Potent NLRP3 Inflammasome Activation by the HIV Reverse Transcriptase Inhibitor Abacavir.

There is experimental and clinical evidence that some exanthematous allergic drug hypersensitivity reactions are mediated by drug-specific T cells. We hypothesized that the capacity of certain drugs to directly stimulate the innate immune system may contribute to generate drug-specific T cells. Here we analyzed whether abacavir, an HIV-1 reverse transcriptase inhibitor often inducing severe delayed-type drug hypersensitivity, can trigger innate immune activation that may contribute to its allergic potential. We show that abacavir fails to generate direct innate immune activation in human monocytes but potently triggers IL-1ß release upon pro-inflammatory priming with phorbol ester or Toll-like receptor stimulation. IL-1ß processing and secretion were sensitive to Caspase-1 inhibition, NLRP3 knockdown, and K+ efflux inhibition and were not observed with other non-allergenic nucleoside reverse transcriptase inhibitors, identifying abacavir as a specific inflammasome activator. It further correlated with dose-dependent mitochondrial reactive oxygen species production and cytotoxicity, indicating that inflammasome activation resulted from mitochondrial damage. However, both NLRP3 depletion and inhibition of K+ efflux mitigated abacavir-induced mitochondrial reactive oxygen species production and cytotoxicity, suggesting that these processes were secondary to NLRP3 activation. Instead, depletion of cardiolipin synthase 1 abolished abacavir-induced IL-1ß secretion, suggesting that mitochondrial cardiolipin release may trigger abacavir-induced inflammasome activation. Our data identify abacavir as a novel inflammasome-stimulating drug allergen. They implicate a potential contribution of innate immune activation to medication-induced delayed-type hypersensitivity, which may stimulate new concepts for treatment and prevention of drug allergies.

Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining.

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

Patients in palliative care-Development of a predictive model for anxiety using routine data.

INTRODUCTION: Anxiety is one of the most common psychological symptoms in patients in a palliative care situation. This study aims to develop a predictive model for anxiety using data from the standard documentation routine. METHODS: Data sets of palliative care patients collected by the German quality management benchmarking system called Hospice and Palliative Care Evaluation (HOPE) from 2007 to 2011 were randomly divided into a training set containing two-thirds of the data and a test set with the remaining one-third. We dichotomized anxiety levels, proxy rated by medical staff using the validated HOPE Symptom and Problem Checklist, into two groups with no or mild anxiety versus moderate or severe anxiety. Using the training set, a multivariable logistic regression model was developed by backward stepwise selection. Predictive accuracy was evaluated by the area under the receiver operating characteristic curve (AUC) based on the test set. RESULTS: An analysis of 9924 data sets suggests a predictive model for anxiety in patients receiving palliative care which contains gender, age, ECOG, living situation, pain, nausea, dyspnea, loss of appetite, tiredness, need for assistance with activities of daily living, problems with organization of care, medication with sedatives/anxiolytics, antidepressants, antihypertensive drugs, laxatives, and antibiotics. It results in a fair predictive value (AUC = 0.72). CONCLUSIONS: Routinely collected data providing individual-, disease- and therapy-related information contain valuable information that is useful for the prediction of anxiety risks in patients receiving palliative care. These findings could thus be advantageous for providing appropriate support for patients in palliative care settings and should receive special attention in future research.

Trends in specialized palliative care for non-cancer patients in Germany--data from the national hospice and palliative care evaluation (HOPE).

INTRODUCTION: The proportion of non-cancer patients (NCs) admitted to palliative care (PC) services increases steadily. Nevertheless, little is known not only about their specific problems, needs that have to be met, but also about improvements that have taken place already. This analysis focuses on developments seen in NC management concerning end-of-life care. METHODS: The German Hospice and Palliative Care Evaluation (HOPE) is a national long-term quality assurance project providing information on PC patients. Data from yearly evaluation periods between 2007 and 2011 are used to investigate differences between NC patients documented from 2002 to 2005 in symptoms, treatment and general condition. RESULTS: The proportion of NC patients increased from 3.5% (147/4182) to 8.1% (558/6854). NC patients, which are now referred to PC services, are younger, show less need for nursing support, die less often during inpatient stay. Overall a greater variety of diagnoses were found and patients suffer from less complex symptoms and problems at admission. CONCLUSIONS: Despite the continuously growing number of patients with non-malignant diseases, their number in PC services is still low. As small steps in the right direction have been taken, integrating PC ideas earlier into treatment of chronic diseases to improve quality of life of NCs during the final stages of their diseases will continue to challenge the health care system in terms of workload, need of more staff and further training of medical professionals dealing with NCs in the future.