Use of endoscopic ultrasound for pancreatic cancer from 2000 to 2016

Background and study aims Pancreatic cancer (PC) is the fourth most common cause of cancer death in the United States. Previous studies have suggested a survival benefit for endoscopic ultrasound (EUS), an important tool for diagnosis and staging of PC. This study aims to describe EUS use over time and identify factors associated with EUS use and its impact on survival. Patients and methods This was a retrospective review of the Surveillance, Epidemiology and End Results (SEER) database linked with Medicare claims. EUS use, clinical and demographic characteristics were evaluated. Chi-squared analysis, Cochran-Armitage test for trend, and logistic regression were used to identify associations between sociodemographic and clinical factors and EUS. Kaplan-Meier and Cox proportional hazard ratios were used for survival analysis. Results EUS use rose during the time period, from 7.4 % of patients in 2000 to 32.4 % in 2015. Patient diversity increased, with a rising share of older, non-White patients with higher Charlson comorbidity scores. Both clinical (receipt of other therapies, PC stage) and nonclinical factors (region of country, year of diagnosis) were associated with receipt of EUS. While EUS was associated with a survival improvement early in the study period, this effect did not persist for PC patients diagnosed in 2012 to 2015 (median survival 3 month ± standard deviation [SD] 9.8 months without vs. 4 months ± SD 8 months with EUS). Conclusions Our data support previous studies, which suggest a survival benefit for EUS when it was infrequently used, but finds that benefit was attenuated as EUS became more widely available.

Assessing Risk of Severe Complications after Endoscopic Transnasal Transsphenoidal Surgery: A Comparison of Frailty, American Society of Anesthesiologists, and Comorbidity Scores

Objective This study aimed to improve age-independent risk stratification for patients undergoing endoscopic transnasal transsphenoidal (TNTS) approach to pituitary mass resection by investigating the associations between frailty, American Society of Anesthesiologists (ASA), and comorbidity scores with severe complications following TNTS. Design This study is a retrospective review. Setting This review was conducted utilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Participants A total of 680 cases of TNTS identified from 2010 to 2013 were included in this study. Main Outcome Measures The modified frailty index (mFI) was calculated to quantify frailty. ASA and Charlson's comorbidity index (CCI) scores were obtained as physiologic status and comorbidity-based prognostic markers. Severe complications were separated into intensive care unit (ICU)-level complications, defined by Clavien–Dindo grade IV (CDIV) criteria, and mortality. Results Overall, 24 CDIV complications (3.5%) and 6 deaths (0.9%) were recorded. Scores for mFI (p = 0.01, R 2 = 0.97) and ASA (p = 0.04., R 2 = 0.87) were significantly correlated with CDIV complications. ASA scores were significantly correlated with mortality (p = 0.03, R 2 = 0.87), as well as independently associated with CDIV complication by multivariable regression models (odds ratio [OR] = 2.96, 95% confidence interval [CI]: 1.35–6.83, p < 0.01), while mFI was not. CCI was not significantly associated with CDIV complications or mortality. A multivariable regression model incorporating ASA had a lower Akaike's Information Criteria (AIC; 188.55) than a model incorporating mFI (195.99). Conclusion Frailty and physiologic status, as measured by mFI and ASA scores respectively, both correlate with ICU-level complications after TNTS. ASA scores demonstrate greater clinical utility than mFI scores; however, as they are more easily generated, uniquely correlated with mortality and independently associated with ICU-level complication risk on multivariable regression analysis.

843. Targeted HIV Testing for Patients Hospitalized for COVID-19

Background SARS-CoV-2 causes a severe respiratory illness known as COVID-19. Treatment options in the early portion of the COVID-19 pandemic included the use of antiretroviral agents i.e. protease inhibitors (PIs) such as lopinavir (LPV) that had been shown to have activity against the main proteases of SARS-CoV-2 in vitro but with very limited clinical data. Prior to the use of PIs, HIV testing would be indicated to ensure that patients who were not previously diagnosed with HIV would start appropriate HIV treatment. In this unique situation, HIV testing would be utilized not based on traditional HIV risk factors. Methods We performed a retrospective search from a specific systems database of patients admitted to Yale-New Haven Health System (YNHHS) with a diagnosis of COVID-19 infection. We identified a subset of patients who were HIV tested. Most were done prior to initiating PI treatment. Demographics, comorbidity scores and specific underlying conditions were also tabulated. We performed Kruskal Wallis and Chi-Squared analysis to test for significance between HIV- and HIV+ patients. Results The total no. of patients admitted to the YNHHS with COVID-19 infection between the period from January 6, 2020 to January 6, 2021 was 5776. A cohort 964 (16.7%) patients were screened for HIV. Much of the testing occurred in the early COVID periods (Figure 1) when PIs were considered as part of the treatment algorithm. Sixty-seven (0.07%) patients tested HIV+ with 3 (0.003%) being newly diagnosed (Fig 2). Compared to HIV- patients, HIV+ were more likely to be identified as Black, with higher mean Elixhauser Comorbidity scores and significant associations with conditions such as hypertension, pulmonary disease, complicated diabetes, liver disease, renal failure and depression (Table 1). These co-morbidities have been correlated with higher risk of hospitalization for people living with HIV (PWH). Figure 2. COVID Admission and HIV Status The graph represents HIV testing results over the entire study period. Table 1. Demographics and Comorbidites Represents demographics and comorbidities of HIV- & HIV+ patients Figure 1. COVID Admissions and HIV Testing COVID admissions over time and the performance of HIV testing Conclusion This is one of the first reports on targeted HIV testing for patients not using identifiable traditional HIV risk factors who were admitted to a large healthcare system for COVID19 infections. The percentage of newly HIV diagnosed patients from this cohort was considered to be &lt; known HIV infection rates for our population. The majority of PWH were already established in care prior to their COVID19 diagnosis. Disclosures All Authors: No reported disclosures

1288. Evaluation of Predictors for Clinical Success in Patients Treated with Eravacycline for Various Infections

Background Eravacycline (ERV) is approved in the United States (US) for the treatment of complicated intra-abdominal infections in adults. We aimed to evaluate the independent predictors of clinical success in patients treated with ERV for various infections. Methods Multicenter, retrospective, observational study conducted from September, 2018 to April, 2021. We included adults treated with ERV for ≥ 72hours. Clinical success was defined as 30-day survival, lack of 30-day infection-recurrence, and resolution of infection signs/symptoms. All outcomes were measured from ERV initiation. Multivariable logistic regression (MLR) was performed to identify independent predictors of clinical success. Clinically relevant variables were selected for model entry based on bivariate comparisons (P&lt; 0.2) in a backward fashion. Results We included 223 patients from 16 medical centers in 13 geographically unique states. The median (IQR) age was 61 (50-69) years, 57% were male and 62% were Caucasian. Median (IQR) APACHE II, and Charlson Comorbidity scores were 15 (10-21), and 3 (1-5), respectively. Sources of infection were primarily intra-abdominal (27%) and respiratory (27%). Common pathogens included Acinetobacter baumannii (21%) and those of the Enterobacterales order (36%). Infectious diseases consultation and surgical interventions were obtained in 93.7% and 52% respectively. Clinical success occurred in 64%, specifically 30-day survival in 78%, absence of 30-day infection-recurrence in 93%, and 74% experienced resolution of infection signs/symptoms. Since characteristics and outcomes were similar among various pathogens, MLR was conducted using the overall cohort. Skin as a source and combination therapy with ERV were independently associated with higher clinical success: odds ratio 3.3 [CI 1.1-10.2] and 2.9 [1.4-5.9], respectively. Whereas, ICU admission at culture time and undergoing surgery within 30 days of culture were independently associated with reduced odds of clinical success: 0.4 [0.17-0.80] and 0.3 [0.11-0.63] respectively. Conclusion Although most ERV treated patients experienced clinical success, factors independently associated with higher clinical success are crucial to consider for optimum antibiotic selection. Disclosures Kimberly C. Claeys, PharmD, GenMark (Speaker’s Bureau) Madeline King, PharmD, tetraphase (Speaker’s Bureau) Michael P. Veve, Pharm.D., Cumberland (Grant/Research Support)Paratek Pharmaceuticals (Research Grant or Support) Bruce M. Jones, PharmD, BCPS, Abbvie (Consultant, Advisor or Review Panel member, Speaker’s Bureau)La Jolla (Speaker’s Bureau)Melinta (Consultant)Merck (Consultant)Paratek (Consultant, Speaker’s Bureau) Susan L. Davis, PharmD, Nothing to disclose Michael J. Rybak, PharmD, MPH, PhD, Paratek Pharmaceuticals (Research Grant or Support)

netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals' genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individual's multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

A comparison of comorbidity measures for predicting mortality after elective hip and knee replacement: A cohort study of data from the National Joint Registry in England and Wales

Background The risk of mortality following elective total hip (THR) and knee replacements (KR) may be influenced by patients’ pre-existing comorbidities. There are a variety of scores derived from individual comorbidities that can be used in an attempt to quantify this. The aims of this study were to a) identify which comorbidity score best predicts risk of mortality within 90 days or b) determine which comorbidity score best predicts risk of mortality at other relevant timepoints (30, 45, 120 and 365 days). Patients and methods We linked data from the National Joint Registry (NJR) on primary elective hip and knee replacements performed between 2011–2015 with pre-existing conditions recorded in the Hospital Episodes Statistics. We derived comorbidity scores (Charlson Comorbidity Index—CCI, Elixhauser, Hospital Frailty Risk Score—HFRS). We used binary logistic regression models of all-cause mortality within 90-days and within 30, 45, 120 and 365-days of the primary operation using, adjusted for age and gender. We compared the performance of these models in predicting all-cause mortality using the area under the Receiver-operator characteristics curve (AUROC) and the Index of Prediction Accuracy (IPA). Results We included 276,594 elective primary THRs and 338,287 elective primary KRs for any indication. Mortality within 90-days was 0.34% (N = 939) after THR and 0.26% (N = 865) after KR. The AUROC for the CCI and Elixhauser scores in models of mortality ranged from 0.78–0.81 after THR and KR, which slightly outperformed models with ASA grade (AUROC = 0.77–0.78). HFRS performed similarly to ASA grade (AUROC = 0.76–0.78). The inclusion of comorbidities prior to the primary operation offers no improvement beyond models with comorbidities at the time of the primary. The discriminative ability of all prediction models was best for mortality within 30 days and worst for mortality within 365 days. Conclusions Comorbidity scores add little improvement beyond simpler models with age, gender and ASA grade for predicting mortality within one year after elective hip or knee replacement. The additional patient-specific information required to construct comorbidity scores must be balanced against their prediction gain when considering their utility.

COVID-19 admission risk tools should include multiethnic age structures, multimorbidity and deprivation metrics for air pollution, household overcrowding, housing quality and adult skills

BackgroundEthnic minorities account for 34% of critically ill patients with COVID-19 despite constituting 14% of the UK population. Internationally, researchers have called for studies to understand deterioration risk factors to inform clinical risk tool development.MethodsMulticentre cohort study of hospitalised patients with COVID-19 (n=3671) exploring determinants of health, including Index of Multiple Deprivation (IMD) subdomains, as risk factors for presentation, deterioration and mortality by ethnicity. Receiver operator characteristics were plotted for CURB65 and ISARIC4C by ethnicity and area under the curve (AUC) calculated.ResultsEthnic minorities were hospitalised with higher Charlson Comorbidity Scores than age, sex and deprivation matched controls and from the most deprived quintile of at least one IMD subdomain: indoor living environment (LE), outdoor LE, adult skills, wider barriers to housing and services. Admission from the most deprived quintile of these deprivation forms was associated with multilobar pneumonia on presentation and ICU admission. AUC did not exceed 0.7 for CURB65 or ISARIC4C among any ethnicity except ISARIC4C among Indian patients (0.83, 95% CI 0.73 to 0.93). Ethnic minorities presenting with pneumonia and low CURB65 (0–1) had higher mortality than White patients (22.6% vs 9.4%; p<0.001); Africans were at highest risk (38.5%; p=0.006), followed by Caribbean (26.7%; p=0.008), Indian (23.1%; p=0.007) and Pakistani (21.2%; p=0.004).ConclusionsEthnic minorities exhibit higher multimorbidity despite younger age structures and disproportionate exposure to unscored risk factors including obesity and deprivation. Household overcrowding, air pollution, housing quality and adult skills deprivation are associated with multilobar pneumonia on presentation and ICU admission which are mortality risk factors. Risk tools need to reflect risks predominantly affecting ethnic minorities.

Utilization, Complications, and Costs of Inpatient versus Outpatient Total Elbow Arthroplasty

Background Although total hip and knee arthroplasty have largely moved to the outpatient setting, total elbow arthroplasty (TEA) remains a predominantly inpatient procedure. Currently, evidence on the safety and potential cost savings of outpatient TEA is limited. Therefore, we aimed to compare the costs and complications associated with performing TEA in the inpatient versus outpatient setting. Methods We identified patients who received elective TEA using the Truven Health MarketScan database. Outcomes of interest were 90-day complication rate, readmission rate, and procedure costs in the inpatient and outpatient settings. We used propensity score matching and logistic regression analysis to assess how patient comorbidities and surgical setting influenced complications and readmission rates. The median cost per patient was compared using the Mann-Whitney U test. Results We identified 307 outpatient and 414 inpatient TEA procedures over a 9-year period. Elixhauser comorbidity scores were higher for the inpatient cohort. The incidence of surgical complications was significantly higher in the inpatient than the outpatient cohort (27% vs 9%). The odds of 90-day readmissions were similar in the 2 groups (37% vs 25%). In terms of cost, the median inpatient TEA was more expensive than outpatient TEA ($26 817 vs $18 412). However, the median cost for occupational therapy within 90 days of surgery was higher for outpatient TEA patients ($687 vs $571). Conclusions The results of this study demonstrate that surgeons can consider a transition toward outpatient TEA for patients without significant comorbidities, as this will substantially reduce health care costs.