Sleep disorders in pregnancy

PURPOSE OF REVIEW: Sleep disturbances are frequent during pregnancy. The spectrum of association between pregnancy and sleep disturbances ranges from an increased incidence of insomnia, nocturnal awakenings, and parasomnias (especially restless legs syndrome) to snoring and excessive sleepiness. These disturbances occur as a result of physiologic, hormonal, and physical changes associated with pregnancy. Although the timing and occurrence of different sleep disorders varies, they are most prevalent during the third trimester. Despite reports of the various sleep problems, the exact nature and incidence of sleep disorders in pregnancy is not known. Given that limitation, we are presenting an up-to-date review of the current understanding of the relation between sleep and pregnancy. RECENT FINDINGS: Studies suggest that pregnancy affects sleep in multiple ways. There are hormonal changes, physiologic changes, physical factors, and behavioral changes in a pregnant woman-all of which may affect her sleep. They may affect the duration and quality of sleep and lead to a variety of sleep disorders. Pregnancy may also affect an existing sleep disorder. Particular attention may be given to obese pregnant women who would gain more weight during pregnancy or those who develop hypertensive conditions (eg, preeclampsia). Snoring may be more common in women with preeclampsia and the pressor responses to obstructive respiratory events during sleep may be enhanced in preeclamptic women when compared with those with obstructive sleep apnea alone. Several investigators have suggested that obstructive sleep apnea (OSA) may be common in pregnant women despite the presence of intrinsic mechanisms that seem to be geared towards preventing sleep apnea. However, the exact incidence and prevalence of sleep apnea in pregnant women is uncertain. In addition, it is unclear if criteria that are used to define sleep apnea in the general population should be applied to pregnant women. Further investigations are needed to determine if lower thresholds for management of OSA should be used in pregnant women to prevent harm to the fetus. SUMMARY: In conclusion, sleep disturbances are common during pregnancy though the full extent of this relation remains undefined. Large, multi-center, prospective studies are needed for better understanding.     

Resident recognition of open-angle glaucoma

To test an educational intervention’s effect on improving detection of glaucoma by direct ophthalmoscopy, 14 medicine residents examined five patients, two with ophthalmoscopic changes of glaucoma and three with normal fundi. The residents observed a standardized slide/narrative educational intervention reviewing glaucomatous ophthalmoscopic changes and then re-examined the same patients eight to 12 weeks later. The intervention’s odds of improving residents’ diagnostic impression were significant (OR=2.2; 95% CI=1.3–36), with significant improvement in sensitivity (p=0.02) and a trend toward improved specificity. These findings confirm that the diagnosis of glaucomatous ocular changes on eye examinations by medicine residents can be improved with a brief educational intervention. Presented in abstract form at the annual meeting of the Society of General Internal Medicine, Medical Education Section, April 29–May 1, 1992, Washington, DC, and at the Southern regional meeting of the Society of General Internal Medicine, Medical Education Section, January 29–31, 1992, New Orleans, Louisiana.     

Incidence of Overuse Musculoskeletal Injuries in Military Service Members With Traumatic Lower Limb Amputation

Objective

To describe the incidence of overuse musculoskeletal injuries in service members with combat-related lower limb amputation.

Editor's Choice: Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy.Materials and methods Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype.Results The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features.Discussion Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable.Conclusion The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.     

Post-acute care referral and inpatient rehabilitation admission criteria for persons with brain injury across two Canadian provinces

Purpose: Investigate health care providers’ perceptions of referral and admission criteria to brain injury inpatient rehabilitation in two Canadian provinces.

Methods: Health care providers (n?=?345) from brain injury programs (13 acute care and 16 rehabilitation facilities) participated in a cross-sectional web-based survey. The participants rated the likelihood of patients (traumatic brain injury and cerebral hypoxia) to be referred/admitted to rehabilitation and the influence of 19 additional factors (e.g., tracheostomy). The participants reported the perceived usefulness of referral/admission policies and assessment tools used.

Results: Ninety-one percent acute care and 98% rehabilitation participants reported the person with traumatic brain injury would likely or very likely be referred/admitted to rehabilitation compared to respectively 43% and 53% for the patient with hypoxia. Two additional factors significantly decreased the likelihood of referral/admission: older age and the combined presence of minimal learning ability, memory impairment and physical aggression. Some significant inter-provincial variations in the perceived referral/admission procedure were observed. Most participants reported policies were helpful. Similar assessment tools were used in acute care and rehabilitation.

Conclusions: Health care providers appear to consider various factors when making decisions regarding referral and admission to rehabilitation. Variations in the perceived likelihood of referral/admission suggest a need for standardized referral/admission practices.

• Implications for Rehabilitation

• Various patient characteristics influence clinicians’ decisions when selecting appropriate candidates for inpatient rehabilitation.

• In this study, acute care clinicians were less likely to refer patients that their rehabilitation counter parts would likely have admitted and a patient with hypoxic brain injury was less likely to be referred or admitted in rehabilitation than a patient with a traumatic brain injury.

• Such discrepancies suggest that policy-makers, managers and clinicians should work together to develop and implement more standardized referral practices and more specific admission criteria in order to ensure equitable access to brain injury rehabilitation services.

     

Model-based prediction of the acute and long-term safety profile of naproxen in rats

Background and Purpose

Despite the increasing importance of biomarkers as predictors of drug effects, toxicology protocols continue to rely on the experimental evidence of adverse events (AEs) as a basis for establishing the link between indicators of safety and drug exposure. Furthermore, biomarkers may facilitate the translation of findings from animals to humans. Combined with a model-based approach, biomarker data have the potential to predict long-term effects arising from prolonged drug exposure. Here, we used naproxen as a paradigm to explore the feasibility of a biomarker-guided approach for the prediction of long-term AEs in humans.

Experimental Approach

An experimental toxicology protocol was set up for evaluating the effects of naproxen in rats, in which four active doses were tested (7.5, 15, 40 and 80 mg·kg⁻¹). In addition to AE monitoring and histology, a few blood samples were also collected for the assessment of drug exposure, TXB₂ and PGE₂ levels. Non-linear mixed effects modelling was used to analyse the data and identify covariate factors on the incidence and severity of AEs.

Key Results

Modelling results showed that besides drug exposure, maximum PGE₂ inhibition and treatment duration were also predictors of gastrointestinal ulceration. Although PGE₂ levels were clearly linked to the incidence rates, it appeared that ulceration severity is better predicted by measures of drug exposure.

Conclusions and Implications

These results show that the use of a model-based approach provides the opportunity to integrate pharmacokinetics, pharmacodynamics and toxicity data, enabling optimization of the design, analysis and interpretation of toxicology experiments.