Citations

Objective Hospitalization, particularly for surgery, represents a period of heightened stress. We evaluated how frailty attenuates the association of rheumatoid arthritis (RA) with negative health outcomes after elective noncardiac surgery. Methods We conducted a cross‐sectional study using the National Inpatient Sample (2018 to 2020), including adults (aged ≥18 years) with elective hospitalizations with noncardiac surgery. Frailty was measured using the Hospital Frailty Risk Score (HFRS). Surgeries were categorized by risk level. Outcomes included discharge health resources need (DHRN), composite adverse clinical events (CACE), and death. Results Among 8,800,359 hospitalizations (2.14% among patients with RA), 73.36% underwent intermediate‐risk surgery and 62.25% had HFRS above 0. DHRN occurred in 28.9%, CACE occurred in 0.93%, and all‐cause mortality occurred in 0.17%. A 10‐point increase in HFRS was associated with a 3.8‐fold increased DHRN, 8.5‐fold increased CACE, and 22.8‐fold increased mortality. RA was not independently associated with CACE (adjusted odds ratio [aOR] 0.91; 95% confidence interval [CI] 0.82–1.01; P = 0.08) or death (aOR 0.90; 95% CI 0.71–1.15; P = 0.41) but was significantly associated with DHRN (aOR 1.28, 95% CI 1.25–1.31; P < 0.001) after adjusting for confounders and frailty. In sensitivity analysis, RA had 10% higher relative odds of DHRN following orthopedic (aOR 1.10; 95% CI 1.07–1.13; P < 0.001) but not nonorthopedic intermediate‐risk surgery (aOR 1.10; 95% CI 0.98–1.23; P = 0.11). Conclusion RA was associated with higher odds of DHRN after orthopedic intermediate‐risk surgery but not CACE or inpatient mortality. Moreover, HFRS was associated with DHRN, CACE, and death. These findings underscore the need for disease‐specific clinical considerations and highlight the potential utility of HFRS in discharge planning.

1597 Background: The impact of COVID19 pandemic on patient health beliefs and healthcare systems has adversely affected mammographic breast cancer screening (MBCS) during the initial stages of the pandemic. The aims of our study were to determine whether the initial decrease in MBCS rates persisted or if there was a rebound in later stages and to quantify the impact on urban and rural populations. Methods: This retrospective observational study analyzed data from March 2017 to March 2023 to assess whether eligible women received annual MBCS during this timeframe. We included women aged 40-75 years, drawn from the integrated Mayo Clinic system, encompassing nearly 60 primary care practices in Arizona, Florida, Iowa, Minnesota, and Wisconsin, representing a broad cross-section of the United States (US) population. US census data was utilized to classify a county as urban or rural. We divided the study period into 4 categories:1) pre-COVID-19 pandemic (March 1, 2017-March 10, 2020), 2) early pandemic (March 11, 2020-March 10, 2021; pre-vaccination period), 3) mid- pandemic (March 11, 2021-March 10, 2022; delta and omicron variant dominant period), and 4) late pandemic (March 11, 2022-March 1, 2023; recovery period). Utilizing the Poisson regression model, we compared the incidence rate ratio (IRR) for rates of MBCS across the 4 predefined time periods. Multivariable modeling was performed to assess the significance of MBCS rates with urbanicity while controlling for the year (2017-2022) and age. Results: By race and ethnicity, the women in this cohort were White (92%), Black (3.1%), Asian (3%) and other (1.9%). The median age was 57 years and the cohort included 239, 804 women aged 40-75 years. MBCS rates was 49.2% in 2017, 47.1% in 2018, 46.4% in 2019, and decreased to 42.1% in 2020 and improved to 44.7% in 2021 and 43.8% in 2022. Compared with the pre-COVID-19 pandemic period, there was a significant decrease in MBCS rate in the early pandemic (IRR= 0.886, 95% CI 0.879, 0.892, p<0.001). Though the MBCS rate improved in the mid-pandemic period, it remained below the pre-pandemic level (IRR=0.941, 95% CI 0.934, 0.948, p<0.001). Similarly, during the late stage of the pandemic, the MBCS rate remained lower than the pre-pandemic level (IRR=0.922, 95% CI 0.915, 0.928, p<0.001). Rural counties had a lower MBCS rate compared with urban counties (IRR = 0.936, 95% CI 0.931, 0.941, p<0.001). Conclusions: This study confirms the negative impact of the early COVID-19 pandemic on MBCS, and it revealed its persistent effect, as the current MBCS rate remains below the pre-pandemic rate. These findings were more evident in rural counties. Factors contributing to the long-lasting impact are being analyzed with the intent to develop and implement targeted solutions to improve the MBCS rate.

Abstract Background We compared long-term mortality and readmission rates after COVID-19 hospitalization based on rural-urban status and assessed the impact of COVID-19 vaccination introduction on clinical outcomes by rurality. Methods The study comprised adults hospitalized for COVID-19 at 17 hospitals in 4 US states between March 2020 and July 2022, followed until May 2023. The main analysis included all patients, whereas a sensitivity analysis focused on residents from 4 states containing 17 hospitals. Additional analyses compared the pre- and postvaccination periods. Results The main analysis involved 9325 COVID-19 hospitalized patients: 31% were from 187 rural counties in 31 states; 69% from 234 urban counties in 44 states; the mean age was 65 years (rural, 66 years; urban, 64 years); 3894 women (rural, 41%; urban, 42%); 8007 Whites (rural, 87%; urban, 83%); 1738 deaths (rural, 21%; urban, 17%); and 2729 readmissions (rural, 30%; urban, 29%). During a median follow-up of 602 days, rural residence was associated with a 22% higher all-cause mortality (log-rank, P &lt; .001; hazard ratio, 1.22; 95% confidence interval, 1.10-1.34, P &lt; .001), and a trend toward a higher readmission rate (log-rank, P = .038; hazard ratio, 1.06; 95% confidence interval, .98-1.15; P = .130). The results remained consistent in the sensitivity analysis and in both pre- and postvaccination time periods. Conclusions and Relevance Patients from rural counties experienced higher mortality and tended to be readmitted more frequently following COVID-19 hospitalization over the long term compared with those from urban counties, a difference that remained even after the introduction of COVID-19 vaccines.

Abstract Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method–based risk prediction tools for outcome prediction after AIS in new data sets.

Introduction: Machine learning models (MLM) hold promise for improved risk stratification and to guide personalized care. Hypothesis: MLM would outperform conventional statistical (CS) methods and existing iScore for predicting composite of recurrent stroke or death at 90 days and 3 years after ischemic stroke (IS) Methods: We built 3 MLM (random forest [RF], support vector machine [SVM], and extreme gradient boosting decision trees [XGBoost]) and two CS models (logistic regression [LR] and least absolute shrinkage and selection operator [LASSO]) to predict composite outcome at 90 days or 3 years after AIS in a retrospective cohort of 721 adults. The performance of these models was evaluated against the existing iScore to predict outcomes after AIS. Results: Composite outcome occurred in11% at 90 days and in 34% at 3 years after AIS. Performance metrics for predicting 90-day and 3-year composite outcome were comparable across all study models except SM LR. Measurements for the area under the receiver operating characteristic curve for each model in validation set was as follow. 1) 90-day prediction: 0.779 for RF, 0.771 for SVM, 0.772 for EXBOOST, 0.613 for LR, 0.811 for LASSO, and 0.720 for iScore; 2) 3-year prediction: 0.743 for RF, 0.777 for SVM, and 0.773 for EXBOOST, 0.724 for LR, 0.790 for LASSO, and 0.710 for iScore. Conclusions: ML-based algorithms did not improve prediction of composite outcome at 90 days or 3 years after AIS compared with CS-based LASSO. iScore was successfully validated and compared broadening its generalizability and applicability.

Background: The association of in-hospital blood pressure variability (BPV) with readmission or survival after acute ischemic stroke (AIS) have not been fully investigated. We evaluated the relationship between intra-individual systolic blood pressure (SBP) variability (SBPV) for the duration of hospitalization, mean SBP, maximal SBP reached and readmission-free survival, all-cause readmission, or all-cause mortality to one year after hospitalization for a new acute ischemic stroke (AIS). Methods: We measured mean SBP, maximal SBP reached, and SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) across a median of 16 SBP readings per patient, every 4 hours, for the duration of hospitalization in 862 patients (age (mean±SD) 75±15 years, 55% females) with AIS admitted from 2003 to 2018, followed-up to 2019. Results: At 1 year, readmission-free survival was 51%, readmission rate was 39%, and mortality was16%. The mean ± SD in mmHg for SBP 146±20, maximal SBP reached 163±26, SD 16±6, and CV 10±5. Adjusted hazard ratios for the highest vs. lowest quartiles of SD or CV were 1.44 (95% confidence interval 1.04 - 1.81) or 1.29 (0.94 - 1.64) for readmission-free survival; 1.29 (0.9 - 1.78) or 1.29 (0.94 - 1.78) for readmission; 1.15 (0.71 - 1.87) or 0.86 (0.55 - 1.36) for mortality. The results remained consistent with mean SBP. However, maximal SBP was inversely associate with readmission-free survival. The accompanying figure shows Kaplan-Meier survival analysis of cumulative incidence of three outcomes by quartiles of SD (upper panel) and CV (lower panel) of SBP. Conculsions: Among patients hospitalized with a new AIS, higher in-hospital SBPV was associated with no decrease in readmission-free survival or increase in rates of readmission or mortality to 1 year. We recommend less emphasis on treating blood pressure and more focus on guideline-directed high-value care for the management AIS patients.