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Acute respiratory distress syndrome (ARDS) is a potential life-threatening, heterogenous, inflammatory lung disease. There are no data available on potential drug–drug interactions (pDDIs) in critically ill patients with ARDS. This study analyzed pDDIs in this specific cohort and aimed to investigate possible associations of coronavirus disease 2019 (COVID-19) as an underlying cause of ARDS and treatment with extracorporeal membrane oxygenation (ECMO) with the occurrence of pDDIs. This retrospective study included patients ≥18 years of age diagnosed with ARDS between January 2010 and September 2021. The Janusmed database was used for the identification of pDDIs. A total of 2694 pDDIs were identified in 189 patients with a median treatment duration of 22 days. These included 323 (12%) clinically relevant drug combinations that are best avoided, corresponding to a median rate of 0.05 per day. There was no difference in the number of pDDIs between COVID-19- and non-COVID-19-associated ARDS. In patients treated with ECMO, the rate of the most severely graded pDDIs per day was significantly higher compared with those who did not require ECMO. PDDIs occur frequently in patients with ARDS. On average, each patient may encounter at least one clinically relevant drug combination that should be avoided during their intensive care unit stay.

Abstract Background PHARAO is a decision support system developed to evaluate the risk for a set of either common or serious side-effects resulting from a combination of pharmacodynamic effects from a patient’s medications. The objective of this study was to investigate the validity of the risk scores for the common side-effects generated by PHARAO in older patients. Methods Side-effects included were sedation, constipation, orthostatic symptoms, anticholinergic and serotonergic effects. The alerts generated by PHARAO were tested in 745 persons ≥65 years old. Dispensed prescriptions retrieved from the Swedish prescribed drug register were used to generate the pharmacological risk scores of patients’ medications. Symptoms possibly related to side-effects were extracted from medical records data. Results The PHARAO system generated 776 alerts, most often for the risk of anticholinergic symptoms. The total specificity estimates of the PHARAO system were 0.95, 0.89 and 0.78 for high, intermediate and low risk alerts, respectively. The corresponding sensitivity estimates were between 0.12 and 0.37. The negative predictive value was 0.90 and the positive predictive value ranged between 0.20–0.25. Conclusions The PHARAO system had a high specificity and negative predictive value to detect symptoms possibly associated with the of patients’ medications, while the sensitivity and positive predictive value were low. The PHARAO system has the potential to minimise the risk of over-alerts in combination with a drug-drug interaction alert system, but should be used in connection with a medical evaluation of the patient.

Janusmed interactions is a drug-drug interactions (DDI) database available online for healthcare professionals (HCP) at all levels of the healthcare system including pharmacies. The database is aimed at HCP but is also open to the public for free, for those individuals who register for a personal account. The aim of this study was to investigate why and how patients use the database Janusmed interactions, how they perceive content and usability, and how they would react if they found an interaction. A web-based questionnaire was sent by email to all users who had registered for Janusmed interactions as a “patient” (n = 3219). A total of 406 patients completed the survey (response rate 12.6%). The study shows that there is an interest among patients to use a DDI database to check their own or a relative’s medication. The respondents found the database easy to use and perceive they understand the information aimed at HCP. Most patients stated they would talk to their HCP if they found an interaction and not adjust their treatment by themselves. However, the respondents in this study are actively searching for information and seem to have high health literacy. Thus, the findings are not generalizable for the general population.

AimsTramadol, a widely used analgesic drug, inhibits the reuptake of noradrenaline and serotonin impairing the aggregation function of thrombocytes. However, the risk for severe bleeding has previously not been studied. The aim of the present study is to investigate the association between tramadol and bleeding peptic ulcer in the Swedish population.MethodsIn this register based case-control study based on the Swedish national patient registry and prescription drug registry, we included 18 306 patients hospitalized with a first-time diagnosis of bleeding peptic ulcer. For every case, 4 matched controls were included. To investigate the temporal aspects of tramadol induced bleeding ulcer, exposure was divided into patients with newly initiated and ongoing treatment. To explore a possible confounding by indication, the effect of codeine, a drug also prescribed for the treatment of moderate pain, but not known to affect thrombocyte function, was investigated. Univariable and multivariable logistic regression was used to analyse the association between tramadol use and bleeding ulcer.ResultsTramadol was associated with an increased risk of bleeding ulcer (adjusted odds ratio (aOR) 2.1, 95% confidence interval: (2.0-2.3). The association was stronger for newly initiated treatment with tramadol 2.8 (2.5-3.2) and weaker for ongoing treatment 1.7 (1.6-1.9). Codeine was also associated with an increased risk of bleeding ulcer 1.9 (1.7-2.1) and this association was also stronger for newly initiated treatment with codeine 2.3 (2.0-2.6) and weaker for ongoing treatment 1.7 (1.5-1.9).ConclusionTreatment with tramadol was associated with an increased risk of bleeding peptic ulcer. Most of this association may be mediated by factors related to the pain condition rather than the pharmacologic effect per se.

PURPOSE: To investigate the extent to which clinicians avoid well-established drug-drug interactions associated with warfarin. We hypothesised that clinicians would avoid combining non-steroidal anti-inflammatory drugs (NSAIDs), tramadol and sulfamethoxazole with warfarin. METHODS: A cross-sectional analysis of nationwide dispensing data was performed in Swedish individuals 18 years or older (n = 7,563,649). Odds ratios of interacting NSAIDs, tramadol and sulfamethoxazole versus respective prevalence of comparator drugs codeine, and ciprofloxacin in patients co-dispensed interacting warfarin versus patients unexposed was calculated. RESULTS: The odds of receiving an interacting NSAID versus the comparator codeine was markedly lower in patients with warfarin than in the remaining population (adjusted OR 0.21; 95% CI 0.20 - 0.22). Also, the interacting drugs tramadol and sulfamethoxazole were less common among patients dispensed warfarin as compared to the remaining population, although the decrease was much more modest (adjusted OR 0.83; CI 0.80-0.87 and 0.81; CI 0.73 - 0.90). CONCLUSIONS: In conclusion, Swedish doctors in the vast majority of cases refrain from prescribing NSAIDs to patients already on warfarin. Tramadol and sulfamethoxazole are however rarely avoided.