Safety of SGLT2 Inhibitors: A Pharmacovigilance Study from 2013 to 2021 Based on FAERS

Background: Sodium-glucose co-transporter-2 inhibitors (SGLT2is) are widely used in clinical practice for their demonstrated cardiorenal benefits, but multiple adverse events (AEs) have been reported. We aimed to describe the distribution of SGLT2i-related AEs in different systems and identify important medical event (IME) signals for SGLT2i.Methods: Data from the first quarter (Q1) of 2013–2021 Q2 in FAERS were selected to conduct disproportionality analysis. The definition of AEs and IMEs relied on the system organ classes (SOCs) and preferred terms (PTs) by the Medical Dictionary for Regulatory Activities (MedDRA-version 24.0). Two signal indicators, the reported odds ratio (ROR) and information component (IC), were used to estimate the association between SGLT2is and IMEs.Results: A total of 57,818 records related to SGLT2i, with 22,537 SGLT2i-IME pairs. Most SGLT2i-related IMEs occurred in monotherapy (N = 21,408, 94.99%). Significant signals emerged at the following SOCs: “metabolism and nutrition disorders” (N = 9,103; IC025 = 4.26), “renal and urinary disorders” (3886; 1.20), “infections and infestations” (3457; 0.85). The common strong signals were observed in diabetic ketoacidosis, ketoacidosis, euglycaemic diabetic ketoacidosis and Fournier’s gangrene. Unexpected safety signals such as cellulitis, osteomyelitis, cerebral infarction and nephrolithiasis were detected.Conclusion: Our pharmacovigilance analysis showed that a high frequency was reported for IMEs triggered by SGLT2i monotherapy. Different SGLT2is caused different types and the association strengths of IMEs, while they also shared some specific PTs. Most of the results are generally consistent with previous studies, and more pharmacoepidemiological studies are needed to validate for unexpected AEs. Based on risk-benefit considerations, clinicians should be well informed about important medical events that may be aggravated by SGLT2is.

Waiting for AIDS in Kuwait

The HIV/AIDS pandemic evoked anxieties that were tied to Kuwait’s particular histories of gendered citizenship and dislocations of globalized labor. In Kuwait, to the best of our knowledge, HIV/AIDS has not reached epidemic levels. But in the midst of global discussions of HIV/AIDS in the late 1980s and early 1990s, anxiety surrounding Kuwait’s integration into transnational networks of travel and tourism brought tensions over gender roles, citizenship, sexuality, and infidelity to the forefront of public discourse. Drawing on local Arabic-language newspapers, public health campaign material, and state-sponsored publications on Islamic interpretations of HIV/AIDS, this article examines the significance of AIDS in a region where reactions to the pandemic centered on the process of constructing a potential medical event. Citizens and noncitizen residents of Kuwait articulated these anxieties in the context of waiting—waiting to be infected, waiting for a national outbreak, waiting in quarantine, and, for noncitizens who tested positive for HIV, waiting to be deported. By the mid-1990s, this process of anticipating and taking concrete legal measures to prevent a future epidemic resulted in the medicalization of social and political patterns of gender inequality, nativism, and differential citizenship.

Using a Biopsychosocial Approach to Examine Differences in Post-Traumatic Stress Symptoms Among Arab and Jewish Israeli Mothers Following A Child’s Traumatic Medical Event

Background: Parents of children following a traumatic medical event (TME) are known to be at high risk for developing severe post-traumatic stress symptoms (PTSS). Findings on the negative impact of TMEs on parents’ PTSS have been described in different cultures and societies worldwide. However, in some cases, a specific ethnic group may also be a minority within a given region or a country, contributing to increased risk for parental PTSS following a child’s TME.Objectives: The current study aimed to examine differences in PTSS between Israeli-Arab and Israeli-Jewish mothers, following a child’s TME. More specifically, we aimed to examine the risk and protective factors affecting mother’s PTSS from a biopsychosocial approach.Methods: Data were collected from medical files of children following TMEs, hospitalized in a pediatric rehabilitation department, during the period 2008–2018. The sample included 47 Israeli-Arab mothers and 47 Israeli-Jewish mothers. Mothers completed the psychosocial assessment tool (PAT), the post-traumatic diagnostic scale (PDS).Results: Arab mothers self-reported significantly higher levels of PTSS than their Jewish counterparts. Further, Arab mothers perceived having more social support than Jewish mothers did. Finally, our prediction model indicated that both Arab ethnicity and pre-trauma family problems predicted higher levels of PTSS among mothers of children following TMEs.Conclusions: Focusing on ethnic and cultural effects following a child’s TME may help improve our understanding of the mental health needs of mothers from different minority ethnic groups and aid in developing appropriate health services and targeted interventions for this population.

An interpretable risk prediction model for healthcare with pattern attention

Background The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events. Methods In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks. Results We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality. Conclusions PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions. Availability The code for this paper is available at: https://github.com/yinchangchang/PAVE.

An Interpretable Risk Prediction Model for Healthcare with Pattern Attention

Background: The availability of the massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (i) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models' performance is highly dependent on imputation accuracy. (ii) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (iii) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events. Methods: In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don't need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks. Results: We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality. Conclusions: PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients' health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions. Availability: The code for this paper is available at: https://github.com/yinchangchang/PAVE.