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.
Patients and methods: This was a prospective study conducted in Hospital Pulau Pinang, Malaysia, between March 2015 and June 2015. Educational intervention was provided to 96 patients (11 males, 85 females; mean age 52.4±12.9 years; range, 20 to 83 years) who fulfilled the inclusion/exclusion criteria. Questionnaires to assess knowledge of CVD risk were given to patients to be answered before reading the informative leaflet, after one hour of intervention, and during their next follow-up three months from the intervention. Both the informative leaflet and questionnaires were prepared in English and then translated into Malay and Chinese languages to suit the need of local patients.
Results: Our results showed that RA patients had good knowledge at baseline regarding risk of smoking, hypertension, and hyperlipidemia on increasing CVD risk and that exercise would not damage their joints. However, they had low knowledge at baseline regarding the amount of exercise needed for lower CVD risks and risk of CVD with use of anti-inflammatory drugs in RA. Total knowledge score increased significantly from baseline immediately after educational intervention. However, total knowledge score decreased after three months compared to immediate post- intervention phase while it was still significantly higher compared to baseline. The improvement was most obvious for knowledge regarding anti- inflammatory drugs and CVD risk and knowledge regarding the number of flares and CVD risk. Our study did not find any significant association between demographic characteristics and traditional cardiovascular risk factors with knowledge of CVD risk.
Conclusion: Rheumatoid arthritis patients have low knowledge regarding their CVD risk related to their disease. The intervention of providing an informative leaflet effectively improved the knowledge of this group of patients on CVD risk particularly in the field related to RA-specific risk.
METHOD: This is a non-interventional, retrospective analysis of documented CPI in a 100-bed, acute-care private hospital in Amman, Jordan. Study consisted of 542 patients, 574 admissions, and 1694 CPI. Team collected demographic and clinical data using a standardized tool. Input consisted of 54 variables with some taking merely repetitive values for each CPI in each patient whereas others varying with every CPI. Therefore, CPI was consolidated to one rejected and/or one accepted per patient per admission. Groups of accepted and rejected CPI were compared in terms of matched and unmatched variables. ANN were, subsequently, trained and internally as well as cross validated for outcomes of interest. Outcomes were length of hospital and intensive care stay after the index CPI (LOSTA & LOSICUA, respectively), readmissions, mortality, and cost of hospitalization. Best models were finally used to compare the two scenarios of approving 80% versus 100% of CPI. Variable impacts (VI) automatically generated by the ANN were compared to evaluate the effect of rejecting CPI. Main outcome measure was Lengths of hospital stay after the index CPI (LOSTA).
RESULTS: ANN configurations converged within 18 s and 300 trials. All models showed a significant reduction in LOSTA with 100% versus 80% accepted CPI of about 0.4 days (2.6 ± 3.4, median (range) of 2 (0-28) versus 3.0 ± 3.8, 2 (0-30), P-value = 0.022). Average savings with acceptance of those rejected CPI was 55 JD (~ 78 US dollars) and could help hire about 1.3 extra clinical pharmacist full-time equivalents.
CONCLUSIONS: Maximizing acceptance of CPI reduced the length of hospital stay in this model. Practicing Clinical Pharmacists may qualify for further privileges including promotion to a fully independent prescriber status.
Objective: To critically examine the literature regarding the involvement of CCB in manifestation of LUTS in humans.
Methods: A systematic literature search was conducted on PubMed, SciELO, Scopus, and OpenGrey databases to find all potentially relevant research studies before August 2016.
Results: Five studies met the inclusion criteria and were included in this review. Three out of five studies stated that CCB were involved in either precipitation or exacerbation of LUTS. As for the remaining two studies, one study found out that only the monotherapy of CCB was associated with increased prevalence of nocturia and voiding symptoms in young females, whereas the other study reported an inverse association of CCB with LUTS. The methodological quality of studies was considered high for four studies and low for one study.
Conclusion: Healthcare providers should make efforts for an earlier identification of the individuals at risk of LUTS prior to the commencement of CCB therapy. Moreover, patients should be counselled to notify their healthcare provider if they notice urinary symptoms after the initiation of CCB.
OBJECTIVE: This study aimed to determine self-monitoring practices, awareness to dietary modifications and barriers to medication adherence among physically disabled type 2 diabetes mellitus patients.
METHODS: Interview sessions were conducted at diabetes clinic - Penang general hospital. The invited participants represented three major ethnic groups of Malaysia (Malay, Chinese & Indians). An openended approach was used to elicit answers from participants. Interview questions were related to participant's perception towards self-monitoring blood glucose practices, Awareness towards diet management, behaviour to diabetes medication and cues of action.
RESULTS: A total of twenty-one diabetes patients between the ages 35 - 67 years with physical disability (P1-P21) were interviewed. The cohort of participants was dominated by Males (n=12) and also distribution pattern showed that majority of participants were Malay (n=10), followed by Chinese (n=7) and rest Indians (n=4). When the participants were asked in their opinion what was the preferred method of recording blood glucose tests, several participants from low socioeconomic status and either divorced or widowed denied to adapt telemontoring instead preferred to record manually. There were mixed responses about the barriers to control diet/calories. Even patients with high economic status, middle age 35-50 and diabetes history of 5-10 years were influenced towards alternative treatments.
CONCLUSION: Study concluded that patients with physical disability required extensive care and effective strategies to control glucose metabolism.
METHODS: A cross-sectional investigation was conducted at General Penang Hospital, Malaysia. Demographic criteria and laboratory tests of patients were investigated. Controlled glycemia (CG) was recognized as glycated hemoglobin (HbA1c) ≤7% depending on American Diabetes Association guidelines 2018. Charlson Comorbidity Index (CCI) was used to estimate the confounding influence of co-morbidities and predict ES-10Y. Data was managed by IBM-SPSS 23.0.
RESULTS: A total of 400 cases categorized to (44.25%) patients with CG, and (55.75%) cases had uncontrolled glycemia (UCG). HbA1c mean in CG and UCG group was (6.8 ± 0.9 vs 9.5 ± 1.6, P-value: 0.001). Fasting blood glucose was (7 ± 2.3 vs. 9.9 ± 4.3, P-value: 0.001) in CG and UCG group. CCI was (3.38 ± 2.38 vs. 4.42 ± 2.70, P-value: 0.001) and, ES-10Y was (62% vs 46.2%, p-value: 0.001) in CG vs. UCG respectively. Spearman test indicates a negative correlation between CG and CCI (r: 0.19, p-value: 0.001). Logistic regression confirmed HbA1c as a significant predictor of CCI (r2: 0.036, P-value: 0.001). CG has a positive correlation with survival (r: 0.16, P-value: 0.001) and logistic regression of survival (r2: 0.26, P-value: 0.001).
CONCLUSIONS: More than one-half of the investigated persons had UCG. Controlled HbA1c was associated with lower co-morbidities and higher ES-10Y.
AIMS: This study intended to assess the association of peripheral neuropathy with statins therapy amongst Type 2 diabetic patients.
METHODS: At Penang General Hospital, 757 cases were categorized into two groups (564 with statins therapy and 193 without statins therapy). The diagnosis of PN was investigated retrospectively for a period of 10 years (2006-2016). Confounding risk factors as age, diabetes period, hypertension, glycemic control, other co-morbidity, and prescriptions were matched.
RESULTS: About 129 (22.9%) cases from 564 statins users had PN. Only 30 (15.5%) subjects had PN from 193 statins non-users. Chi-square test showed a significant variance among statins treatment cohort and statin-free cohort in the occurrence of PN (P-value: 0.001). Spearman's investigation presented a positive correlation (r: 0.078, p-value: 0.031) among statins use and PN prevalence. Binary logistic regression was statistically significant for statins therapy as a predictor of peripheral neuropathy incidence (r2: 0.006, p-value: 0.027) amid diabetic patients. The relative risk of peripheral neuropathy connected with statins therapy is (RR: 1.47, 95% CI: 1.02-2.11). The excess relative risk is 47.1%. While the absolute risk (AR) is 7.3% and the number needed to harm (NNH) is 14.
CONCLUSIONS: The study indicated a positive association between peripheral neuropathy and statins utilization. Peripheral neuropathy was higher amongst statins users than the statins-free group.