Dynamics of cytomegalovirus-specific T-cell recovery in allogeneic hematopoietic cell transplant recipients using a commercially available flow cytometry assay: A pilot study.

BACKGROUND: Cytomegalovirus-specific T-cell-mediated immunity (CMV-CMI) protects from CMV infection in allogeneic hematopoietic cell transplantation (allo-HCT), but to date, there is no validated measure of CMV immunity for this population. METHODS: In this prospective, observational, pilot study, CMV T-cell responses were evaluated monthly and at onset of graft-versus-host disease (GVHD) or CMV infection in CMV-seropositive allo-HCT recipients using a commercial flow cytometry assay, the CMV inSIGHT T-Cell Immunity Panel (CMV-TCIP). The primary endpoint was the time to first positive CMV-TCIP, defined as percentage of interferon-γ-producing CD4+ or CD8+ CMV-specific T cells >0.2%. Letermovir was prescribed from day +10 to ≥100. RESULTS: Twenty-eight allo-HCT recipients were enrolled. The median time to first positive CMV-TCIP result was earlier for CD4+ (60 days [interquartile range, IQR 33‒148]) than for CD8+ T cells (96 days [IQR 33‒155]) and longer for haploidentical and mismatched transplant recipients (77 and 96 days, respectively) than for matched donors (45 and 33 days, respectively). CD4+ and CD8+ CMV-CMI recovery was sustained in 10/10 (100%) and 10/11 (91%) patients, respectively, without GVHD, whereas CD4+ and/or CD8+ CMV-CMI was lost in 4/6 and 2/6 patients, respectively, with GVHD requiring steroids. As a predictor of clinically significant CMV infection in patients with low-level CMV reactivation, the sensitivity and negative predictive value of CMV-TCIP were 90% and 87.5%, respectively, for CD4+ CMV-TCIP and 66.7% and 62.5%, respectively, for CD8+ CMV-TCIP. CONCLUSIONS: There was significant variability in time to CMV-CMI recovery post-HCT, with slower recovery after haploidentical and mismatched HCT. CD4+ CMV-CMI may protect against CS-CMVi, but immunity may be lost with GVHD diagnosis and treatment.

Short-acting opioid prescriptions and Workers' Compensation using the National Ambulatory Medical Care Survey.

BACKGROUND: Short-acting opioids have been utilized for pain management with little known about their use in patients on Workers' Compensation (WC) insurance. Our goal was to investigate this association in the ambulatory care setting. METHODS: Using the National Ambulatory Medical Care Survey, visits from patients aged 18-64 during the years 2010 until 2018 were evaluated (excluding 2017 due to data availability). Demographic and co-morbidity data from each visit was obtained along with the visit year. The first short-acting opioid medication prescribed in the database was considered. Survey-weighted frequencies were evaluated. Logistic regression estimated the crude and adjusted odds ratios (OR) with 95% confidence intervals for the use of short-acting opioid prescription. RESULTS: There were 155,947 included visits with 62.5% for female patients. Most patients were White with 11.7% identifying as Black, and 6% identifying as another race. Over 13% of the sample was of Hispanic descent. WC was the identified insurance type in 1.6% of the sample population. Of these patients, 25.6% were prescribed a short-acting opioid, compared with 10.1% of those with another identified insurance. On multivariable regression, Black patients had increased odds of being prescribed a short-acting opioid compared to white patients (OR: 1.22, 95% CI: 1.11-1.34). Those on WC had 1.7-fold higher odds of being prescribed short-acting opioids (95% CI: 1.46-2.06). CONCLUSION: Certain patient characteristics, including having WC insurance, increased the odds of a short-acting opioid prescription. Further work is needed to identify prescribing patterns in specific high-risk occupational groups, as well as to elicit potential associated health outcomes.

Transition of care in CHD: a single-centre experience: an enigma remains.

Transition of care refers to the continuity of health care during the movement from one healthcare setting to another as care needs change during a chronic illness. We sought to describe social, demographic, and clinical factors related to successful transition in a tertiary urban care facility in patients with CHD. Patients were identified utilising the electronic medical record. Inclusion criteria were patients with CHDs aged ≥15 years seen in the paediatric cardiology clinic between 2013 and 2014. Deceased patients were excluded. Clinical and demographic variables were collected. Patient charts were reviewed in 2015-2021 to determine if included patients were a) still in paediatric cardiology care, b) transitioned to adult cardiology/adult CHD, or were c) lost to follow-up. A total of 322 patients, 53% male (N:172), 46% female (N:149) were included. Majority had moderately complex lesions (N:132, 41%). Most patients had public insurance (N:172, 53%), followed by private insurance (N:67, 21%), while 15% of patients (N:47) were uninsured. Only 49% (N = 159) had successful transition, while 22% (N = 70) continued in care with paediatric cardiology, and 29% (N = 93) were lost to follow-up. Severity of CHD (p = 0.0002), having healthcare insurance (p < .0001), presence of a defibrillator (p = 0.0028), and frequency of paediatric cardiology visits (p = 0.0005) were significantly associated with successful transition. Most patients lost to follow-up (N:42,62%) were either uninsured or had public insurance. Lack of successful transition is multifactorial, and further efforts are needed to improve the process in patients with CHD.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

Associations between mitochondrial biomarkers, urban residential exposures and childhood asthma outcomes over 6 months.

Determining biomarkers of responses to environmental exposures and evaluating whether they predict respiratory outcomes may help optimize environmental and medical approaches to childhood asthma. Relative mitochondrial (mt) DNA abundance and other potential mitochondrial indicators of oxidative stress may provide a sensitive metric of the child's shifting molecular responses to its changing environment. We leveraged two urban childhood cohorts (Environmental Control as Add-on Therapy in Childhood Asthma (ECATCh); Columbia Center for Children's Environmental Health (CCCEH)) to ascertain whether biomarkers in buccal mtDNA associate with airway inflammation and altered lung function over 6 months of time and capture biologic responses to multiple external stressors such as indoor allergens and fine particulate matter (PM2.5). Relative mtDNA content was amplified by qPCR and methylation of transfer RNA phenylalanine/rRNA 12S (TF/RNR1), cytochrome c oxidase (CO1), and carboxypeptidase O (CPO) was measured by pyrosequencing. Data on residential exposures and respiratory outcomes were harmonized between the two cohorts. Repeated measures and multiple regression models were utilized to assess relationships between mitochondrial biomarkers, respiratory outcomes, and residential exposures (PM2.5, allergens), adjusted for potential confounders and time-varying asthma. We found across the 6 month visits, a 0.64 fold higher level of TF/RNR1 methylation was detected among those with asthma in comparison to those without asthma ((parameter estimate (PE) 0.64, standard error 0.28, p = 0.03). In prospective analyses, CPO methylation was associated with subsequent reduced forced vital capacity (FVC; PE -0.03, standard error 0.01, p = 0.02). Bedroom dust mouse allergen, but not indoor PM2.5, was associated with higher methylation of TF/RNR1 (PE 0.015, standard error 0.006, p = 0.01). Select mtDNA measures in buccal cells may indicate children's responses to toxic environmental exposures and associate selectively with asthma and lung function.

Association of Vaccination with the Persistence of Post-COVID Symptoms.

BACKGROUND: Patients who have had COVID-19 often report persistent symptoms after resolution of their acute illness. Recent reports suggest that vaccination may be associated with improvement in post-acute symptoms. We used data from a prospective cohort to assess differences in post-acute sequelae of COVID (PASC) among vaccinated vs. unvaccinated patients. METHODS: We used data from a cohort of COVID-19 patients enrolled into a prospective registry established at a tertiary care health system in New York City. Participants underwent a baseline evaluation before COVID-19 vaccines were available and were followed 6 months later. We compared unadjusted and propensity score-adjusted baseline to 6-month change for several PASC-related symptoms and measures: anosmia, respiratory (cough, dyspnea, phlegm, wheezing), depression, anxiety, post-traumatic stress disorder (PTSD; COVID-19-related and other trauma), and quality-of-life domains among participants who received vs. those who did not receive COVID-19 vaccination. RESULTS: The study included 453 COVID-19 patients with PASC, of which 324 (72%) were vaccinated between the baseline and 6-month visit. Unadjusted analyses did not show significant differences in the baseline to 6-month change in anosmia, respiratory symptoms, depression, anxiety, PTSD, or quality of life (p > 0.05 for all comparisons) among vaccinated vs. unvaccinated patients. Similar results were found in propensity-adjusted comparisons and in secondary analyses based on the number of vaccine doses received. CONCLUSIONS: Our findings suggest that COVID vaccination is not associated with improvement in PASC. Additional studies are needed to better understand the mechanisms underlying PASC and to develop effective treatments.

Child-Turcotte-Pugh versus MELD-XI identify distinct high-risk populations for heart transplantation following ventricular assist device placement.

BACKGROUND: Patients with end-stage heart failure frequently have significant congestive hepatopathy requiring hepatology assessment prior to heart transplantation listing. An elevated Model for End-stage Liver Disease score with modification to exclude INR (MELD-XI) has been associated with increased mortality following heart transplantation (HT). This study's primary aim was to examine whether Child-Turcotte-Pugh (CTP) classification is associated with post-transplant mortality in patients bridged to transplant with left ventricular assist devices. METHODS AND RESULTS: We conducted a retrospective analysis of 134 patients from our center. Age, CTP class, and MELD-XI at HT were included in the multivariate model for the primary outcome, which demonstrated a significant association between 1-year mortality and CTP class (CTP-A HR: .08, CI .01-.46, P < .01; CTP-B HR: .25, CI .05-1.2, P = .08; reference group CTP-C), and MELD-XI (HR: 1.15; CI: 1.03-1.28; P = .01), but no significant difference for age (HR: .97; CI: .93-1.01; P = .15). Only 13/33 patients with CTP improvement after assist device also had improvement in MELD-XI. CONCLUSIONS: Patients with relatively low MELD-XI scores with discordantly high CTP classification may be a distinct subset for whom MELD-XI underestimates the risk of mortality after heart transplantation compared to CTP.

K-means for shared frailty models.

BACKGROUND: The purpose of this research was to see how the k-means algorithm can be applied to survival analysis with single events per subject for defining groups, which can then be modeled in a shared frailty model to further allow the capturing the unmeasured confounding not already explained by the covariates in the model. METHODS: For this purpose we developed our own k-means survival grouping algorithm to handle this approach. We compared a regular shared frailty model with a regular grouping variable and a shared frailty model with a k-means grouping variable in simulations as well as analysis on a real dataset. RESULTS: We found that in both simulations as well as real data showed that our k-means clustering is no different than the typical frailty clustering even under different situations of varied case rates and censoring. It appeared our k-means algorithm could be a trustworthy mechanism of creating groups from data when no grouping term exists for including in a frailty term in a survival model or comparing to an existing grouping variable available in the current data to use in a frailty model.

A flexible approach for variable selection in large-scale healthcare database studies with missing covariate and outcome data.

BACKGROUND: Prior work has shown that combining bootstrap imputation with tree-based machine learning variable selection methods can provide good performances achievable on fully observed data when covariate and outcome data are missing at random (MAR). This approach however is computationally expensive, especially on large-scale datasets. METHODS: We propose an inference-based method, called RR-BART, which leverages the likelihood-based Bayesian machine learning technique, Bayesian additive regression trees, and uses Rubin's rule to combine the estimates and variances of the variable importance measures on multiply imputed datasets for variable selection in the presence of MAR data. We conduct a representative simulation study to investigate the practical operating characteristics of RR-BART, and compare it with the bootstrap imputation based methods. We further demonstrate the methods via a case study of risk factors for 3-year incidence of metabolic syndrome among middle-aged women using data from the Study of Women's Health Across the Nation (SWAN). RESULTS: The simulation study suggests that even in complex conditions of nonlinearity and nonadditivity with a large percentage of missingness, RR-BART can reasonably recover both prediction and variable selection performances, achievable on the fully observed data. RR-BART provides the best performance that the bootstrap imputation based methods can achieve with the optimal selection threshold value. In addition, RR-BART demonstrates a substantially stronger ability of detecting discrete predictors. Furthermore, RR-BART offers substantial computational savings. When implemented on the SWAN data, RR-BART adds to the literature by selecting a set of predictors that had been less commonly identified as risk factors but had substantial biological justifications. CONCLUSION: The proposed variable selection method for MAR data, RR-BART, offers both computational efficiency and good operating characteristics and is utilitarian in large-scale healthcare database studies.

Weight Gain Associated With Integrase Stand Transfer Inhibitor Use in Women.

BACKGROUND: Integrase strand-transfer inhibitor (INSTI)-based antiretroviral therapy (ART) is recommended for human immunodeficiency virus (HIV) management. Although studies have suggested associations between INSTIs and weight gain, women living with HIV (WLHIV) have been underrepresented in research. We evaluated the effect of switching or adding INSTIs among WLHIV. METHODS: Women enrolled in the Women's Interagency HIV Study (WIHS) from 2006-2017 who switched to or added an INSTI to ART (SWAD group) were compared to women on non-INSTI ART (STAY group). Body weight, body mass index (BMI), percentage body fat (PBF), and waist, hip, arm, and thigh circumferences were measured 6-12 months before and 6-18 months after the INSTI switch/add in SWAD participants, with comparable measurement time points in STAY participants. Linear regression models compared changes over time by SWAD/STAY group, adjusted for age, race, WIHS site, education, income, smoking status, and baseline ART regimen. RESULTS: We followed 1118 women (234 SWAD and 884 STAY) for a mean of 2.0 years (+/- 0.1 standard deviation [SD]; mean age 48.8 years, SD +/- 8.8); 61% were Black. On average, compared to the STAY group, the SWAD group experienced mean greater increases of 2.1 kg in body weight, 0.8 kg/m2 in BMI, 1.4% in PBF, and 2.0, 1.9, 0.6, and 1.0 cm in waist, hip, arm, and thigh circumference, respectively (all P values < .05). No differences in magnitudes of these changes were observed by INSTI type. CONCLUSIONS: In WLHIV, a switch to INSTI was associated with significant increases in body weight, body circumferences, and fat percentages, compared to non-INSTI ART. The metabolic and other health effects of these changes deserve further investigation.

Changes in work characteristics over 12 years: Findings from the 2002-2014 US National NIOSH Quality of Work Life Surveys.

OBJECTIVES: To assess changes in work characteristics, socioeconomic status inequalities in changes in work characteristics, and whether US workplaces are becoming more stressful. METHODS: We analyzed data from 5361 employed participants from the 2002, 2006, 2010, and 2014 NIOSH Quality of Work Life Surveys, based on representative samples of US workers. We used regression analyses to assess changes in job characteristics, adjusting for age, sex, race/ethnicity, education, work hours, and unemployment rate. For the regression analyses with continuous job characteristics, we created standardized variables allowing for the magnitude of changes to be directly compared between job characteristics. RESULTS: Over the period 2002-2014, we observed statistically significant increases in job strain (+0.09 standard deviations (SD), P = 0.02), low job control (+0.10 SD, P = 0.03), and work-family conflict (+0.15 SD, P = 0.001). No significant changes were observed for high job demand, low social support, and low reward. The largest increase in low job control was seen among service workers. CONCLUSIONS: The increase in two cardiovascular disease risk factors, job strain, and low job control, might partially explain the slowing of the decline in US heart disease and stroke mortality rates.

Survival analysis of hierarchical learning curves in assessment of cardiac device and procedural safety.

Many Americans rely on cardiac surgical procedures and devices such as pacemakers and thrombolytic catheters to treat or manage their cardiovascular diseases. However, the failure of these cardiac devices and procedures could have grave consequences. One reason cardiac devices tended to fail was due to physician error; there is a learning effect for the physician or operator to come up to speed in skillfully implanting devices and conducting procedures. In order to better understand these learning effects, we had previously modeled the resulting learning curve effects in simulations a hierarchical setting with physicians clustered within institutions using our unique methodology (see the work of Govindarajulu et al 2017). Previously, we had employed these in hierarchical linear modeling and also in generalized estimating equations. In this setting, we have demonstrated how to apply similar methodology but revised in a survival analytic framework or time-to-event analyses. Through simulations and real dataset applications, we found that, out of the three shapes modeled to fit the learning curve, the logarithmic shape tended to have the best fit, similar to previous work (see the work of Govindarajulu et al 2017). However, as seen before, modeling the learning rate can be dataset specific and one shape may be better than another. We learned that modeling the learning rate could also be applied in the survival analysis setting through this new methodology. The goal of this paper is to model cardiac device and procedure learning curve effects in a time-to-event setting so that this knowledge may allow for the improvement of both short and long-term patient survival.

The Hand-eye Coordination of Professional Baseball Players: The Relationship to Batting.

SIGNIFICANCE: A visuomotor skill (eye-hand visual-motor reaction time [EH-VMRT]) important for baseball performance is described. Eye-hand visual-motor reaction time represents the integration of visual information, perceptually based decisions, and motor movements to accomplish a specific task. The speed at which this occurs depends on many factors, some visual, some perceptual, and some motor related. PURPOSE: The purpose of this study was to describe the EH-VMRT ability and evaluate its relationship to the baseball batting performance of professional baseball players. METHODS: A commercially available EH-VMRT system was used on 450 professional baseball players. Results were retrospectively compared with standard, career, plate discipline metrics. RESULTS: Statistically significant correlations were found between the EH-VMRT metrics and plate discipline batting metrics. Better EH-VMRT ability also correlated with longer service in, and likelihood to achieve, the major-league level. The better (top 20%) EH-VMRT group had three fewer at bats before gaining a walk (22% decrease), as well as swinging 10 to 12% less often at pitches outside the strike zone and 6 to 7% less often at pitches in the strike zone as compared with the bottom 20% group. In addition, EH-VMRT displays a threshold-like relationship with the ability to gain a walk. CONCLUSIONS: These results describe the EH-VMRT ability of professional baseball players and show a significant relationship between the EH-VMRT ability and batting performance. These results may suggest a possible role in player selection, indicating that batters with better EH-VMRT may be more likely to reach the major-league level and be more productive for their team. Further studies will be needed to demonstrate whether training better EH-VMRT results in improved batting performance.

Learning curve estimation in medical devices and procedures: hierarchical modeling.

In the use of medical device procedures, learning effects have been shown to be a critical component of medical device safety surveillance. To support their estimation of these effects, we evaluated multiple methods for modeling these rates within a complex simulated dataset representing patients treated by physicians clustered within institutions. We employed unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE) and generalized linear mixed effect models. We found that both methods performed well, but that the GEE may have some advantages over the generalized linear mixed effect models for ease of modeling and a substantially lower rate of model convergence failures. We then focused more on using GEE and performed a separate simulation to vary the shape of the learning curve as well as employed various smoothing methods to the plots. We concluded that while both hierarchical methods can be used with our mathematical modeling of the learning curve, the GEE tended to perform better across multiple simulated scenarios in order to accurately model the learning effect as a function of physician and hospital hierarchical data in the use of a novel medical device. We found that the choice of shape used to produce the 'learning-free' dataset would be dataset specific, while the choice of smoothing method was negligibly different from one another. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data. Copyright © 2017 John Wiley & Sons, Ltd.

Association of restless legs syndrome and mortality in end-stage renal disease: an analysis of the United States Renal Data System (USRDS).

BACKGROUND: Objective of the study is to assess prevalence and survival among end stage renal disease patients with restless legs syndrome (RLS) within a national database (USRDS). METHODS: A case-control, retrospective analysis was performed. Differences in characteristics between the groups, RLS and those with no sleep disorder (NSD), were determined using χ2 tests. Cox proportional hazard regression was used to assess survival between those with RLS and propensity score matched controls. RESULTS: Cases of restless legs syndrome were defined as patients that had received an ICD-9 code of 333.94 at any point during their treatment (n = 372). RLS group demonstrated a significantly higher proportion of patients with major depressive disorder, dysthymic disorder, anxiety, depression, minor depressive disorder, and psychological disorder. The difference between the survival was not statistically significant in those without sleep disorder as compared to those with RLS (HR =1.16±0.14, p = 0.3). CONCLUSIONS: True prevalence of RLS in dialysis patients can only be estimated if knowledge gap for care providers in diagnosis of RLS is addressed. RLS patients also have increased incidence of certain psychological disorders which needs to be addressed.

Comparison of Two Risk Models in Predicting the Incidence of Contrast-Induced Nephropathy after Percutaneous Coronary Intervention.

OBJECTIVES: We sought to compare 2 contrast-induced nephropathy (CIN) risk prediction models in a validation cohort using a consensus definition. BACKGROUND: Contrast-induced nephropathy (CIN) is independently associated with mortality following percutaneous coronary intervention (PCI). Multiple prediction models for the development of CIN have been published using heterogeneous outcome definitions. METHODS: We analyzed 5,540 patients who underwent PCI from January 2005 to June 2012 at a single academic medical center. The primary outcome was development of CIN, defined as an increase in serum creatinine of ≥0.5 mg/dl or a relative increase of ≥25% from baseline. Receiver operator characteristic (ROC) curves were used to evaluate the discriminatory power of Mehran and WBH prediction models. RESULTS: The mean age of our cohort was 68 ± 12 years. The mean baseline creatinine was 1.2 ± 0.53 mg/dl (eGFR 73 ± 27 ml/min). The mean contrast volume used was 212 ± 92 ml. CIN occurred in 436 patients (7.9%). The Mehran risk score demonstrated better discrimination than the William Beaumont Hospital (WBH) risk score to predict the occurrence of CIN (c statistic: 0.82 vs. 0.73, respectively). Mortality at 30 days was approximately 8 times higher among patients with CIN as compared to those without (14.7% vs. 1.8% P < 0.01). CONCLUSIONS: In an independent validation cohort, the Mehran risk model demonstrates greater discriminatory power than the WBH model in predicting the incidence of CIN. Mortality was significantly higher in patients who developed CIN after PCI.

Value of reserve pulse pressure in improving the risk stratification of patients with normal myocardial perfusion imaging.

AIMS: To evaluate the incremental prognostic value of reserve-pulse pressure (reserve-PP: exercise-PP minus rest-PP) to standard risk factors among patients with suspected coronary artery disease (CAD) but normal exercise myocardial perfusion imaging (MPI). METHODS AND RESULTS: We studied 4269 consecutive symptomatic patients without known CAD who were referred for exercise MPI but had normal MPI results (mean age 58 ± 12 years, 56% females, 84% referred for evaluation of chest pain or dyspnoea, 95% with intermediate pretest likelihood of CAD). There were 202 deaths over 5.1 ± 1.4 years of follow-up. Reserve-PP was abnormal (<44 mmHg increase in PP from rest) in 1894 patients (44%). Patients with an abnormal reserve-PP had a higher risk of death compared with patients with normal reserve-PP [hazard ratio (HR): 2.47, 95% CI, 1.8-3.3]. In multivariable models adjusting for age, sex, ejection fraction, medications, heart rate recovery, Duke treadmill score (DTS), and rest-PP, each 10 mmHg lower reserve-PP was associated with a 20.6% increase in risk-adjusted mortality (adjusted HR 0.83, 95% CI 0.76-0.91). Models incorporating reserve-PP significantly reclassified risk compared with models without these parameters (net reclassification index 14.3%, P = 0.0007; integrated discrimination index 0.69, P = 0.01). CONCLUSION: In patients without a history of CAD and a normal MPI, an abnormal reserve-PP identified and reclassified those at higher risk of death independent of known risk factors and DTS.

The COVID-19 Tracheostomy Experience at a Large Academic Medical Center in New York during the First Year.

Background: New York City was the epicenter of the initial surge of the COVID-19 pandemic in the United States. Tracheostomy is a critical procedure in the care of patients with COVID-19. We hypothesized that early tracheostomy would decrease the length of time on sedation, time on mechanical ventilation, intensive care unit length of stay, and mortality. Methods: A retrospective analysis of outcomes for all patients with COVID-19 who underwent tracheostomy during the first year of the COVID-19 pandemic at the Mount Sinai Hospital in New York City, New York. All adult intensive care units at the Mount Sinai Hospital, New York. Patients/subjects: 888 patients admitted to intensive care with COVID-19. Results: All patients admitted to the intensive care unit with COVID-19 (888) from 1 March 2020 to 1 March 2021 were analyzed and separated further into those intubated (544) and those requiring tracheostomy (177). Of those receiving tracheostomy, outcomes were analyzed for early (≤12 days) or late (>12 days) tracheostomy. Demographics, medical history, laboratory values, type of oxygen and ventilatory support, and clinical outcomes were recorded and analyzed. Conclusions: Early tracheostomy resulted in reduced duration of mechanical ventilation, reduced hospital length of stay, and reduced intensive care unit length of stay in patients admitted to the intensive care unit with COVID-19. There was no effect on overall mortality.

Impact of botanical oils on polyunsaturated fatty acid metabolism and leukotriene generation in mild asthmatics.

BACKGROUND: Dietary supplementation with botanical oils that contain n-6 and n-3 eighteen carbon chain (18C)-PUFA such as γ linolenic acid (GLA, 18:3n-6), stearidonic acid (SDA, 18:4n-3) and α linolenic acid (ALA, 18:3n-3) have been shown to impact PUFA metabolism, alter inflammatory processes including arachidonic acid (AA) metabolism and improve inflammatory disorders. METHODS: The diet of mild asthmatics patients was supplemented for three weeks with varying doses of two botanical seed oils (borage oil [Borago officinalis, BO] and echium seed oil [Echium plantagineum; EO]) that contain SDA, ALA and GLA. A three week wash out period followed. The impact of these dietary manipulations was evaluated for several biochemical endpoints, including in vivo PUFA metabolism and ex vivo leukotriene generation from stimulated leukocytes. RESULTS: Supplementation with several EO/BO combinations increased circulating 20-22 carbon (20-22C) PUFAs, including eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and dihommo-gammalinolenic acid (DGLA), which have been shown to inhibit AA metabolism and inflammation without impacting circulating AA levels. BO/EO combinations also inhibited ex vivo leukotriene generation with some combinations attenuating cysteinyl leukotriene generation in stimulated basophils by >50% and in stimulated neutrophils by >35%. CONCLUSIONS: This study shows that dietary supplementation with BO/EO alters 20-22C PUFA levels and attenuates leukotriene production in a manner consistent with a reduction in inflammation.

Adverse pregnancy outcomes and long term risk of ischemic heart disease in mothers: national cohort and co-sibling study.

OBJECTIVE: To examine the associations between five major adverse pregnancy outcomes and long term risks of ischemic heart disease in mothers. DESIGN: National cohort study. SETTING: Sweden. PARTICIPANTS: All 2 195 266 women with a first singleton delivery in Sweden during 1973-2015. MAIN OUTCOME MEASURES: The main outcome measure was incidence of ischemic heart disease from delivery to 2018, identified from nationwide inpatient and outpatient diagnoses. Cox regression was used to calculate hazard ratios for ischemic heart disease associated with preterm delivery, small for gestational age, pre-eclampsia, other hypertensive disorders of pregnancy, and gestational diabetes, adjusting for other adverse pregnancy outcomes and maternal factors. Co-sibling analyses assessed for confounding by shared familial (genetic and environmental) factors. RESULTS: During 53.6 million person years of follow-up, ischemic heart disease was diagnosed in 83 881 (3.8%) women. All five adverse pregnancy outcomes were independently associated with increased risk of ischemic heart disease. In the 10 years after delivery, adjusted hazard ratios for ischemic heart disease associated with specific adverse pregnancy outcomes were 2.09 (95% confidence interval 1.77 to 2.46) for other hypertensive disorders of pregnancy, 1.72 (1.55 to 1.90) for preterm delivery, 1.54 (1.37 to 1.72) for pre-eclampsia, 1.30 (1.09 to 1.56) for gestational diabetes, and 1.10 (1.00 to 1.21) for small for gestational age. The hazard ratios remained significantly increased even 30-46 years after delivery: 1.47 (1.30 to 1.66) for other hypertensive disorders of pregnancy, 1.40 (1.29 to 1.51) for gestational diabetes, 1.32 (1.28 to 1.36) for pre-eclampsia, 1.23 (1.19 to 1.27) for preterm delivery, and 1.16 (1.13 to 1.19) for small for gestational age. These findings were only partially (<45%) explained by shared familial (genetic or environmental) factors. Women who experienced multiple adverse pregnancy outcomes showed further increases in risk (eg, <10 years after delivery, adjusted hazard ratios associated with 1, 2, or ≥3 adverse pregnancy outcomes were 1.29 (1.19 to 1.39), 1.80 (1.59 to 2.03), and 2.26 (1.89 to 2.70), respectively)). CONCLUSIONS: In this large national cohort, women who experienced any of five major adverse pregnancy outcomes showed an increased risk for ischemic heart disease up to 46 years after delivery. Women with adverse pregnancy outcomes should be considered for early preventive evaluation and long term risk reduction to help prevent the development of ischemic heart disease.