Identifying common concerns and predicting vulnerable populations among breast cancer survivors.

e24064 Background: Long-term survival is common after breast cancer treatment with 5-year survival reaching almost 90%. Breast cancer survivors (BCS) face varying degrees of quality of life (QOL) issues depending on their age of diagnosis, disease severity, and treatment. We present a retrospective analysis aimed to describe demographics of BCS, recognize common concerns and identify the vulnerable patients. Methods: This is a retrospective analysis of BCS seen at our Breast Cancer Survivorship Program from October 2016 to May 2021. Patients were given a survey to assess self-reported symptoms following treatment. The descriptive analysis of patient characteristics included age, cancer stage, and treatment type. The bivariate analysis included the relationship between the patient characteristics and their outcomes. Chi-square test was used to analyze group differences. Fisher exact test was employed when any of the expected frequencies was five or less. Logistic regression models were developed to identify significant predictors for outcomes. Results: 902 patients (age 26-94; median 64) were seen. Patient's characteristics studied included cancer stage, age group, and treatment modality. Most common self-reported concerns affecting BCS were fatigue (34%), insomnia (33%), hot flashes (26%), night sweats (23%), pain (22%), trouble concentrating (19%) and neuropathy (21%). Majority of patients (91%) reported having a happy outlook and felt a sense of purpose (89%), but 13% of BCS felt isolated at least 50% of the time. Young survivors (age ≤45) (p = 0.028), higher stage BCS (Stage 2-3) (p = 0.0061) and those who had chemotherapy either alone or as part of their multi-modality treatment (p &lt; 0.0001) were significantly less likely to return back to at least 50% of their pre-treatment baseline (Table). Conclusions: Our study showed that younger BCS, patients with higher cancer stage, and those who underwent chemotherapy are the most vulnerable groups in terms of severity of QOL issues. Identifying vulnerable populations and evaluating common concerns after treatments are important in delivering quality care. Optimizing interventions under standardized approach would help to increase QOL in BCS.[Table: see text] </jats:p

Logistic and neural network models for predicting a hospital admission

Feedforward neural networks are often used in a similar manner as logistic regression models; that is, to estimate the probability of the occurrence of an event. In this paper, a probabilistic model is developed for the purpose of estimating the probability that a patient who has been admitted to the hospital with a medical back diagnosis will be released after only a short stay or will remain hospitalized for a longer period of time. As the purpose of the analysis is to determine if hospital characteristics influence the decision to retain a patient, the inputs to this model are a set of demographic variables that describe the various hospitals. The output is the probability of either a short or long term hospital stay. In order to compare the ability of each method to model the data, a hypothesis test is performed to test for an improvement resulting from the use of the neural network model.Neural networks, logistic regression, prediction, hospital admissions, medical informatics,

Abstract EP28: SO CLOSE YET SO FAR : HOW WELL ARE WE DOING WITH GDMT FOR HEART FAILURE WITH REDUCED EJECTION FRACTION - A COMMUNITY HOSPITAL ANALYSIS

Background: The use of maximum tolerated doses of guideline directed medical therapy (GDMT) for patient with heart failure with reduced ejection fraction (HFrEF) has been suboptimal in routine clinical practice. In particular, the rate of adoption of the angiotensin receptor blocker-neprilysin inhibitor (ARNI) has lagged. In this study, we aim to evaluate the use of optimal GDMT and the effect of ARNI on heart failure related re-admission (HFRR) in a real life setting from a single center community hospital. Method: Retrospective chart review was conducted in patients who were admitted to our hospital system for HFrEF between December 2019-January 2021. Data was collected for patient demographics, comorbidities, admission and discharge medications, length of stay and rate of 30-day HFRR. Results: 357 patients were included in the analysis. 92.7% were discharged on beta blockers, 50% were discharged on ACE-I/ARB, 6.4% were discharged on ARNI and 47.75% were discharged on MRAs. The target doses of each were only achieved in 7.3%, 27%, 100% and &lt;1% respectively. 43% patients were not discharged on either ACE-I/ARB/ARNI. Patients discharged on ARNI had numerically lower rates of chronic kidney disease &gt; stage 3, and lower BNP levels than those discharged on ACE-I/ARB. HFRR was lower in patient discharged on ARNI vs ACE-I/ARB (4.17% vs 16%), although this was not statistically significant due to low sample volume (p = 0.20). Conclusion: Our study shows that the rate of adoption of GDMT, especially ARNI, in hospitalized HFrEF patients in clinical practice lags significantly behind from that reported in clinical trials. Although there was no statistical significance achieved in comparison due to small sample size, we see a trend of decreased heart failure readmission in patients discharged on ARNI. The wide gap of adoption of GDMT in the real world remains a challenge, and adversely impacts patient outcomes. </jats:p

Does Depression Increase Risk for Noncompliance with Warfarin Therapy?

Abstract Introduction Management of warfarin therapy in an outpatient setting has been proven to be challenging despite specialized anticoagulation clinics. It is estimated that 40-50% of INR values are outside and, most commonly, below the therapeutic range. Extended periods of time spent outside the therapeutic INR range have been associated with an increased risk for morbidity and mortality. Sub-therapeutic INRs are associated with a higher risk for thromboembolism, which can lead to ischemic stroke and myocardial infarction; while supra-therapeutic INRs are associated with warfarin-induced hemorrhage, both of which can lead to an increased mortality. Furthermore, it has been found that patients with depressive symptoms have been associated with decreased adherence to any medical management when compared to non-depressed patients and patients with psychosocial or emotional factors are more often found to be outside therapeutic range while on warfarin therapy. However, whether depression has a direct effect on noncompliance with warfarin therapy has yet to be studied. This study intends to prove depression does increase the risk for noncompliance with warfarin therapy and, subsequently, increase their risk of adverse events due to decreased time-in-therapeutic range (TTR). Method A retrospective study was conducted on 91 patients from an outpatient anticoagulation clinic. INR data, past medical history of depression, demographics, and history of complications secondary to warfarin therapy were collected. Patients with history of depression were compared to patients without history of depression on their demographic variables, risk factors and the study outcomes. Chi-square tests were used to determine the significant difference between the two groups on categorical variables. The student t-tests were used to determine the significant difference between the two groups on continuous variables. A p-value ≤ 0.05 was regarded as significant. A logistic regression model was used to determine whether depression had an impact on keeping the patient’s INRs within the therapeutic range 70% of the time while on therapy. All the statistical analyses were completed by SAS version 9.2. Results We found that the group of patients with a history of depression were 67% less likely to have patients who had their INRs within the therapeutic range 70% of the time while on therapy when compared to patients without a history of depression (odds ratio=0.33, CI 0.116 – 0.935, p-value = 0.0370). Additionally, we found that patients with a history of depression had, on average, a lower TTR than patients without a history of depression (p-value = 0.0399). Conclusion The results reveal patients with a history of depression are at an increased risk for noncompliance with warfarin therapy when compared to patients without a history of depression. Furthermore, patients with a history of depression and on warfarin therapy would likely benefit from further interventions to increase their TTR and decrease their risks for adverse events. Disclosures No relevant conflicts of interest to declare. </jats:sec