Modeling and computation of multistep batch testing for infectious diseases.

We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multistep batch testing approach with variable batch sizes. This model and simulation tool dramatically increase the efficiency and efficacy of the tests in a large population at a low cost, particularly when the infection rate is low. The proposed method combines statistical modeling with numerical methods to solve nonlinear equations and obtain optimal batch sizes at each step of tests, with the flexibility to incorporate geographic and demographic information. In theory, this method substantially improves the false positive rate and positive predictive value as well. We also conducted a Monte Carlo simulation to verify this theory. Our simulation results show that our method significantly reduces the false negative rate. More accurate assessment can be made if the dilution effect or other practical factors are taken into consideration. The proposed method will be particularly useful for the early detection of infectious diseases and prevention of future pandemics. The proposed work will have broader impacts on medical testing for contagious diseases in general.

Identification of Risk Factors and Likelihood of Benefit from Adjuvant Chemotherapy for Early Stage Lung Cancer Patients.

The purpose of the research is to develop a statistical decision support algorithm for patients who may benefit from Adjuvant Cisplatin/Vinorelbine (ACT) and improve their survival rates. Genome-wide microarray data are used to identify feasible sets of genes and probe sets that constitute the gene signature. The data are available at the National Center for Biotechnology Information Gene Expression Omnibus (GSE14814). Preliminary studies have shown that high-risk patients who received ACT resulted in an improved prognosis. However, low-risk patients showed no benefit from ACT, and the treatment was possibly detrimental to the patient. Studies using tree-based ensemble statistical learning algorithms have shown that genomic markers could potentially identify a patient's risk factor and likelihood to benefit from ACT; however, it was noted that tree-based ensemble statistical learning algorithms do not provide an estimate of the strength of the treatment effect, nor is it possible to clearly identify subgroups of patients with similar responses to ACT treatment. Building on this idea, Accelerated Failure Time models are used to predict the probability of benefit from receiving chemotherapy or surgery only and provide a treatment recommendation for a new patient. We showed that regardless of whether the model recommended chemotherapy or surgery only, patients who followed the predicted treatment recommendation had significantly longer survival times than patients who did not. The proposed approach provides the likelihood of benefit for each treatment based on a small number of genomic biomarkers.

Development and evaluation of a multimodal marker of major depressive disorder.

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

Subgroup analysis based on prognostic and predictive gene signatures for adjuvant chemotherapy in early-stage non-small-cell lung cancer patients.

In treating patients diagnosed with early Stage I non-small-cell lung cancer (NSCLC), doctors must choose surgery alone, Adjuvant Cisplatin-Based Chemotherapy (ACT) alone or both. For patients with resected stages IB to IIIA, clinical trials have shown a survival advantage from 4-15% with the adoption of ACT. However, due to the inherent toxicity of chemotherapy, it is necessary for doctors to identify patients whose chance of success with ACT is sufficient to justify the risks. This research seeks to use gene expression profiling in the development of a statistical decision-making algorithm to identify patients whose survival rates will improve from ACT treatment. Using the data from the National Cancer Institute, the lasso method in the Cox-Proportional-Hazards regression model is used as the main method to determine a feasible number of genes that are strongly associated with the treatment-related patient survival. Considering treatment groups separately, the patients are assigned a risk category based on the estimation of survival times. These risk categories are used to develop a Random Forests classification model to identify patients who are likely to benefit from chemotherapy treatment. This model allows the prediction of a new patient's prognosis and the likelihood of survival benefit from ACT treatment based on a feasible number of genomic biomarkers. The proposed methods are evaluated using a simulation study.

Traumatic brain injury and bipolar psychosis in the Genomic Psychiatry Cohort.

Approximately three million individuals in the United States sustain traumatic brain injury (TBI) every year, with documented impact on a range of neurological and psychiatric disturbances including mania, depression, and psychosis. Identification of subsets of individuals that may demonstrate increased propensity for posttraumatic symptoms and who may share genetic vulnerabilities for gene-environment interactions can enhance efforts to understand, predict, and prevent these phenomena. A sample of 11,489 cases from the Genomic Psychiatry Cohort (GPC), a NIMH-managed data repository for the investigation of schizophrenia and bipolar disorder, was used for this study. Cases were excluded if TBI was deemed causal to their mental illness. A k-means clustering algorithm was used to probe differences between schizophrenia and bipolar disorder associated with variables including onset age, hallucinations, delusions, head injury, and TBI. Cases were separated into an optimum number of seven clusters, with two clusters including all cases with brain injury. Bipolar disorder with psychosis and TBI were significantly correlated in one cluster in which 72% of cases were male and 99.2% sustained head injury. This cluster also carried the longest average period of unconsciousness. This study demonstrates an association of TBI with psychosis in a subset of bipolar cases, suggesting that traumatic stressors may have the ability to impact gene expression in a vulnerable population, and/or there is a heightened occurrence of TBI in individuals with underlying psychosis. Further studies should more closely examine the interplay between genetic variation in bipolar disorder and susceptibility to psychosis following TBI. © 2015 Wiley Periodicals, Inc.

Multinomial logistic regression ensembles.

This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model.

Classification of high-dimensional data with ensemble of logistic regression models.

A classification method is developed based on ensembles of logistic regression models, with each model fitted from a different set of predictors determined by a random partition of the feature space. The proposed method enables class prediction by an ensemble of logistic regression models for a high-dimensional data set, which is impossible by a single logistic regression model due to the restriction that the sample size needs to be larger than the number of predictors. The proposed classification method is applied to gene expression data on pediatric acute myeloid leukemia (AML) patients to predict each patient's risk for treatment failure or relapse at the time of diagnosis. Hence, specific prognostic biomarkers can be used to predict outcomes in pediatric AML and formulate individual risk-adjusted treatment. Our study shows that the proposed method is comparable to other widely used models in generalized accuracy and is significantly improved in balance between sensitivity and specificity. The proposed ensemble algorithm enables the standard classification model to be used for classification of high-dimensional data.

Feeding acutely stimulates fibrinogen synthesis in healthy young and elderly adults.

Fibrinogen is a positive acute-phase protein and its hepatic synthesis is enhanced following inflammation and injury. However, it is not clear whether fibrinogen synthesis is also responsive to oral nutrients and whether the response to a meal may be affected by age. Our aim in this study was to investigate the acute effect of oral feeding on fibrinogen synthesis in both young and elderly men and women. Fibrinogen synthesis was determined in 3 separate occasions from the incorporation of l[(2)H(5)]phenylalanine (43 mg/kg body weight) in 8 young (21-35 y) and 8 elderly (>60 y) participants following the ingestion of water (control), a complete liquid meal (15% protein, 30% fat, and 55% carbohydrate), or only the protein component of the meal. The ingestion of the complete meal enhanced fibrinogen fractional synthesis rates (FSR) by 17 +/- 6% in the young and by 38 +/- 10% in the elderly participants compared with the water meal (P < 0.02). A comparable stimulation of FSR occurred with only the protein component of the meal in both young (29 +/- 7%) and elderly participants (41 +/- 9%) compared with the water meal (P < 0.005). Similar results were obtained when fibrinogen synthesis was expressed as absolute synthesis rates (i.e. mg.kg(-1).d(-1)). The results demonstrate that fibrinogen synthesis is acutely stimulated after ingestion of a meal and that this effect can be reproduced by the protein component of the meal alone, both in young and elderly adults.

Simulated laparoscopic sigmoidectomy training: responsiveness of surgery residents.

PURPOSE: This study aimed to evaluate the responsiveness of surgery residents to simulated laparoscopic sigmoidectomy training. METHODS: Residents underwent simulated laparoscopic sigmoidectomy training for previously tattooed sigmoid cancer with use of disposable abdominal trays in a hybrid simulator to perform a seven-step standardized technique. After baseline testing and training, residents were tested with predetermined proficiency criteria. Content validity was defined as the extent to which outcome measures departed from clinical reality. Content-valid measures of trays were evaluated by two blinded raters. Simulator-generated metrics included path length and smoothness of instrument movements. Responsiveness was defined as change in performance over time and was assessed by comparing baseline testing with unmentored final testing. RESULTS: For eight weeks, eight postgraduate year 3/4 residents performed 34 resections. Overall operating time (67 vs. 37 min; P = 0.005), flexure (10 vs. 5 min; P = 0.005), inferior mesenteric vessel (8 vs. 5 min; P = 0.04), and ureter (7 vs. 1 min; P = 0.003) times improved significantly. Content-valid measures from trays remained unchanged. Path length (27,155.2 mm) and smoothness (3,575.5 cm/s3) of instrument movement remained unchanged. There were two bowel perforations and 19 anastomotic leaks. Leak rate decreased from 87% to 12.5%. Strong correlation was found between path length and smoothness of instrument movements (r = 0.9; P < 0.001). There was no correlation between simulator-generated metrics and content-valid outcome measures. Interrater reliability was 1.0 for all measures except anastomotic leak (k = 0.56). There was a linear relationship between residents' clinical advanced laparoscopic case volume and responsiveness (r = -0.7; P = 0.04). CONCLUSIONS: Simulated laparoscopic sigmoidectomy training affected responsiveness in surgery residents with significantly decreased operating time and anastomotic leak rate.

Human organ/tissue growth algorithms that include obese individuals and black/white population organ weight similarities from autopsy data.

Physiologically based pharmacokinetic (PBPK) models need the correct organ/tissue weights to match various total body weights in order to be applied to children and the obese individual. Baseline data from Reference Man for the growth of human organs (adrenals, brain, heart, kidneys, liver, lungs, pancreas, spleen, thymus, and thyroid) were augmented with autopsy data to extend the describing polynomials to include the morbidly obese individual (up to 250 kg). Additional literature data similarly extends the growth curves for blood volume, muscle, skin, and adipose tissue. Collectively these polynomials were used to calculate blood/organ/tissue weights for males and females from birth to 250 kg, which can be directly used to help parameterize PBPK models. In contrast to other black/white anthropomorphic measurements, the data demonstrated no observable or statistical difference in weights for any organ/tissue between individuals identified as black or white in the autopsy reports.

Relative importance of symptoms, cognition, and other multilevel variables for psychiatric disease classifications by machine learning.

This study used machine-learning algorithms to make unbiased estimates of the relative importance of various multilevel data for classifying cases with schizophrenia (n = 60), schizoaffective disorder (n = 19), bipolar disorder (n = 20), unipolar depression (n = 14), and healthy controls (n = 51) into psychiatric diagnostic categories. The Random Forest machine learning algorithm, which showed best efficacy (92.9% SD: 0.06), was used to generate variable importance ranking of positive, negative, and general psychopathology symptoms, cognitive indexes, global assessment of function (GAF), and parental ages at birth for sorting participants into diagnostic categories. Symptoms were ranked most influential for separating cases from healthy controls, followed by cognition and maternal age. To separate schizophrenia/schizoaffective disorder from bipolar/unipolar depression, GAF was most influential, followed by cognition and paternal age. For classifying schizophrenia from all other psychiatric disorders, low GAF and paternal age were similarly important, followed by cognition, psychopathology and maternal age. Controls misclassified as schizophrenia cases showed lower nonverbal abilities, mild negative and general psychopathology symptoms, and younger maternal or older paternal age. The importance of symptoms for classification of cases and lower GAF for diagnosing schizophrenia, notably more important and distinct from cognition and symptoms, concurs with current practices. The high importance of parental ages is noteworthy and merits further study.

Importance of the high-molecular-mass isoform of adiponectin in improved insulin sensitivity with rosiglitazone treatment in HIV disease.

The present study was designed to investigate the relationship of isoforms of adiponectin to insulin sensitivity in subjects with HIV-associated insulin resistance in response to treatment with the thiazolidinedione, rosiglitazone. The two isoforms of adiponectin, HMW (high-molecular-mass) and LMW (low-molecular-mass), were separated by sucrose-gradient-density centrifugation. The amount of adiponectin in gradient fractions was determined by ELISA. Peripheral insulin sensitivity (Rd) was determined with hyperinsulinaemic-euglycaemic clamp, whereas hepatic sensitivity [HOMA (Homoeostasis Model Assessment) %S] was based on basal glucose and insulin values. Treatment with rosiglitazone for 3 months resulted in a significant improvement in the index of hepatic insulin sensitivity (86.4+/-15% compared with 139+/-23; P=0.007) as well as peripheral insulin sensitivity (4.04+/-0.23 compared with 6.17+/-0.66 mg of glucose/kg of lean body mass per min; P<0.001). Improvement in HOMA was associated with increased levels of HMW adiponectin (r=0.541, P=0.045), but not LMW adiponectin. The present study suggests that the HMW isoform of adiponectin is important in the regulation of rosiglitazone-mediated improvement in insulin sensitivity in individuals with HIV-associated insulin resistance, particularly in the liver.

Estimation of lag time between onset of and death from an occult tumor.

A new statistical method for estimating the lag time between onset of and death from an occult tumor is proposed for data without cause-of-death information. In this method, the survival function for time to tumor onset, tumor-specific survival function, and competing risks survival function are estimated using the maximum likelihood estimates of the parameters. The proposed method utilizes the estimated survival functions and statistically imputed fatal tumors to estimate the lag time. This approach is developed for rodent tumorigenicity assays that have at least one interim sacrifice and a terminal sacrifice. If the data contain cause-of-death information given by pathologists and it is believed to be reliable, it may be used for estimating the lag time. The proposed method is illustrated using a real data set. The accuracy of the estimation of lag time is evaluated via a Monte Carlo simulation study. This study shows that the estimated lag time is quite reliable.

Identifying high-dimensional biomarkers for personalized medicine via variable importance ranking.

We apply robust classification algorithms to high-dimensional genomic data to find biomarkers, by analyzing variable importance, that enable a better diagnosis of disease, an earlier intervention, or a more effective assignment of therapies. The goal is to use variable importance ranking to isolate a set of important genes that can be used to classify life-threatening diseases with respect to prognosis or type to maximize efficacy or minimize toxicity in personalized treatment of such diseases. A ranking method and present several other methods to select a set of important genes to use as genomic biomarkers is proposed, and the performance of the selection procedures in patient classification by cross-validation is evaluated. The various selection algorithms are applied to published high-dimensional genomic data sets using several well-known classification methods. For each data set, a set of genes selected on the basis of variable importance that performed the best in classification is reported. That classification algorithm with the proposed ranking method is shown to be competitive with other selection methods for discovering genomic biomarkers underlying both adverse and efficacious outcomes for improving individualized treatment of patients for life-threatening diseases.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding.

OBJECTIVE: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce healthcare resources to those who need it the most. DESIGN AND METHODS: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. RESULTS: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model. CONCLUSION: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation.

Ensemble methods for classification of patients for personalized medicine with high-dimensional data.

OBJECTIVE: Personalized medicine is defined by the use of genomic signatures of patients in a target population for assignment of more effective therapies as well as better diagnosis and earlier interventions that might prevent or delay disease. An objective is to find a novel classification algorithm that can be used for prediction of response to therapy in order to help individualize clinical assignment of treatment. METHODS AND MATERIALS: Classification algorithms are required to be highly accurate for optimal treatment on each patient. Typically, there are numerous genomic and clinical variables over a relatively small number of patients, which presents challenges for most traditional classification algorithms to avoid over-fitting the data. We developed a robust classification algorithm for high-dimensional data based on ensembles of classifiers built from the optimal number of random partitions of the feature space. The software is available on request from the authors. RESULTS: The proposed algorithm is applied to genomic data sets on lymphoma patients and lung cancer patients to distinguish disease subtypes for optimal treatment and to genomic data on breast cancer patients to identify patients most likely to benefit from adjuvant chemotherapy after surgery. The performance of the proposed algorithm is consistently ranked highly compared to the other classification algorithms. CONCLUSION: The statistical classification method for individualized treatment of diseases developed in this study is expected to play a critical role in developing safer and more effective therapies that replace one-size-fits-all drugs with treatments that focus on specific patient needs.

Temporal variability of urinary cadmium in spot urine samples and first morning voids.

Cadmium is a carcinogenic heavy metal. Urinary levels of cadmium are considered to be an indicator of long-term body burden, as cadmium accumulates in the kidneys and has a half-life of at least 10 years. However, the temporal stability of the biomarker in urine samples from a non-occupationally exposed population has not been rigorously established. We used repeated measurements of urinary cadmium (U-Cd) in spot urine samples and first morning voids from two separate cohorts, to assess the temporal stability of the samples. Urine samples from two cohorts including individuals of both sexes were measured for cadmium and creatinine. The first cohort (Home Observation of Perinatal Exposure (HOPE)) consisted of 21 never-smokers, who provided four first morning urine samples 2-5 days apart, and one additional sample roughly 1 month later. The second cohort (World Trade Center-Health Program (WTC-HP)) consisted of 78 individuals, including 52 never-smokers, 22 former smokers and 4 current smokers, who provided 2 spot urine samples 6 months apart, on average. Intra-class correlation was computed for groups of replicates from each individual to assess temporal variability. The median creatinine-adjusted U-Cd level (0.19 and 0.21 µg/g in the HOPE and WTC-HP, respectively) was similar to levels recorded in the United States by the National Health and Nutrition Examination Survey. The intra-class correlation (ICC) was high (0.76 and 0.78 for HOPE and WTC-HP, respectively) and similar between cohorts, irrespective of whether samples were collected days or months apart. Both single spot or first morning urine cadmium samples show good to excellent reproducibility in low-exposure populations.

Computerized experience sampling method (ESMc): assessing feasibility and validity among individuals with schizophrenia.

The Experience Sampling Method (ESM) is an ecologically valid, time-sampling of self-reports developed to study the dynamic process of person-environment interactions. ESM with digital wristwatch and booklets (paper-based ESM; ESMp) has been used extensively to study schizophrenia. The present study is designed to test the feasibility and validity of using Computerized ESM (ESMc) among individuals with schizophrenia. ESMc is advantageous in allowing for recording of precise time-stamps of responses. We used PDAs ("Personal Digital Assistant"; Palm handheld computers) to collect data on momentary psychotic symptoms, mood, and thoughts over a one day period among 10 hospitalized schizophrenia patients and 10 healthy controls. ESMc was equally acceptable to both groups, with similar ratings of comfort carrying the PDAs and operating them, interference with daily activities, as well as response rates. The schizophrenia patients reported significantly higher ratings of auditory and visual hallucinations, suspiciousness, sense of unreality, lack of thought control, fear of losing control, difficulty expressing thoughts, as well as depression/sadness, loneliness and less cheerfulness. Significant inverse relationships were found among both groups between ratings of feeling cheerful and being stressed, irritated, and sad/depressed. Among the schizophrenia subjects, the correlation between ratings of suspiciousness on ESMc and Scale for Assessment of Positive Symptoms (SAPS) approached significance, as well as the link between suspiciousness and stress. Our results support the feasibility and validity of using ESMc for assessment of momentary psychotic symptoms, mood, and experiences among individuals with schizophrenia. The authors discuss the potential applications of combining ESMc with ambulatory physiological measures.

Type I error control for tree classification.

Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, say 5%.

Effect of zoledronic Acid on bone mineral density in men with prostate cancer receiving gonadotropin-releasing hormone analog.

Background. Loss of bone density with androgen deprivation therapy for prostate cancer is well recognized. We assessed the effects of quarterly infusion of zoledronic acid on bone mineral density (BMD) and markers of bone turnover over a one-year period in men receiving gonadotropin-releasing hormone analog (GnRH-a) for prostate cancer. Methods. 41 subjects were randomly assigned to treatment with zoledronic acid (4 mg) IV infusion or placebo every 3 months. The primary endpoint was the change in the lumbar spine BMD after 12 months of treatment. Results. The change in vertebral BMD in the zoledronic acid group (+7.93 ± 1.4%) was significantly (P < .05) greater than the change in the placebo group (+0.82 ± 1.7%) as was the change in left femoral neck BMD (+5.05 ± 1.4% for the zoledronic acid group versus -0.48 ± 1.4% for the placebo group). The decrease in biochemical markers of bone turnover was significantly (P < .05) greater in the zoledronic acid group compared to the placebo group. Conclusion. Quarterly infusion of zoledronic acid for 1 year improved vertebral and left femoral neck BMD with a decrease in bone turnover markers in men on GnRH-a treatment. Zoledronic acid treatment appears to be promising in men with low BMD receiving GnRH-a treatment.