Preemptive Intervention Research Articles

MLC performance prognosis using a degradation model based on trajectory log data from a daily test.

This paper investigates the feasibility of implementing a predictive maintenance program for a multileaf collimator (MLC) based on data collected in trajectory logs (TLs) obtained by conducting a simple daily test, with the aim of minimizing unscheduled downtime. A dynamic field test was designed, and the TLs generated in the course of daily administration in a linear accelerator were collected to evaluate trajectory deviations of the MLC leaves as well as interlocks (COL 420219/20, COL 420207/08) reported by the machine. During this evaluation, we observed that the trajectory deviations of some leaves increased up to a threshold value beyond which certain interlocks began to appear in treatment fields in those leaves. An exponential degradation model was therefore developed to predict this drift and determine each leaf's remaining useful life (RUL). Once the applicability of the model was confirmed, we added a second accelerator equipped with an MLC with the same configuration to validate the model. The model was able to predict certain COL 420219/20 interlocks resulting from primary readout/expected position discrepancies and to estimate each leaf's RUL. In total, 11 cases (8 interlocks + 3 potential interlocks avoided due to service interventions [27.3% of the total]) were detected over 7 days in advance, with no false positive results. Scheduling of service interventions several days prior to MLC failure would therefore be possible. When these types of interlocks were not predicted by the model, they were always generated by leaf motor failure. Consequently, intervention time could also be optimized by directly replacing the motor. During the study period, for these types of interlocks, our approach would have reduced downtime from 35.25 to 4.00h (88.7%) and from 34.75 to 22.83h (34.3%) for each accelerator, respectively. For COL 420207/08 interlocks, which are generated by primary/secondary readout discrepancies, no correlation with leaf trajectory deviation increases recorded in the TLs was found. Throughout the study period, these types of interlocks requiring service intervention, also mainly for motor replacement, represented a downtime of 9.50h for the first accelerator (21.2% of total downtime) and by 4.33h (11.1% of total downtime) for the second accelerator. This study demonstrates that by applying a predictive MLC maintenance program based on information collected in TLs, it is possible to predict certain interlocks and therefore schedule preemptive interventions to avoid their occurrence. This could optimize health-care delivery performance and minimize the loss of treatment sessions.

Personalized ctDNA micro-panels can monitor and predict clinical outcomes for patients with triple-negative breast cancer

Circulating tumor DNA (ctDNA) in peripheral blood has been used to predict prognosis and therapeutic response for triple-negative breast cancer (TNBC) patients. However, previous approaches typically use large comprehensive panels of genes commonly mutated across all breast cancers. Given the reduction in sequencing costs and decreased turnaround times associated with panel generation, the objective of this study was to assess the use of custom micro-panels for tracking disease and predicting clinical outcomes for patients with TNBC. Paired tumor-normal samples from patients with TNBC were obtained at diagnosis (T0) and whole exome sequencing (WES) was performed to identify somatic variants associated with individual tumors. Custom micro-panels of 4–6 variants were created for each individual enrolled in the study. Peripheral blood was obtained at baseline, during Cycle 1 Day 3, at time of surgery, and in 3–6 month intervals after surgery to assess variant allele fraction (VAF) at different timepoints during disease course. The VAF was compared to clinical outcomes to evaluate the ability of custom micro-panels to predict pathological response, disease-free intervals, and patient relapse. A cohort of 50 individuals were evaluated for up to 48 months post-diagnosis of TNBC. In total, there were 33 patients who did not achieve pathological complete response (pCR) and seven patients developed clinical relapse. For all patients who developed clinical relapse and had peripheral blood obtained ≤ 6 months prior to relapse (n = 4), the custom ctDNA micro-panels identified molecular relapse at an average of 4.3 months prior to clinical relapse. The custom ctDNA panel results were moderately associated with pCR such that during disease monitoring, only 11% of patients with pCR had a molecular relapse, whereas 47% of patients without pCR had a molecular relapse (Chi-Square; p-value = 0.10). In this study, we show that a custom micro-panel of 4–6 markers can be effectively used to predict outcomes and monitor remission for patients with TNBC. These custom micro-panels show high sensitivity for detecting molecular relapse in advance of clinical relapse. The use of these panels could improve patient outcomes through early detection of relapse with preemptive intervention prior to symptom onset.

Are early social communication skills a harbinger for language development in infants later diagnosed autistic?-A longitudinal study using a standardized social communication assessment.

The early emergence of social communication challenges and their impact on language in infants later diagnosed with autism has sparked many early intervention programs that target social communication skills. While research has consistently shown lower scores on social communication assessments in the first year of life, there is limited research at 12-months exploring associations between different dimensions of social communication and later language. Understanding associations between early social communication skills and language would enhance our ability to choose high priority intervention goals that will impact downstream language skills. The current study used a standardized assessment to profile social communication skills across 516 infants with a high (HL) or low likelihood (LL-Neg) for autism (84% White, 60% Male), based on the presence of a sibling with autism in the family. The primary aim of the study was to profile social communication skill development in the second year of life and to evaluate associations between social communication skills and later language. HL infants who met criteria for autism (HL-ASD, N = 81) demonstrated widespread reductions in social communication skills at 12-months compared to HL infants who did not meet criteria for autism (HL-Neg, N = 277) and LL-Neg (N = 158) infants. Across all infants in the study, those with better social communication skills at 12-months had better language at 24-months. However, within group analyses indicated that infants who met criteria for autism did not show this developmental coupling until 24-months-of-age at which point social communication was positively associated with downstream language skills. The cascading pattern of reduced social communication skills as well as overall significant positive associations with later language provide further evidence for the need to support developing social communication skills prior to formal autism diagnosis, a goal that could possibly be reached through pre-emptive interventions.

Policymaking during COVID-19: Preemptive State Interventions and the Factors Influencing Policy Implementation Success

COVID-19 sparked a public health crisis and created a series of public policy challenges. This article examines how COVID-19 interventions played out at the state level given the absence of guidance and coordinated national response. We focus on how the level of policy rigidness and enforcement of behavioral interventions helps us understand the success and failures of reducing the number of positive test rates over a 20-week period (March–July 2020). Specifically, we examine how four specific interventions (masking, school closures, restaurant closures, and travel restrictions) moved through the policy creation and implementation process as outlined by a modified version of Kingdon’s multiple streams approach. We leverage a pooled-OLS approach to identify the agenda-setting and decision-making windows to verify the narrative derived from applying a modified multiple streams approach to the initial wave of policy making around COVID-19 interventions. Using this technique, we find evidence of two distinct agenda-setting windows and a decision-making window. Using these windows, we ascertain that highly restrictive policies are effective in controlling the spread of COVID-19. We find that governors acting as political entrepreneurs may not play as large of a role in the policy-making process, but they are responsive to constituent policy preferences.

Microbiologically influenced corrosion: The gap in the field

Microorganisms have evolved to inhabit virtually all environments on the planet, from oceanic hot-seeps to pipelines transporting crude and refined hydrocarbons. Often microbial colonization of man-made structures results in the reduction of their service life requiring preemptive or corrective human intervention. Microbiologically Influenced Corrosion (MIC) is caused by a set of intricate bioelectrochemical interactions between a diverse group of microorganisms and metallic surfaces. The complexity of MIC microbiomes and their mechanisms as well as the logistics constraints of industrial facilities are factors to consider when choosing suitable analytical methods for MIC monitoring. These generally reflect only a partial view of the phenomenon and in consequence, might lead to ineffective mitigation measures. This paper acknowledges the discrepancies between the fieldwork for MIC monitoring and the currently available technological advancements. It also highlights the most pressing issues that operators have in the field in light of the diversity of the microbial key players present in corrosive microbiomes. Finally, it compiles and outlines a strategy for the integration of novel molecular approaches aiming for a practical and accurate assessment of the microbial threat.

Impact of remote monitoring in heart failure patients with cardiac implantable electronic devices during COVID-19 pandemic: a single center experience

BackgroundCoronavirus disease 2019 (COVID-19) had spread into a pandemic affecting healthcare providers worldwide. Heart failure patients with implanted cardiac devices require close follow-up in-spite of pandemic related healthcare restrictions.MethodsPatients were retrospectively registered and clinical outcomes were compared of 61 remote monitored (RMG) versus 71 conventionally (in-office only) followed (CFG) cardiac device implanted, heart failure patients. Follow-up length was 12 months, during the COVID-19 pandemic related intermittent insitutional restrictions. We used a specified heart failure detection algorithm in RMG. This investigation compared worsening heart failure-, arrhythmia- and device related adverse events as primary outcome and heart failure hospitalization rates as secondary outcome in the two patient groups.ResultsNo significant difference was observed in the primary composite end-point during the first 12 months of COVID-19 pandemic (p = 0.672).In RMG, patients who had worsening heart failure event had relative modest deterioration in heart failure functional class (p = 0.026), relative lower elevation of N terminal-pro BNP levels (p < 0.01) at in-office evaluation and were less hospitalized for worsening heart failure in the first 6 months of pandemic (p = 0.012) compared to CFG patients.ConclusionsSpecified remote monitoring alert-based detection algorithm and workflow in device implanted heart failure patients may potentially indicate early worsening in heart failure status. Preemptive adequate intervention may prevent further progression of deteriorating heart failure and thus prevent heart failure hospitalizations.

Integrating framework analysis, scenario design, and decision support system for sustainable healthy food system analysis

In the past two decades, a number of researchers have sought to capture the complex interactions of the food system's outcome through frameworks. In several international discourses and expert foresight studies, food system frameworks are frequently used to argue specific approaches and reforms that seek to reduce the multiple burdens of our food system on humans and the planet. To date, policymakers and other stakeholders have been forced to contend with many threats and uncertainties stemming from different dimensions of our food system. While many frameworks for evaluating food system objectives exist, making context-specific choices that reflect current or future situations accounting for various dimensions of the food system is daunting. Therefore, this study examines food system frameworks considering sustainability dimensions, constructs four-styled future food system scenarios using scenario design technique, and develops a decision support system that (1) determines relevant drivers and framework to evaluate predetermined future food system (2) allows the co-creation of the specific context food system and still identify relevant drivers and frameworks. Thus, providing a more selective use of food system frameworks which provides stakeholders with insights to take necessary pre-emptive actions or interventions and make strategic decisions toward potential shocks and uncertainties in such futures. To achieve this, the Complementary Judgment Matrix method was used to translate the preference of ten participants to weights of importance regarding sustainability dimensions, then propagated onto the Fuzzy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision model to evaluate frameworks for each dimension. Finally, the results were integrated using scenario design and digital intelligence to develop a novel decision support system. The findings showed that frameworks are skewed toward food and nutrition, with less economic consideration. Also, the scenarios presented provide an invaluable lens to the future of our food system. Furthermore, the proposed decision support system allows food system stakeholders to navigate the constructed scenarios and re-create plausible future scenarios under different contexts. This study provides an exciting opportunity for future policymakers and scholars at different spatio-temporal levels to design and implement policies that will strengthen and render the food system resilient in front of future adversities.

Challenges and opportunities for the early identification of ADHD across multiple levels of analysis - Areflection on Tobarra-Sanchez etal. (2022).

Challenges and opportunities for the early identification of ADHD across multiple levels of analysis - Areflection on Tobarra-Sanchez etal. (2022).

A graph convolutional network for predicting COVID-19 dynamics in 190 regions/countries.

Introduction:Coronavirus disease (COVID-19) rapidly spread from Wuhan, China to other parts of China and other regions/countries around the world, resulting in a pandemic due to large populations moving through the massive transport hubs connecting all regions of China via railways and a major international airport. COVID-19 will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. Thus, there is urgent need to establish effective implementation of preemptive non-pharmaceutical interventions for appropriate prevention and control strategies, and predicting future COVID-19 cases is required to monitor and control the issue.MethodsThis study attempts to utilize a three-layer graph convolutional network (GCN) model to predict future COVID-19 cases in 190 regions and countries using COVID-19 case data, commercial flight route data, and digital maps of public transportation in terms of transnational human mobility. We compared the performance of the proposed GCN model to a multilayer perceptron (MLP) model on a dataset of COVID-19 cases (excluding the graph representation). The prediction performance of the models was evaluated using the mean squared error.ResultsOur results demonstrate that the proposed GCN model can achieve better graph utilization and performance compared to the baseline in terms of both prediction accuracy and stability.DiscussionThe proposed GCN model is a useful means to predict COVID-19 cases at regional and national levels. Such predictions can be used to facilitate public health solutions in public health responses to the COVID-19 pandemic using deep learning and data pooling. In addition, the proposed GCN model may help public health policymakers in decision making in terms of epidemic prevention and control strategies.

Current Role of Prospective Monitoring and Preemptive and Prophylactic Therapy for Human Herpesvirus 6 After Allogeneic Stem Cell Transplantation.

Human herpesvirus 6 (HHV-6) frequently reactivates after allogeneic stem cell transplantation (SCT). Most patients are asymptomatic and viremia often resolves without therapy; however, transplant-related complications may be associated with reactivation. Multiple presentations have been attributed to HHV-6 reactivation after SCT including encephalitis. Several strategies have been trialed to reduce such risks or complications. Challenges exist with prospective monitoring strategies, and established thresholds of high-level reactivation may be limited. Three published guidelines and extensive trials focusing on preemptive and prophylactic strategies are reviewed. Future areas of investigation and high-risk populations are described. Existing trials and testing platforms have significant limitations, and to date no clear benefit for a preemptive or prophylactic intervention has been demonstrated.

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation.

BackgroundCognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stressful day. Machine learning models are useful tools that can be developed to predict next-day physiological and perceived stress by using data collected from the previous day. Such models can improve our understanding of the specific factors that predict physiological and perceived stress and allow researchers to develop systems that collect selected features for assessment in clinical trials to minimize the burden of data collection.ObjectiveThe aim of this study was to build and evaluate a machine-learned model that predicts next-day physiological and perceived stress by using sensor-based, ecological momentary assessment (EMA)–based, and intervention-based features and to explain the prediction results.MethodsWe enrolled pregnant women into a prospective proof-of-concept study and collected electrocardiography, EMA, and cognitive behavioral therapy intervention data over 12 weeks. We used the data to train and evaluate 6 machine learning models to predict next-day physiological and perceived stress. After selecting the best performing model, Shapley Additive Explanations were used to identify the feature importance and explainability of each feature.ResultsA total of 16 pregnant women enrolled in the study. Overall, 4157.18 hours of data were collected, and participants answered 2838 EMAs. After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. Although any subset of sensor-based, EMA-based, or intervention-based features could reliably predict next-day physiological stress, EMA-based features were necessary to predict next-day perceived stress. The analysis of explainability metrics showed that the prolonged duration of physiological stress was highly predictive of next-day physiological stress and that physiological stress and perceived stress were temporally divergent.ConclusionsIn this study, we were able to build interpretable machine learning models to predict next-day physiological and perceived stress, and we identified unique features that were highly predictive of next-day stress that can help to reduce the burden of data collection.

Changes to the Gut Microbiome in Young Children Showing Early Behavioral Signs of Autism.

The human gut microbiome has increasingly been associated with autism spectrum disorder (ASD), which is a neurological developmental disorder, characterized by impairments to social interaction. The ability of the gut microbiota to signal across the gut-brain-microbiota axis with metabolites, including short-chain fatty acids, impacts brain health and has been identified to play a role in the gastrointestinal and developmental symptoms affecting autistic children. The fecal microbiome of older children with ASD has repeatedly shown particular shifts in the bacterial and fungal microbial community, which are significantly different from age-matched neurotypical controls, but it is still unclear whether these characteristic shifts are detectable before diagnosis. Early microbial colonization patterns can have long-lasting effects on human health, and pre-emptive intervention may be an important mediator to more severe autism. In this study, we characterized both the microbiome and short-chain fatty acid concentrations of fecal samples from young children between 21 and 40 months who were showing early behavioral signs of ASD. The fungal richness and acetic acid concentrations were observed to be higher with increasing autism severity, and the abundance of several bacterial taxa also changed due to the severity of ASD. Bacterial diversity and SCFA concentrations were also associated with stool form, and some bacterial families were found with differential abundance according to stool firmness. An exploratory analysis of the microbiome associated with pre-emptive treatment also showed significant differences at multiple taxonomic levels. These differences may impact the microbial signaling across the gut-brain-microbiota axis and the neurological development of the children.

Pressure Injuries Due to Prone Positioning in Patients with COVID-19.

Preemptive interventions are needed to prevent pressure injuries in patients with COVID-19 who are placed in the prone position.Risk factors for pressure injuries in these patients include severe obesity, a long duration of prone positioning, male sex, a high D-dimer level, and the use of commercial endotracheal tube holders.

Dampened Inflammatory Signalling and Myeloid-Derived Suppressor-Like Cell Accumulation Reduces Circulating Monocytic HLA-DR Density and May Associate With Malignancy Risk in Long-Term Renal Transplant Recipients

BackgroundMalignancy is a major cause of morbidity and mortality in transplant recipients. Identification of those at highest risk could facilitate pre-emptive intervention such as reduction of immunosuppression. Reduced circulating monocytic HLA-DR density is a marker of immune depression in the general population and associates with poorer outcome in critical illness. It has recently been used as a safety marker in adoptive cell therapy trials in renal transplantation. Despite its potential as a marker of dampened immune responses, factors that impact upon monocytic HLA-DR density and the long-term clinical sequelae of this have not been assessed in transplant recipients.MethodsA cohort study of stable long-term renal transplant recipients was undertaken. Serial circulating monocytic HLA-DR density and other leucocyte populations were quantified by flow cytometry. Gene expression of monocytes was performed using the Nanostring nCounter platform, and 13-plex cytokine bead array used to quantify serum concentrations. The primary outcome was malignancy development during one-year follow-up. Risk of malignancy was calculated by univariate and multivariate proportionate hazards modelling with and without adjustment for competing risks.ResultsMonocytic HLA-DR density was stable in long-term renal transplant recipients (n=135) and similar to non-immunosuppressed controls (n=29), though was suppressed in recipients receiving prednisolone. Decreased mHLA-DRd was associated with accumulation of CD14+CD11b+CD33+HLA-DRlo monocytic myeloid-derived suppressor-like cells. Pathway analysis revealed downregulation of pathways relating to cytokine and chemokine signalling in monocytes with low HLA-DR density; however serum concentrations of major cytokines did not differ between these groups. There was an independent increase in malignancy risk during follow-up with decreased HLA-DR density.ConclusionsDampened chemokine and cytokine signalling drives a stable reduction in monocytic HLA-DR density in long-term transplant recipients and associates with subsequent malignancy risk. This may function as a novel marker of excess immunosuppression. Further study is needed to understand the mechanism behind this association.

Intervenção psicológica em demandas judiciais infantojuvenis

The object of study of this essay is the protection of children and adolescents in the face of parental abuse, focusing on the effectiveness of psychological interventions in judicial lawsuits. Law and Psychology have joined forces in an attempt to prove the necessity of multidisciplinarity through demonstrations of early and late psychological interventions with completely different results. A reasonably new problem, since it is through the Divorce Law (BRASIL, Law 6.515/77) and the Federal Constitution of 1988 that the necessity for a new specialization, Family Law, was born. In tandem, Psychology had a consistent rise in recognition in 1972 through the implementation of the Federal Council of Psychology (BRASIL, 1962). With them being considered new sciences, there was a need to investigate whether the lack of scientific recognition of both sciences could be the reason for the damages children and teenagers experienced, lacking support to their needs. The first case experienced from 1981 to 1988 was methodologically compared with the second, from 2018 to 2021. We surveyed data from reliable institutions and brought testimonies from abused children, now adults, transcribed from the following documentaries, both publically available on the internet: "A Morte Inventada (2009)" and "O Amor Que Tu Me Tinhas". From these, it was demonstrated that although we have evolved substantially in terms of legislation, the object of study (children's permanence in harmful environments) does not offer the expected protection, as will be shown. However, our hypothesis demonstrates that qualified and (obligatorily) preemptive psychological intervention can drastically reduce the damage and trauma to children and adolescents.

Risk Factors of Brain Metastasis and Prognosis in HER2-Positive Breast Cancer: A Single-Institution Retrospective Analysis from China.

BackgroundBrain metastasis (BM) frequently occurs in HER2-positive breast cancer (BC) patients, but the risk factors of BM in this type of patients are still unknown. Our study aims to assess the risk factors of BM and prognostic analysis in HER2-positive BC patients.MethodsUnivariate analysis used t-test, chi-square test, and Fisher’s exact test to find out the risk factors for BM, and multivariable analysis was done with stepwise logistic regression analysis. Prognostic data analysis was estimated by the Kaplan–Meier method.ResultsA total of 228 HER2-positive BC patients were included, of whom 214 patients were postoperative metastatic patients and 14 patients were de novo stage IV patients. Through comparing the stratified variables between 51 postoperative metastatic patients with BM and 163 postoperative metastatic patients without BM, the multivariate analysis showed that age ≤40 years (OR 2.321, 95% CI: 1.089 to 4.948) and first metastatic site with lung metastasis (OR 2.168, 95% CI: 1.099 to 4.274) were independent risk factors for BM in HER2-positive BC patients. Prognostic data of all 65 HER2-positive BC patients with BM showed that the time from the diagnosis of BC to the development of breast cancer brain metastasis (BCBM) was 36.3 months (95% CI: 30.0 to 42.1 months). The time from the diagnosis of first recurrence and metastasis stage to the diagnosis of BCBM was 11.35 months (95% CI: 7.1 to 18.4 months). The time from the diagnosis of BCBM to the time of follow-up was 24.1 months (95% CI: 13.9 to 37.5 months). Up until the time of follow-up data, a total of 38 patients had died, and the time from the diagnosis of BM of these 38 patients to death was 11.0 months (95% CI: 9.0 to 20.4 months).ConclusionThe prognosis of HER2-positive BC patients with BM was poor due to the lack of effective treatments for BM. Age ≤40 years and first metastatic site with lung metastasis were the independent risk factors for BM in HER2-positive BC patients. Future research about pre-emptive medical interventions may help to improve the prognosis of HER2-positive BC patients with high risk to develop BM.

Abstract CT004: Product attributes of axicabtagene ciloleucel (axi-cel) that associate differentially with efficacy and toxicity in second-line large B-cell lymphoma

Abstract Background: Axi-cel is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy approved for the treatment of relapsed or refractory large B-cell lymphoma after ≥2 lines of systemic therapy. ZUMA-7, a global Phase 3 randomized study, showed superiority of axi-cel over standard second-line therapy (N=359; event-free survival [EFS] HR 0.398, P&amp;lt;.0001; median EFS 8.3 vs 2 months; estimated 2-year EFS 41% vs 16%; objective response rate [ORR] 83% vs 50%, Locke et al. N Engl J Med. 2021). Here we report axi-cel pharmacokinetics (PK), pharmacodynamics (PD), and product attributes associated with ZUMA-7 clinical outcomes. Methods: Samples from patients who received axi-cel (n=170) were analyzed. PK, PD, and axi-cel T-cell composition (naive, CCR7+CD45RA+; differentiated, CCR7-) were assessed for associations with safety and efficacy using previously described methodologies (Neelapu et al. NEJM. 2017; Locke et al. Blood Adv. 2020). Results: The median (Q1, Q3; n=162) peak CAR T-cell level, time to peak, and area under the curve within the first 28 days of treatment (AUC0-28) were 25.8 cells/μL (8.2, 57.9), 8 days (8, 9), and 236.2 cells/μL*days (76.4, 758.0), respectively. CAR T-cell peak and AUC0-28 correlated with higher ORR (P=.0224 and .0054, respectively) and increased Grade (Gr) ≥3 neurologic events (NEs; P=.0006) but not with durability of response (P=.4894). Rapid transient increases in serum analytes, including granzyme B, ferritin, IL-6, IL-10, CXCL-10, IL-15, ICAM-1, and GM-CSF, occurred early (median peak ≤7 days) and were associated with increased Gr ≥3 NEs and Gr ≥3 cytokine release syndrome (CRS; P&amp;lt;.05). Infusion products richer in naive-like T cells expressing CD27 and CD28 associated with increased EFS, ORR, and complete response (P&amp;lt;.05). In contrast, infusion products richer in differentiated T cells (CCR7-) and with lower % of CCR7+CD45RA+ T cells associated with higher postinfusion peak levels and AUC0-28 of several proinflammatory and immunomodulatory serum analytes (eg, IL-15, ferritin, IFN-γ). Increased rates of Gr ≥3 NEs were found in patients who received axi-cel richer in CCR7- T cells (above median: 30% vs below median: 10%). Similarly, a trend of higher rates of Gr ≥3 NEs and CRS were observed in patients who received axi-cel that secreted higher levels of IFN-γ in co-culture with CD19-expressing targets. Conclusions: Preinfusion axi-cel features and postinfusion PK/PD profiles in the randomized phase 3 ZUMA-7 trial were associated with safety and efficacy outcomes and supported that optimizing product composition towards a juvenile T-cell phenotype (CCR7+CD45RA+) may improve axi-cel therapeutic index. These findings could result in future trials to evaluate if preemptive interventions, including enrichment of naive T cells in the product, could improve outcomes. [SF and SV contributed equally] Citation Format: Simone Filosto, Saran Vardhanabhuti, Miguel Canales, Xavier Poiré, Lazaros J. Lekakis, Sven de Vos, Craig A. Portell, Zixing Wang, Christina To, Paul Cheng, Justin Chou, Adrian Bot, Rhine Shen, Jason R. Westin. Product attributes of axicabtagene ciloleucel (axi-cel) that associate differentially with efficacy and toxicity in second-line large B-cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT004.

Risk profile analysis of uncomplicated type B aortic dissection patients undergoing thoracic endovascular aortic repair: Laboratory andradiographic predictors.

There is emerging evidence to support pre-emptive thoracic endovascular aortic repair (TEVAR) intervention for uncomplicated type B aortic dissection (unTBAD). Pre-emptive intervention would be particularly beneficial in patients that have a higher baseline risk of progressing to complicated TBAD (coTBAD). There remain debate on the optimal clinical, laboratory, morphological, and radiological parameters, which would identify the highest-risk patients that would benefit most from pre-emptive TEVAR. This review summarizes evidence on the clinical, laboratory, and morphological parameters that increase the risk profiles of unTBAD patients. A comprehensive literature search was carried out on multiple electronic databases including PubMed, EMBASE, Ovid, and Scopus to collate all research evidence on the clinical, laboratory, and morphological parameters that increase the risk profiles of unTBAD patients RESULTS: At present, there are no clear clinical guidelines using risk-stratification to inform the selection of unTBAD patients for TEVAR. However, there are noticeable literature trends that can assist with the identification of the most at-risk unTBAD patients. Patients are at particular risk when they have refractory pain and/or hypertension, elevated C-reactive protein (CRP), larger aortic diameter, and larger entry tears. These risks should be considered alongside factors that increase the procedural risk of TEVAR to create a well-balanced approach. Advances in biomarkers and imaging are likely to identify more pertinent parameters in the future to optimize the development of balanced, risk-stratified treatment protocols. There are a variety of risk profiling parameters that can be used to identify the high-risk unTBAD patient, with novel biomarkers and imaging parameters emerging. Longer-term evidence verifying these parameters would be ideal. Further randomized controlled trials and multicentre registry analyses are also warranted to guide risk-stratified selection protocols.

Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database.

Are Neighborhood Characteristics Associated With Outcomes After THA and TKA? Findings From a Large Healthcare System Database.

Influence of climate variables on dengue fever occurrence in the southern region of Thailand.

The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.