Hosmer-Lemeshow Test Research Articles

Development and validation of a nomogram for predicting early acute hydrocephalus after spontaneous intracerebral hemorrhage: a single-center retrospective study.

Acute hydrocephalus is a severe complication that may occur early after an intracerebral hemorrhage (ICH). However, clinical factors predicting the occurrence of acute hydrocephalus have rarely been studied. This study aimed to establish a nomogram model to predict early acute hydrocephalus after ICH. We retrospectively analyzed the data of 930 patients with ICH who were treated at our hospital between January 2017 and May 2024. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen for risk factors for acute hydrocephalus, and stepwise logistic regression analysis was used to construct the prediction model, which was visualized using a nomogram. Data were randomly divided into training (n = 652) and test (n = 278) sets at a 7:3 ratio. A total of 930 patients were included, of whom 123 (13.2%) developed acute hydrocephalus within 6h of being diagnosed with ICH. Univariate analysis revealed that 11 indicators were associated with acute hydrocephalus. In the training set, LASSO and stepwise logistic regression analyses identified four independent risk factors that were used to establish a prediction model. These were the modified Graeb score, age, infratentorial hemorrhage > 15 mL, and thalamic hemorrhage > 15 mL. A graphical nomogram was then developed. The area under the receiver operating characteristic (ROC) curve was 0.974 (95% confidence interval 0.961-0.987). In the Hosmer-Lemeshow test, the p-value was 0.887. The mean absolute error of the calibration plot was 0.012. The decision curve analysis (DCA) validated the fitness and clinical application value of this nomogram. Internal validation showed the test set was in good accordance with the training set. The nomogram prediction model showed good accuracy and could be used to predict the risk of early acute hydrocephalus after ICH, thereby aiding neurologist in making rapid clinical decisions.

Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection.

This study aimed to develop and validate a risk prediction model based on real-world data to assess the risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent video-assisted thoracoscopic (VATS) radical resection. This study is a retrospective study of real-world data. A total of 257 elderly lung adenocarcinoma patients who underwent VATS radical resection from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into delayed recovery (n = 42) and non- delayed recovery group (n = 215) according to whether delayed recovery occurred after anesthesia. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of delayed recovery from anesthesia, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Delayed recovery from anesthesia occurred in 42 of 257 elderly lung adenocarcinoma patients underwent VATS radical resection (16.34%). Logistic regression analysis showed that anesthesia duration, intraoperative infusion volume, inhaled desflurane, preoperative respiratory tract infection, intraoperative hypothermia and diagnosed with hypertension were risk factors for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection (P<0.05). The area under receiver operating characteristic curve was 0.869, 95% CI (0.815 ~ 0.923). The optimal cutoff value was 0.198, the sensitivity was 0.738, and the specificity was 0.823. Hosmer-Lemeshow test showed that χ2 = 7.346, P = 0.500. The decision curve analysis results have shown that the threshold probability is between 0.23 and 0.91, and the net benefit rate of the model is good. The risk prediction model constructed in this study can provide reference for medical staff to screen precisely high-risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection, which is of great significance.

Assessment of fuzzy logic to enhance species distribution modelling of two cryptic wood boring beetle species in Australia.

Fuzzy logic presents a promising approach for Species Distribution Modelling by generating a value that can be used for comparative purposes termed 'environmental favourability'. In contrast to 'presence probability', 'environmental favourability' remains robust regardless of species prevalence. This characteristic facilitates effective comparisons across species with varying levels of prevalence. In this study, presence probability was predicted using three commonly used Species Distribution Models: Generalised Linear Model, Generalised Additive Modelling, and Boosted Regression Trees for two beetle species, Euwallacea fornicatus and Euwallacea perbrevis in Australia. Fuzzy logic was then employed to derive environmental favourability values based on these models. Additionally, Maxent modelling was included to compare prediction outputs and facilitate a comprehensive analysis. Model performance was evaluated using standard metrics (Area under the receiver operating characteristic curve, True statistical skill, Correct classification rate), as well as Hosmer-Lemeshow test. The research explored fuzzy similarity, fuzzy intersection and potential biotic interaction of these closely related borers, and revealed a favourable distribution pattern for Euwallacea fornicatus across Australia. This study supports the efficacy of fuzzy logic in Species Distribution Modelling and highlights the value of environmental favourability function in enhancing the comparative analysis of the geographical relationship across species. This approach offers a more nuanced perspective on Species Distribution Modelling.

Abstract 4137770: Development of a User-Friendly Self-Screening Tool for Assessing Metabolic Syndrome Risk in young adults from economically challenged regions

Background: Metabolic syndrome is a cluster of conditions that increase the risk of heart disease and diabetes. Early identification and management are crucial, particularly in economically challenged regions where access to healthcare may be limited. Research Questions/Hypothesis: User-friendly self-report data accurately predict metabolic outcomes. Aims: To develop and validate nomograms for individualized estimation of metabolic syndrome risk. Methods: Data from 521 college students (60.1% aged 17-20 years; 68.7% female; 28.0% white) were collected in 2022/2023 from two Brazilian cities. These cities are located in the country's poorest states, with Gini indices of 0.56 and 0.43. The potential predictors include demographic and economic variables, school-related factors, behaviors, and body weight. Based on predictors for abdominal obesity identified through multilevel logistic regression, we created a nomogram model. We performed the Hosmer-Lemeshow test to assess model calibration and used a bootstrapping approach (B = 150) for internal validation. To evaluate external validity, we assessed metabolic syndrome in a subset of 375 students. The area under the receiver operating characteristic curve (AUROC), with a threshold of 0.70, was used to evaluate the model's discrimination accuracy. Results: We identified 114 (23.0%) college students who were abdominally obese. We found ten variables associated with the primary outcome: age, biological sex, physical education facilities, enrollment in sports competition (during elementary school); grade retention, preferred subject, physical education classes per week; enrollment in sports training (during secondary school); adherence of 24-hour movement behaviors and body weight. The proposed nomogram showed acceptable performance in the AUROC (0.94 [95% CI: 0.92-0.96). The calibration assessment indicated reasonable consistency of our model (p &gt; 0.05). In the internal validation, we observed a decreased predictive capability (AUROC = 0.86). Conclusion: The 24h-MESYN risk score offers an effective self-screening tool for college students from diverse racial and ethnic backgrounds in economically challenged regions to assess their risk of developing metabolic syndrome.

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer–Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China.

Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer-Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. NomogramI was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for NomogramI were 0.716 in the derivation set and 0.718 in the validation set. NomogramII was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for NomogramII were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that NomogramII outperformed NomogramI. Therefore, the online dynamic NomogramII was established to predict the risks of PH in the plateau population.

QLTI-08. PREDICTING POOR HIGH-VALUE CARE OUTCOMES FOLLOWING SURGICAL RESECTION OF MULTIFOCAL GLIOBLASTOMA

Abstract Presenting with dismal prognoses, treatment of multifocal GBM (mGBM) requires extensive medical infrastructure. Individualized assessment for patients at risk of extended length of stay (LOS) and nonroutine discharge disposition, described as high-value healthcare outcomes, has not previously been described. To develop predictive models for extended LOS and nonroutine discharge disposition for mGBM patients. All mGBM patients undergoing surgical resection at our institution (January 1, 2009-December 31, 2020) were included. Predictive models for nonroutine discharge disposition (discharge to a non-home environment) and extended LOS [upper quartile of the LOS for all patients included in the present study’s cohorts (&amp;gt;6 days)] were developed using multivariate logistic regression models. Optimism-corrected C-statistics for each model were calculated using 1000 bootstrapped samples in order to correct for potential bias associated with the limited population size. The Hosmer-Lemeshow test was used to evaluate model goodness of fit and calibration. A total of 137 mGBM patients were included in the present study. Most patients were male (40.1%), white (83.2%), not Hispanic/Latino (97.8%), not married (55.5%) and underwent elective surgery (85.4%). The average age among our patient cohort was 55.41 ± 13.17, the average KPS score was 77.23 ± 14.54 and the average mFI-5 was 0.613 ± 0.760. On bivariate analysis, older age (p=0.015) and decreasing Karnofsky Performance Status (KPS) score (p&amp;lt;0.001) were significantly associated with nonroutine discharge disposition while emergency admission (p=0.010) and increasing frailty (mFI-5) score (p=0.003) were significantly associated with extended LOS. On multivariate analysis, increasing age (p=0.031) and decreasing KPS (p&amp;lt;0.001) predicted nonroutine discharge while emergent admission (p=0.023) and greater mFI-5 (p=0.044) predicted extended LOS. Our models for nonroutine discharge and extended LOS had optimism-correct c-statistics of 0.7507 and 0.7309 respectively and displayed adequate calibration. Following external validation, our models may have utility as a risk stratification tool for this multifocal GBM patient populations.

Study on the predictive model of delirium risk after surgery for elderly hip fractures based on meta-analysis.

To develop and validate a risk prediction model for postoperative delirium in elderly patients with hip fractures, aiming to identify high-risk patients and implement preventive measures. A systematic search of five authoritative medical databases was conducted, retrieving a total of 1368 relevant articles. After screening, 44 high-quality studies were included in the meta-analysis, analyzing 13 potential risk factors, such as age, gender, diabetes, and history of stroke. A risk prediction model was constructed and validated in a cohort of 189 elderly hip fracture patients. The model's predictive performance was evaluated using ROC curves, with calibration assessed through the Hosmer-Lemeshow test, and clinical utility examined via Decision Curve Analysis (DCA) and Clinical Impact Curves (CIC). The meta-analysis identified the following as independent risk factors for postoperative delirium: age (≥ 70years), male gender, diabetes, history of stroke, preoperative comorbidities (≥ 2), previous delirium, preoperative cognitive impairment, low preoperative albumin levels (≤ 40g/L), prolonged preoperative waiting time (≥ 48h), anemia (≤ 100g/L), ASA classification (≥ 3), use of general anesthesia, and prolonged surgery duration (≥ 2h). The prediction model demonstrated strong efficiency in the validation cohort, with an AUC of 0.956, sensitivity of 87.3%, specificity of 94.8%, and a Brier score of 0.144, indicating high predictive accuracy and calibration. DCA and CIC analyses showed the model to have strong clinical decision-making value and impact across most thresholds. The risk prediction model developed in this study shows high predictive accuracy and clinical utility, making it valuable for identifying high-risk patients and implementing preventive measures in clinical practice. However, the study has limitations, such as potential retrospective bias, and further validation in larger, multicenter prospective studies is needed to confirm the model's broader applicability and stability.

A Comprehensive Nomogram Integrating Phonocardiogram and Echocardiogram Features for the Diagnosis of Heart Failure With Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) is associated with high hospitalization and mortality rates, representing a significant healthcare burden. This study aims to utilize various information including echocardiogram and phonocardiogram to construct and validate a nomogram, assisting in clinical decision-making. This study analyzed 204 patients (68 HFpEF and 136 non-HFpEF) from the First Affiliated Hospital of Chongqing Medical University. A total of 49 features were integrated and used, including phonocardiogram, echocardiogram features, and clinical parameters. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal matching factors, and a stepwise logistic regression was employed to determine independent risk factors and develop a nomogram. Model performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). The nomogram was constructed using five significant indicators, including NT-proBNP (OR = 4.689, p = 0.015), E/e' (OR = 1.219, p = 0.032), LAVI (OR = 1.088, p < 0.01), D/S (OR = 0.014, p < 0.01), and QM1 (OR = 1.058, p < 0.01), and showed a better AUC of 0.945 (95% CI = 0.908-0.982) in the training set and 0.933 (95% CI = 0.873-0.992) in the testing set compared to conventional nomogram without phonocardiogram features. The calibration curve and Hosmer-Lemeshow test demonstrated no statistical significance in the training and testing sets (p = 0.814 and p = 0.736), indicating the nomogram was well-calibrated. The DCA and CIC results confirmed favorable clinical usefulness. The nomogram, integrating phonocardiogram and echocardiogram features, enhances HFpEF diagnostic efficiency, offering a valuable tool for clinical decision-making.

Development and validation of a renal involvement model for patients with hyperuricemia: A cross-sectional study.

To construct a prediction model for renal involvement in patients with hyperuricemia (HUA) based on logistic regression analysis, to achieve early risk stratification. In this cross-sectional study, we collected data from the National Health and Nutrition Examination Survey (NHANES), and constructed a predicted model for renal involvement in HUA patients. The discriminative ability of the model was assessed using the receiver operating characteristic (ROC) curve. Model accuracy was evaluated using the Hosmer-Lemeshow test and calibration curve, while clinical utility was assessed using decision curve analysis (DCA). Furthermore, internal and external validation cohorts were also applied to validate the model. A total of 1669 patients from NHANES between 2007 and 2010 were included in the final analysis for modeling and validation. Six predictive factors including age, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Cr, Uric Acid (UA), and sex were identified by binary logistic regression analysis for renal involvement in HUA patients and used to construct a nomogram with good consistency and accuracy. The AUC values for the predictive model, internal validation, and external validation were 0.881 (95% CI: 0.836-0.926), 0.908 (95% CI: 0.871-0.944), and 0.927 (95% CI: 0.897-0.957), respectively. The calibration curves demonstrated consistency between the nomogram and observed values. The DCA curves of the model and validation cohort indicated good clinical utility. This study developed a predictive model for renal involvement in hyperuricemia patients with strong predictive performance and validated by internal and external cohorts, aiding in the early detection of high-risk populations for renal involvement.

Construction of nomogram for wound recurrence in elderly patients with venous leg ulcers.

Venous Leg Ulcers (VLUs) are one of the most serious and intractable complications of chronic venous insufficiency. This study aims to develop a nomogram based on a theoretical model to predict the probability of wound recurrence in older patients with VLUs. The elderly patients with VLUs attending the five hospitals between September 2021 and October 2022 were enrolled in this research, and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Recurrent events were recorded during a six-month follow-up after the baseline data collection. The univariate analysis, the least absolute shrinkage and selection operator (LASSO) regression method were used to screen variables, and multiple logistic regression was used to establish a risk prediction model, which was presented by nomogram. Receiver operating curves (ROC), Hosmer-Lemeshow test, as well as calibration curves, were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined. A total of 608 elderly patients with VLUs were included in the study. They were randomly divided into the training cohort (N = 421) and the validation cohort (N = 187). In the training cohort, Lasso regression and multivariate logistic regression analysis indicated that previous recurrence number, last ulcer duration, lower extremity DVT history, and frailty were independent risk factors for wound recurrence in elderly patients with VLUs, while daily exercise time and self-efficacy were protective factors. A nomogram was established with a good discrimination capacity and predictive efficiency with and the area under the curve (AUC) of 0.869 (95%CI: 0.831-0.908) in the training set and 0.890 (95%CI: 0.841-0.938) in the validation set. The p values of the Hosmer-Lemeshow test for both sets were 0.887 and 0.772, respectively, both greater than 0.05. The calibration degree charts showed that the data point connection was similar to the diagonal, indicating that the model's prediction probability of wound recurrence in elderly VLUs patients is close to the actual probability. This study constructed a new nomogram to predict the risk of wound recurrence in elderly patients with VLUs. The nomogram has excellent accuracy and reliability, which can help healthcare workers and patients actively monitor and follow up with patients to prevent the recurrence of ulcers and make clinical decisions.

Depression and associated factors among patients with type 2 diabetic mellitus in Ethiopia: a cross sectional study.

Depression is a significant public health concern in both developed and developing countries. The burden of depression is particularly high among patients with chronic illnesses in developing countries, creating a dual challenge for both patients and the community. However, depression goes undiagnosed in 50%-75% of patients with chronic conditions such as diabetes mellitus. Additionally, there is limited information about the prevalence of depression among diabetic patients in Ethiopia. Therefore, this study aimed to assess the prevalence and associated factors of depression among type 2 diabetic patients in Ethiopia. An institution-based cross-sectional study was conducted on 376 randomly selected type 2 diabetic patients. Data were collected through face-to-face interviews and from patients' follow-up records. The data were entered into EpiData version 4.6 and analyzed using STATA 14. Bivariable and multivariate logistic regression analyses were employed to identify associated factors. Variables with a p-value of less than 0.25 in the bivariable analysis were selected for multivariate logistic regression. Model fitness was assessed using Hosmer-Lemeshow's test, and associations were reported using adjusted odds ratios with 95% confidence intervals. In this study, the prevalence of depression among type 2 diabetic patients was found to be 69.72% (95% CI: 64.75, 74.27). Three factors were significantly associated with depression in these patients: the duration of diabetes mellitus since diagnosis [AOR: 1.17; 95% CI (1.02, 1.34)], glycaemic control [AOR: 1.8; 95% CI (1.09, 3.01)] and cigarette smoking [AOR: 2.18; 95% CI (1.07, 4.46)]. The prevalence of depression among type 2 diabetic patients was high. The Federal Ministry of Health, stakeholders, and the Ethiopian Diabetes Association should collaborate to reduce this burden. Mental health assessment and treatment should be integrated into chronic care follow-up services. Additionally, healthcare providers should closely monitor and counsel patients who smoke and those with poor glycemic control.

Discrimination Model Construction for Non-Lactational Mastitis and Breast Cancer Based on Imaging Features.

Aims/Background The clinical presentation of non-lactational mastitis (NLM) shares similarities with some symptoms and examination results of breast cancer (BC), which can lead to misdiagnosis or delayed treatment. Current studies on breast lesions mostly focus on the diagnostic performance of a single imaging technique. This study aims to construct a discrimination diagnostic model for NLM and BC based on such imaging features as ultrasound and magnetic resonance imaging (MRI) and to validate the application value of the model, assisting clinicians in improving disease diagnosis and refining medical decisions. Methods This study is a retrospective analysis. Clinical data of 108 patients suspected of NLM based on imaging diagnosis, admitted to The First Affiliated Hospital with Nanjing Medical University between May 2018 and August 2023, were collected. Among them, 94 cases were pathologically confirmed as NLM and 14 cases as BC. Univariate and multivariate logistic regression analyses were performed on the patients' clinical data, ultrasound features, and MRI features to select the risk factors for discriminating NLM and BC, and construct a discrimination model. The discrimination performance of the model was analyzed with the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve. Results In the NLM group, there were 24 cases of granulomatous lobular mastitis (25.53%) and 70 cases of plasma cell mastitis (74.47%). In the BC group, there were 2 cases of infiltrating ductal carcinoma, 2 cases of atypical hyperplasia, 3 cases of papillary carcinoma, and 7 cases of ductal carcinoma in situ. Age, internal blood flow, calcification, edge, enhancement characteristics, apparent diffusion coefficient (ADC) values, and time-intensity curve (TIC) type were independent factors for differentiating NLM and BC (p < 0.05). The ROC analysis showed that the area under the curve of the model for discriminating NLM and BC was 0.920. The DCA results showed that the model had high net benefits for discriminating NLM and BC. The calibration curve analysis showed that the model had good consistency with the actual diagnosis of NLM and BC, with a chi-square value of 4.545 and a p-value of 0.155 according to the Hosmer-Lemeshow test. Conclusion Age, internal blood flow, calcification, edge, enhancement characteristics, ADC, and TIC curve types are important factors in distinguishing NLM and BC, and the model based on the above characteristics to distinguish NLM and BC has a high net benefit in distinguishing the two.

Albumin creatinine index added to the grace score increases its discriminative capacity for mortality at 12 years of follow-up in patients with acute coronary syndrome

Abstract introduction There is currently no information on the prognostic value of the GRACE score with the additional addition of the albumin-creatinine index (ABI) for the prediction of very long-term mortality in unselected populations of acute coronary syndromes (ACS). Objectives To evaluate the additive value of the albumin-creatinine index in spontaneous urine sample to the GRACE score for the prediction of 12-year mortality from all causes in an unselected cohort of patients with acute coronary syndrome. Material and methods A prospective cohort study was carried out, which included patients admitted to a Community Hospital with a diagnosis of ACS. These patients had their IAC measured in a single spontaneous urine sample based on the principle of immunoturbidimetry in the first 24 hours of admission. The end point of the analysis was all-cause mortality at follow-up. The GRACE 2.0 score was calculated for all patients in the study. The scores were compared according to the following metrics: their power to discriminate between events and non-events (ROC Areas), calibration or goodness of fit, evaluating the agreement between the proportion of events and non-events expected by both scores observed by Hosmer Lemeshow Test. The comparison between the areas under the ROC Curves was carried out using De Long's test with the MEDCALC program version 19.1.7. The net reclassification index and its 95% CI (GRACE score + IAC/GRACE score) were evaluated. Results 600 patients with ACS were analyzed, with an average age of 64 years. The GRACE score risk subgroups were divided by the established cut-off values: low risk (0 to 108), intermediate (109 to 139) and high risk (greater than 139). The area under the curve of the Grace score for mortality during follow-up was 0.79 (95% CI 0.69-0.89), while the area under the ROC curve of the GRACE + IAC score was 0.81 (95% CI 0.74-0.87). ). Comparing the two areas by the De Long test, the difference was statistically significant (P &amp;lt; 0.0001; 95% CI 0.07- 0.015). On the other hand, contingency tables were developed and the Hosmer Lemeshow Test (goodness of fit) was performed on the GRACE score and the GRACE + IAC score, observing agreement between expected events and non-events, throughout all subgroups of risk. In addition, reclassifications were made into deceased and survivors by applying the GRACE + IAC score on the GRACE score. The net reclassification rate was 28% (95% CI 21-35). The chi square of its Mc Nemar Test was 3.69 p &amp;lt; 0.0002. The absolute comprehensive discriminatory improvement was 0.01 (95% CI 0.009-0.02) and the corresponding relative value was 8.2% (95% CI 7.9-8.5). Conclusions In this unselected ACS cohort, the albumin-creatinine index added to the GRACE score significantly increases its ability to discriminate mortality from all causes in a 12-year follow-up.Comparison between ROC curvesHosmer Lemeshow test

Unveiling the determinants of women's cardiovascular disease awareness in northern Italy: insights from the Call for Women project

Abstract Background Despite strides in prevention, diagnosis, and treatment of cardiovascular disease (CVD), CVD remains the leading cause of death (LCOD) in women. While global initiatives aim to enhance awareness, recent studies reveal deficiencies, particularly in Europe where misconceptions prevail, hindering women’s understanding of CVD severity. Purpose To describe determinants affecting awareness of CVD as LCOD among women in the Lombardy region, Italy. Methods A web-based survey (the ‘Call for Women’ Project) was conducted from Sep 2022 to Nov 2023, including women aged 18 and above. The primary focus was awareness of CVD as the LCOD (scaled 1 [adequate] to 2 [inadequate]). The individual predictors (i.e., determinants) of adequate awareness were assessed through a logistic regression (LR) model. Variables exhibiting a point biserial correlation coefficient (rpb) with a p-value ≤ 0.1 were included in the LR model as independent variables. The goodness-of-fit for the LR model was determined using the Hosmer-Lemeshow test and Nagelkerke’s pseudo-R2. Independent variables were entered simultaneously into the model to examine each variable’s relatively unique contribution. Results Out of 3,487 women who completed the survey, only 35.6% (n = 1,240) showed adequate awareness. The Hosmer-Lemeshow test indicated a good model fit (p-value = 0.692), suggesting that the model adequately captures the relationships between the variables and the outcome. Figure 1 shows the contribution of each individual characteristic in modulating cardiovascular risk awareness. The odds of being aware of CVD as the LCOD were significantly influenced by age, decreasing by approximately 1.3% for each additional year of age (Adj. OR = 0.987; 95% CI = 0.981–0.993; p-value &amp;lt; 0.001). Individuals with a lower secondary school diploma exhibited a substantial increase in the odds of awareness, with an Adj. OR of 4.999 (95% CI = 3.074–8.128; p-value &amp;lt; 0.001). Similarly, those with an upper secondary school diploma, a bachelor’s degree, or a master’s degree also demonstrated higher odds of awareness (Adj. OR = 2.427, 1.673, and 1.429, respectively; all p-values &amp;lt; 0.001). Regarding lifestyle, higher number of children was associated with increased odds of awareness, with an Adj. OR of 1.109 (95% CI = 1.027–1.197; p-value = 0.008). Consumption of dry fruits and fruits showed a positive association with an Adj. OR of 1.014 (95% CI = 1.005–1.024; p-value = 0.004), indicating a potential role of dietary habits in influencing awareness. BMI and physical activity did not exhibit statistically significant associations with awareness of CVD risk. Conclusions This pilot study, limited to one region only, reveals key determinants of awareness of CVD as the LCOD among women in northern Italy. Tailoring interventions to address these determinants has the potential to narrow the gaps in CVD awareness among women.

Clinical validated risk prediction model for development of heart failure after contemporary breast cancer treatment

Abstract Background Patients receiving breast cancer (BC) treatment are at risk for heart failure and cardiomyopathy (HF/CM). European Society of Cardiology (ESC) cardio-oncology guidelines recommend baseline cardiovascular (CV) assessment, however, there is limited data about cardiotoxicity risk specific to patients with BC. Our aim was to develop and validate model for prediction of HF/CM in women receiving contemporary treatment for invasive early-stage BC. Methods We assembled a cohort women diagnosed with Stage I-III BC from 2008-2020 and followed through 2021 in a large health care plan. Women age &amp;gt;80, with history of HF/CM, and women who received both anthracyclines and trastuzumab were excluded, leaving N=26,044 for analysis. Study cohort was randomly split into derivation (60%) and validation (40%) datasets. Elastic-net penalized Cox proportional hazard model was created using the derivation dataset to select risk factors and to calculate the panelized coefficient which defined the HF/CM risk score. Risk score system was assessed by Hosmer-Lemeshow goodness-of-fit test and area under the ROC curve (AUC). Event rates of &amp;lt;5% constituted low risk; 5-14% moderate; and &amp;gt;15% high risk category. Incident HF/CM events were examined using Kaplan-Meier approach in derivation and validation datasets. Results HF/CM risk score had high discrimination ability and sufficiently good model of fit regarding HF/CM events (P value=0.163 and 0.667 from Hosmer–Lemeshow test;AUC 0.751 and 0.764, in derivation and validation sets, respectively). There were 10% of women in the high risk; 30% in moderate; and 60% in low-risk group. Variables associated with the higher HF/CM risk score included age, receipt of anthracycline-based chemotherapy or anti-HER2 therapy, presence of hypertension, diabetes, or history of other CV conditions at baseline. The score pattern showed major effect of age, with anthracycline and anti-HER2 therapy increasing the risk across all age groups (Figure 1). Kaplan-Meier analysis of incident HF/CM over 14 years after BC diagnosis showed significantly lower number of HF/CM events in low risk compared to moderate and high-risk score groups (p&amp;lt;0.0001, Figure 2). Conclusion Our robust risk prediction model for HF/CM among women with early-stage BC provides a new tool to identify women at higher CV risk and inform cardiac function monitoring during and after BC treatment. Our findings may guide further validation studies and build evidence for the cardiotoxicity risk stratification in patients with BC.Risk Score for HF/CM by Age/ChemoTxK-M curves of freedom from HF/CM

Prognostic value of neutrophil-to-lymphocyte ratio and cardiac troponin I in patients with immune checkpoint inhibitors-related myocarditis

Abstract Background Over the past decade, immune checkpoint inhibitors (ICIs) have significantly transformed cancer treatment. However, ICIs inevitably may cause a spectrum of immune-related adverse events, among which cardiovascular toxicity, particularly myocarditis, while infrequent, has garnered increasing attention due to its high fatality rate. Purpose We executed a multicenter study aimed at characterizing ICI-related myocarditis and risk factors influencing its prognosis. Methods The study included patients with ICIs-associated myocarditis at four medical centers in China from January 28, 2019 to June 15, 2023. Logistic regression was performed to explore the risk factors for the development of severe myocarditis. Receiver operating characteristic curves were conducted to assess the diagnostic abilities of biomarkers to distinguish severe myocarditis, and the performance and calibration were evaluated using Hosmer-Lemeshow test. Cox regression analysis was employed to investigate the influential risk factors affecting the prognosis of ICI-related myocarditis. Kaplan-Meier survival curve analyses were conducted to assess the differences in survival times among different myocarditis groups. Results 35 ICIs-associated myocarditis patients were identified. 21 patients (60%) were classified as severe myocarditis, and they presented higher cardiac troponin I (cTnI) levels (P=0.013), higher N-terminal pro-B-type natriuretic peptide levels (P=0.031), higher creatine kinase (CK) levels (P=0.018), higher CK-MB levels (P=0.026), and higher neutrophil to lymphocyte ratio (NLR) levels (P=0.016) compared to non-severe myocarditis patients. Multivariate logistic regression showed that cTnI (adjusted odds ratio: 1.021, 95% confidence interval (CI): 1.002-1.039, P=0.03) and NLR (adjusted odds ratio: 1.890, 95% CI: 1.026-3.483, P=0.041) were the independent risk factors of ICI-associated severe myocarditis. The receiver operating characteristic curve showed an area under curve of 0.765 (95% CI: 0.601 to 0.929, P=0.013) for cTnI, and 0.773 for NLR (95% CI: 0.597 to 0.948, P=0.016). Multivariate COX regression showed that NLR (adjusted hazard ratio: 1.189, 95% CI: 1.028-1.376, P=0.020) and pneumonitis (adjusted hazard ratio: 8.142, 95% CI: 1.191-55.651, P=0.032) were the independent prognostic risk factor associated ICI- related myocarditis. Further Kaplan-Meier survival curve analysis showed that the survival rate among patients without pneumonitis and with low level of NLR significantly outstripped that of patients with pneumonitis (P=0.015) and high level of NLR (P=0.024). Conclusions As independent risk factors for the development of ICI-associated severe myocarditis, cTnI and NLR demonstrated a promising predictive utility for the identification of ICI-associated severe myocarditis. NLR and the presence of pneumonitis were identified as independent risk factors that significantly impacted the prognosis of ICI-related myocarditis.

Comparing the ability of GRACE 2.0 and GRACE 3 scores for predicting in-hospital mortality in patients with and without diabetes

Abstract Background Sex-specific refinement of GRACE 2.0 was published as GRACE 3.0 (G3). The relative ability of G3 in predicting in-hospital mortality in diabetic patients has not been explored. Purpose Explore the ability of G3 in determining in-hospital mortality in patients with and without diabetes. Methods Retrospective cohort analysis of data on NSTEMI and UA patients admitted in two hospitals between 2010 and 2019 entered into the Myocardial Infarction National Audit Project database. All baseline characteristics and G2, G3 scores were recorded. In hospital death was recorded from hospital records. The discriminative power of the G2 score was determined by comparing the area under the receiver operating characteristic curve (c-statistics). Accuracy of calibration was evaluated by plotting the mean predicted vs observed mortality in each decile of predicted risk, Hosmer-Lemeshow (HL) goodness-off it test and the Nagelkerke R2 (NR2). Results G3 was calculated in 2768 patients (age 69±14yrs, 62.3% male, 877(31.7%) with diabetes. Median risk for the cohort was 1.3%, (95% CI 1.2 to 1.4). G3 risk was higher in patients with diabetes (median 2.0 IQR 0.6 to 5.5 vs 1.0 IQR 0.3 to 3.7, p&amp;lt;0.0001). In-hospital mortality occurred in 68 (2.5%) patients with no difference between the diabetes groups (3.1% and 2.2% died in the DM and non-DM groups respectively). In the whole cohort, G3 reclassified 139(7.5%) patients in low-intermediate to high risk-category and 159(17.4%) patients in high to low-intermediate category. 10.1% and 6.5% were upgraded and 16.1% and 18.1% were downgraded in DM and non-DM groups. Overall discriminatory power of G3 (AUC 0.87, 95% CI 0.86 to 0.88, p&amp;lt;0.0001) was excellent but no different from G2 (AUC 0.86 95% CI 0.85 to 0.88, p&amp;lt;0.0001) (δAUC 0.009, p=0.461). In patients with diabetes, G3 yielded higher AUC (AUC 0.85, 95% CI 0.83 to 0.87, p&amp;lt;0.0001) than G2 (AUC 0.82, 95% CI 0.79 to 0.84, p&amp;lt;0.00101) but this was not statistically significant (δAUC 0.031, p=0.142). In patients without DM, the difference was smaller (δAUC 0.002, p=0.906). Neither G2 (δAUC 0.061, p=0.185) nor G3 (δAUC 0.028, p=0.473) was better in discrimination in DM compared to non-DM groups (Fig1A,B). In the diabetic group, calibration plot for the G3 score showed suboptimal calibration with consistent overestimation of the risk across all risk deciles (Figure1C), with the HL test showing significant differences between the observed and predicted values (p &amp;lt;0.0001) and the NR2 was low 0.09. The GRACE 2 score showed better calibration (Fig1D) (HL: p=0.09, NR2 0.11). In patients without diabetes, calibration plot for the G3 (HL: p= 0.17; NR2 0.33) (Fig1E) and G2 (HL: p=0.15, NR2 0.25) (Fig1F) shows similar pattern of overestimation of the risk less so for latter. Conclusion G3 and G2 shows excellent discrimination for in-hospital mortality in both diabetic and non-diabetic patients. G2 seems to show better calibration than G3 for diabetic patients.

Construction and Evaluation of a Model for the Prediction of Risk Factors Associated with Severe Pneumonia in Patients with Positive Hepatitis B Core Antibody and COVID-19 Infection in China

Background: Hepatitis B virus (HBV) infection is a high-risk factor for severe COVID-19 cases, leading clinicians to enhance the monitoring of patients with concurrent HBV and COVID-19 infections. However, this focus is typically on patients with active HBV infection. Patients who are HBV surface antigen (HBsAg) negative and HBV core antibody (anti-HBc) positive are often considered to have either a past infection that has naturally cleared or to be in a low-replication state, and thus receive less clinical attention. However, in immunosuppressed states, such as during immunosuppressive treatment following COVID-19 infection, the virus in these patients may reactivate. In such cases, this reactivation can increase the risk of COVID-19 progressing to a severe illness. Therefore, being HBsAg negative and anti-HBc positive could be a potential risk factor for severe COVID-19. Studying the combination of anti-HBc positivity and COVID-19 infection can help identify high-risk populations, allowing for the implementation of targeted prevention and management measures, thereby reducing the occurrence of severe COVID-19 cases. Objectives: To establish and evaluate a model for predicting severe pneumonia in patients with positive anti-HBc combined with COVID-19 infection. Methods: We retrospectively enrolled 380 patients who tested positive for anti-HBc, negative for HBsAg, and HBV e-antigen (HBeAg), combined with COVID-19 infection, in our hospital from December 2022 to May 2023. Based on the inclusion criteria, 163 patients were included in the study. We applied the Lasso binary logistic regression model to optimize feature selection, identifying eight non-zero coefficients using a minimum of one standard error. Using the multiple logistic regression method with backward selection, we screened six factors from the eight selected by the Lasso binary logistic regression model. These six factors were used to construct the predictive model and a nomogram. The validity of our nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow (HL) test, and decision-curve analysis (DCA). Results: Hypertension, diabetes, decreased absolute lymphocyte count, prolonged prothrombin time, elevated aspartate aminotransferase, and decreased albumin are high-risk factors for severe pneumonia in patients with positive anti-HBc combined with COVID-19 infection. The AUC of the predictive model constructed using these six factors is 0.785, with a 95% confidence interval of (0.709-0.862). The HL test, performed using the calibration curve, yielded a p-value of 0.868. The application of this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 75%. Conclusions: The constructed nomogram can be used to predict the risk of patients with positive anti-HBc combined with COVID-19 infection progressing to severe pneumonia, based on routine blood parameters, liver function, coagulation, lactate dehydrogenase levels, and the patient's underlying disease. The predictive model demonstrates good discrimination, calibration, and clinical utility.

Development and validation of a risk prediction model for feeding intolerance in neurocritical patients with enteral nutrition.

This study collects and analyzes clinical data on enteral nutrition therapy in neurocritical patients, develops and validates a feeding intolerance (FI) risk prediction model, and provides a theoretical basis for screening patients with high risk of feeding intolerance (FI) and delivering personalized care. A convenience sampling method was employed to select 300 patients who were admitted to a tertiary hospital in China for early enteral nutrition therapy in the neurointensive care unit between April 2022 and December 2022. Independent risk factors for FI were identified using univariate and logistic regression analyses. A prediction model was established, and the goodness of fit and discriminant validity of the model were evaluated. The incidence of FI in neurocritical patients receiving enteral nutrition was 71%. Logistic regression analysis identified age, Glasgow Coma Scale (GCS) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, mechanical ventilation, feeding via the nasogastric tube route, hyperglycemia, and low serum albumin as independent risk factors for the development of FI (p < 0.05). The predictive formula for FI risk was established as follows: Logit p = -14.737 + 1.184 × mechanical ventilation +2.309 × feeding route +1.650 × age + 1.336 × GCS tertile (6-8 points) + 1.696 × GCS tertile (3-5 points) + 1.753 × APACHE II score + 1.683 × blood glucose value +1.954 × serum albumin concentration. The Hosmer-Lemeshow test showed χ2 = 9.622, p = 0.293, and the area under the ROC curve was 0.941 (95% confidence interval: 0.912-0.970, p < 0.001). The optimal critical value was 0.767, with a sensitivity of 85.9%, a specificity of 90.8%, and a Youden index of 0.715. The early enteral nutrition FI risk prediction model developed in this study demonstrated good predictive ability. This model can serve as a valuable reference for effectively assessing the risk of FI in neurocritical patients, thereby enhancing clinical outcomes.