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Summary: Background: Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based diabetic retinopathy screening programme in Zambia, a lower-middle-income country. Methods: We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated Diabetic Retinopathy Program, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable diabetic retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised severe non-proliferative and proliferative diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable diabetic retinopathy, and sensitivities of vision-threatening diabetic retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. Findings: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 0·973 (95% CI 0·969–0·978), with corresponding sensitivity of 92·25% (90·10–94·12) and specificity of 89·04% (87·85–90·28). Vision-threatening diabetic retinopathy sensitivity was 99·42% (99·15–99·68) and diabetic macular oedema sensitivity was 97·19% (96·61–97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable diabetic retinopathy. Interpretation: An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. Funding: National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Abstract Stroke remains one of the major health challenges due to its high rates of mortality and long-term disability, necessitating the development of effective therapeutic treatment. This study aims to explore the neuroprotective effects of hypoxic postconditioning (HPC) using a cell-based 3D cortical ischemic-hypoxic injury model. Our model employs murine cells to investigate HPC-induced modulation of glial cell reactivity and intercommunication post-oxygen-glucose deprivation-reoxygenation (OGD-R) injury. We found that a single HPC session (1HPC) provided the most significant neuroprotection post-OGD-R compared to multiple intermittent hypoxic treatments, evidenced by improved spheroidal structure, enhanced cell survival and reduced apoptosis, optimal modulation of neuronal phenotypes, dampened ischemic responses, and augmented neurite outgrowth of spheroids. Furthermore, 1HPC suppressed both pro-inflammatory A1 and anti-inflammatory A2 astrocyte phenotypes despite the induction of astrocyte activation while reducing microglial activation with inhibited M1 and M2 reactive states. This was accompanied by a decrease in gene expression of the pro-inflammatory cytokines essential to microglia-astrocyte signaling, collectively suggesting a shift of glial cells away from their traditional reactive states for neuroprotection. This study highlights the potential of 1HPC as a novel therapeutic intervention for ischemic injury via the modulation of neuroprotective glial reactivity. Moreover, the 3D cortical ischemic-hypoxic injury model employed here holds enormous potential serving as a disease model to further elucidate the underlying mechanism of HPC, which can also extend to the applications in brain regeneration, drug development, and the modeling of neural diseases.

Abstract 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). Compared to image-only models, both clinical-data only and multimodal models achieved superior performance at predicting dissatisfaction measured by AUC, clinical-data only model: KSS 0.888 (0.866–0.909), SF-PCS 0.836 (0.812–0.860), SF-MCS 0.833 (0.812–0.854), and OKS 0.806 (0.753–0.859); multimodal model: KSS 0.891 (0.870–0.911), SF-PCS 0.832 (0.808–0.857), SF-MCS 0.835 (0.811–0.856), and OKS 0.816 (0.768–0.863). Our findings highlighted that ML models using clinical or multimodal data were capable to predict post-TKA dissatisfaction.

Lymphoepithelial carcinoma is rare in the salivary glands, with an incidence of 0.4%. The most commonly affected site is the parotid gland, followed by the submandibular gland. Lymphoepithelial carcinoma in the sublingual gland has been reported only four times in the existing English-language literature. Such tumours are characterized by the presence of a poorly differentiated carcinoma that is surrounded and infiltrated by lymphocytes, and they are strongly associated with Epstein–Barr virus infection, patient ethnicity, and prominent radiosensitivity. Wide surgical excision combined with adjuvant therapy has been suggested as the first-choice therapeutic regimen. This report describes the case of a 34-year-old Indonesian woman who was evaluated and treated in Taipei Medical University Hospital. She had a tumour that presented as a painless swelling on the floor of the mouth. The diagnosis was confirmed by conducting an incisional biopsy, and a wide surgical excision with bilateral supraomohyoid neck dissection and free flap reconstruction was performed. The patient also underwent adjuvant chemoradiotherapy. No evidence of local recurrence or distant metastasis was detected during the 6 months of follow-up. Subsequently, the patient returned to her home country, and further follow-ups were not conducted. [ABSTRACT FROM AUTHOR]

Abstract 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 64.1, 43.5% males) from primary care in Singapore with 4.5 median follow-up years. The study cohort included newly diagnosed individuals with hypertension (whose eGFR was mainly in CKD stages G1-G2) and initiated on ACEIs, ARBs, beta-blockers (BBs), calcium channel blockers (CCBs) or diuretics (Ds) as first-line antihypertensive monotherapy. We compared the estimated glomerular filtration rate (eGFR) curve before/after the drug initiation over time of patients under different drug classes and analyzed the time to declining to a more advanced stage CKD. Inverse probability of treatment weighting (IPTW) was used to adjust for baseline confounding factors. Two key findings were observed. First, after initiating antihypertensive drugs, the eGFR almost maintained the same as the baseline in the first follow-up year, compared with dropping 3 mL/min/1.73 m2 per year before drug initiation. Second, ARBs were observed to be slightly inferior to ACEIs (HR = 1.14, 95% CI = (1.04, 1.23)) and other antihypertensive agents (HR = 1.10, 95% CI = (1.01, 1.20)) in delaying eGFR decline to a more advanced CKD stage in the study population. Our results showed that initiating antihypertensive agents can significantly maintain eGFR for those newly diagnosed patients with hypertension. However, RASi may not be superior to other antihypertensive agents in maintaining eGFR levels for non-CKD or early stages CKD patients.

Abstract Evidence on the influence of patient characteristics on HbA1c treatment response for add-on medications in patients with type 2 diabetes (T2D) is unclear. This study aims to investigate the predictors of HbA1c 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. A total of 9748 adult patients with T2D on metformin monotherapy receiving SU, DPP-4 or SGLT-2 add-on were 1:1 propensity score matched to patients receiving other add-on medications. Patient demographics, laboratory results, diabetes related complications, comedications, and treatment response at two endpoints (HbA1c reduction ≥ 1% at 6th month, HbA1c goal attainment

Abstract Background A recent prospective demonstrated that cardiovascular risk factors in early childhood were associated with later cardiovascular events. However, the impact of secondhand smoke (SHS) on children is unclear. The aims of this study is to determine the effects of SHS exposure on the retinal vasculature of children. Methods This is a population-based cross-sectional study of children aged 6 to 8 years. All participants received comprehensive ophthalmic examinations and retinal photography. Data on SHS exposure was derived from a validated questionnaire. A validated deep-learning system was used to automatically estimate retinal arteriolar and venular calibers from retinal photographs. Associations of quantitative retinal vessel caliber values with SHS exposure, number of smokers in the household, and total number of cigarettes smoked were determined by analyses of covariance (ANCOVA) after adjusting for potential confounders. Test of trend was determined by treating categorical risk factors as continuous ordinal variables. Results Here we show children exposed to SHS have wider retinal arteriolar (CRAE 152.1 µm vs. 151.3 µm, p

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.Methods In this study, we validate the association between retinal vessel calibres measured by a DL system (Singapore I Vessel Assessment) and incident myocardial infarction (MI) and assess its incremental performance in discriminating patients with and without MI when added to risk prediction models, using a large UK Biobank cohort.Results Retinal arteriolar narrowing was significantly associated with incident MI in both the age, gender and fellow calibre-adjusted (HR=1.67 (95% CI: 1.19 to 2.36)) and multivariable models (HR=1.64 (95% CI: 1.16 to 2.32)) adjusted for age, gender and other cardiovascular risk factors such as blood pressure, diabetes mellitus (DM) and cholesterol status. The area under the receiver operating characteristic curve increased from 0.738 to 0.745 (p=0.018) in the age–gender-adjusted model and from 0.782 to 0.787 (p=0.010) in the multivariable model. The continuous net reclassification improvements (NRIs) were significant in the age and gender-adjusted (NRI=21.56 (95% CI: 3.33 to 33.42)) and the multivariable models (NRI=18.35 (95% CI: 6.27 to 32.61)). In the subgroup analysis, similar associations between retinal arteriolar narrowing and incident MI were observed, particularly for men (HR=1.62 (95% CI: 1.07 to 2.46)), non-smokers (HR=1.65 (95% CI: 1.13 to 2.42)), patients without DM (HR=1.73 (95% CI: 1.19 to 2.51)) and hypertensive patients (HR=1.95 (95% CI: 1.30 to 2.93)) in the multivariable models.Conclusion Our results support DL-based retinal vessel measurements as markers of incident MI in a predominantly Caucasian population.

Abstract 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. This study aims to present an evidence-based diabetes medication recommendation system (DMRS) underpinned by patient similarity analytics. The DMRS was developed using 10-year electronic health records of 54,933 adult patients with T2DM from six primary care clinics in Singapore. Multiple clinical variables including patient demographics, comorbidities, laboratory test results, existing medications, and trajectory patterns of haemoglobin A1c (HbA1c) were used to identify similar patients. The DMRS was evaluated on four groups of patients with comorbidities such as hyperlipidaemia (HLD) and hypertension (HTN). Recommendations were assessed using hit ratio which represents the percentage of patients with at least one recommended sets of medication matches exactly the diabetes prescriptions in both the type and dosage. Recall, precision, and mean reciprocal ranking of the recommendation against the diabetes prescriptions in the EHR records were also computed. Evaluation against the EHR prescriptions revealed that the DMRS recommendations can achieve hit ratio of 81% for diabetes patients with no comorbidity, 84% for those with HLD, 78% for those with HTN, and 75% for those with both HLD and HTN. By considering patients’ clinical profiles and their trajectory patterns of HbA1c, the DMRS can provide an individualized recommendation that resembles the actual prescribed medication and dosage. Such a system is useful as a shared decision-making tool to assist clinicians in selecting the appropriate medications for patients with T2DM.

Abstract 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. This study aims to use real-world data to determine the effect of OAD initiation, up-titration, down-titration, and discontinuation on HbA1c levels, among Asian patients managed in primary care. Methods A retrospective cohort study over a 5-year period, from Jan 2015 to Dec 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of type 2 diabetes mellitus (T2DM) managed by a network of primary care clinics in Singapore. Nine OADs from five different classes (biguanides, sulphonyurea, dipeptidyl peptidase-4 [DPP-4] inhibitors, sodium-glucose cotransporter-2 [SGLT-2] inhibitors, and alpha-glucosidase inhibitors) were evaluated. Patients were grouped into “No OAD”, “Non-titrators,” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the various OAD titrations were identified. Subsequently, a descriptive analysis of HbA1c values before and after each titration was performed to compute a mean difference for each unique titration identified. Results Among the cohort of 57,910 patients, 43,338 of them had at least one OAD titration, with a total of 76,990 pairs of HbA1c values associated with an OAD titration. There were a total of 206 unique OAD titrations. Overall, initiation of OADs resulted in a reduction of HbA1c by 3 to 12 mmol/mol (0.3 to 1.1%), respectively. These results were slightly lower than those reported in clinical trials of 6 to 14 mmol/mol (0.5 to 1.25%). The change of HbA1c levels due to up-titration, down-titration, and discontinuation were −1 to −8 mmol/mol (−0.1 to −0.7%), +1 to 7 mmol/mol (+0.1 to +0.6%), and +2 to 11 mmol/mol (+0.2 to +1.0%), respectively. The HbA1c lowering effect of initiating newer OADs, namely DPP-4 inhibitors and SGLT-2 inhibitors was 8 to 11 mmol/mol (0.7 to 0.9%) and 7 to 11 mmol/mol (0.6 to 1.0%), respectively. Conclusion The real-world data on Asians with T2DM in this study show that the magnitudes of OAD initiation and dose titration are marginally lower than the results from clinical trials. During shared decision-making in selecting treatment options, the results enable physicians to communicate realistic expectation of the effect of oral medications on the glycemic control of their patients in primary care.

Abstract 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. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.

Abstract 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. Methods A retrospective cohort study over a 5-year period, from April 2014 to March 2019 was conducted on a cohort of multi-ethnic adult Asian patients with clinical diagnosis of Dyslipidaemia in a primary care clinic in Singapore. The statins were classified into low-intensity (LI), moderate-intensity (MI) and high-intensity (HI) groups according to the 2018 American College of Cardiology and American Heart Association (ACC/AHA) Blood Cholesterol Guidelines. Patients were grouped into “No statin”, “Non-titrators” and “Titrators” cohorts based on prescribing patterns. For the “Titrators” cohort, the mean percentage change in LDL-C and absolute change in LDL-C goal attainment rates were computed for each permutation of statin intensity titration. Results Among the cohort of 11,499 patients, with a total of 266,762 visits, there were 1962 pairs of LDL-C values associated with a statin titration. Initiation of LI, MI and HI statin resulted in a lowering of LDL-C by 21.6% (95%CI = 18.9–24.3%), 28.9% (95%CI = 25.0–32.7%) and 25.2% (95%CI = 12.8–37.7%) respectively. These were comparatively lower than results from clinical trials (30 to 63%). The change of LDL-C levels due to up-titration, down-titration, and discontinuation were − 12.4% to − 28.9%, + 13.2% to + 24.6%, and + 18.1% to + 32.1% respectively. The improvement in LDL-C goal attainment ranged from 26.5% to 47.1% when statin intensity was up-titrated. Conclusion In this study based on real-world data of Asian patients in primary care, it was shown that although statin titration substantially affected LDL-C levels and LDL-C goal attainment rates, the magnitude was lower than results reported from clinical trials. These results should be taken into consideration and provide further insight to clinicians when making statin adjustment recommendations in order to achieve LDL-C targets in clinical practice, particularly for Asian populations.

Rice straw fibre was utilized for unidirectional (UD) composites. In this study, the effects of compression temperature, duration, pressure, and fibre volume fraction on the mechanical properties of composites were investigated, respectively. The composite with optimal mechanical properties was prepared at a temperature of 180oC, pressure of 125 kg.cm-2 for 10 min, and at a fibre volume fraction of 40%. Mg(OH)2 was found to be an appropriate additive to enhance the flame retardancy of the composite. Interestingly, this agent also improved the mechanical and thermal insulation properties of the obtained composite

Abstract 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. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p

Acta Ophthalmol. 2010: 88: e234–e240 Purpose: To examine the influence of lens opacity and refraction on the measurement of retinal vascular fractal dimension ( Df). Methods: Optic disc photographs (right eyes) of 3654 baseline Blue Mountains Eye Study participants (aged 49–97 ) were digitized. Retinal vascular D f was quantified using a computer-based program. Summated severity scores for nuclear, cortical and posterior subcapsular (PSC) cataract were assessed from lens photographs. Refraction data were converted to spherical equivalent refraction (SER), as sum spherical plus 0.5 cylinder power. Axial length was measured at 10-year follow-up examinations using an IOL master. Results: Mean D f of the retinal vasculature was 1.444 ± 0.023 for 2859 eligible participants. Increasing lens opacity scores were associated with significant reduction in D f (β = −0.0030, p < 0.0001). Both cortical and PSC cataract involving central lens area were associated with reduced D f, after controlling for confounding factors (ptrend ≤ 0.0105). Increasing myopia severity was associated with reduced D f after adjusting for lens opacity scores and other confounders (ptrend < 0.0001). The slope of D f decrease per SER reduction was 0.0040 in eyes with SER ≤ −4D, compared to −0.0016 in eyes with SER > −4D. For axial length quintiles, there were no significant differences in mean D f in all groups except a reduction in the fifth quintile (axial length ≥24.15 mm) (all p < 0.05). Conclusion: Ocular media opacity independently influenced retinal vascular D f measurement, but we found no evidence supporting any refractive axial magnification effect on this measure. Myopic refraction ≤−4D was associated with a reduction in D f, suggesting rarefaction of retinal vasculature associated with high myopia. [ABSTRACT FROM AUTHOR]

2-Quinolylcarbene 23 and 1-isoquinolylcarbene 33 are generated by flash vacuum thermolysis (FVT) of the corresponding triazolo[1,5-a]quinoline and triazolo[5,1-a]isoquinoline 19 and 29, as well as 2-(5-tetrazolyl)quinoline and 1-(5-tetrazolyl)isoquinoline 20 and 39, respectively. These carbenes rearrange to 1- and 2-naphthylnitrene 21 and 31, respectively, and the nitrenes are also generated by FVT of 1- and 2-naphthyl azides 18 and 28. The products of FVT of both the nitrene and carbene precursors are the 2- and 3-cyanoindenes 26 and 27 together with the nitrene dimers, viz. azonaphthalenes 25 and 35, and the H-abstraction products, aminonaphthalenes 24 and 34. All the azide, triazole, and tetrazole precursors yield 3-cyanoindene 26 as the principal ring contraction product under conditions of low FVT temperature (340-400 °C) and high pressure (1 Torr N2 as carrier gas for the purpose of collisional deactivation). This ring contraction reaction is strongly subject to chemical activation, which caused extensive isomerization of 3-cyanoindene to 2-cyanoindene under conditions of low pressure (10-3 Torr). 2-Cyanoindene is calculated to be ca. 1.7 kcal/mol below 3-cyanoindene in energy; accordingly, high-temperature FVT of these cyanoindenes always gives mixtures of the two compounds with the 2-cyano isomer dominating. Photolysis of trizolo[1,5-a]quinoline 19 and triazolo[5,1-a]isoquinoline 29 in Ar matrixes causes partial ring opening to the corresponding 2-diazomethylquinoline 19' and 1-diazomethylisoquinoline 29'. The photolysis of the former gives rise to a small amount of the cyclic ketenimine 22, the intermediate connecting 2-quinolylcarbene and 1-naphthylnitrene. [ABSTRACT FROM AUTHOR]

Study Objectives: From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature., Methods: We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible., Results: In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized., Conclusions: In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies., Citation: Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med . 2024;20(11):1823-1838., (© 2024 American Academy of Sleep Medicine.)