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.

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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...