Stepwise Regression Research Articles

Serum amyloid A-low-density-lipoprotein complex and cause-specific mortality in patients with suspected or known coronary artery disease: The ANOX Study

Abstract Background Serum amyloid A-low-density-lipoprotein (SAA-LDL) is a complex formed from the oxidative interaction between SAA and LDLs. A relatively small-scale study has shown that circulating SAA-LDL levels may serve as a prognostic marker in patients with stable coronary artery disease (CAD). However, the association between SAA-LDL and cause-specific mortality in patients with suspected or known CAD is unknown. Methods Serum SAA-LDL levels were measured in 2416 patients with suspected or known CAD undergoing elective coronary angiography. The outcomes were all-cause death, cardiovascular (CV) death, non-CV death, cancer death, and other non-CV death. Patients were followed up over a 3-year period. Results During the follow-up, 254 (10.5%) deaths occurred, including 88 (3.6%) CV deaths, 151 (6.3%) non-CV deaths, 66 (2.7%) cancer deaths, 85 (3.5%) other non-CV deaths, and 15 (0.6%) undetermined deaths. Stepwise regression analysis including baseline data on potential clinical confounders and established CV biomarkers (i.e., N-terminal pro-brain natriuretic peptide, high-sensitivity cardiac troponin I, and high-sensitivity C-reactive protein [hs-CRP]) revealed that the strongest independent determinant of SAA-LDL levels was hs-CRP (partial regression coefficient, 0.248; standard error, 0.010; p<0.001). After adjusting for potential clinical confounders and established CV biomarkers, the highest quartile of SAA-LDL levels (vs. the lowest quartile) was significantly associated with all-cause death (adjusted hazard ratio [aHR], 1.56; 95% confidence interval [CI], 1.05–2.34) and non-CV death (aHR, 1.76; 95% CI, 1.02–3.11), but not with CV death (aHR, 1.16; 95% CI, 0.62–2.20), cancer death (aHR, 1.68; 95% CI, 0.71–4.34), or other non-CV death (aHR, 1.65; 95% CI, 0.82–3.45) in the entire cohort. In patients with low hs-CRP levels (≤1.0 mg/L, n=1253), the highest quartile of SAA-LDL levels was significantly associated with all-cause death (aHR, 2.19; 95% CI, 1.11–4.57), non-CV death (aHR, 2.78; 95% CI, 1.05–8.79), and cancer death (aHR, 4.65; 95% CI, 1.12–31.9), but not with CV death (aHR, 1.22; 95% CI, 0.39–3.92) or other non-CV death (aHR, 1.68; 95% CI, 0.42–8.35). In contrast, the highest quartile of SAA-LDL levels was not significantly associated with all-cause death (aHR, 1.18; 95% CI, 0.72–1.96), CV death (aHR, 1.02; 95% CI, 0.45–2.31), non-CV death (aHR, 1.46; 95% CI, 0.75–2.91), cancer death (aHR, 1.35; 95% CI, 0.43–4.58), or other non-CV death (aHR, 1.38; 95% CI, 0.60–3.25) in patients with high hs-CRP levels (>1.0 mg/L, n=1163). Conclusions Elevated SAA-LDL levels were independently associated with all-cause and non-CV mortality, but unexpectedly not with CV mortality, in patients with suspected or known CAD. These associations were pronounced in patients with low hs-CRP levels (≤1.0 mg/L). The association between the SAA-LDL level and cancer mortality suggests its predictive utility in patients with low hs-CRP levels (≤1.0 mg/L).

Predictors of acute ischemic strokes and subsequent mortality in patients hospitalized for percutaneous coronary interventions

Abstract Background Acute ischemic strokes (AIS) occurring post percutaneous coronary intervention (PCI) are an infrequent but severe complication, resulting in increased mortality rates and morbidity in survivors. Purpose We aimed to investigate independent predictors of AIS and subsequent mortality in patients hospitalized for PCI in a nationwide cohort. Methods A retrospective analysis using on ICD-10-GM and OPS-codes obtained from 2006-2021 from the German Federal Statistical Office was performed. Patients with a peri-interventional AIS aged ≥18 years which were admitted for PCI were included in this analysis. A stepwise regression analysis was employed to identify factors independently associated with AIS and to determine variables linked to in-hospital mortality. Results From 4,910,430 PCI cases registered from 2006-2021, AIS occurred in 4,098 (0.08%) patients. Patients with an AIS were 70.9±11.3 years of age, predominantly male (65.3%) and displayed a high burden of comorbidities such as diabetes (36.1%), a prior medical history of atrial fibrillation (34.6%) and chronic kidney disease (19.4%). In patients with AIS, 27.4% presented with a ST-elevation myocardial infarction (STEMI), whilst 38.9% were admitted with a Non-STEMI. Concerning procedural characteristics, 84.2% underwent a coronary stent implantation, and in 5.5% cases a coronary thrombectomy was carried out. Important predictors of peri-interventional AIS after a PCI were: carotid artery disease, history of stroke, presentation with myocardial infraction, atrial fibrillation, and coronary thrombectomy. For variables like hyperlipoproteinemia, obesity and stent implantation an inverse association was noted (also see Figure 1A). With regard to mortality, during a mean in-hospital stay of 18 ± 15.1 days, a total of 18.1% (n=742) patients died. We identified several independent predictors of mortality including clinical presentation with cardiogenic shock, dialysis and STEMI as indication for PCI. A negative correlation with in-hospital mortality was seen for variables such as arterial hypertension and coronary stent implantation (see Figure 1B for full set of independently associated variables). Conclusion In a nationwide cohort of patients hospitalized for PCI over 16 years, we identified important predictors of AIS including carotid artery disease and coronary thrombectomy. Moreover, a substantial rate of in-hospital mortality as well as several predictors of death were recognized. Our findings might help clinicians identify patients undergoing PCI who are at highest risk for ischemic cerebral complications and subsequent mortality.

Built Environment’s Non-Linear Impact on Subway Passenger Flow Through Improved Interpretable Machine Learning

Understanding the complex correlation between the built environment and subway passenger flow can provide unique insights for the development of transportation operations and urban coordination policies. Few studies have systematically analyzed the rationality of selecting built environment variables and further explored the non-linear relationships. In this study, we integrated various sources of built environmental factors and developed an interpretable machine learning analysis framework using backward elimination extreme gradient boosting and SHapley Additive exPlanations (SHAP) values analysis (BE-XGBoost-SHAP). The framework was validated by analyzing passenger flows during the morning peak, non-peak, and evening peak periods at the station level. The research results indicate that there are significant differences between built environment factors and the time-varying passenger flow. Land use characteristics significantly dominate across all three temporal periods. The importance of other variable types in relation to passenger flows varies significantly across the three time periods. It is worth noting that the relationships between all variables and passenger flow at different time periods are non-linear, with the majority displaying threshold effects. Compared with the gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models, the proposed interpretive framework performs better as regards R-square, root mean square error (RMSE), and mean absolute error (MAE) metrics. This study offers valuable insights, elucidating the pivotal land use attributes that notably affect passenger flow, the significance of varied built environment factors across distinct time spans, and the acknowledgment of non-linearities and threshold effects within these relationships. These findings are imperative for urban planning and the enhancement of station area design.

The trajectory of self-care over a 12-month period in patients with heart failure, comparing those with consistently good and poor self-care

Abstract Background Self-care behaviour plays a pivotal role in enhancing the quality of life for patients with heart failure (HF) and reducing mortality and morbidity rates, but many patients struggle with suboptimal self-care behaviours. It is crucial to understand the factors influencing long-term self-care management. Purpose To assess the trajectory of self-care over a 12-month period and assess differences between patients who have constantly good self-care over a 12-month period compared to constantly poor self-care. Method This study use data from a randomised controlled intervention study, called the HF-Wii study. We included patients who had complete data on self-care during on baseline, 3, 6 and 12 months. Self-care was measured with the European Heart Failure Self-care Behaviour scale (EHFSBs). A threshold score of equal or higher than 70 was used to define self-care behaviour as good and a score lower than 70 as poor self-care. Based on literature, the following characteristics (collected from questionnaires and medical records at baseline) were considered in the analysis: age, sex, marital status, education, history of myocardial infarction, duration of HF, New York Heart Association (NYHA) functional class, LVEF, comorbidity. Other factors assessed were Health-related Quality of Life (HrQoL, Minnesota living with HF questionnaire), depressive symptoms (Hospital Anxiety and Depression scale), cognition (Montreal Cognitive Assessment), sleep (Minimal Insomnia Symptom Scale) and level of HF knowledge (Dutch HF knowledge scale). Logistic regression was performed with backwards elimination of the factors considered associated with the differences between patients who have constantly good self-care at baseline, 3, 6 and 12 months compared to constantly poor self-care. Results In total, 407 patients were included in this study. At baseline, the mean self-care was 65±21, with 41% of the patients (n=245) performing good self-care. In total, 126 patients had a constantly good self-care over 12 month (31%), 120 patients had a constantly bad self-care over 12 months (30%) and 161 (39%) fluctuated in their self-care (Figure). In the multivariate analysis, patients that had continuously good self-care over 12-months were of older age (68±10 vs. 64±12, odds ratio (OR) 1.061, P-value<0.001), had higher HF knowledge (score 13±2 vs. 12±2, OR 1.439, P-value<0.001) and had higher HrQoL (score 28±20 vs 36±22, OR 0.984, P-value=0.022). compared to patients with consistently poor self-care. The variance was explained by 20%. Conclusion Younger patients with HF tend to struggle with consistent self-care over a year, underscoring the necessity for tailored interventions for this group. Conversely, patients with greater HF knowledge and higher HrQoL maintained good self-care habits.

Risk Scores for Five Early Life Domains and Odds of Obesity and Hypertension Comorbidity

Abstract Background For outcomes such as obesity and hypertension, determinants are likely to be complex and multidimensional. Therefore, to design realistic interventions, epidemiological research should incorporate information from multiple risk exposure domains to assess the effect on health. In this paper we explore risk scores for five early life domains and odds of obesity and hypertension comorbidity. Methods We used data from 17,196 participants in the 1970 British Cohort Study. The outcome was obesity (BMI of ≥ 30) and hypertension (blood pressure>140/90mm Hg or self-reported doctor’s diagnosis) comorbidity at age 46. Early life domains included: ‘prenatal, antenatal, neonatal and birth’, ‘developmental attributes and behaviour’, ‘child education and academic ability’, ‘socioeconomic factors’ and ‘parental and family environment’. Stepwise backward elimination selected variables for inclusion for each domain. Predicted risk scores of obesity and hypertension for each cohort member within each domain were calculated. Logistic regression investigated the association between domain-specific risk scores and odds of obesity-hypertension, controlling for demographic factors and other domains. Results In unadjusted models, higher domain-specific risk scores were associated with increased odds of obesity-hypertension comorbidity. In adjusted analyses, higher domain-specific risk scores remained associated with increased odds of obesity-hypertension comorbidity, with the strongest associations to the parental and family environment domain (OR1.11 95%CI 1.05-1.18) and the socioeconomic factors domain (OR1.11 95%CI 1.05-1.17). Conclusions Appropriate modelling choices of combined effects of early-life risk factors need to be explored and tested. Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on cardiovascular risk in adulthood. Key messages • Targeted prevention interventions aimed at population groups with shared early-life characteristics could have an impact on obesity-hypertension prevalence. • Appropriate modelling choices of combined effects of early-life risk factors need to be explored and tested.

Lower modified rodnan skin score values and complement 4 levels are associated with arterial endothelial dysfunction in patients with systemic sclerosis

Abstract Background Systemic sclerosis (SSc) is a multisystem autoimmune disease affecting skin and multiple organs. Although immunopathological evidence supports direct arterial involvement leading to tissue ischemia in SSc, factors associated with arterial involvement are not precisely determined due to the rarity of SSc. In this study, we aimed to investigate aortic stiffness and endothelial functions in patients with SSc. Methods Patients under treatment of our Rheumatology department and healthy controls have been included in our study. Aortic stiffness was examined by pulse wave velocity (PWV) exam and endothelial functions were assessed by flow mediated dilation (FMD). Individuals with known atherosclerotic risk factors were excluded from our study. A thorough biochemical examination of all individuals and immunologic markers of patients with SSc was completed. All of the organ involvements in patients with SSc were noted and modified Rodnan skin score (MRSS) was calculated. Results A total of 61 patients and 75 healthy controls were included in our study. Brachial-ankle PWV values were significantly higher (6.89±0.93 vs 6.49±0.22 m/sec; p:0.001) and brachial FMD values were significantly lower (16.7±6.7 % vs 18.7±3.8 %; p: 0.03) in patients with SSc. We have also grouped individuals according to severe FMD impairment (FMD < 10%). None of the healthy controls had severe impairment whereas 15 patients with SSc had FMD values lower than 10%. Higher anti-smooth muscle (Sm) antibody positiveness (40 % vs 15.4 %), lower complement 4 levels (23.6±16.7 vs 31.1±11.1 mg/dL), higher plasma iron values (70.3±43.6 vs 31.4±45.2 mcg/dL) and lower MRSS values (11.4±4.8 vs 20.7±10.9) were observed in SSc patients with severe FMD impairment. When we have put these variables in multivariate logistic regression analysis (backward elimination model), we have found an independent association of complement 4 levels (OR: 0.03, [95 % CI: 0.01-0.491], p:0.04) and MRSS values (OR: 0.853, [95 % CI: 0.761-0.955], p:0.006) with significant FMD impairment in patients with SSc. Conclusion Arterial stiffness is significantly increased and endothelial functions, as assessed by FMD is significantly decreased in patients with SSc, when compared to healthy controls. Lower complement 4 levels and lower modified Rodnan skin scores seem to be independently associated with significant severe FMD impairment. Although our study included a remarkable amount of patients with SSc, an infrequent autoimmune disease, number of patients included in our study is not adequate to make more precise comments. Further prospective studies with higher amount of patients are required for certain results.

Cardiac magnetic resonance in MINOCA patients: a non-tertiary hospital experience

Abstract Background Cardiac magnetic resonance (CMR) is a key tool in the diagnostic process of myocardial infarction with non-obstructive coronary arteries (MINOCA), impacting patient management and prognosis. Its use in non-tertiary hospitals is often constrained mainly by accessibility. Thus, the aim of this study was thoroughly evaluate CMR application in this context to optimize the process. Methods 136 MINOCA patients who were admitted to our hospital between 2016 and January 2024 were analyzed. Each MINOCA patient was reviewed by experts to align with the diagnosis criteria outlined in the 2020 ESC Guidelines and the American Heart Association position paper, which recommended CMR for MINOCA patient’s evaluation. To assess the factors associated with normal-CMR, a logistic regression analysis was performed. In a first step, some factors were detected, showing a trend in the association. In a second step, a backward elimination procedure was used, excluding factors with p>0.1. Results CMR was performed in 65 MINOCA patients (48% of the cohort), with a mean age of 59±15 years old, 35 of them were males (54% of total patients). During the follow-up, there were 4 deaths (6%) and 4 readmissions (6%). Out of the total 65 patients, the CMR diagnosis was myocardial infarction (MI) in 14 (21.5%) and myocarditis in 16 (24.6%), and was normal in 35 (53.9%). Therefore, CMR provided diagnostic information in 46% of the cases. It was noted that a normal CMR was associated with female gender, (OR 2.7, 95% CI 0.96-7.34, p=0.057), hypertension (OR 2.25, 95% CI 0.81-6.22, p=0.11), absence of segmental abnormalities in echocardiography (OR 0.14, 95% CI 0.035-0566, p=0.06) and lower high-sensitivity Troponin T (OR 0.79, 95% CI: 0.66-0.95, p=0.001). In the second step, only segmental abnormalities (OR 0.2, 95%CI 0.04-1.09, p=0.063) and troponin (OR 0.31, 95%CI 0.10-0.99, p=0.048) remained included. Additionally, the Youden Index was determined to obtain the threshold for troponin, resulting a value of 606 nG/mL, area under ROC curve (AUC) = 0.784 (figure). Conclusions - CMR was performed in 48% of the MINOCA patients, reflecting the limitation for its use in routine clinical practice. Furthermore, it was useful in 46% of those patients. - The main factors associated with normal CMR were absence of segmental echocardiography abnormalities and reduced troponin levels. - A troponin cutoff point of 606 ng/mL identified patients with a normal CMR (AUC=0.784).Figure 1.CMR in MINOCA patientsFigure 2.Area under ROC curve = 0.784

Factors associated with sleeping difficulties in patients with heart failure

Abstract Background Sleeping difficulties is prevalent in heart failure (HF) and a predictor of cardiac events in patients with HF. Purpose To describes the prevalence of sleeping difficulties in patients with HF and factors associated. Method This study used data of a multicentre RCT entitled Tele-Yoga study (clincialtrials.gov:NCT03703609). Patients were included in the analysis when they were diagnosed with HF and completed a questionnaire on sleeping difficulties. Sleeping difficulties were measured with the Minimal Insomnia Symptom Scale (MISS), patients with a score a 6 or higher patients were characterised as having sleeping difficulties. Through a literature search we identified factors that could be associated with sleeping difficulties. Demographic and clinical background variables (age, sex, BMI, marital status, working status, having atrial fibrillation and/or diabetes), anxiety and depressive symptoms (Hospital Anxiety and Depression scale) and Health related Quality of Life (EQ-5D index score) were considered. Logistic regression was performed with backwards elimination of the factors considered associated with having or not having sleeping difficulties. Results In total 244 patients were included (mean age 67±10, 28% women), whereas 27% had sleeping difficulties (n = 68, a score a 6 or higher on the MISS); 28% had moderate till very severe difficulties initiating sleep (n=77), 53% had moderate till very severe problems by waking up at night (n=130), 38% had moderate till very severe problems by not feeling refreshed by sleep (n=92). The logistic regression showed that patients with sleeping difficulties were significantly more often single (27% vs. 18%, OR 0.336 P-value=0.023), more often not working (72% vs. 69%, OR 2.524, P-value=0.047) more often experienced symptoms of anxiety (45% vs. 10%, OR 4.882, P-value<001) and had a lower quality of life, (0.637±0.261 vs. 0.786±0.203, OR 0.042 P-Value=0.001) compared to patients with no sleeping difficulties. Conclusion Patients having symptom of anxiety were at five times greater risk of experiencing sleeping difficulties. Being in a relationship and being employed were associated with less sleeping difficulties. Additionally, patients with sleeping problems reported lower quality of life.

Usefulness of mean attenuation of pericoronary adipose tissue as a predictor of coronary microvascular dysfunction

Abstract Background/Introduction Coronary microvascular dysfunction (CMD) is a condition in which the distal microvasculature is compromised, resulting in inadequate blood flow to the subsequent arteries and myocardial ischemia. Index of microvascular resistance (IMR), which can quantify CMD, is becoming the standard for the diagnosis of CMD. Patients with CMD have a 4 to 5 times greater risk of cardiovascular death and myocardial infarction than patients without heart disease and similar cardiovascular risk. Early diagnosis of CMD may reduce cardiovascular risk and improve quality of life with therapeutic intervention. Pericoronary adipose tissue mean attenuation (PCATMA) is an indirect measure of adipocyte size and lipid content around coronary artery, reflecting inflammation status. Previous studies have shown that both PCATMA and CMD are associated with the functional severity of coronary artery disease, but no study has yet examined the relationship between PCATMA and CMD. Purpose We conducted a study comparing PCATMA in patients with and without CMD. Methods Between September 2022 and October 2023, 25 patients without significant stenosis (<50% stenosis) who underwent IMR measurement on invasive coronary angiography and coronary CT angiography (CCTA) were included. CMD was diagnosed when the IMR exceeded 25 as measured by a pressure wire inserted into the left anterior descending artery after invasive coronary angiography had excluded significant stenosis. PCATMA was measured on CCTA using the average of the values of right coronary artery, left anterior descending artery, and left circumflex artery. Multivariable logistic regression analysis with stepwise forward selection was used to assess predictors for patients with abnormal IMR. The following variables were included for adjustment: PCATMA, age, sex, diabetes, dyslipidemia, hypertension. Results The mean age of 25 patients in this study was 63 years, 48.0% were female, 29.2% had diabetes, 62.5% had dyslipidemia and 66.7% had hypertension. PCATMA was significantly higher in patients with abnormal IMR (IMR>25) (-79.6 [IQR: -82.1, -74.6] HU vs -86.3 [IQR: -89.8, -78.9] HU, p = 0.043). Multivariate logistic regression analysis demonstrated that PCATMA remained an independent predictor of patients with abnormal IMR (OR 1.27 [95% CI: 1.05–1.65], p = 0.039). Optimal cut-off value of PCATMA for predicting patients with abnormal IMR by Youden index was -82.1 HU (AUC = 0.75 [95% CI: 0.55-0.95]). Conclusions In patients without significant coronary artery stenosis, PCATMA on CCTA may be used to predict the patients with CMD, noninvasively.

Thyroid function status in patients with hypothyroidism on thyroxine replacement and associated factors: a retrospective cohort study in primary care

BackgroundLong-term management of patients with hypothyroidism on thyroxine replacement requires thyroid function test (TFT) monitoring once every six–12 months as recommended by clinical practice guidelines. This study determined their thyroid function status during two-year follow-up visits in primary care, and the factors influencing their thyroid status, and assessed the optimal interval for TFTs.MethodsA retrospective cohort study was conducted on adults with a clinical diagnosis code for hypothyroidism in their electronic health records taken from a group of polyclinics in Singapore between July 2017 and June 2020. The follow-up thyroid status was categorized as under-replacement (TSH ≥ 3.70mIU/L), over-replacement (TSH ≤ 0.65mIU/L) or euthyroid (TSH 0.65–3.70mIU/L). The patients’ demographic, clinical and TFT data were analyzed using appropriate statistical tests during the two-year follow-up. Stepwise logistic regression analysis identified the factors associated with suboptimal thyroid control. Kaplan–Meier analysis was used to compare their thyroid function status in association with the interval between TFT monitoring.ResultsData from 5,749 eligible subjects (mean age 62.1 ± 13.29 years; 79% female; 79.7% Chinese) were analyzed. After a two-year follow-up, 61.9% (n = 3558) of all subjects were euthyroid, with 29.5% (n = 1694) being under-replaced and 8.6% (n = 497) over-replaced. However, thyroid status did not differ significantly with the various dose regimen (daily, segmented, or alternate days) (p = 0.193). Stepwise logistic regression showed that thyroxine under-replacement was significantly associated with the male gender (AOR = 1.25,95%CI = 1.03–1.51,p = 0.02) and obesity (AOR = 1.34,95%CI = 1.08–1.66,p = 0.008). Every unit (μg/kg body weight) increase in the mean daily thyroxine dose was associated with 2.72 times greater odds of over-replacement. When comparing thyroid function monitoring at intervals of 13-24 months, monitoring at shorter intervals (≤ 12 months) was less likely to detect thyroxine under-replacement (AOR = 0.57,95%CI = 0.44–0.74,p < 0.001) and over-replacement (AOR = 0.62,95%CI = 0.41–0.97,p = 0.033). Among the 3,312 adults who were euthyroid at baseline, 22.2%, 41.7% and 59.6% had suboptimal thyroid control at 6, 12 and 24 months respectively (Kaplan–Meier analysis).ConclusionAround six in ten patients were euthyroid with thyroxine replacement for hypothyroidism in primary care over two years. Thyroxine under-replacement was associated with male gender and obesity. The proportion of euthyroid patients developing abnormal thyroid function doubled with TFTs at six, 12 and 24-month intervals.

Construction and validation of a line chart for gestational diabetes mellitus based on clinical indicators

BackgroundGestational diabetes mellitus (GDM) is a common complication of mid–to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers.MethodsData from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The “rms” package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves.ResultsData from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband– wife relationship, family history, and parental relationships as predictors. The Hosmer–Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (χ2 = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89–0.97), 0.72 (95% CI: 0.62–0.81), and 0.68 (95% CI: 0.53–0.83), respectively.ConclusionIn this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.

HbA1c and leukocyte mtDNA levels as major factors associated with post-COVID-19 syndrome in type 2 diabetes patients

Post-COVID-19 syndrome (PCS) is an emerging health problem in people recovering from COVID-19 infection within the past 3–6 months. The current study aimed to define the predictive factors of PCS development by assessing the mitochondrial DNA (mtDNA) levels in blood leukocytes, inflammatory markers and HbA1c in type 2 diabetes patients (T2D) with regard to clinical phenotype, gender, and biological age. In this case-control study, 65 T2D patients were selected. Patients were divided into 2 groups depending on PCS presence: the PCS group (n = 44) and patients who did not develop PCS (n = 21) for up to 6 months after COVID-19 infection. HbA1c and mtDNA levels were the primary factors linked to PCS in different models. We observed significantly lower mtDNA content in T2D patients with PCS compared to those without PCS (1.26 ± 0.25 vs. 1.44 ± 0.24; p = 0.011). In gender-specific and age-related analyses, the mt-DNA amount did not differ significantly between the subgroups. According to the stepwise multivariate logistic regression analysis, low mtDNA content and HbA1c were independent variables associated with PCS development, regardless of oxygen, glucocorticoid therapy and COVID-19 severity. The top-performing model for PCS prediction was the gradient boosting machine (GBM). HbA1c and mtDNA had a notably greater influence than the other variables, indicating their potential as prognostic biomarkers.

Spectral Variations of Reclamation Vegetation in Rare Earth Mining Areas Using Continuous–Discrete Wavelets and Their Impact on Chlorophyll Estimation

Ion-adsorption rare earth mining areas are primarily situated in the hilly regions of southern China. However, mining activities have led to extensive deforestation of the original vegetation. The reclamation vegetation planted for ecological restoration faces significant challenges in surviving under environmental stresses, including heavy metal pollution, ammonia nitrogen contamination, and soil drought. To rapidly and accurately monitor the growth of reclamation vegetation, this study investigates the spectral variations and their impact on the accuracy of chlorophyll estimation, utilizing hyperspectral data and relative chlorophyll content (SPAD). Specifically, continuous–discrete wavelet transforms were applied, along with the original spectra and first derivative spectra, to enhance spectral anomalies in the reclamation vegetation and identify chlorophyll-sensitive spectral features. Additionally, multiple linear stepwise regression and backpropagation neural network models were employed to estimate chlorophyll content. The results revealed the following: (1) the d5 and d6 scales of the discrete wavelet effectively highlighted spectral anomalies in the reclamation vegetation; (2) Salix japonica (Salix fragilis L.), among typical reclamation species, exhibited poor adaptability to the environmental conditions of the rare earth mining area; (3) the backpropagation neural network model demonstrated superior performance in chlorophyll estimation, with the spectral features Fir, Fir_d4, Fir_d5, and Fir_d6 significantly enhancing the accuracy of the model, achieving an R2 of 0.93 for Photinia glabra (Photinia glabra (Thunb.) Maxim.). The application of continuous–discrete wavelet transforms to hyperspectral data significantly improves the precision of chlorophyll estimation, underscoring the potential of this method for the rapid monitoring of reclamation vegetation growth.

Prediction of live weight from body measurements using stepwise regresion models in Karacabey Merino lambs

Objective: The aim of this study is to determine regression models that can be used to estimate live weight from body measurements in Karacabey Merino lambs of different ages(6th, 8th, and 12th months).. Material-Method: The animal material for the study consisted of 200 Karacabey merino lambs. Some body measurements were taken from all lambs and live weights were also weighed. For live weight prediction equations with multiple linear regression analysis using some measurements according to age groups, stepwise multiple regression procedure was used in SAS (1999). DUNCAN test, one of the tests for multiple comparisons, was used to show the differences between groups. Result: The least squares means for body length (BL), withers height (HW), back height (BH), rump height (RH), breast depth (CD), breast width (CW), rump width (RW), and live weight (LW) were 71.28 cm, 69.91 cm, 70.50 cm, 71.57 cm, 30.05 cm, 87.25 cm, 21.50 cm, 23.32 cm, and 51.05 kg, respectively. It is noteworthy that the live weight estimation models for three different age groups using stepwise regression analysis(second, third, and fourth models) can be recommended for the 6th (R2:0.82), 8th (R2:0.71), and 12th (R2:0.79) months of life. The variables that can be used in the equations to estimate body weight for this breed, at these ages are HW, CW, RW, CG, CD, and BL. Conclusion: Finally, it has been demonstrated that the live weights of Karacabey merino lambs can be estimated with high accuracy using the stepwise regression method based on body measurements.

Forecast of Sugarcane Yield in Chongzuo, Guangxi—LSTM Model Based on Fusion of Trend Yield and Meteorological Yield

Purpose: This paper develops a high-precision yield fusion prediction model for the sugarcane industry in Chongzuo, Guangxi, based on the trend yield and meteorological yield using the long short-term memory (LSTM) model to cope with the multiple factors affecting sugarcane production. Decision support is provided to agricultural producers, policymakers, and supply chain managers so that they can plan resource allocation, market strategies, and policy directions more effectively. Methods: The paper modeled trend yield and weather yield separately to explore the complex relationship between the two in influencing sugarcane production. Trend yields were predicted using the exponential smoothing and multilayer perceptron (MLP) models, while meteorological yields were modeled using stepwise regression. The predicted yields were used again as input variables into the LSTM deep learning network to fit the nonlinear relationship between the two yields. Results: The results showed that (1) the fusion strategy of meteorological yield and MLP trend yield adopted by the model was superior to the fusion strategy of meteorological yield and exponentially smoothed trend yield, achieving a very low mean square error (MSE) of 0.011 and a goodness of fit as high as 0.979, which indicated that the model prediction was highly in agreement with the actual yield, confirming the validity of the method. (2) The prediction curve is basically consistent with the trend of actual sugarcane yield, which predicts that the sugarcane yield in Chongzuo, Guangxi, is expected to maintain a stable and small growth trend in the next eight years. (3) The fusion prediction model proposed in this study provides an accurate and practical solution for sugarcane yield prediction in Chongzuo, Guangxi, with the unique advantage of effectively analyzing and integrating the natural and socio-economic factors affecting the yield, which is of significant reference value for the prediction of sugarcane yield in the local area and even in similar ecoregions.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Salt-related knowledge, attitudes, and practices and their relationship with 24-hour urinary sodium and potassium excretions among a group of healthy residents in the UAE: A cross-sectional study.

This study aimed to measure urinary sodium and potassium as a measure of sodium and potassium intake concerning the knowledge, attitude, and practice (KAP) toward sodium intake among a group of healthy residents in the UAE. A cross-sectional study on a sample of healthy adults in the UAE. In addition to the KAP questionnaire, sodium and potassium excretions, and food records were taken. The United Arab Emirates. A sample of 190 healthy individuals aged between 20-60 years. The mean (± SD) age of the sample was 38.6 (± 12.5) years and 50.5% were females. The mean urinary sodium and potassium intake were 2816.2± 675.7 mg /day and 2533.3± 615 mg/day, respectively. The means were significantly different compared to the World Health Organization (WHO) recommendation of sodium and potassium, (p < 0.001). About 65% of the participants exceeded the WHO recommendations for salt intake, and participants' knowledge classification for health-related issues was fair while food-related knowledge was poor (P=0.001). A two-stage stepwise multiple regression analysis revealed that KAP scores were negatively associated with urinary sodium excretion (r = -0.174; p = 0.017) and those older participants and females had lower urinary sodium excretion (p<0.001). These findings may suggest an increase in the risk of hypertension in the UAE population. Moreover, these findings emphasize the need to establish education and public awareness programs focusing on identifying the sodium contents of foods and establishing national regulations regarding food reformulation, particularly for staple foods such as bread.

Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).

Impact of the macroeconomic factors on the level of energy poverty-case of the Czech Republic, Poland and Slovakia

Energy poverty is a socio-economic topic that is not only related to the sustainability problem of natural resource extraction but also human activities. It reflects a situation in which households are unable to fully meet their energy needs. It mainly affects countries with a lower level of development, as well as those whose energy mix is largely based on non-renewable sources (such as coal). These undoubtedly include the Czech Republic, Poland and Slovakia. Investigations are still being conducted to explain the causes of the problem of energy poverty. The paper’s main aim is to assess the impact of selected macroeconomic factors on the level of energy poverty. A backward stepwise regression procedure was used to achieve this aim. Models have been developed for each of the three countries: the Czech Republic, Poland, and Slovakia separately and for all of them together. These models are intended to identify the most prominent macroeconomic factors across all three countries. Results from all four models have highlighted two prominent variables which may impact the level of energy poverty in selected countries. They are following: electricity prices for household consumers with all taxes and net electricity imports. In three of the four developed models, they explained the level of energy poverty in a statistically significant manner. In conclusion, it can be suggested that the governments of these countries take action on these variables, which can help manage energy poverty. Their identification and subsequent impact may help to reduce this problem in the analysed post-coal economies.

Biomarkers in lymphedema assessment: integrating elastography and muti-frequency bioimpedance analysis.

Aim: Multi-frequency bioimpedance analysis (MFBIA) is used to measure lymphedema, but it is a biomarker that is sensitive to stiffness. Lymphedema is a condition that can be accompanied by stiffness, but no studies have considered this, so we tried to use non-invasive elastography as a biomarker for stiffness.Methods & results: This retrospective study included 102 patients with lymphedema, divided into two groups according to the elastography strain ratio: stiff group (elastography strain ratio <0.7, n=48) and non-stiff group (elastography strain ratio >0.7, n=54). We estimated the volume of the affected arm based the extracellular water (ECW) volume calculated using MFBIA through a simple linear regression method. The adjusted R2 was 0.044 in the stiff group and 0.729 in the non-stiff group. Stepwise multivariate linear regression was used to investigate the significant factors for estimating the affected arm volume for each group. In the non-stiff group, the significantly associated factors were impedance at 50kHz, weight, and height (adjusted R2=0.724; p=0.003). In the stiff group, significant associations were observed among impedance at 250kHz, impedance at 1kHz, weight, and height (adjusted R2=0.705, p=0.041).Conclusion: Considering the characteristics of lymphedema, using MFBIA concurrently with elastography can be useful biomarker for estimating lymphedema.