Lemeshow Test Research Articles

Predicting risk of the subsequent early pregnancy loss in women with recurrent pregnancy loss based on preconception data

BackgroundFor women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.MethodsA prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.ResultsIn the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer–Lemeshow test was 7.427, and P = 0.505.ConclusionsPredicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.Trial registrationThis study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

Hygiene Practices and Factors Associated With Hygiene Practice Among Meat Handlers at Butcher Houses and Restaurants in Gambela Town, Southwest Ethiopia

Meat contamination during slaughter and sale affects 600 million people globally annually, leading to diseases and 420,000 deaths. However, evidence on hygienic practices in low-income countries, particularly in Ethiopia, limits the extent of this problem, highlighting the need for research on hygienic practices. The aim of this study was to assess the magnitude of meat hygiene practices and factors associated with poor practices among food handlers in Gambela town, Ethiopia. The study was an institution-based, cross-sectional study among food handlers. A total of 422 participants were chosen at random using systematic sampling. Data were collected using structured questions through interviews and observations. The study analyzed the prevalence of good hygiene practices and factors associated with meat hygiene practices using binary logistic regression. Statistical significance was determined using adjusted odds ratios, 95% confidence interval, and p-value of .05, and the model was checked using Hosmer and Lemeshow tests. The study result shows that good hygiene practices were only 40.6% (36.0–45.6) among meat handlers at butcher houses and restaurants in Gambela town. Low hygiene practices were significantly associated with the education level of food handlers (AOR: 2.5, 95%CI [1.17–5.41]) and meat hygiene training (AOR: 5.1, 95%CI [2.55–10.23]). Therefore, intensive food safety training for the workers, engaging educated professionals in food safety services, and mitigating the health risks and consequences of contamination demand intervention in the setting.

Postoperative satisfaction and perception among laparoscopic cholecystectomy patients in Diyala Province, Iraq

Background: Over the past two decades, laparoscopic surgeries have significantly progressed, and surpassing traditional surgical methods across multiple medical fields. This study aims to assess the patients’ satisfaction and the related factors toward the laparoscopic cholecystectomy in Iraq. Methods: A cross-sectional study was conducted from April 2023 to 30th March 2024 at the Alkalis General Hospital and Al Hayat Private Hospital, Diyala province, Iraq. Semi-structured questionnaire was recruited to collect the pre and post operative data from patients. Statistical analysis was carried out using SPSS-21, with significance set at p < 0.05. Results: Data of 157 patients with mean age (+ Standard Deviation) of 47.57 (SD +12.80) were analyzed. Most of them (53.5%) were females, operated in public hospitals (52.2%), unemployed (51.6%), and earned less than USD 400 (60.5%). The majority of patients expressed high overall satisfaction with the laparoscopic cholecystectomy procedure (42.7% being strongly satisfied and 38.9% satisfied). In the multiple logistic regression, the patients who were operated in private hospital (OR = 4.506, 95% CI: 1.394 to 14-577; P-value= 0.012), with monthly income exceeds USD 400 (OR = 4.887, 95% CI: 1.531 to 15.601; P-value=0.007), involved in the choosing of surgery technique (OR = 5.596, 95% CI: 1.425 to 21.982; P-value=0.014), and those recommend laparoscopic cholecystectomy for others (OR = 9.100, 95% CI: 1.425 to 21.982; P-value=0.001), had the highest odds ratios. The Hosmer and Lemeshow test indicated a good fit (p = 0.552). Conclusion: Patient satisfaction is high especially among high income patients operated in private hospital however, the perception of postoperative outcomes remains important areas of focus, particularly in regions with unique healthcare challenges like Iraq.

Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle

The study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer–Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.

A Nomogram Based on MRI Visual Decision Tree to Evaluate Vascular Endothelial Growth Factor in Hepatocellular Carcinoma

BackgroundsAnti‐vascular endothelial growth factor (VEGF) therapy has been developed and recognized as an effective treatment for hepatocellular carcinoma (HCC). However, there remains a lack of noninvasive methods in precisely evaluating VEGF expression in HCC.PurposeTo establish a visual noninvasive model based on clinical indicators and MRI features to evaluate VEGF expression in HCC.Study TypeRetrospective.PopulationOne hundred forty HCC patients were randomly divided into a training (N = 98) and a test cohort (N = 42).Field Strength/Sequence3.0 T, T2WI, T1WI including pre‐contrast, dynamic, and hepatobiliary phases.AssessmentThe fusion model constructed by history of smoking, albumin‐to‐globulin ratio (AGR) and the Radio‐Tree model was visualized by a nomogram.Statistical TestsPerformances of models were assessed by receiver operating characteristic (ROC) curves. Student's t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact test, univariable and multivariable logistic regression analysis, DeLong's test, integrated discrimination improvement (IDI), Hosmer–Lemeshow test, and decision curve analysis were performed. P < 0.05 was considered statistically significant.ResultsHistory of smoking and AGR ≤1.5 were clinical independent risk factors of the VEGF expression. In training cohorts, values of area under the curve (AUCs) of Radio‐Tree model, Clinical‐Radiological (C‐R) model, fusion model which combined history of smoking and AGR with Radio‐Tree model were 0.821, 0.748, and 0.871. In test cohort, the fusion model showed highest AUC (0.844) than Radio‐Tree and C‐R models (0.819, 0.616, respectively). DeLong's test indicated that the fusion model significantly differed in performance from the C‐R model in training cohort (P = 0.015) and test cohort (P = 0.007).Data ConclusionThe fusion model combining history of smoking, AGR and Radio‐Tree model established with ML algorithm showed the highest AUC value than others.Evidence Level4Technical EfficacyStage 2

Magnitude of in-hospital mortality and its associated factors among patients undergone laparotomy at tertiary public hospitals, West Oromia, Ethiopia, 2022

IntroductionLaparotomy surgery, which involves making an incision in the abdominal cavity to treat serious abdominal disease and save the patient’s life, causes significant deaths in both developed and developing countries, including Ethiopia. The number studies examining in-hospital mortality rates among individuals that undergone laparotomy surgery and associated risk factors is limited.ObjectiveTo assess the magnitude of in-hospital mortality and its associated factors among patients undergone laparotomy at tertiary hospitals, West Oromia, Ethiopia, 2022.MethodsAn institutional based retrospective cross-sectional study was conducted from January 1, 2017, to December 31, 2021. Data were collected using systematic random sampling and based on structured and pretested abstraction sheets from 548 medical records and patient register log. Data were checked for completeness and consistency, coded, imported using Epi-data version 4.6, cleaned and analyzed using SPSS version 25 software. Variables with p < 0.2 in the Bi-variable logistic regression analysis were included in the multivariate logistic regression analysis. The fit of the model was checked by the Hosmer‒Lemeshow test. Using the odds ratio adjusted to 95% CI and a p value of 0.05, statistical significance was declared.ResultsA total of 512 patient charts were reviewed, and the response rate was 93.43%. The overall magnitude of in-hospital mortality was 7.42% [95% CI: 5.4–9.8]. American society of Anesthesiology physiological status greater than III [AOR = 7.64 (95% CI: 3.12–18.66)], systolic blood pressure less than 90 mmHg [AOR = 6.11 (95% CI: 1.98–18.80)], preoperative sepsis [AOR = 3.54 (95% CI: 1.53–8.19)], ICU admission [AOR = 4.75 (95% CI: 1.50-14.96)], and total hospital stay greater than 14 days [(AOR = 6.76 (95% CI: 2.50-18.26)] were significantly associated with mortality after laparotomy surgery.ConcussionIn this study, overall in- hospital mortality was high. Early identification patient’s American Society of Anesthesiologists physiological status and provision of early appropriate intervention, and pays special attention to patients admitted with low systolic blood pressure, preoperative sepsis, intensive care unit admission and prolonged hospital stay to improve patient outcomes after laparotomy surgery.

A predictive model of pregnancy loss using pre-pregnancy endocrine and immunological parameters in women with abnormal glucose/lipid metabolism and previous pregnancy loss

ObjectiveTo investigate the clinical and endocrine risk factors for pregnancy loss in women with abnormal glucose/lipid metabolism and a history of pregnancy loss, and to develop a predictive model to assess the risk of pregnancy loss in these women’s subsequent pregnancies.MethodsPatients with a history of pregnancy loss who had abnormal glucose/lipid metabolism were retrospectively included in this study, and their pre-pregnancy baseline and clinical characteristics were collected. A predictive nomogram was constructed based on the results of the multivariable logistic regression model analysis, and its calibration and discriminatory capabilities were evaluated. The internal validation was then performed and the net benefits were assessed by the clinical decision curve.ResultsThe predictive model was eventually incorporated eight variables, including maternal age, previous pregnancy losses, anticardiolipin antibody (aCL) IgG, aCL IgM, thyroid peroxidase antibody, complement 4, free thyroxine and total cholesterol. The area under the curve (AUC) of the nomogram was 0.709, and Chi-square value and P value of the Hosmer–Lemeshow test were 12.786 and 0.119, respectively, indicating that the nomogram had a satisfactory calibration and discriminatory performance. The validation cohort showed a similar result for the discrimination of the nomogram (AUC = 0.715). The clinical decision curve demonstrated the nomogram had good positive net benefits.ConclusionsThis is the first study to predict the risks of subsequent pregnancy loss in women with abnormal glucose/lipid metabolism and history of pregnancy loss using pre-pregnancy clinical and endocrine parameters. This predictive nomogram may provide clinicians assistance to personalize the management of subsequent pregnancies in these patients.

Development and validation of a nomogram to predict risk of septic cardiomyopathy in the intensive care unit

The aim of this study was to develop a simple but effective nomogram to predict risk of septic cardiomyopathy (SCM) in the intensive care unit (ICU). We analyzed data from patients who were first admitted to the ICU for sepsis between 2008 and 2019 in the MIMIC-IV database, with no history of heart disease, and divided them into a training cohort and an internal validation cohort at a 7:3 ratio. SCM is defined as sepsis diagnosed in the absence of other cardiac diseases, with echocardiographic evidence of left (or right) ventricular systolic or diastolic dysfunction and a left ventricular ejection fraction (LVEF) of less than 50%. Variables were selected from the training cohort using the Least Absolute Shrinkage and Selection Operator (LASSO) regression to develop an early predictive model for septic cardiomyopathy. A nomogram was constructed using logistic regression analysis and its receiver operating characteristic (ROC) and calibration were evaluated in two cohorts. A total of 1562 patients participated in this study, with 1094 in the training cohort and 468 in the internal validation cohort. SCM occurred in 13.4% (147 individuals) in the training cohort, 16.0% (75 individuals) in the internal validation cohort. After adjusting for various confounding factors, we constructed a nomogram that includes SAPS II, Troponin T, CK-MB index, white blood cell count, and presence of atrial fibrillation. The area under the curve (AUC) for the training cohort was 0.804 (95% CI 0.764–0.844), and the Hosmer–Lemeshow test showed good calibration of the nomogram (P = 0.288). Our nomogram also exhibited good discriminative ability and calibration in the internal validation cohort. Our nomogram demonstrated good potential in identifying patients at increased risk of SCM in the ICU.

Quality of life of children with cerebral palsy: Evidence from Assam, India

BackgroundCerebral palsy (CP) is a common childhood physical disability, which often leads to secondary conditions and affects the quality of life (QOL) of the children suffering from it. This study measures the QOL of children with CP using Children with Cerebral Palsy Quality of Life Questionnaire (CP-QOL) and the factors affecting it. MethodsThis cross-sectional study covering 140 purposively chosen caregivers of children with CP aged 4–12 years was conducted in Assam, India, 2023. Subscale scores of CP-QOL were computed to determine the overall QOL score. Cronbach's α assessed the subscales' reliability and interval consistency. Bivariate analysis was conducted to present the socio-demographic variation in QOL, and logistic regression revealed the determinants. Regression model diagnostics were performed, which included the Omnibus test, the Hosmer–Lemeshow test, a classification table showing the model's sensitivity, specificity, and overall accuracy, and a ROC curve displaying the model's overall accuracy in predicting the outcome variable. ResultsThe overall QOL score was 44.94 ± 16.28. QOL scores varied significantly with children's age, Gross Motor Function Classification System (GMFCS) levels, special school enrolment, mother's education, family structure, and the behaviour of relatives and neighbours. Nearly three-fourths of the caregivers perceived a poor QOL of their children with CP. Children's CP type, GMFCS level, associated conditions, and religion were significantly associated with their QOL. ConclusionThe QOL of children with CP are significantly lower, the primary factor being the GMFCS level. Efforts to educate caregivers and customized policies and programs will help improve CP children's QOL.

Development, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer–Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram (https://mr.cscps.com.cn/wscoringtool/index.html) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

A nomogram for predicting hemorrhagic shock in pediatric patients with multiple trauma

The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer–Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Development and validation of a social alienation predictive model for older maintenance hemodialysis patients based on latent profile analysis—a cross-sectional study

BackgroundSocial alienation refers to the state of feeling isolated, helpless, and unsatisfied due to maintaining distance from others or avoiding social interaction and activities. This phenomenon is caused by a lack of social skills, social anxiety, physical health problems, and other reasons. Older maintenance hemodialysis patients are exposed to a higher risk of social alienation. However, previous studies have been performed using the total score of the scale, which does not allow the identification of the characteristics of various patient groups with different levels of social alienation. In contrast, latent profile analysis can classify individuals into different categories based on continuous observational indicators, which improves accuracy and provides a more objective assessment by accounting for the uncertainty of variables. Given the concealed nature of social alienation and the differences in characteristics and treatment measures between different profiles, developing a predictive model for social alienation in older maintenance hemodialysis patients holds significance.ObjectiveTo explore the latent profile analysis of social alienation in older maintenance hemodialysis patients and to develop and validate a predictive model for social alienation in this population.MethodsA total of 350 older maintenance hemodialysis patients were selected as the study subjects using convenience sampling. A cross-sectional survey was conducted using a general information questionnaire, the Generalized Alienation Scale, and the Self-Perceived Burden Scale. Based on the results of the Generalized Alienation Scale, a latent profile analysis was performed, followed by univariate analysis and multinomial logistic regression to develop a predictive model. The effectiveness of the predictive model was evaluated in terms of its authenticity, reliability, and predictive ability.ResultsThree hundred nineteen valid questionnaires were collected. The social alienation of older maintenance hemodialysis patients based on latent profile analysis were divided into three profiles, which were named the low/medium/high-symptom groups, comprising 21%, 38.9%, and 40.1% of participants, respectively. Based on male, monthly social activity hours, Age-Adjusted Charlson Comorbidity Index, dialysis age, and Self-Perceived Burden Scale, a predictive model of social alienation for older maintenance hemodialysis patients was developed, and the Hosmer–Lemeshow tests showed no statistical significance (P > 0.05). The model has high predictive efficiency in authenticity, reliability and predictability.ConclusionOlder maintenance hemodialysis patients exhibited moderate to high levels of social alienation. The latent profile analysis based method was used to divide patients into low/medium/high-symptom profiles, and the predictive model demonstrates excellent authenticity, reliability, and predictability.

#2271 Cardiovascular event prediction in kidney transplant recipients: a new risk score derivation and internal validation

Abstract Background and Aims Kidney transplant recipients (KTRs) have a cardiovascular (CV) risk 3 to 5-fold higher than that of the general population.CV disease represents the main cause of death in KTRs, both due to the presence of traditional risk factors and factors strictly related to transplant. The main CV risk calculators proved to be poorly predictive in the KTR population. We, therefore, aimed to compare the performance of CV risk scores already in use and to develop and validate a CV risk score in our KTRs population. Method Our analysis included 371 adult KTRs in follow-up at the outpatient clinic from 1 January 2015 to 31 December 2016. The composite outcome was the occurrence of MACE. We compared the performance of three main CV risk calculators (Framingham Risk Score-FRS-, INDANA score and PORT score). Thereafter, we built a predictive model selecting study variables by backward stepwise Cox regression. The risk score for each variable was weighted according to the HR of the final multivariable model. Internal and external validation of the prediction score and its discriminative capacity was assessed via area under the curve (AUC) curve, and the calibration via the Hosmer–Lemeshow test. Results After a mean follow-up of 68 months, CV events occurred in 71 (19%) KTRs. FRS, INDANA and PORT were demonstrated to have low predictive power in our population (Fig. 1). The accuracy did not improve after adjustment for immunological and transplant-related variables. Therefore, we derived a model including significative variables at multivariate analysis: age, dialysis vintage, systolic blood pressure, eGFR and 24-hour proteinuria (Akaike information criteria 713). The model discrimination was good (Harrel's c: 0.73). According to the hazard ratio, 3 points have been attributed to age higher than 60 years and 24 h proteinuria higher than 1 g/d, 2 points to dialysis vintage longer than 5 years, systolic blood pressure higher than 140 mmHg and eGFR less than 30 ml/min/1.73 m2. The new score demonstrated good discrimination (AUC 0.70, Fig. 2) and calibration (Hosmer-Lemeshow test 11.34, P = 0.12). The new score was internally validated by 10-fold cross-validation (mean AUC 0.70, 95% CI 0.60-0.77). For each point of the new score, the risk of the event increased by 40% and a score higher than three was associated with a 4-fold increased risk of composite endpoint. Conclusion We confirmed the poor predictive power of the known CV risk assessment tools in our real-world KTRs population. Moreover, their discriminatory power was not improved by adjustment for the occurrence of biopsy-proven rejection, HLA compatibility and donor-specific antibody. A new score for CV event prediction including traditional risk factors, such as age and systolic blood pressure, and variables related to renal function and the effects of previous chronic kidney disease, was developed and internally validated.

Development and Validation of a Nomogram for Predicting Nutritional Risk Based on Frailty Scores in Older Stroke Patients

BackgroundIn older stroke patients with frailty, nutritional deficiencies can amplify their susceptibility, delay recovery, and deteriorate prognosis. A precise predictive model is crucial to assess their nutritional risk, enabling targeted interventions for improved clinical outcomes.ObjectiveTo develop and externally validate a nutritional risk prediction model integrating general demographics, physical parameters, psychological indicators, and biochemical markers. The aim is to facilitate the early identification of older stroke patients requiring nutritional intervention.MethodsThis was a multicenter cross-sectional study. A total of 570 stroke patients were included, 434 as the modeling set and 136 as the external validation set. The least absolute shrinkage selection operator (LASSO) regression analysis was used to select the predictor variables. Internal validation was performed using Bootstrap resampling (1000 iterations). The nomogram was constructed based on the results of logistic regression. The performance assessment relied on the receiver operating characteristic curve (ROC), Hosmer–-Lemeshow test, calibration curves, Brier score, and decision curve analysis (DCA).ResultsThe predictive nomogram encompassed seven pivotal variables: Activities of Daily Living (ADL), NIHSS score, diabetes, Body Mass Index (BMI), grip strength, serum albumin levels, and depression. Together, these variables comprehensively evaluate the overall health and nutritional status of elderly stroke patients, facilitating accurate assessment of their nutritional risk. The model exhibited excellent accuracy in both the development and external validation sets, evidenced by AUC values of 0.934 and 0.887, respectively. Such performance highlights its efficacy in pinpointing elderly stroke patients who require nutritional intervention. Moreover, the model showed robust goodness of fit and practical applicability, providing essential clinical insights to improve recovery and prognosis for patients prone to malnutrition.ConclusionsElderly individuals recovering from stroke often experience significant nutritional deficiencies. The nomogram we devised accurately assesses this risk by combining physiological, psychological, and biochemical metrics. It equips healthcare providers with the means to actively screen for and manage the nutritional care of these patients. This tool is instrumental in swiftly identifying those in urgent need of targeted nutritional support, which is essential for optimizing their recovery and managing their nutrition more effectively.

A preoperative scoring system for predicting the extraprostatic extension of prostate cancer following radical prostatectomy using magnetic resonance imaging and clinical factors.

We aimed to develop a preoperative prediction model for extraprostatic extension (EPE) in prostate cancer (PCa) patients following radical prostatectomy (RP) using MRI and clinical factors. This retrospective study enrolled 266 consecutive patients who underwent RP for PCa in 2022. These patients were divided into a training set (n = 187) and a test set (n = 79) through random assignment. The evaluated variables included age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), index tumor length on MRI, Prostate Imaging-Reporting and Data System (PI-RADS) category, and EPE-related MRI features as defined by PI-RADS v2.1. A predictive model was constructed through multivariable logistic regression and subsequently translated into a scoring system. The performance of this scoring system in terms of prediction and calibration was assessed using C statistics and the Hosmer‒Lemeshow test. Among patients in the training and test cohorts, 74 (39.6%) and 25 (31.6%), respectively, exhibited EPE after RP. The formulated scoring system incorporated the following factors: PSAD, index tumor length, bulging prostatic contour, and tumor-capsule interface > 10mm as identified on MRI. This scoring system demonstrated strong prediction performance for EPE in both the training (C statistic, 0.87 [95% confidence interval, 0.86-0.87]) and test cohorts (C statistic, 0.85 [0.83-0.89]). Furthermore, the scoring system exhibited good calibration in both cohorts (P = 0.988 and 0.402, respectively). Our scoring system, built upon MRI features defined by the PI-RADS, offers valuable assistance in assessing the likelihood of EPE after RP.

3D intraoral scanning techniques support the effects of crown morphology on dental caries

BackgroundWith the development and utilization of three-dimensional (3D) intraoral scanning (IOS) technology, the morphological characteristics of teeth were quantitatively assessed. In this research, we aimed to explore the prevalence of dental caries in relation to each measurable morphological indicator of the tooth body via 3D intraoral scanning techniques.MethodsA hospital-based single-centre study was conducted at our hospital from Dec. 2021 to Apr. 2023. A total of 53 patients were involved in the study, providing complete morphological data for 79 teeth. Each patient completed an oral hygiene routine questionnaire and underwent examination by an experienced dentist to evaluate caries conditions before undergoing 3D intraoral scanning to obtain a digital dental model. Geomagic Studio 2014 was used to extract oral morphological data from the models. The acquired data were entered, cleaned and edited using Excel 2016 and subsequently exported to SPSS version 25.0 for analysis. Chi-square analysis and logistic regression analyses were employed to test the associations.ResultsAmong the participants, 33 (61.1%) were female, with a mean age of 26.52 ± 10.83 years. Significant associations were found between dental caries and the vertical distance between the distal tip and the gum (OR 14.02; 95% CI 1.80-109.07; P = 0.012), the distal lateral horizontal distance of occlusion (OR 0.40; 95% CI 0.18–0.90; P = 0.026), and the mesial horizontal distance of occlusion (OR 2.20; 95% CI 1.12–4.31; P = 0.021). The Hosmer–Lemeshow test indicated a P value of 0.33.ConclusionsThe vertical distance between the distal tip and the gum, the distal lateral horizontal distance of the occlusion and the mesial horizontal distance of the occlusion were the influencing factors for dental caries (identified as independent risk factors). We hypothesize that these factors may be associated with the physiological curvature of teeth and the role of chewing grooves in plaque formation over time. However, further studies involving larger population samples and more detailed age stratification are still needed.

Development and validation of a predictive model for the risk of sarcopenia in the older adults in China

BackgroundSarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults.MethodsThis study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators.Results3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75–0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73–0.79). Hosmer–Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties.ConclusionsThe constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

Prediction of surface urban heat island in Ho Chi Minh City, Viet Nam using remote sensing and logistic regression model

Urban heat island is a concerning environmental issue in major cities, particularly in Ho Chi Minh City. With a high rate of urbanization, the city’s extensive impervious surface area contributes to land surface temperature increase, leading to the expansion of the urban heat island phenomenon. This study was conducted in the Ho Chi Minh city region from 2009 to 2020, images from Landsat 5 (TM) and Landsat 8 (OLI/TIRS) satellites were employed to estimate land surface temperature (LST), examine the spatial distribution of the urban heat island (SUHI), and analyze changes in impervious surface area (ISA). A logistic regression model was established to predict the relationship between SUHI and ISA, with the percentage of ISA calculated by the Kernel Density Estimation method (KDE). The results revealed that the impervious surface area in the city doubled from 2009 to 2020, accounting for 29.54% of the city’s area, which correlated with a significant expansion of SUHI from 37,000 ha to 53,000 ha. The SUHI predicting model exhibited good discrimination ability (AUC = 0.98) and total accuracy of 96% through Hosmer & Lemeshow Test in R. The probability of the predicted SUHI showed a high correlation (R2 = 0.885) with the actual SUHI. In areas where ISA increased from 60% to 80%, the probability of SUHI occurrence also increased from 52.85% to 98%. This study presented a novel approach, utilizing statistical modeling to accurately quantify SUHI based on changes in impervious surface density. This method can contribute to an effective evaluation of heat-related health risks, provide the government and urban planners with a precise orientation in management toward sustainable development.

Intraoperative hypothermia in patients with laparoscopic surgery: Influencing factors and prevention strategies

BackgroundEffective body temperature management is crucial for the favorable prognosis of patients undergoing surgery. The purpose of this study is to explore the risk factors of intraoperative hypothermia and to develop a risk prediction model to provide basis for clinical treatment. MethodsPatients who underwent laparoscopic surgery in a tertiary hospital in China from February 1, 2023 to January 31, 2024 were included. The body temperature characteristics of patients in hypothermia group and non-hypothermia group were collected and evaluated. Univariate and Logistic regression analysis were used to evaluate the influencing factors. Based on the regression coefficients of risk factors, a risk prediction model of hypothermia was established. The model was assessed by Hosmer's Lemeshow (H- L) test and receiver working characteristic (ROC) curve. ResultsIn 216 patients undergoing laparoscopic surgery, the incidence of hypothermia was 52.78 %. BMI≤23 kg/m2(OR = 2.061, 95%CI: 1.413–3.263), basal body temperature≤36.1 °C (OR = 3.715, 95%CI: 3.011–4.335), operating room temperature≤22 °C (OR = 2.481, 95%CI: 1.906–3.014), length of surgery≥120 min (OR = 2.228, 95%CI: 1.925–2.981) were the risk factors of hypothermia in patients undergoing laparoscopic surgery (all P < 0.05). The P value of H-L test was 0.098, the area under ROC curve and 95%CI were 0.806 (0.746–0.869). The sensitivity and specificity of the model in this study were good. ConclusionsPatients undergoing laparoscopic surgery are at a high risk of developing hypothermia, a condition influenced by a multitude of factors. This model is designed to be integrated into clinical practice, enabling healthcare providers to identify patients with a higher risk and to implement targeted preventive measures.

External Validation of the fullPIERS Risk Prediction Model in a U.S. Cohort of Women With Preeclampsia [ID 2683408

INTRODUCTION: Hypertensive disorders of pregnancy are a leading cause of maternal morbidity and adverse pregnancy outcomes. The fullPIERS model was developed to predict severe adverse maternal outcomes within 48 hours of admission in patients with preeclampsia. This model has not been validated in the U.S. cohort. We sought to examine whether the fullPIERS model was predictive in the U.S. cohort. METHODS: This was a retrospective study of patients with preeclampsia who delivered at 23 weeks or greater from January 1, 2010, to December 31, 2020. Our primary outcome was a composite of maternal mortality or other serious complications of preeclampsia that occurred within 48 hours of admission. Using the prediction model that considers gestational age at admission, chest pain or dyspnea, serum creatinine, platelet, aspartate transaminase, and oxygen saturation, we calculated the probability of the composite outcome. We assessed performance using the area under the curve (AUC) of the receiver operating characteristic curve. Hosmer–Lemeshow test was used to assess the goodness-of-fit. RESULTS: Of 1,552 patients with preeclampsia, 77 (5.0%) had the composite outcome within 48 hours. The fullPIERS model had an AUC of 0.73 (95% CI, 0.66–0.80). However, the Hosmer–Lemeshow test showed poor calibration with a value of P&lt;.001. The calibration plot showed the model underestimated when the predicted probability was less than 10% and overestimated when the predicted probability was over 28%. CONCLUSION: The fullPIERS model had good AUC in the U.S. cohort of patients with preeclampsia but poor calibration. The fullPIERS model may not be clinically useful in the United States.