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 by developing risk prediction models for readmissions for fluid overload in people living with diabetes. METHODS: Single-center retrospective cohort study of 1531 adults with diabetes and hospitalized for fluid overload, including 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) were independently associated with 90-day readmissions in individuals with diabetic kidney disease. Additionally, thiazide at discharge reduced 90-day readmission in diabetic kidney disease while 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 were 22.4% and 28.9%, respectively. The NRI for the clinico-psychosocial models to predict 30- and 90-day readmissions in diabetic kidney disease were 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. .

Hallux valgus with second metatarsalgia: Is second metatarsal Weil osteotomy necessary?

BACKGROUND: The role of concomitant Weil osteotomy to address second toe metatarsalgia during hallux valgus correction is unclear. We aimed to critically analyse outcomes of an additional Weil osteotomy versus isolated Scarf osteotomy.in the management of hallux valgus and second metatarsalgia. METHODS: Patients with second toe metatarsalgia who underwent first metatarsal Scarf osteotomy for hallux valgus were enrolled retrospectively. Demographics, radiographic measurements and functional outcomes were assessed at baseline, 6-months and 2-years postoperatively. Between-group significance was established with Fisher exact test, Chi-square or Mann-Whitney U test. Within-group changes from baseline were assessed with paired t-test and Wilcoxon signed-rank test. RESULTS: 48 feet (34 isolated Scarf, 14 concomitant Weil osteotomy) were included. Both cohorts demonstrated significant improvements across all measures of functional outcome. However, patients with additional Weil osteotomy reported poorer short-term outcomes. CONCLUSION: Superiority of additional Weil osteotomy versus isolated Scarf osteotomy in addressing second toe metatarsalgia or improving functional outcomes was not demonstrated.

Cryotherapy in extra-abdominal desmoid tumors: A systematic review and meta-analysis.

INTRODUCTION: Desmoid tumor is a locally-invasive neoplasm that causes significant morbidity. There is recent interest in cryotherapy for treatment of extra-abdominal desmoid tumors. This systematic review assesses evidence on safety and efficacy of cryotherapy in the treatment of extra-abdominal desmoid tumors. MATERIALS AND METHODS: The systematic review was conducted with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Literature search was performed using MEDLINE and the Cochrane Central Register of Controlled Trials. 9 full text papers were reviewed and meta-analysis was performed for measures of safety, efficacy and symptom relief. RESULTS: The estimated pooled proportion of major and minor complications was 4.2% (95% CI, 1.8-9.6; I 2 = 0%) and 10.2% (95% CI, 5.7-17.8; I 2 = 0%) respectively. The estimated pooled proportion of non-progressive disease rate of all studies was 85.8% (95% CI, 73.4-93.0; I 2 = 32.9%). The estimated progression free survival rate at 1 year was 84.5% (95% CI:74.6-95.8) and 78.0% at 3 years (95% CI: 63.8-95.3). As for pain control, the estimated pooled proportion of patients with decrease in visual analogue scale (VAS) > = 3 for those with VAS > = 3 before treatment for 2 studies was 87.5% (95% CI, 0.06-100; I 2 = 71.5%) while 37.5% to 96.9% of patients were reported to have experienced partial or complete symptom relief in the other studies. CONCLUSION: Cryotherapy is a safe and effective treatment modality for extra-abdominal desmoid tumors with efficacy similar to those treated with traditional strategies in the short to medium term.

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation.

BACKGROUND: Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE: The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS: The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS: The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS: Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

Establishment of the SingHealth Diabetes Registry.

PURPOSE: To describe the inception and structure of the SingHealth Diabetes Registry (SDR) as well as the methodology used to set up the registry. The SDR was established to facilitate systematic and standardized data collection for diabetes mellitus within Singapore Health Services (SingHealth), which is an Academic Medical Center (AMC) and Singapore's largest group of healthcare institutions. The diabetes casemix and outcome variables within the registry cohort are also provided. MATERIALS AND METHODS: The SDR is built from SingHealth's electronic medical records (EMR) and clinical databases. It covers all individuals aged 18 and above with diabetes mellitus, excluding those with pre-diabetes. Cases are annually ascertained using criteria that include diagnosis codes, prescription records and laboratory test records. Data collection of casemix and outcome variables for the period 2013 to 2019 is complete. RESULTS: The SDR stands at 208,102 ascertained individuals, distributed across 8 healthcare sites within the AMC. The cohort is broadly reflective of the local gender and ethnic compositions but has a high proportion of older individuals with a mean age of 65.8 ± 13.7 years. Majority (>99%) have type 2 diabetes mellitus, with multiple other comorbidities (hypertension 84.1%, hyperlipidemia 86.2%, established cardiovascular disease 34.1%). At present, majority of individuals are able to meet key process indicators and 52.7% have a mean HbA1c of <7% (53 mmol/mol). Areas of potential improvement include increasing eye and foot screening rates, as well as glycemic control for the 19.5% of individuals with mean HbA1c >8% (64 mmol/mol). CONCLUSION: The SDR is a large-scale, comprehensive, and representative diabetes registry that incorporates EMR data across the primary and hospital-based care continuum, in a major AMC in Singapore. The SDR has identified areas of improvement in diabetes processes and outcomes. It will support future quality assessment and improvements in diabetes care.