NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
NUR 705 Discussion 11.1: Statistical Tests That Predict Outcomes
Some current issues that have been occurring in my practice environment include HAPIs and infiltrated IVs. I work on a cardiac floor and we use IV medications such as amiodarone. One of the main side effects of amiodarone is that it can cause swelling, tenderness, pain, redness, thrombphebelitis, and/or necrosis. The worst thing that can happen is that a patient can lose a limb; unfortunately, I have seen it happen. The second most common issue I see is HAPIs or health-acquired pressure injuries. This occurs when a patient is lying on one side for too long and not turned every 2 hours (as stated in our protocol). This can be because of nurse error or a patient refused. These two concerns have led to extended hospital stays and worsening patient outcomes. Looking at regression a statistical model that can be used is linear regression. The reason I would choose linear regression is that it examines the relationship between an independent and dependent variable and I can see how strong the relationship is. Examples I would use are examining the relationship between age and the number of pressure injuries acquired to patients OR looking at the relationship between IV infiltration and the duration a patient is on IV amiodarone. For the first example, age would be the independent variable, and the number of pressure injuries would be the dependent variable. For the second example, IV infiltration would be the dependent variable and the duration a patient is on IV amiodarone would be the independent variable. For the IV infiltration example, I would measure the duration in minutes. That said, I would look at the relationship to see how many minutes it would take each participant to acquire an infiltrated IV. Looking at my pressure injury example, I would examine the relationship in a quantitative manner to see if a patient’s age has a relationship with an increased number of pressure injuries. To gather my information, I would organize the results in a spreadsheet and then put them in a linear regression graph. This type of analysis is an excellent way to find the relationship between variables. Being able to utilize this type of data can help shape hospital policies and create better outcomes for patients.
Hello Rana,
I agree with you that linear regression examines the relationship between dependent and independent variable. Both variables are important in the study. Choosing the right variables in the study may trigger confusion Therefore, an individual should identify independent and dependent variables before applying other procedures of linear regression (Diel et al., 2021). For instance, in investigating health-acquired pressure injuries as the current health issue the variables may be age and the number of the pressure injuries. Age can be identified as independent whereas HAPIs considered as dependent variable. Linear regression provides information that can be used to predict the future (Bartlett et al., 2020). The information provided in the graph reveals a trend that can be used for prediction. Healthcare settings have the information about the future helps in planning (Santiago et al., 2018). When the right data is collected for linear regression, the strategy will provide accurate and reliable information. The trends is important aspect of linear regression.
References
Bartlett, P. L., Long, P. M., Lugosi, G., & Tsigler, A. (2020). Benign overfitting in linear regression. Proceedings of the National Academy of Sciences, 117(48), 30063-30070. https://doi.org/10.1073/pnas.1907378117 (Links to an external site.)
Diel, A., Weigelt, S., & Macdorman, K. F. (2021). A meta-analysis of the uncanny valley’s independent and dependent variables. ACM Transactions on Human-Robot Interaction (THRI), 11(1), 1-33. https://doi.org/10.1145/3470742 (Links to an external site.)
Santiago, C. B., Guo, J. Y., & Sigman, M. S. (2018). Predictive and mechanistic multivariate linear regression models for reaction development. Chemical science, 9(9), 2398-2412. DOI: 10.1039/C7SC04679K (Links to an external site.)
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