Treatment Outcomes of Multidrug-Resistant Tuberculosis: A Systematic Review and Meta-Analysis

BACKGROUND:Treatment outcomes for multidrug-resistant Mycobacterium Tuberculosis (MDRTB) are generally poor compared to drug sensitive disease. We sought to estimate treatment outcomes and identify risk factors associated with poor outcomes in patients with MDRTB. METHODOLOGY/PRINCIPAL FINDINGS:We performed a systematic search (to December 2008) to identify trials describing outcomes of patients treated for MDRTB. We pooled appropriate data to estimate WHO-defined outcomes at the end of treatment and follow-up. Where appropriate, pooled covariates were analyzed to identify factors associated with worse outcomes. Among articles identified, 36 met our inclusion criteria, representing 31 treatment programmes from 21 countries. In a pooled analysis, 62% [95% CI 57-67] of patients had successful outcomes, while 13% [9]-[17] defaulted, 11% [9]-[13] died, and 2% [1]-[4] were transferred out. Factors associated with worse outcome included male gender 0.61 (OR for successful outcome) [0.46-0.82], alcohol abuse 0.49 [0.39-0.63], low BMI 0.41[0.23-0.72], smear positivity at diagnosis 0.53 [0.31-0.91], fluoroquinolone resistance 0.45 [0.22-0.91] and the presence of an XDR resistance pattern 0.57 [0.41-0.80]. Factors associated with successful outcome were surgical intervention 1.91 [1.44-2.53], no previous treatment 1.42 [1.05-1.94], and fluoroquinolone use 2.20 [1.19-4.09]. CONCLUSIONS/SIGNIFICANCE:We have identified several factors associated with poor outcomes where interventions may be targeted. In addition, we have identified high rates of default, which likely contributes to the development and spread of MDRTB

Early Outcomes of MDR-TB Treatment in a High HIV-Prevalence Setting in Southern Africa

BACKGROUND: Little is known about treatment of multidrug-resistant tuberculosis (MDR-TB) in high HIV-prevalence settings such as sub-Saharan Africa. METHODOLOGY/PRINCIPAL FINDINGS: We did a retrospective analysis of early outcomes of the first cohort of patients registered in the Lesotho national MDR-TB program between July 21, 2007 and April 21, 2008. Seventy-six patients were included for analysis. Patient follow-up ended when an outcome was recorded, or on October 21, 2008 for those still on treatment. Fifty-six patients (74%) were infected with HIV; the median CD4 cell count was 184 cells/microl (range 5-824 cells/microl). By the end of the follow-up period, study patients had been followed for a median of 252 days (range 12-451 days). Twenty-two patients (29%) had died, and 52 patients (68%) were alive and in treatment. In patients who did not die, culture conversion was documented in 52/54 patients (96%). One patient had defaulted, and one patient had transferred out. Death occurred after a median of 66 days in treatment (range 12-374 days). CONCLUSIONS/SIGNIFICANCE: In a region where clinicians and program managers are increasingly confronted by drug-resistant tuberculosis, this report provides sobering evidence of the difficulty of MDR-TB treatment in high HIV-prevalence settings. In Lesotho, an innovative community-based treatment model that involved social and nutritional support, twice-daily directly observed treatment and early empiric use of second-line TB drugs was successful in reducing mortality of MDR-TB patients. Further research is urgently needed to improve MDR-TB treatment outcomes in high HIV-prevalence settings

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications