Relative contrast estimation and inference for treatment recommendation.

When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale-invariant contrasts between the conditional treatment effects. By showing that all scale-invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two-step approach that minimizes a doubly robust loss function for initial estimation and then performs a one-step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.

Robust estimation and variable selection for the accelerated failure time model.

This article concerns robust modeling of the survival time for cancer patients. Accurate prediction of patient survival time is crucial to the development of effective therapeutic strategies. To this goal, we propose a unified Expectation-Maximization approach combined with the L1 -norm penalty to perform variable selection and parameter estimation simultaneously in the accelerated failure time model with right-censored survival data of moderate sizes. Our approach accommodates general loss functions, and reduces to the well-known Buckley-James method when the squared-error loss is used without regularization. To mitigate the effects of outliers and heavy-tailed noise in real applications, we recommend the use of robust loss functions under the general framework. Furthermore, our approach can be extended to incorporate group structure among covariates. We conduct extensive simulation studies to assess the performance of the proposed methods with different loss functions and apply them to an ovarian carcinoma study as an illustration.

The Causal Relationship Between Portal Usage and Self-Efficacious Health Information-Seeking Behaviors: Secondary Analysis of the Health Information National Trends Survey Data.

BACKGROUND: Patient portals have drawn much attention, as they are considered an important tool for health providers in facilitating patient engagement. However, little is known about whether the intensive use of patient portals contributes to improved management of patients' health in terms of their confidence in acquiring health information and exercising self-care. There is a lack of randomized trials with these outcomes measured both pre- and postadoption of patient portals. OBJECTIVE: The aim of this study was to examine the causal relationship between the usage of patient portals and patients' self-efficacy toward obtaining health information and performing self-care. METHODS: This study was a secondary data analysis that used data from a US national survey, the National Cancer Institute's Health Information National Trends Survey 5 Cycle 1. Patient portal usage frequency was used to define the treatment. Survey items measuring self-efficacy on a Likert-type scale were selected as the main outcomes, including patients' confidence in obtaining health information and performing self-care. To establish causality using survey data, we adopted the instrumental variables method. To determine the direction of the causal relationship in the presence of high-dimensional confounders, we further proposed a novel testing framework that employs conditional independence tests in a directed acyclic graph. The average causal effect was measured using the two-stage least squares regression method. RESULTS: We showed that frequently using patient portals improves patients' confidence in obtaining health information. The estimand of the weighted average causal effect was 0.14 (95% CI 0.06-0.23; P<.001). This means that when increasing the portal usage intensity, for instance, from 1-2 times to 3-5 times per year, the expected average increase in confidence level measured on a Likert-type scale would be 0.14. However, we could not conclusively determine the causal effect between patient portal usage and patients' confidence in exercising self-care. CONCLUSIONS: The results support the use of patient portals and encourage better support and education to patients. The proposed statistical method can be used to exploit the potential of national survey data for causal inference studies.

Characteristics of Patients Using Different Patient Portal Functions and the Impact on Primary Care Service Utilization and Appointment Adherence: Retrospective Observational Study.

BACKGROUND: Patient portals are now widely available and increasingly adopted by patients and providers. Despite the growing research interest in patient portal adoption, there is a lack of follow-up studies describing the following: whether patients use portals actively; how frequently they use distinct portal functions; and, consequently, what the effects of using them are, the understanding of which is paramount to maximizing the potential of patient portals to enhance care delivery. OBJECTIVE: To investigate the characteristics of primary care patients using different patient portal functions and the impact of various portal usage behaviors on patients' primary care service utilization and appointment adherence. METHODS: A retrospective, observational study using a large dataset of 46,544 primary care patients from University of Florida Health was conducted. Patient portal users were defined as patients who adopted a portal, and adoption was defined as the status that a portal account was opened and kept activated during the study period. Then, users were further classified into different user subgroups based on their portal usage of messaging, laboratory, appointment, and medication functions. The intervention outcomes were the rates of primary care office visits categorized as arrived, telephone encounters, cancellations, and no-shows per quarter as the measures of primary care service utilization and appointment adherence. Generalized linear models with a panel difference-in-differences study design were then developed to estimate the rate ratios between the users and the matched nonusers of the four measurements with an observational window of up to 10 quarters after portal adoption. RESULTS: Interestingly, a high propensity to adopt patient portals does not necessarily imply more frequent use of portals. In particular, the number of active health problems one had was significantly negatively associated with portal adoption (odds ratios [ORs] 0.57-0.86, 95% CIs 0.51-0.94, all P<.001) but was positively associated with portal usage (ORs 1.37-1.76, 95% CIs 1.11-2.22, all P≤.01). The same was true for being enrolled in Medicare for portal adoption (OR 0.47, 95% CI 0.41-0.54, P<.001) and message usage (OR 1.44, 95% CI 1.03-2.03, P=.04). On the impact of portal usage, the effects were time-dependent and specific to the user subgroup. The most salient change was the improvement in appointment adherence, and patients who used messaging and laboratory functions more often exhibited a larger reduction in no-shows compared to other user subgroups. CONCLUSIONS: Patients differ in their portal adoption and usage behaviors, and the portal usage effects are heterogeneous and dynamic. However, there exists a lack of match in the patient portal market where patients who benefit the most from patient portals are not active portal adopters. Our findings suggest that health care delivery planners and administrators should remove the barriers of adoption for the portal beneficiaries; in addition, they should incorporate the impact of portal usage into care coordination and workflow design, ultimately aligning patients' and providers' needs and functionalities to effectively deliver patient-centric care.

A Computational-Based Approach to Identify Estrogen Receptor α/ß Heterodimer Selective Ligands.

The biologic effects of estrogens are transduced by two estrogen receptors (ERs), ERα and ERß, which function in dimer forms. The ERα/α homodimer promotes and the ERß/ß inhibits estrogen-dependent growth of mammary epithelial cells; the functions of ERα/ß heterodimers remain elusive. Using compounds that promote ERα/ß heterodimerization, we have previously shown that ERα/ß heterodimers appeared to inhibit tumor cell growth and migration in vitro. Further dissection of ERα/ß heterodimer functions was hampered by the lack of ERα/ß heterodimer-specific ligands. Herein, we report a multistep workflow to identify the selective ERα/ß heterodimer-inducing compound. Phytoestrogenic compounds were first screened for ER transcriptional activity using reporter assays and ER dimerization preference using a bioluminescence resonance energy transfer assay. The top hits were subjected to in silico modeling to identify the pharmacophore that confers ERα/ß heterodimer specificity. The pharmacophore encompassing seven features that are potentially important for the formation of the ERα/ß heterodimer was retrieved and subsequently used for virtual screening of large chemical libraries. Four chemical compounds were identified that selectively induce ERα/ß heterodimers over their respective homodimers. Such ligands will become unique tools to reveal the functional insights of ERα/ß heterodimers.

Risk prediction for heterogeneous populations with application to hospital admission prediction.

This article is motivated by the increasing need to model risk for large hospital and health care systems that provide services to diverse and complex patients. Often, heterogeneity across a population is determined by a set of factors such as chronic conditions. When these stratifying factors result in overlapping subpopulations, it is likely that the covariate effects for the overlapping groups have some similarity. We exploit this similarity by imposing structural constraints on the importance of variables in predicting outcomes such as hospital admission. Our basic assumption is that if a variable is important for a subpopulation with one of the chronic conditions, then it should be important for the subpopulation with both conditions. However, a variable can be important for the subpopulation with two particular chronic conditions but not for the subpopulations of people with just one of those two conditions. This assumption and its generalization to more conditions are reasonable and aid greatly in borrowing strength across the subpopulations. We prove an oracle property for our estimation method and show that even when the structural assumptions are misspecified, our method will still include all of the truly nonzero variables in large samples. We demonstrate impressive performance of our method in extensive numerical studies and on an application in hospital admission prediction and validation for the Medicare population of a large health care provider.

Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms.

With the advancement in drug development, multiple treatments are available for a single disease. Patients can often benefit from taking multiple treatments simultaneously. For example, patients in Clinical Practice Research Datalink with chronic diseases such as type 2 diabetes can receive multiple treatments simultaneously. Therefore, it is important to estimate what combination therapy from which patients can benefit the most. However, to recommend the best treatment combination is not a single label but a multilabel classification problem. In this paper, we propose a novel outcome weighted deep learning algorithm to estimate individualized optimal combination therapy. The Fisher consistency of the proposed loss function under certain conditions is also provided. In addition, we extend our method to a family of loss functions, which allows adaptive changes based on treatment interactions. We demonstrate the performance of our methods through simulations and real data analysis.

On the effect of electronic patient portal on primary care utilization and appointment adherence.

BACKGROUND: The objective of this study was to investigate the impact of patient portal adoption on patients' primary care utilization and appointment adherence. METHODS: We conducted a retrospective observational study using a panel difference-in-differences (DID) framework to investigate the use of primary care services by patients, adjusting for their disease burden and allowing for time-dependent portal effect. A large dataset with 46,544 patients of University of Florida (UF) Health during the study period July 2013 - June 2016 was used. The main outcome measures are disease burden adjusted rates of office visits arrived, no-show, and cancellation to primary care physicians (PCPs) per quarter between patient portal adopters (denoted as users) and non-users. RESULTS: At the time of adoption, the quarterly PCP office visit rate ratio (RR) of patient portal users to non-users was 1.33 (95% CI, 1.27-1.39; p < 0.001). The RRs were between 0.94 to 0.99 up to four quarters after portal adoption (p = 0.749, 0.100, 0.131, and 0.091, respectively), and were significantly less than one at the seventh (RR =0.82; 95% CI, 0.73-0.91; p < 0.001) and the eighth (RR = 0.80; 95% CI, 0.70-0.90; p < 0.001) quarters post adoption. The quarterly no-show rates of the users were significantly smaller (RRs were between 0.60 and 0.83) except for the seventh, eighth and tenth quarters post adoption. In these three quarters, the no-show rates were not significantly changed (p = 0.645, 0.295, and 0.436, respectively). Quarterly cancellation rates were not significantly affected by portal adoption (p > 0.05 for all cases). CONCLUSIONS: Patient portal users' disease burden adjusted PCP office visit rate was significantly reduced in one and a half year and thereafter post portal adoption. PCP appointment no-show rate was also significantly reduced and cancellation rate was not affected, implying improved care engagement of patients.

MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images.

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly available at https://zenodo.org/records/10475293.

Risk-Specific Training Cohorts to Address Class Imbalance in Surgical Risk Prediction.

Importance: Machine learning tools are increasingly deployed for risk prediction and clinical decision support in surgery. Class imbalance adversely impacts predictive performance, especially for low-incidence complications. Objective: To evaluate risk-prediction model performance when trained on risk-specific cohorts. Design, Setting, and Participants: This cross-sectional study performed from February 2024 to July 2024 deployed a deep learning model, which generated risk scores for common postoperative complications. A total of 109 445 inpatient operations performed at 2 University of Florida Health hospitals from June 1, 2014, to May 5, 2021 were examined. Exposures: The model was trained de novo on separate cohorts for high-risk, medium-risk, and low-risk Common Procedure Terminology codes defined empirically by incidence of 5 postoperative complications: (1) in-hospital mortality; (2) prolonged intensive care unit (ICU) stay (≥48 hours); (3) prolonged mechanical ventilation (≥48 hours); (4) sepsis; and (5) acute kidney injury (AKI). Low-risk and high-risk cutoffs for complications were defined by the lower-third and upper-third prevalence in the dataset, except for mortality, cutoffs for which were set at 1% or less and greater than 3%, respectively. Main Outcomes and Measures: Model performance metrics were assessed for each risk-specific cohort alongside the baseline model. Metrics included area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), F1 scores, and accuracy for each model. Results: A total of 109 445 inpatient operations were examined among patients treated at 2 University of Florida Health hospitals in Gainesville (77 921 procedures [71.2%]) and Jacksonville (31 524 procedures [28.8%]). Median (IQR) patient age was 58 (43-68) years, and median (IQR) Charlson Comorbidity Index score was 2 (0-4). Among 109 445 operations, 55 646 patients were male (50.8%), and 66 495 patients (60.8%) underwent a nonemergent, inpatient operation. Training on the high-risk cohort had variable impact on AUROC, but significantly improved AUPRC (as assessed by nonoverlapping 95% confidence intervals) for predicting mortality (0.53; 95% CI, 0.43-0.64), AKI (0.61; 95% CI, 0.58-0.65), and prolonged ICU stay (0.91; 95% CI, 0.89-0.92). It also significantly improved F1 score for mortality (0.42; 95% CI, 0.36-0.49), prolonged mechanical ventilation (0.55; 95% CI, 0.52-0.58), sepsis (0.46; 95% CI, 0.43-0.49), and AKI (0.57; 95% CI, 0.54-0.59). After controlling for baseline model performance on high-risk cohorts, AUPRC increased significantly for in-hospital mortality only (0.53; 95% CI, 0.42-0.65 vs 0.29; 95% CI, 0.21-0.40). Conclusion and Relevance: In this cross-sectional study, by training separate models using a priori knowledge for procedure-specific risk classes, improved performance in standard evaluation metrics was observed, especially for low-prevalence complications like in-hospital mortality. Used cautiously, this approach may represent an optimal training strategy for surgical risk-prediction models.

Severe trauma leads to sustained muscle loss, induced frailty, and distinct temporal changes in myokine and chemokine profiles of older patients.

INTRODUCTION: Sarcopenia is a known risk factor for adverse outcomes across multiple disease states, including severe trauma. Factors such as age, hyperinflammation, prolonged immobilization, and critical illness may not only exacerbate progression of this disease but may also contribute to the development of induced sarcopenia, or sarcopenia secondary to hospitalization. This study seeks to (1) determine the effects of severe traumatic injury on changes in skeletal muscle mass in older adults; (2) test whether changes in skeletal muscle mass are associated with clinical frailty, physical performance, and health-related quality of life; and (3) examine trauma-induced frailty and temporal changes in myokine and chemokine profiles. METHODS: A prospective, longitudinal cohort study of 47 critically ill, older (≥45 years) adults presenting after severe blunt trauma was conducted. Repeated measures of computed tomography-based skeletal muscle index, frailty, and quality of life were obtained in addition to selected plasma biomarkers over 6 months. RESULTS: Severe trauma was associated with significant losses in skeletal muscle mass and increased incidence of sarcopenia from 36% at baseline to 60% at 6 months. Severe trauma also was associated with a transient worsening of induced frailty and reduced quality of life irrespective of sarcopenia status, which returned to baseline by 6 months after injury. Admission biomarker levels were not associated with skeletal muscle index at the time points studied but demonstrated distinct temporal changes across our entire cohort. CONCLUSIONS: Severe blunt trauma in older adults is associated with increased incidence of induced sarcopenia and reversible induced frailty. Despite muscle wasting, functional decline is transient, with a return to baseline by 6 months, suggesting a need for holistic definitions of sarcopenia and further investigation into long-term functional outcomes in this population.

Development and optimization of a diluted whole blood ELISpot assay to test immune function.

Sepsis remains a leading cause of death worldwide with no proven immunomodulatory therapies. Stratifying Patient Immune Endotypes in Sepsis ('SPIES') is a prospective, multicenter observational study testing the utility of ELISpot as a functional bioassay specifically measuring cytokine-producing cells after stimulation to identify the immunosuppressed endotype, predict clinical outcomes in septic patients, and test potential immune stimulants for clinical development. Most ELISpot protocols call for the isolation of PBMC prior to their inclusion in the assay. In contrast, we developed a diluted whole blood (DWB) ELISpot protocol that has been validated across multiple laboratories. Heparinized whole blood was collected from healthy donors and septic patients and tested under different stimulation conditions to evaluate the impact of blood dilution, stimulant concentration, blood storage, and length of stimulation on ex vivo IFNγ and TNFα production as measured by ELISpot. We demonstrate a dynamic range of whole blood dilutions that give a robust ex vivo cytokine response to stimuli. Additionally, a wide range of stimulant concentrations can be utilized to induce cytokine production. Further modifications demonstrate anticoagulated whole blood can be stored up to 24 h at room temperature without losing significant functionality. Finally, we show ex vivo stimulation can be as brief as 4 h allowing for a substantial decrease in processing time. The data demonstrate the feasibility of using ELISpot to measure the functional capacity of cells within DWB under a variety of stimulation conditions to inform clinicians on the extent of immune dysregulation in septic patients.

Adverse outcomes and an immunosuppressed endotype in septic patients with reduced IFN-γ ELISpot.

BACKGROUNDSepsis remains a major clinical challenge for which successful treatment requires greater precision in identifying patients at increased risk of adverse outcomes requiring different therapeutic approaches. Predicting clinical outcomes and immunological endotyping of septic patients generally relies on using blood protein or mRNA biomarkers, or static cell phenotyping. Here, we sought to determine whether functional immune responsiveness would yield improved precision.METHODSAn ex vivo whole-blood enzyme-linked immunosorbent spot (ELISpot) assay for cellular production of interferon γ (IFN-γ) was evaluated in 107 septic and 68 nonseptic patients from 5 academic health centers using blood samples collected on days 1, 4, and 7 following ICU admission.RESULTSCompared with 46 healthy participants, unstimulated and stimulated whole-blood IFN-γ expression was either increased or unchanged, respectively, in septic and nonseptic ICU patients. However, in septic patients who did not survive 180 days, stimulated whole-blood IFN-γ expression was significantly reduced on ICU days 1, 4, and 7 (all P < 0.05), due to both significant reductions in total number of IFN-γ-producing cells and amount of IFN-γ produced per cell (all P < 0.05). Importantly, IFN-γ total expression on days 1 and 4 after admission could discriminate 180-day mortality better than absolute lymphocyte count (ALC), IL-6, and procalcitonin. Septic patients with low IFN-γ expression were older and had lower ALCs and higher soluble PD-L1 and IL-10 concentrations, consistent with an immunosuppressed endotype.CONCLUSIONSA whole-blood IFN-γ ELISpot assay can both identify septic patients at increased risk of late mortality and identify immunosuppressed septic patients.TRIAL REGISTRYN/A.FUNDINGThis prospective, observational, multicenter clinical study was directly supported by National Institute of General Medical Sciences grant R01 GM-139046, including a supplement (R01 GM-139046-03S1) from 2022 to 2024.

Length-to-width ratio in Mohs defects: what is the golden rule?

A length-to-width ratio (LWR) of 3:1 for linear closures is often cited in the literature. However, there are limited studies evaluating this ratio in relation to various surgical sites. This study analyzes LWRs for 3318 patients undergoing Mohs micrographic surgery (MMS) and linear repair to identify the average LWRs stratified by patient age, anatomic location, gender, and surgeon. Average LWRs ranged between 2.89 and 3.82. The LWR for all anatomic sites averaged between 3:1 and 4:1, except for closures on the trunk. Locations with the highest LWR included the cheek, ear, and perioral sites.

Allostatic load and risk of all-cause, cancer-specific, and cardiovascular mortality in older cancer survivors: an analysis of the National Health and Nutrition Examination Survey 1999-2010.

Background: Allostatic load has been linked to an increased risk of death in various populations. However, to date, there is no research specifically investigating the effect of allostatic load on mortality in older cancer survivors. Aims: To investigate the association between allostatic load (AL) and mortality in older cancer survivors. Method: A total of 1,291 adults aged 60 years or older who survived for ≥1 year since cancer diagnoses were identified from the 1999-2010 National Health and Nutrition Examination Survey. AL was the exposure of interest incorporating 9 clinical measures/biomarkers; one point was added to AL if any of the measures/biomarkers exceeded the normal level. The sum of points was categorized as an ordinal variable to reflect low, moderate, and high AL. Our outcomes of interest were all-cause, cancer-specific, and cardiovascular disease (CVD)-specific mortality. Death was identified by linkage to the National Death Index. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (aHR) and 95% confidence intervals (CI) of mortality by AL category. Results: Overall, 53.6% of participants were male and 78.4% were white. The mean age of study participants at interview was 72.8 years (SD=7.1). A total of 546 participants died during the follow-up (median follow-up time: 8.0 years). Among them, 158 died of cancer and 106 died of cardiovascular events. Results from multivariable Cox proportional hazards models showed that higher ALS was positively associated with higher all-cause mortality (ALS=4-9 vs. ALS =0-1: aHR=1.52, 95% CI =1.17-1.98, p-trend<0.01) and higher cancer-specific mortality (ALS=4-9 vs. ALS =0-1: aHR=1.80, 95% CI =1.12-2.90, p-trend=0.01). The association between ALS and cardiovascular mortality was positive but non-significant (ALS=4-9 vs. ALS =0-1: aHR=1.59, 95% CI =0.86-2.94, p-trend=0.11). Conclusions: Our study suggests that older cancer survivors can have a higher risk of death if they have a high burden of AL.

Low muscle mass is associated with a higher risk of all-cause and cardiovascular disease-specific mortality in cancer survivors.

OBJECTIVES: Individuals with prior cancer diagnosis are more likely to have low muscle mass (LMM) than their cancer-free counterparts. Understanding the effects of LMM on the prognosis of cancer survivors can be clinically important. The aim of this study was to investigate whether risks for all-cause and cardiovascular disease (CVD)-specific mortality differ by status of LMM in cancer survivors and a matched cohort without cancer history. METHODS: We used cohort data from the 1999-2006 and 2011-2014 National Health and Nutrition Examination Survey. Participants included 946 adults surviving for ≥1 since cancer diagnosis and a matched cohort (by age, sex, and race) without cancer history (N = 1857). LMM was defined by appendicular lean mass and body height (men <7.26 kg/m2, women <5.45 kg/m2). Death was ascertained via the National Death Index and cause of death was assessed via International Classification of Diseases, Tenth Revision. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratio (aHR) and 95% confidence interval (CI) of LMM. RESULTS: The mean age of cancer survivors and matched cohort was 60.6 y (SD 15) and 60.2 y (SD 14.9), respectively. The median follow-up was 10.5 y for survivors and 10.9 y for matched cohort. Overall, 22.2% of cancer survivors and 19.7% of the matched cohort had LMM, respectively. In all, 321 survivors (33.9%) and 495 participants (26.7%) in the matched cohort died during follow-up. CVD-specific deaths were identified in 58 survivors (6.1%) and 122 participants in the matched cohort (6.6%). The multivariable Cox model suggested that LMM was positively associated with all-cause (aHR, 1.73; 95% CI, 1.31-2.29) and CVD-specific (aHR, 2.13; 95% CI, 1.14-4.00) mortality in cancer survivors. The associations between LMM and risk for all-cause (aHR, 1.24; 95% CI, 0.98-1.56) and CVD-specific (aHR, 1.21; 95% CI, 0.75-1.93) mortality were not statistically significant in the matched cohort. CONCLUSION: Cancer survivors with LMM have an increased risk for all-cause and CVD-specific mortality. This increase appears to be larger than that in counterparts without cancer history.

Adverse Long-Term Outcomes and an Immune Suppressed Endotype in Sepsis Patients with Reduced Interferon-γELISpot: A Multicenter, Prospective Observational Study.

BACKGROUND: Sepsis remains a major clinical challenge for which successful treatment requires greater precision in identifying patients at increased risk of adverse outcomes requiring different therapeutic approaches. Predicting clinical outcomes and immunological endotyping of septic patients has generally relied on using blood protein or mRNA biomarkers, or static cell phenotyping. Here, we sought to determine whether functional immune responsiveness would yield improved precision. METHODS: An ex vivo whole blood enzyme-linked immunosorbent (ELISpot) assay for cellular production of interferon-γ (IFN-γ) was evaluated in 107 septic and 68 non-septic patients from five academic health centers using blood samples collected on days 1, 4 and 7 following ICU admission. RESULTS: Compared with 46 healthy subjects, unstimulated and stimulated whole blood IFNγ expression were either increased or unchanged, respectively, in septic and nonseptic ICU patients. However, in septic patients who did not survive 180 days, stimulated whole blood IFNγ expression was significantly reduced on ICU days 1, 4 and 7 (all p<0.05), due to both significant reductions in total number of IFNγ producing cells and amount of IFNγ produced per cell (all p<0.05). Importantly, IFNγ total expression on day 1 and 4 after admission could discriminate 180-day mortality better than absolute lymphocyte count (ALC), IL-6 and procalcitonin. Septic patients with low IFNγ expression were older and had lower ALC and higher sPD-L1 and IL-10 concentrations, consistent with an immune suppressed endotype. CONCLUSIONS: A whole blood IFNγ ELISpot assay can both identify septic patients at increased risk of late mortality, and identify immune-suppressed, sepsis patients.

Desiccation resistance differences in Drosophila species can be largely explained by variations in cuticular hydrocarbons.

Maintaining water balance is a universal challenge for organisms living in terrestrial environments, especially for insects, which have essential roles in our ecosystem. Although the high surface area to volume ratio in insects makes them vulnerable to water loss, insects have evolved different levels of desiccation resistance to adapt to diverse environments. To withstand desiccation, insects use a lipid layer called cuticular hydrocarbons (CHCs) to reduce water evaporation from the body surface. It has long been hypothesized that the water-proofing capability of this CHC layer, which can confer different levels of desiccation resistance, depends on its chemical composition. However, it is unknown which CHC components are important contributors to desiccation resistance and how these components can determine differences in desiccation resistance. In this study, we used machine-learning algorithms, correlation analyses, and synthetic CHCs to investigate how different CHC components affect desiccation resistance in 50 Drosophila and related species. We showed that desiccation resistance differences across these species can be largely explained by variation in CHC composition. In particular, length variation in a subset of CHCs, the methyl-branched CHCs (mbCHCs), is a key determinant of desiccation resistance. There is also a significant correlation between the evolution of longer mbCHCs and higher desiccation resistance in these species. Given that CHCs are almost ubiquitous in insects, we suggest that evolutionary changes in insect CHC components can be a general mechanism for the evolution of desiccation resistance and adaptation to diverse and changing environments.

Radioembolization Followed by Transarterial Chemoembolization in Hepatocellular Carcinoma.

BACKGROUND AND OBJECTIVE: In recent years, combination therapies for hepatocellular carcinoma (HCC) have been increasingly used with superior treatment responses compared to monotherapies. However, the safety and efficacy of the transarterial chemoembolization (TACE) and transarterial radioembolization (TARE) combinations for HCC patients have not been investigated in the literature. In this study, our aim was to evaluate the safety and outcomes of TACE after TARE in HCC patients. MATERIALS AND METHODS: All TARE procedures performed on HCC patients at a single institution between January 2008 and November 2016 were retrospectively reviewed. Seventy-three patients who did not receive any additional transarterial therapy in the areas targeted by TARE were assigned to the "TARE group," while 27 patients who received TACE after TARE to the same target area were assigned to the "Combo group." Post-procedural liver toxicity, tumor response, overall survival (OS), and time to progression (TTP) were evaluated. RESULTS: Fewer patients in the Combo group had worsening liver function than the TARE group based on the change in bilirubin levels (19% vs. 40%; p=0.029) and Child-Pugh score increase (28% vs. 51%; p=0.056). The median OS time of all patients was 11.04 months. The Combo group had a significantly longer median OS of 36.8 months (vs. 10.6, p=0.003) and median TTP of 14.4 months (vs. 5.5, p=0.018). After accounting for selection bias, OS and TTP were still in favor of the Combo group, with hazard ratios of 0.651 (p<0.05) and 0.63 (p<0.05), respectively. CONCLUSION: The addition of TACE to TARE is a safe and effective treatment in unresectable HCC patients and can be considered in select patients with a lack of complete response or disease progression.

Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.

With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.