Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Construction and validation of a clinical risk model based on machine learning for screening characteristic factors of lymphovascular space invasion in endometrial cancer.

This study aimed to identify factors that affect lymphovascular space invasion (LVSI) in endometrial cancer (EC) using machine learning technology, and to build a clinical risk assessment model based on these factors. Samples were collected from May 2017 to March 2022, including 312 EC patients who received treatment at Xuzhou Medical University Affiliated Hospital of Lianyungang. Of these, 219 cases were collected for the training group and 93 for the validation group. Clinical data and laboratory indicators were analyzed. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used to analyze risk factors and construct risk models. The LVSI and non-LVSI groups showed statistical significance in clinical data and laboratory indicators (P < 0.05). Multivariable logistic regression analysis identified independent risk factors for LVSI in EC, which were myometrial infiltration depth, cervical stromal invasion, lymphocyte count (LYM), monocyte count (MONO), albumin (ALB), and fibrinogen (FIB) (P < 0.05). LASSO regression identified 19 key feature factors for model construction. In the training and validation groups, the risk scores for the logistic and LASSO models were significantly higher in the LVSI group compared with that in the non-LVSI group (P < 0.001). The model was built based on machine learning and can effectively predict LVSI in EC and enhance preoperative decision-making. The reliability of the model was demonstrated by the significant difference in risk scores between LVSI and non-LVSI patients in both the training and validation groups.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Alcohol and substance use in older adults with treatment-resistant depression.

INTRODUCTION: Alcohol and substance use are increasing in older adults, many of whom have depression, and treatment in this context may be more hazardous. We assessed alcohol and other substance use patterns in older adults with treatment-resistant depression (TRD). We examined patient characteristics associated with higher alcohol consumption and examined the moderating effect of alcohol on the association between clinical variables and falls during antidepressant treatment. METHODS: This secondary and exploratory analysis used baseline clinical data and data on falls during treatment from a large randomized antidepressant trial in older adults with TRD (the OPTIMUM trial). Multivariable ordinal logistic regression was used to identify variables associated with higher alcohol use. An interaction model was used to evaluate the moderating effect of alcohol on falls during treatment. RESULTS: Of 687 participants, 51% acknowledged using alcohol: 10% were hazardous drinkers (AUDIT-10 score ≥5) and 41% were low-risk drinkers (score 1-4). Benzodiazepine use was seen in 24% of all participants and in 21% of drinkers. Use of other substances (mostly cannabis) was associated with alcohol consumption: it was seen in 5%, 9%, and 15% of abstainers, low-risk drinkers, and hazardous drinkers, respectively. Unexpectedly, use of other substances predicted increased risk of falls during antidepressant treatment only in abstainers. CONCLUSIONS: One-half of older adults with TRD in this study acknowledged using alcohol. Use of alcohol concurrent with benzodiazepine and other substances was common. Risks-such as falls-of using alcohol and other substances during antidepressant treatment needs further study.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Risk factor analysis and nomogram for predicting poor symptom control in smoking asthmatics.

BACKGROUND: Smoking induces and modifies the airway immune response, accelerating the decline of asthmatics' lung function and severely affecting asthma symptoms' control level. To assess the prognosis of asthmatics who smoke and to provide reasonable recommendations for treatment, we constructed a nomogram prediction model. METHODS: General and clinical data were collected from April to September 2021 from smoking asthmatics aged ≥14 years attending the People's Hospital of Zhengzhou University. Patients were followed up regularly by telephone or outpatient visits, and their medication and follow-up visits were recorded during the 6-months follow-up visit, as well as their asthma control levels after 6 months (asthma control questionnaire-5, ACQ-5). The study employed R4.2.2 software to conduct univariate and multivariate logistic regression analyses to identify independent risk factors for 'poorly controlled asthma' (ACQ>0.75) as the outcome variable. Subsequently, a nomogram prediction model was constructed. Internal validation was used to test the reproducibility of the model. The model efficacy was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve. RESULTS: Invitations were sent to 231 asthmatics who smoked. A total of 202 participants responded, resulting in a final total of 190 participants included in the model development. The nomogram established five independent risk factors (P<0.05): FEV1%pred, smoking index (100), comorbidities situations, medication regimen, and good or poor medication adherence. The area under curve (AUC) of the modeling set was 0.824(95%CI 0.765-0.884), suggesting that the nomogram has a high ability to distinguish poor asthma control in smoking asthmatics after 6 months. The calibration curve showed a C-index of 0.824 for the modeling set and a C-index of 0.792 for the self-validation set formed by 1000 bootstrap sampling, which means that the prediction probability of the model was consistent with reality. Decision curve analysis (DCA) of the nomogram revealed that the net benefit was higher when the risk threshold probability for poor asthma control was 4.5 - 93.9%. CONCLUSIONS: FEV1%pred, smoking index (100), comorbidities situations, medication regimen, and medication adherence were identified as independent risk factors for poor asthma control after 6 months in smoking asthmatics. The nomogram established based on these findings can effectively predict relevant risk and provide clinicians with a reference to identify the poorly controlled population with smoking asthma as early as possible, and to select a better therapeutic regimen. Meanwhile, it can effectively improve the medication adherence and the degree of attention to complications in smoking asthma patients.

Exploring the association between Frailty Index and low back pain in middle-aged and older Chinese adults: a cross-sectional analysis of data from the China Health and Retirement Longitudinal Study (CHARLS).

OBJECTIVES: This study explored the association between the Frailty Index (FI) and low back pain (LBP) in middle-aged and older Chinese adults. We hypothesised that a higher FI correlates with increased LBP prevalence. DESIGN: Cross-sectional analysis. SETTING: The study used data from the China Health and Retirement Longitudinal Study (CHARLS) across various regions of China. PARTICIPANTS: The analysis included 6375 participants aged 45 and above with complete LBP and FI data from the CHARLS for 2011, 2013 and 2015. We excluded individuals under 45, those with incomplete LBP data, participants with fewer than 30 health deficit items and those missing covariate data. OUTCOME MEASURES: We constructed an FI consisting of 35 health deficits. Logistic multivariable regression examined the relationship between FI and LBP, using threshold analysis to identify inflection points. Sensitivity analyses were performed to ensure the robustness of the findings. RESULTS: Of the participants, 27.2% reported LBP. A U-shaped association was observed between FI and LBP, with the highest quartile (Q4, FI ≥0.23) showing more than a twofold increased risk of LBP (OR=2.90, 95% CI: 2.45-3.42, p<0.001). Stratified analysis showed a significant association in participants under 60, particularly in the lowest FI quartile (OR=1.43, 95% CI: 1.14 to 1.79). Sensitivity analysis upheld the robustness of the primary results. CONCLUSIONS: The findings suggest a complex relationship between frailty and LBP, highlighting the need for early screening and tailored interventions to manage LBP in this demographic. Further research is necessary to understand the mechanisms of this association and to validate the findings through longitudinal studies.

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study.

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We collected retrospective data on demographic characteristics, blood tests, and urine tests of the patients who underwent kidney biopsy. The dataset was divided into derivation and validation cohorts, with temporal validation. We employed five machine learning models-eXtreme Gradient Boosting (XGBoost), LightGBM, Random Forest, Artificial Neural Networks, and 1 Dimentional-Convolutional Neural Network (1D-CNN)-and logistic regression, evaluating performance via the area under the receiver operating characteristic curve (AUROC) and explored variable importance through SHapley Additive exPlanations method. The study included 1268 participants, with 353 (28%) diagnosed with IgA nephropathy. In the derivation cohort, LightGBM achieved the highest AUROC of 0.913 (95% CI 0.906-0.919), significantly higher than logistic regression, Artificial Neural Network, and 1D-CNN, not significantly different from XGBoost and Random Forest. In the validation cohort, XGBoost demonstrated the highest AUROC of 0.894 (95% CI 0.850-0.935), maintaining its robust performance. Key predictors identified were age, serum albumin, IgA/C3, and urine red blood cells, aligning with existing clinical insights. Machine learning can be a valuable non-invasive tool for IgA nephropathy.

Individual and community-level factors associated with repeat induced abortion among women in Ghana: a multivariable complex sample logistic regression analysis of 2017 Ghana maternal health survey.

BACKGROUND: Repeat induced abortion is a serious public health issue that has been linked to adverse maternal health outcomes. However, knowledge about repeat induced abortion and its associated factors among reproductive age women in Ghana is very scarce. The objective of this study is to examine individual and community factors associated with repeat induced abortion in Ghana which would be helpful to design appropriate programmes and policies targeted at improving the sexual and reproductive health of women. METHODS: We used secondary cross-sectional data from the 2017 Ghana Maternal Health Survey. The study included a weighted sample of 4917 women aged 15-49 years with a history of induced abortion. A multivariable complex sample logistic regression analysis was used to investigate individual and community factors associated with repeat induced abortion among women in Ghana. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) was used to measure the association of variables. RESULTS: Of the 4917 reproductive women with a history of abortion, 34.7% have repeat induced abortion. We find that, compared to women who experience single induced abortion, women who experience repeat abortion are age 25-34 years (AOR:2.16;95%CI = 1.66-2.79) or 35-49 years (AOR:2.95;95%CI:2.18-3.99), have Middle/JHS education (AOR:1.69;95%CI = 1.25-12.27), use contraceptive at the time of conception (AOR:1.48: 95%CI = 1.03-2.14), had sexual debut before 18 years (AOR:1.57; 95%CI: 1.33-1.85) and reside in urban areas (AOR:1.29;95%CI = 1.07-1.57). On the other hand, women who reside in Central (AOR:0.68;95%CI: 0.49-0.93), Northern (AOR:0.46;95%CI:0.24-0.88), Upper West (AOR:0.24; 95%CI: 0.12-0.50) and Upper East (AOR:0.49; 95%CI = 0.24-0.99) regions were less likely to have repeat induced abortion. CONCLUSION: The study showed that both individual and community level determinants were significantly associated with repeat induced abortion. Based on the findings, it is recommended to promote sexual and reproductive health education and more emphasis should be given to adult, those with early sexual debut, those with Middle/JHS education and those who live in urban centers.

Multilevel analysis of discrimination of people living with HIV/AIDS and associated factors in Ghana: demographic health survey of 2022 Ghana data.

Introduction: The negative effects of stigma and discrimination in communities and families include medication non-adherence, heightened psychological distress, verbal and physical abuse, a lack of social support, isolation, and dangerous health behaviors such as hiding prescriptions. Despite the huge burden of HIV/AIDS discriminatory attitudes, limited studies were conducted in Ghana. Therefore, this study examines the burden of discriminatory attitudes and their determinant factors on people who are living with HIV/AIDS in Ghana. Objective: This study aimed to determine the prevalence of discriminatory attitudes and associated factors among people who are living with HIV/AIDS in Ghana based on recent DHS data. Method: Secondary data analysis was used for this multilevel logistic regression analysis based on the Ghana Demographic Health Survey of 2022. Data extraction, cleaning, and analysis were conducted using Stata version 14. The community of Ghana, from the 15 to 49 age group, was used for this study, with a final sample size of 22,058 participants. Four separate models were fitted, incorporating individual and community levels. Multilevel logistic regression models were calibrated to determine the associated factors at the individual and community level with discriminatory attitudes, with a 95% CI and AOR. Results: The prevalence of discriminatory attitudes toward people living with HIV/AIDS was 60.92%, with a 95% CI (60.13, 61.70) among Ghana DHS. Lower wealth status, having no comprehensive knowledge of HIV, low educational status at the individual level, and low wealth status at the community level, poorest and poorer [AOR =2.03; 95% CI: (1.04, 3.94)] and [AOR = 2.09; 95% CI: (1.84, 8.65)], respectively, no comprehensive knowledge [AOR = 3.42; 95% CI: (1.74, 6.73)], no and primary education [AOR = 3.18; 95% CI: (2.48, 5.51)] and [AOR = 3.78; 95% CI: (2.68, 5.92)], respectively, at the individual level and low wealth status [AOR = 1.58; 95% CI: (1.00, 2.46)] community level were the associated factors. Conclusion: The prevalence of discriminatory attitudes toward people living with HIV/AIDS was high (60.92%) in Ghana's DHS. The associated factors for this study were lower wealth status, having no comprehensive knowledge of HIV, and low educational status at the individual level.

Low phase angle: A predictor of functional status and discharge disposition in acute stroke older patients.

BACKGROUND & AIMS: This study aimed to investigate the effects of low phase angle (PhA) on functional status and discharge disposition during the acute phase in older patients with acute stroke. METHODS: We included consecutive patients who experienced acute stroke between October 2021 and December 2022. The exclusion criteria included: age<65 years, admission from other than home, death during hospitalization, inability to measure bioelectrical impedance analysis owing to implantation, and missing data. We defined low PhA (<5.28° for male and <4.62° for female) and categorized them into the low PhA group and normal group. The clinical outcomes were functional independence by the modified Rankin Scale (mRS) score (0-2, independence; 3-5, nonindependence) and discharge disposition (home or others). We used multivariate logistic regression analysis to examine the effect of low PhA on the mRS score at discharge and discharge disposition. RESULTS: Ultimately, a total of 205 patients were included in this analysis. More patients in the low PhA group were unable to be independent (27.7% vs. 66.7%, P < 0.001) and were unable to be discharged home (53.4% vs. 82.5%, P < 0.001) than in the normal group. Logistic regression analysis of the mRS scores showed that baseline low PhA decreased the likelihood of functional independence (odds ratio [OR] = 0.275, P = 0.003) and home discharge (OR = 0.378, P = 0.044). CONCLUSIONS: Low PhA is a risk factor for low functional status at hospital discharge; it decreases the likelihood of home discharge in older patients with acute stroke.

[Incidence of blood transfusion and associated risk factors during hip arthroplasty hospitalization].

OBJECTIVE: To investigate the incidence and risk factors of blood transfusion during hospitalization in patients receiving hip arthroplasty. METHODS: Clinical data of 347 hip arthroplasty patients admitted between January and January 2019 and December 2021. Patients were divided into 184 patients in the transfusion group and 164 patients in the nontransfusion group according to whether they received blood transfusion during hospitalization. The basic medical history data, biochemical results and surgical conditions of the patients in two groups were collected and compared. They were divided into total hip arthroplasty (THA) and hemiarthroplasty (HA) according to the different surgical methods. One-way analysis and Spearman correlation were used to analyze the factors associated with blood transfusion in hip arthroplasty patients. Multi-factor logistic regression analysis was performed for statistically significant(P<0.05) indicators, thus screening for independent risk factors for blood transfusion during hospitalization in hip arthroplasty patients. The receiver operating characteristic(ROC)curves for intraoperative bleeding in all hip arthroplasty patients, total hip arthroplasty patients, and hemi arthroplasty patients were plotted and compared, and area under curve(AUC) and the optimal threshold were calculated. RESULTS: A total of 347 patients were included for hip arthroplasty, including 207 total hip arthroplasty and 140 hemi arthroplasty. The transfusion rates of all hip arthroplasty patients, total hip arthroplasty patients and hemi arthroplasty patients were 53.03%(184/347), 53.14%(110/207) and 52.86%(74/140), respectively. Multifactorial logistic regression analysis showed that preoperative cystatin C (OR=2.739, P=0.001), hemoglobin at admission (OR=0.960, P<0.000 1), intraoperative bleeding (OR=1.010, P<0.000 1), postoperative pneumonia (OR=1.897, P=0.024), and right hip arthroplasty (OR=2.277, P=0.002) were independent risk factors for all hip arthroplasty patients;hemoglobin at admission (OR=0.978, P=0.016), intraoperative bleeding (OR=1.012, P<0.000 1), and postoperative pneumonia (OR=2.769, P=0.013) were independent risk factors for total hip arthroplasty;hemoglobin at admission (OR=0.930, P<0.000 1), intraoperative bleeding (OR=1.010, P<0.000 1), preoperative cystatin C (OR=2.277, P=0.023), and right hip arthroplasty (OR=2.428, P=0.046) were independent risk factors for hemi arthroplasty. Hemoglobin on admission and intraoperative bleeding were common risk factors for total and hemi arthroplasty. The AUCs were 0.688, 0.778, and 0.652 for total hip arthroplasty patients, total hip arthroplasty patients, and hemi arthroplasty patients, respectively. CONCLUSION: Intraoperative bleeding volume and preoperative hemoglobin are important risk factors for transfusion during hip arthroplasty hospitalization, and cystatin C may be a new biomarker for transfusion during hip arthroplasty hospitalization. At the same time, given the high incidence and potential risk of blood transfusion in hip arthroplasty, interventions should be made during hospitalization for identified risk factors.

Time-varying living arrangements and suicide death in the general population sample: 14-year causal survival analysis via pooled logistic regression.

AIMS: While past research suggested that living arrangements are associated with suicide death, no study has examined the impact of sustained living arrangements and the change in living arrangements. Also, previous survival analysis studies only reported a single hazard ratio (HR), whereas the actual HR may change over time. We aimed to address these limitations using causal inference approaches. METHODS: Multi-point data from a general Japanese population sample were used. Participants reported their living arrangements twice within a 5-year time interval. After that, suicide death, non-suicide death and all-cause mortality were evaluated over 14 years. We used inverse probability weighted pooled logistic regression and cumulative incidence curve, evaluating the association of time-varying living arrangements with suicide death. We also studied non-suicide death and all-cause mortality to contextualize the association. Missing data for covariates were handled using random forest imputation. RESULTS: A total of 86,749 participants were analysed, with a mean age (standard deviation) of 51.7 (7.90) at baseline. Of these, 306 died by suicide during the 14-year follow-up. Persistently living alone was associated with an increased risk of suicide death (risk difference [RD]: 1.1%, 95% confidence interval [CI]: 0.3-2.5%; risk ratio [RR]: 4.00, 95% CI: 1.83-7.41), non-suicide death (RD: 7.8%, 95% CI: 5.2-10.5%; RR: 1.56, 95% CI: 1.38-1.74) and all-cause mortality (RD: 8.7%, 95% CI: 6.2-11.3%; RR: 1.60, 95% CI: 1.42-1.79) at the end of the follow-up. The cumulative incidence curve showed that these associations were consistent throughout the follow-up. Across all types of mortality, the increased risk was smaller for those who started to live with someone and those who transitioned to living alone. The results remained robust in sensitivity analyses. CONCLUSIONS: Individuals who persistently live alone have an increased risk of suicide death as well as non-suicide death and all-cause mortality, whereas this impact is weaker for those who change their living arrangements.

Impact of Preoperative Neutrophil to Prealbumin Ratio Index (NPRI) on Short-Term Complications and Long-Term Prognosis in Patients Undergoing Laparoscopic Radical Surgery for Colorectal Cancer.

Objective: This study aims to evaluate the impact and predictive value of the preoperative NPRI on short-term complications and long-term prognosis in patients undergoing laparoscopic radical surgery for colorectal cCancer (CRC). Methods: A total of 302 eligible CRC patients were included, assessing five inflammation-and nutrition-related markers and various clinical features for their predictive impact on postoperative outcomes. Emphasis was on the novel indicator NPRI to elucidate its prognostic and predictive value for perioperative risks. Results: Multivariate logistic regression analysis identified a history of abdominal surgery, prolonged surgical duration, CEA levels ≥5 ng/mL, and NPRI ≥ 3.94 × 10-2 as independent risk factors for postoperative complications in CRC patients. The Clavien--Dindo complication grading system highlighted the close association between preoperative NPRI and both common and severe complications. Multivariate analysis also identified a history of abdominal surgery, tumor diameter ≥5 cm, poorly differentiated or undifferentiated tumors, and NPRI ≥ 2.87 × 10-2 as independent risk factors for shortened overall survival (OS). Additionally, a history of abdominal surgery, tumor maximum diameter ≥5 cm, tumor differentiation as poor/undifferentiated, NPRI ≥ 2.87 × 10-2, and TNM Stage III were determined as independent risk factors for shortened disease-free survival (DFS). Survival curve results showed significantly higher 5-year OS and DFS in the low NPRI group compared to the high NPRI group. The incorporation of NPRI into nomograms for OS and DFS, validated through calibration and decision curve analyses, attested to the excellent accuracy and practicality of these models. Conclusion: Preoperative NPRI independently predicts short-term complications and long-term prognosis in patients undergoing laparoscopic colorectal cancer surgery, enhancing predictive accuracy when incorporated into nomograms for patient survival.

Association between red cell distribution width and hypertension: Results from NHANES 1999-2018.

The relationship between red cell distribution width (RDW) and hypertension remains a contentious topic, with a lack of large-scale studies focusing on the adults in the United States. This study aimed to investigate the association between RDW and hypertension among US adults from 1999 to 2018. METHODS: Data were derived from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. RDW values were obtained from the Laboratory Data's Complete Blood Count with 5-part Differential-Whole Blood module. Hypertension data were obtained through hypertension questionnaires and blood pressure measurements. Multivariable weighted logistic regression analyses were conducted to assess the association between RDW and hypertension, followed by subgroup and smooth curve analyses. RESULTS: Compared to the non-hypertensive group, the hypertensive group exhibited higher RDW values (13.33±1.38 vs. 12.95±1.27, P <0.001). After adjusting for covariates, weighted multivariable logistic regression analysis revealed a positive correlation between RDW and hypertension prevalence (OR: 1.17, 95% CI 1.13, 1.21, P <0.001). When RDW was included as a categorical variable, participants in the fourth quartile had the highest risk of hypertension (OR: 1.86, 95% CI 1.70, 2.03, P <0.001). Subgroup analysis showed that, except for age, BMI and weak/failing kidneys, gender, race, education level, smoking, alcohol use, congestive heart failure, and stroke did not significantly influence this correlation (all P-values for interaction >0.05).Smooth curve fitting analysis revealed a reverse J-shaped relationship between RDW and hypertension prevalence, with an inflection point at 12.93%. CONCLUSION: We first explored the relationship between RDW and hypertension among US adults and discovered a reverse J-shaped association, providing further insights into the relationship between blood cell counts and hypertension and offering a new foundation for hypertension prevention and control.

Visualising and quantifying the usefulness of new predictors stratified by outcome class: The U-smile method.

Binary classification methods encompass various algorithms to categorize data points into two distinct classes. Binary prediction, in contrast, estimates the likelihood of a binary event occurring. We introduce a novel graphical and quantitative approach, the U-smile method, for assessing prediction improvement stratified by binary outcome class. The U-smile method utilizes a smile-like plot and novel coefficients to measure the relative and absolute change in prediction compared with the reference method. The likelihood-ratio test was used to assess the significance of the change in prediction. Logistic regression models using the Heart Disease dataset and generated random variables were employed to validate the U-smile method. The receiver operating characteristic (ROC) curve was used to compare the results of the U-smile method. The likelihood-ratio test demonstrated that the proposed coefficients consistently generated smile-shaped U-smile plots for the most informative predictors. The U-smile plot proved more effective than the ROC curve in comparing the effects of adding new predictors to the reference method. It effectively highlighted differences in model performance for both non-events and events. Visual analysis of the U-smile plots provided an immediate impression of the usefulness of different predictors at a glance. The U-smile method can guide the selection of the most valuable predictors. It can also be helpful in applications beyond prediction.

Analysis of the Outcomes of Tuberculosis Treatment and Factors Associated with Successful Treatment at the Bamenda Regional Hospital: A 10-year Retrospective Study.

BACKGROUND: Tuberculosis (TB) remains a global public health issue, impacting millions of people worldwide. This study determined the outcomes of TB treatment managed within a 10 year period at the Bamenda Regional Hospital in Cameroon. METHODS: A retrospective study was carried out among 2428 patients diagnosed and treated for active TB infection from 2013 to 2022, at the Bamenda Regional Hospital. Data collection was done from March to April 2023 using a data extraction form. Bivariate and multivariate logistic regression models were used to identify factors associated with successful TB treatment outcomes. Data was analyzed using SPSS software version 26. RESULTS: Of the 2428 patients with TB, 1380 (56.8%) were cured, 739 (30.4%) completed treatment, treatment failures were recorded in 10 (0.4%) patients, and 200 (8.2%) died during or after receiving treatment. Treatment default was the outcome in 99 (4.1%). Successful treatment outcomes were reported in 2119 (87.3%). Patients within age groups 41-50 (P = 0.010), 51-60 (P = 0.041), and >60 years (P = 0.006), male (P = 0.004), and human immunodeficiency virus-positive patients (P < 0.001) had decreased odds of successful treatment outcomes. CONCLUSION: The outcomes of treatment within a 10 year period showed that the treatment success was 2.7% below the World Health Organizations target. Prioritizing vulnerable patient groups in TB management and implementing public health interventions such as financial assistance and nutritional support will go a long way in improving treatment outcomes.

Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn's disease for point of care risk evaluation.

BACKGROUND: Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk. METHODS: We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index. RESULTS: There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%. CONCLUSIONS: Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Higher oxidative balance score was associated with decreased risk of erectile dysfunction: a population-based study.

BACKGROUND: Erectile dysfunction (ED) is a prevalent condition that is thought to be significantly impacted by oxidative stress. The oxidative balance score (OBS) has been built to characterize the state of antioxidant/pro-oxidant balance. There is less known regarding the relationship of OBS with ED. METHODS: This study conducted cross-sectional analyses on 1860 males who participated in the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2004. OBS was constructed by the 16 dietary components and 4 lifestyle factors. Self-reported ED was defined as men who indicated that they "never" or "sometimes" could achieve or keeping an erection adequate for satisfactory intercourse. Multivariate logistic regression models were applied to examine the association between OBS and the risk of ED. RESULTS: Among 1860 participants, the median OBS was 20 (IQR 15-26), and OBS was lower in males with ED vs. those without ED (P = 0.001). The results of our analyses indicated a negative correlation between OBS and ED among male subjects. Specifically, each one-unit increase in the continuous OBS was relate to 3% reduction in the odds of ED after full adjustment. Moreover, when extreme OBS quartiles were compared, the adjusted odds ratio (95% confidence interval) for the 4th OBS category was 0.53 (0.32 to 0.88) after full adjustment (P for trend < 0.05). There was also statistical significance in the relationships between dietary/lifestyle OBS with ED, and the association between lifestyle OBS and ED may be even tighter. For each unit increase in lifestyle OBS, the odds of ED decreased by 11% after full adjustment. CONCLUSION: Higher OBS was associated with reduced risk of ED in U.S. males. These findings suggested that adopting an antioxidant-rich diet and engaging in antioxidant-promoting lifestyle behaviors may contribute to a lower incidence of ED. These results provided recommendations for a comprehensive dietary and lifestyle antioxidants for ED patients.