Discussion Filtered information and unfiltered information
Discussion Filtered information and unfiltered information
PICOT
The PICOT guidelines are questions that help clinicians discover the answers to their research (Walden Student Center for Success, 2012). With these guidelines in mind I formulated the question “Is the daily use of CHG for all pediatric inpatients who have no allergy to CHG associated with a lower incidence of bacterial infection in these same patients?”
The P in the acronym stands for population or patients, in the case the characteristics of the population would be all patients who are staying in the hospital, especially for an extended period or those who have risk factors such as central lines.
The I stands for the intervention, which would be the daily chlorahexadine baths. This intervention would help reduce the risk of hospital acquired infections such as MRSA or c diff. Comparison is the next step and is what the C stands for.
In this case the comparison of the effectiveness of daily CHG baths would be compared to not doing CHG baths. The O stands for outcomes that we would hope to see, which would be a decrease in hospital acquired infections.
The T is the last and final letter and stands for time. In my hospital, the study was conducted over a three-month period to see if we saw a decrease in our hospital acquired infections. Using evidence based research I will find the conclusion to this question.
Evidence Based Research
When conducting research, it is very important to have filtered information and unfiltered information. “Filtered information is information that has been appraised for quality and clinical relevance (Hierarchy of Evidence Pyramid).”
Filtered information includes systematic review, critically appraised topics and critically appraised individual articles (Hierarchy of Evidence Pyramid). “Unfiltered information is evidence that has not necessarily been appraised for quality.
This information tends to come from primary sources (Hierarchy of Evidence Pyramid).” Unfiltered information includes randomized controlled trials, cohort studies, case-controlled studies and expert opinion (Hierarchy of Evidence Pyramid).
When searching the Walden Database for articles on my PICOT question, I used the search terms “CHG Bath,” “CHG Bath in Pediatric Patients,” and “Reducing infection using CHG Bath.” The first article I found multiple studies done on multiple patients with bone marrow transplants.
The purpose of the study was to see if bathing them daily with CHG would decrease the acquired infections, which it did. This article would be considered a systemic review because it had multiple resources and multiple studies.
When searching for critically appraised topics it was very difficult to find one that had a cohort study that had to do with CHG baths, there were some articles about other ways to reduce infection, but none that involved CHG.
The next article I found falls under the topic of expert opinion, in the case the expert opinion came from the nurses. In this study done in 2017, they interviewed nurses, nurse’s aides and nurse managers. They found that all interviewed did find a decrease in infection when CHG baths were used, however many times the nurses did not have time to administer the baths.
Research Advice
I think that when conducting a search for evidence base practice it is important to stay open minded and patient. Staying open minded will help you think of different search terms that may yield different search results. It is also important to be patient while searching so that you can stay focused and weed out the unwanted results.
References
Laureate Education (Producer). 2012g). Hierarchy of evidence pyramid. Baltimore, MD:Author
Musuuza, J. S., Roberts, T. J., Carayon, P., & Safdar, N. (2017). Assessing the sustainability of daily chlorhexidine bathing in the intensive care unit of a Veteran’s Hospital by examining nurses’ perspectives and experiences. BMC Infectious Diseases, 17(1)
Polit, D.F., & Beck C.T. (2017). Nursing research: Generating and assessing evidence for
nursing practice (10Th ed.). Philadelphia, PA: Wolters Kluwer
Robeson, P., Dobbins, M., DeCorby,K., &Tirillis, D. (2010). Facilitating access to pre-processed research evidence in public health. BMC Public Health,10,95.
Rosselet, R., Termuhlen, A., Skeens, M., Garee, A., Laudick, M., & Ryan-Wenger, N. (2009). CH
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Problem Statement (PICOT)
Some healthcare conditions, such as cancer, diabetes, and heart disease, have high morbidity, mortality, and healthcare costs. They also increase the workload for healthcare providers, and attending to them is integral. Healthcare providers periodically evaluate population problems and develop evidence-based interventions to prevent risks, reduce compilations, and improve the health outcomes of populations. Some populations are vulnerable to specific health conditions.
For example, youths between 15-24 years are prone to sexually transmitted illnesses, while females between 40-55 years are prone to post-menopausal syndrome. Healthcare providers assess their population’s needs to determine their risks and intervene for better health outcomes. This paper presents a population problem, expounds on the population affected and the risks, and explores interventions that could help reduce the problem’s effects and promote better health outcomes.
Problem of Interest
Metabolic syndrome features at least three medical conditions occurring together, increasing a population’s risk for diabetes, stroke, and heart disease (Nilsson et al., 2019). These conditions include high blood pressure, blood sugar abnormalities, excess belly fat, and abnormal cholesterol and triglyceride levels. Having one f these conditions does not mean one has the disease but has an increased risk for diabetes, stroke, and heart disease. The condition presents less apparent symptoms such as body fat around the waist and some symptoms of diabetes such as thirst and fatigue. Metabolic syndrome is caused by increased insulin resistance, overweight and obesity, and inactivity.
The risk factors for the condition include diabetes, age (risk increases with age), ethnicity (Hispanic women are at the most significant risk), and other diseases such as non-alcoholic fatty liver disease and sleep apnea (Nilsson et al., 2019). Medications such as second-generation psychotropics that lead to weight gain, increased insulin resistance, and alteration in body fats and glucose metabolism significantly increase the risk for metabolic syndrome. Aggressive lifestyle and therapy changes can help reduce the risk of developing metabolic syndromes or metabolic syndrome complications. The condition’s prevalence is gradually rising, affecting about a third of the US population leading to poor quality of life through reduced abilities and increased susceptibility to life-threatening illnesses (Hirode & Wong, 20). The condition is preventable, and there is a need to implement change interventions that can help alleviate the problem
Population of Interest
Patients with mental health conditions such as bipolar disorder and schizophrenia are some of the most neglected populations. Caring for mentally ill patients requires long-term treatment interventions. Healthcare providers prescribe medications and other interventions such as cognitive behavior therapy depending on patient needs and response to medications. These medications affect other areas, such as hypertension in CNS-acting drugs. Second-generation antipsychotics are associated with increased risk for metabolic syndrome due to their effects on weight gain and insulin resistance. Thus, populations with mental health issues such as bipolar disorder and schizophrenia are thus at risk for metabolic syndrome. The risk for mental health illnesses increases with age; thus, the population of interest is adults aged 20 and above.
The area of interest is a healthcare facility in the Bronx, New York, ZXIPI CODE 10451-5253, serving minority black and Hispanics. The target population is the minority blacks and Hispanics, ethnic groups that carry the most significant risk for metabolic syndrome. Hispanics, especially Hispanic women, have the most significant risk for metabolic syndrome (Phenninx & Lange, 2022). The population is also prone to poor access to mental health care and other social determinants of health such as low income, unemployment, cultural practices (eating practices), and genetic predisposition. Hispanic whites are also exposed to mental health issues due to similar determinants of health, such as low-income families and unemployment. Mental health issues and the genetic predisposition to the condition increase the risk and severity of metabolic syndrome in this population.
Comparison of Approaches
Measures to prevent metabolic syndrome are varied depending on the cause. The most common interventions in mental health include lifestyle changes such as increasing exercise and activity, diet changes, quitting smoking, treatment for obesity and overweight, and changes in treatment therapies associated with the development of metabolic syndrome. Nilsson et al. (2019) note that diet plays a significant role in determining the high-density and low-density lipoprotein levels and their effects on weight gain, obesity, and overweight. Nilsson et al. (2019) also note that patients who adhere to changes in diet and physical activity have better health outcomes than controls. However, interventions should be crafted to meet long-term sustainability without resulting in unhealthy behaviors.
Medication therapy changes are often the medication of choice due to the adherence issues for mentally ill patients. Changing afflicting medications while maintaining the targeted medication therapeutic outcomes has been used to help manage the condition. Changing medication does not eliminate all risks but significantly reduces the risk for metabolic disorders. Piras et al. (2022) note that switching the medications, often from second-generation to first-generation antipsychotics, reduce the risks significantly and leads to attained health, such as the arrest of weight gain and control of blood sugars. Hence, changing psychiatric medications is the intervention of choice, while maintaining the desired therapeutic outcomes is the intervention of choice.
Outcome Approach
The desired outcomes of the specific approaches depend on the confounders. The desired outcome is the prevention of metabolic syndrome in mentally ill patients. The desire is to ensure patients do not develop the condition during therapy. As mentioned earlier, metabolic syndrome results from either of the five conditions. These conditions are affected by various factors, especially in adults with mental health illnesses. Thus, the desired outcomes are attaining a healthy weight with decreased waist circumference, normal triglyceride levels, increased high-density lipoproteins, normal blood pressure, and blood sugars (Nilsson et al., 2019). The effectiveness of the interventions in preventing metabolic syndrome should be evaluated against these values because changes in any three could lead to metabolic syndrome.
Time Approach
Healthcare interventions vary in their effectiveness. Psychotropic medications take a short time to produce side effects such as weight gain. A more extended period, six months, is the idea to help monitor patients and ensure these effects do not appear later. The effects of the proposed intervention can be evaluated after six months of the intervention. According to Nilsson et al. (2019), management interventions for more than six months produce more permanent changes and reduce symptoms of relapse. Six months is the optimum period for developing, implementing, and evaluating the effectiveness of an intervention. Thus, the PICOT question is: Among mentally ill patients, do first-generation antipsychotics reduce the risk for metabolic syndrome, compared to second-generation antipsychotics, in six months?
Literature Review
The prevalence of metabolic syndrome in the US is rising gradually due to lifestyle changes and increased associated conditions such as diabetes, overweight, and obesity. Hirode and Wong (2020) conducted a study using the National Health and Nutrition Examination Survey 2011-2016 data to study the metabolic syndrome trends for adults above age 20. According to the study, the weighted prevalence of metabolic syndrome was 34.7%, with individuals between ages 20-39 presenting the lowest percentage (19%) and individuals above 60 years having the highest prevalence (48%) (Hirode & Wong, 2020). Thus, the risk for the disease increases with age.
Gurka et al. (2019) note that the geographical prevalence of metabolic syndrome was high in areas such as the high number of Hispanic women. Metabolic syndrome is associated with factors such as poor dieting, such as food with large volumes of fat leading to substantial or uncontrolled weight gain. The location of interest is a health facility dealing with minority blacks and Hispanics and dealing with many patients with diabetes, obesity, and mental health problems. It is thus a suitable setting to manage metabolic syndrome among mentally ill patients.
Phennix and Lange (2022) note that patients with mental health illnesses are at risk for premature mortality related to cardiovascular disorders. The most common cause of these cardiovascular disorders is metabolic syndrome, often caused by psychotropic medications. The most common disorders with increased risk for metabolic syndrome are bipolar disorder, major depression, and schizophrenia. Phennix and Lange (2022) show that bipolar disorder patients under psychotropic medications had a 1.72 times risk for developing metabolic syndrome than bipolar disorder patients without psychotropic medications. The study also revealed that 72% of the patients receiving second-generation antipsychotics reported weight gain and metabolic alterations (Phennix & Lange, 2022). The results are synonymous with other studies, such as Scaini et al. (2021).
Scaini et al. (2021) show that second-generation antipsychotics lead to mitochondrial activity alterations and subsequent metabolic syndrome results, especially in patients with schizophrenia and schizophrenia spectrum disorders. Phennix and Lange (2022) note that alterations in pathways involving neuroreceptors for dopamine and other neurotransmitters, such as serotonin, leading to metabolic syndrome development. Piras et al. (2022) agree with the study and show that psychotropic drugs induce weight gain and increase the risk for metabolic syndrome in these populations. Mental health issues increase with age, and so do metabolic syndrome, making mentally ill adults above age 20 a population of interest.
In another study, Abo Alrob et al. (2019) studied the effects of long-term use of second-generation antipsychotics on a Jordanian population. After six months of treatment with second-generation antipsychotics, 44% of the patients reported increased systolic pressure, 54.9% reported elevated triglyceride, and 31.9% developed glucose regulation problems (Abo ASlrob et al., 2019). In addition, the number of participants with metabolic syndrome increased from 14% at baseline to 31% at the end of the study. These results are supported by other studies, such as Fang et al. (2019), which report a direct correlation between second-generation antipsychotics and metabolic syndrome.
Fang et al. (2019) show that the prevalence of metabolic syndrome among schizophrenic patients on second-generation antipsychotics was 33%, presenting results similar to most other studies. In addition, Buhagiar and Jabbar (2019) note that individuals under first-generation antipsychotics report lower lipid level abnormalities rates than individuals on first-generation antipsychotics. However, first-generation antipsychotics are avoided due to their severe side effects, such as tardive dyskinesia. From these studies, it is clear that second-generation antipsychotics are associated with high rates and the development of metabolic syndrome compared to controls which include placebo and first-generation antipsychotics.
Gurusamy et al. (2021) note that diet and exercise can help alleviate complications of metabolic syndrome in individuals with schizophrenia. Diet and exercise help reduce lipid levels and promote maintenance of healthy body weight, thus alleviating metabolic syndrome. However, Gurusamy et al. (2021) note that patients with schizophrenia also present with exercise and diet adherence problems. Studies have shown that only a tiny percentage of patients adhere to diet and exercise regimens due to personal factors and other social determinants of health, such as income and education level. Despite the effectiveness of diet and exercise in preventing and alleviating metabolic syndrome, they remain underutilized. Swarup et al. (2021) also note that exercises are the most effective interventions in regulating risk factors for the condition. However, their compatibility with individuals with mental illnesses is the most significant barrier to their effectiveness.
Mazza et al. (2018) note that antipsychotic medications relay different effects on populations. Cariprazine is associated with less weight gain than other drugs such as olanzapine, quetiapine, and risperidone and can be used to replace them when patients report marked weight gain (Mazza et al., 2018). These medications, if unmonitored, can increase insulin resistance, lead to weight gain, and increase the risk for metabolic syndrome. The efficacy of the changed therapies remains in question hence the need for periodic evaluation and therapy changes as the need arises in these patients. Rimvall et al. (2021) note that patients manifest differently and respond differently to some interventions. A patient-centered transdiagnostic approach is vital to managing mental health illnesses and preventing complications.
The National Institute of Mental Health is the state agency with the mandate to control and prevent mental health illnesses in the population. The institute requires all studies to focus on using human subjects for research t ensure the studies are IRB-approved (NIMH, n.d.). Studies involving mentally ill patients should be conducted with their consent if they are deemed fit to give consent or with their care providers. Other ethical considerations applying to the general population should also be addressed. For example, the care provider/researcher should not withhold a proven intervention or lead to delays in any care delivery to these patients. In addition, other organizations such as HIPSS regulate information sharing, and researchers should ensure data privacy and the protection of the participants from population access to their personal information.
Sneller et al. (2021) note that patients are often provided with polypharmacy when care providers want to eliminate some drug side effects when achieving the targeted therapeutic outcomes. For example, care providers can prescribe lipids lowering drugs to patients reporting weight gain with lithium without stopping or lowering the lithium dose. Healthcare providers should practice safe prescriptions concerning policies regulating polypharmacy due to its consequences, such as superimposed side effects and widespread poor drug adherence (Ijaz et al., 2018). Ijaz et al. (2018) showed that polypharmacy has no significant effect on metabolic syndrome prevalence but could lead to other potentially harmful consequences.
Policies and regulations help support efforts in managing healthcare conditions. Swarup et al. (2021) note that the joint commission recommends blood pressure regulation to ensure it is less than 140/90 in the general population, below 130/80 for diabetic patients, and below 150/90 in individuals above 60 years. The regulations should be observed in mentally ill patients, and their regular evaluation will help healthcare providers intervene and prevent metabolic syndrome in the long run.
Notably, metabolic syndrome does not occur in mentally ill patients and the general population. Factors in the general population include physical inactivity, insulin resistance, poor nutrition, and dieting. Thus, interventions such as changing medications may be ineffective hence the need to address the specific causes of the diseases in individual patients. The literature review helps appreciate the role played by second-generation antipsychotics in metabolic syndrome development. However, patients taking first-generation antipsychotics or other mental health illnesses can develop the disorder when factors such as poor diet and nutrition, inactivity, and smoking are in play (Hirode & Wong, 2022). Thus, individualized care is essential despite implementing community-wide interventions.
Conclusion
Healthcare providers play vital roles in assessing population health and promoting better outcomes. The interest population is mentally ill adults aged 20 and above receiving care in the Bronx. The problem of interest is metabolic syndrome, a diagnosis of three conditions: elevated low-density lipoproteins, low high-density lipoprotein, increased waist circumference, poor glucose regulation, and elevated blood pressure. The risk factors include diabetes, age, overweight and obesity, and medications affecting metabolic activities.
The mentally ill are at risk for the disease precisely due to the second-generation antipsychotic medications’ ability to alter metabolic functions in the mitochondria and the CNS. Interventions such as diet, exercise, and changes in therapy target one or more conditions in metabolic syndrome. These interventions have been implemented with varying degrees of success in varied populations. Assessing population needs will help develop interventions that produce the desired outcomes-prevention of metabolic syndrome in mentally ill patients.
References
Abo Alrob, O., Alazzam, S., Alzoubi, K., Nusair, M. B., Amawi, H., Karasneh, R., Rababa’h, A., & Nammas, M. (2019). The effect of long-term second-generation antipsychotics use on the metabolic syndrome parameters in Jordanian population. Medicina, 55(7), 320. https://doi.org/10.3390/medicina55070320
Buhagiar, K., & Jabbar, F. (2019). Association of first-vs. second-generation antipsychotics with lipid abnormalities in individuals with severe mental illness: a systematic review and meta-analysis. Clinical Drug Investigation, 39(3), 253-273. https://doi.org/10.1007/s40261-019-00751-2
Fang, X., Wang, Y., Chen, Y., Ren, J., & Zhang, C. (2019). Association between IL-6 and metabolic syndrome in schizophrenia patients treated with second-generation antipsychotics. Neuropsychiatric Disease and Treatment, 15, 2161. https://doi.org/10.2147/NDT.S202159
Gurka, M. J., Filipp, S. L., & DeBoer, M. D. (2018). Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutrition & Diabetes, 8(1), 1-8. https://doi.org/10.1038/s41387-018-0024-2
Gurusamy, J., Gandhi, S., Damodharan, D., Ganesan, V., & Palaniappan, M. (2018). Exercise, diet and educational interventions for metabolic syndrome in persons with schizophrenia: A systematic review. Asian Journal of Psychiatry, 36, 73-85. https://doi.org/10.1016/j.ajp.2018.06.018
Hirode, G., & Wong, R. J. (2020). Trends in the prevalence of metabolic syndrome in the United States, 2011-2016. JAMA, 323(24), 2526-2528. https://doi.org/10.1001/jama.2020.4501
Ijaz, S., Bolea, B., Davies, S., Savović, J., Richards, A., Sullivan, S., & Moran, P. (2018). Antipsychotic polypharmacy and metabolic syndrome in schizophrenia: a review of systematic reviews. BMC Psychiatry, 18(1), 1-13. https://doi.org/10.1186/s12888-018-1848-y
Mazza, M., Marano, G., Traversi, G., Carocci, V., Romano, B., & Janiri, L. (2018). Cariprazine in bipolar depression and mania: state of the art. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders), 17(10), 723-727. https://doi.org/10.2174/1871527317666180828120256
National Institute of Mental Health (NIMH), (n.d.). Human Subject Research Issues. Mental Health Information. https://www.nimh.nih.gov/funding/managing-your-grant/human-subjects-research-issues
Nilsson, P. M., Tuomilehto, J., & Rydén, L. (2019). The metabolic syndrome–What is it, and how should it be managed? European Journal Of Preventive Cardiology, 26(2_suppl), 33-46. https://doi.org/10.1177/2047487319886404
Penninx, B. W., & Lange, S. M. (2022). Metabolic syndrome in psychiatric patients: overview, mechanisms, and implications. Dialogues in Clinical Neuroscience. https://doi.org/10.31887/DCNS.2018.20.1/bpenninx
Piras, M., Ranjbar, S., Laaboub, N., Grosu, C., Gamma, F., Plessen, K. J., Gunten, A., Conus, P., & Eap, C. B. (2022). Evolutions of Metabolic Parameters Following Switches of Psychotropic Drugs: A Longitudinal Cohort Study. Schizophrenia Bulletin. https://doi.org/10.1093/schbul/sbac133
Rimvall, M. K., van Os, J., & Jeppesen, P. (2021). Promoting a patient-centered, transdiagnostic approach to prevention of severe mental illness. European Child & Adolescent Psychiatry, 30(5), 823-824. https://doi.org/10.1007/s00787-020-01563-y
Scaini, G., Quevedo, J., Velligan, D., Roberts, D. L., Raventos, H., & Walss-Bass, C. (2018). Second generation antipsychotic-induced mitochondrial alterations: Implications for increased risk of metabolic syndrome in patients with schizophrenia. European Neuropsychopharmacology, 28(3), 369-380. https://doi.org/10.1016/j.euroneuro.2018.01.004
Sneller, M. H., De Boer, N., Everaars, S., Schuurmans, M., Guloksuz, S., Cahn, W., & Luykx, J. J. (2021). Clinical, biochemical and genetic variables associated with metabolic syndrome in patients with schizophrenia spectrum disorders using second-generation antipsychotics: a systematic review. Frontiers in Psychiatry, 12, 625935. https://doi.org/10.3389/fpsyt.2021.625935
Swarup, S., Goyal, A., Grigorova, Y., & Zeltser, R. (2021). Metabolic syndrome. In StatPearls [internet]. StatPearls Publishing.
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