Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).

BACKGROUND: Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). RESULTS: First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen's Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. CONCLUSIONS: Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.

Development and evaluation of a risk assessment tool for the personalized screening of breast cancer in Chinese populations: A prospective cohort study.

INTRODUCTION: Breast cancer is a significant contributor to female mortality, exerting a public health burden worldwide, especially in China, where risk-prediction models with good discriminating accuracy for breast cancer are still scarce. METHODS: A multicenter screening cohort study was conducted as part of the Cancer Screening Program in Urban China. Dwellers aged 40-74 years were recruited between 2014 and 2019 and prospectively followed up until June 30, 2021. The entire data set was divided by year of enrollment to develop a prediction model and validate it internally. Multivariate Cox regression was used to ascertain predictors and develop a risk-prediction model. Model performance at 1, 3, and 5 years was evaluated using the area under the curve, nomogram, and calibration curves and subsequently validated internally. The prediction model incorporates selected factors that are assigned appropriate weights to establish a risk-scoring algorithm. Guided by the risk score, participants were categorized into low-, medium-, and high-risk groups for breast cancer. The cutoff values were chosen using X-tile plots. Sensitivity analysis was conducted by categorizing breast cancer risk into the low- and high-risk groups. A decision curve analysis was used to assess the clinical utility of the model. RESULTS: Of the 70,520 women enrolled, 447 were diagnosed with breast cancer (median follow-up, 6.43 [interquartile range, 3.99-7.12] years). The final prediction model included age and education level (high, hazard ratio [HR], 2.01 [95% CI, 1.31-3.09]), menopausal age (≥50 years, 1.34 [1.03-1.75]), previous benign breast disease (1.42 [1.09-1.83]), and reproductive surgery (1.28 [0.97-1.69]). The 1-year area under the curve was 0.607 in the development set and 0.643 in the validation set. Moderate predictive discrimination and satisfactory calibration were observed for the validation set. The risk predictions demonstrated statistically significant differences between the low-, medium-, and high-risk groups (p < .001). Compared with the low-risk group, women in the high- and medium-risk groups posed a 2.17-fold and 1.62-fold elevated risk of breast cancer, respectively. Similar results were obtained in the sensitivity analyses. A web-based calculator was developed to estimate risk stratification for women. CONCLUSIONS: This study developed and internally validated a risk-adapted and user-friendly risk-prediction model by incorporating easily accessible variables and female factors. The personalized model demonstrated reliable calibration and moderate discriminative ability. Risk-stratified screening strategies contribute to precisely distinguishing high-risk individuals from asymptomatic individuals and prioritizing breast cancer screening. PLAIN LANGUAGE SUMMARY: Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening. Accurate risk-prediction models for breast cancer remain scarce in China. We established and validated a risk-adapted and user-friendly risk-prediction model by incorporating routinely available variables along with female factors. Using this risk-stratified model helps accurately identify high-risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer. Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers.

PAGE-B incorporating moderate HBV DNA levels predicts risk of HCC among patients entering into HBeAg-positive chronic hepatitis B.

BACKGROUND & AIMS: Recent studies reported that moderate HBV DNA levels are significantly associated with hepatocellular carcinoma (HCC) risk in hepatitis B e antigen (HBeAg)-positive, non-cirrhotic patients with chronic hepatitis B (CHB). We aimed to develop and validate a new risk score to predict HCC development using baseline moderate HBV DNA levels in patients entering into HBeAg-positive CHB from chronic infection. METHODS: This multicenter cohort study recruited 3,585 HBeAg-positive, non-cirrhotic patients who started antiviral treatment with entecavir or tenofovir disoproxil fumarate at phase change into CHB from chronic infection in 23 tertiary university-affiliated hospitals of South Korea (2012-2020). A new HCC risk score (PAGED-B) was developed (training cohort, n = 2,367) based on multivariable Cox models. Internal validation using bootstrap sampling and external validation (validation cohort, n = 1,218) were performed. RESULTS: Sixty (1.7%) patients developed HCC (median follow-up, 5.4 years). In the training cohort, age, gender, platelets, diabetes and moderate HBV DNA levels (5.00-7.99 log10 IU/ml) were independently associated with HCC development; the PAGED-B score (based on these five predictors) showed a time-dependent AUROC of 0.81 for the prediction of HCC development at 5 years. In the validation cohort, the AUROC of PAGED-B was 0.85, significantly higher than for other risk scores (PAGE-B, mPAGE-B, CAMD, and REAL-B). When stratified by the PAGED-B score, the HCC risk was significantly higher in high-risk patients than in low-risk patients (sub-distribution hazard ratio = 8.43 in the training and 11.59 in the validation cohorts, all p <0.001). CONCLUSIONS: The newly established PAGED-B score may enable risk stratification for HCC at the time of transition into HBeAg-positive CHB. IMPACT AND IMPLICATIONS: In this study, we developed and validated a new risk score to predict hepatocellular carcinoma (HCC) development in patients entering into hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) from chronic infection. The newly established PAGED-B score, which included baseline moderate HBV DNA levels (5-8 log10 IU/ml), improved on the predictive performance of prior risk scores. Based on a patient's age, gender, diabetic status, platelet count, and moderate DNA levels (5-8 log10 IU/ml) at the phase change into CHB from chronic infection, the PAGED-B score represents a reliable and easily available risk score to predict HCC development during the first 5 years of antiviral treatment in HBeAg-positive patients entering into CHB. With a scoring range from 0 to 12 points, the PAGED-B score significantly differentiated the 5-year HCC risk: low <7 points and high ≥7 points.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification.

BACKGROUND: The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC. METHODS: The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic. RESULTS: Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events. CONCLUSION: In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

Feasibility of risk assessment for breast cancer molecular subtypes.

PURPOSE: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. METHODS: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. RESULTS: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. CONCLUSION: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

Clinical, radiological and histopathological features of patients with familial pulmonary fibrosis.

BACKGROUND: In familial pulmonary fibrosis (FPF) at least two biological relatives are affected. Patients with FPF have diverse clinical features. RESEARCH QUESTION: We aimed to characterize demographic and clinical features, re-evaluate high-resolution computed tomography (HRCT) scans and histopathology of surgical lung biopsies, assess survival and investigate the suitability of risk prediction models for FPF patients. STUDY DESIGN: A retrospective cohort study. METHODS: FPF data (n = 68) were collected from the medical records of Oulu University Hospital (OUH) and Oulaskangas District Hospital between 1 Jan 2000 and 11 Jan 2023. The inclusion criterion was pulmonary fibrosis (PF) (ICD 10-code J84.X) and at least one self-reported relative with PF. Clinical information was gathered from hospital medical records. HRCT scans and histology were re-evaluated. RESULTS: Thirty-seven (54.4%) of the patients were men, and 31 (45.6%) were women. The mean ages of the women and men were 68.6 and 61.7 years, respectively (p = 0.003). Thirty-seven (54.4%) patients were nonsmokers. The most common radiological patterns were usual interstitial pneumonia (UIP) (51/75.0%), unclassifiable (8/11.8%) and nonspecific interstitial pneumonia (NSIP) (3/4.4%). Pleuroparenchymal fibroelastosis (PPFE) was observed as a single or combined pattern in 13.2% of the patients. According to the 2022 guidelines for idiopathic pulmonary fibrosis (IPF), the patients were categorized as UIP (31/45.6%), probable UIP (20/29.4%), indeterminate for UIP (7/10.3%) or alternative diagnosis (10/14.7%). The histopathological patterns were UIP (7/41.2%), probable UIP (1/5.9%), indeterminate for UIP (8/47.2%) and alternative diagnosis (1/5.9%). Rare genetic variants were found in 9 patients; these included telomerase reverse transcriptase (TERT, n = 6), telomerase RNA component (TERC, n = 2) and regulator of telomere elongation helicase 1 (RTEL1, n = 1). Half of the patients died (n = 29) or underwent lung transplantation (n = 5), with a median survival of 39.9 months. The risk prediction models composite physiology index (CPI), hazard ratio (HR) 1.07 (95.0% CI 1.04-1.10), and gender-age-physiology index (GAP) stage I predicted survival statistically significantly (p<0.001) compared to combined stages II and III. CONCLUSIONS: This study confirmed the results of earlier studies showing that FPF patients' radiological and histopathological patterns are diverse. Moreover, radiological and histological features revealed unusual patterns and their combinations.

Validation in type 2 diabetes of a metabolomic signature of all-cause mortality.

CONTEXT: Mortality in type 2 diabetes is twice that of the normoglycemic population. Unravelling biomarkers that identify high-risk patients for referral to the most aggressive and costly prevention strategies is needed. OBJECTIVE: To validate in type 2 diabetes the association with all-cause mortality of a 14-metabolite score (14-MS) previously reported in the general population and whether this score can be used to improve well-established mortality prediction models. METHODS: This is a sub-study consisting of 600 patients from the "Sapienza University Mortality and Morbidity Event Rate" (SUMMER) study in diabetes, a prospective multicentre investigation on all-cause mortality in patients with type 2 diabetes. Metabolic biomarkers were quantified from serum samples using high-throughput proton nuclear magnetic resonance metabolomics. RESULTS: In type 2 diabetes, the 14-MS showed a significant (p < 0.0001) association with mortality, which was lower (p < 0.0001) than that reported in the general population. This difference was mainly due to two metabolites (histidine and ratio of polyunsaturated fatty acids to total fatty acids) with an effect size that was significantly (p = 0.01) lower in diabetes than in the general population. A parsimonious 12-MS (i.e. lacking the 2 metabolites mentioned above) improved patient discrimination and classification of two well-established mortality prediction models (p < 0.0001 for all measures). CONCLUSIONS: The metabolomic signature of mortality in the general population is only partially effective in type 2 diabetes. Prediction markers developed and validated in the general population must be revalidated if they are to be used in patients with diabetes.

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population.

BACKGROUND: Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. METHODS: A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. RESULTS: Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). CONCLUSIONS: Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.

Risk prediction model for gastric cancer within 5 years in healthy Korean adults.

BACKGROUND: Although endoscopy is commonly used for gastric cancer screening in South Korea, predictive models that integrate endoscopy results are scarce. We aimed to develop a 5-year gastric cancer risk prediction model using endoscopy results as a predictor. METHODS: We developed a predictive model using the cohort data of the Kangbuk Samsung Health Study from 2011 to 2019. Among the 260,407 participants aged ≥20 years who did not have any previous history of cancer, 435 cases of gastric cancer were observed. A Cox proportional hazard regression model was used to evaluate the predictors and calculate the 5-year risk of gastric cancer. Harrell's C-statistics and Nam-D'Agostino χ2 test were used to measure the quality of discrimination and calibration ability, respectively. RESULTS: We included age, sex, smoking status, alcohol consumption, family history of cancer, and previous results for endoscopy in the risk prediction model. This model showed sufficient discrimination ability [development cohort: C-Statistics: 0.800, 95% confidence interval (CI) 0.770-0.829; validation cohort: C-Statistics: 0.799, 95% CI 0.743-0.856]. It also performed well with effective calibration (development cohort: χ2 = 13.65, P = 0.135; validation cohort: χ2 = 15.57, P = 0.056). CONCLUSION: Our prediction model, including young adults, showed good discrimination and calibration. Furthermore, this model considered a fixed time interval of 5 years to predict the risk of developing gastric cancer, considering endoscopic results. Thus, it could be clinically useful, especially for adults with endoscopic results.

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia.

BACKGROUND: This study aimed to develop a risk prediction model for stress urinary incontinence (SUI) throughout pregnancy in Indonesian women. METHODS: We conducted a multicenter retrospective longitudinal study involving pregnant women in Indonesia, who sought care at obstetrics clinics from January 2023 to March 2023, encompassing all stages of pregnancy. We collected data on their predictive factors and SUI outcome. SUI was diagnosed based on responses to the "leaks when you are physically active/exercising" criterion in the ICIQ-UI-SF questionnaire during our investigation of the participants. The models underwent internal validation using a bootstrapping method with 1000 resampling iterations to assess discrimination and calibration. RESULTS: A total of 660 eligible pregnant women were recruited from the two study centers, with an overall SUI prevalence of 39% (258/660). The final model incorporated three predictive factors: BMI during pregnancy, constipation, and previous delivery mode. The area under the curve (AUROC) was 0.787 (95% CI: 0.751-0.823). According to the max Youden index, the optimal cut-off point was 44.6%, with a sensitivity of 79.9% and specificity of 65.9%. A discrimination slope of 0.213 was found. CONCLUSION: The developed risk prediction model for SUI in pregnant women offers a valuable tool for early identification and intervention among high-risk SUI populations in Indonesian pregnant women throughout their pregnancies. These findings challenge the assumption that a high BMI and multiple previous deliveries are predictors of SUI in Indonesian women. Further research is recommended to validate the model in diverse populations and settings.

Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study.

PURPOSE: This study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk. METHODS: The study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed. RESULTS: Univariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p < 0.05). The constructed model exhibited a consistency index (C-index) and an area under the ROC curve of 0.738. The calibration curve and DCA demonstrated that these indicators had a good degree of agreement and utility. The external validation results of the model also indicated good predictability (AUC = 0.700, p < 0.05). CONCLUSION: The personalized scoring prediction model constructed by gender, length of hospital stays, BUN_max levels, as well as the use of adrenaline and amiodarone, can effectively identify AAD patients with high mortality risk within one year after discharge.

Construction and Validation of a Risk Prediction Model for Mild Cognitive Impairment in Non-Dialysis Chronic Kidney Disease Patient.

INTRODUCTION: The aims of this study are to explore the factors affecting mild cognitive impairment in patients with chronic kidney disease (CKD) who are not undergoing dialysis and to construct and validate a nomogram risk prediction model. METHODS: Using a convenience sampling method, 383 non-dialysis CKD patients from two tertiary hospitals in Chengdu were selected between February 2023 and August 2023 to form the modeling group. The patients were divided into a mild cognitive impairment group (n = 192) and a non-mild cognitive impairment group (n = 191), and factors such as demographics, disease data, and sleep disorders were compared between the two groups. Univariate and multivariate binary logistic regression analyses were used to identify independent influencing factors, followed by collinearity testing, and construction of the regression model. The final risk prediction model was presented through a nomogram and an online calculator, with internal validation using Bootstrap sampling. For external validation, 137 non-dialysis CKD patients from another tertiary hospital in Chengdu were selected between October 2023 and December 2023. RESULTS: In the modeling group, 192 (50.1%) of the non-dialysis CKD patients developed mild cognitive impairment, and in the validation group, 56 (40.9%) patients developed mild cognitive impairment, totaling 248 (47.7%) of all sampled non-dialysis CKD patients. Age, educational level, Occupation status, Use of smartphone, sleep disorders, hemoglobin, and platelet count were independent factors influencing the occurrence of mild cognitive impairment in non-dialysis CKD patients (all p < 0.05). The model evaluation showed an area under the ROC curve of 0.928, 95% CI (0.902, 0.953) in the modeling group, and 0.897, 95% CI (0.844, 0.950) in the validation group. The model's Youden index was 0.707, with an optimal cutoff value of 0.494, sensitivity of 0.853, and specificity of 0.854, indicating good predictive performance; calibration curves, Hosmer-Lemeshow test, and clinical decision curves indicated good calibration and clinical benefit. Internal validation results showed a consistency index (C-index) of 0.928, 95% CI (0.902, 0.953). CONCLUSION: The risk prediction model developed in this study shows excellent performance, demonstrating significant predictive potential for early screening of mild cognitive impairment in non-dialysis CKD patients. The application of this model will provide a reference for healthcare professionals, helping them formulate more targeted intervention strategies to optimize patient treatment and management outcomes.

Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer.

BACKGROUND: In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model. METHODS: A total of 750 postoperative patients with breast cancer were selected from the First People's Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients. RESULTS: SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685-0.8613), 0.768 (0.727-0.809), and 0.7661 (0.724--0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848-0.918) and 0.937 (0.893-0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848-0.919) and 0.885 (0.818-0.951), respectively. CONCLUSION: We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model.

Development and Validation of a Risk Prediction Model to Predict Postextubation Dysphagia in Elderly Patients After Endotracheal Intubation Under General Anesthesia: A Single-Center Cross-Sectional Study.

At present, the incidence and risk factors for dysphagia after extubation in elderly inpatients are still unclear, and we aimed to develop and validate a risk prediction model that prospectively identifies high-risk patients to reduce the occurrence rate of dysphagia. The 469 patients recruited were randomly divided into modeling and validation groups in a 7:3 ratio. In the modeling group, the postextubation dysphagia (PED) risk factors were analyzed, and a risk prediction model was established. In the validation group, the model was validated and evaluated. The model was constructed based on the risk factors determined by a binary logistic regression analysis. The discrimination ability of the model was evaluated by the receiver operating characteristic (ROC) curve. The calibration curve and Hosmer‒Lemeshow test were performed to evaluate the model's calibration ability. The clinical utility of the risk prediction model was analyzed by decision curve analysis (DCA). The results showed that the incidence of PED was 15.99%, and age, duration of indwelling gastric tube, difficult endotracheal intubation, atomization after extubation, anesthesia risk level and frailty assessment were identified as important risk factors. The model was validated to have favorable discrimination, calibration ability and clinical utility. It has a certain extension value and clinical applicability, providing a feasible reference for preventing the occurrence of PED.

Risk factor analysis and model construction for fish bone foreign body in children's pharynx.

OBJECTIVES: This study aimed to assess the risk factors for predicting the presence of fish bone foreign bodies and to develop a risk prediction model. METHODS: Data of 1405 children who underwent video-guided laryngoscope for suspected fish bone foreign body ingestion were retrospectively analyzed. Multi-factor logistic regression analyses were performed to analyze the risk factors for the presence of fish bone foreign body in patients, and a risk prediction model was established based on the results of the logistic regression analysis. RESULTS: The results of the statistical analysis showed the presence of an ulcerated surface increased the risk of having a fishbone foreign body in the pharynx by approximately 55.36-fold (95 % confidence interval (CI): 15.78-194.24), followed by a clear chief complaint site, which increased the risk of having a fishbone foreign body in the pharynx by approximately 7.963-fold (95 % CI: 4.820-13.15), and a tingling sensation, which increased the risk of having a fishbone foreign body by approximately 7-fold (95 % CI: 3.483, 14.233). A clinical prediction model (nomogram) was developed and its validation was performed using receiver operating characteristic (ROC) curve analysis, in which an area under the curve (AUC) value of 0.808 indicated that the model had a great prediction capability. CONCLUSION: The predictive capability of a logistic regression model for the detection of fish bone foreign bodies following ingestion is significant. Clinicians can concentrate on monitoring these risk factors and implementing appropriate interventions to reduce the risks of patients presenting with fish bone foreign bodies.

Prediction of the risk of 3-year chronic kidney disease among elderly people: a community-based cohort study.

OBJECTIVE: We conducted a community-based cohort study to predict the 3-year occurrence of chronic kidney disease (CKD) among population aged ≥60 years. METHOD: Participants were selected from two communities through randomized cluster sampling in Jiading District of Shanghai, China. The two communities were randomly divided into a development cohort (n = 12012) and a validation cohort (n = 6248) with a 3-year follow-up. Logistic regression analysis was used to determine the independent predictors. A nomogram was established to predict the occurrence of CKD within 3 years. The area under the curve (AUC), the calibration curve and decision curve analysis (DCA) curve were used to evaluate the model. RESULT: At baseline, participants in development cohort and validation cohort were with the mean age of 68.24 ± 5.87 and 67.68 ± 5.26 years old, respectively. During 3 years, 1516 (12.6%) and 544 (8.9%) new cases developed CKD in the development and validation cohorts, respectively. Nine variables (age, systolic blood pressure, body mass index, exercise, previous hypertension, triglycerides, fasting plasma glucose, glycated hemoglobin and serum creatinine) were included in the prediction model. The AUC value was 0.742 [95% confidence interval (CI), 0.728-0.756] in the development cohort and 0.881(95%CI, 0.867-0.895) in the validation cohort, respectively. The calibration curves and DCA curves demonstrate an effective predictive model. CONCLUSION: Our nomogram model is a simple, reasonable and reliable tool for predicting the risk of 3-year CKD in community-dwelling elderly people, which is helpful for timely intervention and reducing the incidence of CKD.

A nomogram prediction model for mild cognitive impairment in non-dialysis outpatient patients with chronic kidney disease.

BACKGROUND: The high prevalence of mild cognitive impairment (MCI) in non-dialysis individuals with chronic kidney disease (CKD) impacts their prognosis and quality of life. OBJECTIVE: This study aims to investigate the variables associated with MCI in non-dialysis outpatient patients with CKD and to construct and verify a nomogram prediction model. METHODS: 416 participants selected from two hospitals in Chengdu, between January 2023 and June 2023. They were categorized into two groups: the MCI group (n = 210) and the non-MCI (n = 206). Univariate and multivariate binary logistic regression analyses were employed to identify independent influences (candidate predictor variables). Subsequently, regression models was constructed, and a nomogram was drawn. The restricted cubic spline diagram was drawn to further analyze the relationship between the continuous numerical variables and MCI. Internally validated using a bootstrap resampling procedure. RESULTS: Among 416 patients, 210 (50.9%) had MCI. Logistic regression analysis revealed that age, educational level, occupational status, use of smartphones, sleep disorder, and hemoglobin were independent influencing factors of MCI (all p<.05). The model's area under the curve was 0.926,95% CI (0.902, 0.951), which was a good discriminatory measure; the Calibration curve, the Hosmer-Lemeshow test, and the Clinical Decision Curve suggested that the model had good calibration and clinical benefit. Internal validation results showed the consistency index was 0.926, 95%CI (0.925, 0.927). CONCLUSION: The nomogram prediction model demonstrates good performance and can be used for early screening and prediction of MCI in non-dialysis patients with CKD. It provides valuable reference for medical staff to formulate corresponding intervention strategies.

Evaluation of the risk prediction model of pressure injuries in hospitalized patient: A systematic review and meta-analysis.

AIMS AND OBJECTIVES: The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high-risk patients. BACKGROUND: Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear. DESIGN: Systematic review and meta-analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine). METHODS: This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case-control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta-analysis. RESULTS: Sixty-two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries. CONCLUSION: Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high-risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high-risk patients based on the actual situation and provide care targeting the high-risk factors to prevent the occurrence of diseases. RELEVANCE TO CLINICAL PRACTICE: The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high-risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients. REGISTRATION NUMBER (PROSPERO): CRD42023445258.

Preoperative Variables of 30-Day Mortality in Adults Undergoing Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND AND AIM: Risk adjustment following percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and clinical decision-making. There remains significant variation in the accuracy and nature of risk adjustment models utilised in international PCI registries/databases. Therefore, the current systematic review aims to summarise preoperative variables associated with 30-day mortality among patients undergoing PCI, and the other methodologies used in risk adjustments. METHOD: The MEDLINE, EMBASE, CINAHL, and Web of Science databases until October 2022 without any language restriction were systematically searched to identify preoperative independent variables related to 30-day mortality following PCI. Information was systematically summarised in a descriptive manner following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The quality and risk of bias of all included articles were assessed using the Prediction Model Risk Of Bias Assessment Tool. Two independent investigators took part in screening and quality assessment. RESULTS: The search yielded 2,941 studies, of which 42 articles were included in the final assessment. Logistic regression, Cox-proportional hazard model, and machine learning were utilised by 27 (64.3%), 14 (33.3%), and one (2.4%) article, respectively. A total of 74 independent preoperative variables were identified that were significantly associated with 30-day mortality following PCI. Variables that repeatedly used in various models were, but not limited to, age (n=36, 85.7%), renal disease (n=29, 69.0%), diabetes mellitus (n=17, 40.5%), cardiogenic shock (n=14, 33.3%), gender (n=14, 33.3%), ejection fraction (n=13, 30.9%), acute coronary syndrome (n=12, 28.6%), and heart failure (n=10, 23.8%). Nine (9; 21.4%) studies used missing values imputation, and 15 (35.7%) articles reported the model's performance (discrimination) with values ranging from 0.501 (95% confidence interval [CI] 0.472-0.530) to 0.928 (95% CI 0.900-0.956), and four studies (9.5%) validated the model on external/out-of-sample data. CONCLUSIONS: Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods.

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors.

BACKGROUND: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors. METHODS: A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve. RESULTS: Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. CONCLUSIONS: Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

We aimed to evaluate a risk prediction model of gestational diabetes mellitus (GDM) based on traditional and genetic factors. A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to collect general data. Among traditional factors, age ≥30 years old, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Both genetic and traditional factors increase the risk of GDM in women.