BackgroundLow dose dexamethasone demonstrated clinical improvement in patients with coronavirus disease 2019 (COVID-19) needing oxygen therapy; however, evidence on the efficacy of high dose of dexamethasone is limited.MethodsWe performed a randomised, open-label, controlled trial involving hospitalised patients with confirmed COVID-19 pneumonia needing oxygen therapy. Patients were randomly assigned in a 1:1 ratio to receive low dose dexamethasone (6 mg once daily for 10 days) or high dose dexamethasone (20 mg once daily for 5 days, followed by 10 mg once daily for additional 5 days). The primary outcome was clinical worsening within 11 days since randomisation. Secondary outcomes included 28-day mortality, time to recovery, and clinical status at day 5, 11, 14 and 28 on an ordinal scale ranging from 1 (discharged) to 7 (death).ResultsA total of 200 patients (mean (sd) age, 64 (14) years; 62% male) were enrolled. Thirty-two patients of 102 (31.4%) enrolled in the low dose group and 16 of 98 (16.3%) in the high dose group showed clinical worsening within 11 days since randomisation (rate ratio, 0.427; 95% CI, 0.216–0.842; p=0.014). The 28-day mortality was 5.9% in the low dose group and 6.1% in the high dose group (p=0.844). There was no significant difference in time to recovery, and in the 7-point ordinal scale at day 5, 11, 14 and 28.ConclusionsAmong hospitalised COVID-19 patients needing oxygen therapy, high dose of dexamethasone reduced clinical worsening within 11 days after randomisation as compared with low dose.

ObjectiveThe aim of this study was to assess whether the CYP2C9*2 and/or *3 variants might modify the risk for NSAID-related upper gastrointestinal bleeding (UGIB) in NSAID users.Patients and methodsWe conducted a multicenter, case–control study in which cases were patients aged more than 18 years with a diagnosis of UGIB, and controls were matched (1 : 3) by sex, age, date of admission, and hospital. Exposure was defined as the mean number of defined daily doses (DDDs) of NSAIDs metabolized by CYP2C9 in the week preceding the index date. Three DDD categories were defined (0, ≤0.5, and >0.5). Exposure was constructed taking both NSAID use and CYP2C9 polymorphisms into account. Patients of non-European origin were excluded from the analysis.ResultsA total of 577 cases and 1343 controls were finally included in the analysis: 103 cases and 89 controls consumed NSAIDs metabolized by CYP2C9, and 88 cases and 177 controls were CYP2C9*3 carriers. The adjusted odds ratios (aORs) of UGIB associated with the CYP2C9*2 and wild-type alleles proved to be similar [OR=8.79 (4.50–17.17) and 10.15 (2.92–35.35), respectively] and lower than those of the CYP2C9*3 allele [aOR=18.07 (6.34–51.53)] for consumers taking more than 0.5 DDDs of NSAIDs metabolized by CYP2C9. Grouping genotypes into carriers and noncarriers of the CYP2C9*3 variant resulted in aORs of 16.92 (4.96–57.59) for carriers and 9.72 (4.55–20.76) for noncarriers, where DDDs were greater than 0.5.ConclusionThe presence of the CYP2C9*3 variant increases the risk for UGIB associated with NSAID for DDDs greater than 0.5. The presence of the CYP2C9*2 allele shows no such effect.

The number of available community-based investigations on periodontal knowledge is scarce and restricted to areas with a very high level of human development. Gaps of knowledge exist in every geographic area, with the most relevant issues of low awareness and poor knowledge about the etiology of periodontal diseases and their relation with systemic disorders. These results highlight the need for local, community-based investigations about periodontal knowledge and barriers hampering early diagnosis, as well as for adequate educational interventions focused on these issues.

The final 11 item KAAR questionnaire appears to be valid, reliable and responsive.

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.