The objective of this study was to describe outcomes of tuberculosis (TB) contact investigations, factors correlated with those outcomes, and current successes and ways to improve TB contact investigations. We abstracted clinic records of a representative U.S. urban sample of 1,080 pulmonary, sputum-smear(+) TB patients reported to CDC July 1996 through June 1997 and the cohort of their 6,225 close contacts. We found a median of four close contacts per patient. Fewer contacts were identified for homeless patients. A visit to the patient's residence resulted in two additional (especially child) contacts identified. Eighty-eight percent of eligible contacts received tuberculin skin tests (TSTs). Recording the last exposure date to the infectious patient facilitated follow-up TST provision. Thirty-six percent of contacts were TST(+). Household contacts and contacts to highly smear(+) or cavitary TB patients were most likely to be TST(+). Seventy-four percent of TST(+) contacts started treatment for latent TB infection (LTBI), of whom 56% completed. Sites using public health nurses (PHNs) started more high-risk TST(-) contacts on presumptive treatment for LTBI. Using directly observed treatment (DOT) increased the likelihood of treatment completion. We documented outcomes of contact investigation efforts by urban TB programs. We identified several successful practices, as well as suggestions for improvements, that will help TB programs target policies and procedures to enhance contact investigation effectiveness.

No abstract

IMPORTANCE Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. DESIGN, SETTING, AND PARTICIPANTS This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. EXPOSURES Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). MAIN OUTCOMES AND MEASURES The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. RESULTS The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (continued) Key Points Question Can machine learning models predict improvement of various depressive symptoms with antidepressant treatment based on pretreatment symptom scores and electroencephalographic measures? Findings In this prognostic study, using the machine learning approach of gradient-boosted decision trees, the ElecTreeScore algorithm could reliably distinguish the patients who r...

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these “functional connectomes” are reliable over time. In a large public sample of healthy participants ( N = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6–8%) to excellent (0.08–0.10%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging.

No abstract

ImportanceIn patients with severe aortic valve stenosis at intermediate surgical risk, transcatheter aortic valve replacement (TAVR) with a self-expanding supra-annular valve was noninferior to surgery for all-cause mortality or disabling stroke at 2 years. Comparisons of longer-term clinical and hemodynamic outcomes in these patients are limited.ObjectiveTo report prespecified secondary 5-year outcomes from the Symptomatic Aortic Stenosis in Intermediate Risk Subjects Who Need Aortic Valve Replacement (SURTAVI) randomized clinical trial.Design, Setting, and ParticipantsSURTAVI is a prospective randomized, unblinded clinical trial. Randomization was stratified by investigational site and need for revascularization determined by the local heart teams. Patients with severe aortic valve stenosis deemed to be at intermediate risk of 30-day surgical mortality were enrolled at 87 centers from June 19, 2012, to June 30, 2016, in Europe and North America. Analysis took place between August and October 2021.InterventionPatients were randomized to TAVR with a self-expanding, supra-annular transcatheter or a surgical bioprosthesis.Main Outcomes and MeasuresThe prespecified secondary end points of death or disabling stroke and other adverse events and hemodynamic findings at 5 years. An independent clinical event committee adjudicated all serious adverse events and an independent echocardiographic core laboratory evaluated all echocardiograms at 5 years.ResultsA total of 1660 individuals underwent an attempted TAVR (n = 864) or surgical (n = 796) procedure. The mean (SD) age was 79.8 (6.2) years, 724 (43.6%) were female, and the mean (SD) Society of Thoracic Surgery Predicted Risk of Mortality score was 4.5% (1.6%). At 5 years, the rates of death or disabling stroke were similar (TAVR, 31.3% vs surgery, 30.8%; hazard ratio, 1.02 [95% CI, 0.85-1.22]; P = .85). Transprosthetic gradients remained lower (mean [SD], 8.6 [5.5] mm Hg vs 11.2 [6.0] mm Hg; P < .001) and aortic valve areas were higher (mean [SD], 2.2 [0.7] cm2 vs 1.8 [0.6] cm2; P < .001) with TAVR vs surgery. More patients had moderate/severe paravalvular leak with TAVR than surgery (11 [3.0%] vs 2 [0.7%]; risk difference, 2.37% [95% CI, 0.17%- 4.85%]; P = .05). New pacemaker implantation rates were higher for TAVR than surgery at 5 years (289 [39.1%] vs 94 [15.1%]; hazard ratio, 3.30 [95% CI, 2.61-4.17]; log-rank P < .001), as were valve reintervention rates (27 [3.5%] vs 11 [1.9%]; hazard ratio, 2.21 [95% CI, 1.10-4.45]; log-rank P = .02), although between 2 and 5 years only 6 patients who underwent TAVR and 7 who underwent surgery required a reintervention.Conclusions and RelevanceAmong intermediate-risk patients with symptomatic severe aortic stenosis, major clinical outcomes at 5 years were similar for TAVR and surgery. TAVR was associated with superior hemodynamic valve performance but also with more paravalvular leak and valve reinterventions.

Hispanic populations have low HPV vaccination rates, although the vaccine is safe and efficacious. We surveyed a low-income Hispanic population to characterize knowledge gaps about the HPV vaccine and understand factors associated with the decision to vaccinate a child to determine how physicians can enhance vaccination rates. Surveys in English and Spanish were distributed to parents of children under age 18. Statistical analysis included logistic regression. Knowledge that the vaccine can prevent invasive cervical cancer most impacted intent to vaccinate. Physician recommendation to vaccinate was far more influential in a parent's decision compared to TV and other sources. Girls are more likely to receive the HPV vaccine over boys. While physician recommendation is critical, they have minimal time for education. Our results suggest that physicians should focus on the vaccine's link to cancer prevention, leaving other knowledge areas for the interdisciplinary care team.

To describe the policies and procedures used by 11 urban tuberculosis control programs to conduct contact investigations, written policies were reviewed and semi-structured interviews were conducted with program managers and staff. Qualitative analysis showed that contact investigation policies and procedures vary widely. Most policies address risk factor assessment and contact prioritization; however, none of the policies provide comprehensive guidance for the entire process. Staffing patterns vary, but, overall, staff receive little formal training; informal monitoring practices predominate. Comprehensive guidelines and programmatic support are needed to improve the quality of contact investigation processes.