PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).
EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.
KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.
CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.
Materials and Methods: In two tertiary care selected hospitals, the included diabetic patients were randomly divided into two study arms. In the control group, 200 patients who were receiving usual treatment from hospitals were included. However, in the intervention group, those 200 patients who were receiving usual treatment along with counseling sessions from pharmacists under the Diabetes Medication Therapy Adherence Clinic (DMTAC) program were included. The study continued for 1 year, and there were four follow-up visits for both study arms. A prevalidated data collection form was used to measure the improvement in predictors of diabetic foot in included patients. Data were analyzed by using the Statistical Package for the Social Sciences (SPSS) software program, version 24.0.
Results: With the average decrease of 1.97% of HbA1c values in the control group and 3.43% in the intervention group, the univariate and multivariate analysis showed a statistically significant difference between both of the study arms in the improvement of predictors belonging to the diabetic foot (P < 0.05). The proportion of patients without any signs and symptoms of the diabetic foot in the intervention group was 91.7%, which increased from 42.3% at baseline (P < 0.05). However, this proportion in the control group was 76.9% at the fourth follow-up, from 48.3% at baseline (P < 0.05).
Conclusion: A statistically significant reduction in the signs and symptoms of diabetic foot was observed in the intervention group at the end of 1 year. The progression of diabetic foot was significantly decreased in the pharmacist intervention group.
MATERIALS AND METHODS: This retrospective cohort study included only COVID-19 positive patients hospitalized in a Private Hospital in West Jakarta between March and September 2020. All patients were not vaccinated during this period and treatment was based on the guidelines by the Ministry of Health Indonesia. A convenience sampling method was used and all patients who met the inclusion criteria were enrolled.
RESULTS: The clinical outcome of COVID-19 patients following medical therapy was either cured (85.7%) or died (14.3%), with 14.3% patients reported to have cytokine storm, from which 23.1% led to fatalities. A plasma immunoglobulin (Gammaraas®) and/or tocilizumab (interleukin-6 receptor antagonist; Actemra®) injection was utilised to treat the cytokine storm while remdesivir and oseltamivir were administered to ameliorate COVID-19. Most (61.5%) patients who experienced the cytokine storm were male; mean age 60 years. Interestingly, all patients who experienced the cytokine storm had hypertension or/ and diabetes complication (100%). Fever, cough and shortness of breath were also the common symptoms (100.0%). Almost all (92.3%) patients with cytokine storm had to be treated in the intensive care unit (ICU). Most (76.9%) patients who had cytokine storm received hydroxychloroquine and all had antibiotics [1) azithromycin + levofloxacin or 2) meropenam for critically ill patients] and vitamins such as vitamins C and B-complex as well as mineral. Unfortunately, from this group, 23.1% patients died while the remaining 70% of patients recovered. A significant (p<0.05) correlation was established between cytokine storms and age, the presence of comorbidity, diabetes, hypertension, fever, shortness of breath, having oxygen saturation (SPO2) less than 93%, cold, fatigue, ward of admission, the severity of COVID-19 disease, duration of treatment as well as the use of remdesivir, Actemra® and Gammaraas®. Most patients recovered after receiving a combination treatment (oseltamivir + remdesivir + Antibiotics + Vitamin/Mineral) for approximately 11 days with a 90% survival rate. On the contrary, patients who received oseltamivir + hydroxychloroquine + Gammaraas® + antibiotics +Vitamin/Mineral, had a 83% survival rate after being admitted to the hospital for about ten days.
CONCLUSION: Factors influencing the development of a cytokine storm include age, duration of treatment, comorbidity, symptoms, type of admission ward and severity of infection. Most patients (76.92%) with cytokine storm who received Gammaraas®/Actemra®, survived although they were in the severe and critical levels (87.17%). Overall, based on the treatment duration and survival rate, the most effective therapy was a combination of oseltamivir + favipiravir + hydroxychloroquine + antibiotics + vitamins/minerals.
METHODOLOGY: A single-center cohort study was performed at Indus Hospital and Health Network, Karachi, Pakistan, between April 1, 2021, and October 31, 2021. This study included 333 hospitalized hypertensive COVID-19 patients and evaluated their clinical characteristics and survival outcomes. A multivariate logistic regression model was applied in IBM SPSS 27.0 to determine the predictors of mortality.
RESULTS: The majority of patients were females (54.7%), the median age was 62 [55-70] years, with co-existing diabetes (56.5%) and severely ill (52.6%). The independent predictors of mortality identified were age ≥ 65 years (aOR 20.89, 95% CI, 5.81-75.15; p < 0.001), pulse rate (aOR 1.03, 95% CI 1.01-1.63; p = 0.006), serum creatinine (aOR 1.34, 95% CI 1.11-1.63; p = 0.002), use of antibiotics (aOR 3.40, 95% CI 1.29-8.98; p = 0.014)), corticosteroid (aOR 49.68, 95% CI 1.83-1350.31; p = 0.020), and who needed high flow oxygen supply (aOR 13.08, 95% CI 1.70-100.54; p < 0.001), non-invasive mechanical ventilation (aOR 229.01, 95% CI 29.30-1789.71; p < 0.001) and invasive mechanical ventilation (aOR 379.54, 95% CI 36.60-3935.87; p < 0.001).
CONCLUSIONS: Our study suggests that older age, elevated pulse rate, serum creatinine, use of antibiotics and corticosteroids, and the need for mechanical ventilation predict mortality among hypertensive COVID-19.
OBJECTIVE: The aim of this study was to determine the rate, factors, and medications associated with ADR-related hospitalisations among HF patients.
SETTING: Two government hospitals in Dubai, United Arab Emirates.
METHODS: This was a prospective, observational study. Consecutive adult HF patients who were admitted between December 2011 and November 2012 to the cardiology units were included in this study. The circumstances of their admission were analysed.
MAIN OUTCOME MEASURES: ADRs-related admissions of HF patients to cardiology units were identified and further assessed for their nature, causality, and preventability.
RESULTS: Of 511 admissions, 34 were due to ADR-related hospitalisation (6.65, 95 % confidence interval 4.8-8.5 %). Number of medications taken by HF patients was the only predictors of ADR-related hospitalisations, where higher number of medications was associated with the odd ratio of 1.11 (95 % CI, 1.03-1.20, P = 0.005). More than one-third of ADR-related hospitalisations (35 %) were preventable The most frequent drugs causing ADR-related hospitalisation were diuretics (32 %), followed by non-steroidal anti-inflammatory drugs (15 %), thiazolidinediones (9 %), anticoagulants (9 %), antiplatelets (6 %), and aldosterone blockers (6 %).
CONCLUSION: ADR-related hospitalisations account for 6.7 % of admissions of HF patients to cardiac units, one-third of which are preventable. Number of medications taken by HF patients is the only predictors of ADR-related hospitalisations. Diuretic induced volume depletion, and sodium and water retention caused by thiazolidinediones and NSAIDs medications are the major causes of ADR-related hospitalisations of HF patients.