Predictors of high-intensity care at the end of life among older adults with solid tumors: A population-based study.

INTRODUCTION: High-intensity end-of-life (EoL) care can be burdensome for patients, caregivers, and health systems and does not confer any meaningful clinical benefit. Yet, there are significant knowledge gaps regarding the predictors of high-intensity EoL care. In this study, we identify risk factors associated with high-intensity EoL care among older adults with the four most common malignancies, including breast, prostate, lung, and colorectal cancer. MATERIALS AND METHODS: Using SEER-Medicare data, we conducted a retrospective analysis of Medicare beneficiaries aged 65 and older who died of breast, prostate, lung, or colorectal cancer between 2011 and 2015. We used multivariable logistic regression to identify clinical, demographic, socioeconomic, and geographic predictors of high-intensity EoL care, which we defined as death in an acute care hospital, receipt of any oral or parenteral chemotherapy within 14 days of death, one or more admissions to the intensive care unit within 30 days of death, two or more emergency department visits within 30 days of death, or two or more inpatient admissions within 30 days of death. RESULTS: Among 59,355 decedents, factors associated with increased likelihood of receiving high-intensity EoL care were increased comorbidity burden (odds ratio [OR]:1.29; 95% confidence interval [CI]:1.28-1.30), female sex (OR:1.05; 95% CI:1.01-1.09), Black race (OR:1.14; 95% CI:1.07-1.23), Other race/ethnicity (OR:1.20; 95% CI:1.10-1.30), stage III disease (OR:1.11; 95% CI:1.05-1.18), living in a county with >1,000,000 people (OR:1.23; 95% CI:1.16-1.31), living in a census tract with 10%-<20% poverty (OR:1.09; 95% CI:1.03-1.16) or 20%-100% poverty (OR:1.12; 95% CI:1.04-1.19), and having state-subsidized Medicare premiums (OR:1.18; 95% CI:1.12-1.24). The risk of high-intensity EoL care was lower among patients who were older (OR:0.98; 95% CI:0.98-0.99), lived in the Midwest (OR:0.69; 95% CI:0.65-0.75), South (OR:0.70; 95% CI:0.65-0.74), or West (OR:0.81; 95% CI:0.77-0.86), lived in mostly rural areas (OR:0.92; 95% CI:0.86-1.00), and had poor performance status (OR:0.26; 95% CI:0.25-0.28). Results were largely consistent across cancer types. DISCUSSION: The risk factors identified in our study can inform the development of new interventions for patients with cancer who are likely to receive high-intensity EoL care. Health systems should consider incorporating these risk factors into decision-support tools to assist clinicians in identifying which patients should be referred to hospice and palliative care.

Combining multiple biomarkers linearly to minimize the Euclidean distance of the closest point on the receiver operating characteristic surface to the perfection corner in trichotomous settings.

The performance of individual biomarkers in discriminating between two groups, typically the healthy and the diseased, may be limited. Thus, there is interest in developing statistical methodologies for biomarker combinations with the aim of improving upon the individual discriminatory performance. There is extensive literature referring to biomarker combinations under the two-class setting. However, the corresponding literature under a three-class setting is limited. In our study, we provide parametric and nonparametric methods that allow investigators to optimally combine biomarkers that seek to discriminate between three classes by minimizing the Euclidean distance from the receiver operating characteristic surface to the perfection corner. Using this Euclidean distance as the objective function allows for estimation of the optimal combination coefficients along with the optimal cutoff values for the combined score. An advantage of the proposed methods is that they can accommodate biomarker data from all three groups simultaneously, as opposed to a pairwise analysis such as the one implied by the three-class Youden index. We illustrate that the derived true classification rates exhibit narrower confidence intervals than those derived from the Youden-based approach under a parametric, flexible parametric, and nonparametric kernel-based framework. We evaluate our approaches through extensive simulations and apply them to real data sets that refer to liver cancer patients.

Circulating microRNA Panel for Prediction of Recurrence and Survival in Early-Stage Lung Adenocarcinoma.

Early-stage lung adenocarcinoma (LUAD) patients remain at substantial risk for recurrence and disease-related death, highlighting the unmet need of biomarkers for the assessment and identification of those in an early stage who would likely benefit from adjuvant chemotherapy. To identify circulating miRNAs useful for predicting recurrence in early-stage LUAD, we performed miRNA microarray analysis with pools of pretreatment plasma samples from patients with stage I LUAD who developed recurrence or remained recurrence-free during the follow-up period. Subsequent validation in 85 patients with stage I LUAD resulted in the development of a circulating miRNA panel comprising miR-23a-3p, miR-320c, and miR-125b-5p and yielding an area under the curve (AUC) of 0.776 in predicting recurrence. Furthermore, the three-miRNA panel yielded an AUC of 0.804, with a sensitivity of 45.8% at 95% specificity in the independent test set of 57 stage I and II LUAD patients. The miRNA panel score was a significant and independent factor for predicting disease-free survival (p < 0.001, hazard ratio [HR] = 1.64, 95% confidence interval [CI] = 1.51-4.22) and overall survival (p = 0.001, HR = 1.51, 95% CI = 1.17-1.94). This circulating miRNA panel is a useful noninvasive tool to stratify early-stage LUAD patients and determine an appropriate treatment plan with maximal efficacy.

Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings.

Tuberculosis (TB) studies often involve four different states under consideration, namely, "healthy," "latent infection," "pulmonary active disease," and "extra-pulmonary active disease." While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so-called "tree" or "umbrella" orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non-parametric kernel-based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.

Lineage specific extracellular vesicle-associated protein biomarkers for the early detection of high grade serous ovarian cancer.

High grade serous ovarian carcinoma (HGSOC) accounts for ~ 70% of ovarian cancer cases. Non-invasive, highly specific blood-based tests for pre-symptomatic screening in women are crucial to reducing the mortality associated with this disease. Since most HGSOCs typically arise from the fallopian tubes (FT), our biomarker search focused on proteins found on the surface of extracellular vesicles (EVs) released by both FT and HGSOC tissue explants and representative cell lines. Using mass spectrometry, 985 EV proteins (exo-proteins) were identified that comprised the FT/HGSOC EV core proteome. Transmembrane exo-proteins were prioritized because these could serve as antigens for capture and/or detection. With a nano-engineered microfluidic platform, six newly discovered exo-proteins (ACSL4, IGSF8, ITGA2, ITGA5, ITGB3, MYOF) plus a known HGSOC associated protein, FOLR1 exhibited classification performance ranging from 85 to 98% in a case-control study using plasma samples representative of early (including stage IA/B) and late stage (stage III) HGSOCs. Furthermore, by a linear combination of IGSF8 and ITGA5 based on logistic regression analysis, we achieved a sensitivity of 80% with 99.8% specificity and a positive predictive value of 13.8%. Importantly, these exo-proteins also can accurately discriminate between ovarian and 12 types of cancers commonly diagnosed in women. Our studies demonstrate that these lineage-associated exo-biomarkers can detect ovarian cancer with high specificity and sensitivity early and potentially while localized to the FT when patient outcomes are more favorable.

Characteristics, staging and outcomes of differentiated thyroid cancer in patients with and without Graves' disease.

Background: The incidence of thyroid cancer has increased over the last three decades with studies showing incidence of thyroid cancer is higher among patients with Graves' Disease (GD) when compared to Toxic multinodular goiter.1 We conducted a retrospective study to further investigate characteristics and outcomes in patients with thyroid cancer and GD. Methods: We retrospectively reviewed 62 patients with a diagnosis of Differentiated Thyroid Cancer (DTC). We compared age at diagnosis, type, size of tumor, radioactive iodine (RAI) use, and DTC recurrence amongst patients with GD, non-GD patients. We used Chi-square to test for independence among categorical variables at a nominal level of 0.05; comparison was based on t-test. Results: Out of 62 patients, 29 patients had GD and DTC (47%). 94% had papillary thyroid cancer. Patients with GD were diagnosed with DTC at a younger age (mean 46 years) in comparison to patients without GD (mean 53 years). There was no difference in the type of DTC. Patients with GD had significantly smaller tumor size (mean size 1.035 cm; p value = 0.002), more Stage 1 and 2 compared to patients without GD (p-value = 0.009). Both groups of patients had similar rates of recurrence on follow up and RAI use. Conclusion: We found patients with GD had smaller tumor size, early-stage DTC when compared to patients without GD and potentially favorable prognosis. More data is needed to understand whether this is due to pathogenesis like Graves antibodies promoting tumor formation or merely earlier detection of DTC in GD.

Identification of small extracellular vesicle protein biomarkers for pediatric Ewing Sarcoma.

Ewing Sarcoma (EWS) is the second most common osseous malignancy in children and young adults after osteosarcoma, while it is the fifth common osseous malignancy within adult age population. The clinical presentation of EWS is quite often non-specific, with the most common symptoms at presentation consisting of pain, swelling or general discomfort. The dearth of clinically relevant diagnostic or predictive biomarkers continues to remain a pressing clinical challenge. Identification of tumor specific biomarkers can lend towards an early diagnosis, expedited initiation of therapy, monitoring of therapeutic response, and early detection of recurrence of disease. We carried-out a complex analysis of cell lines and cell line derived small extracellular vesicles (sEVs) using label-free-based Quantitative Proteomic Profiling with an intent to determine shared and distinct features of these tumor cells and their respective sEVs. We analyzed EWS cells with different EWS-ETS fusions (EWS-FLI1 type I, II, and III and EWS-ERG) and their corresponding sEVs. Non-EWS controls included osteosarcoma, rhabdomyosarcoma, and benign cells, i.e., osteoid osteoma and mesenchymal stem cells. Proteomic profiling identified new shared markers between cells and their corresponding cell-derived sEVs and markers which were exclusively enriched in EWS-derived sEVs. These exo-biomarkers identified were validated by in silico approaches of publicly available protein databases and by capillary electrophoresis based western analysis (Wes). Here, we identified a protein biomarker named UGT3A2 and found its expression highly specific to EWS cells and their sEVs compared to control samples. Clinical validation of UGT3A2 expression in patient tumor tissues and plasma derived sEV samples demonstrated its specificity to EWS, indicating its potential as a EWS biomarker.

Lineage specific extracellular vesicle-associated protein biomarkers for the early detection of high grade serous ovarian cancer.

High grade serous ovarian carcinoma (HGSOC) accounts for ~ 70% of ovarian cancer cases. Non-invasive, highly specific blood-based tests for pre-symptomatic screening in women are crucial to reducing the mortality associated with this disease. Since most HGSOCs typically arise from the fallopian tubes (FT), our biomarker search focused on proteins found on the surface of extracellular vesicles (EVs) released by both FT and HGSOC tissue explants and representative cell lines. Using mass spectrometry, 985 EV proteins (exo-proteins) were identified that comprised the FT/HGSOC EV core proteome. Transmembrane exo-proteins were prioritized because these could serve as antigens for capture and/or detection. With a nano-engineered microfluidic platform, six newly discovered exo-proteins (ACSL4, IGSF8, ITGA2, ITGA5, ITGB3, MYOF) plus a known HGSOC associated protein, FOLR1 exhibited classification performance ranging from 85-98% in a case-control study using plasma samples representative of early (including stage IA/B) and late stage (stage III) HGSOCs. Furthermore, by linear combination of IGSF8 and ITGA5 based on logistic regression analysis, we achieved a sensitivity of 80% (99.8% specificity). These lineage-associated exo-biomarkers have potential to detect cancer while localized to the FT when patient outcomes are more favorable.

Statistical assessment of the prognostic and the predictive value of biomarkers-A biomarker assessment framework with applications to traumatic brain injury biomarker studies.

Studies that investigate the performance of prognostic and predictive biomarkers are commonplace in medicine. Evaluating the performance of biomarkers is challenging in traumatic brain injury (TBI) and other conditions when both the time factor (i.e. time from injury to biomarker measurement) and different levels or doses of treatments are in play. Such factors need to be accounted for when assessing the biomarker's performance in relation to a clinical outcome. The Hyperbaric Oxygen in Brain Injury Treatment (HOBIT) trial, a phase II randomized control clinical trial seeks to determine the dose of hyperbaric oxygen therapy (HBOT) for treating severe TBI that has the highest likelihood of demonstrating efficacy in a phase III trial. Hyperbaric Oxygen in Brain Injury Treatment will study up to 200 participants with severe TBI. This paper discusses the statistical approaches to assess the prognostic and predictive performance of the biomarkers studied in this trial, where prognosis refers to the association between a biomarker and the clinical outcome while the predictiveness refers to the ability of the biomarker to identify patient subgroups that benefit from therapy. Analyses based on initial biomarker levels accounting for different levels of HBOT and other baseline clinical characteristics, and analyses of longitudinal changes in biomarker levels are discussed from a statistical point of view. Methods for combining biomarkers that are of complementary nature are also considered and the relevant algorithms are illustrated in detail along with an extensive simulation study that assesses the performance of the statistical methods. Even though the discussed approaches are motivated by the HOBIT trial, their applications are broader. They can be applied in studies assessing the predictiveness and prognostic ability of biomarkers in relation to a well-defined therapeutic intervention and clinical outcome.

Proteomic Profiling of Fallopian Tube-Derived Extracellular Vesicles Using a Microfluidic Tissue-on-Chip System.

The human fallopian tube epithelium (hFTE) is the site of fertilization, early embryo development, and the origin of most high-grade serous ovarian cancers (HGSOCs). Little is known about the content and functions of hFTE-derived small extracellular vesicles (sEVs) due to the limitations of biomaterials and proper culture methods. We have established a microfluidic platform to culture hFTE for EV collection with adequate yield for mass spectrometry-based proteomic profiling, and reported 295 common hFTE sEV proteins for the first time. These proteins are associated with exocytosis, neutrophil degranulation, and wound healing, and some are crucial for fertilization processes. In addition, by correlating sEV protein profiles with hFTE tissue transcripts characterized using GeoMx® Cancer Transcriptome Atlas, spatial transcriptomics analysis revealed cell-type-specific transcripts of hFTE that encode sEVs proteins, among which, FLNA, TUBB, JUP, and FLNC were differentially expressed in secretory cells, the precursor cells for HGSOC. Our study provides insights into the establishment of the baseline proteomic profile of sEVs derived from hFTE tissue, and its correlation with hFTE lineage-specific transcripts, which can be used to evaluate whether the fallopian tube shifts its sEV cargo during ovarian cancer carcinogenesis and the role of sEV proteins in fallopian tube reproductive functions.

Epidermal Growth Factor Receptor Inhibitor Treatment Timing does not Impact Survival in Stage 4 Colon Cancer Treatment: A Retrospective Study.

Introduction: Colon cancer impacts the lives of Kansans and those across the United States. Epidermal growth factor receptor (EGFR) inhibitors, such as panitumumab and cetuximab, have gained popularity as first-line treatment for stage 4 colon cancer despite their toxicities and have been used by clinicians in later lines of therapy. EGFR inhibitors have been proven to be an efficacious first-line treatment for stage 4 colon cancer, but no study has investigated outcomes comparing EGFR inhibitors as first-line treatment to its use as second- or third-line treatment. This study investigated EGFR inhibitor therapy estimated overall survival when used as first-, second-, and third-line treatment for stage 4 colon cancer. Methods: A retrospective review was done for patients with stage 4 colon cancer who underwent EGFR inhibitor treatment at a large academic center from November 2007 to August 2021. The patients were stratified into five groups by the line in which they received the EGFR inhibitor treatment. A log-rank test was used to analyze the groups, and the median survival for each group was determined. Results: A total of 68 patients were reviewed; 18 received first-line, 23 received second-line, 18 received third-line, 6 received fourth-line, and 3 received sixth-line treatment with an EGFR inhibitor. Fourth- and sixth-line therapies were excluded due to small patient size. There was no significant difference in estimated survival time between any of the lines. Median survival of the therapies was found. Conclusions: There was no statistical difference in survival between the first-, second-, or third-line groups, which may provide justification for its use as a second- or third-line therapy.

Plasma metabolomic profiling as a tool to identify predictive biomarkers of methotrexate efficacy in rheumatoid arthritis.

OBJECTIVE: Methotrexate (MTX) remains the first-choice disease-modifying therapy in rheumatoid arthritis (RA). However, clinical response is variable, and no reliable predictive biomarkers of efficacy currently exist. In this study, plasma metabolomic profiling is evaluated as a tool to identify pretreatment biomarkers of MTX response in RA. METHODS: Plasma collected from RA patients initiating MTX therapy (n = 20) were analyzed by metabolomic profiling totaling 648 identified metabolites. Pretreatment metabolomic profiles were compared based on clinical response after 16-weeks of MTX therapy. Clinical response to MTX was defined by a clinically meaningful reduction in disease activity score in 28 joints (DAS28-ESR) of greater than 1.2. RESULTS: Pretreatment plasma levels of 19 metabolites were found to differ (p < 0.05) between RA patients based on response to MTX at 16-weeks. Spearman's correlation of pretreatment plasma metabolite levels with change in DAS28-ESR over the treatment period further supported three of the identified metabolites as associated with MTX response (p < 0.05). The identified metabolite levels were all found to be lower in RA patients responsive to MTX but were not found to be intercorrelated. Receiver operating characteristic analysis of each of the identified metabolites, alone or in combination, demonstrated an excellent discrimination between responders and non-responders based on pretreatment plasma levels of nornicotine (AUC = 0.84), N-methylisoleucine (AUC = 0.82), 2,3-dihydroxybutanoic acid (AUC = 0.82), and a combination biomarker panel score (AUC = 0.98). CONCLUSION: Pretreatment plasma metabolomic profiling identified multiple metabolites associated with early response to MTX therapy in RA and represents a promising approach for the identification of clinical biomarkers of MTX response in RA.

Construction of joint confidence spaces for the optimal true class fraction triplet in the ROC space using alternative biomarker cutoffs.

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. Finding new biomarkers for its early detection is of high clinical importance. As with many other diseases, cancer has a progressive nature. In cancer biomarker studies, it is often the case that the true disease status of the recruited individuals exhibits more than two classes. The receiver operating characteristic (ROC) surface is a well-known statistical tool for assessing the biomarkers' discriminatory ability in trichotomous settings. The volume under the ROC surface (VUS) is an overall measure of the discriminatory ability of a marker. In practice, clinicians are often in need of cutoffs for decision-making purposes. A popular approach for computing such cutoffs is the Youden index and its recent three-class generalization. A drawback of such a method is that it treats the data in a pairwise fashion rather than consider all the data simultaneously. The use of the minimized Euclidean distance from the perfection corner to the ROC surface (also known as closest to perfection method) is an alternative to the Youden index that may be preferable in some settings. When such a method is employed, there is a need for inferences around the resulting true class rates/fractions that correspond to the optimal operating point. In this paper, we provide an inferential framework for the derivation of marginal confidence intervals (CIs) and joint confidence spaces (CSs) around the corresponding true class fractions, when dealing with trichotomous settings. We explore parametric and nonparametric approaches for the construction of such CIs and CSs. We evaluate our approaches through extensive simulations and apply them to a real data set that refers to HCC patients.

On optimal biomarker cutoffs accounting for misclassification costs in diagnostic trilemmas with applications to pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the most deadly cancer and currently there is strong clinical interest in novel biomarkers that contribute to its early detection. Assessing appropriately the accuracy of such biomarkers is a crucial issue and often one needs to take into account that many assays include biospecimens of individuals coming from three groups: healthy, chronic pancreatitis, and PDAC. The ROC surface is an appropriate tool for assessing the overall accuracy of a marker employed under such trichotomous settings. A decision/classification rule is often based on the so-called Youden index and its three-dimensional generalization. However, both the clinical and the statistical literature have not paid the necessary attention to the underlying false classification (FC) rates that are of equal or even greater importance. In this article we provide a framework to make inferences around all classification rates as well as comparisons. We explore the trinormal model, flexible models based on power transformations, and robust non-parametric alternatives. We provide a full framework for the construction of confidence intervals, regions, and spaces for joint inferences or for clinically meaningful points of interest. We further discuss the implications of costs related to different FCs. We evaluate our approaches through extensive simulations and illustrate them using data from a recent PDAC study conducted at the MD Anderson Cancer Center.

An Omega-3-rich Anti-inflammatory Diet Improved Widespread Allodynia and Worsened Metabolic Outcomes in Adult Mice Exposed to Neonatal Maternal Separation.

Inflammation plays a key role in the progression and maintenance of chronic pain, which impacts the lives of millions of Americans. Despite growing evidence that chronic pain can be improved by treating underlying inflammation, successful treatments are lacking and pharmaceutical interventions are limited due to drug side effects. Here we are testing whether a 'healthy human' diet (HHD), with or without anti-inflammatory components (HHAID), improves pain-like behaviors in a preclinical model of chronic widespread hypersensitivity induced by neonatal maternal separation (NMS). The HHD and HHAID are isocaloric and macronutrient-matched, have a low glycemic index, and fat content (35 kcal%) that is high in omega-3 fatty acids, while only the HHAID includes a combination of key anti-inflammatory compounds, at clinically relevant doses. Mice on these diets were compared to mice on a control diet with a macronutrient composition commonly used in rodents (20% protein, 70% carbohydrate, 10% fat). Our results demonstrate a benefit of the HHAID on pain-like behaviors in both male and female mice, despite increased caloric intake, adiposity, and weight gain. In female mice, HHAID specifically increased measures of metabolic syndrome and inflammation compared to the HHD and control diet groups. Male mice were susceptible to worsening metabolic measures on both the HHAID and HHD. This work highlights important sexual dimorphic outcomes related to early life stress exposure and dietary interventions, as well as a potential disconnect between improvements in pain-like behaviors and metabolic measures.

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation.

When amino acids vary during evolution, the outcome can be functionally neutral or biologically-important. We previously found that substituting a subset of nonconserved positions, "rheostat" positions, can have surprising effects on protein function. Since changes at rheostat positions can facilitate functional evolution or cause disease, more examples are needed to understand their unique biophysical characteristics. Here, we explored whether "phylogenetic" patterns of change in multiple sequence alignments (such as positions with subfamily specific conservation) predict the locations of functional rheostat positions. To that end, we experimentally tested eight phylogenetic positions in human liver pyruvate kinase (hLPYK), using 10-15 substitutions per position and biochemical assays that yielded five functional parameters. Five positions were strongly rheostatic and three were non-neutral. To test the corollary that positions with low phylogenetic scores were not rheostat positions, we combined these phylogenetic positions with previously-identified hLPYK rheostat, "toggle" (most substitution abolished function), and "neutral" (all substitutions were like wild-type) positions. Despite representing 428 variants, this set of 33 positions was poorly statistically powered. Thus, we turned to the in vivo phenotypic dataset for E. coli lactose repressor protein (LacI), which comprised 12-13 substitutions at 329 positions and could be used to identify rheostat, toggle, and neutral positions. Combined hLPYK and LacI results show that positions with strong phylogenetic patterns of change are more likely to exhibit rheostat substitution outcomes than neutral or toggle outcomes. Furthermore, phylogenetic patterns were more successful at identifying rheostat positions than were co-evolutionary or eigenvector centrality measures of evolutionary change.

Estimation and construction of confidence intervals for biomarker cutoff-points under the shortest Euclidean distance from the ROC surface to the perfection corner.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer with a 5-year survival rate of less than 5%. As in many other diseases, its diagnosis might involve progressive stages. It is common that in biomarker studies referring to PDAC, recruitment involves three groups: healthy individuals, patients that suffer from chronic pancreatitis, and PDAC patients. Early detection and accurate classification of the state of the disease are crucial for patients' successful treatment. ROC analysis is the most popular way to evaluate the performance of a biomarker and the Youden index is commonly employed for cutoff derivation. The so-called generalized Youden index has a drawback in the three-class case of not accommodating the full data set when estimating the optimal cutoffs. In this article, we explore the use of the Euclidean distance of the ROC to the perfection corner for the derivation of cutoffs in trichotomous settings. We construct an inferential framework that involves both parametric and nonparametric techniques. Our methods can accommodate the full information of a given data set and thus provide more accurate estimates in terms of the decision-making cutoffs compared with a Youden-based strategy. We evaluate our approaches through extensive simulations and illustrate them on a PDAC biomarker study.

Statistical inference for the difference between two maximized Youden indices obtained from correlated biomarkers.

Currently, there is global interest in deriving new promising cancer biomarkers that could complement or substitute the conventional ones. Clinical decisions can often be based on the cutoff that corresponds to the maximized Youden index when maximum accuracy drives decisions. When more than one classification criteria are measured within the same individuals, correlated measurements arise. In this work, we propose hypothesis tests and confidence intervals for the comparison of two correlated receiver operating characteristic (ROC) curves in terms of their corresponding maximized Youden indices. We explore delta-based techniques under parametric assumptions, or power transformations. Nonparametric kernel-based methods are also examined. We evaluate our approaches through simulations and illustrate them using data from a metabolomic study referring to the detection of pancreatic cancer.

The length of the receiver operating characteristic curve and the two cutoff Youden index within a robust framework for discovery, evaluation, and cutoff estimation in biomarker studies involving improper receiver operating characteristic curves.

During the early stage of biomarker discovery, high throughput technologies allow for simultaneous input of thousands of biomarkers that attempt to discriminate between healthy and diseased subjects. In such cases, proper ranking of biomarkers is highly important. Common measures, such as the area under the receiver operating characteristic (ROC) curve (AUC), as well as affordable sensitivity and specificity levels, are often taken into consideration. Strictly speaking, such measures are appropriate under a stochastic ordering assumption, which implies, without loss of generality, that higher measurements are more indicative for the disease. Such an assumption is not always plausible and may lead to rejection of extremely useful biomarkers at this early discovery stage. We explore the length of a smooth ROC curve as a measure for biomarker ranking, which is not subject to directionality. We show that the length corresponds to a ϕ divergence, is identical to the corresponding length of the optimal (likelihood ratio) ROC curve, and is an appropriate measure for ranking biomarkers. We explore the relationship between the length measure and the AUC of the optimal ROC curve. We then provide a complete framework for the evaluation of a biomarker in terms of sensitivity and specificity through a proposed ROC analogue for use in improper settings. In the absence of any clinical insight regarding the appropriate cutoffs, we estimate the sensitivity and specificity under a two-cutoff extension of the Youden index and we further take into account the implied costs. We apply our approaches on two biomarker studies that relate to pancreatic and esophageal cancer.

Risk Factors Associated With All-Cause 30-Day Mortality in Nursing Home Residents With COVID-19.

Importance: The coronavirus disease 2019 (COVID-19) pandemic has severely affected nursing homes. Vulnerable nursing home residents are at high risk for adverse outcomes, but improved understanding is needed to identify risk factors for mortality among nursing home residents. Objective: To identify risk factors for 30-day all-cause mortality among US nursing home residents with COVID-19. Design, Setting, and Participants: This cohort study was conducted at 351 US nursing homes among 5256 nursing home residents with COVID-19-related symptoms who had severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection confirmed by polymerase chain reaction testing between March 16 and September 15, 2020. Exposures: Resident-level characteristics, including age, sex, race/ethnicity, symptoms, chronic conditions, and physical and cognitive function. Main Outcomes and Measures: Death due to any cause within 30 days of the first positive SARS-CoV-2 test result. Results: The study included 5256 nursing home residents (3185 women [61%]; median age, 79 years [interquartile range, 69-88 years]; and 3741 White residents [71%], 909 Black residents [17%], and 586 individuals of other races/ethnicities [11%]) with COVID-19. Compared with residents aged 75 to 79 years, the odds of death were 1.46 (95% CI, 1.14-1.86) times higher for residents aged 80 to 84 years, 1.59 (95% CI, 1.25-2.03) times higher for residents aged 85 to 89 years, and 2.14 (95% CI, 1.70-2.69) times higher for residents aged 90 years or older. Women had lower risk for 30-day mortality than men (odds ratio [OR], 0.69 [95% CI, 0.60-0.80]). Two comorbidities were associated with mortality: diabetes (OR, 1.21 [95% CI, 1.05-1.40]) and chronic kidney disease (OR, 1.33 [95%, 1.11-1.61]). Fever (OR, 1.66 [95% CI, 1.41-1.96]), shortness of breath (OR, 2.52 [95% CI, 2.00-3.16]), tachycardia (OR, 1.31 [95% CI, 1.04-1.64]), and hypoxia (OR, 2.05 [95% CI, 1.68-2.50]) were also associated with increased risk of 30-day mortality. Compared with cognitively intact residents, the odds of death among residents with moderate cognitive impairment were 2.09 (95% CI, 1.68-2.59) times higher, and the odds of death among residents with severe cognitive impairment were 2.79 (95% CI, 2.14-3.66) times higher. Compared with residents with no or limited impairment in physical function, the odds of death among residents with moderate impairment were 1.49 (95% CI, 1.18-1.88) times higher, and the odds of death among residents with severe impairment were 1.64 (95% CI, 1.30-2.08) times higher. Conclusions and Relevance: In this cohort study of US nursing home residents with COVID-19, increased age, male sex, and impaired cognitive and physical function were independently associated with mortality. Understanding these risk factors can aid in the development of clinical prediction models of mortality in this population.