Physical examination (PE), mammography (MG), breast MRI, FDG-PET and pathologic evaluation are used to assess primary breast cancer. Their accuracy has not been well studied in patients receiving neoadjuvant chemotherapy. Accuracies of each modality in tumor and nodal assessment in patients with T3/4 tumors receiving neoadjuvant chemotherapy were compared. METHODS-45 patients of a prospective clinical trial studying T3-T4M0 tumors were included. Patients received neoadjuvant chemotherapy: docetaxel/carboplatin with or without trastuzumab before and/or after surgery (depending on HER-2/neu status and randomization). Tumor measurements by PE, MG, and MRI and nodal status by PE and PET were obtained before and after neoadjuvant chemotherapy. Concordance among different clinical measurements was assessed and compared with the tumor and nodal staging by pathology. Spearman corr (r) and root mean square error (RMSE) were used to measure the accuracy of measurements among all modalities and between modalities and pathological tumor size. RESULTS-Comparing to the tumor size measured by PE, MRI was more accurate than MG at baseline (r 0.559, RMSE 35.4% vs. r 0.046, RMSE 66.1%). After neoadjuvant chemotherapy, PE correlated better with pathology than MG or MRI (r 0.655, RMSE 88.6% vs. r 0.146, RMSE 147.1% and r 0.364, RMSE 92.6%). Axillary nodal assessment after neoadjuvant chemotherapy showed high specificity but low sensitivity by PET and PE. CONCLUSION-Findings suggested that MRI was a more accurate imaging study at baseline for T3/T4 tumor and PE correlated best with pathology finding. PET and PE both correctly predicted positive axillary nodes but not negative nodes.
To date, the effect of tooth loss on all-cause mortality among elderly patients with a different weight group has not been assessed. This retrospective cohort study evaluated the data obtained from a government-sponsored, annual physical examination program for elderly citizens residing in Taipei City during 2005 to 2007, and follow-up to December 31, 2010. We recruited 55,651 eligible citizens of Taipei City aged ≥65 years, including 29,572 men and 26,079 women, in our study. Their mortality data were ascertained based on the national death files. The number of missing teeth was used as a representative of oral health status. We used multivariate Cox proportional hazards regression analysis to determine the association between tooth loss and all-cause mortality. After adjustment for all confounders, the hazard ratios (HRs) of all-cause mortality in participants with no teeth, 1 to 9 teeth, and 10 to 19 teeth were 1.36 [95% confidence interval (CI): 1.15–1.61], 1.24 (95% CI: 1.08–1.42), and 1.19 (95% CI: 1.09–1.31), respectively, compared with participants with 20 or more teeth. A significant positive correlation of body mass index (BMI) with all-cause mortality was found in underweight and overweight elderly patients and was represented as a U-shaped curve. Subgroup analysis revealed a significant positive correlation in underweight (no teeth: HR = 1.49, 95% CI: 1.21–1.83; 1–9 teeth: HR = 1.23, 95% CI: 1.03–1.47; 10–19 teeth: HR = 1.20, 95% CI: 1.06–1.36) and overweight participants (no teeth: HR = 1.37, 95% CI: 1.05–1.79; 1–9 teeth: HR = 1.27, 95% CI: 1.07–1.52). The number of teeth lost is associated with an increased risk of all-cause mortality, particularly for participants with underweight and overweight.
ObjectiveStroke survivors generally have problems completing instrumental activities of daily living (IADL; eg, preparing meals, chores, taking a bath, and managing finances). However, it is unclear how stroke survivors might stave off IADL issues. Studies indicating that sleep has restorative neurological effects provide potential mechanisms to address issues with IADL. The aim of this study was to ascertain the association between sleep duration (short or long sleep duration) and IADL among stroke survivors and those without a stroke history.MethodsData of 486,619 participants were analyzed from the 2000 to 2015 National Health Interview Survey (NHIS), a nationally representative sample. Measures of self-reported stroke, sociodemographic variables, sleep duration, and IADL problems were collected. Binary logistic regression was utilized to analyze the relationship of short (≤6 hours) and long (≥9 hours) sleep duration with limitations to IADL.ResultsOf the sample, 3% reported a physician-diagnosed stroke event. The mean age was 45.73 years; 52.7% were female; 77.4% were White; 14.2% were Black; 41.3% were married, 62.7% were employed; 31.1% reported that annual family income was less than $35,000; 87% reported good-to-excellent health; and 29.7% reported short sleep (≤6 hours). Approximately 30% of stroke survivors reported IADL problems, and 34.4% who reported IADL problems were short sleepers. Among stroke survivors, long sleepers were 97% more likely than average sleepers to report IADL problems (OR =1.97, 95% CI =1.71–2.26, P<0.001) adjusting for age, sex, race, marital status, poverty, and health.ConclusionFindings from our study indicate that, among stroke survivors, long sleepers were more likely to report IADL problems compared to average sleepers (7–8 hours). Future studies should investigate other potential mediators such as severity of stroke, medication, comorbidities, level of impairment, and whether improving sleep among stroke survivors may improve IADL.
Supplement, 2017 n=300) is a case control study 1:1 matched by age (±5 years), sex, race and body mass index (BMI±5kg/m 2 ) including those >18 years with paroxysmal AF (PAF) and controls without AF. Participants underwent administration of STOP-Bang, NoSAS (neck circumference, obesity, snoring and sex), Berlin and Epworth Sleepiness Scale (ESS) questionnaires and16-channel research-grade polysomnography. We examined questionnaire diagnostic performance characteristics for moderate to severe OSA (apnea hypopnea index≥15) separately in PAF and without, including area under the curve (AUC, 95% confidence intervals). Analyses were performed in SAS software (version 9.4; Cary, NC). Results: The analytic sample was comprised of 300 participants (n=150 cases and n=150 controls): age 61.9 ± 11.9 years, 63.3% male, and BMI 31.4 ± 6.7 kg/m 2 . Sensitivity for the 4 questionnaires was lower, albeit comparable, in PAF (range: 52-79%) versus controls (range: 61-75%). NoSAS showed highest sensitivity in PAF (79%). Specificity range was overall lower in PAF (43-60%) versus controls (56-80%) The positive predictive value range was lower in PAF (23-27%) versus controls (54-72%). Conversely, the negative predictive value range was higher in PAF (84-89%) versus controls (63-75%). The AUC was lower in PAF versus controls except comparable for STOP-BANG (0.66, 0.56-0.77 versus 0.65, 0.57-0.74) and higher for NoSAS (0.79, 0.72-0.86 versus 0.64, 0.53-0.75) respectively. Conclusion: In this systematic assessment of standard OSA screening instruments, the NoSAS questionnaire performed most optimally in terms of sensitivity and reasonable discriminative ability of moderate to severe OSA detection in those with PAF. Further investigation is needed to identify effective OSA screening strategies with focused efforts on development/refinement of novel OSA screening tools in the AF population. Support (If Any):
Introduction Initial download and use of sleep tracking is very high, but prolonged use is very low. Poor prolonged use may be attributable to several factors such as engagement, functionality, aesthetics, information, and recommendation. We appraised these five factors in 16 consumer- and research/medical- grade digital sleep devices. Methods Three reviewers independently assessed 16 consumer- and medical-grade sleep digital devices using the Mobile Application Rating Scale (MARS) App quality ratings, which measures engagement (engagement, entertainment, interest, customization, interactivity, target group), functionality (functionality, performance, ease of use, navigation, gestural design), aesthetics (layout, graphics, visual appeal), information (Accuracy. Goals, Quality of information, Quantity of information, Visual information, Credibility, and Evidence base) and recommended on a Likert scale, with 1- Inadequate to 5 Excellent. Each subcategory is rated on a 1-5 Likert scale which is summed for each category: engagement (30), functionality (25), aesthetics (15), information (35) and recommended (yes or no). Results Devices that had the highest engagement score were Fitbit (27), Apple Watch (27), Garmin (27), and Dreem 2 headband (25.5). Apple Watch (30) had highest score; while Fitbit (13), Apple Watch (13), Garmin (13), Samsung Gear (13) had highest aesthetic score. While for information, ActiGraph (35), SOMNOwatch plus (35), CleveMed SleepView Monitor (35), CleveMed Sapphire PSG (35), SOMNOscreen plus (35), Nox T3 Sleep Monitor (35) and Nox A1 PSG System (35) had the highest ratings. The Dreem 2 headband has the potential induce prolong use among users with and without sleep disorders, based on high scores on engagement (25.5), Functionality (20.5), and Information (26.5). Conclusion Consumer- and research-grade digital devices that measure sleep have varying levels of engagement, functionality, aesthetics, information and recommendations to facilitate prolong use. Consumer grade devices had higher engagement, functionality and aesthetics scores, while research grade devices had higher information and recommendation scores. If consumer- and research-grade devices are to have prolonged use, standardization is needed across the five MARS components. Support K01HL135452, R01MD007716, R01HL142066, and K07AG052685
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