Which is a better treatment for hypertensive patients with diabetes: a combination of losartan and hydrochlorothiazide or a maximum dose of losartan?

Thiazide Diuretic-Induced Change in Fasting Plasma Glucose: a Meta-analysis of Randomized Clinical Trials

BACKGROUND

Prior meta-analyses measuring thiazide-induced glycemic change have demonstrated an increased risk of incident diabetes; however, this measure's definition has changed over time.

AIM

To determine the magnitude of change in fasting plasma glucose (FPG) for thiazide diuretics.

DATA SOURCES

A research librarian designed and conducted searches in Medline®, EMBASE, and EBM Reviews-Cochrane Central Register of Controlled Trials (inception through July 2018) and International Pharmaceutical Abstracts (inception to December 2014).

STUDY SELECTION

Randomized, controlled trials comparing a thiazide or thiazide-like diuretic to any comparator reporting FPG were identified. Trials enrolling < 50 participants, those with a follow-up period of < 4 weeks, and conference abstracts were excluded.

DATA EXTRACTION

Independent duplicate screening of citations and full-text articles, data extraction, and assessment of risk of bias was conducted.

DATA SYNTHESIS

Ninety-five studies were included (N = 76,608 participants), with thiazides compared with placebo, beta-blockers, calcium channel blockers, renin-angiotensin-aldosterone-system inhibitors, potassium-sparing diuretic, and others alone or in combination. Thiazide diuretics marginally increased FPG (weighted mean difference 0.20 mmol/L (95% CI 0.15-0.25); I2 = 84%) (1 mmol/L = 18 mg/dL). Results did not change substantially when considering dose or duration, comparing thiazides with placebo or an active comparator, or using thiazides as monotherapy or combination therapy, even when combined with a potassium-correcting agent.

CONCLUSION

Thiazide diuretics have a small and clinically unimportant impact on FPG.

Applying a "Big Data" Literature System to Recommend Antihypertensive Drugs for Hypertension Patients with Diabetes Mellitus

BACKGROUND The explosive increase in medical literature has changed therapeutic strategies, but it is challenging for physicians to keep up-to-date on the medical literature. Scientific literature data mining on a large-scale of can be used to refresh physician knowledge and better improve the quality of disease treatment. MATERIAL AND METHODS This paper reports on a reformulated version of a data mining method called MedRank, which is a network-based algorithm that ranks therapy for a target disease based on the MEDLINE literature database. MedRank algorithm input for this study was a clear definition of the disease model; the algorithm output was the accurate recommendation of antihypertensive drugs. Hypertension with diabetes mellitus was chosen as the input disease model. The ranking output of antihypertensive drugs are based on the Joint National Committee (JNC) guidelines, one through eight, and the publication dates, ≤1977, ≤1980, ≤1984, ≤1988, ≤1993, ≤1997, ≤2003, and ≤2013. The McNemar's test was used to evaluate the efficacy of MedRank based on specific JNC guidelines. RESULTS The ranking order of antihypertensive drugs changed with the date of the published literature, and the MedRank algorithm drug recommendations had excellent consistency with the JNC guidelines in 2013 (P=1.00 from McNemar's test, Kappa=0.78, P=1.00). Moreover, the Kappa index increased over time. Sensitivity was better than specificity for MedRank; in addition, sensitivity was maintained at a high level, and specificity increased from 1997 to 2013. CONCLUSIONS The use of MedRank in ranking medical literature on hypertension with diabetes mellitus in our study suggests possible application in clinical practice; it is a potential method for supporting antihypertensive drug-prescription decisions.