Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models.

Identifying and preventing patients who are not likely to benefit long-term from total knee arthroplasty (TKA) would decrease healthcare expenditure significantly. We trained machine learning (ML) models (image-only, clinical-data only, and multimodal) among 5720 knee OA patients to predict postoperative dissatisfaction at 2 years. Dissatisfaction was defined as not achieving a minimal clinically important difference in postoperative Knee Society knee and function scores (KSS), Short Form-36 Health Survey [SF-36, divided into a physical component score (PCS) and mental component score (MCS)], and Oxford Knee Score (OKS).

Association between deep learning measured retinal vessel calibre and incident myocardial infarction in a retrospective cohort from the UK Biobank.

Background: Cardiovascular disease is a leading cause of global death. Prospective population-based studies have found that changes in retinal microvasculature are associated with the development of coronary artery disease. Recently, artificial intelligence deep learning (DL) algorithms have been developed for the fully automated assessment of retinal vessel calibres.

Comparative effectiveness of first-line antihypertensive drug classes on the maintenance of estimated glomerular filtration rate (eGFR) in real world primary care.

Renin-angiotensin system inhibitors (RASi), particularly angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs), are commonly used in the treatment of hypertension and are recommended for kidney protection. Uncertainty remains about the effectiveness of RASi being used as first-line antihypertensive therapy on eGFR maintenance compared to its alternatives, especially for those with no or early-stage chronic kidney disease (CKD). We conducted a retrospective cohort study of 19,499 individuals (mean age 64.

Predictors of HbA treatment response to add-on medication following metformin monotherapy: a population-based cohort study.

Evidence on the influence of patient characteristics on HbA treatment response for add-on medications in patients with type 2 diabetes (T2D) is unclear. This study aims to investigate the predictors of HbA treatment response for three add-on medications (sulfonylureas (SU), dipeptidyl peptidase-4 (DPP-4) and sodium-glucose cotransporter-2 (SGLT-2) inhibitor) in metformin monotherapy treated patients with T2D. This retrospective cohort study was conducted using the electronic health record data from six primary care clinics in Singapore.

Diabetes medication recommendation system using patient similarity analytics.

Type-2 diabetes mellitus (T2DM) is a medical condition in which oral medications avail to patients to curb their hyperglycaemia after failed dietary therapy. However, individual responses to the prescribed pharmacotherapy may differ due to their clinical profiles, comorbidities, lifestyles and medical adherence. One approach is to identify similar patients within the same community to predict their likely response to the prescribed diabetes medications.

Using similar patients to predict complication in patients with diabetes, hypertension, and lipid disorder: a domain knowledge-infused convolutional neural network approach.

Objective: This study aims to develop a convolutional neural network-based learning framework called domain knowledge-infused convolutional neural network (DK-CNN) for retrieving clinically similar patient and to personalize the prediction of macrovascular complication using the retrieved patients.

Materials And Methods: We use the electronic health records of 169 434 patients with diabetes, hypertension, and/or lipid disorder. Patients are partitioned into 7 subcohorts based on their comorbidities.

Risk of Normal Tension Glaucoma Progression From Automated Baseline Retinal-Vessel Caliber Analysis: A Prospective Cohort Study.

Purpose: To determine the relationship between baseline retinal-vessel calibers computed by a deep-learning system and the risk of normal tension glaucoma (NTG) progression.

Design: Prospective cohort study.

Methods: Three hundred and ninety eyes from 197 patients with NTG were followed up for at least 24 months.

Deep-learning retinal vessel calibre measurements and risk of cognitive decline and dementia.

Previous studies have explored the associations of retinal vessel calibre, measured from retinal photographs or fundus images using semi-automated computer programs, with cognitive impairment and dementia, supporting the concept that retinal blood vessels reflect microvascular changes in the brain. Recently, artificial intelligence deep-learning algorithms have been developed for the fully automated assessment of retinal vessel calibres. Therefore, we aimed to determine whether deep-learning-based retinal vessel calibre measurements are predictive of risk of cognitive decline and dementia.

Glycaemic control of Asian patients with type-2 diabetes mellitus on tiered up-titration of metformin monotherapy: A one-year real-world retrospective longitudinal study in primary care.

Aims: To determine the glycaemic control and associated factors among patients with type-2 diabetes mellitus on tiered metformin monotherapy over one-year.

Methods: Adult Asian patients on metformin monotherapy with tiered dosage up-titration (low < 500 mg/day; medium 500-<1000 mg/day and high ≥ 1000 mg/day) are divided into four sub-cohorts based on their baseline HbA1c < 7%(C); 7%-<8%(C); 8%-<9%(C) and ≥ 9%(C). The HbA1c absolute reduction, time to reach glycaemic control (HbA1c < 7%), and time from glycaemic control to failure (HbA1c ≥ 7%) after the dosage up-titration were the outcomes.

The effect of oral diabetes medications on glycated haemoglobin (HbA1c) in Asians in primary care: a retrospective cohort real-world data study.

Background: Clinical trials have demonstrated that initiating oral anti-diabetic drugs (OADs) significantly reduce glycated hemoglobin (HbA1c) levels. However, variability in lifestyle modifications and OAD adherence impact on their actual effect on glycemic control. Furthermore, evidence on dose adjustments and discontinuation of OAD on HbA1c is lacking.

Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics.

Patient similarity analytics has emerged as an essential tool to identify cohorts of patients who have similar clinical characteristics to some specific patient of interest. In this study, we propose a patient similarity measure called D3K that incorporates domain knowledge and data-driven insights. Using the electronic health records (EHRs) of 169,434 patients with either diabetes, hypertension or dyslipidaemia (DHL), we construct patient feature vectors containing demographics, vital signs, laboratory test results, and prescribed medications.

Patient similarity analytics for explainable clinical risk prediction.

Background: Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility.

LDL-cholesterol change and goal attainment following statin intensity titration among Asians in primary care: a retrospective cohort study.

Background: Clinical trials have demonstrated that either initiating or up-titrating a statin dose substantially reduce Low-Density Lipoprotein-Cholesterol (LDL-C) levels. However, statin adherence in actual practice tends to be suboptimal, leading to diminished effectiveness. This study aims to use real-world data to determine the effect on LDL-C levels and LDL-C goal attainment rates, when selected statins are titrated in Asian patients.

A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations.

Background: Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies.

Methods: We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China.

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre.

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.

Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy.

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR.

Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

Purpose Of Review: This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated.

Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study.

In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes.

Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.

Background: Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms.

Objective: The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.

Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.

Design, Setting, And Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images.

Comparison of Common Retinal Vessel Caliber Measurement Software and a Conversion Algorithm.

Purpose: To compare three commonly used retinal vessel caliber measurement software systems, and propose an algorithm for conversion between measurement systems.

Methods: We used 120 retinal photographs to evaluate the agreement between three commonly used software (Retinal Analysis [RA], Integrative Vessel Analysis [IVAN], and Singapore I Vessel Assessment [SIVA]). Bland-Altman plots were used to evaluate agreement of retinal arteriolar (central retinal artery equivalent, CRAE) and venular (central retinal vein equivalent, CRVE) calibers.