Hosmer Lemeshow Research Articles

A Predictive Model of Early Readmission for Patients with Heart Failure

Background: Readmission within 30 days of discharge for heart failure (HF) has become a challenging public health issue. Predicting the risk of 30-day readmission may assist clinicians in making individualized treatment plans for HF patients. Methods: A total of 2254 patients were enrolled in this study. The risk predictors associated with 30-day readmission were selected using the least absolute shrinkage and the selection operator regression model. The performance of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow (HL) test, and decision curve analysis (DCA). Results: The 30-day all-cause readmission rate was 7.1%. Thirteen clinical parameters were identified as the risk predictors, including age, cystatin C, albumin, red cell distribution width coefficient variation, neutrophils, N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin T, myoglobin, sex, dyslipidaemia, left ventricular ejection fraction, left ventricular end-diastolic dimension, and atrial fibrillation. The nomogram showed good discrimination, with an area under the ROC curve of 0.653 (95% confidence interval: 0.608–0.698) and good calibration results (HL test p = 0.328). The DCA showed that the nomogram would have good clinical utility. Conclusions: This predictive model based on clinical data makes it simple for clinicians to assess the 30-day HF readmission risk.

Establishment and evaluation of a prediction model for acute gastrointestinal injury in patients with prolonged disorder of consciousness

ObjectiveTo establish a prediction model for acute gastrointestinal injury (AGI) in patients with prolonged disorder of consciousness (pDOC) and to evaluate and apply the prediction model. MethodsThe clinical data of 165 patients with pDOC admitted to the hyperbaric oxygen department from January 2021 to December 2021 were retrospectively reviewed, and the patients were divided into an AGI group (n = 91) and an N-AGI group (n = 74) according to whether AGI occurred. A prediction model was built by fitting multiple independent influencing factors through logistic regression. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the model, the Hosmer–Lemeshow (H–L) test was used to evaluate the goodness-of-fit of the model, and the ROC curve and calibration curve were drawn to evaluate the predictive performance. A nomogram was plotted to visualize the prediction model.ResultsAccording to the multivariate logistic regression analysis results, the prediction model was finally constructed with the CRS-R score, DAO, PCT, ALB, and I-FABP, and a nomogram was generated. The area under the ROC curve (AUC) of the prediction model was 0.931, the sensitivity was 83.5%, and the specificity was 93.2%. The data were divided into 5 groups for the H–L test (χ2 = 2.54, P = 0.468 > 0.05) and into 10 groups for the H–L test (χ2 = 9.98, P = 0.267 > 0.05). A calibration curve was drawn based on the test results, indicating that the prediction model has a good goodness-of-fit and good prediction stability.ConclusionThe prediction model for AGI in pDOC patients constructed in this study can be used in clinical practice and is helpful to predict the occurrence of AGI in pDOC patients.

Utilisation de l’application mobile TestObs pour l’évaluation de l’observance thérapeutique dans une population d’hémodialysés chroniques : modèle de monitoring technologique de l’adhérence au traitement et ses facteurs déterminants

IntroductionNon-adherence behaviors are very common in chronic hemodialysis patients, it is estimated that only one patient out of two complies with medical prescriptions, these behaviors are associated with a higher risk of morbidity and adverse events as well as increased expenses for health systems. The aim of our study was to assess adherence to long-term prescribed medications in chronic hemodialysis patients, using a mobile application named TestObs, as well as to determine the main factors influencing medication adherence. MethodsWe conducted a prospective descriptive study, between January and June 2019. We developed a mobile application named TestObs, downloadable on playstore for android devices, which assesses with the Girerd questionnaire, the adherence to the main medications taken by chronic hemodialysis patients. We included adult patients, with a duration of dialysis of more than 6 months, all patients who downloaded TestObs, tested their adherence to their medication by answering the questionnaire. We created a web-based platform, where data was collected from the application and then analyzed and tabulated. Regarding the statistical analysis, the normal distribution of the variables was studied by the Kolmogorov-Smirnov test, the analysis of the qualitative variables used the Pearson's Chi2 and Fisher's statistical test, the Hosmer Lemeshow test was used to examine the quality of the final logistic regression model. ResultsWe collected 90 adult chronic hemodialysis patients, 51 of them (56%) were selected to enter the study. We found good compliance in 46.15% of patients, minor noncompliance in 32.87%, and noncompliance in 20.98%. In multivariate analysis, the factors influencing adherence were the presence of other comorbidities (diabetes and vision problems) and the number of pills per day. DiscussionIn this study, we report treatment adherence problems in 53.85% of patients, our results are close to the data reported in hemodialysis patients in the literature, different factors influence the quality of treatment adherence, in our study poly-medication and the presence of other comorbidities were the statistically significant determinants. The new technology assessment instruments were used in hemodialysis patients and were able to provide real-time monitoring of adherence behaviors. ConclusionWe believe that mobile health technologies hold promise for assessing and improving medication adherence in hemodialysis patients, so we suggest that TestObs represents an accessible and free of charge tool, based on a validated questionnaire, that can allow patients to benefit from new technologies for medical monitoring, and may eventually constitute an interventional program to improve medication adherence; however, this technological tool should not replace traditional therapeutic education; prior targeting of non-adherent patients and an optimal combination of several tools can help improve adherence in these patients.

Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients.

This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic kidney disease (DKD) patients and develop an HUA risk model based on a retrospective study in Ningbo, China. Six hundred and ten DKD patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the HUA risk prediction model were screened out by the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation, and the model was presented by nomogram. The C-index and receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the discriminatory power, degree of fitting, and clinical applicability of the risk model. Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA in the DKD population. The characteristic variables (gender, family history of T2DM, drinking history, BMI, and hyperlipidemia) were screened out by LASSO combined with 10-fold cross-validation and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712-0.810) and excellent degree of fit (Hosmer-Lemeshow test, P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9-79%. Meanwhile, the risk model was also well validated in the validation cohort, where the C-index was 0.843 (95% CI: 0.780-0.906), the degree of fit was good, and the DCA risk threshold probability was 7-100%. The development of risk models contributes to the early identification and prevention of HUA in the DKD population, which is vital for preventing and reducing adverse prognostic events in DKD.

Construction and internal validation of a novel nomogram for predicting prognosis of infective endocarditis

To develop a nomogram prediction model capable of early identification of high-risk infective endocarditis (IE) patients. We retrospectively analyzed the clinical data of 383 patients with IE and divided them into survival and non-survival groups according to different hospitalization outcomes. Univariate and multivariate logistic regression methods were used to screen independent risk factors affecting the survival outcome of IE, and a Nomogram prediction model was constructed by these factors. The Hosmer–Lemeshow goodness-of-fit test was applied to assess the model fit, the discrimination and calibration of the model were evaluated by plotting ROC curves and calibration curves. Advanced age, embolic symptoms, abnormal leukocyte count, low hemoglobin level and double-sided IE were associated with higher in-hospital mortality in patients with IE (P < 0.05). The Hosmer–Lemeshow goodness-of-fit test for the model was χ2 = 7.107, P = 0.311. The AUC of the ROC curve of the model was 0.738 (95% CI 0.677–0.800). The bootstrap method was used to validate the prediction model. The results showed that the prediction accuracy of the model in the validation cohort was 0.842. The nomogram prediction model can accurately predict the in-hospital mortality risk of IE and can help clinicians identify high-risk IE patients early.

A risk prediction model for evaluating thrombosis extension of muscle calf venous thrombosis after craniotomy.

ObjectiveTo explore the risk factors of muscle calf venous thrombosis (MCVT) after craniotomy and construct a risk prediction model, so as to provide tool for evaluating the prognosis of MCVT after craniotomy.MethodsRetrospective analysis was performed on the data of patients undergoing craniotomy complicated with MCVT from January 1, 2018 to December 31, 2020. A prediction model was established by Logistic regression, and the predictive efficacy of the model was tested by ROC curve. The accuracy of the risk model was evaluated by Hosmer-Lemeshow (H-L) test, and the model was verified internally by cross validation.ResultsAmong the 446 patients who underwent craniotomy complicated with MCVT, 112 cases (25.11%) had thrombosis extension. D-dimer, Capirini scores, length of hospital stay, malignant tumor, fracture, use of dehydrating agents and hemostatic agents were independently related to thrombosis extension after craniotomy. The area under ROC curve (AUROC) of the prediction model was 0.918 (0.888, 0.942), and the sensitivity and specificity of the maximum Youden index were 85.3% and 78.2%, respectively. H-L test showed that the prediction model was accurate (χ2 = 12.426, P = 0.133). The internal verification results of the prediction model showed that the AUROC value of the prediction model is 0.892.ConclusionThe prediction model has a good prediction efficacy on the prognosis of post-craniotomy patients complicated with MCVT, and can be used as a tool to evaluate the risk of thrombosis extension.

Non-Invasive Assessment of Intracranial Hypertension in Patients with Traumatic Brain Injury Using Computed Tomography Radiomic Features: A Pilot Study.

This study aimed to assess intracranial hypertension in patients with traumatic brain injury non-invasively using computed tomography (CT) radiomic features. Fifty patients from the primary cohort were enrolled in this study. The clinical data, pre-operative cranial CT images, and initial intracranial pressure readings were collected and used to develop a prediction model. Data of 20 patients from another hospital were used to validate the model. Clinical features including age, sex, midline shift, basilar cistern status, and ventriculocranial ratio were measured. Radiomic features-i.e., 18 first-order and 40 second-order features- were extracted from the CT images. LASSO method was used for features filtration. Multi-variate logistic regression was used to develop three prediction models with clinical (CF model), first-order (FO model), and second-order features (SO model). The SO model achieved the most robust ability to predict intracranial hypertension. Internal validation showed that the C-statistic of the model was 0.811 (95% confidence interval [CI]: 0.691-0.931) with the bootstrapping method. The Hosmer Lemeshow test and calibration curve also showed that the SO model had excellent performance. The external validation results showed a good discrimination with an area under the curve of 0.725 (95% CI: 0.500-0.951). Although the FO model was inferior to the SO model, it had better prediction ability than the CF model. The study shows that the radiomic features analysis, especially second-order features, can be used to evaluate intracranial hypertension non-invasively compared with conventional clinical features, given its potential for clinical practice and further research.

Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand.

Individuals with metabolic risks are at high risk of cognitive impairment. We aimed to investigate whether the Thai Cardiovascular Risk (TCVR) score can be used to predict mild cognitive impairment (MCI) in Thai adults with metabolic risks. The study was conducted using secondary data of patients with metabolic risks from Maharaj Nakorn Chiang Mai Hospital. MCI was indicated by an MoCA score of less than 25. Six different TCVR models were used with various combinations of ten different variables for predicting the risk of MCI. The area under the receiver operator characteristic curve (AuROC) and Hosmer–Lemeshow goodness of fit tests were used for determining discriminative performance and model calibration. The sensitivity of the discriminative performance was further evaluated by stratifying by age and gender. From a total of 421 participants, 348 participants had MCI. All six TCVR models showed a similar AuROC, varying between 0.58 and 0.61. The anthropometric-based model showed the best risk prediction performance in the older age group (AuROC 0.69). The laboratory-based model provided the highest discriminative performance for the younger age group (AuROC 0.60). There is potential for the development of an MCI risk model based on values from routine cardiovascular risk assessments among patients with metabolic risks.

A predictive nomogram for intradiscal cement leakage in percutaneous kyphoplasty for osteoporotic vertebral compression fractures combined with intravertebral cleft.

BackgroundFor patients with osteoporotic vertebral compression fractures (OVCFs) treated with percutaneous kyphoplasty (PKP), the occurrence and risk factors of intradiscal cement leakage should be characteristic of the presence of intravertebral cleft (IVC). This study aimed to identify risk factors for intradiscal leakage in individuals with OVCFs combined with IVC treated with PKP and build a powered and well-calibrated predictive nomogram.MethodsThis study retrospectively reviewed consecutive patients who underwent PKP at our center between January 2016 and May 2021. Patients diagnosed with OVCFs combined with IVC were identified, and the incidence of different types of bone cement leakage was recorded. Risk factors for intradiscal leakage among the demographic, perioperative baseline, and radiologic data were identified, following which a nomogram was developed and verified.ResultsA total of 109 eligible patients were included, and the intradiscal leakage rate was 32.1%. Compression rate (odds ratio [OR] 0.025; 95% confidence interval [CI] 0.002–0.264; P = 0.002) and cemented vertebral body fraction (OR 44.122; 95% CI 2.790–697.740; P = 0.007) were identified as independent risk factors. A predictive nomogram with good predictive power (C-statistic = 0.786) and fitness of data (Hosmer–Lemeshow goodness-of-fit test, P = 0.092) was established to build a quantitative relationship between the risk factors and intradiscal leakage.ConclusionThe incidence rate of intradiscal leakage in PKP for OVCFs combined with IVC was 32.1%. Compression rate and cemented vertebral body fraction were identified as independent risk factors. A powered and well-calibrated nomogram was established to accurately predict the probability of intradiscal leakage. Further prospective and multicenter studies are required to verify and calibrate our findings.

Breastfeeding performance index and associated factors among children aged 0-6 months in Ethiopia: Analysis of the 2019 Ethiopia Mini Demographic and Health Survey.

BackgroundInfants under the age of 6 months are commonly affected by malnutrition globally. The higher the breastfeeding performance index (BPI), the greater the advantage of breastfeeding will be. However, there is a lack of literature in the context of Ethiopia. Therefore, this study is aimed at investigating the magnitude and determinants of the breastfeeding performance index score among mothers of children under the age of 6 months in Ethiopia.MethodsThis study was conducted using the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) dataset. A stratified, two-stage cluster sampling technique was used in the study. The survey data were weighted using the “svy” function in STATA version 16. Descriptive statistics, bivariable and multivariable logistic regression were employed in the analysis. An adjusted odds ratio (AOR) with a 95% confidence interval (CI) was reported. The results were considered statistically significant if the p-value was < 0.05. The goodness of fit of the model was checked using the Hosmer–Lemeshow test.ResultsA total of 4,273 mothers with children under the age of 6 months were included in the analysis. Our analysis revealed that the prevalence of low breastfeeding performance index was 79.05% (95% CI: 78.01, 81.59). A unit increase in child age (AOR = 11.56; 95% CI: 6.97, 19.17), the richest wealth quintile (AOR = 2.76; 95% CI: 1.18, 6.5), a higher level of education (AOR = 5.41; 95% CI: 2.08, 14.05), being married or living with partner (AOR = 2.73; 95% CI: 1.18, 6.27), being women from Somali (AOR = 5.11; 95% CI: 2.08, 12.56), Afar (AOR = 3.03; 95% CI: 1.16, 7.91), Oromia (AOR = 1.88; 95% CI: 1.03, 3.41), Diredawa city administration (AOR = 2.89; 95% CI: 1.04, 8.07), and antenatal care (ANC) visit (AOR = 2.05; 95% CI: 1.31, 3.19) were positively associated with the low breastfeeding performance index.ConclusionThe prevalence of the low breastfeeding performance index was found to be high. Hence, the findings of the study suggest the need to target interventions aimed at improving breastfeeding performance toward mothers with higher socioeconomic and demographic status and educational status. Antenatal care clients are among the targets of the intervention.

Development and validation of a nomogram model for the prediction of 4L lymph node metastasis in thoracic esophageal squamous cell carcinoma.

ObjectivesThe left tracheobronchial (4L) lymph nodes (LNs) are considered as regional LNs for esophageal squamous cell carcinoma (ESCC), but there is a controversy about routine prophylactic 4L LN dissection for all resectable ESCCs. This study aimed to develop a nomogram for preoperative prediction of station 4L lymph node metastases (LNMs).MethodsA total of 522 EC patients in the training cohort and 370 in the external validation cohort were included. The prognostic impact of station 4L LNM was evaluated, and multivariable logistic regression analyses were performed to identify independent risk factors of station 4L LNM. A nomogram model was developed based on multivariable logistic regression analysis. Model performance was evaluated in both cohorts in terms of calibration, discrimination, and clinical usefulness.ResultsThe incidence of station 4L LNM was 7.9% (41/522) in the training cohort. Patients with station 4L LNM exhibited a poorer 5-year overall survival rate than those without (43.2% vs. 71.6%, p < 0.001). In multivariate logistic regression analyses, six variables were confirmed as independent 4L LNM risk factors: sex (p = 0.039), depth of invasion (p = 0.002), tumor differentiation (p = 0.016), short axis of the largest 4L LNs (p = 0.001), 4L conglomeration (p = 0.006), and 4L necrosis (p = 0.002). A nomogram model, containing six independent risk factors, demonstrated a good performance, with the area under the curve (AUC) of 0.921 (95% CI: 0.878–0.964) in the training cohort and 0.892 (95% CI: 0.830–0.954) in the validation cohort. The calibration curve showed a good agreement on the presence of station 4L LNM between the risk estimation according to the model and histopathologic results on surgical specimens. The Hosmer–Lemeshow test demonstrated a non-significant statistic (p = 0.691 and 0.897) in the training and validation cohorts, which indicated no departure from the perfect fit. Decision curve analysis indicated that the model had better diagnostic power for 4L LNM than the traditional LN size criteria.ConclusionsThis model integrated the available clinical and radiological risk factors, facilitating in the precise prediction of 4L LNM in patients with ESCC and aiding in personalized therapeutic decision-making regarding the need for routine prophylactic 4L lymphadenectomy.

Influence of Educational Level and Healthy Habits on the Prevalence of Diabesity in a Spanish Working Population.

Background: Diabesity is a growing problem internationally. Taking into account the importance of physical activity and diet in its prevention and treatment, the objective of this study was to delve into the impact of healthy habits on diabesity. Methods: A descriptive, cross-sectional study was carried out in 386,924 Spanish adult workers. Obesity was determined according to eleven different formulas. Life habits were also valued; sociodemographic variables; and educational level; as well as analytical and clinical variables such as blood pressure and blood glucose levels. The association between the different variables was performed using the chi-square and the Student’s t-tests when the samples were independent. A multivariate analysis was performed using the multinomial logistic regression test by calculating the odds ratio and a 95% confidence interval. The Hosmer–Lemeshow goodness-of-fit test was also performed. Results: The overall prevalence of diabesity ranged between 0.3% (95% CI 0.3–0.4) when obesity was assessed according to the abdominal volume index and 8.3% (95% CI 8.2–8.4) when evaluated according to the CUN-BAE (Clínica Universitaria de Navarra Body Adiposity Estimator) formula. The prevalence of diabesity was also higher in workers with a non-heart-healthy diet and in those who did not exercise regularly. Conclusions: The most disadvantaged socioeconomic classes are those with the highest prevalence of diabesity. It is important to prioritise prevention in populations and communities with the most unfavourable social and environmental conditions to reduce the burden of diabesity.

Isogency Hosmer–Lemeshow Logistic Regression-Based Secured Information Sharing for Pharma Supply Chain

Counterfeit drugs are forgery-tagged medicines that are considered to be drugs without vigorous active pharmaceutical ingredients (API). India, being the world’s largest producer of drugs, faces a crucial issue of counterfeits. Moreover, counterfeits identify their path into the pharmaceutical supply chain (PSC) effortlessly owing to the dearth of security and traceability in the prevailing system. This is because the software applications currently in use stockpile the information about drugs on centralized servers and are accessed by manufacturers, distributors and retailers via the internet. The security of such systems is found to be weak. To address these issues, in this work, a novel method called Supersingular Isogeny and Hosmer–Lemeshow Logistic Regression-based (SI-HLLR) secured information sharing for the pharmaceutical supply chain is proposed. The SI-HLLR method is split into two sections, block validation and authentication. First, with the pharmaceutical sales data provided as input, the supersingular isogeny Diffie–Hellman key exchange model is applied for block validation and then is implemented using a blockchain. Next, with the validated blocks, the authentication mechanism is performed using Hosmer–Lemeshow logistic regression-based authentication that in turn eliminates the counterfeit drugs from the pharmaceutical supply chain. The hyperledger fabric blockchain solution using SI-HLLR leads to improved security ensuring data integrity and better authentication accuracy in the proposed method.

Кардиальная автономная нейропатия как предиктор развития фибрилляции предсердий у пациентов с сахарным диабетом 2 типа после инфаркта миокарда

BACKGROUND: Type 2 diabetes mellitus is a widespread chronic disease having many factors that negatively affect the course of cardiovascular events. One of these factors is cardiac autonomic neuropathy. The possibility of assessing the effect of cardiac autonomic neuropathy on the risk of development of atrial fibrillation after myocardial infarction may help in choosing the management tactics for patients with myocardial infarction and type 2 diabetes mellitus. AIM: To evaluate the effect of cardiac autonomic neuropathy on the risk of development of atrial fibrillation in patients with past myocardial infarction. To develop a method for assessing the risk of atrial fibrillation in 12-month period in patients with type 2 diabetes mellitus. MATERIALS AND METHODS: The study involved 60 individuals with type 2 diabetes mellitus and 60 individuals with newly diagnosed type 2 diabetes hospitalized with anterior and anteroseptal myocardial infarction with Q wave. At the initial stage of the study, the formed groups were homogeneous in age, gender, and all general clinical research methods. The diagnosis of type 2 diabetes mellitus was made on the basis of anamnesis data and available medical documentation confirmed by examination of glycosylated hemoglobin and glycemic profile. The newly found diabetes mellitus was diagnosed based on the data of glycosylated hemoglobin &gt; 6.5%, blood glucose &gt; 11.1 mmol/l recorded two or more times, according to national clinical guidelines. On the 3rd day, in 3 and 12 months after hospitalization, a set of tests for detecting cardiac autonomic neuropathy was performed; the presence of atrial fibrillation, the dynamics of left ventricular hypertrophy were assessed by electrocardiography and echocardiography. Statistical processing of the obtained results was performed using Microsoft Office Excel 2019, SPSS Statistics Standart ver. 28 programs for Windows. In order to choose the method of analysis, the distribution was estimated using Kolmogorov–Smirnov test. Comparison of the groups was carried out using Mann–Whitney test. The risk of atrial fibrillation was assessed using logistic, multifactorial and ROC analysis. RESULTS: Prognostic models were formed to determine the risk of development of atrial fibrillation in patients with type 2 diabetes mellitus. The model for diabetes mellitus had determination coefficient (R2 = 0.7), Hosmer–Lemeshow criterion 0.83, and the area under the curve 0.93. The model for newly diagnosed diabetes mellitus had R2 coefficient 0.72, Hosmer–Lemeshow goodness-of-fit test 0.85, and the area under the curve 0.95. CONCLUSION: Use of the developed models based on the results of tests aimed at detecting cardiac autonomic neuropathy, permits to predict the development of atrial fibrillation in patients with existing and newly diagnosed type 2 diabetes mellitus in combination with diabetic polyneuropathy in 12-month period after anterior, anteroseptal myocardial infarction with high accuracy.

A nine-consensus-prognostic -gene-based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies.

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers. Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model. Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44–5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892–3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554–2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer–Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis. Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

PREDICTIVE VALUE ANALYSIS OF MR IMAGING FEATURES ON THE RISK OF KNEE REPLACEMENT IN PATIENTS WITH KNEE ARTHRITIS

This study used knee MR imaging features to quantify the severity of knee injury in patients and analyzed the predictive value of knee MR imaging features in their risk of knee replacement. A total of 120 patients with knee arthritis were included from a public knee arthritis database FNIH OAI. First, univariate logistic regression was used to screen clinical features to obtain the clinical risk factors. Then the minimum redundancy maximum correlation (mRMR) method was used to reduce the image features, and the LASSO method was used to further screen the retained image features to construct a Rad model. Next, the multivariate logistic regression method was used to combine the Rad model and the screened clinical risk factors to construct a combined model and its corresponding nomogram. Finally, ROC curve and its related metrics, Hosmer–Lemeshow test and Delong test were used to evaluate and compare the accuracy and consistency of the model performance. Age and body mass index (BMI) were found as significant clinical risk factors for knee replacement. After using mRMR and LASSO methods, 147 image features were reduced to 30 and 7 features, respectively, then these 7 features were linearly combined to construct the Rad model. Age, BMI, and the Rad model were combined to establish the combined model and its corresponding nomogram. The resulted two models showed high accuracy ([Formula: see text]) and consistency (H-L test: [Formula: see text]) on both the training set and test set. Finally, the comparison results showed that the prediction performance of the combined model was better than that of the Rad model, but their difference was not significant (Delong test: [Formula: see text]). This paper studied the predictive performance of MR imaging features on the risk of knee replacement and built a model that can be used to predict the potential risk of knee replacement in patients with knee arthritis. The resulting models showed good predictive accuracy and consistency. The drawn nomogram could be used as a useful tool to personalize the prognosis of patients with knee arthritis and guide clinical decisions on knee replacement.

Health behavior changes and mortality among South Korean cancer survivors.

Considering the rapid growth in the number of cancer survivors, the successful management of their health behaviors requires further attention. However, there are lack of information about cancer survivors’ health behaviors and the risk of mortality using Korean cohort data. This study aimed to examine the effects of health behavior changes on mortality among cancer survivors and to develop a validated nomogram. This cohort study was conducted using claims data. Data from adult cancer survivors from the National Health Insurance Service–National Sample Cohort, conducted between 2002 and 2015, were included. Individuals who were alive for five years after their cancer diagnosis were defined as cancer survivors. Cox proportional-hazards regression was used to estimate the target associations. Discrimination (Harrell’s C-index) and calibration (Hosmer–Lemeshow test) were employed to validate the nomogram. Data from 9300 cancer survivors were used for analysis. Compared to non-smokers, those who started or quit smoking had a higher risk of all-cause mortality. Those who were physically inactive had a higher risk of all-cause mortality than those who were continuously active. In the nomogram, the C-index value was 0.79 in the training data and 0.81 in the testing data. Hosmer–Lemeshow test was not significant, demonstrating a good fit. We found that individuals with unhealthy behaviors had a higher risk of mortality, thereby highlighting the importance of managing health behaviors among cancer survivors. The development of a validated nomogram may provide useful insights regarding official policies and existing practices in healthcare systems, which would benefit cancer survivors. Our study could provide the evidence to inform the priority of guideline for managing the health behavior among cancer survivors.

Development and validation of a nomogram for predicting the risk of pressure injury in adult patients undergoing abdominal surgery

ObjectivesThis study aimed to develop a nomogram for predicting the risk of pressure injury (PI) in adult patients undergoing abdominal surgery and validate its effectiveness among these patients. MethodsThis study retrospectively included 11,247 adult patients, who underwent abdominal surgery and postoperative supervision in ICU, in a tertiary care hospital in western China between January 2017 and December 2020. All datasets were extracted from the patient’s medical records and randomly divided into the training cohort (8,997) and the validation cohort (2,250) by 8:2. The univariable logistic regression was used to select potentially relevant features. Then, multivariable logistic regression was also conducted and utilized to establish the nomogram. The nomogram was compared with the Braden scale for predicting PI in the validation cohort through the area under the curve (AUC) of the receiver operator characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA). Results873 (7.8%) patients suffered PIs. Logistic regression analysis showed that time of operation, weight, type of operation, albumin, and Braden scale score were independent risk factors for PI. A nomogram integrating five selected characteristics was constructed. The AUC of the ROC curve for the nomogram was 0.831, with a specificity of 85.2% and sensitivity of 63.7%. The AUC of the ROC curve for the Braden scale was 0.567, with a specificity of only 33.0%. The P-values of the H-L test were 0.45 (nomogram) and 0.22 (Braden scale), both indicating good calibration. The DCA also displayed that the nomogram had better predictive validity. ConclusionCompared with the Braden scale, the nomogram showed a better predictive performance. This nomogram is informative and has the potential to better guide caregivers for risk stratification and prevention of PI, although it requires further validation.

Prediction model for the pretreatment evaluation of mortality risk in anti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease.

BackgroundAnti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease (anti-MDA5 DM-ILD) is a disease with high mortality. We sought to develop an effective and convenient prediction tool to estimate mortality risk in patients with anti-MDA5 DM-ILD and inform clinical decision-making early.MethodsThis prognostic study included Asian patients with anti-MDA5 DM-ILD hospitalized at the Nanjing Drum Hospital from December 2016 to December 2020. Candidate laboratory indicators were retrospectively collected. Patients hospitalized from 2016 to 2018 were used as the discovery cohort and applied to identify the optimal predictive features using a least absolute shrinkage and selection operator (LASSO) logistic regression model. A risk score was determined based on these features and used to construct the mortality risk prediction model in combination with clinical characteristics. Results were verified in a temporal validation comprising patients treated between 2019 and 2020. The primary outcome was mortality risk within one year. The secondary outcome was overall survival. The prediction model’s performance was assessed in terms of discrimination, calibration, and clinical usefulness.ResultsThis study included 127 patients, (72 men [56.7%]; median age, 54 years [interquartile range, 48-63 years], split into discovery (n = 87, 70%) and temporal validation (n=37, 30%) cohorts. Five optimal features were selected by LASSO logistic regression in the discovery cohort (n = 87) and used to construct a risk score, including lymphocyte counts, CD3+CD4+ T-cell counts, cytokeratin 19 fragment (CYFRA21-1), oxygenation index, and anti-Ro52 antibody. The retained predictive variables in the final prediction model were age, Heliotrope, fever, and risk score, and the most predictive factor was the risk score. The prediction model showed good discrimination (AUC: 0.915, 95% CI: 0.846–0.957), good calibration (Hosmer–Lemeshow test, P = 0.506; Brier score, 0.12), and fair clinical usefulness in the discovery cohort. The results were verified among patients in the temporal validation cohort (n = 38). We successfully divided patients into three risk groups with very different mortality rates according to the predictive score in both the discovery and validation cohorts (Cochran-Armitage test for trend, P < 0.001).ConclusionsWe developed and validated a mortality risk prediction tool with good discrimination and calibration for Asian patients with anti-MDA5 DM-ILD. This tool can offer individualized mortality risk estimation and inform clinical decision-making.

A nomogram for predicting malignant cerebral artery infarction in the modern thrombectomy era.

ObjectiveThis study aimed to develop and validate a nomogram to predict malignant cerebral artery infarction (MMI) after endovascular treatment (EVT) in patients with acute ischemic stroke (AIS) in the modern thrombectomy era.MethodsWe retrospectively analyzed data from a prospective cohort of consecutive patients with AIS who underwent EVT at Xuanwu hospital between January 2013 and June 2021. A multivariable logistic regression model was employed to construct the nomogram for predicting MMI after EVT. The discrimination and calibration of the nomogram were assessed both in the derivation and validation cohorts.ResultsA total of 605 patients were enrolled in this study, with 425 in the derivation cohort and 180 in the validation cohort. The nomogram was developed based on admission systolic blood pressure (SBP), the National Institute of Health Stroke Score (NIHSS), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), vessel occlusion site, EVT time window, and recanalization status. The nomogram displayed good discrimination with the area under the receiver operating characteristics (ROCs) curve (AUC) of 0.783 [95% confidence interval (CI), 0.726–0.840] in the derivation cohort and 0.806 (95% CI, 0.738–0.874) in the validation cohort. The calibration of the nomogram was good as well, with the Hosmer–Lemeshow test of p = 0.857 in the derivation cohort and p = 0.275 in the validation cohort.ConclusionIn the modern thrombectomy era, a nomogram containing admission SBP, NIHSS, ASPECTS, vessel occlusion site, EVT time window, and recanalization status may predict the risk of MMI after EVT in patients with AIS.