RESUMEN

Alzheimer's disease is a type of neurodegenerative disorder that is characterized by the progressive degeneration of brain cells, leading to cognitive decline and memory loss. It is the most common cause of dementia and affects millions of people worldwide. While there is currently no cure for Alzheimer's disease, early detection and treatment can help to slow the progression of symptoms and improve quality of life. This research presents a diagnostic tool for classifying mild cognitive impairment and Alzheimer's diseases using feature-based machine learning applied to optical coherence tomographic angiography images (OCT-A). Several features are extracted from the OCT-A image, including vessel density in five sectors, the area of the foveal avascular zone, retinal thickness, and novel features based on the histogram of the range-filtered OCT-A image. To ensure effectiveness for a diverse population, a large local database for our study was collected. The promising results of our study, with the best accuracy of 92.17,% will provide an efficient diagnostic tool for early detection of Alzheimer's disease.

Asunto(s)

Enfermedad de Alzheimer , Disfunción Cognitiva , Tomografía de Coherencia Óptica , Humanos , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/clasificación , Enfermedad de Alzheimer/patología , Disfunción Cognitiva/diagnóstico por imagen , Disfunción Cognitiva/diagnóstico , Tomografía de Coherencia Óptica/métodos , Angiografía/métodos , Aprendizaje Automático , Masculino , Anciano , Femenino

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[This corrects the article DOI: 10.3389/fmed.2022.1060990.].

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Background: Frailty has been increasingly recognized as a public health problem for aging populations with significant social impact, particularly in low- and middle-income countries. We aimed to develop a modified version of the Thai Frailty Index (TFI) and explore the association between different frailty statuses, socioeconomic factors, and mortality in community-dwelling older people from a middle-income country. Methods: The data from participants aged ≥60 years in the Fourth Thai National Health Examination Survey were used to construct the 30-item TFI. Cutoff points were created based on stratum-specific likelihood ratio. TFI ≤ 0.10 was categorized as fit, 0.10-0.25 as pre-frail, 0.25-0.45 as mildly frail, and >0.45 as severely frail. The association of frailty status with mortality was examined using Cox proportional hazard models. Findings: Among 8,195 older adults with a mean age of 69.2 years, 1,284 died during the 7-year follow-up. The prevalence of frailty was 16.6%. The adjusted hazard ratio (aHR) for mortality in pre-frail was 1.76 (95% CI = 1.50-2.07), mildly frail 2.79 (95% CI = 2.33-3.35), and severely frail 6.34 (95% CI = 4.60-8.73). Having a caretaker in the same household alleviated mortality risk for severely frail participants with an aHR of 2.93 (95% CI = 1.92-4.46) compared with an aHR of 6.89 (95% CI = 3.87-12.26) among those living without a caretaker. Interpretation: The severity of frailty classified by the modified TFI can predict long-term mortality risk for community-dwelling older adults. Identification of severely frail older people to provide appropriate care might alleviate mortality risk. Our findings can inform policymakers to appropriately allocate services in a resource-limited setting.

RESUMEN

Background: The Nursing Delirium Screening Scale (Nu-DESC) is an effective instrument for assessing postoperative delirium (POD). This study translated the Nu-DESC into Thai ("Nu-DESC-Thai"), validated it, and compared its accuracy with the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5). Methods: The translation process followed the International Society for Pharmacoeconomics Outcome Research guidelines. Recruited participants were ≥ 70 years old, fluent in Thai, and scheduled for surgery. The exclusion criteria were cancellation or postponement of an operation, severe visual or auditory impairment, and patients with a Richmond Agitation Sedation Scale score of -4 or less before delirium assessment. Post-anesthesia care unit (PACU) nurses and residents on wards each used the Nu-DESC to assess delirium in 70 participants (i.e., 140 assessments) after the operation and after patient arrival at wards, respectively. Geriatricians confirmed the diagnoses using video observations and direct patient contact. Results: The participants' mean age was 76.5 ± 4.6 years. The sensitivity and specificity of the Nu-DESC-Thai at a threshold of ≥ 2 were 55% (95% CI, 31.5-76.9%) and 90.8% (84.2-95.3%), respectively, with an area under a receiver operating characteristic curve (AUC) of 0.73. At a threshold of ≥ 1, the sensitivity and specificity were 85% (62.1-96.8%) and 71.7% (62.7-79.5%), respectively (AUC, 0.78). Adding 1 point for failing backward-digit counting (30-1) to the Nu-DESC-Thai and screening at a threshold of ≥ 2 increased its sensitivity to 85% (62.1-96.8%) with the same specificity of 90.8% (84.2-95.3%). Conclusion: The Nu-DESC-Thai showed good validity and reliability for postoperative use. Its sensitivity was inadequate at a cutoff ≥ 2. However, the sensitivity improved when the threshold was ≥ 1 or with the addition of backward counting to Nu-DESC-Thai and screening at a threshold of ≥ 2.