Background The aim of this study was to determine perioperative risk factors associated with anastomotic leak (AL) after minimally invasive esophagectomy (MIE) and its association with cancer recurrence and overall survival. Methods This retrospective observational study of electronic health record data included patients who underwent MIE for esophageal cancer between September 2013 and July 2023 at a tertiary center. The primary outcome was AL after esophagectomy, whereas the secondary outcomes included time to cancer recurrence and overall survival. Perioperative patient factors were evaluated to determine their associations with the primary and the secondary outcomes. Propensity score-matched logistic regression assessed the associations between perioperative factors and AL. Kaplan-Meier survival curves compared cancer recurrence and overall survival by AL. Results A total of 251 consecutive patients with esophageal cancer were included in the analysis; 15 (6%) developed AL. Anemia, hospital complications, hospital length of stay, and 30-day readmissions significantly differed from those with and without AL (P = .037, <.001, <.001, and.016, respectively). Moreover, 30- and 90-day mortality were not statistically affected by the presence of AL (P = .417 and 0.456, respectively). Logistic regression modeling showed drug history and anemia were significantly associated with AL (P = .022 and.011, respectively). The presence of AL did not significantly impact cancer recurrence or overall survival (P = .439 and.301, respectively). Conclusion The etiology of AL is multifactorial. Moreover, AL is significantly associated with drug history, preoperative anemia, hospital length of stay, and 30-day readmissions, but it was not significantly associated with 30- or 90-day mortality, cancer recurrence, or overall survival. Patients should be optimized before undergoing MIE with special consideration for correcting anemia. Ongoing research is needed to identify more modifiable risk factors to minimize AL development and its associated morbidity.

Introduction Symptomatic mammary hypertrophy (SMH) refers to excessive breast weight exceeding 3% of total body weight, impacting not only the breast but also the nipples and areola. Breast reduction surgery (BRS) has a complication that adversely affects the nipple-areolar complex (NAC) sensation. The purpose of this study was to estimate the degree to which the specialized infrared camera-computer system (SPY) may predict postoperative sensation of the NAC following BRS. Methods A retrospective, observational study that included 408 SMH patients who underwent BRS was conducted at the Division of Plastics Surgery at the University of Florida College of Medicine - Jacksonville. Breast surgery patients were grouped according to whether SPY was used intraoperatively (SPY group) or not used intraoperatively (NOSPY group) during surgery. A chi-square test was used to evaluate whether a percentage difference existed between the SPY group and the NOSPY group. The main outcomes were unchanged, decreased, or increased NAC sensation. An Eta square was used to measure the effect sizes of profusion variance associated with reported nipple sensation. An area under the curve (AUC) was performed to determine the most favorable cutoff for SPY profusion sensitivity and specificity. A nominal regression model was used to determine the correlation between NAC sensation and SPY usage. A probability value less than 0.05 was considered statistically significant. Results Of the 408 SMH patients included in the study, SPY technology was incorporated for 63 patients (15.4%). The percentage of those who reported decreased nipple sensation with the use of SPY was 29 (47.6%). The percentage of those who reported increased nipple sensation with the use of SPY was 4 (6.4%). The percentage of patients who reported no change in nipple sensitivity with the use of SPY was 29 (46.0%). The chi-square test was statistically significant (χ2 = 302.29, df = 2, p < 0.001). The Eta squared for the right breast SPY profusion percent was 0.74 and for the left breast SPY profusion percent was 0.81. Both percentages represent large effect sizes as the proportion of variance associated with the reported nipple sensation. The AUC for the SPY profusion was 0.471, which was not statistically significant. The most favorable receiver operator characteristic curve SPY profusion sensitivity was 0.651, with a specificity of 0.690 and an associated cutoff of 65.5. The outcome variable, NAC sensation, was determined to be significantly correlated with SPY use predicting decreased and increased NAC sensitivities (OR 3.6; p < 0.001 and OR 6.4; p = 0.007), respectively. Conclusions Although SPY technology has traditionally been utilized to assess tissue perfusion, our study demonstrates its potential as a predictive tool for postoperative NAC sensation.

Background The objective of this study is to evaluate if access to Samaritan, a digital support platform, improves the social determinants of health (SDOH) needs for patients enrolled in a jail diversion program in Jacksonville, FL. Methodology A total of 59 patients who were enrolled in a jail diversion program for homeless mentally ill misdemeanor offenders in Jacksonville, FL, participated in the study. Of the 59 patients, 47 individuals consented to participate in Samaritan while 12 declined participation. Demographics and the Health Leads Social Needs Screening Tool scores from the electronic health record were compared between groups along with average financial support from Samaritan. These non -normally distributed variables were compared using Wilcoxon rank -sum tests. Results The majority of study participants were male (92%, n = 43). The average age of study participants was 42 years. The average income from donors on the platform over three months for those who opted in was $48.80 (SD = 53.75). Among the individual Health Leads Social Needs Screening Tool questions, intact Housing was statistically significant (Z = -2.002, p = 0.045), suggesting access to a digital technology such as Samaritan might help improve SDOH needs. Conclusions Access to digital technologies, such as Samaritan, might help offenders with mental illness adjust to the many challenges they face upon reentry into the community. As such, these devices may represent one means for improving SDOH needs for disadvantaged mental health patients.

BackgroundDelirium, an acute confusional state highlighted by inattention, has been reported to occur in 10% to 50% of patients with COVID-19. People hospitalized with COVID-19 have been noted to present with or develop delirium and neurocognitive disorders. Caring for patients with delirium is associated with more burden for nurses, clinicians, and caregivers. Using information in electronic health record data to recognize delirium and possibly COVID-19 could lead to earlier treatment of the underlying viral infection and improve outcomes in clinical and health care systems cost per patient. Clinical data repositories can further support rapid discovery through cohort identification tools, such as the Informatics for Integrating Biology and the Bedside tool. ObjectiveThe specific aim of this research was to investigate delirium in hospitalized older adults as a possible presenting symptom in COVID-19 using a data repository to identify neurocognitive disorders with a novel group of International Classification of Diseases, Tenth Revision (ICD-10) codes. MethodsWe analyzed data from 2 catchment areas with different demographics. The first catchment area (7 counties in the North-Central Florida) is predominantly rural while the second (1 county in North Florida) is predominantly urban. The Integrating Biology and the Bedside data repository was queried for patients with COVID-19 admitted to inpatient units via the emergency department (ED) within the health center from April 1, 2020, and April 1, 2022. Patients with COVID-19 were identified by having a positive COVID-19 laboratory test or a diagnosis code of U07.1. We identified neurocognitive disorders as delirium or encephalopathy, using ICD-10 codes. ResultsLess than one-third (1437/4828, 29.8%) of patients with COVID-19 were diagnosed with a co-occurring neurocognitive disorder. A neurocognitive disorder was present on admission for 15.8% (762/4828) of all patients with COVID-19 admitted through the ED. Among patients with both COVID-19 and a neurocognitive disorder, 56.9% (817/1437) were aged ≥65 years, a significantly higher proportion than those with no neurocognitive disorder (P<.001). The proportion of patients aged <65 years was significantly higher among patients diagnosed with encephalopathy only than patients diagnosed with delirium only and both delirium and encephalopathy (P<.001). Most (1272/4828, 26.3%) patients with COVID-19 admitted through the ED during our study period were admitted during the Delta variant peak. ConclusionsThe data collected demonstrated that an increased number of older patients with neurocognitive disorder present on admission were infected with COVID-19. Knowing that delirium increases the staffing, nursing care needs, hospital resources used, and the length of stay as previously noted, identifying delirium early may benefit hospital administration when planning for newly anticipated COVID-19 surges. A robust and accessible data repository, such as the one used in this study, can provide invaluable support to clinicians and clinical administrators in such resource reallocation and clinical decision-making.

Abstract Frailty among older adults is associated with higher morbidity and mortality rates and poorer hospital outcomes. Although frailty is not routinely assessed in the hospital setting, elements of frailty are captured in nursing assessments. The purpose of this study was to examine components of the Risk Analysis Index (RAI) captured in electronic health record (EHR) assessment data and their associations with discharge disposition. This was a retrospective observational study of EHR assessment data which included encounters of older adult patients (≥ 65 years) who were admitted from home to a medical/surgical unit of an academic hospital in North Central Florida between January 2012 and May 2021. The components of the RAI included in the study were sex, age, cancer, renal failure, heart failure, cognitive decline, unintentional weight loss, poor appetite, and shortness of breath at rest. Descriptive statistics were generated and compared between patients discharged to home versus those who were discharged to Post-Acute Care (PAC) facilities. Unadjusted associations were assessed using univariate logistic regression. Consistent with existing literature on frailty, all but one of the included RAI components (i.e., male sex) exhibited higher odds of discharge to a PAC facility with strong statistical support. Recognizing functional decline due to frailty is integral to the mission of improving patient safety. This study provides a preliminary proof of concept for leveraging existing assessment data in the EHR to capture frailty in older adults. Future studies of the overall predictive value of RAI with disposition are warranted.

Background This study aimed to investigate the actual weight change documented as a goal of treatment after patients were newly diagnosed with obstructive sleep apnea (OSA). We hypothesized that patients with OSA and classified as overweight and obese based on BMI would fail to achieve significant weight loss over a two- to five-year period. Methodology This retrospective review included adults aged 18 years or older who were newly diagnosed with OSA in 2015, as indicated by a full nocturnal polysomnogram and using the 4% rule for the definition of hypopnea. Data collected were between January 01, 2015, and December 31, 2020. Patients received either usual care for weight reduction or bariatric surgery to assess the overall weight loss and identify barriers. Statistical analysis included independent t-tests, Mann-Whitney U tests and related samples McNemar change statistics, Cox proportional hazards regression, and Kaplan-Meier curves to analyze age, gender, ethnicity, and weight differences between usual care and bariatric surgery groups. Results The number of participants included for usual care and bariatric surgery was 100 and 24, respectively. Over five years, 87% of the usual care patients remained in the same BMI classification, 7% lowered their classification, and 6% raised theirs. For usual care patients, the average net weight per individual of 2.19 kg gained represented a 1.96% weight change. Bariatric patients lost an average net weight of 30.40 kg (22.39%). Cox proportional hazards regression showed that the overall model fit was statistically significant (χ2 = 55.40, degrees of freedom [df] = 9, and P-value < 0.001). The significant variables were time-dependent weight change and ethnicity. The Kaplan-Meier curve revealed that weight loss reduced over time in treatment. Conclusions This study confirmed that despite the direction to lose weight, only 7% of OSA patients lowered their BMI classification. Patient instruction and provider-driven weight loss strategies seem equally ineffective to achieve sustained weight reduction among high-risk groups. More research is needed to investigate optimal strategies that include interprofessional collaborative practices for sustained weight loss.

Purpose/Objective: There is a need for innovative methods to provide high-quality care to vulnerable populations in an effort to reduce disparities. This is particularly important for women facing a new breast cancer diagnosis. The purpose of the present research was to investigate what social determinants may play a role in the existing disparities impacting adherence to care recommendations in communities with poor access to primary care and higher rates of morbidity and mortality. This study characterized adherence with breast health follow-up care in a diverse population of patients where inequalities exist that may negatively impact adherence to treatment recommendations. The objective of the study was to characterize factors associated with adherence to treatment among women with newly diagnosed breast cancer. Materials/Methods: Women diagnosed with stage I through IV breast cancer treated at the University of Florida College of Medicine-Jacksonville between 01/01/2014 and 12/31/2019 were included in the sample. Patterns of adherence were categorized using machine learning methods with data derived from the electronic medical record and the UF Health Jacksonville Tumor registry. Age, race, and Area Deprivation Index (ADI) state rank in 2019 as a disparity proxy were used to build a machine learning model and classify compliance to treatment. Included patients had a diagnostic procedure that identified breast cancer. Compliance to treatment was fulfilled if the patient received surgery following diagnostic confirmation. A machine learning model was used to stratify patients by risk of non-adherence to treatment following a diagnostic procedure. The models were evaluated using their area under the curve (AUC). Results: A total of 6,951 women were included, 629 who were adherent and 6322 non-adherent patients with breast cancer. The average age of the participants was 61.4 years, (Standard Deviation = 12.8 years). The majority of patients were Black (48%) or Caucasian (45%), 2% were Asian, and 5% were Other races. Payer type at diagnosis showed 45% had Medicare, 30% had commercial insurance, 17% were covered by Medicaid, 7% were charity, and 1% had other sources of pay. Most women were diagnosed with stage III breast cancer. Of 346 patients who received surgery that data was available, 127 (36.7%) had surgery within 30 days of diagnosis, 102 (29.5%) between 31 and 60 days, and 37 (10.7%) between 61 and 90 days. Fifteen models were compared using the PyCaret Python library. The ADI appeared as the most important factor to predict adherence in the model, followed by race and characterized by an AUC of 0.63. Conclusion: Our clinic treats predominantly more women diagnosed with biologically aggressive and advanced breast cancer especially in young African American population. The role social conditions play that precipitate and perpetuate health care disparities were investigated to determine their impact on adherence to treatment. At our safety net hospital, over one third were able to undergo surgery within 30 days of diagnosis. The ADI appeared as the most important feature to predict adherence, followed by race. This demonstrated the necessity to better understand the relation between socio-economical determinants and care received by patients. A more detailed description of the patients’ circumstances, such as access to transport, proximity of the hospital, and insurance status may further improve the model. There is a need for innovative methods of providing quality health care to vulnerable populations. Machine learning models can be used to stratify patients by risk of non-adherence to diagnostic follow-up and treatment following a diagnosis of breast cancer. Future research needs to move from identification of non-adherence risk factors to implementation of interventions to improve breast cancer outcomes. Citation Format: Guillaume Labilloy, Brian Celso, Bharti Jasra, Leigh Neumayer, Erin Mobley, Carmen Smotherman, Jennifer Brailsford. Risk factors for lack of adherence with diagnostic follow-up care in breast cancer patients [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-14-14.

TOPIC: Sleep Disorders TYPE: Original Investigations PURPOSE: Current evidence suggests that patient-driven weight loss strategies are not effective in achieving sustained weight loss. It is not clear if provider-driven discussions regarding weight loss are effective at helping patients achieve weight loss amongst patients with OSA, who are at a high risk of being overweight or obese. The purpose of the present research is to investigate the progression of weight over a 2-5 year period in patients with OSA. The patients were counseled routinely during routine clinical visits on weight loss through diet and exercise, as part of their treatment goals for the management of OSA. METHODS: A retrospective chart review of patients aged 18 years and older referred to the sleep clinic during 2015 for an evaluation of OSA was conducted. Inclusion criteria were patients newly diagnosed via polysomnography with OSA (defined as AHI ≥ 5/hr) and a minimum follow-up of 2 years. Data on gender, age, ethnicity, weight, medications, and co-morbidities were collected until December 31, 2020. A McNemar test utilized a 2x2 classification table and analyzed the difference between the paired proportions, (weight classification at the initial assessment with weight classification at the last recorded weight) and a regression equation calculated to estimate BMI. Significance was set at 0.05. RESULTS: The total sample size was 74 patients and consisted of 29 men (39.2%) and 45 women (60.8%). The average age was 52.99 years (SD=12.78). Approximately 42% identified as African-American, 1.4% as Asian, 54.2% as Caucasian, 1.4% as Hispanic, and 1.4% as Other. The median number of comorbidities and known medications that potentially increase weight were 2 and 1, respectively. The average participant weight was 120.23 Kg (SD=43.87) and average BMI was 41.28 (SD=14.16). The percentages of BMI Classifications were underweight (1.4%), normal (2.7%), overweight (16.2%), and obese (79.7%). The majority, 87.8% of patients remained in the same BMI classification, 9.5% lowered their classification and 2.7% raised the classification. The average net amount of weight lost per individual was 0.64 Kg over the five years, a 0.5% weight change. The McNemar chi-squared statistic was 58.6, (p-value<0.001). A regression with BMI as the Dependent Variable showed the overall model fit was statistically significant (F=271.96, p<0.001). The significant variables for the predicted change in BMI were gender, weight, comorbidities, and medications CONCLUSIONS: The present research confirmed that the majority of our patients with OSA (79.7%) were obese based on BMI. However, despite counseling by providers in sleep clinic, the overwhelming majority of patients maintained their BMI classification (87.8%). Only 9.5% of patients actually lowered their BMI classification. Based on this data, routine counseling regarding diet and exercise does not appear to be effective at producing long-term weight loss amongst this high-risk group. CLINICAL IMPLICATIONS: The results from this study suggest that routine counseling during office visits by clinicians targeting weight loss is not effective in helping patients achieve long-term weight loss in patients with OSA. More research investigating optimal strategies by providers targeting weight loss, including multi-disciplinary approaches, is needed. DISCLOSURES: No relevant relationships by Brian Celso, source=Web Response No relevant relationships by Mariam Louis, source=Web Response No relevant relationships by Bijal Patel, source=Web Response No relevant relationships by Edward Prange, source=Web Response

SESSION TITLE: Sleep 1 SESSION TYPE: Original Investigation Poster PRESENTED ON: Wednesday, November 1, 2017 at 01:30 PM - 02:30 PM PURPOSE: Obstructive Sleep pnea (OSA) and insomnia are often conceptualized and treated as two separate sleep disorders in the primary care setting. While in some cases this is true, studies have shown that 30-70% of patients treated for OSA also exhibited symptoms of insomnia. To date, there are no published reports that study how Insomnia and OSA are assessed in the primary clinical setting and if co-morbid diseases affect time to diagnose OSA. Our hypothesis is that there is a delay in referral for a sleep study to diagnose OSA for patients treated for Insomnia with associated specific co-morbidities. METHODS: We performed a rretrospective chart review of 78 patients referred to our institution's sleep laboratory from primary care clinics between 01/01/11 and 12/31/16 for a sleep study. Inclusion criteria were participants > 18 years old, and have a diagnosis of both OSA and Insomnia. Exclusion criteria were pregnancy, chronic, severe mental disorders, and/or history of a Substance Use Disorder. Comorbidities were categorized into four domains: Respiratory (Asthma, COPD, Nasal Allergies); Cardiovascular (HTN, CVA, CAD; Endocrine/GI (DM, GERD, Thyroid Disease) and Nervous (Anxiety, Depression, Seizures). A Multiple Regression was performed to assess the magnitude of the relationships between the predictor variables; age, gender, ethnic group, Body Mass Index (BMI), and comorbidities with the outcome variable time to referral for a sleep study in days. Significance was set at 0.05. RESULTS: There were 24 men (30.8%) and 54 women (69.2%), with an average age of 53.9 years, SD = 11.85, and average BMI of 36.66, SD = 9.02 . Patients identified as 41% Black, 41% White, 1.3% Hispanic, and 16.7% as Other. The Multiple Regression performed explained 25% of the variance for time to diagnosis of OSA. The overall test of the regression model was significant (F=2.86, p=0.008). Of the predictor variables, BMI was significant and negative (t=-0.065, p=0.008). Of the comorbidities, endocrine and GI disorders was significant (t=0.448, p=0.05), as well as contributed the greatest weight to time of diagnosis of OSA. Interestingly, respiratory disorders had a negative effect on time to diagnosis although the result was not significant. CONCLUSIONS: This study shows that in the primary care setting, patients who have insomnia, a delay in the diagnosis of OSA was the longest in patients suffering from endocrine and GI (especially GERD) and shortest in those with increased BMI. Patients suffering from cardiovascular and neurological/psychiatric conditions were in the middle. No referral bias based on age, gender, race was dobserved. CLINICAL IMPLICATIONS: This study is the first to look at potential referral bias for sleep studies amongst patients with insomnia and co-existing diseases in the primary care setting. Depending on the co-morbidities, time to referral varied. Prompt diagnosis and treatment of OSA in patients can improve quaility of life metrics, decrease MVAs and potentialy stablize co-existing diseases. This study highlights areas where greater awareness regarding the recognition of OSA symptoms is needed in patients who have OSA, insomnia and underlying co-morbid diseases. The most effective means by which this can be achieved needs further research. DISCLOSURE: The following authors have nothing to disclose: Mariam Louis, Ramon Rodriquez-Quijano, Peter Staiano, Nimeh Najjar, Tracy Ashby, Brian Celso No Product/Research Disclosure Information