Publications

Most lung transplantation immunosuppression regimens include tacrolimus. Single nucleotide polymorphisms (SNPs) in genes important to tacrolimus bioavailability and clearance (ABCB1, CYP3A4, and CYP3A5) are associated with differences in tacrolimus pharmacokinetics. We hypothesized that polymorphisms in these genes would impact immunosuppression-related outcomes. We categorized ABCB1, CYP3A4, and CYP3A5 SNPs for 321 lung allograft recipients. Genotype effects on time to therapeutic tacrolimus level, interactions with antifungal medications, concentration to dose (C /D), acute kidney injury, and rejection were assessed using linear models adjusted for subject characteristics and repeat measures. Compared with CYP3A poor metabolizers (PM), time to therapeutic tacrolimus trough was increased by 5.1 ± 1.6 days for CYP3A extensive metabolizers (EM, P < 0.001). In the post-operative period, CYP3A intermediate metabolizers spent 1.2 ± 0.5 days less (P = 0.01) and EM spent 2.1 ± 0.5 days less (P < 0.001) in goal tacrolimus range than CYP3A PM. Azole antifungals interacted with CYP3A genotype in predicting C /D (P < 0.001). Increased acute kidney injury rates were observed in subjects with high ABCB1 function (OR 3.0, 95% CI 1.1-8.6, P = 0.01). Lower rates of acute cellular rejection were observed in subjects with low ABCB1 function (OR 0.36, 95% CI 0.07-0.94, P = 0.02). Recipient genotyping may help inform tacrolimus dosing decisions and risk of adverse clinical outcomes.

We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. To analyze if these representations are transferable across tasks, we evaluate them in multiple supervised setups to predict patient mortality, primary diagnostic and procedural category, and gender. We compare their performance with sparse representations obtained from a bag-of-words model. We observe that the learned generalized representations significantly outperform the sparse representations when we have few positive instances to learn from, and there is an absence of strong lexical features. 2. We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts. In the latter case, concepts represent problems, treatments, and tests. We find that concept identification does not improve the classification performance. 3. We propose novel techniques to facilitate model interpretability. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate feature sensitivity across two networks to identify the most significant input features for different classification tasks when we use these pretrained representations as the supervised input. We successfully extract the most influential features for the pipeline using this technique.

Drug-induced thrombotic microangiopathies (DTMAs) are increasingly being recognized as an important category of thrombotic microangiopathies (TMAs). Cancer therapeutic agents including proteasome inhibitors (PIs) are among the most common medications reported to cause DTMA. PIs could cause DTMA either by an immune mechanism or dose-dependent/cumulative toxicity. Eleven cases of DTMA have been reported with bortezomib and carfilzomib. To the best of our knowledge, only one case of DTMA has been reported with ixazomib due to an immune-mediated mechanism. Here, we report the first case of ixazomib-induced DTMA due to cumulative toxicity rather than immune-mediated mechanism. In this article, we discuss the precipitating factors for cumulative toxicity of ixazomib, resulting in DTMA, diagnostic workup, and management of DTMA. We also discuss clinical reasoning based analysis of DTMA versus cancer-associated TMA as well as DTMA versus cyclic thrombocytopenia seen in PI use.

BACKGROUND

Incomplete adherence to tuberculosis (TB) treatment increases the risk of delayed culture conversion with continued transmission in the community, as well as treatment failure, relapse, and development or amplification of drug resistance. We conducted a systematic review and meta-analysis of adherence interventions, including directly observed therapy (DOT), to determine which approaches lead to improved TB treatment outcomes.

METHODS AND FINDINGS

We systematically reviewed Medline as well as the references of published review articles for relevant studies of adherence to multidrug treatment of both drug-susceptible and drug-resistant TB through February 3, 2018. We included randomized controlled trials (RCTs) as well as prospective and retrospective cohort studies (CSs) with an internal or external control group that evaluated any adherence intervention and conducted a meta-analysis of their impact on TB treatment outcomes. Our search identified 7,729 articles, of which 129 met the inclusion criteria for quantitative analysis. Seven adherence categories were identified, including DOT offered by different providers and at various locations, reminders and tracers, incentives and enablers, patient education, digital technologies (short message services [SMSs] via mobile phones and video-observed therapy [VOT]), staff education, and combinations of these interventions. When compared with DOT alone, self-administered therapy (SAT) was associated with lower rates of treatment success (CS: risk ratio [RR] 0.81, 95% CI 0.73-0.89; RCT: RR 0.94, 95% CI 0.89-0.98), adherence (CS: RR 0.83, 95% CI 0.75-0.93), and sputum smear conversion (RCT: RR 0.92, 95% CI 0.87-0.98) as well as higher rates of development of drug resistance (CS: RR 4.19, 95% CI 2.34-7.49). When compared to DOT provided by healthcare providers, DOT provided by family members was associated with a lower rate of adherence (CS: RR 0.86, 95% CI 0.79-0.94). DOT delivery in the community versus at the clinic was associated with a higher rate of treatment success (CS: RR 1.08, 95% CI 1.01-1.15) and sputum conversion at the end of two months (CS: RR 1.05, 95% CI 1.02-1.08) as well as lower rates of treatment failure (CS: RR 0.56, 95% CI 0.33-0.95) and loss to follow-up (CS: RR 0.63, 95% CI 0.40-0.98). Medication monitors improved adherence and treatment success and VOT was comparable with DOT. SMS reminders led to a higher treatment completion rate in one RCT and were associated with higher rates of cure and sputum conversion when used in combination with medication monitors. TB treatment outcomes improved when patient education, healthcare provider education, incentives and enablers, psychological interventions, reminders and tracers, or mobile digital technologies were employed. Our findings are limited by the heterogeneity of the included studies and lack of standardized research methodology on adherence interventions.

CONCLUSION

TB treatment outcomes are improved with the use of adherence interventions, such as patient education and counseling, incentives and enablers, psychological interventions, reminders and tracers, and digital health technologies. Trained healthcare providers as well as community delivery provides patient-centered DOT options that both enhance adherence and improve treatment outcomes as compared to unsupervised, SAT alone.