Differences in Health-Related Outcomes and Health Care Resource Utilization in Breast Cancer Survivors With and Without Type 2 Diabetes.

PURPOSE: Up to 74% of breast cancer survivors (BCS) have at least one preexisting comorbid condition, with diabetes (type 2) common. The purpose of this study was to examine differences in health-related outcomes (anemia, neutropenia, and infection) and utilization of health care resources (inpatient, outpatient, and emergency visits) in BCS with and without diabetes. METHODS: In this retrospective cohort study, data were leveraged from the electronic health records of a large health network linked to the Indiana State Cancer Registry. BCS diagnosed between January 2007 and December 2017 and who had received chemotherapy were included. Multivariable logistic regression and generalized linear models were used to determine differences in health outcomes and health care resources. RESULTS: The cohort included 6851 BCS, of whom 1121 (16%) had a diagnosis of diabetes. BCS were, on average, 55 (standard deviation: 11.88) years old, the majority self-reported race as White (90%), and 48.8% had stage II breast cancer. BCS with diabetes were significantly older (mean age of 60.6 [SD: 10.34] years) than those without diabetes and were often obese (66% had body mass index of ≥33). BCS with diabetes had higher odds of anemia (odds ratio: 1.43; 95% CI: 1.04, 1.96) and infection (odds ratio: 1.86; 95% CI: 1.35, 2.55) and utilized more outpatient resources (P<0.0001). CONCLUSIONS: Diabetes has a deleterious effect on health-related outcomes and health care resource utilization among BCS. These findings support the need for clinical practice guidelines to help clinicians manage diabetes among BCS throughout the cancer trajectory and for coordinated models of care to reduce high resource utilization.

A Computational Framework to Analyze the Associations Between Symptoms and Cancer Patient Attributes Post Chemotherapy Using EHR Data.

Patients with cancer, such as breast and colorectal cancer, often experience different symptoms post-chemotherapy. The symptoms could be fatigue, gastrointestinal (nausea, vomiting, lack of appetite), psychoneurological symptoms (depressive symptoms, anxiety), or other types. Previous research focused on understanding the symptoms using survey data. In this research, we propose to utilize the data within the Electronic Health Record (EHR). A computational framework is developed to use a natural language processing (NLP) pipeline to extract the clinician-documented symptoms from clinical notes. Then, a patient clustering method is based on the symptom severity levels to group the patient in clusters. The association rule mining is used to analyze the associations between symptoms and patient attributes (smoking history, number of comorbidities, diabetes status, age at diagnosis) in the patient clusters. The results show that the various symptom types and severity levels have different associations between breast and colorectal cancers and different timeframes post-chemotherapy. The results also show that patients with breast or colorectal cancers, who smoke and have severe fatigue, likely have severe gastrointestinal symptoms six months after the chemotherapy. Our framework can be generalized to analyze symptoms or symptom clusters of other chronic diseases where symptom management is critical.

Analyzing the symptoms in colorectal and breast cancer patients with or without type 2 diabetes using EHR data.

This research extracted patient-reported symptoms from free-text EHR notes of colorectal and breast cancer patients and studied the correlation of the symptoms with comorbid type 2 diabetes, race, and smoking status. An NLP framework was developed first to use UMLS MetaMap to extract all symptom terms from the 366,398 EHR clinical notes of 1694 colorectal cancer (CRC) patients and 3458 breast cancer (BC) patients. Semantic analysis and clustering algorithms were then developed to categorize all the relevant symptoms into eight symptom clusters defined by seed terms. After all the relevant symptoms were extracted from the EHR clinical notes, the frequency of the symptoms reported from colorectal cancer (CRC) and breast cancer (BC) patients over three time-periods post-chemotherapy was calculated. Logistic regression (LR) was performed with each symptom cluster as the response variable while controlling for diabetes, race, and smoking status. The results show that the CRC and BC patients with Type 2 Diabetes (T2D) were more likely to report symptoms than CRC and BC without T2D over three time-periods in the cancer trajectory. We also found that current smokers were more likely to report anxiety (CRC, BC), neuropathic symptoms (CRC, BC), anxiety (BC), and depression (BC) than non-smokers.

Association of Comorbid Diabetes With Clinical Outcomes and Healthcare Utilization in Colorectal Cancer Survivors.

OBJECTIVES: To compare clinical outcomes and healthcare utilization in colorectal cancer (CRC) survivors with and without diabetes. SAMPLE & SETTING: CRC survivors (N = 3,287) were identified from a statewide electronic health record database using International Classification of Diseases (ICD) codes. Data were extracted on adults aged 21 years or older with an initial diagnosis of stage II or III CRC with diabetes present before CRC diagnosis or no diagnosis of diabetes (control). METHODS & VARIABLES: ICD codes were used to extract diabetes diagnosis and clinical outcome variables. Healthcare utilization was determined by encounter type. Data were analyzed using descriptive statistics, multivariable logistic, and Cox regression. RESULTS: CRC survivors with diabetes were more likely to develop anemia and infection than CRC survivors without diabetes. In addition, CRC survivors with diabetes were more likely to utilize emergency resources sooner than CRC survivors without diabetes. IMPLICATIONS FOR NURSING: Oncology nurses can facilitate the early identification of high-risk survivor groups, reducing negative clinical outcomes and unnecessarily high healthcare resource utilization in CRC survivors with diabetes.

Survival, Chemotherapy Treatments, and Health Care Utilization Among Patients with Advanced Small Cell Lung Cancer: An Observational Study.

INTRODUCTION: Most cases of small cell lung cancer (SCLC) are diagnosed at an advanced stage. The objective of this study was to investigate patient characteristics, survival, chemotherapy treatments, and health care use after a diagnosis of advanced SCLC in subjects enrolled in a health system network. METHODS: This was a retrospective cohort study of patients aged ≥ 18 years who either were diagnosed with stage III/IV SCLC or who progressed to advanced SCLC during the study period (2005-2015). Patients identified from the Indiana State Cancer Registry and the Indiana Network for Patient Care were followed from their advanced diagnosis index date until the earliest date of the last visit, death, or the end of the study period. Patient characteristics, survival, chemotherapy regimens, associated health care visits, and durations of treatment were reported. Time-to-event analyses were performed using the Kaplan-Meier method. RESULTS: A total of 498 patients with advanced SCLC were identified, of whom 429 were newly diagnosed with advanced disease and 69 progressed to advanced disease during the study period. Median survival from the index diagnosis date was 13.2 months. First-line (1L) chemotherapy was received by 464 (93.2%) patients, most commonly carboplatin/etoposide, received by 213 (45.9%) patients, followed by cisplatin/etoposide (20.7%). Ninety-five (20.5%) patients progressed to second-line (2L) chemotherapy, where topotecan monotherapy (20.0%) was the most common regimen, followed by carboplatin/etoposide (14.7%). Median survival was 10.1 months from 1L initiation and 7.7 months from 2L initiation. CONCLUSION: Patients in a regional health system network diagnosed with advanced SCLC were treated with chemotherapy regimens similar to those in earlier reports based on SEER-Medicare data. Survival of patients with advanced SCLC was poor, illustrating the lack of progress over several decades in the treatment of this lethal disease and highlighting the need for improved treatments.

Glucagon-like peptide 1-based therapies and risk of pancreatitis: a self-controlled case series analysis.

PURPOSE: Previous studies have suggested a link between glucagon-like peptide 1 (GLP-1)-based therapies and acute pancreatitis, while other studies have found no association. Because differences in diabetes severity may confound this relationship, a self-controlled case series (SCCS) analysis has been suggested as a means to control for individual-level confounding. METHODS: We evaluated the relationship between GLP-1-based therapies and pancreatitis by SCCS method using a large observational database. We calculated the incidence density ratio of pancreatitis for exposure versus non-exposure to each drug. To examine the robustness of our findings, we performed sensitivity analyses by varying risk windows, using two pancreatitis definitions and including incident pancreatitis or all occurrences. RESULTS: From dispensing data on 1.2 million patients, we found 7992 sitagliptin-exposed patients and 3552 exenatide-exposed patients between 2004 and 2009. Using an ICD9/CPT-based case definition of pancreatitis, we identified 207 sitagliptin and 82 exenatide cases. Augmenting this definition with laboratory criteria increased our cohort to 245 sitagliptin and 96 exenatide cases. For sitagliptin and exenatide cases, respectively, the mean duration of observation was 5.2 and 5.5 years, and the mean duration of drug exposure was 0.7 and 0.5 years. For all analyses (including different pancreatitis definitions, risk periods, and incident or recurrent events), the incidence density ratios for development of pancreatitis during exposure versus non-exposure ranged from 0.68 to 1.46, with all having 95% confidence intervals containing 1. CONCLUSIONS: We found no association between the use of GLP-1-based therapies and pancreatitis using SCCS analysis in a large observational database.