Product Images Losartan Potassium
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Product Label Images
The following 6 images provide visual information about the product associated with Losartan Potassium NDC 68788-4007 by Preferred Pharmaceuticals Inc., such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.
Figure 1 - spl image2 figure1

This text provides information on a clinical study involving Atenolol and Losartan Potassium. It mentions the percentage of patients reaching the primary endpoint, an adjusted risk reduction of 13% with a p-value of 0.021. The timeline of the study is also indicated, with the months labeled from 0 to 66.*
Figure 2 - spl image3 figure2

This text provides information comparing the risk reduction of Atenolol and Losartan Potassium, with an adjusted risk reduction of 25% and a p-value of 0.001. It also shows the percentage of patients with fatal/non-fatal stroke across different study months from 6 to 66.*
Figure 3 - spl image4 figure3

This text provides data on primary endpoint events within demographic subgroups from a study comparing the efficacy of Losartan Potassium and Atenolol in preventing stroke (fatal/non-fatal). The demographic subgroups analyzed include age, gender, race, presence of isolated systolic hypertension (ISH), diabetes, and history of cardiovascular disease (CVD). The results are presented in terms of event rates, hazard ratios, and confidence intervals. The study adjusted for baseline Framingham risk score and left ventricular hypertrophy levels. The sample sizes for each subgroup are represented proportionally by symbols.*
Figure 4 - spl image5 figure4

This is a graph showing the percentage of patients with an event over a period of 48 months for Losartan Potassium compared to a Placebo. The risk reduction is calculated to be 16.1% with a statistically significant p-value of 0.022.*
* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.