians with chronic kidney disease.

Backgraund: Cardiovascular disease (CVD) and mortality is elevated in chronic kidney disease (CKD). Retinal vessel calibre in retinal photographs is associated with cardiovascular risk and automated measurements may aid CVD risk prediction. Methods: Retrospective cohort study of 860 Chinese, Malay and Indian participants aged 40-80 years with CKD [estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2] who attended the baseline visit (2004-2011) of the Singapore Epidemiology of Eye Diseases Study. Retinal vessel calibre measurements were obtained by a deep learning system (DLS). Incident CVD [non-fatal acute myocardial infarction (MI) and stroke, and death due to MI, stroke and other CVD] in those who were free of CVD at baseline was ascertained until 31 December 2019. Risk factors (established, kidney, and retinal features) were examined using Cox proportional hazards regression models. Model performance was assessed for discrimination, fit, and net reclassification improvement (NRI). Results: Incident CVD occurred in 289 (33.6%) over mean follow-up of 9.3 (4.3) years. After adjusting for established cardiovascular risk factors, eGFR [adjusted HR 0.98 (95% CI: 0.97-0.99)] and retinal arteriolar narrowing [adjusted HR 1.40 (95% CI: 1.17-1.68)], but not venular dilation, were independent predictors for CVD in CKD. The addition of eGFR and retinal features to established cardiovascular risk factors improved model discrimination with significantly better fit and better risk prediction according to the low (<15%), intermediate (15-29.9%), and high (30% or more) risk categories (NRI 5.8%), and with higher risk thresholds (NRI 12.7%). Conclusions: Retinal vessel calibre measurements by DLS were significantly associated with incident CVD independent of established CVD risk factors. Addition of kidney function and retinal vessel calibre parameters may improve CVD risk prediction among Asians with CKD.

Circulating Metabolites and Cardiovascular Disease in Asians with Chronic Kidney Disease.

INTRODUCTION: Chronic kidney disease (CKD) is a growing public health problem, with significant burden of cardiovascular disease and mortality. The risk of cardiovascular disease in CKD is elevated beyond that predicted by traditional cardiovascular risk factors, suggesting that other factors may account for this increased risk. Through metabolic profiling, this study aimed to investigate the associations between serum metabolites and prevalent cardiovascular disease in Asian patients with CKD to provide insights into the complex interactions between metabolism, cardiovascular disease and CKD. METHODS: This was a single-center cross-sectional study of 1,122 individuals from three ethnic cohorts in the population-based Singapore Epidemiology of Eye Disease (SEED) study (153 Chinese, 262 Indians, and 707 Malays) aged 40-80 years with CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2). Nuclear magnetic resonance spectroscopy was used to quantify 228 metabolites from the participants' serum or plasma. Prevalent cardiovascular disease was defined as self-reported myocardial infarction, angina, or stroke. Multivariate logistic regression identified metabolites independently associated with cardiovascular disease in each ethnic cohort. Metabolites with the same direction of association with cardiovascular disease in all three cohorts were selected and subjected to meta-analysis. RESULTS: Cardiovascular disease was present in 275 (24.5%). Participants with cardiovascular disease tend to be male; of older age; with hypertension, hyperlipidemia, and diabetes; with lower systolic and diastolic blood pressure (BP); lower high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol than those without cardiovascular disease. After adjusting for age, sex, systolic BP, diabetes, total cholesterol, and HDL cholesterol, 10 lipoprotein subclass ratios and 6 other metabolites were significantly associated with prevalent cardiovascular disease in at least one cohort. Meta-analysis with Bonferroni correction for multiple comparisons found that lower tyrosine, leucine, and valine concentrations and lower cholesteryl esters to total lipid ratio in intermediate-density lipoprotein (IDL) were associated with cardiovascular disease. CONCLUSION: In Chinese, Indian, and Malay participants with CKD, prevalent cardiovascular disease was associated with tyrosine, leucine, valine, and cholesteryl esters to total lipid ratios in IDL. Increased cardiovascular risk in CKD patients may be contributed by altered amino acid and lipoprotein metabolism. The presence of CKD and ethnic differences may affect interactions between metabolites in health and disease, hence greater understanding will allow us to better risk stratify patients, and also individualize care with consideration of ethnic disparities.

Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes.

OBJECTIVE: To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. MATERIALS AND METHODS: We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). RESULTS: In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. DISCUSSION AND CONCLUSION: There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.

Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of Asian adults.

Background: Machine learning (ML) techniques improve disease prediction by identifying the most relevant features in multidimensional data. We compared the accuracy of ML algorithms for predicting incident diabetic kidney disease (DKD). Methods: We utilized longitudinal data from 1365 Chinese, Malay, and Indian participants aged 40-80 y with diabetes but free of DKD who participated in the baseline and 6-year follow-up visit of the Singapore Epidemiology of Eye Diseases Study (2004-2017). Incident DKD (11.9%) was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 with at least 25% decrease in eGFR at follow-up from baseline. A total of 339 features, including participant characteristics, retinal imaging, and genetic and blood metabolites, were used as predictors. Performances of several ML models were compared to each other and to logistic regression (LR) model based on established features of DKD (age, sex, ethnicity, duration of diabetes, systolic blood pressure, HbA1c, and body mass index) using area under the receiver operating characteristic curve (AUC). Results: ML model Elastic Net (EN) had the best AUC (95% CI) of 0.851 (0.847-0.856), which was 7.0% relatively higher than by LR 0.795 (0.790-0.801). Sensitivity and specificity of EN were 88.2 and 65.9% vs. 73.0 and 72.8% by LR. The top 15 predictors included age, ethnicity, antidiabetic medication, hypertension, diabetic retinopathy, systolic blood pressure, HbA1c, eGFR, and metabolites related to lipids, lipoproteins, fatty acids, and ketone bodies. Conclusions: Our results showed that ML, together with feature selection, improves prediction accuracy of DKD risk in an asymptomatic stable population and identifies novel risk factors, including metabolites. Funding: This study was supported by the National Medical Research Council, NMRC/OFLCG/001/2017 and NMRC/HCSAINV/MOH-001019-00. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Readability, Understandability, and Actionability of Online Cardiovascular Risk Assessment Tools and Patient Educational Material: A Systematic Review.

Introduction: Individuals with kidney diseases have increased risk of cardiovascular disease and death. Online cardiovascular risk assessment tools can educate patients on risks and modifiable factors. Since patients have variable health literacy, we evaluated the readability, understandability, and actionability of publicly available online cardiovascular risk assessment tools. Methods: We systematically searched, reviewed, characterized, and assessed English-language cardiovascular risk assessment tools online for readability (Flesch-Kincaid Grade Level [FKGL] score), understandability, and actionability (Patient Education Materials Assessment Tool for printable materials [PEMAT-P]). Results: After screening 969 websites, 69 websites employing 76 risk tools were included. The most frequently used tools were the Framingham Risk Score (n = 13) and the Atherosclerotic Cardiovascular Disease score (n = 12). Most tools were intended for the general population and estimated the 10-year incident cardiovascular risk. Patient education was provided in the form of targets for blood pressure (n = 17), lipids (n = 15), or glucose (n = 5); and advice regarding diet (n = 18), exercise (n = 19), and smoking cessation (n = 20). The median FKGL, PEMAT understandability, and actionability scores were 6.2 (4.7, 8.5), 84.6% (76.9%, 89.2%), and 60% (40%, 60%), respectively. Conclusion: The online cardiovascular risk tools were generally easy to read and understand, but only a third provided education on risk modification. Judicious selection of an online cardiovascular risk assessment tool may help patients in self-management.

Impact of focal segmental glomerulosclerosis over the past decade.

OBJECTIVE: This is a study on the demographics and clinical outcomes including the response to therapy of patients with focal segmental glomerulosclerosis (FSGS) over the past decade. MATERIALS AND METHODS: All histologically proven FSGS cases diagnosed between 2008 and 2018 were analyzed for their clinical, laboratory, and histological characteristics including treatment that could influence the disease progression and renal outcome of these patients. We used the Columbia Classification for FSGS for the renal biopsy. RESULTS: There were two subgroups of FSGS patients; those with nephrotic syndrome and those without nephrotic syndrome. Patients with FSGS with non-nephrotic syndrome had poorer survival rates compared to the nephrotic group. For those without nephrotic syndrome, the indices responsible for progression involved more tubular and blood vessel lesions in addition to glomerular pathology compared to those with nephrotic syndrome. Patients with FSGS with nephrotic syndrome responded to immunosuppressants more favorably compared to the non-nephrotic group, though both groups responded with decreasing proteinuria. The nephrotic group had a better 10-year long-term survival rate of 92 vs. 72% for the non-nephrotic group (log-rank 0.002). The 10-year survival for the whole group of FSGS patients was 64%. CONCLUSION: Our data suggest that in FSGS, one of the significant components of the disease is the vascular and tubular damage, apart from the underlying glomerular pathology, resulting in varying responses to therapy, and the difference is reflected in inherently poorer response to immunosuppressant therapy in those without nephrotic syndrome as opposed to those with nephrotic syndrome, who responded to immunosuppressant therapy (IST) with stabilization of renal function and had less blood vessel and tubular lesions.