Individual treatment effects of sodium-glucose co-transporter-2 inhibitors on the risk of chronic kidney disease in patients with type 2 diabetes: A counterfactual prediction model based on real-world data.

AIM: To estimate individual treatment effects (ITEs) of sodium-glucose co-transporter-2 inhibitors (SGLT2is) on lowering the risk of developing chronic kidney disease (CKD) in patients with type 2 diabetes (T2D) and to identify those most probable to benefit from treatment. METHODS: This study followed a T2D cohort from Ramathibodi Hospital, Thailand, from 2015 to 2022. A counterfactual model was constructed to predict factual and counterfactual risks of CKD if patients did/did not receive SGLT2is. ITEs were estimated by subtracting the factual risk from the counterfactual risk of CKD. RESULTS: There were 1619 and 15 879 patients included in the SGLT2i and non-SGLT2i groups, respectively. The estimated ITEs varied from -1.19% to -17.51% with a median of -4.49%, that is, 50% of patients had a 4.49% or greater lower CKD risk if they received an SGLT2i. Patients who gained the greatest benefit from SGLT2is were more probable to be male, aged at least 60 years, with a history of diabetes duration of at least 3 months, hypertension, peripheral arterial disease, diabetic retinopathy and low high-density lipoprotein cholesterol. CONCLUSIONS: Our prediction model provides individualized information that helps target T2D patients who may benefit more from SGLT2is. This could help clinical decision making and implementation of personalized medicine in clinical practice, especially in resource-limited settings.

Efficacy of migraine prophylaxis treatments for treatment-naïve patients and those with prior treatment failure: a protocol for systematic review and network meta-analysis of randomised controlled trials.

INTRODUCTION: Migraine headache is a significant health problem affecting patients' psychological well-being and quality of life. Several network meta-analyses (NMAs) have compared the efficacy of migraine prophylaxis medications. However, some have focused exclusively on oral medications, while others were limited to injectable medications. Moreover, none of these NMAs conducted a stratified analysis between treatment-naïve patients and those with prior treatment failure. Therefore, this systematic review and NMA will compare the efficacy among all treatments for migraine prophylaxis, stratified by the treatment status of patients (ie, treatment-naïve and previous treatment failure). METHODS AND ANALYSIS: Randomised-controlled trials that included patients with chronic or episodic migraine, assessed the efficacy of oral or injectable treatments for migraine prophylaxis and measured the outcomes as monthly migraine day, monthly headache day, migraine-related disability, health-related quality of life or adverse drug events will be eligible for inclusion in this review. Relevant studies will be searched from Medline, Scopus, the US National Institutes of Health Register, and the World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP) databases since inception through 15 August 2023. Risk of bias assessment will be performed using a revised tool for assessing the risk of bias in randomised trials. Two-stage NMA will be applied to compare relative treatment effects among all treatments of migraine prophylaxis. Surface under the cumulative ranking curve will be applied to estimate and rank the probability to be the best treatment. Consistency assumption will be assessed using a design-by-treatment interaction model. Publication bias will be assessed by comparison-adjusted funnel plot. All analyses will be stratified according to patients' status (ie, treatment-naïve and prior treatment failure). ETHICS AND DISSEMINATION: This study is a systematic review protocol collecting data from published literature and does not require approval from an institutional review board. Results from this systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42020171843.

External validation and revision of Penn incisional hernia prediction model: A large-scale retrospective cohort of abdominal operations.

BACKGROUND: Incisional hernia (IH) manifests in 10%-15% of abdominal surgeries and patients at elevated risk of this complication should be identified for prophylactic intervention. This study aimed to externally validate the Penn hernia risk calculator. METHODS: The Ramathibodi abdominal surgery cohort was constructed by linking relevant hospital databases from 2010 to 2021. Penn hernia risk scores were calculated according to the original model which was externally validated using a seven-step approach. An updated model which included four additional predictor variables (i.e., age, immunosuppressive medication, ostomy reversal, and transfusion) added to those of the three original predictors (i.e., body mass index, chronic liver disease, and open surgery) was also evaluated. The area under the receiver operating characteristic curve (AUC) was estimated, and calibration performance was compared using the Hosmer-Lemeshow goodness-of-fit method for the observed/expected (O/E) ratio. RESULTS: A total of 12,155 abdominal operations were assessed. The original Penn model yielded fair discrimination with an AUC (95% confidence interval (CI)) of 0.645 (0.607, 0.683). The updated model that included the additional predictor variables achieved an acceptable AUC (95% CI) of 0.733 (0.698, 0.768) with the O/E ratio of 0.968 (0.848, 1.088). CONCLUSION: The updated model achieved improved discrimination and calibration performance, and should be considered for the identification of high-risk patients for further hernia prevention strategy.

Delayed versus primary closure to minimize risk of surgical-site infection for complicated appendicitis: A secondary analysis of a randomized trial using counterfactual prediction modeling.

OBJECTIVE: To evaluate the risk of surgical site infection (SSI) following complicated appendectomy in individual patients receiving delayed primary closure (DPC) versus primary closure (PC) after adjustment for individual risk factors. DESIGN: Secondary analysis of randomized controlled trial (RCT) with prediction model. SETTING: Referral centers across Thailand. PARTICIPANTS: Adult patients who underwent appendectomy via a lower-right-quadrant abdominal incision due to complicated appendicitis. METHODS: A secondary analysis of a published RCT was performed applying a counterfactual prediction model considering interventions (PC vs DPC) and other significant predictors. A multivariable logistic regression was applied, and a likelihood-ratio test was used to select significant predictors to retain in a final model. Factual versus counterfactual SSI risks for individual patients along with individual treatment effect (iTE) were estimated. RESULTS: In total, 546 patients (271 PC vs 275 DPC) were included in the analysis. The individualized prediction model consisted of allocated intervention, diabetes, type of complicated appendicitis, fecal contamination, and incision length. The iTE varied between 0.4% and 7% for PC compared to DPC; ∼38.1% of patients would have ≥2.1% lower SSI risk following PC compared to DPC. The greatest risk reduction was identified in diabetes with ruptured appendicitis, fecal contamination, and incision length of 10 cm, where SSI risks were 47.1% and 54.1% for PC and DPC, respectively. CONCLUSIONS: In this secondary analysis, we found that most patients benefited from early PC versus DPC. Findings may be used to inform SSI prevention strategies for patients with complicated appendicitis.

Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches.

BACKGROUND: Severe periodontitis affects 26% of Thai adults and 11.2% of adults globally and is characterized by the loss of alveolar bone height. Full-mouth examination by periodontal probing is the gold standard for diagnosis but is time- and resource-intensive. A screening model to identify those at high risk of severe periodontitis would offer a targeted approach and aid in reducing the workload for dentists. While statistical modelling by a logistic regression is commonly applied, optimal performance depends on feature selections and engineering. Machine learning has been recently gaining favor given its potential discriminatory power and ability to deal with multiway interactions without the requirements of linear assumptions. OBJECTIVE: We aim to compare the performance of screening models developed using statistical and machine learning approaches for the risk prediction of severe periodontitis. METHODS: This study used data from the prospective Electricity Generating Authority of Thailand cohort. Dental examinations were performed for the 2008 and 2013 surveys. Oral examinations (ie, number of teeth and oral hygiene index and plaque scores), periodontal pocket depth, and gingival recession were performed by dentists. The outcome of interest was severe periodontitis diagnosed by the Centre for Disease Control-American Academy of Periodontology, defined as 2 or more interproximal sites with a clinical attachment level ≥6 mm (on different teeth) and 1 or more interproximal sites with a periodontal pocket depth ≥5 mm. Risk prediction models were developed using mixed-effects logistic regression (MELR), recurrent neural network, mixed-effects support vector machine, and mixed-effects decision tree models. A total of 21 features were considered as predictive features, including 4 demographic characteristics, 2 physical examinations, 4 underlying diseases, 1 medication, 2 risk behaviors, 2 oral features, and 6 laboratory features. RESULTS: A total of 3883 observations from 2086 participants were split into development (n=3112, 80.1%) and validation (n=771, 19.9%) sets with prevalences of periodontitis of 34.4% (n=1070) and 34.1% (n=263), respectively. The final MELR model contained 6 features (gender, education, smoking, diabetes mellitus, number of teeth, and plaque score) with an area under the curve (AUC) of 0.983 (95% CI 0.977-0.989) and positive likelihood ratio (LR+) of 11.9 (95% CI 8.8-16.3). Machine learning yielded lower performance than the MELR model, with AUC (95% CI) and LR+ (95% CI) values of 0.712 (0.669-0.754) and 2.1 (1.8-2.6), respectively, for the recurrent neural network model; 0.698 (0.681-0.734) and 2.1 (1.7-2.6), respectively, for the mixed-effects support vector machine model; and 0.662 (0.621-0.702) and 2.4 (1.9-3.0), respectively, for the mixed-effects decision tree model. CONCLUSIONS: The MELR model might be more useful than machine learning for large-scale screening to identify those at high risk of severe periodontitis for periodontal evaluation. External validation using data from other centers is required to evaluate the generalizability of the model.

Proteomic analysis investigating kidney transplantation outcomes- a scoping review.

BACKGROUND: Kidney transplantation is the optimal treatment option for most patients with end-stage kidney disease given the significantly lower morbidity and mortality rates compared to remaining on dialysis. Rejection and graft failure remain common in transplant recipients with limited improvement in long-term transplant outcomes despite therapeutic advances. There is an unmet need in the development of non-invasive biomarkers that specifically monitor graft function and predict transplant pathologies that affect outcomes. Despite the potential of proteomic investigatory approaches, up to now, no candidate biomarkers of sufficient sensitivity or specificity have translated into clinical use. The aim of this review was to collate and summarise protein findings and protein pathways implicated in the literature to date, and potentially flag putative biomarkers worth validating in independent patient cohorts. METHODS: This review followed the Joanna Briggs' Institute Methodology for a scoping review. MedlineALL, Embase, Web of Science Core Collection, Scopus and Google Scholar databases were searched from inception until December 2022. Abstract and full text review were undertaken independently by two reviewers. Data was collated using a pre-designed data extraction tool. RESULTS: One hundred one articles met the inclusion criteria. The majority were single-centre retrospective studies of small sample size. Mass spectrometry was the most used technique to evaluate differentially expressed proteins between diagnostic groups and studies identified various candidate biomarkers such as immune or structural proteins. DISCUSSION: Putative immune or structural protein candidate biomarkers have been identified using proteomic techniques in multiple sample types including urine, serum and fluid used to perfuse donor kidneys. The most consistent findings implicated proteins associated with tubular dysfunction and immunological regulatory pathways such as leukocyte trafficking. However, clinical translation and adoption of candidate biomarkers is limited, and these will require comprehensive evaluation in larger prospective, multicentre trials.

Efficacy of Time-Restricted Eating and Behavioral Economic Intervention in Reducing Fasting Plasma Glucose, HbA1c, and Cardiometabolic Risk Factors in Patients with Impaired Fasting Glucose: A Randomized Controlled Trial.

This randomized controlled trial is aimed at assessing the efficacy of combining time-restricted eating (TRE) with behavioral economic (BE) interventions and comparing it to TRE alone and to the usual care for reducing fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), and other cardiometabolic risk factors among patients with impaired fasting glucose (IFG). Seventy-two IFG patients aged 18-65 years were randomly allocated for TRE with BE interventions (26 patients), TRE alone (24 patients), or usual care (22 patients). Mean FPG, HbA1c, and other cardiometabolic risk factors among the three groups were compared using a mixed-effect linear regression analysis. Mean body weight, FPG, HbA1c, fasting insulin, and lipid profiles did not significantly differ among the three groups. When considering only patients who were able to comply with the TRE protocol, the TRE group showed significantly lower mean FPG, HbA1c, and fasting insulin levels compared to the usual care group. Our results did not show significant differences in body weight, blood sugar, fasting insulin, or lipid profiles between TRE plus BE interventions, TRE alone, and usual care groups. However, TRE might be an effective intervention in lowering blood sugar levels for IFG patients who were able to adhere to the TRE protocol.

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.

Optimizing the Yield of Abnormal Preoperative Chest Radiographs in Elective Non-cardiothoracic Surgery: Development of a Risk Prediction Score and External Validation.

BACKGROUND: Guideline recommendations for preoperative chest radiographs vary to the extent that individual patient benefit is unclear. We developed and validated a prediction score for abnormal preoperative chest radiographs in adult patients undergoing elective non-cardiothoracic surgery. METHODS: Our prospective observational study recruited 703 adult patients who underwent elective non-cardiothoracic surgery at Ramathibodi Hospital. We developed a risk prediction score for abnormal preoperative chest radiographs with external validation using data from 411 patients recruited from Thammasat University Hospital. The discriminative performance was assessed by receiver operating curve analysis. In addition, we assessed the contribution of abnormal chest radiographs to perioperative management. RESULTS: Abnormal preoperative chest radiographs were found in 19.5% of the 703 patients. Age, pulmonary disease, cardiac disease, and diabetes were significant factors. The model showed good performance with a C-statistics of 0.739 (95% CI, 0.691-0.786). We classified patients into four groups based on risk scores. The posttest probabilities in the intermediate-, intermediate-high-, and high-risk groups were 33.2%, 59.8%, and 75.7%, respectively. The model fitted well with the external validation data with a C statistic of 0.731 (95% CI, 0.674-0.789). One (0.4%) abnormal chest radiograph from the low-risk group and three (2.4%) abnormal chest radiographs from the intermediate-to-high-risk group had a major impact on perioperative management. CONCLUSIONS: Four predictors including age, pulmonary disease, cardiac disease, and diabetes were associated with abnormal preoperative chest radiographs. Our risk score demonstrated good performance and may help identify patients at higher risk of chest abnormalities.

Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.

BACKGROUND: Stroke has multiple modifiable and nonmodifiable risk factors and represents a leading cause of death globally. Understanding the complex interplay of stroke risk factors is thus not only a scientific necessity but a critical step toward improving global health outcomes. OBJECTIVE: We aim to assess the performance of explainable machine learning models in predicting stroke risk factors using real-world cohort data by comparing explainable machine learning models with conventional statistical methods. METHODS: This retrospective cohort included high-risk patients from Ramathibodi Hospital in Thailand between January 2010 and December 2020. We compared the performance and explainability of logistic regression (LR), Cox proportional hazard, Bayesian network (BN), tree-augmented Naïve Bayes (TAN), extreme gradient boosting (XGBoost), and explainable boosting machine (EBM) models. We used multiple imputation by chained equations for missing data and discretized continuous variables as needed. Models were evaluated using C-statistics and F1-scores. RESULTS: Out of 275,247 high-risk patients, 9659 (3.5%) experienced a stroke. XGBoost demonstrated the highest performance with a C-statistic of 0.89 and an F1-score of 0.80 followed by EBM and TAN with C-statistics of 0.87 and 0.83, respectively; LR and BN had similar C-statistics of 0.80. Significant factors associated with stroke included atrial fibrillation (AF), hypertension (HT), antiplatelets, HDL, and age. AF, HT, and antihypertensive medication were common significant factors across most models, with AF being the strongest factor in LR, XGBoost, BN, and TAN models. CONCLUSIONS: Our study developed stroke prediction models to identify crucial predictive factors such as AF, HT, or systolic blood pressure or antihypertensive medication, anticoagulant medication, HDL, age, and statin use in high-risk patients. The explainable XGBoost was the best model in predicting stroke risk, followed by EBM.

New anti-diabetic agents for the treatment of non-alcoholic fatty liver disease: a systematic review and network meta-analysis of randomized controlled trials.

Objectives: This network meta-analysis aims to compare the efficacy and safety of new anti-diabetic medications for the treatment of non-alcoholic fatty liver disease (NAFLD). Materials and methods: PubMed and Scopus were searched from inception to 27th March 2022 to identify all randomized controlled trials (RCTs) in NAFLD patients. Outcomes included reductions in intrahepatic steatosis (IHS) and liver enzyme levels. The efficacy and safety of DPP-4 inhibitors, GLP-1 agonists, SGLT-2 inhibitors, and other therapies were indirectly compared using a NMA approach. Unstandardized mean difference (USMD) with 95% confidence intervals (CI) were calculated. Results: 2,252 patients from 31 RCTs were included. "Add-on" GLP-1 agonists with standard of care (SoC) treatment showed significantly reduced IHS compared to SoC alone [USMD (95%CI) -3.93% (-6.54%, -1.33%)]. Surface under the cumulative ranking curve (SUCRA) identified GLP-1 receptor agonists with the highest probability to reduce IHS (SUCRA 88.5%), followed by DPP-4 inhibitors (SUCRA 69.6%) and pioglitazone (SUCRA 62.2%). "Add-on" GLP-1 receptor agonists were also the most effective treatment for reducing liver enzyme levels; AST [USMD of -5.04 (-8.46, -1.62)], ALT [USMD of -9.84 (-16.84, -2.85)] and GGT [USMD of -15.53 (-22.09, -8.97)] compared to SoC alone. However, GLP-1 agonists were most likely to be associated with an adverse event compared to other interventions. Conclusion: GLP-1 agonists may represent the most promising anti-diabetic treatment to reduce hepatic steatosis and liver enzyme activity in T2DM and NAFLD patients. Nevertheless, longer-term studies are required to determine whether this delays progression of liver cirrhosis in patients with NAFLD and T2DM. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021259336.1.

Evaluation of transitions from early hypertension to hypertensive chronic kidney disease, coronary artery disease, stroke and mortality: a Thai real-world data cohort.

Objective: Systemic arterial hypertension (HT) is a major modifiable risk factor for cardiovascular disease (CVDs), associated with all-cause death (ACD). Understanding its progression from the early state to late complications should lead to more timely intensification of treatment. This study aimed to construct a real-world cohort profile of HT and to estimate transition probabilities from the uncomplicated state to any of these long-term complications; chronic kidney disease (CKD), coronary artery disease (CAD), stroke, and ACD. Methods: This real-world cohort study used routine clinical practice data for all adult patients diagnosed with HT in the Ramathibodi Hospital, Thailand from 2010 to 2022. A multi-state model was developed based on the following: state 1-uncomplicated HT, 2-CKD, 3-CAD, 4-stroke, and 5-ACD. Transition probabilities were estimated using Kaplan-Meier method. Results: A total of 144,149 patients were initially classified as having uncomplicated HT. The transition probabilities (95% CI) from the initial state to CKD, CAD, stroke, and ACD at 10-years were 19.6% (19.3%, 20.0%), 18.2% (17.9%, 18.6%), 7.4% (7.1%, 7.6%), and 1.7% (1.5%, 1.8%), respectively. Once in the intermediate-states of CKD, CAD, and stroke, 10-year transition probabilities to death were 7.5% (6.8%, 8.4%), 9.0% (8.2%, 9.9%), and 10.8% (9.3%, 12.5%). Conclusions: In this 13-year cohort, CKD was observed as the most common complication, followed by CAD and stroke. Among these, stroke carried the highest risk of ACD, followed by CAD and CKD. These findings provide improved understanding of disease progression to guide appropriate prevention measures. Further investigations of prognostic factors and treatment effectiveness are warranted.

An updated meta-analysis of effects of curcumin on metabolic dysfunction-associated fatty liver disease based on available evidence from Iran and Thailand.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common cause of chronic liver disease and can progress to nonalcoholic steatohepatitis and cirrhosis. This study aims to summarize the evidence for the effects of curcumin on MAFLD progression. Studies were identified from Medline and Scopus databases until April 2022. Systematic reviews and meta-analyses (SRMA) and randomized controlled trials (RCT) were selected based on pre-specified criteria. Three reviewers independently extracted data and assessed quality of included studies. Of the 427 identified records, 6 SRMAs and 16 RCTs were included in the analysis. Very high overlap was observed among SRMAs with corrected covered area of 21.9%. From an updated meta-analysis, curcumin demonstrated significant improvement in aspartate and alanine aminotransferase with pooled mean difference [95% confidence interval (CI)] of -3.90 (-5.97, -1.82) and -5.61 (-9.37, -1.85) units/L, respectively. Resolution and improvement of hepatic steatosis was higher in curcumin than control group with pooled relative risk (95% CI) of 3.53 (2.01, 6.22) and 3.41 (1.36, 8.56), respectively. Curcumin supplementation also led to lower fasting blood sugar, body mass index, and total cholesterol. Further trials should be conducted to assess the effect of curcumin on liver histology, especially regarding non-invasive hepatic fibrosis and steatosis.

Efficacy and safety of monoclonal antibody therapy in patients with neuromyelitis optica spectrum disorder: A systematic review and network meta-analysis.

Introduction: Neuromyelitis optica spectrum disorder (NMOSD) is a devastating inflammatory CNS demyelinating disease. Two groups of monoclonal antibodies (mAbs) are used to prevent disease relapse, i.e., Food and Drug Administration (FDA)-approved mAbs (e.g., eculizumab satralizumab, inebilizumab), and off-label mAb drugs (e.g., rituximab and tocilizumab). The FDA-approved mAbs have high efficacy but more expensive compared to the off-labels, and thus are less accessible. This systematic review and network meta-analysis (NMA) was to assess the efficacy and safety of both classes of mAbs compared to the current standard treatments. Methods: Systematically searches were conducted in MEDLINE and SCOPUS from inception until July 2021. Randomized-controlled trials (RCTs) were eligible if they compared any pair of treatments (mAbs, immunosuppressive drugs, or placebo) in adult patients with NMOSD. Studies with AQP4-IgG positive or negative were used in the analysis. Probability of relapse and time to event were extracted from the Kaplan-Meier curves using Digitizer. These data were then converted into individual patient time-to-event data. A one-stage mixed-effect survival model was applied to estimate the median time to relapse and relative treatment effects using hazard ratios (HR). Two-stage NMA was used to determine post-treatment annualized relapse rate (ARR), expanded disability status score (EDSS) change, and serious adverse events (SAE). Risk of bias was assessed using the revised cochrane risk of bias tool. Results: A total of 7 RCTs with 776 patients were eligible in the NMA. Five of the seven studies were rated low risk of bias. Both FDA-approved and off-label mAbs showed significantly lower risk of relapse than standard treatments, with HR (95% CI) of 0.13 (0.07, 0.24) and 0.16 (0.07, 0.37) respectively. In addition, the FDA-approved mAbs had 20% lower risk of relapse than the off-label mAbs, but this did not reach statistical significance. The ARRs were also lower in FDA-approved and off-label mAbs than the standard treatments with the mean-difference of-0.27 (-0.37,-0.16) and-0.31(-0.46,-0.16), respectively. Conclusion: The off-label mAbs may be used as the first-line treatment for improving clinical outcomes including disease relapse, ARR, and SAEs for NMOSD in countries where resources and accessibility of the FDA-approved mAbs are limited. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=283424, identifier: CRD42021283424.

Clinical effectiveness of second-line antihyperglycemic drugs on major adverse cardiovascular events: An emulation of a target trial.

Introduction: The cardiovascular benefits of multiple antihyperglycemic drugs as add-on therapies to metformin in the real-practice are unclear. This study aimed to directly compare major adverse cardiovascular events (CVE) associated with these multiple drugs. Methods: An emulation of a target trial was conducted using a retrospective-cohort data of type 2 diabetes mellitus (T2DM) prescribed with second-line drugs on top of metformin, including sodium-glucose cotransporter 2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP4i), thiazolidinediones (TZD) and sulfonylureas (SUs). We applied inverse probability weighting and regression adjustment using intention-to-treat (ITT), per-protocol analysis (PPA) and modified ITT. Average treatment effects (ATE) were estimated using SUs as the reference. Results and Discussion: Among 25,498 patients with T2DM, 17,586 (69.0%), 3,261 (12.8%), 4,399 (17.3%), and 252 (1.0%) received SUs, TZD, DPP4i, and SGLT2i. Median follow-up time was 3.56 (1.36-7.00) years. CVE was identified in 963 patients. The ITT and modified ITT approaches showed similar results; the ATE (i.e., the difference of CVE risks) for SGLT2i, TZD, and DPP4i compared to SUs were -0.020(-0.040, -0.0002), -0.010(-0.017, -0.003), and -0.004(-0.010, 0.002), respectively, indicating 2% and 1% significant absolute risk reduction in CVE in SGLT2i and TZD compared to SUs. These corresponding effects were also significant in the PPA with ATEs of -0.045(-0.060, -0.031), -0.015(-0.026, -0.004), and -0.012(-0.020, -0.004). In addition, SGLT2i had 3.3% significant absolute risk reduction in CVE relative to DPP4i. Our study demonstrated benefits of SGLT2i and TZD in reducing CVE in T2DM patients compared to SUs when added to metformin.

Treatment outcomes of advanced hepatocellular carcinoma in real-life practice: Chemotherapy versus multikinase inhibitors.

BACKGROUND: Multikinase inhibitors (MKIs) represent the main treatment options for advanced hepatocellular carcinoma (aHCC). However, accessibility in developing countries is limited. A chemotherapy, Fluorouracil and Oxaliplatin (FOLFOX), offers a less expensive treatment. Therefore, this study sought to compare the clinical effectiveness of FOLFOX with Sorafenib as a first-line treatment for aHCC in real-life practice. METHODS: A retrospective aHCC cohort from four Thai hospitals was investigated for patients who received FOLFOX or Sorafenib between 2013-2019. Multiple imputation by chained equations addressed missing covariate data in a treatment effect model using Weight-adjusted-censoring inverse-probability-weighted regression adjustment; overall survival (OS) and progression-free survival (PFS) were estimated. RESULTS: A total of 504 patients were included, (Sorafenib [n = 382] and FOLFOX [n = 122]). The treatment effect model estimated a median OS for Sorafenib and FOLFOX of 11.38 and 8.22 months, representing a significantly shorter OS (95% confidence interval) of -3.16 (-6.21, -0.11) months for FOLFOX, p = 0.042. A significant shorter median PFS of FOLFOX to Sorafenib of -2.13 (-3.03, -1.24) months, p < 0.001, was reported. CONCLUSION: Despite significantly shorter median OS and PFS than Sorafenib, FOLFOX still extended OS by 8.22 months. This evidence may offer clinical utility to physicians considering treatment options for aHCC in low resource settings.

Evaluation of the Effectiveness of Behavioral Economic Incentive Programs for Goal Achievement on Healthy Diet, Weight Control and Physical Activity: A Systematic Review and Network Meta-analysis.

BACKGROUND: Healthy diet, weight control and physical activity to reduce obesity can be motivated by financial incentives (FI). Behavioral-economic approaches may improve the incentivization effectiveness. This study compares and ranks the effectiveness of standard and behavioral incentivization for healthy diet, weight control, and physical activity promotion. PURPOSE: To investigate whether behavioral-economic insights improve incentivization effectiveness. METHODS: A systematic search of Medline and Scopus was performed from database inception to December 2020. Study characteristics, program designs, and risk ratio (RR) were extracted. A two-stage network meta-analysis pooled and ranked intervention effects. RESULTS: There were 35 eligible RCTs. For diet-weight control, standard FI, deposit contract (deposit), lottery-based incentive (lottery), and standard-FI + lottery increased goal achievement compared to no-FI but only deposit was statistically significant with pooled RRs and 95% confidence intervals (CI) of 1.21 (0.94, 1.56), 1.79 (1.04, 3.05), 1.45 (0.99, 2.13), and 1.73 (0.83, 3.63). For physical activity, standard-FI, deposit, and lottery significantly increased goal achievement compared to no-FI, with pooled RRs of 1.38 (1.13, 1.68), 1.63 (1.24, 2.14) and 1.43 (1.14, 1.80), respectively. In a follow-up period for physical activity, only deposit significantly increased goal achievement compared to no-FI, with pooled RRs of 1.39 (1.11, 1.73). CONCLUSION: Deposit, followed by lottery, were best for motivating healthy diet, weight control and physical activity at program end. Post-intervention, deposit then standard-FI were best for motivating physical activity. Behavioral insights can improve incentivization effectiveness, although lottery-based approaches may offer only short-term benefit regarding physical activity. However, the imprecise intervention effects were major concerns.

Healthy diet, weight control and physical activity to reduce obesity can be motivated by financial incentives (FI). Behavioral-economic approaches may improve the effectiveness of FI programs. This study aims to investigate whether behavioral-economic insights improve incentivization effectiveness for healthy diet, weight control, and physical activity promotion. We conducted a systematic review of published randomized controlled trials (RCTs), then pooled the interested results, compared and ranked the effectiveness of standard and behavioral incentivization programs by a two-stage network meta-analysis. There were 35 eligible RCTs. For diet-weight control, standard FI, deposit contract (deposit), lottery-based incentive (lottery), and standard-FI + lottery increased goal achievement compared to no-FI but only deposit was statistically significant. For physical activity, standard-FI, deposit, and lottery significantly increased goal achievement compared to no-FI. In a follow-up period for physical activity, only deposit significantly increased goal achievement compared to no-FI. In conclusion, deposit, followed by lottery, were best for motivating healthy diet, weight control and physical activity at program end. Post-intervention, deposit then standard-FI were best for motivating physical activity. This shows that behavioral insights can improve incentivization effectiveness, although lottery-based approaches may offer only short-term benefit regarding physical activity.

Comparative cardiovascular benefits of individual SGLT2 inhibitors in type 2 diabetes and heart failure: a systematic review and network meta-analysis of randomized controlled trials.

Background: In patients with type 2 diabetes (T2D) and a history of heart failure (HF), sodium-glucose cotransporter-2 inhibitors (SGLT2is) have demonstrated cardiovascular (CV) benefits. However, the comparative efficacy of individual SGLT2is remains uncertain. This network meta-analysis (NMA) compared the efficacy and safety of five SGLT2is (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, and sotagliflozin) on CV outcomes in these patients. Materials and methods: PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched up to September 23, 2022, to identify all randomized controlled trials (RCTs) comparing SGLT2is to placebo in T2D patients with HF. The main outcomes included composite CV death/heart failure hospitalization (HFH), HFH, CV death, all-cause mortality, and adverse events. Pairwise and NMA approaches were applied. Results: Our analysis included 11 RCTs with a total of 20,438 patients with T2D and HF. All SGLT2is significantly reduced HFH compared to standard of care (SoC) alone. "Add-on" SGLT2is, except ertugliflozin, significantly reduced composite CV death/HFH relative to SoC alone. Moreover, canagliflozin had lower composite CV death/HFH compared to dapagliflozin. Based on the surface under the cumulative ranking curve (SUCRA), the top-ranked SGLT2is for reducing HFH were canagliflozin (95.5%), sotagliflozin (66.0%), and empagliflozin (57.2%). Head-to-head comparisons found no significant differences between individual SGLT2is in reducing CV death. "Add-on" SGLT2is reduced all-cause mortality compared with SoC alone, although only dapagliflozin was statistically significant. No SGLT2is were significantly associated with serious adverse events. A sensitivity analysis focusing on HF-specific trials found that dapagliflozin, empagliflozin, and sotagliflozin significantly reduced composite CV death/HFH, consistent with the main analysis. However, no significant differences were identified from their head-to-head comparisons in the NMA. The SUCRA indicated that sotagliflozin had the highest probability of reducing composite CV death/HFH (97.6%), followed by empagliflozin (58.4%) and dapagliflozin (44.0%). Conclusion: SGLT2is significantly reduce the composite CV death/HFH outcome. Among them, canagliflozin may be considered the preferred treatment for patients with diabetes and a history of heart failure, but it may also be associated with an increased risk of any adverse events compared to other SGLT2is. However, a sensitivity analysis focusing on HF-specific trials identified sotagliflozin as the most likely agent to reduce CV death/HFH, followed by empagliflozin and dapagliflozin. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022353754.

Efficacy of pharmacotherapy and non-pharmacotherapy of Alzheimer dementia: A protocol for systematic review and meta-analysis.

BACKGROUND: Alzheimer disease (AD) is the most common cause of dementia worldwide. Despite the publication of numerous systematic reviews and meta-analyses that have summarized the evidence associated with pharmacotherapies (PTs) and non-pharmacotherapies (NPTs) for the wide range of interventions available for AD treatment, their comparative safety and efficacy remains insufficiently defined. METHODS: Systematic reviews of randomized controlled trials (RCTs) will be selected according to the following criteria: conducted in elderly patients aged 60 years or older with AD living in community or institutionalized settings, applied pairwise meta-analysis (PMA) or network meta-analysis (NMA) approaches providing pooled relative treatment effects for at least 1 pair of PTs or NPTs, and providing at least 1 of the following outcomes for patients/caregivers: cognitive, functional status, behavior, quality of life (QoL), and caregiver stress or burden. All article screening, data extraction, and risk of bias assessment will be completed independently by 2 reviewers. Relative treatment rankings will be reported with mean ranks and surface under the cumulative ranking curves. RESULTS AND CONCLUSION: We will determine the most efficacious treatment strategies for AD patients from the most highly ranked treatments. These results will help to guide clinical decision-making and improve patient care.

Epigenome-wide meta-analysis identifies DNA methylation biomarkers associated with diabetic kidney disease.

Type 1 diabetes affects over nine million individuals globally, with approximately 40% developing diabetic kidney disease. Emerging evidence suggests that epigenetic alterations, such as DNA methylation, are involved in diabetic kidney disease. Here we assess differences in blood-derived genome-wide DNA methylation associated with diabetic kidney disease in 1304 carefully characterised individuals with type 1 diabetes and known renal status from two cohorts in the United Kingdom-Republic of Ireland and Finland. In the meta-analysis, we identify 32 differentially methylated CpGs in diabetic kidney disease in type 1 diabetes, 18 of which are located within genes differentially expressed in kidneys or correlated with pathological traits in diabetic kidney disease. We show that methylation at 21 of the 32 CpGs predict the development of kidney failure, extending the knowledge and potentially identifying individuals at greater risk for diabetic kidney disease in type 1 diabetes.