Smaller Akaike Information Criterion Research Articles

Diagnostic performance of TILs-US score and LPBC in biopsy specimens for predicting pathological complete response in patients with breast cancer.

Tumor-infiltrating lymphocytes-ultrasonography (TILs-US) score is used to predict lymphocyte-predominant breast cancer (LPBC) in surgical specimens. We aimed to compare diagnostic performance of TILs-US score for predicting pathological complete response (pCR) with that of LPBC in biopsy specimens. TILs ≥ 50% in biopsy specimens was defined as biopsy-LPBC, and TILs-US score ≥ 4 was categorized as TILs-US score-high. Basic nomogram for pCR was developed using stepwise logistic regression based on the smallest Akaike Information Criterion, and biopsy-LPBC and TILs-US score nomograms were developed by integrating biopsy-LPBC or TILs-US scores into a basic nomogram. The diagnostic performance of the nomograms for pCR was compared using area under the curve (AUC), categorical net reclassification improvement (NRI), and integrated discrimination improvement (IDI). This retrospective study evaluated 118 patients with breast cancer, including 33 (28.0%) with biopsy-LPBC, 52 (44.1%) with TILs-US score-high, with 34 (28.8%) achieving pCR. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and AUC for predicting pCR were 0.53, 0.82, 2.96, 0.57, and 0.68, respectively, for biopsy-LPBC, and 0.76, 0.69, 2.47, 0.34, and 0.73, respectively, for TILs-US score. The biopsy-LPBC nomogram showed significant improvements in categorical NRI (p = 0.023) and IDI (p = 0.007) but not in AUC (p = 0.25), compared with the basic nomogram. The TILs-US nomogram exhibited significant improvements in AUC (p = 0.039), categorical NRI (p = 0.010), and IDI (p < 0.001). The TILs-US score may serve as a novel marker for prediction of pCR in patients with breast cancer. An external validation study is warranted to confirm our findings.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches

BackgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.MethodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).ResultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.ConclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure.Trials registrationORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Determinants of microbial community structure in supraglacial pool sediments of monsoonal Tibetan Plateau.

Supraglacial pools are prevalent on debris-covered mountain glaciers, yet only limited information is available on the microbial communities within these habitats. Our research questions for this preliminary study were: (1) What microbes occur in supraglacial pool sediments of monsoonal Tibet?; (2) Which abiotic and biotic habitat variables have the most influence on the microbial community structure?; and (3) Does microbial composition of supraglacial pool sediments differ from that of glacial-melt stream pool sediments? We collected microbial samples for 16S rRNA sequencing and invertebrates for enumeration and identification and measured 14 abiotic variables from 46 supraglacial pools and nine glacial-melt stream pools in 2018 and 2019. Generalized linear model analyses, small sample Akaike information criterion, and variable importance scores were used to identify the best predictor variables of microbial community structure. Multi-response permutation procedure (MRPP) was used to compare taxa composition between supraglacial pools and stream pools. The most abundant phyla in supraglacial pool sediments were Proteobacteria, Actinobacteria, Bacteroidota, Chloroflexi, and Cyanobacteria. Genera richness, indicator genera richness, and Polaromonas relative abundance were best predicted by Chironomidae larvae abundance. Angustibacter and Oryzihumus relative abundance were best predicted by pH, Acidiphilium relative abundance was best predicted by turbidity, and Sphingomonas relative abundance was best predicted by glacier zone. Taxa composition was similar between supraglacial and stream pools at the class, genus, and ASV taxonomic levels. Our results indicate that Chironomidae larvae may play a keystone species role in shaping bacterial communities of supraglacial pools on debris-covered glaciers.IMPORTANCEGlacier meltwater habitats (cryoconite holes, supraglacial pools, supraglacial ponds and lakes, glacial streams) and their biota have not been well-studied, especially on debris-covered glaciers in temperate monsoonal regions. Our study is the first to document the microbial community-habitat relationships in supraglacial pools on a debris-covered glacier in Tibet. Microbial genera richness, indicator genera richness, and Polaromonas relative abundance declined with increasing larval Chironomidae abundance, which is a novel finding that highlights the importance of larval insects in structuring microbial communities in supraglacial pools.

HANDLING OF OVERDISPERSION CASES IN MORBIDITY DATA IN SELUMA REGENCY

The problem of overdispersion as a violation of the assumption of equidispersion in Poisson regression is generally caused by sources of unobserved heterogeneity, missing observations on predictor variables, outliers in the data, errors in the specification of the bridging function, and many observed values that are zero. The purpose of this study is to find out the right model and the variables that affect data that occurs overdispersion and excess zero in the case of the number of days of disruption at work, school, or other daily activities due to health complaints. The methods used were Poisson Regression, Negative Binomial Regression, Hurdle Poisson Regression, Zero Inflated Poisson Regression, Zero Inflated Negative Binomial Regression, and Hurdle Negative Binomial Regression. The data used were morbidity taken from data on the number of days of disruption at work, school or other daily activities due to health complaints in Seluma district, Bengkulu Province. It was found that the best model is Zero Inflated Negative Poisson with the smallest Akaike Information Criterion (AIC) value of 1620.609 and the variables that have a significant effect on the log model and the logit model are marital status and work variables.

A nomogram to predict the pathological complete response in patients with breast cancer based on the TILs-US score.

The tumor-infiltrating lymphocytes-ultrasonography score is a calculation system for predicting lymphocyte-predominant breast cancers in surgical specimens. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score was developed to predict the pathological complete response in breast cancer treated with neoadjuvant chemotherapy. A retrospective evaluation was conducted on 118 patients with breast cancer treated with neoadjuvant chemotherapy at Hiroshima University Hospital. Tumor-infiltrating lymphocytes-ultrasonography scores ≥4 were classified as high. A nomogram was developed using a stepwise logistic regression model for pathological complete response (ypT0 ypN0), based on the smallest Akaike information criterion. The predictive ability and clinical usefulness of the nomogram were also evaluated. Among 118 patients, 34 (28.8%) achieved a pathological complete response, and 52 (44.1%) exhibited high tumor-infiltrating lymphocytes-ultrasonography. In multivariate logistic regression analysis, high tumor-infiltrating lymphocytes-ultrasonography (odds ratio, 6.01; P<0.001), clinical complete response (odds ratio, 4.83; P=0.004) and hormone receptor (odds ratio, 3.48; P=0.038) were independent predictors of pathological complete response. A nomogram based on tumor-infiltrating lymphocytes-ultrasonography score, clinical complete response, hormone receptor and clinical N status was developed. The nomogram showed an area under the curve of 0.831 and a bias-corrected area under the curve of 0.809. The calibration plot showed a good fit between the expected and actual pathological complete response values. Decision curve analysis also showed the clinical utility of the nomogram for predicting pathological complete responses. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score exhibited a favorable predictive ability for pathological complete response in patients with breast cancer, which can be useful in predicting the residual disease status after neoadjuvant chemotherapy.

MODELING THE MANY EARTHQUAKES IN SUMATRA USING POISSON HIDDEN MARKOV MODELS AND EXPECTATION MAXIMIZATION ALGORITHM

Sumatra Island is one of the islands that are prone to earthquakes because Sumatra Island is located at the confluence of three plates, namely the large Indo-Australian plate, the Eurasian plate and the Philippine plate. In general, the number of earthquake events follows the Poisson distribution, but there are cases where there is overdispersion in the Poisson distribution. The Poisson Hidden Markov Models (PHMMs) method is used to overcome overdispersion, then applying the Expectation-Maximization Algorithm (EM algorithm) to each model to obtain the estimated parameters. From the models obtained, the best model will be selected based on the smallest Akaike Information Criterion (AIC) value. The data used is secondary data on earthquake events on the island of Sumatra from January 2000 to December 2022 with a depth of ≤ 70 Km and a magnitude of ≥ 4.4 Mw. From the research, the model with m = 3 is the best estimation model with an AIC value of 1503,286. From the best model, estimates are obtained for Poisson Hidden Markov Models with an average occurrence of earthquakes of 5.7633 ≈ 6 events within one month.

Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients.

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients. A total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model. Patients with a high PNI (≥53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram. The pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients.

Lipid parameters, adipose tissue distribution and prognosis prediction in chronic kidney Disease patients

BackgroundLipid management in clinic is critical to the prevention and treatment of Chronic kidney disease (CKD), while the manifestations of lipid indicators vary in types and have flexible association with CKD prognosis.PurposeExplore the associations between the widely used indicators of lipid metabolism and their distribution in clinic and CKD prognosis; provide a reference for lipid management and inform treatment decisions for patients with non-dialysis CKD stage 3–5.MethodsThis is a retrospective cohort study utilizing the Self-Management Program for Patients with Chronic Kidney Disease Cohort (SMP-CKD) database of 794 individuals with CKD stages 3–5. It covers demographic data, clinical diagnosis and medical history collection, laboratory results, circulating lipid profiles and lipid distribution assessments. Primary endpoint was defined as a composite outcome(the initiation of chronic dialysis or renal transplantation, sustained decline of 40% or more in estimated glomerular filtration rate (eGFR), doubled of serum creatinine (SCr) from the baseline, eGFR less than 5 mL/min/1.73m2, or all-cause mortality). Exposure variables were circulating lipid profiles and lipid distribution measurements. Association were assessed using Relative risks (RRs) (95% confidence intervals (CIs)) computed by multivariate Poisson models combined with least absolute shrinkage and selection operator (LASSO) regression according to categories of lipid manifestations. The best model was selected via akaike information criterion (AIC), area under curve (AUC), receiver operating characteristic curve (ROC) and net reclassification index (NRI). Subgroup analysis and sensitivity analysis were performed to assess the interaction effects and robustness..Results255 individuals reached the composite outcome. Median follow-up duration was 2.03 [1.06, 3.19] years. Median age was 58.8 [48.7, 67.2] years with a median eGFR of 33.7 [17.6, 47.8] ml/min/1.73 m2. Five dataset were built after multiple imputation and five category-based Possion models were constructed for each dataset. Model 5 across five datasets had the best fitness with smallest AIC and largest AUC. The pooled results of Model 5 showed that total cholesterol (TC) (RR (95%CI) (per mmol/L) :1.143[1.023,1.278], P = 0.018) and percentage of body fat (PBF) (RR (95%CI) (per percentage):0.976[0.961,0.992], P = 0.003) were significant factors of composite outcome. The results indicated that comprehensive consideration of lipid metabolism and fat distribution is more critical in the prediction of CKD prognosis..ConclusionComprehensive consideration of lipid manifestations is optimal in predicting the prognosis of individuals with non-dialysis CKD stages 3–5.

Correlation between the Density of Acipenser sinensis and Its Environmental DNA.

Since the construction of the Gezhouba Dam, Chinese sturgeon (Acipenser sinensis) numbers have gradually declined, rendering this species critically endangered according to the International Union for the Conservation of Nature. Environmental DNA (eDNA) technology plays an important role in monitoring the abundance of aquatic organisms. Species density and biomass have been proven to be estimable by researchers, but the level of accuracy depends on the specific species and ecosystem. In this study, juvenile A. sinensis, an endangered fish, were selected as the research target. Under controlled laboratory conditions in an aquarium, one, two, four, six, and eight juvenile A. sinensis were cultured in five fish tanks, respectively. Water samples were filtered at eight different time points for eDNA content analysis. Additionally, eDNA yield was tested at six different time points after a 0.114 ind./L density of A. sinensis was removed, and the employed degradation model was screened using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The results showed that eDNA content remained stable after 3 days and exhibited a significant positive linear correlation with the density of A. sinensis (R2 = 0.768~0.986). Furthermore, eDNA content was negatively correlated with the 3-day period after the removal of A. sinensis. The power function had the smallest AIC and BIC values, indicating better fitting performance. This study lays a momentous foundation for the application of eDNA for monitoring juvenile A. sinensis in the Yangtze Estuary and reveals the applicability of eDNA as a useful tool for assessing fish density/biomass in natural environments.

Studying the spatial non-stationary relationships of some physical parameters on the Earth's surface temperature using GWR in Upper Awash basin, Ethiopia

Exploring the spatial non-stationary relationships between land surface temperature (LST) and their driving environmental factors is important for selecting appropriate strategies to mitigate and regulate the thermal environment of watersheds. To examine the influence of various biophysical factors on LST in the Upper Awash Basin (UAB) of Ethiopia, the study used two models: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. As a global model, the OLS model was initially used to capture the overall relationship between LST and some biophysical factors. And then the GWR, a local spatial modeling approach, was used to examine the spatial non-stationary relationships between LST and its influencing biophysical factors. Landsat 8 OLI/TIRS image and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) were used to generate the LST and its influencing biophysical factors. Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST. The result showed that the GWR model, with a higher coefficient of determination (R2) (GWR: 0.98; OLS: 0.52) and a smaller Akaike Information Criterion (AIC) (GWR: 12354; OLS: 65412), provides a better prediction than the traditional OLS model, reflecting the spatial non-stationarity relationships. The results also showed that increased LST was significantly affected by NDBI, NDBaI, and albedo, with NDBI having the greatest effect. Conversely, EVI, MNDVI, and DEM showed a negative correlation with LST, with EVI having the greatest impact. These findings highlighted the importance of considering the spatial non-stationarity relationships between LST and pertinent driving factors, and they also offer recommendations for mitigating measures to control the thermal environment of a river basin.

Modelling Infant Mortality Rate using Time Series Models

The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

On Hybrid Autoregressive Integrated Moving Average Artificial Neural Network Time Series Analysis of the Nigeria External Reserves

The amount of the external reserves possessed by a country is an essential component in the measure of its economic status. In essence, a country with substantial amount of money in its external reserves would experience a buoyant and friendly economy among other nations. This study employs three different time series models to examine the status of the Nigeria External reserves for 22 years (1960-2022). These include the Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANN) and the hybrid ARIMA-ANN models. ARIMA (1,0,1)(0,0,2)12 emerged as the optimum model among other fitted linear models, having the smallest Akaike Information Criterion (AIC) value of 14365.84 and was used to fit the nonlinear Hybrid ARIMA-ANN model and for the prediction of the External Reserves series. The Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) values of (590.1479), (358.3421), and (24.10321) for the Hybrid ARIMA-ANN model were also the least in comparison with their corresponding values for the other models. These show that the Hybrid model has the least error value compared with those of the independent ARIMA and ANN models. Hence, the nonlinear Hybrid ARIMA-ANN model performed excellently in the estimation and the generation of the forecast values than the conventional linear ARIMA and the nonlinear ANN models.

A multi‐site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals

AbstractBackgroundDeveloping effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi‐site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating β‐amyloid positivity (A+/‐) and predicting disease progression.MethodsT1‐weighted MRI and cognitive data of 2819 CN (A‐/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A‐. In 563 CN with cross‐sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross‐sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported.ResultsSignificant differences between A+/A‐ (Table 2) were observed in cross‐sectional tau (most significant), cross‐sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1‐a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional information. The selected biomarkers can identify fast versus slow progressors with AUCs ranging from 0.63‐0.78 (Figure 1‐b).ConclusionsThe results demonstrated that both cross‐sectional (tau PET and cognition) and longitudinal biomarkers (MRI and cognition) are sensitive to amyloid status in CN. In addition, baseline tau PET and cognitive measures provide complementary information in predicting disease progression regardless of amyloid status. This indicates that these biomarkers may have important utility in preclinical AD clinical trials and normal aging studies.

Abstract 16300: Genetic Influences on Cardiovascular Deaths During Various Follow-Up Lengths in the 45-Year Longitudinal Twin Study

Background: Genetic factors are known to affect cardiovascular and coronary heart death. The more deaths occur during the longer follow-up period in a longitudinal study. However, it is unclear whether follow-up lengths affect genetic influences on the mortality from total cardiovascular (CVD) and coronary heart diseases (CHD). Objective: To determine genetic influences on the mortality from CVD and CHD over various years of follow-up in the 45-year longitudinal National Heart, Lung, and Blood Institute (NHLBI) Twin Study. Method: A total of 1024 middle-aged, white male, veteran twins (512 pairs), including 253 monozygotic and 261 dizygotic twin pairs, were initially enrolled during 1969-1973 and then followed up on vital status and causes of death through December 31, 2015. Tetrachoric correlation coefficients were estimated for CHD and CVD death at 20, 30, and 45 years of follow-up, respectively. Genetic and environmental influences on the death were quantified using the best-fitting structural equation model selected with the smallest Akaike's Information Criterion value and the parsimony rule. Results: The age at death ranged from 43.9 to 97.3 years over a 45-year follow-up. Tetrachoric correlation coefficients in dizygotic twins were less than half of that in monozygotic twins over 20, 30, and 45 years of follow-up for CVD but over 20 and 30 years for CHD, respectively. Dominant genetic factors explained 40% (95% CI 5% ~ 68%), 39 % (16% ~ 59%), and 24% (4% ~ 43%) of variation in CVD death over 20, 30, and 45 years of follow-up, respectively, while the remaining variation in CVD death was explained by unique environmental factors during each follow-up period. For CHD death, unique environmental factors explained all variation over 20 and 45 years of follow-up. By contrast, dominant genetic factors explained 26% (95% CI -6% ~ 53%) variation in CHD death over 30 years of follow-up while the remaining variation was explained by unique environmental factors. Conclusion: Dominant genetic factors consistently influence cardiovascular mortality over 20 to 45 years of follow-up, implying a twin study of cardiovascular death can control for genetic confounding during this period.

Derivation and validation of a risk score to predict acute kidney injury in critically ill cirrhotic patients.

Acute kidney injury (AKI) is a common complication in critically ill cirrhotic patients with substantial mortality. Given AKI can be prevented via early detection, it is urgent to develop an easy model to identify high-risk patients. A total of 1149 decompensated cirrhotic (DC) patients from the eICU Collaborative Research Database were enrolled for model development and internal validation. The variables used for analysis mainly included laboratory tests. We first built an ensemble model (random forest, gradient boosting machine, K-nearest neighbor, and artificial neural network) named DC-AKI using machine learning methods. Based on the Akaike Information Criterion (AIC), we then constructed a risk score, which was externally validated in 789 DC patients from the Medical Information Mart for Intensive Care database. AKI developed in 212 (26%) of 804 patients in the derivation cohort and 355 (45%) of 789 patients in the external validation cohort. DC-AKI identified the 8 variables most strongly associated with the outcome: serum creatinine (sCr), total bilirubin, magnesium, shock index (SI), prothrombin time, mean corpuscular hemoglobin, lymphocytes and arterial oxygen saturation (SaO2 ). Based on the smallest AIC, a 6-variable model was eventually used to construct the scoring system (sCr, total bilirubin, magnesium, SI, lymphocytes and SaO2 ). The scoring system showed good discrimination with the AUROC of 0.805 and 0.772 in two validation cohorts. The scoring system using routine laboratory data was able to predict the development of AKI in critically ill cirrhotic patients. The utility of this score in clinical care requires further research. This article is protected by copyright. All rights reserved.

On the Earthquake Distribution Modeling in Sumatra by Cauchy Cluster Process: Comparing Log-Linear and Log-Additive Intensity Models

Inhomogeneous cluster point processes have been considered for modeling the distribution of earthquake epicenters with the spatial trend and clustering patterns. In particular, the spatial trend is assessed by the intensity model involving geological variables. However, for intensity with a log-linear form, it may be too restrictive and not appropriate for earthquake distribution. In this study, we consider the Cauchy cluster process with the log-additive intensity model to analyze the distribution of major earthquake occurrences in Sumatra, Indonesia. The estimation procedure follows the standard two-step estimation technique, where the first step adapts the method for the Generalized Additive Models (GAMs) using penalized iteratively reweighted least squares (PIRLS) algorithm, and the second step employs the second-order composite likelihood. For the earthquake analysis in Sumatra, the log-additive intensity shows more flexibility to determine the contribution of each geological factor, especially to capture the effect of the nearest distance to the fault which is far from log-linear. In addition, compared to the log-linear model, the Cauchy cluster process with a log-additive intensity model performs better with a smaller Akaike Information Criterion’s (AIC) value and a sharper envelope K-function. The estimated number of mainshocks is around 114 with aftershocks spread by 14 km around the mainshocks. We detect three hotspots for the major earthquake in Sumatra: the northern part (Aceh and North Sumatra), the western part (Mentawai, Nias, and Simeulue), and Bengkulu.

Determinants of maternal morbidity during pregnancy in urban Bangladesh.

Maternal morbidities especially life-threatening pregnancy complications are major health concerns in developing countries. The main aim is to investigate the prevalence of maternal morbidity during pregnancy and its determinants among women from urban areas of Bangladesh. The secondary data were used and extracted from the latest Bangladesh Urban Health Survey (BUHS) 2013. Several statistical models: Poisson, negative binomial (NB) and mixed Poisson were adapted and compared to explore the best model for investigating potential determinants of maternal morbidity. Pearson chi-square statistic was used for the detection of overdispersion in the data. Results Overall 13.5% of the urban women in Bangladesh suffered from at least two pregnancy complications. The study detected the overdispersion existing in the maternal morbidity count data and found the NB regression as the best choice for analyzing the data because of its smallest Akaike information criterion. Administrative division (Rangpur: p = 0.003, incidence rate ratio, IRR = 1.34, 95% confidence interval, CI: 1.11 to 1.63; Sylhet: p = 0.006, incidence rate ratio, IRR = 1.42, 95% CI: 1.11 to 1.82), unwanted pregnancy (p<0.001, IRR = 1.25, 95% CI: 1.11 to 1.40), place of delivery (p<0.001, IRR = 1.68, 95% CI: 1.53 to 1.86) and wealth index (Poor: p<0.001, IRR = 1.34, 95% CI: 1.19 to 1.50; Middle: p = 0.003, IRR = 1.21, 95% CI: 1.08 to 1.36) were found to be statistically significant determinants for maternal morbidity during pregnancy among the urban women in Bangladesh. Urban women in Bangladesh with an unwanted pregnancy, from the poor/middle-income group; and living in Rangpur and Sylhet divisional cities have a higher risk of maternal morbidity during pregnancy. Study findings may help the government and relevant authorities to take necessary steps for reducing maternal morbidity and mortality due to pregnancy-related complications.

A Nomogram Based on the Log Odds of Positive Lymph Nodes Predicts the Prognosis of Patients with Colon Neuroendocrine Tumors After Surgery: A Surveillance, Epidemiology, and End Results Population-Based Study.

This work focused on determining the highly efficient nodal classification system from American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) classification (eighth edition), positive lymph node, log odds of positive lymph nodes (LODDS), lymph node ratio, examined lymph node, and establishing the new nomogram for predicting cancer-specific survival in colon neuroendocrine tumors (CNETs). From the Surveillance, Epidemiology, and End Results database, 943 CNET cases undergoing radical operation were enrolled, and randomized as training (n = 663) or validation set (n = 280). For the above 5 lymph node classification systems, their prediction performances were compared with C-index, Akaike information criterion (AIC), and area under the receiver operating characteristic curve. Univariate together with multivariate regression was carried out for identifying independent risk factors. Afterward, this work established 1 nomogram and confirmed its accuracy based on C-index, calibration curves, together with the area under the curve value. Besides, it was compared with the AJCC TNM classification system with regard to model prediction performance. LODSS achieved the greatest area under the curve and C-index, whereas the smallest AIC. Upon multivariate regression, age, histologic grade, T stage, M stage, and LODDS independently predicted the risk of CNETs. For the validation set, the C-index of the nomogram was 0.794, and the area under the curves at 1, 3, and 5 years was 0.826, 0.857, and 0.870, separately. Additionally, as revealed by the C-index, AIC, decision curve analysis, as well as Kaplan-Meier analysis, our nomogram had superior performance to the AJCC TNM classification system. For postoperative patients with CNETs, the LODDS might achieve the best prediction performance. Moreover, the LODDS-based nomograms might show superior survival prediction performance to the AJCC TNM classification system (eighth edition).

Development and External Validation of a Nomogram to Predict the Benefit of Regional Node Irradiation in Patients with pT1-2N1M0 Breast Cancer

<h3>Purpose/Objective(s)</h3> To establish a nomogram for predicting the value of regional node irradiation (RNI) in reducing locoregional recurrence (LRR) in patients with pT1-2N1M0 breast cancer. <h3>Materials/Methods</h3> A total of 4262 women with pT1–2N1M0 breast cancer treated at two institutions between 1999 and 2014 were retrospectively reviewed. All had received lumpectomy or mastectomy and axillary lymph node dissection without neoadjuvant chemotherapy. All received whole breast irradiation ± RNI if underwent breast-conserving surgery. Patients from the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS, n=2745) formed the training set and patients from the Sun Yat-Sen University Cancer Center (SYSUCC, n = 1517) the validation set. The prognostic model for LRR was obtained from the full multivariate Cox proportional-hazard model by backward selection. The model with the smallest Akaike Information Criterion was selected as the final model. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. <h3>Results</h3> The median age was 48 (range, 23-84) years. Four hundred and four (9.5%) patients had lumpectomy and 3858 (90.5%) patients had mastectomy. A total of 3995 (93.7%) patients received adjuvant chemotherapy. Among 3296 patients with hormone receptor (HR) positive disease, 2862 (86.8%) patients received endocrine therapy. Among 859 patients with Her2 positive disease, 233 (27.1%) patients received anti-Her2 targeted therapy. There were 576 (21.1%) patients in CHCAMS and 356 (23.5%) patients in SYSUCC who had received RNI. After median follow-up time of 70 months (range, 6-226 months), 332 (7.8%) patients had LRR. The 5-year LRR was 4.0% and 7.2% with and without RNI (P = 0.001). The nomogram incorporated seven risk factors derived from multivariable analysis of the training set including age, tumor quadrant, T stage, number of positive nodes, histological grade, HR status, and RNI. Our nomogram showed good discrimination in both the training set and the validation set (the C-indexes were 0.717 [95% CI: 0.679-0.756] and 0.712 [95% CI: 0.650-0.773], respectively). For 3-year and 5-year LRR, the AUC was 0.743 and 0.717 in the training set, and 0.723 and 0.728 in the validation set. Both in the training and validation sets, the calibration curve showed good agreement between the nomogram-based predictions and the actual observations. <h3>Conclusion</h3> The proposed nomogram appears to accurately estimate the LRR risk of an individual case with and without RNI and could help radiation oncologists to decide on use of RNI for patients with pT1-2N1M0 breast cancer.

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV.

BACKGROUND:We previously reported the added value of 24-hour lactate concentration alone and in combination with 24-hour lactate clearance and lactate concentration at admission for the prediction of inhospital mortality in critically ill patients with sepsis. We aimed to validate this finding.DERIVATION COHORT:The derivation cohort from Leiden, The Netherlands, consisted of 451 critically ill patients with sepsis.VALIDATION COHORT:The validation cohort consisted of 4,440 critically ill adult patients with sepsis from the Medical Information Mart for Intensive Care cohort admitted to the ICU of Beth Israel Deaconness Medical Center, Boston, MA, between January 2006 and 2018.PREDICTION MODEL:Predictors of mortality were: age, chronic comorbidities, length of stay pre-ICU, Glasgow Coma Scale, and Acute Physiology Score. Lactate concentration at 24-hour alone, in combination with 24-hour lactate clearance and in combination with lactate concentration at admission, was added to assess improvement of the prediction model. The outcome was inhospital mortality.RESULTS:Inhospital mortality occurred in 160 patients (36%) in the derivation cohort and in 2,347 patients (53%) in the validation cohort. The Acute Physiology and Chronic Health Evaluation (APACHE) IV model had a moderate discriminative performance (recalibrated C-statistic, 0.62; 95% CI, 0.60–0.63). Addition of 24-hour lactate concentration increased the recalibrated C-statistic to 0.64 (95% CI, 0.62–0.66). The model with 24-hour lactate concentration and lactate concentration at admission showed the best fit as depicted by the smallest Akaike Information Criterion in both the derivation and validation data.CONCLUSION:The 24-hour lactate concentration and lactate concentration at admission contribute modestly to prediction of inhospital mortality in critically ill patients with sepsis. Future updates and possible modification of APACHE IV should consider the incorporation of lactate concentration at baseline and at 24 hours.