Treatment of Complex Cutaneous Leishmaniasis with Liposomal Amphotericin B

Background: There is no consensus for the best treatment of complex cutaneous leishmaniasis (CL). We aimed to describe a cohort of CL, focusing on liposomal amphotericin B (L-AmB) treatment outcome. Methods: We performed a retrospective study in Vall d’Hebron University Hospital (Barcelona, Spain). All patients with parasitologically proven CL diagnosed from 2012 to 2018 were included. Results: The analysis included 41 patients with CL. The median age was 39 years (IQR 12- 66); 12 (29%) were children, and 29 (71%) were men. Regarding treatment, 24 (59%) received local treatment, whereas 17 (41%) had complex CL and were offered intravenous systemic treatment. Sixteen patients received L-AmB; eight (50%) had adverse events, and three (19%) discontinued treatment for safety reasons. All cases were considered cured within the first year post-treatment. Conclusions: L-AmB for complex CL showed no treatment failures, offering an alternative treatment option for patients with complex CL. Clinicians should pay close attention to the potential adverse events of L-AmB and adopt an active drug safety surveillance scheme to rapidly detect reversible side effects.

Concomitant drugs associated with increased mortality for MDMA users reported in a drug safety surveillance database

Abstract3,4-Methylenedioxymethamphetamine (MDMA) is currently being evaluated by the Food and Drug Administration (FDA) for the treatment of post-traumatic stress disorder (PTSD). If MDMA is FDA-approved it will be important to understand what medications may pose a risk of drug–drug interactions. The goal of this study was to evaluate the risks due to MDMA ingestion alone or in combination with other common medications and drugs of abuse using the FDA drug safety surveillance data. To date, nearly one thousand reports of MDMA use have been reported to the FDA. The majority of these reports include covariates such as co-ingested substances and demographic parameters. Univariate and multivariate logistic regression was employed to uncover the contributing factors to the reported risk of death among MDMA users. Several drug classes (MDMA metabolites or analogs, anesthetics, muscle relaxants, amphetamines and stimulants, benzodiazepines, ethanol, opioids), four antidepressants (bupropion, sertraline, venlafaxine and citalopram) and olanzapine demonstrated increased odds ratios for the reported risk of death. Future drug–drug interaction clinical trials should evaluate if any of the other drug–drug interactions described in our results actually pose a risk of morbidity or mortality in controlled medical settings.

A Drug Safety Surveillance Study of a Ciprofloxacin/Dexamethasone Ophthalmic Fixed Combination in Peruvian Population

(1) Background: drugs provide a significant benefit for patients who require medical treatment; however, their use implies an intrinsic potential danger, with the possibility of causing unwanted effects. These effects are known as adverse drug reactions (ADRs). Post-marketing drug safety surveillance detects unknown risks that have not been identified in clinical trials, and it is necessary to monitor marketed medications under real-life practice. Due to the scarce information about fixed combination of ciprofloxacin 0.3%/dexamethasone 0.1% (SDO), we performed a drug safety surveillance study. (2) Methods: A prospective non-controlled drug safety surveillance study was conducted in Peruvian population. A total of 236 patients prescribed SDO were included derived from 12 sites. Patients’ standardized information was collected through two phone calls, including demographics, medical history, prescribing patterns of SDO, concomitant medication, and ADRs in detail. The ADRs were classified by causality and severity, followed by outcome measures to identify new risk. (3) Results: 236 patients prescribed with SDO participated in the study and 220 were included. A total of 82 ADRs/220 patients were reported after the use of SDO, presenting a ratio 0.37 ADR/patient. The most frequent ADR with SDO administration was eye irritation (30%). All ADRs were classified as non-serious, and 97.5% (n = 80) were classified as mild while 2.5% as moderate (n = 2). No cases under the severe category were identified. (4) Conclusion: No new risks were found in the population where this study was conducted.

A drug safety surveillance study of a ciprofloxacin / dexamethasone ophthalmic fixed combination in Peruvian population

(1) Background: drugs provide a significant benefit; however, their use implies an intrinsic potential danger, with the possibility to cause unwanted effects. These effects are known as adverse drug reactions (ADRs). Post-marketing drug safety surveillance detects unknown risks that have not been identified in clinical trials and it is necessary to monitor marketed medications under real-life practice. Due to the scarce information about fixed combination of ciprofloxacin 0.3% / dexamethasone 0.1% (SDO), we performed a drug safety surveillance study. (2) Methods: A prospective non-controlled drug safety surveillance study was conducted in Peruvian population. A total of 236 patients prescribed SDO were included derivates from 12 sites. Patients' standardized information was collected through two phone calls, including demographics, medical history, prescribing patterns of SDO, concomitant medication, and ADRs in detail. The ADRs were classified by causality and severity, followed by outcome measures to identify new risk. (3) Results: 236 patients prescribed with SDO participated in the study and 220 were included. A total of 82 ADRs/220 patients were reported after the use of SDO, presenting a ratio 0.37 ADR/patient. The most frequent ADR with SDO administration was eye irritation (30%). The totality of the ADR was classified as non-serious, and the 97.5% (n=80) was classified as mild and 2.5% as moderate (n=2). No cases under the severe category were identified. (4) Conclusion: No new risks were found in the population where this study was conducted.

Mapping scientific landscapes in UMLS research: a scientometric review

Objective The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. Materials and Methods We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. Results The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain’s growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. Discussion Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. Conclusion The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models

Background An adverse drug event (ADE) is commonly defined as “an injury resulting from medical intervention related to a drug.” Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient’s ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes. Objective This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients. Methods This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning–based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient. Results Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug−reason (F1=0.650 versus F1=0.579) and drug−ADE (F1=0.490 versus F1=0.476) relations. Conclusions This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning–based concepts and relation extraction. This study demonstrates the potential for deep learning–based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance.

Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models (Preprint)

BACKGROUND An adverse drug event (ADE) is commonly defined as “an injury resulting from medical intervention related to a drug.” Providing information related to ADEs and alerting caregivers at the point of care can reduce the risk of prescription and diagnostic errors and improve health outcomes. ADEs captured in structured data in electronic health records (EHRs) as either coded problems or allergies are often incomplete, leading to underreporting. Therefore, it is important to develop capabilities to process unstructured EHR data in the form of clinical notes, which contain a richer documentation of a patient’s ADE. Several natural language processing (NLP) systems have been proposed to automatically extract information related to ADEs. However, the results from these systems showed that significant improvement is still required for the automatic extraction of ADEs from clinical notes. OBJECTIVE This study aims to improve the automatic extraction of ADEs and related information such as drugs, their attributes, and reason for administration from the clinical notes of patients. METHODS This research was conducted using discharge summaries from the Medical Information Mart for Intensive Care III (MIMIC-III) database obtained through the 2018 National NLP Clinical Challenges (n2c2) annotated with drugs, drug attributes (ie, strength, form, frequency, route, dosage, duration), ADEs, reasons, and relations between drugs and other entities. We developed a deep learning–based system for extracting these drug-centric concepts and relations simultaneously using a joint method enhanced with contextualized embeddings, a position-attention mechanism, and knowledge representations. The joint method generated different sentence representations for each drug, which were then used to extract related concepts and relations simultaneously. Contextualized representations trained on the MIMIC-III database were used to capture context-sensitive meanings of words. The position-attention mechanism amplified the benefits of the joint method by generating sentence representations that capture long-distance relations. Knowledge representations were obtained from graph embeddings created using the US Food and Drug Administration Adverse Event Reporting System database to improve relation extraction, especially when contextual clues were insufficient. RESULTS Our system achieved new state-of-the-art results on the n2c2 data set, with significant improvements in recognizing crucial drug−reason (F1=0.650 versus F1=0.579) and drug−ADE (F1=0.490 versus F1=0.476) relations. CONCLUSIONS This study presents a system for extracting drug-centric concepts and relations that outperformed current state-of-the-art results and shows that contextualized embeddings, position-attention mechanisms, and knowledge graph embeddings effectively improve deep learning–based concepts and relation extraction. This study demonstrates the potential for deep learning–based methods to help extract real-world evidence from unstructured patient data for drug safety surveillance.