Hosmer-Lemeshow Goodness Of Fit Test Research Articles

Model of predicting fear of cancer recurrence in patients with digestive tract cancer: A cross-sectional study.

To investigate the incidence of fear of cancer recurrence in patients with digestive tract cancers analyse its influencing factors, and further establish a visual risk prediction model. A cross-sectional study. A cross-sectional survey was conducted among 570 patients with digestive tract tumours admitted to a local hospital, from May 2023 to December 2023 by convenient sampling method. Univariate analysis and logistic analysis were performed on the influencing factors, and the risk prediction nomogram model of fear of cancer recurrence in patients with digestive tract cancer was constructed by using R 4.1.3 software. ROC curve was used to evaluate the differentiation of the nomogram model. The calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the consistency of the model. This study was reported using the TRIPOD checklist. In this study, 272 (47.7%) patients developed fear of recurrence. The risk prediction model of recurrence fear column chart for digestive tract cancer patients incorporated six variables of gender, therapy, alimentary tract haemorrhage, pain, depression and social support. The C-statistic was (.976), and the calibration curve showed that the predicted probability was more in line with the actual probability of occurrence, and the decision curve showed that the predictive model had better practicality. The column-line diagram prediction model constructed in this study is effective and facilitates timely intervention and management by healthcare professionals based on their risk factors. Nomogram is helpful to calculate the risk probability of FCR in patients with digestive tract cancer, identify FCR patients in time, and formulate comprehensive and personalized countermeasures, to provide a good quality of life and prolong the survival cycle of patients with digestive tract cancer. Participants were hospitalized patients or patients with digestive tract cancer undergoing follow-up. First of all, before the investigation and research, a team is formed to discuss the concept, research purpose, method, significance, etc., and determine the research tools. Second, by reasonably explaining the study to patients to seek informed consent from the patient and sign it, patients filled in the questionnaire independently. For patients with low education levels who could not fill in the questionnaire, the team members made objective explanations to help them choose reasonable options.

A nomogram prediction model for short-term aortic-related adverse events in patients with acute Stanford type B aortic intramural hematoma: development and validation

BackgroundThis study is to examine the factors associated with short-term aortic-related adverse events in patients with acute type B aortic intramural hematoma (IMH). Additionally, we develop a risk prediction nomogram model and evaluate its accuracy.MethodsThis study included 197 patients diagnosed with acute type B IMH. The patients were divided into stable group (n = 125) and exacerbation group (n = 72) based on the occurrence of aortic-related adverse events. Logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) method for variables based on baseline assessments with significant differences in clinical and image characteristics were employed to identify independent predictors. A nomogram risk model was constructed based on these independent predictors. The nomogram model was evaluated using various methods such as the receiver operating characteristic curve, calibration curve, decision analysis curve, and clinical impact curve. Internal validation was performed using the Bootstrap method.ResultsA nomogram risk prediction model was established based on four variables: absence of diabetes, anemia, maximum descending aortic diameter (MDAD), and ulcer-like projection (ULP). The model demonstrated a discriminative ability with an area under the curve (AUC) of 0.813. The calibration curve indicated a good agreement between the predicted probabilities and the actual probabilities. The Hosmer-Lemeshow goodness of fit test showed no significant difference (χ2 = 7.040, P = 0.532). The decision curve analysis (DCA) was employed to further confirm the clinical effectiveness of the nomogram.ConclusionThis study introduces a nomogram prediction model that integrates four important risk factors: ULP, MDAD, anemia, and absence of diabetes. The model allows for personalized prediction of patients with type B IMH.

Predictive Value of Jugulo-omohyoid Lymph Nodes in Lateral Lymph Node Metastasis of Papillary Thyroid Cancer

BackgroundJugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery.MethodsA total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study.ResultsThyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful.ConclusionJOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients.

Construction and evaluation of weight retention nomogram prediction model for women 1 year after delivery

To analyze the influencing factors of body weight retention in woman at 1 year postpartum, and to construct and evaluate a nomogram prediction model for postpartum 1-year weight retention. From September 2010 to February 2011, 468 pregnant women in the third trimester were recruited from Yuexiu District and Baiyun District Maternal and Child Health Hospital in Guangzhou, and followed up to 1 year postpartum. The basic demographic information of pregnant women was collected by self-made questionnaire. Dietary intake in the third trimester was investigated by 3-day 24-hour dietary review. The weight of women before delivery and one year after delivery were measured. According to whether the weight retention at 1 year postpartum is greater than 0 kg, the study subjects were divided into the 1-year postpartum weight retention group and weight recovery group. Logistic regression analysis were used to screen the influencing factors of weight retention at 1 year postpartum. R 4.2.3 software was used to construct the nomogram prediction model. The subject working characteristic curve, calibration curve, Hosmer-Lemeshow goodness of fit test and clinical decision curve were used to evaluate the model's differentiation, accuracy and clinical applicability. Among 329 subjects in the model training set, the 1-year postpartum weight retention was 68.09%, and the median and quartile levels of retained body weight were 5.0(3.0, 10.0)kg. After Logistic analysis, a nomogram prediction model was constructed based on five factors: pre-pregnancy body mass index(BMI), pregnancy weight gain, parity, gravitity, 0-6 months postpartum feeding pattern. The model had good discrimination(AUC_(training)=0.778, AUC_(testing)=0.767) and accuracy(Hosmer-Lemeshow test: P_(training)=0.946, P_(testing)=0.891). The 1-year postnatal weight retention nomogram model based on women's pre-pregnancy BMI, pregnancy weight gain, parity, gravitity, 0-6 months postpartum feeding pattern has good differentiation, accuracy and clinical applicability.

Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study

BackgroundThe prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level.MethodsAccording to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs.ResultsIndependent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05).ConclusionThe linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

Prognostic nomogram for uncontrolled type 2 diabetes using Thailand nation-wide cross-sectional studies.

Uncontrolled type 2 diabetes (T2DM) and limited hemoglobin A1c (HbA1c) levels examination are a burden in community hospitals in Thailand. The nomogram from the patients' information might be a practical solution to identify a high-risk group of diabetic complications. Thus, this study aimed to establish an effective prognostic nomogram for patients with uncontrolled T2DM. Sequential nationwide cross-sectional studies of T2DM patients in 2018 and 2015 were utilized for development and validation groups, respectively, with this chronological order aiming to capture recent trends during development and assess the nomogram's robustness across diverse timeframes. The predictive outcome was uncontrolled T2DM, defined as HbA1c ≥9%. The model was determined by multivariable regression analysis and established an effective prognostic nomogram. The receiver operating characteristic curve, Hosmer-Lemeshow goodness of fit test, and decision curve analysis (DCA) was applied to evaluate the performance of the nomogram. In 2018, 24% of the 38,568 participants in the development group had uncontrolled T2DM (defined as Hba1c ≥9%). The predictive nomogram of uncontrolled diabetes consisted of demographic characteristics, prescription medications, history of diabetic complications, and laboratory results (C-statistic of 0.77). The goodness of fit test and DCA showed good agreement between the result and clinical application for T2DM. The predictive nomogram demonstrates simplicity, accuracy, and valuable prediction to enhance diabetic care in resource-limited countries, including Thailand.

Predictors of successful trial of labor after cesarean section (TOLAC) in women with one prior transverse cesarean section at Tertiary Hospitals in northwest Ethiopia: a multicenter study

BackgroundTrials of labor after cesarean section is the preferred strategy to decrease the cesarean delivery rate and reducing complications associated with multiple cesarean sections. The success rate of trials of labor after cesarean section and associated factors have not been well documented in Ethiopia. Hence, this study was aimed to determine the success rate and factors associated with the trial of labor after one cesarean section in five Comprehensive Specialized Hospitals located in northwest Ethiopia.MethodsAn institutional-based cross-sectional study was conducted among 437 women who came for the trial of labor from December 1, 2021, to March 30, 2022. All women who fulfilled the eligibility criteria were included to this study. Data was collected using structured and pre-tested questionnaire. Then, the data was entered into Epi Data 4.6 software and exported to SPSS version 26 for analysis. To identify the variables influencing the outcome variable, bivariable and multivariable logistic regression analyses were conducted. The model’s fitness was checked using the Hosmer-Lemeshow goodness of fit test, and an adjusted odds ratio with a 95% confidence interval was used to declare the predictors that are significantly associated with TOLAC.ResultsThe success rate of the trial of labor after one cesarean section was 56.3% (95% CI, 51.3%, 61.2%). Maternal age ≥ 35 years (AOR: 3.3, 95% CI 1.2, 9.3), the fetal station at admission ≤ zero (AOR: 5. 6, 95% CI 3.3, 9.5), vaginal delivery before cesarean section (AOR: 1.9, 95% CI 1.2, 3.2), and successful vaginal birth after cesarean delivery (AOR 2.2, 95% CI 1.2, 4.1) were found to have a significant association with the success rate of trial of labor after cesarean section.ConclusionsIn this study, the success rate of the trial of labor after a cesarean section was low as compared to the ACOG guideline and other studies in different countries. Therefore, the clinicians ought to offer counsel during antenatal and intrapartum period, encourage the women to make informed decision on the mode of delivery, and the practitioners need to follow fetal and maternal conditions strictly to minimize adverse birth outcomes.

A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

A nomogram for predicting the risk of heart failure with preserved ejection fraction

BackgroundThis study purposed to design and establish a nomogram to predict the risk of having heart failure with preserved ejection fraction. MethodThe clinical data of 1031 patients diagnosed with heart failure (HF) in the First Affiliated Hospital of Jinan University from January 2018 to December 2022 were retrospectively analyzed, among which 618 patients were diagnosed with heart failure with preserved ejection fraction (HFpEF). Patients were randomly divided into a training set (70%, n = 722) and a validation set (30%, n = 309). The prediction model of HFpEF was established by using clinical characteristic data parameters, and the risk of having HFpEF was predicted by using a nomogram. Single-factor analysis was used to select independent risk factors (P < 0.05), and then binary logistic regression was used to screen predictive variables (P < 0.05). The discrimination ability of the model was evaluated by the ROC curve and calculating the area under the curve (AUC). In addition, the predictive ability of the established nomogram was evaluated using calibration curves and the Hosmer-Lemeshow goodness of fit test (HL test), and the clinical net benefit was evaluated using decision curve analysis (DCA). ResultsThe results of binary logistic regression analysis showed that age, gender, hypertension, coronary heart disease, glycosylated hemoglobin, serum creatinine, E/e’ septal, relative wall thickness (RWT), left ventricular mass index (LVMI) and pulmonary hypertension (PH) were independent influencing factors for the risk of having HFpEF (P < 0.05). Based on the results of logistic regression analysis, a nomogram was established and calibration curves were made. The prediction model showed that the AUC of the training dataset was 0.876 (95%CI, 0.851–0.902), and 0.837 (95%CI, 0.791–0.883) in the validation set. According to the calibration curves and HL test, the nomogram shows good calibration, and DCA shows that our model is clinically useful. ConclusionA nomogram prediction model was constructed to predict the patient's risk of having HFpEF. This prediction model indicated that the combination of creatinine, E/e’, RWT, LVMI and PH may be valuable in the diagnosis of HFpEF.

Factors influencing Teenage Pregnancy among Girls attending Kampala International University Teaching Hospital, Bushenyi, Uganda

Teenage pregnancy and subsequent childbirth to women less than 20 years of age continue to be a major global public health concern in both developed and developing countries, affecting more than 16 million girls and young women, or an estimated 11% of all births worldwide. This study determined the factors influencing teenage pregnancy among teenage girls aged 13–19 attending ANC at KIU-TH, Bushenyi District. A cross-sectional study design was employed for this study. Quantitative data was collected using structured interviews. Questionnaire tools were checked for their accuracy and data completeness, then the data was coded and entered into Epi Info version 7, then exported into SPSS version 22.0 for analysis. A binary logistic regression model was used to identify the determinant factors. A collinearity diagnostic test was conducted using tolerance to check for collinearity between independent variables and the interaction effect. Variables in the bivariable analysis having a p-value &lt;0.2 were considered for the multivariate analysis to adjust the confounders. The strength and presence of a statistical association were assessed by the OR, p-value, and 95% confidence interval (95% CI). Variables with a p-value ≤0.05 were considered statistically significant determinants of teenage pregnancy. The Hosmer-Lemeshow goodness-offit test (p = 0.9289) was used to assess the fitness of the model. A total of 310 adolescents were included in the study, with a response rate of 100%. The majority (39.7%) were aged 16–17, and more than half (59.0%) were from rural areas. The proportion of teenage pregnancies among study participants was 19.0. In the multivariate logistic regression model, age, residence, parents’ marital status, level of education, monthly income, substance abuse, history of use of contraceptives, age of first sexual contact, and awareness of safe sex were statistically significant. The study showed a high prevalence of teenage pregnancy. Age, residence, parents’ marital status, level of education, monthly income, substance abuse, history of use of contraceptives, age of first sexual contact, and awareness of safe sex were found to have a statistically significant association. Keywords: Teenage pregnancy, Childbirth, Girls, Marital status, Age.

Risk factors and nomogram to predict skip metastasis in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy. Skip metastases of PTCs are easily misdiagnosed before surgery, and it could lead to re-operation and affect the prognosis. Although there are a few studies about nomograms for predicting central lymph node metastases (CLNM) or lateral lymph node metastases (LLNM) of PTCs, there are few studies about nomograms for skip metastases. Based on the clinical and ultrasonographic characteristics of patients with PTCs, the aim of our study was to investigate the risk factors and establish a nomogram for predicting the risk of skip metastases in PTCs. This study enrolled 218 PTCs patients with lateral cervical lymph node metastases and their data were analyzed retrospectively. According to the postoperative pathological results, the patients were divided into skip-positive group and skip-negative group. In order to establish the nomogram, univariate and multivariate analyses were used to estimate risk factors of skip metastases. The receiver operating characteristic (ROC) curve, internal calibration plot and decision curve analysis (DCA) were used to evaluate the nomogram model's efficacy. There were statistical differences between skip-positive group and skip-negative group in tumor location, the maximum diameter (D) and capsule invasion (P<0.05). No statistical differences were observed in sex, age, Hashimoto's thyroiditis, multifocality, anteroposterior diameter/transverse diameter (A/T) ratio, shape, margin, microcalcification, intra-nodular vascularity and preoperative serum thyroglobulin (Tg) (P≥0.05). The risk factors of skip metastases in PTCs were D ≤10 mm, location in the upper portion and capsule invasion. The area under the curve (AUC) of nomogram was 0.877, the accuracy was 85.32%, the sensitivity was 60.98%, and the specificity was 90.96%. The calibration curve and the Hosmer-Lemeshow goodness of fit test showed that the consistency between the nomogram and the actual observation was good. The DCA showed that most PTC patients might benefit from the predictive nomogram model. A nomogram for predicting skip metastases in PTCs may be useful in clinical diagnosis and treatment.

Construction and verification of a nomogram of factors influencing the risk of death in patient with sepsis-associated thrombocytopenia

To construct a nomogram prediction model for predicting the risk of death in patients with sepsis-associated thrombocytopenia (SAT) in intensive care unit (ICU) for early indentification and active intervention. Clinical data of SAT patients admitted to ICU of the First Affiliated Hospital of Nanjing Medical University from December 2019 to August 2021 were retrospectively collected, including demographic data, laboratory indicators, etc. According to the prognosis at 28 days, the patients were divided into the death group and the survival group, and the differences of clinical variables between the two groups were compared. Multivariate Logistic regression analysis was performed to analyze the independent risk factors influencing mortality of patients within 28 days, then a nomogram predictive model was constructed and its performance was verified with internal data. Receiver operator characteristic curve (ROC curve) was used to evaluate the diagnostic effectiveness of the nomogram model, and the clinical applicability of this model was evaluated by clinical decision curve analysis (DCA). A total of 275 patients were included, with 95 deaths at 28 days and a 28-day mortality of 34.5%. Compared with the survival group, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), lactic acid (Lac), platelet distribution width (PDW) on day 5 of ICU admission, blood urea nitrogen (BUN), total bilirubin (TBIL), aspartate aminotransferase (AST), C-reactive protein (CRP) of patients in the death group were higher, activated partial thromboplastin time (APTT) and prothrombin time (PT) were longer, platelet count (PLT) on day 3 and day 5 of ICU admission, direct bilirubin (DBIL), fibrinogen (FIB) were lower, the history of chronic lung disease, mixed site infection, lung infection, bloodstream infection, Gram-negative bacterial infection and fungal infection accounted for a higher proportion, the history of diabetes mellitus, urinary tract infection and no pathogenic microorganisms cultured accounted for a lower proportion, and the proportion of the use of vasoactive drugs, mechanical ventilation (MV), continuous renal replacement therapy (CRRT), bleeding events and platelet transfusion were higher. Multivariate Logistic regression analysis showed that APACHE II score at the day of ICU admission [odds ratio (OR) = 1.417, 95% confidence interval (95%CI) was 1.153-1.743, P = 0.001], chronic lung disease (OR = 72.271, 95%CI was 4.475-1 167.126, P = 0.003), PLT on day 5 of ICU admission (OR = 0.954, 95%CI was 0.922-0.987, P = 0.007), vasoactive drug (OR = 622.943, 95%CI was 10.060-38 575.340, P = 0.002), MV (OR = 91.818, 95%CI was 3.973-2 121.966, P = 0.005) were independent risk factors of mortality in SAT patients. The above independent risk factors were used to build a nomogram prediction model, and the area under the curve (AUC), sensitivity and specificity were 0.979, 94.7% and 91.7%, respectively, suggesting that the model had good discrimination. The Hosmer-Lemeshow goodness of fit test showed a good calibration with P > 0.05. At the same time, DCA showed that the nomogram model had good clinical applicability. Patients with SAT has a higher risk of death. The nomogram model based on APACHE II score at the day of ICU admission, chronic lung disease, PLT on day 5 of ICU admission, the use of vasoactive drug and MV has good clinical significance for the prediction of 28-day mortality, and the discrimination and calibration are good, however, further verification is needed.

Minimum acceptable diet and associated factors among children aged 6–23 months in Jig-Jiga, Somali region, eastern Ethiopia, 2022

BackgroundThe minimum acceptable diet is the proportion of children aged 6–23 months who consumed the minimum meal frequency and minimum dietary diversity during the previous day or night. The minimum acceptable diet assesses both micronutrient adequacy and the quantity of food consumed during the previous day or night. Inappropriate infant and young child feeding practices during this period result in significant threats to child health through impaired cognitive development. Therefore, this study aims to assess the minimum acceptable diet and associated factors among children aged 6–23 months in Jig-Jiga, Somali region, Eastern Ethiopia.MethodsA community-based, cross-sectional study was conducted in Jig-Jiga town. A systematic random sampling technique was used to select 536 children aged 6–23 months with their mothers. Data were collected using a pre-tested, structured questionnaire. The data were entered into Epi-data 3.1. The data were cleaned and analyzed using SPSS v20. Bi-variable and multivariable logistic regression analyses were done, and model fitness was checked and tested by the Hosmer-Lemeshow goodness of fit test. The results of the adjusted odds ratio with 95% confidence intervals and P < 0.05 were considered statistically significant.ResultThe overall prevalence of a minimum acceptable diet was 47.2% (95% CI: 43.1–51.6). Occupation of fathers (AOR = 0.5, 95%CI: 0.3–0.8), child age of 6–11 months (AOR = 3.6, 95%CI: 1.7–7.7), age of the mother 15–24 years (AOR = 7.6, 95%CI: 1.5–38.146), 25–34 years (AOR = 5.56, 95%CI: 1.17–26.325), mothers who had only one under-five child (AOR = 2.1, 95%CI: 1.298–3.471), and media as a source of information (AOR = 0.16, 95%CI: 0.061–0.433) were associated with the minimum acceptable diet.ConclusionThis study showed that the prevalence of a minimum acceptable diet was low. Factors associated with a minimum acceptable diet included the father’s occupation, the child’s age, the mother’s age, having one under-five child, and the media as a source of information. Therefore, interventional strategies that focus on family planning and advocacy for infant and young child feeding are required to improve the provision of a minimum acceptable diet.

Prognostic value of right ventricular trabecular complexity in patients with arrhythmogenic cardiomyopathy.

The present study aimed to investigate the incremental prognostic value of the right ventricular fractal dimension (FD), a novel marker of myocardial trabecular complexity by cardiac magnetic resonance (CMR) in patients with arrhythmogenic cardiomyopathy (ACM). Consecutive patients with ACM undergoing CMR were followed up for major cardiac events, including sudden cardiac death, aborted cardiac arrest, and appropriate implantable cardioverter defibrillator intervention. Prognosis prediction was compared by Cox regression analysis. We established a multivariable model supplemented with RV FD and evaluated its discrimination by Harrell's C-statistic. We compared the category-free, continuous net reclassification improvement (cNRI) and integrated discrimination index (IDI) before and after the addition of FD. A total of 105 patients were prospectively included from three centers and followed up for a median of 60 (48, 66) months; experienced 36 major cardiac events were recorded. Trabecular FD displayed a strong unadjusted association with major cardiac events (p < 0.05). In the multivariable Cox regression analysis, RV maximal apical FD maintained an independent association with major cardiac events (hazard ratio, 1.31 (1.11-1.55), p < 0.002). The Hosmer-Lemeshow goodness of fit test displayed good fit (X2 = 0.68, p = 0.99). Diagnostic performance was significantly improved after the addition of RV maximal apical FD to the multivariable baseline model, and the continuous net reclassification improvement increased 21% (p = 0.001), and the integrated discrimination index improved 16% (p = 0.045). In patients with ACM, CMR-assessed myocardial trabecular complexity was independently correlated with adverse cardiovascular events and provided incremental prognostic value. The application of FD values for assessing RV myocardial trabeculae may become an accessible and promising parameter in monitoring and early diagnosis of risk factors for adverse cardiovascular events in patients with ACM. • Ventricular trabecular morphology, a novel quantitative marker by CMR, has been explored for the first time to determine the severity of ACM. • Patients with higher maximal apical fractal dimension of RV displayed significantly higher cumulative incidence of major cardiac events. • RV maximal apical FD was independently associated with major cardiac events and provided incremental prognostic value in patients with ACM.

Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer.

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.

Establishment of Risk Nomogram Model of Postpartum Hemorrhage After Second Cesarean Section.

To establish and evaluate a nomogram model for predicting the risk of postpartum hemorrhage in second cesarean section. A total of 440 parturients who underwent the second cesarean section surgery and were registered in our hospital from August 2019 to July 2021 were selected as the study subjects. They were randomly divided into 220 modeling group and 220 validation group based on simple randomization. The two groups were divided into postpartum hemorrhage group and postpartum non bleeding group according to whether postpartum hemorrhage occurred. In the modeling group, the incidence of postpartum hemorrhage in the second cesarean section was 15.00%; the Logistic regression model showed that placenta previa, operation time, prenatal anemia, placenta accreta, uterine inertia were the independent risk factors of postpartum hemorrhage in the second cesarean section (P < 0.05). ROC results showed that AUC of predicting the risk of postpartum hemorrhage in the second cesarean section was 0.824. The slope of calibration curve is close to 1, Hosmer-Lemeshow goodness of fit test showed x2= 7.585, P = 0.250. The external verification results show that the AUC is 0.840, and the predicted probability of the calibration curve is close to the actual probability. Based on the five risk factors of postpartum hemorrhage in the second cesarean section, including placenta previa, operation time, prenatal anemia, placenta accreta and uterine inertia, the nomogram model for predicting the risk of postpartum hemorrhage in the second cesarean section has good accuracy and differentiation.

Post-abortion contraceptive utilization and associated factors among women who attended abortion services: A health facility cross-sectional study.

Background: Post-abortion contraceptive utilization prevents unintended pregnancies, reduces the number of abortions, and lowers the morbidity and mortality related to obstetric complications. It plays a central role in improving maternal health and reducing child mortality. However, many women are suffering from unintended pregnancy and its complications after abortion care. Hence, the main objective of this study was to determine the magnitude of post-abortion contraceptive utilization and its associated factors in Bahir Dar City. Methods: Health-facility-based cross-sectional study was conducted in Bahir Dar city health facilities from April 1 to May 30, 2018. A systematic random sampling technique was used to select 354 eligible study participants. A pre-tested semi-structured questionnaire was used to collect the data. Data entry was done using Epi Data version 3.7 software and analyzed by SPSS v23 software. Descriptive statistics were done based on the nature of the data. A simple logistic regression model was used to identify the association and strength of exploratory variables and the outcome variables. Associations were announced at a 95% confidence interval and p-value <0.05 with adjusted odds ratio. Model fitness was checked by the Hosmer-Lemeshow goodness of fit test for logistic regression. Results: In this study, 348 aborted participants were involved with a mean age of 24.37 (±5.73) years. About 40% of the participants were not currently married and 12.9% were unable to read and write. The magnitude of post-abortion contraceptive utilization was 65.8%. The number of alive children (adjusted odds ratio: 7.0, 95% confidence interval: 1.54, 31.95), lower income (adjusted odds ratio: 0.14, 95% confidence interval: 0.03, 0.60), and (adjusted odds ratio: 0.11, 95% confidence interval: 0.02, 0.46), primary school education (adjusted odds ratio: 0.18, 95% confidence interval: 0.03, 0.97), and currently unemployed (adjusted odds ratio: 0.23, 95% confidence interval: 0.06, 0.85) were significantly associated with the post-abortion contraceptive utilization. Conclusion: The level of post-abortion contraceptive utilization is low as per the national plan. The number of alive children, lower income, lower education, and currently unemployed were the identified factors affecting post-abortion contraceptive utilization. Therefore, a collaborative effort is needed among stakeholders to increase the utilization and avoid factors that prevent the utilization of post-abortion contraceptive utilization.

Developing a Predictive Model for Minimal or Mild Endometriosis as a Clinical Screening Tool in Infertile Women: Uterosacral Tenderness as a Key Predictor

To develop a noninvasive predictive model based on patients with infertility for identifying minimal or mild endometriosis. A retrospective cohort study. This study was conducted at a tertiary referral center. A total of consecutive 1365 patients with infertility who underwent laparoscopy between January 2013 and August 2020 were divided into a training set (n = 910) for developing the predictive model and a validation set (n = 455) to confirm the model's prediction efficiency. The patients were randomly assigned in a 2:1 ratio. Sensitivities, specificities, area under the curve, the Hosmer-Lemeshow goodness of fit test, Net Reclassification Improvement index, and Integrated Discrimination Improvement index were evaluated in the training set to select the optimum model. In the validation set, the model's discriminations, calibrations, and clinical use were tested for validation. In the training set, there were 587 patients with minimal or mild endometriosis and 323 patients without endometriosis. The combination of clinical parameters in the model was evaluated for both statistical and clinical significance. The best-performing model ultimately included body mass index, dysmenorrhea, dyspareunia, uterosacral tenderness, and serum cancer antigen 125 (CA-125). The nomogram based on this model demonstrated sensitivities of 87.7% and 93.3%, specificities of 68.6% and 66.4%, and area under the curve of 0.84 (95% confidence interval 0.81-0.87) and 0.85 (95% confidence interval 0.80-0.89) for the training and validation sets, respectively. Calibration curves and decision curve analyses also indicated that the model had good calibration and clinical value. Uterosacral tenderness emerged as the most valuable predictor. This study successfully developed a predictive model with high accuracy in identifying infertile women with minimal or mild endometriosis based on clinical characteristics, signs, and cost-effective blood tests. This model would assist clinicians in screening infertile women for minimal or mild endometriosis, thereby facilitating early diagnosis and treatment.

Optimal postoperative delirium prediction after coronary artery bypass grafting surgery: a prospective cohort study.

Postoperative delirium (POD) presents as a serious neuropsychiatric syndrome in patients undergoing off-pump coronary artery bypass grafting (OPCABG) surgery. This is correlated with higher mortality, cognitive decline, and increased costs. The Age-adjusted Charlson Comorbidity Index (ACCI) is recognized as an independent predictor for mortality and survival rate. The purpose of our study is to estimate the predictive value of the ACCI on the POD in patients undergoing OPCABG surgery. This prospective cohort study enrolled patients undergoing OPCABG surgery between December 2020 and May 2021 in Qilu Hospital. Patients were divided into the low-ACCI group (score, 0-3) and the high-ACCI group (score ≥4) according to their ACCI scores. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and CAM were used to diagnose POD within 7 days after surgery. The general, laboratory, and clinical data of the patients were recorded and collected. The characteristic ROC curve was applied to further assess the predictive value of the ACCI for POD in patients following OPCABG surgery. A total of 89 patients were enrolled, including 45 patients in the low-ACCI group and 44 patients in the high-ACCI group. The incidence of POD was higher in the high-ACCI group than in the low-ACCI group (45.5% vs. 15.6%, P = 0.003). Multivariate logistic regression analyses showed that the ACCI (OR, 2.433; 95% CI, 1.468-4.032; P = 0.001) was an independent risk factor for POD. The ACCI accurately predicted POD in patients following OPCABG surgery with an AUC of 0.738, and the Hosmer-Lemeshow goodness of fit test yielded X2 = 5.391 (P = 0.145). The high-ACCI group showed a high incidence of POD. The ACCI was an independent factor associated with POD in patients following OPCABG surgery. In addition, the ACCI could accurately predict POD in patients following OPCABG surgery. ClinicalTrials.gov, identifier CHiCTR2100052811.

Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty

To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA). A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects, including 318 males and 220 females, aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed, and the validity and discrimination of the prediction model were evaluated. The proportion of patients with osteoporosis, trauma history, and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05), and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177, 95%CI(1.815, 9.617), P<0.05], trauma history[OR=7.481, 95%CI(3.104, 18.031), P<0.05], and hip revision[OR=11.371, 95%CI(3.220, 40.153, P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA, cemented prosthesis [OR=0.067, 95%CI(0.019, 0.236), P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ2=7.864, P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve. The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination, which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA, and facilitates individualized fracture prevention.