Lowest Bayesian Information Criterion Research Articles

A fully automatic tool for development of population pharmacokinetic models.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection.

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was proposed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model's performance was optimised using the AHT tuning technique. The AHT-SEML model's efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub-Saharan cities, at mmWaves frequencies. The AHT-SEML model's performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML's t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p-values of 1.91×10−14 and 2.50×10−13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML's performance.

A Data-driven Approach for Generating Leaf Tissue Nutrient Interpretation Ranges for Greenhouse Lettuce

In the absence of controlled sufficiency studies, foliar interpretations for many horticultural crops are based on survey concentrations from small data sets. In addition, both survey and sufficiency ranges provide little interpretation regarding zones that are above or below the concentration range deemed “sufficient.” While providing a critical initial set of ranges, it was based on a limited set of data and therefore improvements in interpretation of data are needed. This study presents a novel method based on 1950 data points to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient (lowest 2.5%), low (2.5% to 25%), sufficient (25% to 75%), high (75% to 97.5%), and excessive (highest 2.5%). Data were analyzed by fitting Normal, Gamma, and Weibull distributions. Corresponding P values were calculated based on the Shapiro-Wilk test for normality for the Normal and Gamma distributions, and the Kolmogorov-Smirnov test was used for the Weibull distribution. The optimal distribution was selected based on the lowest Bayesian Information Criterion (BIC) value and visual fitness. The Weibull distribution best represented nitrogen, phosphorus, potassium, calcium, manganese, zinc, and copper, and the Gamma distribution best represented magnesium, sulfur, iron, and boron. Using the selected distributions, we propose a refined set of nutrient evaluation ranges for greenhouse-grown lettuce. These refined standards will aid growers and technical specialists in more accurately interpreting leaf tissue sample data.

Environmental drivers of the rising incidence of early-onset colorectal cancer in the United States.

Incidence of early-onset (diagnosed before age 50) colorectal cancer (EOCRC) has increased alarmingly since the 1990s in the United States. This study investigated what environmental exposures may have driven this increase. We obtained EOCRC incidence data from the Surveillance, Epidemiology, and End Results Program, and data for 11 exposures, for example, body mass index (BMI), from long-term national surveys. We aggregated these data for 30 to 49-year-olds during 1992 to 2016 by population subgroups defined by calendar period, age, race and sex, and used negative binomial regression models to identify and estimate associations of EOCRC with multiple exposures. Furthermore, we used counterfactual modeling to quantify contributions of identified risk factors to EOCRC incidence. The top models (with lowest Bayesian Information Criteria) consistently identified excess body weight, represented by overweight and obesity (BMI ≥25) or obesity alone (BMI ≥30), as the strongest risk factor. The best-performing model estimated increased EOCRC incidence due to overweight and obesity, with an incidence rate ratio (95% confidence interval) of 1.20 (1.17-1.22) for white men, 1.04 (1.00-1.08) for black men, 1.17 (1.15-1.21) for white women and 1.03 (0.97-1.08) for black women. Increases in overweight and obesity prevalence contributed to an estimated 30% (standard error: 1%) for men and 28% (standard error: 2%) for women of ECORC incidence during 1992 to 2016. These findings suggest excess body weight substantially contributed to and is likely a primary driver of the rising incidence of EOCRC in the United States. Prevention of excess weight gain may help lower colorectal cancer risk early in life.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

Comparison of Devices That Measure Sickle Red Cell Deformability

Background With the emergence of novel pharmacologic and gene-based therapies, identifying the rheological and biophysical RBC abnormalities of sickle cell disease (SCD) not captured by clinical laboratory techniques is crucial. Ektacytometry (LORRCA), the current standard to assess RBC function, does not reflect mechanical stress that RBCs experience in capillary microvasculature in the body. The device is costly, requires a nitrogen gas tank, and its use is difficult to translate outside of large-scale laboratory settings. In comparison, the Microfluidic Impedance Red Cell Assay (MIRCA) is a low-cost, portable device that mimics capillary microvasculature with micropillar arrays spaced 3-12 µm apart. Thus, MIRCA measures mechanical deformability, which is more physiologic, mimicking RBCs squeezing through microvasculature. An impedance analyzer calculates the MIRCA Occlusion Index (OI), representing the % occlusion of the chip. Here we compare the MIRCA to the LORRCA, assessing the correlations of OI and Elongation Index maximum (EI) to conventional laboratory tests and SCD related complications. Methods Peripheral blood from 53 adult (n = 28) and pediatric (n = 25) individuals (HbSS = 35, HbSB0 = 6, HbSC = 5, HbSB0 = 1, and HbAA controls = 6) were obtained under an Emory University IRB approved protocol. Patients transfused < 90 days prior were excluded. Hospitalizations / emergency department (ED) visits for pain within 12 months of sample collection were determined by chart review. Samples were collected in EDTA, stored at 4°C up to 48 hours, centrifuged at 500g, washed, and resuspended to a 20% hematocrit to run on MIRCA, an oxygen gradient ektacytometer (LORRCA), and an ADVIA hematology analyzer. Polydimethylsiloxane (PDMS) fabricated MIRCA chips were bonded to a standard glass slide with pairs of gold-sputtered electrodes adjacent to each array. Chips were prepared by perfusing ethanol, 1X phosphate-buffered saline (PBS), and 3% bovine-serum albumin (BSA) in 1X PBS via syringe pump and were incubated overnight at 4°C prior to use. Baseline impedance was taken for a 2-minute perfusion of 1X PBS, then OIs were calculated from impedance values 10 minutes after sample introduction normalized to baseline. The data was analyzed using the Mann-Whitney U test, Spearman correlation, and linear regression. OriginPro (Northampton, MA, USA) and Stata 18 (College Station, TX, USA) were used for the analyses and a p < 0.05 was considered significant. Results The correlation coefficients for OI and EI with laboratory tests (Table 1) and to each other (Figure 1) were comparable (p < 0.05). HbSS/SB0 had higher OIs than HbSC/SB+ (median = 13.7% vs 6.8%, p < 0.01) and HbAA (median = 4.6%, p < 0.01). Patients with ≥ 1 vaso-occlusive events (VOE) in the past year had higher OIs and lower EIs compared to those without a VOE (median = 15.3% vs 9.6%, p <0.01; median = 0.4 vs 0.5, p = 0.02, respectively). Patients that received acute medical care (admission or ED visit) had higher OIs and lower EIs compared to those that did not (median = 15.7% vs 9.8%, p < 0.01; median = 0.4 vs 0.5, p = 0.02, respectively). In multiple linear regression, HbSS/SB0 (p < 0.01), adults (p = 0.01), and DRBC (p < 0.01) were associated with OI while HbSS/SB0 (p = 0.01), DRBC (p < 0.01), hemoglobin % (p = 0.04), absolute neutrophil count (p = 0.02), and VOE+ (p = 0.02) were associated with EI. The models explained 55.2% and 66.8% variability of OI and EI, respectively. Lower Akaike's information criterion (AIC, 186 vs -80) and Bayesian information criterion (BIC, 192 vs -71) of the EI model suggest a higher correlation to conventional laboratory values and clinical features compared to OI; OI may possibly be a unique biomarker that both laboratory tests and LORRCA fail to fully characterize. Conclusion MIRCA OI and LORRCA EI normoxic deformability are correlated to each other, and both show statistically comparable correlations to many parameters associated with SCD severity. Both OI and EI are significantly associated with SCD outcomes like VOE and acute care. Due to lower device size, cost, and relative ease-of-use, MIRCA may be more convenient for routine clinical use and for use in low resource settings where LORRCA costs and requirements hamper implementation. As the addition of chemical hypoxia to MIRCA is now underway, future work will examine OI under hypoxia and its associations with SCD-relevant outcomes like pain events, stroke and acute chest syndrome.

Two is better than one: the double diffusion technique in classifying heart failure.

Heart failure (HF) is a chronic condition in which the heart does not pump enough blood to meet the body's demands. Diffusing capacity of the lung for nitric oxide (DLNO) and carbon monoxide (DLCO) may be used to classify patients with HF, as DLNO and DLCO are lung function measurements that reflect pulmonary gas exchange. Our objectives were to determine 1) if DLNO added to DLCO testing predicts HF better than DLCO alone and 2) whether the binary classification of HF is better when DLNO z-scores are combined with DLCO z-scores than using DLCO z-scores alone. This was a retrospective secondary data analysis in 140 New York Heart Association Class II HF patients (ejection fraction <40%) and 50 patients without HF. z-scores for DLNO, DLCO and DLNO+DLCO were created from reference equations from three articles. The model with the lowest Bayesian Information Criterion was the best predictive model. Binary HF classification was evaluated with the Matthews Correlation Coefficient (MCC). The top two of 12 models were combined z-score models. The highest MCC (0.51) was from combined z-score models. At most, only 32% of the variance in the odds of having HF was explained by combined z-scores. Combined z-scores explained 32% of the variation in the likelihood of an individual having HF, which was higher than models using DLNO or DLCO z-scores alone. Combined z-score models had a moderate ability to classify patients with HF. We recommend using the NO-CO double diffusion technique to assess gas exchange impairment in those suspected of HF.

Growth curve analysis in different generations of Boer x Central Highland goats using alternative estimation models.

Growth curve analysis can help to optimize the management, determine nutritional requirements, predict the weight of animals at a specific age, and to select highly productive animals. Therefore, this study aimed to find the best-fitted nonlinear functions to provide a specific shape of the growth curve from birth to yearling age in different generations of Boer x Central Highland goats. Gompertz, Logistic, Brody, Von Bertalanffy, Monomolecular, Negative exponential, and Richards models were evaluated to quantify their ability to describe the biological growth curve. Root mean square error (RMSE), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR2), and Akaike's information criterion (AIC) were used to evaluate the goodness of fit and flexibility of the models. Data were analyzed using the nonlinear regression procedure of SAS. High AdjR2 and lower AIC, BIC, and RMSE values are indicators of best-fitted model. The best-fitting model for the first filial generation (F1), second filial generation (F2), and male goats' growth data was Brody function, whereas the Richards model, followed by Brody, best described the growth of third filial generation (F3) and female goats. The values of parameter A (asymptotic weight) for F1, F2, F3, female, and male goats based on the Brody model were 30.5±1.32, 28.2±1.38, 24.4±1.04, 27.8±0.94, and 29.8±1.32 kg for F1, F2, F3, female, and male goats, respectively. As per the best-fitted growth function, the asymptotic weight tended to reduce when the filial generation increased. The asymptotic weight for male goats was higher than for female goats. F1 had a slightly small value of parameter K, followed by F2 and F3. Both males and females had similar maturity rates. Based on the Brody function, the correlation between maturation rate and mature weight was high (-0.98, P<0.001). The correlation estimates for A-B and B-K were 0.27 and -0.15, respectively. Brody was best fitted for most goat categories, although Richards, followed by Brody, was best fitted for female and F3 goats. Besides, Brody could be better than Richards due to the ease of interpretation, convergence, and applicability for a small sample size. Therefore, the Brody function can predict the mature body weight, maturation rate, and growth rate of Boer x Central Highland goats and be used to formulate breeding and management strategies for profitable goat farming.

Trajectories of depressive symptoms 10 years after stroke and associated risk factors: a prospective cohort study

BackgroundPrevious studies have investigated the risk factors for post-stroke depression at only one timepoint, neglecting its dynamic nature. We aimed to identify trajectories of post-stroke depression from multiple assessments and explore their risk factors. MethodsWe did a population-based cohort study with the South London Stroke Register (1995–2019). All stroke patients with three or more measurements of the Hospital Anxiety and Depression Scale were included. We identified trajectories of post-stroke depression over a 10-year follow-up using group-based trajectory modelling. We determined the optimal number and shape of trajectories based on the lowest Bayesian information criterion, average posterior probability of assignment of each group over 0·70, and inclusion of at least 5% of participants within each group. We used multinomial logistic regression adjusted for age, sex, ethnicity, comorbidity, physical disability, stroke severity, history of depression and cognitive impairment to explore associations with different trajectories. FindingsThe analysis comprised 1968 participants (mean age 64·9 years [SD 13·8], 56·6% male and 43·4% female, 65·1% white ethnicity, 30·7% severe disability and 32·7% severe stroke). We identified four patterns of symptoms: no depressive symptoms (14·1%, n=277), low symptoms (41·7%, n=820), moderate symptoms and symptoms worsening early and then improving (34·6%, n=681), and high and increasing symptoms (9·7%, n=190). Compared with no depressive symptom trajectory, patients with severe disability, severe stroke, pre-stroke depression, and cognitive impairment were more likely to be in the moderate and high symptom groups (adjusted odds ratios [ORs] 2·26 [95% CI 1·56–3·28], 1·75 [1·19–2·57], 2·20 [1·02–4·74], and 2·04 [1·25–3·32], respectively). Female sex was associated with high depression (OR 1·65 [1·13–2·41]), while older age (≥65 years) was associated with moderate depression (OR 1·82 [1·36–2·45]). In men, the ORs for patients with severe disability, severe stroke, pre-stroke depression, and cognitive impairment being in the high depression group were 1·91 (1·01–3·60), 2·41 (1·26–4·60), 2·57 (0·84–7·88), and 2·68 (1·28–5·60), respectively. In women, the ORs were 1·08 (0·52–2·23), 1·30 (0·60–2·79), 19·2 (2·35–156·05), and 3·80 (1·44–10·01), respectively. InterpretationFemale sex and older age were associated with distinct courses of depressive symptoms. In men, high depressive symptom trajectory was associated with severe stroke and severe disability, which was not the case in women. These findings were limited to patients with three or more assessments, who tended to have less severe disabilities than excluded patients and might not generalise to all stroke survivors. FundingNational Institute for Health and Care Research (NIHR).

Numeric rating scale for pain should be used in an ordinal but not interval manner. A retrospective analysis of 346,892 patient reports of the quality improvement in postoperative pain treatment registry.

To assess postoperative pain intensity in adults, the numeric rating scale (NRS) is used. This scale has shown acceptable psychometric features, although its scale properties need further examination. We aimed to evaluate scale properties of the NRS using an item response theory (IRT) approach. Data from an international postoperative pain registry (QUIPS) was analyzed retrospectively. Overall, 346,892 adult patients (age groups: 18-20 years: 1.6%, 21-30 years: 6.7%, 31-40 years: 8.3%, 41-50 years: 13.2%, 51-60 years: 17.1%, 61-70 years: 17.3%, 71-80 years: 16.4%, 81-90 years: 3.9%, >90: 0.2%) were included. Among the patients, 55.7% are female and 38% had preoperative pain. Three pain items (movement pain, worst pain, least pain) were analyzed using 4 different IRT models: partial credit model (PCM), generalized partial credit model (GPCM), rating scale model (RSM), and graded response model (GRM). Fit indices were compared to decide the best fitting model (lower fit indices indicate a better model fit). Subgroup analyses were done for sex and age groups. After collapsing the highest and the second highest response category, the GRM outperformed other models (lowest Bayesian information criterion) in all subgroups. Overlapping categories were found in category boundary curves for worst and minimum pain and particularly for higher pain ratings. Response category widths differed depending on pain intensity. For female, male, and age groups, similar results were obtained. Response categories on the NRS are ordered but have different widths. The interval scale properties of the NRS should be questioned. In dealing with missing linearity in pain intensity ratings using the NRS, IRT methods may be helpful.

Trajectories of Controller Therapy Use Before and After Asthma-Related Hospitalization in Children and Adults: Population-Based Retrospective Cohort Study.

Inappropriate use of inhaled corticosteroids (ICSs) for asthma impairs control and may cause exacerbation, including asthma-related hospitalization (ARH). In prospective studies, ICS use peaked around ARH, but information on routine care use is limited. Since ARH is a major outcome, controller therapy use in routine care before and after ARH should be documented. This study aimed to distinguish ICS use typologies (trajectories) before and after ARH, and assess their relationships with sociodemographic, disease, and health care characteristics. A retrospective cohort study was performed using a 1% random sample of the French claims database. All patients hospitalized for asthma between January 01, 2013, and December 31, 2015, were classified as either children (aged 1-10 years) or teens/adults (aged ≥11 years). Health care resource use was assessed between 24 and 12 months before ARH. ICS use was computed with the Continuous Measures of Medication Acquisition-7 (CMA7) for the 4 quarters before and after ARH. Initially, the overall impact of hospitalization on the CMA7 value was studied using a segmented regression analysis in both children and teens/adults. Then, group-based trajectory modeling differentiated the groups with similar ICS use. We tested different models having 2 to 5 distinct trajectory groups before selecting the most appropriate trajectory form. We finally selected the model with the lowest Bayesian Information Criterion, the highest proportion of patients in each group, and the maximum estimated probability of assignment to a specific group. Overall, 863 patients were included in the final study cohort, of which 447 (51.8%) were children and 416 (48.2%) were teens/adults. In children, the average CMA7 value was 12.6% at the start of the observation period, and there was no significant quarter-to-quarter change in the value (P=.14) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 34.9% (P=.001), before a significant decrease (P=.01) of 7.0% per quarter. In teens/adults, the average CMA7 value was 31.0% at the start, and there was no significant quarter-to-quarter change in the value (P=.08) before hospitalization. Immediately after hospitalization, the average CMA7 value rose by 26.9% (P=.002), before a significant decrease (P=.01) of 7.0% per quarter. We identified 3 and 5 trajectories before ARH in children and adults, respectively, and 5 after ARH for both groups. Trajectories were related to sociodemographic characteristics (particularly, markers of social deprivation) and to potentially inappropriate health care, such as medical management and choice of therapy. Although ARH had an overall positive impact on ICS use trajectories, the effect was often transient, and patient behaviors were heterogeneous. Along with overall trends, distinct trajectories were identified, which were related to specific patients and health care characteristics. Our data reinforce the evidence that inappropriate use of ICS paves the way for ARH.

Comparative analysis of methodologies for predicting overall survival in patients with non-small cell lung cancer based on the number and rate of resected positive lymph nodes: A study based on the SEER database for 2010 through 2019.

Lymph node (LN) metastasis is crucial in non-small cell lung cancer (NSCLC) prognosis and treatment, but the TNM system lacks LN quantity consideration. Our goal is to investigate the role of positive LNs (nPLN) and positive LN rate (LNR) in overall survival (OS) and assess whether they offer higher value in prognostic assessment of NSCLC than N-stage. Patients were stratified into four subgroups using X-Tile software. Statistical analysis was conducted using the Kaplan-Meier method, univariate analysis, and multivariate Cox regression analysis. Model performance was evaluated using the Harrell consistency index (C-index), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The prognostic performance of the nodal classification was validated using overall survival as the endpoint. The survival curves demonstrate distinct disparities between each nPLN and LNR category. A pronounced trend toward deteriorating overall survival from N-PLN 1 to N-PLN 2+ was observed across all tumor size categories. However, the differences between each LNR category were only significant for tumors ≤3cm and 5-7cm. Notably, both nPLN and LNR classifications displayed a higher C-index, lower AIC, and lower BIC compared with the N staging. Furthermore, the LNR classification provided superior prognostic stratification when compared with the nPLN classification. Our results demonstrate that nPLN and LNR classifications may offer improved prognostic performance compared with the current N classification for LN-positive NSCLC patients. Nonetheless, more studies are needed to assess the feasibility of incorporating these classifications into the next TNM staging system.

Subphenotypes of SARS-CoV-2-Associated ARDS Overlap Each Other: A Retrospective Analysis.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus-associated pneumonia and acute respiratory distress syndrome (ARDS) were often associated with hyperinflammation and elevation of several serum inflammatory markers but usually less than what is observed in non-coronavirus disease (COVID) ARDS. Elevated inflammatory markers such as C-reactive protein, interleukin (IL)-6, etc., are associated with severe infection. This study identified subphenotypes of COVID-19 ARDS patients by latent profile analysis in a cohort of Indian patients. Methods Data of n = 233 adult Indian patients with laboratory-confirmed SARS-CoV-2 infection admitted to a tertiary care teaching hospital were analyzed in this retrospective study. Only patients with acute respiratory failure (defined by partial pressure of oxygen/fraction of inspired oxygen ratio < 200 mm Hg) and chest X-ray showing bilateral infiltrates were included. Results The patients' mean (standard deviation) age was 53.3 (14.9) years, and 62% were male. A two subphenotypic model was formulated based on the lowest Bayesian information criterion. Neutrophil-to-lymphocyte ratio and serum IL-6 were latent variables in that model (entropy 0.91). The second phenotype (hyperinflammatory) had lower platelet count ( p = 0.02), higher serum creatinine ( p = 0.004), higher C-reactive protein ( p = 0.001), higher ferritin ( p < 0.001), and serum lactate dehydrogenase ( p = 0.009). Age-adjusted hospital mortality ( p = 0.007), duration of hospital stay ( p < 0.001), and duration of intensive care unit stay ( p < 0.001) were significantly higher in the second subphenotype. Conclusion Two distinct but overlapping subphenotypes were identified in SARS-CoV-2-associated respiratory failure. Hyperinflammatory subphenotype was associated with significantly poor short-term outcomes.

Modeling and forecasting of lifetime milk production in Hariana cattle

The present study was conducted on performance records of 648 Hariana cattle with respect to first lactation and lifetime traits spread over a period of 57 years (1962-2018) from DUVASU farm, Mathura and 23 years (1995– 2017) from State livestock farm Babugarh, Hapur. Lifetime production traits, viz. Lifetime 2 Milk Yield (LT2MY), Lifetime 3 Milk Yield (LT3MY), Lifetime 4 Milk Yield (LT4MY) and Lifetime 5 Milk Yield (LT5MY) were predicted by multiple linear regression analysis. Step-wise regression procedure was used to predict the lifetime milk yield from first lactation traits, viz. age at first calving (AFC), first calving interval (FCI) and first lactation total milk yield (FLTMY). The optimum models were selected with the help of Akaike information criterion (AIC), Bayesian information criterion (BIC), Mallow’s conceptual predictive value and variance inflation factor. The models with lower AIC, BIC and RMSE values and with higher co-efficient of determination were selected as optimum. The optimal models for lifetime production traits, viz. LT2MY, LT3MY, LT4MY and LT5MY with co-efficient of determination were 75.46%, 66.63%, 59.4% and 55.03%, respectively.

Severity Analysis of Multi-Truck Crashes on Mountain Freeways Using a Mixed Logit Model

Many studies have analyzed the road characteristics that affect the severity of truck crashes. However, most of these studies have only examined road alignment or grade separately, without considering their combined effects. The purpose of this article is to address this gap in the literature. Our study uses truck crash data from 2015 to 2019 on freeways in the Yunnan Province of China, where the severity levels of the crashes were determined by taking into account economic loss and the number of injuries and fatalities. Our study develops three models to examine the severity of truck crashes: a multinomial logit model, a mixed logit model, and a generalized ordered logit model. The findings suggest that the mixed logit model, which can suffer from unobserved heterogeneity, is more suitable because of the higher pseudo-R-squared (ρ2) value and lower Akaike Information Criterion and Bayesian Information Criterion. The estimation results show that the combination of curve and slope significantly increases the severity of truck crashes compared to curves and slopes alone. In addition, risk factors such as crash type, vehicle type, surface condition, time of day, pavement structure, and guardrails have a significant impact on the severity of truck crashes on mountainous freeways. Based on these findings, we developed policy recommendations for reducing the severity of multi-truck collisions on mountainous highways and improving transport sustainability. For example, if possible, the combination of curve and slope should be avoided. Additionally, it is recommended that trucks use tires with good heat resistance.

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors.

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).

Abstract P516: Multilevel Factors Are Related to Blood Pressure in Native Hawaiian Communities

The association between blood pressure and cardiovascular disease events (e.g., stroke, heart attack) is continuous, consistent, and independent. Hypertension prevalence is 55% among Native Hawaiians, the indigenous people of Hawai‘i, compared to 23% among Whites. Native Hawaiians have high rates of physical inactivity and poor diet. Discrimination and acculturation have been associated with blood pressure in Native Hawaiians. Household characteristics (e.g., food insecurity) have been linked to hypertension in Hawai‘i. Areas with high proportions of Native Hawaiians have more fast-food outlets. Limited research has been done to examine how multilevel factors impact blood pressure in this high-risk population. The purpose of this study was to examine the contribution of individual-level behavioral, biological, psychosocial; household-level; and community-level factors in explaining variability in systolic blood pressure (SBP) and diastolic blood pressure (DBP). Adult participants (n=150) were recruited from 6 predominantly Native Hawaiian communities across Hawai‘i. Height, weight, and blood pressure were measured using standardized protocols. Behavioral factors included consumption of foods measured by the NCI dietary screener questionnaire and physical activity category and sedentary time measured by the International Physical Activity Questionnaire. Psychosocial factors included cultural identity and perceived discrimination. Household factors included size, financial stability, and food insecurity. Community factors included neighborhood walkability. Sociodemographic factors were also assessed. Four multilevel models for SBP and DBP were developed. The level-1 model for both SBP and DBP included sociodemographic factors. The level-2 model for both SBP and DBP adds behavioral factors (e.g., BMI, physical activity, daily intake of diet components). The level-3 model adds financial stability, food insecurity for SBP and cultural identity and discrimination for DBP. The level-4 model adds walkability for SBP and financial stability, food insecurity, and family use of free school lunches for DBP. For SBP and DBP models, Akaike’s Information Criterion (AIC) decreased from 740.9 and 663.2 to 529.5 and 515.8, respectively, and Bayesian Information Criterion (BIC) decreased from 740.4 and 663.0 to 529.3 and 514.4, respectively. For SBP, each successive model produced a lower AIC and BIC, indicating that the inclusion of behavioral, household, and community factors improved the models’ ability to predict SBP. Similarly, for DBP, the inclusion of behavioral, psychosocial, and household-level factors improved the model's ability to predict DBP. These results illustrate the importance of including multilevel factors in research to understand and address hypertension and cardiovascular disease in Native Hawaiian communities.

Association Between Arterial Stiffness and Measures of Autonomic Dysfunction in People With Chronic Kidney Disease.

Autonomic nervous system (ANS) dysfunction and vascular stiffness increase cardiovascular risk in people with chronic kidney disease (CKD). Chronic elevations in sympathetic activity can lead to increased arterial stiffness; however, the relationship between these variables is unknown in CKD. To explore the association between measures of autonomic function and arterial stiffness in patients with moderate-to-severe CKD. This study was a prespecified secondary analysis of a randomized controlled trial. This included the following measures: 24-hour ambulatory blood pressure (BP), carotid-femoral and carotid-radial pulse wave velocity (PWV), and postexercise heart rate recovery (HRR). We used mixed effect linear regression models with Bayesian information criteria (BIC) to assess the contribution of ANS measurements. Forty-four patients were included in the analysis. Mean carotid-femoral and carotid-radial PWV were 7.12 m/s (95% CI 6.13, 8.12) and 8.51 m/s (7.90, 9.11), respectively. Mean systolic dipping, calculated as percentage change in mean systolic readings from day to night, was 10.0% (95% CI 7.79, 12.18). Systolic dipping was independently associated with carotid-radial PWV, MD -0.09 m/s (95% CI -0.15, -0.02) and had the lowest BIC. Systolic dipping was associated with carotid-radial PWV in people with moderate-to-severe CKD; however, there was no association with carotid-femoral PWV. Systolic dipping may be a feasible surrogate of ANS function, as the association with carotid-radial PWV was consistent with the minimal clinically important difference (MCID). Future studies are needed to define the relationship between ANS function, arterial stiffness, and CV events over time in people with CKD.

Combined Model of OCT Angiography and Structural OCT Parameters to Predict Paracentral Visual Field Loss in Primary Open-Angle Glaucoma

To assess a model combining OCT angiography (OCTA) and OCT parameters to predict the severity of paracentral visual field (VF) loss in primary open-angle glaucoma (POAG). Cross-sectional study. Forty-four patients with POAG and 42 control subjects underwent OCTA and OCT imaging with a swept-source OCT device. The circumpapillary microvasculature was quantified for vessel density (cpVD) and flow (cpFlow) after delineation of Bruch's membrane opening and removal of large vessels. Retinal nerve fiber layer thickness (RNFLT) and Bruch's membrane opening-minimum rim width (BMO-MRW) were measured from structural OCT. Paracentral total deviation (PaTD) was defined as the average of the total deviation values within the central 10 degrees on Humphrey VF testing (24-2) for upper and lower hemifields. The OCT and OCTA parameters were measured in the affected hemisphere corresponding to the hemifield with lower PaTD for POAG patients. Models were created to predict affected PaTD based on RNFLT alone; RNFLT and BMO-MRW; OCTA alone; or RNFLT, BMO-MRW and OCTA parameters. The models were compared using coefficient of determination (r2) and Bayesian information criterion (BIC) score. Bayesian information criterion decrease of ≥6 indicates strong evidence for model improvement. Performance of models containing OCT and OCTA parameters in predicting PaTD. Patients with POAG and controls were similar in age and sex (65.9 ± 9.5 years and 38.4% male overall, P ≥ 0.56 for both). Average RNFLT, minimum RNFLT, average BMO-MRW, minimum BMO-MRW, cpVD, and cpFlow were all significantly lower (all P < 0.001) in the affected hemisphere in patients with POAG than in controls. In patients with POAG, the average mean deviation was -4.33 ± 3.25 dB; the PaTD of the affected hemifield averaged -4.55 ± 5.26 dB and correlated significantly with both OCTA and structural OCT parameters (r ≥ 0.43, P ≤ 0.004 for all). The model containing RNFLT, BMO-MRW, and OCTA parameters was superior in predicting affected PaTD (r2= 0.47, BIC= 290.7), with higher r2 and lower BIC compared with all 3 other models. A combined model of OCTA and structural OCT parameters can predict the severity of paracentral VF loss of the affected hemifield, supporting clinical utility of OCTA in patients with POAG with paracentral VF loss. Proprietary or commercial disclosure may be found after the references.