Purpose: To determine if falls in the hospital occur more frequently in patients receiving intravenous (IV) antihypertensive therapy compared to patients that are not receiving any such treatment.

Methods: Using data retrospectively collected from the institution’s electronic records, we filtered dispensing records for select IV antihypertensive medications from January 2018 through December 2019. We then compared the patient identifying numbers from the dispensing records with the patient identifying numbers from the reported inpatient fall records during the same time frame.

Results: There was a total of 617 inpatient falls at the hospital between January 2018 and December 2019. One hundred twenty-three patients out of the 617 (20%) fell while receiving IV antihypertensive agents. During the same time there were a total of 9,226 orders for IV metoprolol, labetalol, and hydralazine. This indicates that out of the 9,226 orders for IV antihypertensives, 1.3% of patients fell while in the hospital because of those medications. Of the 617 falls, 124 of those were associated with an injury report. There were 22 patients (18%) receiving IV antihypertensives that fell and were injured.

Conclusions & Recommendations: Our findings support the need for additional research to determine what factors are contributing to patients falling while on IV antihypertensive therapy. IV antihypertensive medications should be discontinued at the earliest time to prevent falls especially when patients are restarted on oral antihypertensive medications and blood pressure is controlled. Patients should be monitored closely for orthostasis in all settings when antihypertensive therapy is started or changed. Patients and caregivers should be educated particularly on this point especially in frail, elderly patients.

Sterile filter validation is an important process in the pharmaceutical, biotechnology, and healthcare industries to ensure the effectiveness and integrity of sterile filtration systems. The validation process involves testing and verifying the performance of sterile filters to ensure they can effectively remove microorganisms and particles from a fluid stream while maintaining sterility. Selecting a sterilizing grade filter requires consideration of many important issues, such as materials of construction and their compatibility with the product to be filtered. The processes requirements and validation need differ based on the filtration requirement. Validation has always been a key aspect of assuring sterility of the final formulation produced under the aseptic conditions. As sterilizing-grade filters play a major role in obtaining a high sterility assurance level, validation of these filtration processes has become a subject of increasing awareness and regulatory scrutiny. The bacterial challenge test serves on the major functions. the filter manufacturer uses it to classify filters as sterilizing grade if the filter provides a sterile effluent with a minimum of 107 cells of Brevundimonas diminuta ATCC 19146/cm2 of effective filter surface area. Therefore, the design, validation and ongoing monitoring of a sterile filtration system are essential for assuring the quality and safety of the pharmaceutical product. Proper understanding and testing of the sterile filtration system according to international regulatory standards is important for both the validation and ongoing monitoring of the system.

Lung Cancer in Never-Smokers (LCINS) is one of the leading causes of cancer patient deaths in the United States. Unlike lung cancer onset by cigarette smoking, LCINS is not as readily understood and research on the subject has been conflicting. Thus, early diagnosis and prevention are key in reducing mortality among LCINS patients. In this study, the Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset contains more than 155,000 participants and over 36,000 never-smokers. Data from more than 5,000 patients with LCINS were analyzed using R and Excel software to determine risk factors of LCINS. The factors analyzed for predictive power in LCINS incidence were age, height, weight, body mass index, race, income, family history, and secondary smoke exposure. Multiple statistical methods, including t-tests, ANOVA tests, and logistic regression, were implemented to assess each factor. Through comparison and corroboration of results from these statistical methods, age and race were identified as the key factors that had statistically significant evidence as potential influences in LCINS incidence. This was indicated by strong positive correlation between age/race and the predicted probability of LCINS development with a statistically significant p value. Other physical characteristics did not appear to have a significant impact on the likelihood of developing LCINS. In addition, the statistical method that provided the most information regarding a factor’s power was logistical regression due to the binomial outcome of whether or not a patient has LCINS. It enables a deeper elucidation of how individual factors influence the probability of LCINS development. These results provide valuable insights into demographic risk factors for LCINS and lay the groundwork for future investigations, potentially including the application of advanced analytical techniques such as deep learning algorithms to explore additional predictive factors for LCINS and other lung cancer subtypes.