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Background While increased CD8 counts and low CD4/CD8 ratio during treated HIV correlate with immunosenescence, their additional predictive values to identify individuals with HIV at higher risk of clinical events remain controversial. Methods We selected treatment-naive individuals initiating ART from ACTG studies 384, 388, A5095, A5142, A5202, and A5257 who had achieved viral suppression at year 2. We examined the effect of CD8+ T cell counts and CD4/CD8 at year 2 on the probability of AIDS and serious non-AIDS events in years 3
7. We used inverse probability weighting methods to address informative censoring, combined with multivariable logistic regression models. Findings We analyzed 5133 participants with a median age of 38 years; 959 (19%) were female, pre-ART median CD4 counts were 249 (Q1-Q3 91
372) cell/µL. Compared to participants with CD8 counts between 500/µL and 1499/µL, those with >1500/µL had a higher risk of clinical events during years 3
7 (aOR 1.75; 95%CI 1.33
2.32). CD4/CD8 ratio was not predictive of greater risk of events through year 7. Additional analyses revealed consistent CD8 count effect sizes for the risk of AIDS events and noninfectious non-AIDS events, but opposite effects for the risk of severe infections, which were more frequent among individuals with CD8 counts <500/µL (aOR 1.70; 95%CI 1.09
2.65). Interpretation The results of this analysis with pooled data from clinical trials support the value of the CD8 count as a predictor of clinical progression. People with very high CD8 counts during suppressive ART might benefit from closer monitoring and may be a target population for novel interventions. 

Objectives: The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods: Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM and extend the method to deal with this problem. Results: We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions: The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error. 

Background. Rapid blood culture diagnostics are of unclear benefit for patients with gram-negative bacilli (GNB) bloodstream infections (BSIs). We conducted a multicenter, randomized, controlled trial comparing outcomes of patients with GNB BSIs who had blood culture testing with standard-of-care (SOC) culture and antimicrobial susceptibility testing (AST) vs rapid organism identification (ID) and phenotypic AST using the Accelerate Pheno System (RAPID). Methods. Patients with positive blood cultures with Gram stains showing GNB were randomized to SOC testing with antimicrobial stewardship (AS) review or RAPID with AS. The primary outcome was time to first antibiotic modification within 72 hours of randomization. Results. Of 500 randomized patients, 448 were included (226 SOC, 222 RAPID). Mean (standard deviation) time to results was faster for RAPID than SOC for organism ID (2.7 [1.2] vs 11.7 [10.5] hours; P < .001) and AST (13.5 [56] vs 44.9 [12.1] hours; P < .001). Median (interquartile range [IQR]) time to first antibiotic modification was faster in the RAPID arm vs the SOC arm for overall antibiotics (8.6 [2.6–27.6] vs 14.9 [3.3–41.1] hours; P = .02) and gram-negative antibiotics (17.3 [4.9–72] vs 42.1 [10.1–72] hours; P < .001). Median (IQR) time to antibiotic escalation was faster in the RAPID arm vs the SOC arm for antimicrobial-resistant BSIs (18.4 [5.8–72] vs 61.7 [30.4–72] hours; P = .01). There were no differences between the arms in patient outcomes. Conclusions. Rapid organism ID and phenotypic AST led to faster changes in antibiotic therapy for gram-negative BSIs. 

Background: Carbapenem-resistant Enterobacteriaceae (CRE) are a global threat. Here, we describe the clinical and molecular characteristics of Centers for Disease Control and Prevention (CDC)-defined CRE in the US. Methods: The second Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae (CRACKLE-2, ClinicalTrials.gov: NCT03646227) is a prospective, multicenter, cohort study. Patients hospitalized in 49 US hospitals, with clinical cultures positive for CDC-defined CRE between 30 April 2016 and 31 August 2017 were included. Primary outcome was desirability of outcome ranking (DOOR) at 30 days. Clinical data and bacteria were collected, and whole genome sequencing (WGS) was performed. Findings: 1,040 patients with unique isolates were included; 449 (43%) with infection and 591 (57%) with colonization. CDC-defined CRE admission rate was 57 CDC-defined CRE admissions/100,000 admissions (95% CI: 45–71). Three subsets of CDC-defined CRE were identified: carbapenemase-producing Enterobacteriaceae (618/1,040, 59%); non-carbapenemase-producing CRE (194/1,040, 19%); and unconfirmed CRE (228/1,040, 22%; initially reported as CRE, but susceptible to carbapenems in two central laboratories). Klebsiella pneumoniae carbapenemase (KPC)-producing clonal group 258 K. pneumoniae was the most common carbapenemase-producing Enterobacteriaceae. In 449 patients with CDC-defined CRE infections, DOOR outcomes were not significantly different in patients with carbapenemase-producing Enterobacteriaceae, non-carbapenemase-producing CRE, and unconfirmed CRE. At 30 days 107/449 (24%, 95% CI 20–28%) patients had died. Interpretation: Among patients with CDC-defined CRE, similar outcomes were observed among three subgroups, including the novel unconfirmed CRE group. CDC-defined CRE represent diverse bacteria, whose spread may not respond to interventions directed to carbapenemase-producing Enterobacteriaceae. 

Patient management is not based on a single decision. Rather, it is dynamic: based on a sequence of decisions, with therapeutic adjustments made over time. Adjustments are personalized: tailored to individual patients as new information becomes available. However, strategies allowing for such adjustments are infrequently studied. Traditional antibiotic trials are often nonpragmatic, comparing drugs for definitive therapy when drug susceptibilities are known. COMparing Personalized Antibiotic StrategieS (COMPASS) is a trial design that compares strategies consistent with clinical practice. Strategies are decision rules that guide empiric and definitive therapy decisions. Sequential, multiple-assignment, randomized (SMART) COMPASS allows evaluation when there are multiple, definitive therapy options. SMART COMPASS is pragmatic, mirroring clinical, antibiotic-treatment decision-making and addressing the most relevant issue for treating patients: identification of the patient-management strategy that optimizes the ultimate patient outcomes. SMART COMPASS is valuable in the setting of antibiotic resistance, when therapeutic adjustments may be necessary due to resistance. 

Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no unmeasured confounding. In the presence of unmeasured confounders, the unobserved potential outcomes are not missing at random, and standard G-estimation leads to biased effect estimates. To remedy this, we investigate the sensitivity of G-estimators of coarse SNMMs to unmeasured confounding, assuming a nonidentifiable bias function which quantifies the impact of unmeasured confounding on the average potential outcome. We present adjusted G-estimators of coarse SNMM parameters and prove their consistency, under the bias modeling for unmeasured confounding. We apply this to a sensitivity analysis for the effect of the ART initiation time on the mean CD4 count at year 2 after infection in HIV-positive patients, based on the prospective Acute and Early Disease Research Program. 

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of two anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.