Hyperglycemia and Cancer: An Algorithm to Guide Oncology Nurses

Veronica Sampayo

Cindy S. Tofthagen

professional development, nurse education, diabetes, cancer, hyperglycemia
CJON 2017, 21(3), 345-352. DOI: 10.1188/17.CJON.345-352

Background: A dual diagnosis of cancer and hyperglycemia has demonstrated untoward effects on patients’ cancer treatment, prognosis, and survival.

Objectives: The purpose of this evidence-based project is to improve knowledge and awareness of the consequences of hyperglycemia in patients with cancer, increasing nurses’ capability to effectively intervene. In addition, a clinical algorithm based on current evidence was developed and is presented.

Methods: An educational program was developed and pilot tested. The program addressed the etiology of hyperglycemia and its effects on patients with cancer. Knowledge of hyperglycemia in patients with cancer was assessed with a pre- and post-test.

Findings: All participants found the educational program effective and deemed the clinical algorithm useful. Results improved significantly after participation in the educational intervention.

Jump to a section

    About 8%–18% of individuals with cancer have preexisting diabetes at the time of diagnosis (Barone et al., 2008; Hammer & Voss, 2012; Hershey et al., 2014). Cancer therapies, including chemotherapeutic, biologic, and hormonal agents, have the potential to cause hyperglycemia (Hershey et al., 2014). Researchers have found that patients with elevated fasting blood glucose (FBG) levels and body mass index (BMI) have a reduced time to tumor progression (Pantano et al., 2013). Elevated FBG and BMI are not only predictive factors for time to progression (Pantano et al., 2013), but also are risk factors for diabetes (American Diabetes Association [ADA], 2015).

    Oncology nurses need information and resources to guide them in providing care to patients at risk for hyperglycemia. Identifying modifiable and nonmodifiable risk factors for hyperglycemia, understanding how cancer treatments can affect glycemic control, and recognizing potential cancer-related and non–cancer-related complications of hyperglycemia are important to providing the best possible care to patients at risk for hyperglycemia. However, these types of resources may not be readily available to nurses across treatment settings. The goal of this article is to provide information and resources to help guide oncology nurses caring for individuals with, or at risk for, hyperglycemia.

    Review of the Literature

    Diabetes mellitus is a group of metabolic disorders that share one common trait, hyperglycemia. Many factors contribute to an individual’s hyperglycemic state, including decreased insulin production, decreased glucose uptake, and an increase in glucose secretion (Powers, 2015a). Type I diabetes is an autoimmune disorder that causes destruction of pancreatic beta cells. The destruction of insulin-producing beta cells causes individuals to be dependent on exogenous insulin (Powers, 2015a). Type II diabetes accounts for more than 95% of all diagnosed cases (Giovannucci et al., 2010). It is characterized by insulin resistance, impaired insulin secretion, and increased glucose production (Powers, 2015a). A long, asymptomatic phase often occurs before a diagnosis is made (ADA, 2015). Prediabetes is a condition associated with impaired fasting glucose and impaired glucose tolerance (McCance & Huether, 2010). Hammer et al. (2015) found that 26% of patients with cancer undergoing cancer treatment for solid tumors had prediabetes.

    Corticosteroids are often used in chemotherapy regimens and for the treatment and prevention of chemotherapy-related side effects. Corticosteroids affect the insulin-signaling pathway by decreasing expression of insulin receptor substrate, causing a down-regulation of glucose transport in the muscle, which requires more insulin for cellular glucose uptake, and stimulating the liver to secrete glycogen stores, with a resultant surge in circulating glucose (De Vos-Schmidt & Dilworth, 2014). Prediabetic patients who are prescribed corticosteroids often develop diabetes, and patients with preexisting diabetes can develop severe hyperglycemia (Vigneri, Frasca, Sciacca, Pandini, & Vigneri, 2009). Docetaxel (Taxotere®), mTOR inhibitors, and androgen deprivation therapy are additional cancer therapies that may contribute to hyperglycemia (Hershey et al., 2014).

    Modifiable Risk Factors

    Obesity is associated with insulin resistance and an increased incidence of type II diabetes, and higher BMIs are associated with increased cancer risk, particularly cancers of the breast, colon, rectum, endometrium, pancreas, esophagus, kidney, gallbladder, and liver (Giovannucci et al., 2010). Diets high in sugar and refined carbohydrates are known to contribute to obesity and increase the risk for developing type II diabetes (Barclay et al., 2008; Giovannucci et al., 2010; Hammer & Voss, 2012). Some research suggests that an excess intake of refined carbohydrates and sugars are strongly associated with increased cancer risk (Vigneri et al., 2009). The American Cancer Society (ACS), World Research Fund, and American Institute for Cancer Research have all recommended limiting excess intake of these foods (Giovannucci et al., 2010). A consensus statement by ACS and ADA emphasizes the benefits of increased physical activity (Giovannucci et al., 2010). Decreased physical activity is associated with adiposity, insulin resistance, and inflammation (Lynch, Courneya, Sethi, Patrao, & Hawkes, 2014).

    ADA (n.d.) describes elevated blood pressure as a risk factor for diabetes mellitus. Patients with blood pressures greater than 120/80 are recommended to undergo lifestyle modifications, including increasing physical activity, adjusting diet, and exercising weight control. Uncontrolled hypertension not only increases the risk of diabetes but also increases the risk of cardiovascular events, ocular complications, and kidney dysfunction, all of which can complicate the course of cancer treatment. Abnormal lipid metabolism is also associated with an increased risk of diabetes (ADA, n.d.).

    Patients with cancer may be prescribed potentially hyperglycemia-inducing medications unrelated to cancer treatment. Beta blockers and thiazide diuretics may increase serum glucose levels, particularly in patients with diabetes (Rehman, Setter, & Vue, 2011). Antihypertensives most commonly associated with elevating serum glucose levels include propranolol (Hemangeol™), metoprolol (Lopressor®), atenolol (Tenormin®), and hydrochlorothiazide (Microzide). The onset of hyperglycemic induction among these medications varies; however, onset of hyperglycemia typically occurs from 9–18 weeks after initiation (Rehman et al., 2011). Table 1 summarizes recommendations for modifiable risk factors.

    Nonmodifiable Risk Factors

    Increasing age is a risk factor for diabetes and cancer (Giovannucci et al., 2010). Older adult patients are at increased risk for complications associated with their comorbidities and treatments (American Geriatrics Society Expert Panel on the Care of Older Adults With Multimorbidity, 2012). The number of older adults with comorbidities is expected to increase, as is the incidence of cancer and diabetes (American Geriatrics Society Expert Panel on the Care of Older Adults With Multimorbidity, 2012; Giovannucci et al., 2010). An estimated 78% of all newly diagnosed patients with cancer are aged older than 55 years (ACS, 2015). Type II diabetes is more prevalent in older adults, and more than 29 million older adults in the United States have diabetes (Centers for Disease Control and Prevention [CDC], 2014). ADA (2015) suggests diabetic screening for all patients aged older than 45 years. In addition, family history is a known risk factor for the development of diabetes, particularly in first-degree relatives (CDC, 2014).

    Complications of Hyperglycemia and Diabetes in Patients With Cancer

    Peripheral neuropathy is prevalent in 30%–66% of diabetics, and the longer that patients are diabetic, the more likely they are to develop peripheral neuropathy (Watson & Dyck, 2015). Patients with cancer undergoing treatment are also at increased risk for peripheral neuropathy because of frequently prescribed neurotoxic drugs, including chemotherapy and biotherapy. Patients with peripheral neuropathy are at increased risk for physical impairments and decreased quality of life. Patients can experience physical distress from neuropathic pain and progressive neuromuscular weakness, which ultimately affects patients’ ability to perform activities of daily living (Tofthagen, Visovsky, & Hopgood, 2013). Evidence suggests that diabetic patients undergoing neurotoxic chemotherapy for colorectal cancer develop peripheral neuropathy at lower cumulative doses than those without diabetes (Uwah, Ackler, Leighton, Pomerantz, & Tester, 2012). Similarly, Vissers et al. (2015) found that patients with colorectal cancer and diabetes reported a higher incidence of neuropathic symptoms following chemotherapy than those without diabetes. In a study by Hershman et al. (2016), diabetic patients receiving taxane-based chemotherapy had more than twice the odds of developing chemotherapy-induced neuropathy. The researchers concluded that diabetes and increased age are independent predictors of neuropathy. Overall, diabetic patients are at a greater risk for occurrence of peripheral neuropathy and should be monitored closely for glucose levels and symptoms of neuropathy.

    Patients with coexisting diabetes and cancer are at risk for developing kidney dysfunction because of the effects of diabetes and chemotherapy on the kidneys. Diabetic kidney disease occurs in about 20%–40% of patients with diabetes and is the leading cause of end-stage renal disease (ADA, 2015). Caution should be maintained when diabetic patients with cancer receive nephrotoxic chemotherapy, as they are already predisposed to renal dysfunction. Acute kidney injury resulting from nephrotoxic chemotherapy has been shown to decrease glomerular filtration rates long-term (Bhat et al., 2015). In addition, compromised renal function is associated with increased mortality. Diabetic patients with cancer should be closely monitored for kidney dysfunction.

    Patients with cancer with hyperglycemia are at risk for developing infections. Hyperglycemia causes prolonged inflammatory cytokine expression, ultimately leading to inhibition of the signaling pathway that detects and eliminates foreign microorganisms (Storey & Von Ah, 2012). Patients with coexisting cancer and hyperglycemia have an increased risk for life-threatening infections with ensuing poor outcomes (Magliano et al., 2015).

    Diabetic patients with cancer are at risk for developing hyperglycemic crisis. Predisposing factors for this condition include infection and dehydration, which are also common factors associated with cancer and diabetes. Patients in this state develop severe hyperglycemia (often greater than 300 mg/dl) because of insulin deficiency, dehydration, and acid base abnormalities (Powers, 2015b).

    Diagnosis of Prediabetes and Diabetes

    The hemoglobin A1c test measures the average serum glucose levels during the previous one to three months (Nicoll, Lu, Pignone, & McPhee, 2012). It is one of the diagnostic tests used by providers to evaluate diabetic status and is convenient because it does not require fasting (ADA, 2015). In the general population, this is an excellent tool that can be used to diagnose and manage hyperglycemia. However, several factors may influence A1c results, including age, ethnicity (ADA, 2015), smoking, and conditions that affect red blood cell turnover, such as iron deficiency anemia (Hong et al., 2015) and blood transfusions (Spencer, Grossman, & Scott, 2011). Evidence suggests that A1c levels are inaccurate in individuals with anemia (Hong et al., 2015) and in those receiving blood transfusions (Spencer et al., 2011). As a result, ADA (2015) has advised against using A1c results in patients with conditions that affect red blood cell turnover. This is noteworthy because anemia is reported in 40%–75% of patients with cancer (Gobel, Triest-Robertson, & Vogel, 2015). Table 2 summarizes the diagnostic criteria for prediabetes and diabetes.

    Developing Education Program

    Focusing on the information previously described, an educational program was designed to increase nurse knowledge and awareness of the consequences of hyperglycemia in patients with cancer. The authors also designed an algorithm to guide screening and nursing management of hyperglycemia in an outpatient oncology setting. The specific aims of this project were to (a) develop an evidence-based algorithm to guide nursing management of hyperglycemia in oncology settings and (b) evaluate efficacy of an educational program for management of hyperglycemia in an outpatient oncology setting.

    Project Implementation

    Sample and Setting

    The convenience sample consisted of 11 oncology nurses at University of Florida Health Cancer Center at Orlando Health. All nurses at the facility who were involved in the care of patients with cancer undergoing chemotherapy and had a current RN license in Florida were eligible.

    Hyperglycemic Algorithm and Program Content

    The clinical algorithm was based on the latest standards of care from ADA (2015). The algorithm guides the nursing process from assessment of glycemic level to intervention, which includes collaborating with the interdisciplinary team and educating the patient on self-management strategies (see Figure 1). The algorithm directs the nurse to assess for hyperglycemic crisis and intervene when glucose levels are greater than 300 mg/dl. The algorithm also includes recommended resources for glycemic control.

    The educational program included information on (a) epidemiology of cancer and diabetes, (b) pathophysiology and possible biologic links between diabetes and cancer, (c) etiology and risk factors for the development of hyperglycemia and diabetes in cancer, (d) implications of hyperglycemia and diabetes in cancer, (e) criteria for the diagnosis of prediabetes and diabetes, and (f) best practices for nursing management.


    Administrative approval for this project was obtained from the unit manager of the ambulatory treatment center, the Orlando Health Nursing Research Council, and the human resources committee. The project was granted exempt status by the University of South Florida Institutional Review Board (IRB) and the Orlando Health IRB. Participation in the educational program was voluntary. Notice of the educational program was distributed to potential participants throughout the center through word of mouth. The unit’s learning specialist announced the event at staff meetings and huddles about one month prior to the event.

    The evaluation method employed a pre-/post-test design. Knowledge of hyperglycemia was measured immediately before and after the intervention. The pre- and post-test questionnaires were modeled after the Drass, Muir-Nash, Boykin, Turek, and Baker (1989) Diabetes Basic Knowledge Test (DBKT). DBKT is a reliable (Cronbach alpha = 0.79) instrument used to assess a nurse’s basic knowledge of diabetes and its treatment (Drass et al., 1989). Items were created to align with the objectives of this program (see Figure 2).

    The pretest assessed the nurses’ baseline knowledge of hyperglycemia, diabetes, and implications for patients with cancer. The pre- and post-test included multiple-choice, choose-all-that-apply, and Likert-type scale items. The post-test assessment included an additional question that assessed the nurses’ attitudes toward implementation of a clinical algorithm. Demographic information, including level of education, employment status (full-time, part-time, or per diem), years of nursing experience, and years of oncology nursing experience, was collected. To maintain anonymity, no other identifying information (e.g., name, age, race) was collected.

    Data Analysis

    Descriptive statistics, including means, percentages, and frequencies, were used to describe the sample. Frequencies and percentages of correct scores for each item, as well as total scores, were calculated pre- and post-test. Total differences in percentage from pre- to post-test were calculated, and student t tests were used to compare the results from pre- to post-test to assess knowledge gained. The likelihood of implementing the clinical algorithm into practice was also described using frequencies and percentages.



    Eleven oncology nurses with varying degrees of experience and education attended the educational program (see Table 3). The mean overall nursing experience was 14.7 years, and the mean oncology experience was 9.5 years. Most participants were full-time employees at the center; only one was a per diem nurse. Before the educational program, participants reported a mean score of 5.5 on a 10-point scale when asked how confident they felt caring for a patient with cancer with hyperglycemia.

    Pre-/Post-Test Results

    Results of the pre- and post-test are presented in Table 4. Participants had a mean pretest score of 63%. Although the pretest scores revealed areas for improvement, participants did relatively well with identifying the etiology of diabetes, biologic links between diabetes and patients with cancer, nonmodifiable risk factors, hyperglycemia-inducing medications, effective management of hyperglycemia, and associated complications. Prior to the intervention, nurses were not likely to request a baseline hemoglobin A1c test. In addition, nurses were not likely to use a clinical algorithm when managing hyperglycemia in patients with cancer. The mean post-test score was 80%, which was an improvement of 17% from mean pretest scores. This indicates a statistically significant (p < 0.001) change in the post-test scores. Areas of greatest improvement were identification of hyperglycemic-inducing chemotherapies and nonmodifiable risk factors. All participants believed the clinical algorithm was very useful. In addition, all participants believed the educational program was very effective at increasing knowledge and awareness of hyperglycemia in patients with cancer.


    The findings of the current study support the need for specific nursing education related to hyperglycemia in patients with cancer. Pretest scores indicated that nurses did not know the implications of hyperglycemia in patients with cancer or which medications posed a greater risk for inducing hyperglycemia. This may be because of the lack of education for nurses regarding this issue, as postintervention results demonstrated a significant increase in these scores. Most participants indicated that more than two years had passed since receiving education on hyperglycemia. Many participants indicated never receiving continuing education regarding hyperglycemia or diabetes. Hyperglycemia is often overlooked in outpatient oncology settings, and nurses in these settings may not be prepared to manage this problem. Educational programs should emphasize the role of glycemic control on improved morbidity and mortality, including cancer-related outcomes, and provide realistic guidelines for monitoring and managing glucose levels.

    This program identified areas for needed improvement in nursing knowledge related to management of hyperglycemia in oncology. Although this program was specifically designed for oncology nurses, nurses are just one element of the interdisciplinary team. All members of the team must understand the consequences of hyperglycemia in patients with cancer so that effective management may take place. Additional work should aim to increase provider awareness of all implications of hyperglycemia in patients with cancer and recommend a baseline evaluation of risk and laboratory factors prior to cancer treatment planning. With this baseline knowledge, providers may incorporate prevention strategies prior to the initiation of potential hyperglycemic-inducing cancer treatments.

    This educational program was the first of its kind at the center. The results of this project demonstrate a statistically significant increase in nursing knowledge and awareness of the consequences of hyperglycemia in patients with cancer. In addition, nurses who participated in the program were more likely to use an algorithm guiding nursing management and intervention for hyperglycemic patients with cancer.


    This project had several limitations that may limit its integration into other practice settings. First, the authors evaluated this program specifically in the infusion center of a large urban cancer center. Other settings may differ in protocols or the nurses’ educational background. The current sample size was small, which was intentional because the goal was to improve management of hyperglycemia on a specific unit; however, the educational program and algorithm provided likely could be useful to other facilities interested in improving nursing care in this area.


    The goal of this educational program was to provide nurses with the knowledge and tools to deliver nursing interventions for hyperglycemic patients during their cancer treatment. Educating nurses about the possible effects of hyperglycemia in patients with cancer may empower them to advocate for their patients, provide appropriate education, and promote patient self-care, ultimately leading to improved patient outcomes. This program encouraged nurses to advocate for their patients, communicate with the multidisciplinary team, and provide evidence-based recommendations to patients. Overall, the program brought excitement and awareness to this important issue of hyperglycemia in patients with cancer. Implementation of the algorithm on the unit and throughout the facility is planned.

    About the Author(s)

    Veronica Sampayo, DNP, ARNP-C, is a nurse practitioner in the gastrointestinal medical oncology division of the University of Florida Health Cancer Center at Orlando Health, and Cindy Tofthagen, PhD, ARNP, AOCNP®, FAANP, FAAN, is an associate professor in the College of Nursing at the University of South Florida in Tampa. The authors take full responsibility for this content and did not receive honoraria or disclose any relevant financial relationships. The article has been reviewed by independent peer reviewers to ensure that it is objective and free from bias. Mention of specific products and opinions related to those products do not indicate or imply endorsement by the Oncology Nursing Society. Sampayo can be reached at, with copy to (Submitted April 2016. Accepted September 7, 2016.)



    American Cancer Society. (2015). Cancer facts and figures 2015. Retrieved from…

    American Diabetes Association. (2015). Standards of medical care in diabetes—2015: Summary of revisions. Diabetes Care, 38(Suppl. 4), S4. doi:10.2337/dc15-S003

    American Diabetes Association. (n.d.). Lower your risk. Retrieved from…

    American Geriatrics Society Expert Panel on the Care of Older Adults With Multimorbidity. (2012). Patient-centered care for older adults with multiple chronic conditions: A stepwise approach from the American Geriatrics Society. Journal of the American Geriatrics Society, 60, 1957–1968. doi:10.1111/j.1532-5415.2012.04187.x

    Barclay, A.W., Petocz, P., McMillan-Price, J., Flood, V.M., Prvan, T., Mitchell, P., & Brand-Miller, J.C. (2008). Glycemic index, glycemic load, and chronic disease risk—A meta-analysis of observational studies. American Journal of Clinical Nutrition, 87, 627–637.

    Barone, B.B., Yeh, H.C., Snyder, C.F., Peairs, K.S., Stein, K.B., Derr, R.L., . . . Brancati, F.L. (2008). Long-term all-cause mortality in cancer patients with preexisting diabetes mellitus: A systematic review and meta-analysis. JAMA, 300, 2754–2764. doi:10.1001/jama.2008.824

    Bhat, Z.Y., Cadnapaphornchai, P., Ginsburg, K., Sivagnanam, M., Chopra, S., Treadway, C.K., . . . Doshi, M.D. (2015). Understanding the risk factors and long-term consequences of cisplatin-associated acute kidney injury: An observational cohort study. PLOS ONE, 10, e0142225. doi:10.1371/journal.pone.0142225

    Centers for Disease Control and Prevention. (2014). Diabetes report card 2014. Atlanta, GA: U.S. Department of Health and Human Services. Retrieved from

    De Vos-Schmidt, D., & Dilworth, K. (2014). Management strategy for steroid-induced malglycemia during cancer treatment. Clinical Journal of Oncology Nursing, 18, 41–44. doi:10.1188/14.CJON.41-44

    Drass, J.A., Muir-Nash, J., Boykin, P.C., Turek, J.M., & Baker, K.L. (1989). Perceived and actual level of knowledge of diabetes mellitus among nurses. Diabetes Care, 12, 351–356.

    Giovannucci, E., Harlan, D.M., Archer, M.C., Bergenstal, R.M., Gapstur, S.M., Habel, L.A., . . . Yee, D. (2010). Diabetes and cancer: A consensus report. CA: A Cancer Journal for Clinicians, 60, 207–221. doi:10.3322/caac.20078

    Gobel, B.H., Triest-Robertson, S., & Vogel, W.H. (2015). Advanced oncology nursing certification review and resource manual (2nd ed.). Pittsburgh, PA: Oncology Nursing Society.

    Hammer, M.J., Aouizerat, B.E., Schmidt, B.L., Cartwright, F., Wright, F., & Miaskowski, C. (2015). Glycosylated hemoglobin A1c and lack of association with symptom severity in patients undergoing chemotherapy for solid tumors. Oncology Nursing Forum, 42, 581–590. doi:10.1188/15.ONF.581-590

    Hammer, M.J., & Voss, J.G. (2012). Malglycemia and cancer: Introduction to a conceptual model [Online exclusive]. Oncology Nursing Forum, 39, E275–E287. doi:10.1188/12.ONF.E275-E287

    Hershey, D.S., Bryant, A.L., Olausson, J., Davis, E.D., Brady, V.J., & Hammer, M. (2014). Hyperglycemic-inducing neoadjuvant agents used in treatment of solid tumors: A review of the literature [Online exclusive]. Oncology Nursing Forum, 41, E343–E354. doi:10.1188/14.ONF.E343-E354

    Hershman, D.L., Till, C., Wright, J.D., Awad, D., Ramsey, S.D., Barlow, W.E., . . . Unger, J. (2016). Comorbidities and risk of chemotherapy-induced peripheral neuropathy among participants 65 years or older in Southwest Oncology Group clinical trials. Journal of Clinical Oncology, 34, 3014–3022. doi:10.1200/JCO.2015.66.2346

    Hong, J.W., Ku, C.R., Noh, J.H., Ko, K.S., Rhee, B.D., & Kim, D.J. (2015). Association between the presence of iron deficiency anemia and hemoglobin A1c in Korean adults: The 2011–2012 Korea National Health and Nutrition Examination Survey. Medicine, 94, E825–E825.

    Lynch, B.M., Courneya, K.S., Sethi, P., Patrao, T.A., & Hawkes, A.L. (2014). A randomized controlled trial of a multiple health behavior change intervention delivered to colorectal cancer survivors: Effects on sedentary behavior. Cancer, 120, 2665–2672. doi:10.1002/cncr.28773

    Magliano, D.J., Harding, J.L., Cohen, K., Huxley, R.R., Davis, W.A., & Shaw, J.E. (2015). Excess risk of dying from infectious causes in those with type 1 and type 2 diabetes. Diabetes Care, 38, 1274–1280. doi:10.2337/dc14-2820

    McCance, K.L., & Huether, S.E. (2010). Pathophysiology: The biologic basis for disease in adults and children (6th ed.). Maryland Heights, MO: Mosby Elsevier.

    Nicoll, D., Lu, C.M., Pignone, M., & McPhee, S.J. (2012). Pocket guide to diagnostic tests (6th ed.). New York, NY: McGraw-Hill Companies.

    Pantano, F., Guida, F.M., Vivaldi, C., Vincenzi, B., Grande, E., Garrote, M.R., . . . Santini, D. (2013). Body mass index and impaired fasting blood glucose as predictive factor of time to progression (TTP) in cetuximab-based colorectal cancer treatment. Cancer Biology and Therapy, 14, 467–468. doi:10.4161/cbt.24346

    Powers, A.C. (2015a). Diabetes mellitus: Diagnosis, classification, and pathophysiology. In D. Kasper, A. Fauci, S. Hauser, D. Longo, J.L. Jameson, & J. Loscalzo (Eds.), Harrison’s principles of internal medicine (19th ed.). New York, NY: McGraw-Hill Education.

    Powers, A.C. (2015b). Diabetes mellitus: Management and therapies. In D. Kasper, A. Fauci, S. Hauser, D. Longo, J. L. Jameson, & J. Loscalzo (Eds.), Harrison’s principles of internal medicine (19th ed.). New York, NY: McGraw-Hill Education.

    Rehman, A., Setter, S.M., & Vue, M.H. (2011). Drug-induced glucose alterations part 2: Drug-induced hyperglycemia. Diabetes Spectrum, 24, 234–238. doi:10.2337/diaspect.24.4.234

    Spencer, D.H., Grossman, B.J., & Scott, M.G. (2011). Red cell transfusion decreases hemoglobin A1c in patients with diabetes. Clinical Chemistry, 57, 344–346. doi:10.1373/clinchem.2010.157321

    Storey, S., & Von Ah, D. (2012). Impact of malglycemia on clinical outcomes in hospitalized patients with cancer: A review of the literature. Oncology Nursing Forum, 39, 458–465. doi:10.1188/12.ONF.458-465

    Tofthagen, C., Visovsky, C.M., & Hopgood, R. (2013). Chemotherapy-induced peripheral neuropathy: An algorithm to guide nursing management. Clinical Journal of Oncology Nursing, 17, 138–144. doi:10.1188/13.CJON.138-144

    Uwah, A.N., Ackler, J., Leighton, J.C., Jr., Pomerantz, S., & Tester, W. (2012). The effect of diabetes on oxaliplatin-induced peripheral neuropathy. Clinical Colorectal Cancer, 11, 275–279. doi:10.1016/j.clcc.2012.05.002

    Vigneri, P., Frasca, F., Sciacca, L., Pandini, G., & Vigneri, R. (2009). Diabetes and cancer. Endocrine-Related Cancer, 16, 1103–1123. doi:10.1677/erc-09-0087

    Vissers, P.A., Mols, F., Thong, M.S., Pouwer, F., Vreugdenhil, G., & van de Poll-Franse, L.V. (2015). The impact of diabetes on neuropathic symptoms and receipt of chemotherapy among colorectal cancer patients: Results from the PROFILES registry. Journal of Cancer Survivorship, 9, 523–531. doi:10.1007/s11764-015-0429-z

    Watson, J.C., & Dyck, P.J. (2015). Peripheral neuropathy: A practical approach to diagnosis and symptom management. Mayo Clinic Proceedings, 90, 940–951. doi:10.1016/j.mayocp.2015.05.004