Association between habitual snoring,middle ear disease,and speech problems in young children with non-syndromic cleft palate anomalies

The purpose of this study was to investigate the association between habitual snoring (HS), middle ear disease (MED), and speech problems in children with cleft palate. This cross-sectional study included children aged 2.0–7.9 years with non-syndromic cleft palate anomalies. Parents completed the Pediatric Sleep Questionnaire and a questionnaire about MED. Audiograms and speech assessment were also conducted. Ninety-five children were enrolled; 15.2% of families reported HS, 97.6% MED, and 17.1% speech problems. HS (37.5% vs 10.3%, P = 0.007) and early episodes of MED (92.3% vs 58.2%, P = 0.021) were more likely to be reported for children with isolated cleft palate when compared to those with cleft lip and palate. Children with cleft lip and palate had a higher frequency of MED with effusion compared to those with Robin sequence (86.4% vs 57.1%, P = 0.049). The odds ratio for HS in children with ≥1 episode of MED in the last year was 7.37 (95% confidence interval 1.55–35.15, P = 0.012). There was a trend for children with speech problems reported by parents to have HS (30.8% vs 11.5%, P= 0.076). Anatomical factors play a role in the frequency of upper airway symptoms in children with cleft palate. A recent history of at least one episode of MED was associated with an increased frequency of HS.     

Association between dysglycemia and the Charlson Comorbidity Index among hospitalized patients with diabetes

AimInpatient dysglycemia has been linked to short-term mortality, but longer-term mortality data are lacking. Our aim was to evaluate the association between inpatient dysglycemia and one-year mortality risk.MethodsRetrospective chart review of adults with diabetes hospitalized between 2015 and 2019. The Charlson Comorbidity Index (CCI) was used to estimate 1-year mortality risk, stratified into low (CCI ≤ 5) and high risk (CCI ≥6). Simple and multivariable logistic regression was used to evaluate the association between dysglycemic measures and high mortality risk.ResultsAmong 22,639 unique admissions, BG ≥ 180, ≥300, ≤70, <54 and <40 mg/dL were associated with adjusted odds of 1.43 (95 % CI, 1.33, 1.54), 1.58 (95 % CI, 1.48, 1.68), 2.16 (95 % CI, 2.01, 2.32), 2.58 (95 % CI, 2.32, 2.86), and 2.56 (95 % CI, 2.19, 2.99) for high mortality risk, respectively. Older age and Black race were positively associated with hyperglycemia and hypoglycemia. Myocardial infarction, congestive heart failure (CHF), and moderate to severe liver disease were most strongly associated with hyperglycemia, while renal disease, CHF, peripheral vascular disease, and peptic ulcer disease were most strongly associated with hypoglycemia.ConclusionsInpatient hypoglycemia and hyperglycemia were both positively associated with higher one-year mortality risk, with stronger magnitude of association observed for hypoglycemia. The association appears to be mediated mainly by presence of diabetes-related complications.     

Rationale for a Clinical Trial That Compares Acute Stroke Rehabilitation at Inpatient Rehabilitation Facilities to Skilled Nursing Facilities: Challenges and Opportunities

In the United States, approximately 400,000 patients with acute stroke are discharged annually to inpatient rehabilitation facilities (IRFs) or skilled nursing facilities (SNFs). Typically, IRFs provide time-intensive therapy for an average of 2-3 weeks, whereas SNFs provide more moderately intensive therapy for 4-5 weeks. The factors that influence discharge to an IRF or SNF are multifactorial and poorly understood. The complexity of these factors in combination with subjective clinical indications contributes to large variations in the use of IRFs and SNFs. This has significant financial implications for health care expenditure, given that stroke rehabilitation at IRFs costs approximately double that at SNFs. To control health care spending without compromising outcomes, the Institute of Medicine has stated that policy reforms that promote more efficient use of IRFs and SNFs are critically needed. A major barrier to the formulation of such policies is the highly variable and low-quality evidence for the comparative effectiveness of IRF- vs SNF-based stroke rehabilitation. The current evidence is limited by the inability of observational data to control for residual confounding, which contributes to substantial uncertainty around any magnitude of benefit for IRF- vs SNF-based care. Furthermore, it is unclear which specific patients would receive the most benefit from each setting. A randomized controlled trial addresses these issues, because random treatment allocation facilitates an equitable distribution of measured and unmeasured confounders. We discuss several measurement, practical, and ethical issues of a trial and provide our rationale for design suggestions that overcome some of these issues.     

Novel pathogenic variants in an Indian cohort with epidermolysis bullosa: Expanding the genotypic spectrum

BackgroundEpidermolysis bullosa (EB) is a genodermatosis characterized by skin fragility and blisters with variable severity. Patients with Dystrophic EB (DEB) or Junctional EB (JEB) mainly present to clinic due to greater functional impairment. Pathogenic sequence variations in COL7A1 are implicated in DEB.ObjectiveWe have tried to decipher the molecular spectrum and genotype phenotype correlation of 21 Indian patients with EB.MethodsNext generation sequencing (NGS) was performed to determine the pathogenic variants. Sanger sequencing was also done for validation of the variants in eleven individuals.ResultsPathogenic variants were detected in 20 individuals (diagnostic yield of 95%). Majority of them (90%) had sequence variation in COL7A1 while two had pathogenic variants in ITGB4 and KRT14 respectively. Out of the 18 patients confirmed to have DEB, 3 had Dominant DEB (DDEB) whereas 15 patients had Recessive DEB (RDEB). Amongst 23 sequence variations identified, 12 were found to be novel (3 were missense, 5 were premature termination codon variants while 4 were splice-site changes).ConclusionGenotype phenotype correlation was noted with milder manifestations in those with dominant inheritance types. Exact molecular diagnosis can be ascertained by NGS in majority of cases.     

Quantifying and leveraging predictive uncertainty for medical image assessment

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy. Finally, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.     

Directional-TV algorithm for image reconstruction from limited-angular-range data

Investigation of image reconstruction from data collected over a limited-angular range in X-ray CT remains a topic of active research because it may yield insight into the development of imaging workflow of practical significance. This reconstruction problem is well-known to be challenging, however, because it is highly ill-conditioned. In the work, we investigate optimization-based image reconstruction from data acquired over a limited-angular range that is considerably smaller than the angular range in short-scan CT. We first formulate the reconstruction problem as a convex optimization program with directional total-variation (TV) constraints applied to the image, and then develop an iterative algorithm, referred to as the directional-TV (DTV) algorithm for image reconstruction through solving the optimization program. We use the DTV algorithm to reconstruct images from data collected over a variety of limited-angular ranges for breast and bar phantoms of clinical- and industrial-application relevance. The study demonstrates that the DTV algorithm accurately recovers the phantoms from data generated over a significantly reduced angular range, and that it considerably diminishes artifacts observed otherwise in reconstructions of existing algorithms. We have also obtained empirical conditions on minimal-angular ranges sufficient for numerically accurate image reconstruction with the DTV algorithm.