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.

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.

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.

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.

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.

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.

Effects of second-line antihyperglycemic drugs on the risk of chronic kidney disease: applying a target trial approach to a hospital-based cohort of Thai patients with type 2 diabetes.

BACKGROUND: The reno-protective effect of second-line treatments in type 2 diabetes has been assessed by clinical trials but generalizability to routine clinical practice is still uncertain. We aimed to assess the effectiveness of these treatments, when added to metformin, on the risk of chronic kidney disease (CKD). METHODS: A real-world, hospital-based, type 2 diabetes cohort was retrospectively assembled at Ramathibodi Hospital from 2010 to 2019. Patients who received sulfonylureas (SU), thiazolidinediones (TZD), dipeptidyl peptidase-4 inhibitors (DPP4i), or sodium-glucose cotransporter-2 inhibitors (SGLT2i), as second-line antihyperglycemic treatment were included. Treatment effect models with inverse probability weighting and regression adjustment were used to estimate CKD risk according to treatment. RESULTS: CKD was identified in 4,132 of the 24,777 patients with type 2 diabetes (16.7%). The CKD incidence (95% CI) was 4.1% (2.2%, 6.9%), 13.5% (12.5%, 14.6%), 14.8% (13.5%, 16.1%), and 18.0% (17.4%, 18.5%) for patients receiving SGLT2i, DPP4i, TZD, and SU treatment, respectively. The average treatment effects (i.e., the difference in CKD risk) for SGLT2i, DPP4i, and TZD compared to SU were - 0.142 (- 0.167, - 0.116), - 0.046 (- 0.059, - 0.034), and - 0.004 (- 0.023, 0.014), respectively, indicating a significant reduction in CKD risk of 14.2% and 4.6% in the SGLT2i and DPP4i groups, respectively, compared to the SU group. Furthermore, SGLT2i significantly reduced CKD risk by 13.7% (10.6%, 16.8%) and 9.5% (6.8%, 12.2%) when compared to TZD and DPP4i, respectively. CONCLUSIONS: Our study identified 14.2%, 13.7%, and 9.5% reduced CKD risk in Thai patients with type 2 diabetes who were treated with SGLT2i compared to those treated with SU, TZD, and DPP4i, respectively, in real-world clinical data. Previous evidence of a reno-protective effect of SGLT2i reported in other populations is consistent with our observations in this Southeast Asian cohort.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia. METHODS: In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid ß burden (testing the deep learning model between amyloid ß positive vs amyloid ß negative). FINDINGS: 12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid ß positive and those who were amyloid ß negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]). INTERPRETATION: A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting. FUNDING: BrightFocus Foundation.

Cost-effectiveness analysis of mesh fixation techniques for laparoscopic and open inguinal hernia surgeries.

PURPOSE: This study reports economic evaluation of mesh fixation in open and laparoscopic hernia repair from a prospective real-world cohort study, using cost-effectiveness analysis (CEA) and cost-utility analysis (CUA). METHODS: A prospective real-world cohort study was conducted in two university-based hospitals in Thailand from November 2018 to 2019. Patient data on hernia features, operative approaches, clinical outcomes, associated cost data, and quality of life were collected. Models were used to determine each group's treatment effect, potential outcome means, and average treatment effects. An incremental cost-effectiveness ratio was used to evaluate the incremental risk of hernia recurrences. RESULTS: The 261 patients in this study were divided into six groups: laparoscopic with tack (LT, n = 47), glue (LG, n = 26), and self-gripping mesh (LSG, n = 30), and open with suture (OS, n = 117), glue (OG, n = 18), and self-gripping mesh (OSG, n = 23). Hernia recurrence was most common in LSG. The mean utility score was highest in OG and OSG (both 0.99). Treatment costs were generally higher for laparoscopic than open procedures. The cost-effectiveness plane for utility and hernia recurrence identified LSG as least cost effective. Cost-effectiveness acceptability curves identified OG as having the highest probability of being cost effective at willingness to pay levels between $0 and $3,300, followed by OSG. CONCLUSION: Given the similarity of hernia recurrence among all major procedures, the cost of surgery may impact the decision. According to our findings, open hernia repair with adhesive or self-gripping mesh appears most cost-effective.

Retinal vascular measures from diabetes retinal screening photographs and risk of incident dementia in type 2 diabetes: A GoDARTS study.

Objective: Patients with diabetes have an increased risk of dementia. Improved prediction of dementia is an important goal in developing future prevention strategies. Diabetic retinopathy screening (DRS) photographs may be a convenient source of imaging biomarkers of brain health. We therefore investigated the association of retinal vascular measures (RVMs) from DRS photographs in patients with type 2 diabetes with dementia risk. Research Design and Methods: RVMs were obtained from 6,111 patients in the GoDARTS bioresource (635 incident cases) using VAMPIRE software. Their association, independent of Apo E4 genotype and clinical parameters, was determined for incident all cause dementia (ACD) and separately Alzheimer's disease (AD) and vascular dementia (VD). We used Cox's proportional hazards with competing risk of death without dementia. The potential value of RVMs to increase the accuracy of risk prediction was evaluated. Results: Increased retinal arteriolar fractal dimension associated with increased risk of ACD (csHR 1.17; 1.08-1.26) and AD (HR 1.33; 1.16-1.52), whereas increased venular fractal dimension (FDV) was associated with reduced risk of AD (csHR 0.85; 0.74-0.96). Conversely, FDV was associated with increased risk of VD (csHR 1.22; 1.07-1.40). Wider arteriolar calibre was associated with a reduced risk of ACD (csHR 0.9; 0.83-0.98) and wider venular calibre was associated with a reduced risk of AD (csHR 0.87; 0.78-0.97). Accounting for competing risk did not substantially alter these findings. RVMs significantly increased the accuracy of prediction. Conclusions: Conventional DRS photographs could enhance stratifying patients with diabetes at increased risk of dementia facilitating the development of future prevention strategies.

A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events.

BACKGROUND: Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction models. This study aims to develop novel DNAm surrogates for cardiovascular diseases (CVD) risk factors and develop a composite biomarker predictive of CVD risk. We compared the prediction performance of our newly developed risk score with the state-of-the-art DNAm risk scores for cardiovascular diseases, the 'next-generation' epigenetic clock DNAmGrimAge, and the prediction model based on traditional risk factors SCORE2. RESULTS: Using data from the EPIC Italy cohort, we derived novel DNAm surrogates for BMI, blood pressure, fasting glucose and insulin, cholesterol, triglycerides, and coagulation biomarkers. We validated them in four independent data sets from Europe and the USA. Further, we derived a DNAmCVDscore predictive of the time-to-CVD event as a combination of several DNAm surrogates. ROC curve analyses show that DNAmCVDscore outperforms previously developed DNAm scores for CVD risk and SCORE2 for short-term CVD risk. Interestingly, the performance of DNAmGrimAge and DNAmCVDscore was comparable (slightly lower for DNAmGrimAge, although the differences were not statistically significant). CONCLUSIONS: We described novel DNAm surrogates for CVD risk factors useful for future molecular epidemiology research, and we described a blood DNAm-based composite biomarker, DNAmCVDscore, predictive of short-term cardiovascular events. Our results highlight the usefulness of DNAm surrogate biomarkers of risk factors in epigenetic epidemiology to identify high-risk populations. In addition, we provide further evidence on the effectiveness of prediction models based on DNAm surrogates and discuss methodological aspects for further improvements. Finally, our results encourage testing this approach for other NCD diseases by training and developing DNAm surrogates for disease-specific risk factors and exposures.

Association of TGFB1 rs1800469 and BCMO1 rs6564851 with coronary heart disease and IL1B rs16944 with all-cause mortality in men from the Northern Ireland PRIME study.

BACKGROUND: Historically, high levels of morbidity and mortality have been associated with cardiovascular disease in the Northern Ireland population. Previously reported associations between single nucleotide polymorphisms (SNPs) and cardiovascular disease within other populations have not always been consistent. OBJECTIVE: To investigate associations between 33 SNPs with fatal or non-fatal incident coronary heart disease (CHD) events and all-cause mortality in the Northern Irish participants of the Prospective Epidemiological Study of Myocardial Infarction (PRIME). METHOD: Phase 2 of the PRIME study prospectively evaluated 2,010 men aged 58-74 years in Northern Ireland for more than 10 years for incident CHD events (myocardial infarction, percutaneous coronary intervention, coronary artery bypass, and cardiac death) and more than 15 years for all-cause mortality. SNPs previously reported in association with cardiovascular outcomes were evaluated against incident CHD events and all-cause mortality using Cox's proportional hazards models adjusted for established cardiovascular disease risk factors. RESULTS: During the follow-up period, 177 incident CHD events were recorded, and 821 men died. Both BCMO1 rs6564851 (Hazard ratio [HR] = 0.76; 95% confidence intervals [CI]: 0.60-0.96; P = 0.02) and TGFB1 rs1800469 (HR = 1.30; CI: 1.02-1.65; P = 0.04) were significantly associated with incident CHD events in adjusted models. Only IL1B rs16944 was significantly associated with all-cause mortality (HR = 1.18; CI: 1.05-1.33; P = 0.005). No associations remained significant following Bonferonni correction for multiple testing. CONCLUSION: We report a novel association between BCMO1 rs6564851 and risk of incident CHD events. In addition, TGFB1 rs1800469 and IL1B rs16944 were associated with the risk of incident CHD events and all-cause mortality outcomes respectively, supporting previously reported associations.

Mesh-fixation technique for inguinal hernia repair: umbrella review.

BACKGROUND: Mesh-based repair is the standard of surgical care for symptomatic inguinal hernias. Many systematic reviews and meta-analyses (SRMAs) addressed various aspects of these procedures. This umbrella review aimed to report the evidence from all previous SRMAs for open and laparoscopic inguinal hernia repair. METHODS: SRMAs were identified from MEDLINE, Scopus, Cochrane, Embase, DARE, PROSPERO, CINAHL, JBISRIS, EPPI-Centre, Wiley Online Library and ScienceDirect database according to PRISMA guidelines. Data including mesh-fixation techniques and surgical approach were extracted from selected SRMAs. The corrected covered area was calculated to address study overlap across reviews, and an excess significance test was used to assess potential bias. The outcomes of interest were hernia recurrence, chronic groin pain, operating time, postoperative pain, duration of hospital stay, return to daily life activities, and postoperative complication. RESULTS: Thirty SRMAs were included between 2010 and 2019: 16 focused on open repair, and 14 focused on laparoscopic repair, with a high degree of overlap (open repairs, 41 per cent; laparoscopic repairs, 30-57 per cent). Sufficient evidence was available on hernia recurrence, chronic groin pain, and operative time. Effects of glue on hernia recurrence were inconclusive in open and laparoscopy approaches, P = 0.816 and 0.946 respectively. Glue was significantly associated with lower persistent groin pain, in open repair (versus suture) and in laparoscopic repair (versus tack). SRMAs suggested that self-gripping mesh was associated with shorter operating time in open surgery, although with only a few minutes of improvement (0.36-7.85 min, P < 0.001). CONCLUSION: In this umbrella review, chronic groin pain and operating time were the only outcomes for which there was sufficient evidence supporting the effectiveness respectively of glue and self-gripping mesh.

Prevalence of risk alleles in the lysyl oxidase-like 1 gene in pseudoexfoliation glaucoma patients in India.

Purpose: The purpose of this study was to genotype two previously identified SNPs (rs1048661:R141L, and rs3825942:G153D) in the lysyl oxidase-like 1 (LOXL1) gene and determine their association with pseudoexfoliation glaucoma (XFG) in patients from Pune, India. Methods: All subjects underwent detailed phenotyping, and DNA extraction was performed on blood samples by using standardized techniques. Exon 1 of the LOXL1 gene containing the SNPs (rs3825942:G153D; rs1048661:R141L) were Sanger sequenced, and the results were analyzed using sequence analysis software SeqScape 2.1.1. Results: Data were analyzed from 71 patients with XFG and 81 disease-negative, age-matched controls. There was a strong association between the G allele of rs3825942 and XFG with an odds ratio of 10.2 (CI: 3.92-26.6; P < 0.001). The G allele of rs1048661 also showed an increase in risk relative to the T allele (OR = 1.49; CI: 0.88-2.51; P = 0.13), but this was not significant. Haplotype combination frequencies were estimated for rs1048661 and rs3825942; the GG haplotype was associated with a significant increase in risk (OR = 3.91; CI: 2.27-6.73; P < 0.001). Both the GA and TG haplotypes were associated with decreased XFG risk, although the latter was not significant (GA: OR = 0.08; CI: 0.03-0.21; P < 0.001; TG: OR = 0.67; CI: 0.40-1.13; P = 0.13). Conclusion: The risk G allele in rs3852942 (G153D) is strongly associated with the development of XFG in the Western Indian population. Genetic screening strategies to identify LOXL1 risk alleles in the population can assist in case definition and early diagnosis, targeting precious resources to high-risk patients.