Amblyopia is a developmental visual disorder characterized by abnormal form vision and binocular functions. Past research has differentiated the performance of subjects with strabismic and anisometropic subtypes of amblyopia.^1-6 Often the motivation is to better understand the pathogenesis of amblyopia. An important source of evidence is monocular position acuity tasks. For example, Vernier acuity loss is more severe in subjects with strabismus compared with that in subjects with anisometropia. In subjects with strabismus, the deficit is larger than that predicted by linearly scaling their grating acuity,⁵ indicating that additional deficits are involved. Different models attribute this acuity loss according to the size, shape, or density of receptive fields in the visual cortex, or to spatial disarray in their topographical location.^2,7-9 In general, these models suggest that abnormal “noise” arises in the visual cortex, probably in areas beyond the primary visual cortex.

We examined the association between the colonic adherent microbiota and nocturnal sleep duration in humans. In a cross-sectional study, 63 polyp-free adults underwent a colonoscopy and donated 206 mucosal biopsies. The gut microbiota was profiled using the 16S rRNA gene sequencing targeting the V4 region. The sequence reads were processed using UPARSE and DADA2, respectively. Lifestyle factors, including sleep habits, were obtained using an interviewer-administered questionnaire. We categorized the participants into short sleepers (<6 h per night; n = 16) and normal sleepers (6–8 h per night; n = 47) based on self-reported data. Differences in bacterial biodiversity and the taxonomic relative abundance were compared between short vs. normal sleepers, followed by multivariable analysis. A false discovery rate-adjusted p value (q value) < 0.05 indicated statistical significance. The bacterial community composition differed in short and normal sleepers. The relative abundance of Sutterella was significantly lower (0.38% vs. 1.25%) and that of Pseudomonas was significantly higher (0.14% vs. 0.08%) in short sleepers than in normal sleepers (q values < 0.01). The difference was confirmed in the multivariable analysis. Nocturnal sleep duration was associated with the bacterial community composition and structure in the colonic gut microbiota in adults.

Rationale: Central sleep apnea (CSA) is associated with high mortality. Current knowledge stems from studies with limited sample size (fewer than 100 subjects) and in homogeneous populations such as heart failure (HF). Objectives: To address this knowledge gap, we compared the mortality pattern and time to death between the CSA and obstructive sleep apnea (OSA) patients in the large Veterans Health Administration patient population using the big data analytic approach. Methods: This is a retrospective study using national Veterans Health Administration electronic medical records from October 1, 1999, through September 30, 2020. We grouped the patients with underlying sleep disorders into CSA and OSA, using the International Classification of Diseases, Ninth and Tenth Revision codes. We applied Cox regression analysis to compare the mortality rate and hazard ratio (HR) among the two groups and adjusted HR by gender, race, body mass index (BMI), age, and Charlson Comorbidity Index. In CSA groups, a machine-learning algorithm was used to determine the most important predictor of time to death. Further subgroup analysis was also performed in patients that had comorbid HF. Results: Evaluation of patients resulted in 2,961 grouped as CSA and 1,487,353 grouped as OSA. Patients with CSA were older (61.8 ± 15.6 yr) than those with OSA (56.7 ± 13.9 yr). A higher proportion of patients with CSA (25.1%) died during the study period compared with the OSA cohort (14.9%). The adjusted HR was 1.53 (95% confidence interval [CI], 1.43–4.65). Presence of HF history of cerebrovascular disease, hemiplegia, and having a BMI less than 18.5 were among the highest predictors of mortality in CSA. The subgroup analysis revealed that the presence of HF was associated with increased mortality both in CSA (HR, 7.4; 95% CI, 6.67–8.21) and OSA (HR, 4.3; 95% CI, 4.26–4.34) groups. Conclusions: Clinically diagnosed CSA was associated with a shorter time to death from the index diagnostic date. Almost one-fifth of patients with CSA died within 5 years of diagnosis. The presence of HF, history of cerebrovascular disease and hemiplegia, male sex, and being underweight were among the highest predictors of mortality in CSA. CSA was associated with higher mortality than OSA, independent of associated comorbidity.

Background: There is a need to better understand the association between sleep and chronic diseases. In this study we developed a natural language processing (NLP) algorithm to mine polysomnography (PSG) free-text notes from electronic medical records (EMR) and evaluated the performance. Methods: Using the Veterans Health Administration EMR, we identified 46,093 PSG studies using CPT code 95,810 from 1 October 2000–30 September 2019. We randomly selected 200 notes to compare the accuracy of the NLP algorithm in mining sleep parameters including total sleep time (TST), sleep efficiency (SE) and sleep onset latency (SOL), wake after sleep onset (WASO), and apnea-hypopnea index (AHI) compared to visual inspection by raters masked to the NLP output. Results: The NLP performance on the training phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. The NLP performance on the test phase was >0.90 for precision, recall, and F-1 score for TST, SOL, SE, WASO, and AHI. Conclusions: This study showed that NLP is an accurate technique to extract sleep parameters from PSG reports in the EMR. Thus, NLP can serve as an effective tool in large health care systems to evaluate and improve patient care.