The potential interaction between MTX and doxycycline may cause pharmacokinetic changes, and its clinical repercussions on the quality of life of the patient and associated costs should be considered.
The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers’ performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.
MMF was effective in the majority of patients with uveitis with an acceptable profile of side-effects. TDM of MMF in patients with uveitis is clinically practicable and may help to optimize individual immunosuppressive therapy. We estimated that MMF dosages in the range of 0.5-1.5 g/day might be sufficient for treating uveitis and we recommend an initial target range of 2-4 microg/mL, which included 50% of our results. Randomized controlled trials are essential to confirm the efficacy of MMF in uveitis.
Background:Medication non-adherence is associated with treatment failure. Some authors show a positive relationship between patient activation and both, adherence to treatment for chronic conditions and improved outcomes.Objectives:To present preliminary results of a study measuring adherence to biological therapy and its relationship with the Patient Activation Measure (PAM) in the outpatient setting.Methods:Ambispective longitudinal observational descriptive study in a general tertiary university hospital.Patients on treatment with the same biological drug for ≥6 months were included in order of arrival. Patients with some degree of mental disability, which prevented understanding of the purpose and parameters of the study, were excluded.Demographic variables (sex, age, environment, educational level), diagnosis and treatment were collected. To measure adherence, the Simplified Medication Adherence Questionnaire (SMAQ), validated in Spain, and the medication possession ratio (MPR) were used. Patients were considered as non-adherent if MPR<80% and/ or non-adherent SMAQ. To measure patient's ability to play an active role in their health care, PAM questionnaire, consisting on 13 items and validated in Spain, was used. It sorts patients into 4 activation levels, which were grouped together into not activated (PAM 1–2) or activated (PAM 3–4).Relationship between adherence to treatment, as a combined variable, and PAM was analyzed using chi-square, considering significance level p<0,05. Statistical analysis were performed with spss v17.0.Results:Fifty patients (58% women) were included. Mean age: 48 years (95% CI: 33 to 63); 92% lived in urban areas, 28% completed elementary education, 44% high-school and 28% university studies.Diagnosis: rheumatoid arthritis (38%), Crohn's disease (20%), psoriasis (20%), ankylosing spondylitis (16%) and psoriatic arthritis (6%). Treatment: adalimumab (44%), etanercept (16%), tocilizumab (16%), secukinumab (12%), ustekinumab (6%), golimumab (4%) and ixekizumab (2%). Median time on the biological drug treatment: 26 months (IQR 53).PAM level: 2, 16, 54 and 28% for levels 1,2,3 and 4, respectively.Table 1Global distribution and relationship between adherence and PAM n(%)Adherent patients Total SMAQMPRCombined 38 (76%)29 (59%)*23 (47%)* Activated patientsYes32 (78%)25 (63%)*20 (50%)*41 (82%)No6 (67%)4 (44%)*3 (33%)*9 (18%)χ2; p0,524; 0,6680,991; 0,4560,819; 0,472*1 lost patient.The proportion of adherent patients was 47% (23/49), being higher (50%) among the activated patients compared to the non-activated patients (33%), even though the differences were not statistically significant.Conclusions:Among biologic treated patients, 82% show a high degree of activation on their disease and treatment self-management. However, only 47% were adherent to treatment, when combining the SMAQ questionnaire and the medication possession ratio quantification.The greater proportion of adherence found among patients with a higher degree of activation could indicate a positive relationship between activat...
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