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Little is known about the relationship between habitual sleep duration, cardiovascular health (CVH) and their impact on healthcare costs and resource utilization. We describe the relationship between sleep duration and ideal CVH, and the associated burden of healthcare expenditure and utilization in a large South Florida employee population free from known cardiovascular disease.The study used data obtained from a 2014 voluntary Health Risk Assessment among 8629 adult employees of Baptist Health South Florida. Health expenditures and resource utilization information were obtained through medical claims data. Frequencies of the individual and cumulative CVH metrics across sleep duration were computed. Mean and marginal per-capita healthcare expenditures were estimated.The mean age was 43 years, 57% were of Hispanic ethnicity. Persons with 6-8.9hours and ≥9 hours of sleep were significantly more likely to report optimal goals for diet, physical activity, body mass index, and blood pressure when compared to those who slept less than 6 hours. Compared to those who slept less than 6 hours, those sleeping 6-8.9hours and ≥9hours had approximately 2- (odds ratio 2.1, 95% confidence interval: 1.9-3.0) and 3-times (odds ratio 3.0, 95% confidence interval: 1.6-5.6) higher odds of optimal CVH. Both groups with 6 or more hours of sleep had lower total per-capita expenditure (approximately $2000 and $2700 respectively), lower odds of visiting an emergency room, or being hospitalized compared to those who slept6 hours.Sleeping 6 or more hours was associated with better CVH, lower healthcare expenditures, and reduced healthcare resource utilization.

Purpose There are very few studies that have compared the short-term outcomes of laparoscopic Roux-en-Y gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG). Among short-term outcomes, hospital readmission after these procedures is an area for quality enhancement and cost reduction. In this study, we compared 30-day readmission rates after LSG and LRYGB through analyzing a nationalized dataset. In addition, we identified the reasons of readmission. Materials and Methods The current study was a retrospective analysis of data from National Surgical Quality Improvement Program (NSQIP) All adult patients, ≥ 18 years of age and who had LSG or LRYGB during 2014 to 2019 were included. Current Procedural Terminology (CPT) codes were used to identify the procedures. Multivariate logistic regressions were used to calculate propensity score adjusted odds ratios (ORs) for all cause 30-day re-admissions. Results There were 109,900 patients who underwent laparoscopic bariatric surgeries (67.5% LSG and 32.5% LRYGB). Readmissions were reported in 4168 (3.8%) of the patients and were more common among RYGB recipients compared to LSG (5.6% versus 2.9%, P Conclusions Readmission rates were significantly higher among those receiving LRYGB, compared to LSG. Readmission was also affected by many patient factors. The factors could help patients and providers to make informed decisions for selecting appropriate procedures. Graphical Abstract

Catheter ablation (CA) and left atrial appendage closure (LAAC) require transseptal access; combining both in a single procedure may have advantages. However, the safety of this approach has not been extensively studied. The objective of this study was to compare in hospital outcomes among patients receiving CA, LAAC, and combination of both treatments on the same day.We conducted a retrospective cohort analysis of the National Inpatient Sample database. The primary outcome was the presence of major adverse cardiovascular and cerebrovascular events (MACCE) during index hospitalization. Secondary outcomes included stroke, pericardial effusion, pericardiocentesis, and bleeding.A total of 69,285 hospitalizations with AF were included in the analysis, of which 71.7% received LAAC, 27.8% received CA, and 0.5% received combination of both treatments on the same day. MACEE (OR, 1.63; 95% CI, 0.39-6.70), stroke (OR, 2.98; 95% CI, 0.55-16.01), pericardial effusion (OR, 0.33; 95% CI, 0.07-1.41), pericardiocentesis (OR, 1.00; 95% CI, 0.25-3.86), and bleeding (OR, 3.25; 95% CI, 0.87-12.07) did not differ significantly between CA and combination treatment. Similarly, MACCE (OR, 1.11; 95% CI, 0.28-4.41), stroke (OR, 1.03; 95% CI, 0.24-4.35), pericardial effusion (OR, 0.45; 95% CI, 0.11-1.90), pericardiocentesis (OR, 0.63; 95% CI, 0.14-2.83), and bleeding (OR, 2.04; 95% CI, 0.65-6.39) did not differ significantly between LAAC and combination treatment.The combined approach is infrequently used in clinical practice ( 1%). However, major life-threatening adverse events did not differ between CA and LAAC when performed in isolation or combined in a single procedural stage on the same day.

Frailty could affect outcomes of autologous hematopoietic stem cell transplantation (aHSCT). This study sought to examine the effects of frailty on hospital outcomes among patients with non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), and multiple myeloma (MM) who received aHSCT.This study was a retrospective analysis of Nationwide Inpatient Sample database, 2005 to 2014. Outcome variables were in-hospital mortality, prolonged length of stay and hospitalization cost. Frail patients were defined using the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator.There were 20,573 NHL, 8,974 HL, and 40,750 MM patients. Among them, 5.5% NHL, 3.8% HL, and 4.8% MM patients were frail. Among patients with NHL, there were significant associations between frailty and in-hospital mortality (Odds Ratio [OR], 4.04, 95% CI: 2.11-7.76), and prolonged length of stay (OR, 2.32, 95% CI: 1.56-3.46). Similarly, among HL, there were significant associations between frailty and in-hospital mortality (OR, 1.82, 95% CI: 1.43-2.76), and prolonged length of stay (OR, 1.55, 95% CI: 1.34-2.84). Likewise, for MM, there were significant associations between frailty and in-hospital mortality (OR, 4.28, 95% CI: 2.16-8.48), and prolonged length of stay (OR, 3.00, 95% CI: 2.00-4.51). These associations remained significant after stratifying by age and comorbidities. Significant differences were observed in hospitalization cost between frail and non-frail patients.Among patients with lymphoid malignancies undergoing HSCT, frailty was associated with greater in-hospital mortality, longer length of stay, and higher hospitalization costs. Comprehensive health status assessments for identifying and managing frail patients in this population could improve patient outcomes.

Transesophageal echocardiogram (TEE) is the preferred imaging modality to guide transcatheter left atrial appendage closure (LAAC). Intracardiac echocardiography (ICE) has evolved as a less invasive alternative to TEE. Several observational studies have shown similar success rates and perioperative complications between TEE and ICE for LAAC.We sought to examine the temporal trends and patient characteristics of TEE versus ICE use in LAAC using a national database. We also evaluated hospital outcomes including periprocedural complications, mortality, and length of hospital stay.This is a retrospective analysis of data from the National Readmission Database, collected from 2016 to 2018. The primary outcome was major adverse events (MAE) defined as in-hospital mortality, cardiac arrest, pericardial effusion with or without tamponade, pericardiocentesis or window pericardiocentesis and pericardial window, pericardial effusion and tamponade, and hemorrhage requiring transfusion.Trend analysis showed that TEE-guided LAAC increased from 96.6% in 2016 to 98.4% in 2018 (relative increase, 1.9%), while ICE-guided LAAC decreased from 3.4% to 1.6% during the same period (relative decrease, 53%, p for trend = 0.08). In the unmatched cohorts, the MAE was significantly lower in TEE-guided LAAC compared to ICE-guided LAAC (6.5% vs. 9.3%, p = 0.022). In the propensity score matching analysis, MAE remained significant (5.6% vs. 9.4%, p 0.001). The incidence of pericardial effusion with or without tamponade remained significantly lower in the TEE group (2.3% vs. 5.8%, p 0.001). Length of stay (3.4 vs. 1.9 days, p 0.001) and hospitalization cost ($34,826 vs. $20,563, p 0.001) remained significantly lower for TEE-guided LAAC.Compared to ICE, the incidence of MAE was significantly lower for TEE-guided LAAC, driven mainly by less pericardial effusion events. Large-scale randomized trials are needed to confirm the findings of the current and previous studies.

Purpose: To assess the effects of COVID-19 on hospitalizations for intracranial meningioma resection using a large database. Methods: We conducted a retrospective analysis of the California State Inpatient Database (SID) 2019 and 2020. All adult (18 years or older) hospitalizations were included for the analysis. The primary outcomes were trends in hospitalization for intracranial meningioma resection between 2019 and 2020. Secondary outcomes were Clavien–Dindo grade IV complications, in-hospital mortality, and prolonged length of stay, which was defined as length of stay ≥75 percentile. Results: There were 3,173,333 and 2,866,161 hospitalizations in 2019 and 2020, respectively (relative decrease, 9.7%), of which 921 and 788 underwent intracranial meningioma resection (relative decrease, 14.4%). In 2020, there were 94,114 admissions for COVID-19 treatment. Logistic regression analysis showed that year in which intracranial meningioma resection was performed did not show significant association with Clavien–Dindo grade IV complications and in-hospital mortality (OR, 1.23, 95% CI: 0.78–1.94) and prolonged length of stay (OR, 1.05, 95% CI: 0.84–1.32). Conclusion: Our findings show that neurosurgery practice in the US successfully adapted to the unforeseen challenges posed by COVD-19 and ensured the best quality of care to the patients.

Simple Summary: All fields of healthcare were adversely affected by the COVID-19 pandemic. In this study, we sought to understand the effects of COVID-19 on hospitalizations for intracranial meningioma resection using a large database. We compared hospitalization rates as well as hospital outcomes such as Clavien-Dindo grade IV complications, in-hospital mortality, and prolonged length of stay for intracranial meningioma resection during 2019 and 2020. Our findings showed that though hospitalization rates decreased slightly during the COVID-19 pandemic, hospital outcomes were not adversely affected. The findings of our study show that with adequate planning and preparations, better hospital outcomes could be sustained even during healthcare emergencies such as COVID-19 pandemic. Our findings assure that neurosurgery practice in the US ensured the best quality of care to their patients even during COVID-19 pandemic. Abstract: Purpose: To assess the effects of COVID-19 on hospitalizations for intracranial meningioma resection using a large database. Methods: We conducted a retrospective analysis of the California State Inpatient Database (SID) 2019 and 2020. All adult (18 years or older) hospitalizations were included for the analysis. The primary outcomes were trends in hospitalization for intracranial meningioma resection between 2019 and 2020. Secondary outcomes were Clavien-Dindo grade IV complications, in-hospital mortality, and prolonged length of stay, which was defined as length of stay >= 75 percentile. Results: There were 3,173,333 and 2,866,161 hospitalizations in 2019 and 2020, respectively (relative decrease, 9.7%), of which 921 and 788 underwent intracranial meningioma resection (relative decrease, 14.4%). In 2020, there were 94,114 admissions for COVID-19 treatment. Logistic regression analysis showed that year in which intracranial meningioma resection was performed did not show significant association with Clavien-Dindo grade IV complications and in-hospital mortality (OR, 1.23, 95% CI: 0.78-1.94) and prolonged length of stay (OR, 1.05, 95% CI: 0.84-1.32). Conclusion: Our findings show that neurosurgery practice in the US successfully adapted to the unforeseen challenges posed by COVD-19 and ensured the best quality of care to the patients.

Coronavirus disease 2019 (COVID-19) could be a risk factor for acute ischemic stroke (AIS) due to the altered coagulation process and hyperinflammation. This study examined the risk factors, clinical profile, and hospital outcomes of COVID-19 hospitalizations with AIS. This study was a retrospective analysis of data from California State Inpatient Database (SID) during 2019 and 2020. COVID-19 hospitalizations with age ≥ 18 years during 2020 and a historical cohort without COVID-19 from 2019 were included in the analysis. The primary outcomes studied were in-hospital mortality and discharge to destinations other than home. There were 91,420 COVID-19 hospitalizations, of which, 1027 (1.1%) had AIS. The historical control cohort included 58,083 AIS hospitalizations without COVID-19. Conditional logistic regression analysis showed that the odds of in-hospital mortality, discharge to destinations other than home, DVT, pulmonary embolism, septic shock, and mechanical ventilation were significantly higher among COVID-19 hospitalizations with AIS, compared to those without AIS. The odds of in-hospital mortality, DVT, pulmonary embolism, septic shock, mechanical ventilation, and respiratory failure were significantly higher among COVID-19 hospitalizations with AIS, compared to AIS hospitalizations without COVID-19. Although the prevalence of AIS was low among COVID-19 hospitalizations, it was associated with higher mortality and greater rates of discharges to destinations other than home.

Many case reports have indicated that myocarditis could be a prognostic factor for predicting morbidity and mortality among patients with COVID-19. In this study, using a large database we examined the association between myocarditis among COVID-19 hospitalizations and in-hospital mortality and other adverse hospital outcomes. The present study was a retrospective analysis of data collected in the California State Inpatient Database during 2020. All hospitalizations for COVID-19 were included in the analysis and grouped into those with and without myocarditis. The outcomes were in-hospital mortality, cardiac arrest, cardiogenic shock, mechanical ventilation, and acute respiratory distress syndrome. Propensity score matching, followed by conditional logistic regression, was performed to find the association between myocarditis and outcomes. Among 164,417 COVID-19 hospitalizations, 578 (0.4%) were with myocarditis. After propensity score matching, the rate of in-hospital mortality was significantly higher among COVID-19 hospitalizations with myocarditis (30.0% vs 17.5%, p0.001). Survival analysis with log-rank test showed that 30-day survival rates were significantly lower among those with myocarditis (39.5% vs 46.3%, p0.001). Conditional logistic regression analysis showed that the odds of cardiac arrest (odds ratio [OR] 1.90, 95% confidence interval [CI] 1.16 to 3.14), cardiogenic shock (OR 4.13, 95% CI 2.14 to 7.99), mechanical ventilation (OR 3.30, 95% CI 2.47 to 4.41), and acute respiratory distress syndrome (OR 2.49, 95% CI 1.70 to 3.66) were significantly higher among those with myocarditis. Myocarditis was associated with greater rates of in-hospital mortality and adverse hospital outcomes among patients with COVID-19, and early suspicion is important for prompt diagnosis and management.

Acromegaly is a rare disease associated with comorbidities that are common in the general population. Most patients undergo screening for classic phenotypical (CP) or mass effect manifestations. By retrospective review of pituitary tumor surgeries performed between 1994 and 2016 (1836), we identified patients with acromegaly (112). Main presentations were: CP (43%), mass effect (26%), incidentally detected (ID) tumors (17%), and other (14%). We compared the ID and CP groups regarding prevalence, clinical, biochemical, radiological and histopathological characteristics, and postoperative outcomes. The prevalence of ID among all surgeries increased after 2011 from 0.6% to 1.9% (p=0.01), while prevalence of CP remained stable (2.8% and 2.33%, p=0.65). Almost half of ID (47.4%) presented with otolaryngological manifestations. The ID and CP groups were similar regarding age, gender, comorbidities (hypertension, diabetes, hypopituitarism), tumor diameter and cavernous sinus invasion. Median insulin-like growth factor (IGF-1) and growth hormone (GH) levels were lower in the ID than CP (p

Objective: Machine learning (ML) may enhance prediction of outcomes such as mortality or acute kidney injury (AKI) among cardiac patients after coronary artery bypass graft (CABG). In this study, we used meta-analyses of reported ML models to assess what ML has been able to accomplish in this field, by evaluating the model performance in studies with CABG patients. Methods: We performed a literature search using Google Scholar and included studies that reported AUC and 95% CI for various models in our analysis. In addition, total participants, year of publication, type of analytical method (gradient boosting, random forest, etc.) and type of outcome (mortality or AKI) were extracted. We combined effect sizes using random effects model, and tested for heterogeneity, and publication bias. Results: 5 models from 5 studies were included in the analysis (patients= 35,152; with outcome mortality =3,080, AKI=933). Combined mean AUC was 0.796 (95% CI: 0.776, 0.815). Test of heterogeneity showed high variation between studies (I 2 = 66.7%). Egger’s test intercept was -1.03 (95% CI: -7.22, 5.17, p > .25) indicating no small study bias. Meta regression showed newer publications had a positive association ( coef = 0.003) and number of variables in the study had a negative association with higher AUC values ( coef = -0.0002). In subgroup analysis, the pooled AUC values for mortality and AKI groups were 0.795 and 0.805 respectively. The highest individual AUC was from ensemble model predicting AKI with AUC 0.84 and lowest was from gradient boosting model predicting mortality with AUC 0.77. Conclusion: Among the presented models for CABG ensemble methods performed well, but surprisingly methods with lesser number of variables tended to have higher predictive power. In near future, ML-based models may form the basis to build intelligent decision support systems for patient selection and risk stratification prior to CABG and could be applied to other cardiac surgeries.

Developing and implementing analytical models for predicting mortality or readmission related outcomes among heart failure (HF) patients are challenging. In this study, we used meta-analyses of reported predictive models to assess what machine learning (ML) has been able to accomplish in this field, by evaluating the ML model performance for studies in HF. We performed a literature search using Google Scholar, Web of Science and PubMed. The studies reporting AUC and 95% CI for various models were included. In addition, total participants, year of publication, type of analytical method (logistic regression, RF, etc.) and type of outcome (mortality, readmission, etc.) were extracted. We combined effect sizes using random effects (RF) model, and tested for heterogeneity, and publication bias. 12 studies were included in the analysis (patients= 123,832; AUC=15, with outcome mortality =17,471, readmission=15,703, hospitalization=67,523). Combined mean AUC was 0.77 (95% CI: 0.72, 0.82). Test of heterogeneity showed high variation between studies (I2=98.9%). Egger’s test intercept was 5.2 (95% CI: -4.2, 14.7, p > .25) indicating no small study effects/bias. Meta regression showed newer publications provide better AUC values (p < 0.03). In subgroup analysis, the pooled AUC for readmission, hospitalization, and mortality groups were 0.71, 0.80, and 0.78 respectively. The highest individual AUC was from neural networks (NN) predicting hospitalization with AUC 0.96 and lowest was from RF predicting readmission with AUC 0.65. Presented models were diverse, ranking in quality from fair to very good, and being varied for different clinical outcomes among HF patients. Situation known from studies using classical statistical methods holds also for methods using ML, with better predictive values for hospitalizations, and lower for other outcomes. Methods using NN and methods using higher numbers of variables performed very well and had highest predictive power.

Objective: Identifying patients at high risk of AE after TAVR is essential to prolong their survival. Current prediction models for AE after TAVR suffer from a lack of accuracy and external validation. Modern ML approaches can account for higher-dimensional relationships among variables, potentially improving the prediction of outcomes. We performed a systematic review and meta-analysis to estimate the discriminative ability of recently developed ML-based models, which predict various AE after TAVR. Methods: We searched Pubmed, Google Scholar, and Web of Science for studies (Jan 2019 to Jan 2022) that used ML approaches to predict AE after TAVR. Inputs in the meta-analysis were study-reported c-index values and 95% CI. Subgroup analyses separated models by outcome (mortality or clinical AE). Combined effect sizes using a random-effects model, test for heterogeneity, and Egger’s test to assess publication bias were considered. Results: Eight studies were included in the systematic review (patients = 26,023; outcomes = 1,014), of which five models had sufficient data for the meta-analysis. The number of features included in each model ranged from 6 to 107. The two most common models were random forest (n=2) and logistic regression (n=2). The most common outcome was mortality (n=5). The meta-analysis showed that models predicting mortality performed better (0.90; 95% CI: 0.81, 1.01) than models predicting clinical AE (0.80; 95% CI: 0.79, 0.95). The combined mean c-index was 0.87 (95% CI: 0.79-0.95). Test of heterogeneity showed high variation among studies ( I 2 =98.5%). Egger’s test did not indicate publication bias ( β = 1.48; 95% CI: –18.14, 21.09, p = 0.848). Conclusion: Although relatively few studies have applied ML for predicting AE after TAVR, the results are very promising. The time of complex sophisticated models has arrived with improved predictive accuracy through advanced ML methods able to help identify patients who are at risk for clinical AE early in their care.

To understand the effects of frailty on hospital outcomes such as in-hospital mortality, length of stay, and healthcare cost among patients with cancer using a nationally representative database.This study was a retrospective observational analysis of Nationwide Inpatient Sample (NIS) data collected during 2005-2014. Participants included adult patients with cancer ≥45 years identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. 'Frail' versus 'non-frail' hospitalizations were determined using the Johns Hopkins Adjusted Clinical Groups (ACG) frailty-defining diagnosis indicator. Main outcome measures were in-hospital mortality, hospital length of stay, and hospitalization cost. We defined prolonged length of stay as hospital stay ≥75th percentile of the study sample. Propensity score match analysis was done to examine whether frailty was associated with length of stay and in-hospital mortality.There were 10,463,083 cancer hospitalizations during 2005-2014, of which 1,022,777 (9.8%) were frail. Patients having length of stay ≥8 days were significantly higher among frail group, compared to non-frail group (53.3% versus 25.3%, P 0.001). Similarly, unadjusted mortality (12.0% versus 5.3%, P 0.001) and hospitalization costs ($29,726 versus $18,595, P 0.001) were significantly higher for frail patients. Nearly $28 billion was expended on hospitalization of frail patients with cancer during the study period. In propensity score match analysis, the odds of in-hospital mortality (odds ratio [OR], 1.54; 95% confidence interval [CI], 1.50-1.58) and length of stay (OR, 2.23; 95% CI, 2.18-2.27) were significantly greater for frail patients.Frailty was associated with adverse hospital outcomes such as increased length of stay, mortality, and hospitalization cost among all cancer types. Our findings could be valuable for frailty-based risk stratification of patients with cancer. Concerted efforts by the physiatrists, oncologists, and surgeons towards identifying frailty and incorporating it in risk estimation measures could help in optimizing management strategies for cancer.

Background: The prevalence of obesity among people of reproductive age has steadily increased. The rates of both maternal/parental and fetal adverse outcomes increased significantly with higher BMI. In this study, we examined the national estimates of adverse maternal/parental and fetal outcomes among pregnant individuals with pre-pregnancy obesity using a nationally representative database. Methods: This was a retrospective observational analysis of data retrieved from the Nationwide Inpatient Sample database (2010-2014). The primary outcomes of this study were various adverse maternal/parental and fetal outcomes. Delivery related hospitalizations and all outcomes were identified using the ICD-9 codes. Additionally, propensity-score matching analysis was conducted. Results: There were a total of 18,687,217 delivery-related hospitalizations, of which 1,048,323 were among people with obesity. The mean age of pregnant persons with obesity was 28.5 years while among those without obesity was 28.0 years. Between both groups, most pregnant persons were White, followed by Hispanic and Black. Pregnant persons with obesity were more likely to have labor inductions (AOR: 1.51; 95% CI: 1.42, 1.60), cesarean deliveries (1.70; 1.62, 1.79), and greater length of stay after both cesarean deliveries (1.14; 1.08, 1.36) and vaginal deliveries (1.48; 1.23, 1.77). They were also more likely to have risk factors for adverse obstetrical outcomes such as gestational hypertension (2.17; 2.06, 2.29), preeclampsia (2.06; 1.42, 2.99), gestational diabetes (2.75; 2.60, 2.90), premature rupture of membranes (1.17; 1.08, 1.27), chorioamnionitis (1.39; 1.25, 1.55), and venous thromboembolism (1.63; 1.34, 1.99). Additionally, pregnant persons with obesity were more likely to have adverse fetal outcomes such as excessive fetal growth (3.18; 2.96, 3.43) and fetal distress (1.28; 1.21, 1.35). Conclusion: Pregnant persons with obesity had significantly greater risk for adverse obstetrical outcomes. Risk stratification of pregnant individuals based on obesity can inform obstetrical providers’ clinical decision-making and potentially improve patient outcomes and decrease costs. There is an opportunity to address these risks with pre-conception interventions. Future studies could examine the effect of pre-conception interventions such as counseling on pre-pregnancy weight management, lifestyle modifications addressing nutrition and activity, and pre-pregnancy bariatric surgery.

Introduction: Prior research has shown that hospital outcomes such as mortality among COVID-19 patients receiving methylprednisolone and dexamethasone differed despite having similar anti-inflammatory effect. In this study, we compared mortality rate among three groups (usual care with no steroids [NS], methylprednisolone [MP], or dexamethasone [DE]) of COVID-19 patients. Methods: Data was collected retrospectively from a large healthcare system in the South Florida region. All eligible COVID-19 patients from April 1 to Dec 31, 2020 were included in this study. Propensity scores (PS) was utilized to match patients on their demographics and acuity scores (Age Group, Ethnicity, Gender, APR risk of mortality, and APR severity of illness, etc.). All-cause mortality was compared among the three groups after accounting for demographics, length of stay (hospital and ICU), days on MP or DE, ventilator status, days on ventilator, other medication history (HCQ, Tocilizumab, etc.), pO2, and lab values (hCRP, Ferritin, Glucose, etc.). Multivariate Logistic regression and Survival analysis was utilized for the analysis. Results: There were a total of 2445 eligible COVID-19 cases. After PS matching, 292 cases in each group (n = 876) were included in the analysis. There were 42% females and the majority of participants were White Hispanic (81%). The average age (SD) of the PS matched cohort was 64 (17.1) years, and there was a significant difference between LOS among the three groups with MP being the highest (Mean: 12.4; SD: 12.1). All-cause mortalities in the NS, MP, and DE groups were about 6%, 19% and 7% (p < 0.0001) respectively. Logistic regression showed that the odds of mortality among NS (AOR: 1.9, 95% CI: 0.10, 41.8) group were higher, and DE (AOR: 0.15, 95% CI: 0.04, 0.57) group were significantly lower than MP group respectively. Similar results were observed in the survival analysis after accounting for total hospital length of stay. The adjusted hazard ratios suggest that patients who were in the DE group had lower mortality as compared to MP group but, the effect was not significant (HR: 0.70, p < 0.18). In a subgroup survival analysis among patients who were ventilated (n = 69) in the matched cohort, mortality was higher in DE group compared to MP group however the effect was not significant (HR: 1.8, 95% CI: 0.82, 4.0). Conclusions: Among hospitalized COVID-19 cases, preliminary results show that patients who received DE had lower mortality as compared to those who received MP. Among COVID-19 patients who require mechanical ventilation, sufficiently dosed MP may lead to a decreased mortality as compared to DE. However more in depth analysis with higher number of patients and better characterization of variables such as dosage of the drugs, are needed to confirm these findings.

Optimal treatment of femoral neck fractures (FNFs) remains debated. Recent data suggest that total hip arthroplasty (THA) confers improved functional outcomes compared to hemiarthroplasty (HA) in active patients. However, temporal trends in complication rates between these treatments lack study.The National Surgical Quality Improvement Program database was retrospectively queried to compare differences between HA and THA over time (2010-2012, 2013-2015, and 2016-2017) in blood transfusions, operation time, major complications, minor complications, and 30-day readmission, among FNF patients aged ≥50 years. Analyses adjusted for age, gender, anesthesia type, smoking, body mass index, hypertension, bleeding disorder, steroid use, and American Society of Anesthesiologists classification.In total, 16,213 patients were identified. THA was associated with higher transfusion rates in 2010-2012 (mean = 0.34 vs 0.28, P = .001) and 2013-2015 (mean = 0.21 vs 0.19, P = .002), but not in 2016-2017 (mean = 0.13 vs 0.14, P = .146). Operation time was significantly higher for THA across all periods (P's.001), but declined over time. In recent years, THA was associated with less major (2016-2017: 5.4% vs 10.2%, P = .02; 2013-2015: 5.3% vs 10.3%, P.001) and minor (2016-2017: 6.2% vs 9.8%, P = .02; 2013-2015: 7.2% vs 12.4%, P.001) complications compared to 2010-2012 (major: 7.2% vs 10.6%, P = .87; minor: 12.6% vs 10.1%, P = .89). No differences in 30-day readmission were noted.THA was associated with less major and minor complications in recent time periods compared to HA for the treatment of FNF, controlling for comorbidities. THA trends in transfusions and operation duration have improved over time compared to HA.

Objective: Malignancy-related ascites (MRA) is the terminal stage of many advanced cancers, and the treatment is mainly palliative. This study looked for epidemiology and inpatient hospital outcomes of patients with MRA in the United States using a national database. Methods: The current study was a cross-sectional analysis of 2015 National Inpatient Sample data and consisted of patients ≥18 years with MRA. Descriptive statistics were used for understanding demographics, clinical characteristics, and MRA hospitalization costs. Multivariate regression models were used to identify predictors of length of hospital stay and in-hospital mortality. Results: There were 123 410 MRA hospitalizations in 2015. The median length of stay was 4.7 days (interquartile range [IQR]: 2.5-8.6 days), median cost of hospitalization was US$43 543 (IQR: US$23 485-US$82 248), and in-hospital mortality rate was 8.8% (n = 10 855). Multivariate analyses showed that male sex, black race, and admission to medium and large hospitals were associated with increased hospital length of stay. Factors associated with higher in-hospital mortality rates included male sex; Asian or Pacific Islander race; beneficiaries of private insurance, Medicaid, and self-pay; patients residing in large central and small metro counties; nonelective admission type; and rural and urban nonteaching hospitals. Conclusions: Our study showed that many demographic, socioeconomic, health care, and geographic factors were associated with hospital length of stay and in-hospital mortality and may suggest disparities in quality of care. These factors could be targeted for preventing unplanned hospitalization, decreasing hospital length of stay, and lowering in-hospital mortality for this population.

OBJECTIVE The objective of this study was to understand recent trends in direct health care expenditures among cancer survivors using novel cost-estimation methods and a nationally representative database. MATERIALS AND METHODS This study was a retrospective analysis of 193,003 adults, ≥18 years of age, using the Medical Expenditure Panel Survey during the years 2009-2016. Manning and Mullahy two-part model was used to calculate adjusted mean and incremental medical expenditures after adjusting for covariates. RESULTS The mean direct annual health care expenditure among cancer survivors ($13,025.0 [$12,572.0 to $13,478.0]) was nearly 3 times greater than noncancer participants ($4689.3 [$4589.2 to $4789.3]) and were mainly spent on inpatient services, office-based visits, and prescription medications. Cancer survivors had an additional health care expenditure of $4407.6 ($3877.6, $4937.6) per person per year, compared with noncancer participants after adjusting for covariates (P

Objective: There are several challenges in implementing models for predicting stroke or stroke related outcomes. Most of these models have average concordance, and several of the important variables cannot be modified. In this study, we updated and performed a meta-analysis of commonly utilized models to predict stroke related outcomes. Our primary aim was to evaluate the discriminative ability of the concordance statistic by adding additional studies. Methods: Studies reporting c-index and SE (or 95% CI) for predicting stroke or related outcomes were included in our analysis. In addition to the c-index, total participants, year of publication, type of analytical method (survival, logistic regression, neural network, etc.) and type of outcome (predicting stroke or composite outcome) were utilized. Combined effect sizes with the random model, test for heterogeneity, and publication bias were considered. Egger’s test was used to assess funnel asymmetry. Results: Twenty-seven models were included (patients= 1762461; c-index=14, Harrell’s c-index= 13; only stroke =21, composite=6) in the analysis. Combined mean c-index was 0.76 (95% CI: 0.71, 0.81; 95% predictive interval: 0.59, 0.93). Combined mean Harrell’s c-index was 0.65 (95% CI: 0.61, 0.69; 95% predictive interval: 0.56, 0.74). Test of heterogeneity showed high variation between studies reporting c-index and Harrell’s c-index (I2=97.49% and 80.0% respectively). Egger’s test intercept was -2.1 (95% CI: -7.2, 3.0, P > .40) for c-statistic and 1.2 (95% CI: -1.2, 3.5, P > .32) for Harrell’s c-index studies. Conclusion: Current studies have not improved the prediction interval significantly as compared to our previous meta-analysis for predictive or explanatory model available for stroke risk. However, recent studies were found to be more inclusive of non-traditional biomarkers (e.g., genetic, or polygenic scores) and utilized various machine learning methods that were not used before.

Background: Acute ischemic stroke (AIS) patients show a good prognosis with Carotid endarterectomy (CEA). However, lowering short-term readmissions after CEA for AIS cases remains a significant challenge. Several machine learning (ML) models predicting readmissions fail to explain the predictors. Hence the utility of such ML models in clinical settings is limited. This study used interpretable machine learning (ML) methods to identify the predictors associated with 30-day readmission. Methods: We utilized the National Surgical Quality Improvement Program registry (2014-2017) for this study. Patients aged >18 years and who underwent CEA for AIS were included. ICD-9, ICD-10, and CPT codes were used to identify AIS and CEA cases. Decision Trees and Random Forest classification techniques were utilized to identify predictors of 30-day readmission. Results: A total of 22,373 AIS patients underwent CEA during the study period. The mean (SD) age of the patients was 70.7 (9.4) years, and the majority (61%) were males. About 80% were Non-Hispanic White, followed by non-Hispanic Black (4.6%). About 7% of AIS patients who underwent CEA had 30-day readmission. Random Forest classification and Decision Tree were able to provide clinically relevant predictors and cut-off values. For example, one of the top predictors was pre-operative Hematocrit, and its cut-off value of ≤33% with Diabetes showed a higher risk for readmission. Conclusion: Our study showed that interpretable ML models could be helpful for clinicians to stratify patients based on their pre-operative risk for a 30-day readmission and could help optimize management strategies for improving patient outcomes. Incorporating these cut-off values in EMR could help clinical decision-making and plan interventions to reduce readmissions by managing risk factors. This would improve the quality of care and save high healthcare costs.

Context Acromegaly (ACM) and Cushing’s disease (CD) are caused by functioning pituitary adenomas secreting growth hormone and ACTH respectively. Objective To determine the impact of race on presentation and postoperative outcomes in adults with ACM and CD, which has not yet been evaluated. Methods This is a retrospective study of consecutive patients operated at a large-volume pituitary center. We evaluated (1) racial distribution of patients residing in the metropolitan area (Metro, N = 124) vs 2010 US census data, and(2) presentation and postoperative outcomes in Black vs White for patients from the entire catchment area (N = 241). Results For Metro area (32.4% Black population), Black patients represented 16.75% ACM (P = .006) and 29.2% CD (P = .56). Among the total 112 patients with ACM, presentations with headaches or incidentaloma were more common in Black patients (76.9% vs 31% White, P = .01). Black patients had a higher prevalence of diabetes (54% vs 16% White, P = .005), significantly lower insulin-like growth factor (IGF)-1 deviation from normal (P = .03) and borderline lower median growth hormone levels (P = .09). Mean tumor diameter and proportion of tumors with cavernous sinus invasion were similar. Three-month biochemical remission (46% Black, 55% White, P = .76) and long-term IGF-1 control by multimodality therapy (92.3% Black, 80.5% White, P = .45) were similar. Among the total 129 patients with CD, Black patients had more hypopituitarism (69% vs 45% White, P = .04) and macroadenomas (33% vs 15% White, P = .05). At 3 months, remission rate was borderline higher in White (92% vs 78% Black, P = 0.08), which was attributed to macroadenomas by logistic regression. Conclusion We identified disparities regarding racial distribution, and clinical and biochemical characteristics in ACM, suggesting late or missed diagnosis in Black patients. Large nationwide studies are necessary to confirm our findings.

Background: Carotid endarterectomy (CEA) is associated with improved overall clinical outcomes in patients with acute ischemic stroke (AIS). However, studies on rates and factors associated with readmission following CEA for AIS are scarce. In this study, we used machine learning (ML) methods to identify the factors associated with readmission using a large-scale national database. Methods: We used National Surgical Quality Improvement Program (NSQIP) registry (2014-2017) and included patients 18 years or older, who underwent CEA for AIS. AIS and CEA were identified using ICD-9 and ICD-10 diagnosis and CPT procedure codes, respectively. We used Naïve Bayes, Boosted Decision Trees, and Bootstrapped Random Forest classification techniques to explore the predictors of 30-day readmission using demographics, past medical history, and preoperative variables. Results: There were a total of 22,373 AIS patients who underwent CEA. Mean (SD) age of the patients was 70.7 (9.4) years, and 61% were men. Majority were non-Hispanic White (80%), followed by non-Hispanic Black (4.6%). During the study period, 1 in 15 AIS patients who underwent CEA experienced 30-day readmission. Bootstrapped Random Forest classification performed best and Naïve Bayes worst with an AUROC of 92% and 59% respectively. The top 5 predictors of 30-day readmission after CEA were Hematocrit, BUN, Creatinine, WBC count, and Platelet count, all collected pre-operatively. Conclusion: Our study showed that ML techniques could accurately predict 30-day readmission using pre-operative risk factors. This ML model could be incorporated in EMR as a potential clinical decision support system. Implementing this system could help in early identification of patients who are at high risk for readmission following CEA. This could help physicians to plan and intervene effectively and prevent short-term readmissions; thereby improving quality of care and saving healthcare costs.

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.