Lowest Akaike Information Criterion Value Research Articles

A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts.

Comparative analysis of adolescent hypertension definitions for predicting early-adulthood carotid artery intima-media thickness: Tehran lipid and glucose study.

Definitions of childhood and adolescent hypertension (HTN) do not precisely elucidate the relationship between HTN and cardiovascular outcomes. Carotid intima-media thickness (CIMT), as a substitute for cardiovascular outcomes, enables the early identification of cardiovascular events throughout early adulthood. This study aimed to compare the ability of childhood HTN definitions to predict a high CIMT in early adulthood. This prospective cohort study included 921 individuals aged 10-17 years from the Tehran Lipid and Glucose Study (TLGS). The CIMT was measured after 18 years of follow-up. Participants were categorized into normal blood pressure (BP), high-normal BP, HTN stage 1, and HTN stage 2 groups based on the childhood HTN definitions of the 4th report, European Society of Hypertension (ESH), and American Academy of Pediatrics Clinical Practice Guidelines (AAP-CPG). Akaike's information criterion (AIC) and relative efficiencies (RE) were calculated to compare the ability of each to predict a high CIMT (≥95th percentile) during early adulthood. The highest and lowest prevalence of stage 1 HTN was observed with the AAP-CPG (17.7%) and ESH (8.8%), respectively. Similarly, the highest and lowest prevalence of stage 2 HTN was noted with the AAP-CPG (1.5%) and ESH (0.8%), respectively. According to the RE values, the highest to lowest predictive abilities belonged to the 4th report, ESH, and AAP-CPG, respectively. In all models, the 4th report's pediatric HTN definition had the lowest AIC value and offered the best predictive ability. Among the various definitions of pediatric HTN, the 4th report offered the best ability to predict a high CIMT during early adulthood, followed by the ESH and AAP-CPG. Because the reference population of the 4th report includes overweight, obese, and normal-weight individuals, our findings suggest that excessive adiposity is among the main predictors of early adulthood atherosclerosis risk.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement.

The objective of this investigation is to explore the capability of baseline 18F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI). 126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index). 62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05). The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.

Prevalence and associated factors of teenage childbearing among Ethiopian women using semi-parametric and parametric proportional hazard and accelerated failure time models

BackgroundTeenage childbearing is a common issue for young people’s sexual and reproductive health in the world, particularly in low-income countries, and affects teenagers between the ages of 13 and 19. According to several academics, adolescent pregnancy accounts for the majority of Ethiopia’s population increase, and there has been little effort to address this threat. This study aimed to determine the prevalence and associated factors of the time to teenage childbearing in Ethiopia.MethodThis paper compares the results of the semi-parametric proportional hazard (PH), parametric PH, and accelerated failure time (AFT) models to find the model that best fits the data. The Akaike Information Criterion (AIC) was used to evaluate the performance of models examined in this investigation. Time to teenage childbearing was the study’s outcome variable, while the analysis considered various independent variables. We analyze data from the 2016 National Demographic Health Survey to assess the influence of different risk factors on teenage pregnancy among Ethiopian women.ResultsOut of the 10,274 teenagers (aged between 13 and 19) who participated in the 2016 survey, 6,430 (62.59%) were parents. The study findings revealed that these teenage parents were influenced by various time-related factors before becoming parents. The log-normal AFT model has the lowest AIC value and hence it is the best fit for this data. Results from this model indicated that significant factors influencing the time to teenage childbearing include the age of the household head, current age of the respondents, region, religion, educational attainment, wealth status, intention to use contraception, and recent sexual activity.ConclusionThis study reveals that 62.59% of surveyed teenagers aged 13 to 19 were parents. Various factors at both the individual and community levels: including the age of the household head, regional differences, religious affiliation, educational level, economic status, contraceptive intentions, and recent sexual activity, determine the time to teenage childbearing. Targeted interventions addressing these factors are essential for reducing teenage pregnancies and supporting adolescent parents effectively.

SQEAIR: an Improved Infectious Disease Dynamics Model

The Covid-19 pandemic poses a significant threat to human health and life. Timely and accurate prediction of the epidemic’s trajectory is crucial for devising effective prevention and control strategies. Traditional infectious disease models may not capture the complexity of modern epidemics, especially when governments implement diverse policies. Drawing from China’s epidemic prevention strategies and Covid-19 transmission characteristics, this study introduces two distinct categories quarantined cases and asymptomatic cases to enhance the traditional SEIR model in depicting disease dynamics. To address the intricate nature of prevention and control efforts, the quarantined cases are further segmented into three subgroups: exposed quarantined, asymptomatic quarantined, and infected quarantined cases. Consequently, a novel SQEAIR model is proposed to model the dynamics of Covid-19. Evaluation metrics such as the Akaike information criterion (AIC) and Absolute Percentage Error (MAE) are employed to assess the efficacy and accuracy of both the newly proposed and traditional models. By fitting the models to the number of infected cases in Shanghai (March to May 2022) and Guangzhou (November 2022), it was observed that the SQEAIR model exhibited a lower AIC value compared to the SEIR model, indicating superior fitting accuracy for Covid-19 infections. Moreover, the high accuracy of the SQEAIR model enabled precise predictions of confirmed cases in Guangzhou. Leveraging the SQEAIR model, various parameters were tested to simulate the impact of different influencing factors, enabling the evaluation of defense strategies. These findings underscore the effectiveness of key epidemic control measures, such as quarantining exposed cases, in enhancing public health and promoting awareness of personal protection.

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.

The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Application of Mixture and Non-mixture Cure Models in Survival Analysis of Patients With COVID-19.

Background Due to the emergence of new COVID-19 mutations and an increase in re-infection rates, it has become an important priority for the medical community to identify the factors affecting the short- and long-term survival of patients. This study aimed to determine the risk factors of short- and long-term survival in patients with COVID-19 based on mixture and non-mixture cure models. Methodology In this study, the data of 880 patients with COVID-19 confirmed with polymerase chain reaction in Fereydunshahr city (Isfahan, Iran) from February 20, 2020, to December 21, 2021, were gathered, and the vital status of these patients was followed for at least one year. Due to the high rate of censoring, mixture and non-mixture cure models were applied to estimate the survival. Akaike information criterion values were used to evaluate the fit of the models. Results In this study, the mean age of the patients was 48.9 ± 21.23 years, and the estimated survival rates on the first day, the fourth day, the first week, the first month, and at one year were 0.997, 0.982, 0.973, 0.936, and 0.928, respectively. Among the parametric models, the log-logistic mixed cure model with the logit link, which showed the lowest Akaike information criterion value, demonstrated the best fit. The variables of age and prescribed medication type were significant predictors of long-term survival, while occupation was influential in the short-term survival of patients. Conclusions According to the results of this study, it can be concluded that elderly patients should observe health protocols more strictly and consider receiving booster vaccine doses. The log-logistic cure model with a logit link can be used for survival analysis in COVID-19 patients, a fraction of whom have long-term survival.

Relationship Between Tenofovir Diphosphate Concentrations in Dried Blood Spots and Virological Outcomes After Initiating Tenofovir-Lamivudine-Dolutegravir as First-Line or Second-Line Antiretroviral Therapy.

Tenofovir diphosphate (TFV-DP) concentration in dried blood spots is a marker of long-term adherence. We investigated the relationship between TFV-DP concentrations and virological outcomes in participants initiating tenofovir-lamivudine-dolutegravir (TLD) as first-line or second-line antiretroviral therapy. Three primary care clinics in Khayelitsha, Cape Town, South Africa. We conducted a post hoc analysis of 2 randomized controlled trials of participants initiating TLD. TFV-DP concentrations and viral loads were measured at 12, 24, and 48 weeks. Multivariable logistic regression was performed to assess the association with virological suppression (<50 copies/mL) per natural logarithm increase in TFV-DP concentration. Generalized estimating equations with logit link were used to assess associations with virological rebound. The Akaike Information Criterion and Quasi-likelihood Information Criteria were used to compare models built on continuous TFV-DP data to 4 previously defined concentration categories. We included 294 participants in the analysis, 188 (64%) of whom initiated TLD as second-line therapy. Adjusted odds ratios (95% CIs) of virological suppression were 2.12 (1.23, 3.75), 3.11 (1.84, 5.65), and 4.69 (2.81, 8.68) per natural logarithm increase in TFV-DP concentration at weeks 12, 24, and 48, respectively. In participants with virological suppression at week 12, the adjusted odds ratio for remaining virologically suppressed was 3.63 (95% CI: 2.21 to 5.69) per natural logarithm increase in TFV-DP concentration. Models using continuous TFV-DP data had lower Akaike Information Criterion and Quasi-likelihood Information Criteria values than those using categorical data for predicting virological outcomes. TFV-DP concentrations in dried blood spots exhibit a dose-response relationship with viral load. Analyzing TFV-DP concentrations as continuous variables rather than conventional categorization may be appropriate.

Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.

Development and evaluation of the Newstage system: integrating tumor regression grade and lymph node status for improved prognostication in neoadjuvant treatment of gastric cancer

BackgroundThe predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy.MethodsA retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system.ResultsIn a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001).ConclusionThe Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor.

Anterior Knee Pain Scale (AKPS): structural and criterion validity in Brazilian population with patellofemoral pain

PurposeTo identify the best internal structure of the Brazilian version of the Anterior Knee Pain Scale (AKPS), comparing different instrument structures (structural validity) and correlating the scores of the versions (criterion validity).MethodsWe included Brazilian volunteers, aged ≥ 18 years, with patellofemoral pain (PFP) for at least 3 months. We used the confirmatory factor analysis and considered the following fit indices: chi-square/degrees of freedom (DF), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA). We considered the structure with the lowest values of the Akaike information criterion (AIC), sample size adjusted Bayesian information criterion (SABIC), and assessed criterion validity using Pearson correlation coefficient (r) to correlate the long and short versions.ResultsThe study included 101 participants, mostly women (65.3%), young adults (~ 31 years old), overweight (BMI > 25 kg/m2), incomplete higher education (37.6%), and physically active (64.4%). The original 1-domain, 13-item structure showed adequate fit indices (chi-square/GL < 3.00, TLI and CFI > 0.90, and RMSEA < 0, 08). However, items 11 and 12 had a factorial load of less than 0.23. Therefore, we excluded items 11 and 12 and found adequate fit indices (chi-square/GL < 3.00, TLI and CFI > 0.90, and RMSEA < 0, 08) and lower AIC and SABIC values. We observed a correlation coefficient above the acceptable cutoff of 0.70 (r = 0.966, p-value < 0.001) between the versions.ConclusionThe 11-item AKPS (without items 11 and 12) is the version with the most adequate internal structure and correlates satisfactorily with the long version of the instrument.

Perhitungan Premi Murni Kendaraan Bermotor Menggunakan Generalized Linear Models

Insurance is a form of agreement between the insurer against the insured party (insurance company), where the insurer will pay a premium to the insured every month as a form of collateral for loss or damage that befalls him for certain events. Researchers took data by distributing questionnaires to respondents. Some covariate variables used in the study include: gender, vehicle age, vehicle insurance costs, vehicle use, vehicle mileage every year, and claims records in the last three years. Meanwhile, the dependent variable is frequency claim and claim severity. The researcher estimates the Generalized Linear Models (GLZ) parameters using the maximum likelihood estimation (MLE). The next step, researchers conducted a model compatibility test on the dependent variable using calculations on the Akaike Information Criteria (AIC), where the model chosen was the model with the lowest AIC value. Based on this, the researcher can determine covariate variables that affect the dependent variable and model it. The final step is the researcher calculates the probability of claim frequency, claim severity, and estimates the amount of pure premiums charged to respondents.

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.

The liver-heart axis in patients with severe obesity: The association between liver fibrosis and chronic myocardial injury may be explained by shared risk factors of cardiovascular disease

BackgroundSevere obesity is associated with increased risk of non-alcoholic fatty liver disease and cardiovascular disease. We hypothesized that liver fibrosis as quantified by the Enhanced Liver Fibrosis (ELF) test would be predictive of myocardial injury and fibrosis, expressed by higher concentrations of cardiac troponin T and I measured by high-sensitivity assays (hs-cTnT and hs-cTnI, respectively). Material and methodsWe performed cross-sectional analyses of baseline data from 136 patients (mean age 45 years, 38 % male) with severe obesity participating in the non-randomized clinical trial Prevention of Coronary Heart Disease in Morbidly Obese Patients (ClinicalTrials.gov NCT00626964). Associations between ELF scores, hs-cTnT, and hs-cTnI concentrations were assessed using linear regression analysis. ResultsELF scores were associated with hs-cTnT in the unadjusted model (B 0.381, 95 % Confidence Interval [CI] 0.247, 0.514), but the association was attenuated upon adjustment for potential confounders (B −0.031, 95 % CI −0.155, 0.093). Similarly, for hs-cTnI, an observed association with ELF scores in the unadjusted model was attenuated upon adjustment for potential confounders ((B 0.432, 95 % CI 0.179, 0.685) and (B 0.069, 95 % CI −0.230, 0.367), respectively). Age, sex, hypertension, and estimated glomerular filtration rate were amongst the shared predictors of ELF score, hs-cTnT, and hs-cTnI that provided the univariable models with the highest R-squared and lowest Akaike Information Criterion values. ConclusionsContrary to our hypothesis, ELF score did not predict myocardial injury and fibrosis, but we rather demonstrated an association between liver fibrosis and myocardial injury and fibrosis may be explained by shared risk factors of cardiovascular disease.

ASGARD risk scores for long-term, individualized cardiovascular surveillance of low-risk patients with asymptomatic non-severe aortic valve stenosis: development and internal validation

Abstract Background Routine echocardiography of asymptomatic patients with non-severe aortic valve stenosis (AS) mainly focuses on the valve condition at 1-2 year intervals but has limited ability to predict the risk of other cardiovascular comorbidities. Thus, there is a compelling need to replace routine echocardiography for low-risk patients with more individualized cardiovascular surveillance for a safe period. Aim First, to develop and validate separate risk stratification models for AS-related events (AVE) and major cardiovascular events (MCE) to identify low-risk AS patients who can safely postpone routine echocardiography replaced by tailored risk stratification and adequate risk factor management. Second, to estimate optimal follow-up intervals according to risk stratification for each outcome. Methods We analyzed serial measurements from 69% of 1579 patients with mild-to-moderate AS who were asymptomatic and event-free one year after inclusion in the randomized, multicenter Simvastatin and Ezetimibe in Aortic Stenosis (SEAS) trial. We developed two ASGARD risk scores using multivariable Cox proportional hazard regression analyses, designed as landmark analyses starting at year-1 and with a clinically relevant 2-year follow-up. The ASGARD scores estimated the risk of AVE (composite of aortic valve replacement or hospitalization with heart failure) and MCE (composite of myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft, stroke, and all-cause mortality). We estimated the prognostic models with backward elimination, selecting the lowest Akaike information criterion values and p&amp;lt;0.05. There were 7 predictors for AVE and 6 for MCE among current demographic, electrocardiograms, laboratory data, and echocardiographic measures one year earlier (baseline) (Figures 1A, 2A). We defined optimal follow-up for risk score quartiles at rates ≤5% visualized on Kaplan-Meier plots. We assessed model performance in 31% of patients held out for internal validation. Results ASGARD development models for both AVE and MCE risk demonstrated overall consistent predictive performance in the internal validation cohort, as evidenced by c-statistics, calibration-in-the-large, calibration slope, Brier score, and goodness-of-fit (all p&amp;gt;0.05) (Figure 1B-C, 2B-C). ASGARD predicted AVE risk similar to a new Vmax measurement (area under the curve [95% CI], 0.82 [0.78-0.87] vs. 0.85 [0.82-0.89) (Figure 1B), and MCE risk better than Framingham cardiovascular risk score (0.76 [0.70-0.82] vs. 0.68 [0.62-0.75]) (Figure 2B). The one-year event rates were ≤5% for the 2 lowest risk score quartiles (Figure 1D, 2D). Conclusion The ASGARD risk scores show promise for long-term, individualized cardiovascular surveillance of low-risk patients with non-severe AS without unnecessary echocardiography. Further studies are warranted for external model validation, continuous optimization, and exploring the potential for implementation in clinical practice.Figure 1Figure 2

Utility of the revised FIGO2023 staging with molecular classification in endometrial cancer

ObjectivesMolecular classification was introduced in endometrial cancer staging following the transition of the International Federation of Gynecology and Obstetrics (FIGO) 2008 to FIGO2023. In the early stages, p53 abnormal endometrial carcinoma with myometrial involvement was upstaged to stage IICm, in addition to the downstaging of POLE mutation endometrial cancer to stage IAm. This study compared the goodness of fit and discriminatory ability of FIGO2008, FIGO2023 without molecular classification (FIGO2023), and FIGO2023 with molecular classification (FIGO2023m); no study has been externally validated to date. MethodsThe study included 265 patients who underwent initial surgery at the National Cancer Center Hospital between 1997 and 2019 and were pathologically diagnosed with endometrial cancer. The three classification systems were compared using Harrell's concordance index (C-index), Akaike information criterion (AIC), and time-dependent receiver operating characteristic (ROC) curves. A higher C-index score and a lower AIC value indicated a more accurate model. ResultsAmong the three classification systems, FIGO2023m had the lowest AIC value (FIGO2023m: 455.925; FIGO2023: 459.162; FIGO2008: 457.901), highest C-index (FIGO2023m: 0.768; FIGO2023: 0.743; FIGO2008: 0.740), and superior time-dependent ROC curves within 1 year after surgical resection. In the stage IIIC, patients with p53 abnormalities had considerably lower 5-year overall survival than those with a p53 wild-type pattern (24.3% vs. 83.7%, p = 0.0005). ConclusionsFIGO2023m had the best discriminatory ability compared with FIGO2008 and FIGO2023. Even in advanced stages, p53 status was a poor prognostic factor. When feasible, molecular subtypes can be added to the staging criteria to allow better prognostic prediction in all stages.

Determining the optimal time to report mortality after lobectomy for lung cancer: An analysis of the time-varying risk of death

ObjectiveSurgical mortality has traditionally been assessed at arbitrary intervals out to 1 year, without an agreed optimum time point. The aim of our study was to investigate the time-varying risk of death after lobectomy to determine the optimum period to evaluate surgical mortality rate after lobectomy for lung cancer. MethodsWe performed a retrospective study of patients undergoing lobectomy for lung cancer at our institution from 2015 to 2022. Parametric survival models were assessed and compared with a nonparametric kernel estimate. The hazard function was plotted over time according to the best-fit statistical distribution. The time points at which instantaneous hazard rate peaked and stabilized in the 1-year period after surgery were then determined. ResultsDuring the study period, 2284 patients underwent lobectomy for lung cancer. Cumulative mortality at 30, 90, and 180 days was 1.3%, 2.9%, and 4.9%, respectively. Log-logistic distribution showed the best fit compared with other statistical distribution, indicated by the lowest Akaike information criteria value. The instantaneous hazard rate was greatest during the immediate postoperative period (0.129; 95% confidence interval, 0.087-0.183) and diminishes rapidly within the first 30 days after surgery. Instantaneous hazard rate continued to decrease past 90 days and stabilized only at approximately 180 days. ConclusionsIn-hospital mortality is the optimal follow-up period that captures the early-phase hazard during the immediate postoperative period after lobectomy. Thirty-day mortality is not synonymous to “early mortality,” as instantaneous hazard rate remains elevated well past the 90-day time point and only stabilizes at approximately 180 days after lobectomy.

15-item Roland-Morris disability questionnaire for general pain (RMDQ-g): Structural validity and criterion validity on Brazilian patients with chronic pain

BackgroundRoland-Morris Disability Questionnaire for general pain (RMDQ-g) is an instrument adapted to assess disability in patients with pain in any region of the body. ObjectiveTo perform the structural and criterion validity of the RMDQ-g in Brazilian patients with chronic pain. DesignA cross-sectional study. MethodsWe included native speakers of Brazilian Portuguese, of both sexes, aged ≥18 years, with pain for at least 3 months in any region of the body. Participants eligible for the study responded to an online form containing personal and clinical data, and assessment instruments. We used the confirmatory factor analysis and considered the following fit indices: chi-square/degree of freedom (DF), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA). In the comparison between models, we considered the structure with the lowest values of the Akaike information criterion (AIC) and sample-size adjusted Bayesian information criterion (SABIC). We assessed criterion validity via Spearman's correlation coefficient (rho) to correlate the long and short versions. ResultsThe study consisted of 297 participants with chronic pain. The main sites of pain were the lumbar region (40.7%), thoracic (21.5%), and neck (19.5%). Mean pain intensity was greater than 5 points. The 24-item long version and the 15-item short version had adequate fit indices (chi-square/DF ≤ 1.77, CFI ≥0.97, TLI ≥0.96, and RMSEA ≤0.05). However, when comparing structures, the short version was the most appropriate because it had the lowest values of AIC (2562.05) and SABIC (2577.72). Criterion validity was acceptable (rho = 0.94) and internal consistency as well (Cronbach's alpha = 0.87). ConclusionThe structural validity and criterion validity of the RMDQ-g with one domain and 15 items is the most appropriate version and should be considered in the clinical environment and in research for measuring disability in patients with chronic pain in any region of the body.

Interictal respiratory variability predicts severity of hypoxemia after generalized convulsive seizures.

Severe respiratory dysfunction induced by generalized convulsive seizures (GCS) is now thought to be a common mechanism for sudden unexpected death in epilepsy (SUDEP). In a mouse model of seizure-induced death, increased interictal respiratory variability was reported in mice that later died of respiratory arrest after GCS. We studied respiratory variability in epilepsy patients as a predictive tool for severity of postictal hypoxemia, a potential biomarker for SUDEP risk. We then explored the relationship between respiratory variability and central CO2 drive, measured by the hypercapnic ventilatory response (HCVR). We reviewed clinical, video-electroencephalography, and respiratory (belts, airflow, pulse oximeter, and HCVR) data of epilepsy patients. Mean, SD, and coefficient of variation (CV) of interbreath interval (IBI) were calculated. Primary outcomes were: (1) nadir of capillary oxygen saturation (SpO2 ) and (2) duration of oxygen desaturation. Poincaré plots of IBI were created. Covariates were evaluated in univariate models, then, based on Akaike information criteria (AIC), multivariate regression models were created. Of 66 GCS recorded in 131 subjects, 30 had interpretable respiratory data. In the multivariate model with the lowest AIC value, duration of epilepsy was a significant predictor of duration of oxygen desaturation. Duration of tonic phase and CV of IBI during the third postictal minute correlated with SpO2 nadir, whereas CV of IBI during non-rapid eye movement sleep had a negative correlation. Poincaré plots showed that long-term variability was significantly greater in subjects with ≥200 s of postictal oxygen desaturation after GCS compared to those with <200 s desaturation. Finally, HCVR slope showed a negative correlation with measures of respiratory variability. These results indicate that interictal respiratory variability predicts severity of postictal oxygen desaturation, suggesting its utility as a potential biomarker. They also suggest that interictal respiratory control may be abnormal in some patients with epilepsy.

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.