Product Images Spironolactone

View Photos of Packaging, Labels & Appearance

Product Label Images

The following 4 images provide visual information about the product associated with Spironolactone NDC 55154-5517 by Cardinal Health 107, Llc, 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.

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9572e6f6 b46c 44a0 8027 5942b02d2f86 00

This is a description of a medication called Spironolactone in a 25mg dose. The medication is in tablet form, with 10 tablets in each container. There are instructions to refer to the product insert for more dosage information, precautions, and warnings. The recommended storage condition is at 20-25°C, with protection from light. The package contains a warning that it is intended for institutional use only and is not child-resistant. The medication is manufactured by Mylan Pharmaceuticals Inc. in West Virginia, U.S.A, and distributed by Mylan Institutional Inc. in Illinois, U.S.A, and Cardinal Health in Ohio, U.S.A.*

bag label - image 01

bag label - image 01

bag label - image 02

bag label - image 02

bag label - image 03

bag label - image 03

This appears to be a table of medical data, possibly related to heart failure. The categories include subgroup, age range, sex, race, left ventricular ejection fraction (LVEF), heart failure classification based on NYHA criteria, diabetes, serum creatinine levels, estimated creatinine clearance, potassium levels, medication use (including digoxin, beta blockers, and ACE inhibitors), and the results of certain tests (such as the proportion of patients with ischemic vs non-ischemic heart disease). It appears that there are some missing values or not-applicable categories. The last row suggests that the table may be related to a study comparing the effectiveness of spironolactone vs placebo for treating heart failure.*

* 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.