Evaluation of a nomogram model for predicting in-hospital mortality risk in patients with acute ST-elevation myocardial infarction and acute heart failure post-PCI.

OBJECTIVES: This study aims to identify the risk factors contributing to in-hospital mortality in patients with acute ST-elevation myocardial infarction (STEMI) who develop acute heart failure (AHF) post-percutaneous coronary intervention (PCI). Based on these factors, we constructed a nomogram to effectively identify high-risk patients. METHODS: In the study, a collective of 280 individuals experiencing an acute STEMI who then developed AHF following PCI were evaluated. These subjects were split into groups for training and validation purposes. Utilizing lasso regression in conjunction with logistic regression analysis, researchers sought to pinpoint factors predictive of mortality and to create a corresponding nomogram for forecasting purposes. To evaluate the model's accuracy and usefulness in clinical settings, metrics such as the concordance index (C-index), calibration curves, and decision curve analysis (DCA) were employed. RESULTS: Key risk factors identified included blood lactate, D-dimer levels, gender, left ventricular ejection fraction (LVEF), and Killip class IV. The nomogram demonstrated high accuracy (C-index: training set 0.838, validation set 0.853) and good fit (Hosmer-Lemeshow test: χ2 = 0.545, p = 0.762), confirming its clinical utility. CONCLUSION: The developed clinical prediction model is effective in accurately forecasting mortality among patients with acute STEMI who develop AHF after PCI.

Clinical Characteristics of Moxifloxacin-Related Arrhythmias and Development of a Predictive Nomogram: A Case Control Study.

This study aimed to analyze the incidence, clinical characteristics, and risk factors of moxifloxacin-related arrhythmias and electrocardiographic alterations in hospitalized patients using real-world data. Concurrently, a nomogram was established and validated to provide a practical tool for prediction. Retrospective automatic monitoring of inpatients using moxifloxacin was performed in a Chinese hospital from January 1, 2017, to December 31, 2021, to obtain the incidence of drug-induced arrhythmias and electrocardiographic alterations. Propensity score matching was conducted to balance confounders and analyze clinical characteristics. Based on the risk and protective factors identified through logistic regression analysis, a prediction nomogram was developed and internally validated using the Bootstrap method. Arrhythmias and electrocardiographic alterations occurred in 265 of 21,711 cases taking moxifloxacin, with an incidence of 1.2%. Independent risk factors included medication duration (odds ratio [OR] 1.211, 95% confidence interval [CI] 1.156-1.270), concomitant use of meropenem (OR 4.977, 95% CI 2.568-9.644), aspartate aminotransferase >40 U/L (OR 3.728, 95% CI 1.800-7.721), glucose >6.1 mmol/L (OR 2.377, 95% CI 1.531-3.690), and abnormally elevated level of amino-terminal brain natriuretic peptide precursor (OR 2.908, 95% CI 1.640-5.156). Concomitant use of cardioprotective drugs (OR 0.430, 95% CI 0.220-0.841) was a protective factor. The nomogram showed good differentiation and calibration, with enhanced clinical benefit. The incidence of moxifloxacin-related arrhythmias and electrocardiographic alterations is in the range of common. The nomogram proves valuable in predicting the risk in the moxifloxacin-administered population, offering significant clinical applications.

Development and Validation of a Clinical Predictive Nomogram for Assessing the Risk of Recurrence of Acute Pancreatitis in Combined Hypertriglyceridemia.

BACKGROUND: The objective of this study is to develop and validate a new nomogram-based scoring system for anticipating the recurrence of acute pancreatitis (AP) in combined hypertriglyceridemia (HTG). METHODS: A total of 292 patients diagnosed with AP combined with HTG participated in this research. Among them, 201 patients meeting the inclusion criteria were randomly divided into training and validation sets at a ratio of 7:3. Clinical data were collected for all patients. In the training set, predictive indicators were chosen through backward stepwise multivariable logistic regression analysis. Subsequently, a nomogram was developed based on the selected indicators. Finally, the model's performance was validated in both the training and validation sets. RESULTS: By employing backward stepwise multivariable logistic regression analysis, we identified diabetes, gallstones, alcohol consumption, and triglyceride levels as predictive indicators. Subsequently, a clinical nomogram that incorporates these four independent risk factors was constructed. Model validation demonstrated an AUC of 0.726 (95% CI 0.644-0.809) in the training set and an AUC of 0.712 (95% CI 0.583-0.842) in the validation set, indicating a good discriminative ability. The Hosmer-Lemeshow test yielded P-values of 0.882 and 0.536 in the training and validation sets, respectively, suggesting good calibration. Calibration curves further confirmed good agreement. Ultimately, decision curve analysis (DCA) emphasized the clinical utility of our model. CONCLUSION: We have developed a nomogram for predicting the recurrence of AP combined with HTG in patients, and this nomogram demonstrates good discriminative ability, calibration, and clinical utility. This tool holds the potential to assist clinicians in offering more personalized treatment strategies for AP combined with HTG.

Oral lichenoid drug eruption due to osimertinib for lung cancer.

INTRODUCTION: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR) are linked with side effects involving skin and mucosa. Herein, we present a unique case of oral lichenoid drug eruption (LDE) in a patient treated with osimertinib. CASE REPORT: A 75-year-old woman was diagnosed with metastatic EGFR-mutated lung adenocarcinoma, and started on osimertinib 80 mg PO daily. At 24 months of therapy, the patient developed a painful, red, and white striated oral lesion involving the left buccal mucosa and the adjacent buccal aspect of gingivae. Biopsy showed oral LDE. Causality assessment between osimertinib and the oral LDE via Naranjo Adverse Drug Reaction probability scale revealed a score of 5. MANAGEMENT AND OUTCOME: Osimetinib discontinuation was not felt to be in the best interest of the patient. Therefore, diphenhydramine HCL mouthwash every 6 h PRN (before meals) was started. Spicy and hot foods were discontinued. At a four-week follow-up visit, the patient reported moderate improvement in her symptoms. CONCLUSION: Oral LDEs are considered premalignant lesions as they can transform into squamous cell carcinoma; therefore, regular follow-up is needed. Awareness of this potential side effect of osimertinib would also prevent unnecessary (and potentially costly) work-up and lead to its prompt diagnosis and treatment.

A meta-analysis of Chinese men's penile size in a global context.

BACKGROUND: Penile size is a topic of significant interest among men and healthcare professionals. However, data on adult male penile dimensions in China are scarce, hindering clinical counseling and research. This study aimed to establish normative data for Chinese penile size and compare it with a global reference population. METHODS: We conducted a meta-analysis of published studies on penile size, focusing on Chinese and international data. Studies from East and Southeast Asia were excluded to minimize regional confounding factors. The analysis included 23 Chinese studies encompassing 34,060 men aged 16-57 years, and 19 international studies encompassing 15,216 men aged 16-91 years. Penile length and circumference were assessed in both flaccid and erect states. RESULTS: This study established the first nomogram and percentile distribution chart for adult male penile size in China. The mean flaccid length was 7.42 cm (standard deviation [SD] ± 0.95 cm), and the mean flaccid circumference was 8.54 cm (SD ± 0.62 cm). The mean erect length was 12.42 cm (SD ± 1.63 cm), and the mean erect circumference was 10.75 cm (SD ± 1.34 cm). Compared with the global reference population (flaccid length: 9.09 cm ± 1.51 cm, flaccid circumference: 9.12 cm ± 0.93 cm; p < 0.05 for both), Chinese men exhibited statistically significant shorter flaccid lengths and circumferences. No significant differences were observed in erect length or circumference between Chinese men and the global reference population (p > 0.05 for both). Notably, the growth coefficient during erection (defined as the percentage increase from flaccid length to erect length) was significantly higher in Chinese men (67.39%) compared with the global average (43.45%; p < 0.05), supporting the hypothesis of a proportionally greater increase in shorter penises. CONCLUSIONS: This study provides novel and comprehensive data on penile size in Chinese men. Compared with a global reference population, Chinese men exhibited shorter flaccid penises but a higher proportional increase upon erection. These findings may be of value for clinical counseling and future research on penile size variations. The newly developed nomogram and percentile chart can serve as a valuable tool for both patients and healthcare professionals.

GATIS score for predicting the prognosis of rectal neuroendocrine neoplasms: A Chinese multicenter study of 12-year experience.

BACKGROUND: There is currently a shortage of accurate, efficient, and precise predictive instruments for rectal neuroendocrine neoplasms (NENs). AIM: To develop a predictive model for individuals with rectal NENs (R-NENs) using data from a large cohort. METHODS: Data from patients with primary R-NENs were retrospectively collected from 17 large-scale referral medical centers in China. Random forest and Cox proportional hazard models were used to identify the risk factors for overall survival and progression-free survival, and two nomograms were constructed. RESULTS: A total of 1408 patients with R-NENs were included. Tumor grade, T stage, tumor size, age, and a prognostic nutritional index were important risk factors for prognosis. The GATIS score was calculated based on these five indicators. For overall survival prediction, the respective C-indexes in the training set were 0.915 (95% confidence interval: 0.866-0.964) for overall survival prediction and 0.908 (95% confidence interval: 0.872-0.944) for progression-free survival prediction. According to decision curve analysis, net benefit of the GATIS score was higher than that of a single factor. The time-dependent area under the receiver operating characteristic curve showed that the predictive power of the GATIS score was higher than that of the TNM stage and pathological grade at all time periods. CONCLUSION: The GATIS score had a good predictive effect on the prognosis of patients with R-NENs, with efficacy superior to that of the World Health Organization grade and TNM stage.

A self-reported symptom-based decision-making model helps to rule out outpatient cases at low risk for CRC before colonoscopy.

Objectives: Effective exclusion of low-risk symptomatic outpatient cases for colorectal cancer (CRC) remains diagnostic challenges. We aimed to develop a self-reported symptom-based decision-making model for application in outpatient scenarios. Methods: In total, 8233 symptomatic cases at risk for CRC, as judged by outpatient physicians, were involved in this study at seven medical centers. A decision-making model was constructed using 60 self-reported symptom parameters collected from the questionnaire. Further internal and external validation cohorts were built to evaluate the discriminatory power of the CRC model. The discriminatory power of the CRC model was assessed by the C-index and calibration plot. After that, the clinical utility and user experience of the CRC model were evaluated. Results: Nine symptom parameters were identified as valuable predictors used for modeling. Internal and external validation cohorts verified the adequate discriminatory power of the CRC model. In the clinical application step, all 17 physicians found the model easy to grasp, 99.9 % of the patients were satisfied with the survey form. Application of this model detected all CRC cases. The total consistency ratio of outpatient cases undergoing colonoscopy was 81.4 %. None of the low-risk patients defined by the CRC model had been diagnosed with CRC. Conclusion: This multicenter study developed and validated a simple and user-friendly decision-making model covering self-reported information. The CRC model has been demonstrated to perform well in terms of rapid outpatient decision-making scenarios and clinical utility, particularly because it can better rule out low-risk outpatient cases.

Risk factors and nomogram predictive model of severe postoperative complications in elderly patients with intertrochanteric fractures.

Objective: To analyze risk factors of severe postoperative complications in elderly patients with intertrochanteric fractures (ITF), and to construct a predictive model. Methods: The medical records of 316 elderly patients with ITF who underwent surgical treatment in Suzhou Hospital of Integrated Traditional Chinese and Western Medicine from January 2020 to December 2022 were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to identify risk factors of severe postoperative complications. A nomogram prediction model was constructed using the RMS package of R4.1.2 software. Accuracy and stability of the model was assessed using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis. Results: Age, American Society of Anesthesiologists (ASA) grading, combined medical diseases, preoperative bedridden condition, frailty, and preoperative albumin levels were all risk factors for severe postoperative complications in ITF patients were noted. These factors were then used to build a risk prediction model that had an area under the ROC curve (AUC) of 0.899 (95% confidence interval (CI): 0.846-0.951). The internal validation results of the Bootstrap method showed that the C-index value of the model was 0.899, and the calibration curve had a good fit with the ideal curve. Conclusions: Age, ASA grading, combined medical diseases, preoperative bedridden condition, frailty, and preoperative albumin levels were independent risk factors for severe postoperative complications in elderly ITF patients. The constructed prediction model based on the above risk factors has a high predictive value.

Developing a nomogram for forecasting the 28-day mortality rate of individuals with bleeding esophageal varices using MELD scores.

BACKGROUND: The goal is to create a nomogram using the model for end-stage liver disease (MELD) that can better predict the risk of 28-day mortality in patients with bleeding esophageal varices. METHODS: Data on patients with bleeding esophageal varices were gathered retrospectively from the marketplace for medical information in intensive care (MIMIC) database. Variables were selected using least absolute shrinkage and selection operator (LASSO)-Logistic regression, then used to construct a prognostic nomogram. The nomogram was evaluated against the MELD model through various methods including receiver operating characteristic (ROC) analysis, calibration plotting, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). RESULTS: A total of 280 patients were included in the study. Patient's use of vasopressin and norepinephrine, respiratory rate (RR), temperature, mean corpuscular volume (MCV), and MELD score were included in the nomogram. The area under the curve (AUC), NRI, IDI, and DCA of the nomogram showed that it performs better than the MELD alone. CONCLUSION: A nomogram was created that outperformed the MELD score in forecasting the risk of 28-day mortality in individuals with bleeding esophageal varices.

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis.

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

Establishment and evaluation of a clinical prediction model for cognitive impairment in patients with cerebral small vessel disease.

BACKGROUND: There are currently no effective prediction methods for evaluating the occurrence of cognitive impairment in patients with cerebral small vessel disease (CSVD). AIMS: To investigate the risk factors for cognitive dysfunction in patients with CSVD and to construct a risk prediction model. METHODS: A retrospective study was conducted on 227 patients with CSVD. All patients were assessed by brain magnetic resonance imaging (MRI), and the Montreal Cognitive Assessment (MoCA) was used to assess cognitive status. In addition, the patient's medical records were also recorded. The clinical data were divided into a normal cognitive function group and a cognitive impairment group. A MoCA score < 26 (an additional 1 point for education < 12 years) is defined as cognitive dysfunction. RESULTS: A total of 227 patients (mean age 66.7 ± 6.99 years) with CSVD were included in this study, of whom 68.7% were male and 100 patients (44.1%) developed cognitive impairment. Age (OR = 1.070; 95% CI = 1.015 ~ 1.128, p < 0.05), hypertension (OR = 2.863; 95% CI = 1.438 ~ 5.699, p < 0.05), homocysteine(HCY) (OR = 1.065; 95% CI = 1.005 ~ 1.127, p < 0.05), lacunar infarct score(Lac_score) (OR = 2.732; 95% CI = 1.094 ~ 6.825, P < 0.05), and CSVD total burden (CSVD_score) (OR = 3.823; 95% CI = 1.496 ~ 9.768, P < 0.05) were found to be independent risk factors for cognitive decline in the present study. The above 5 variables were used to construct a nomogram, and the model was internally validated by using bootstrapping with a C-index of 0.839. The external model validation C-index was 0.867. CONCLUSIONS: The nomogram model based on brain MR images and clinical data helps in individualizing the probability of cognitive impairment progression in patients with CSVD.

Development and validation of a nomogram for predicting mechanical ventilation need among acutely intoxicated patients with impaired consciousness.

BACKGROUND: A considerable portion of acutely intoxicated patients is presented with impaired consciousness. Early identification of those patients who require advanced medical care, such as mechanical ventilation (MV), can improve their prognosis. METHODS: This study included 330 acutely intoxicated patients who were presented with impaired consciousness and admitted to Tanta University Poison Control Center, Egypt, in the period from January 2021 to December 2023. Patients were enrolled in derivation (257 patients) and validation (73 patients) cohorts. Patients' data were analyzed to develop and validate a predictive nomogram to determine the probability of MV need in acutely intoxicated patients. RESULTS: Significant predictors for MV need were mean arterial blood pressure (OR = 0.96, p = .014), PaO2 (OR = 0.96, p = .001), pH (OR = 0.00, p < . 001), and glucose/potassium ratio (OR = 1.59, p = .030). These four parameters were used to formulate a bedside nomogram. Receiver-operating characteristic (ROC) analysis for the proposed nomogram shows that area under the curve (AUC) = 95.7%, accuracy = 93.4%, sensitivity = 88.9%, and specificity = 95.1%. The internal validation for the developed nomogram was assessed using a bootstrapping method and calibration curve. Regarding external validation, AUCs for the developed nomogram probability was 96.5%, and for predicted probability using the developed nomogram was 97.8%. CONCLUSION: The current study provides a validated nomogram that could be used as a reliable tool for the accurate prediction of MV need among acutely intoxicated patients with impaired consciousness. It could assist in the early identification of patients who will require MV, especially in low-income countries with limited resources.

Nomograms established for predicting microvascular invasion and early recurrence in patients with small hepatocellular carcinoma.

BACKGROUND: In this study, we aimed to establish nomograms to predict the microvascular invasion (MVI) and early recurrence in patients with small hepatocellular carcinoma (SHCC), thereby guiding individualized treatment strategies for prognosis improvement. METHODS: This study retrospectively analyzed 326 SHCC patients who underwent radical resection at Wuhan Union Hospital between April 2017 and January 2022. They were randomly divided into a training set and a validation set at a 7:3 ratio. The preoperative nomogram for MVI was constructed based on univariate and multivariate logistic regression analysis, and the prognostic nomogram for early recurrence was constructed based on univariate and multivariate Cox regression analysis. We used the receiver operating characteristic (ROC) curves, area under the curves (AUCs), and calibration curves to estimate the predictive accuracy and discriminability of nomograms. Decision curve analysis (DCA) and Kaplan-Meier survival curves were employed to further confirm the clinical effectiveness of nomograms. RESULTS: The AUCs of the preoperative nomogram for MVI on the training set and validation set were 0.749 (95%CI: 0.684-0.813) and 0.856 (95%CI: 0.805-0.906), respectively. For the prognostic nomogram, the AUCs of 1-year and 2-year RFS respectively reached 0.839 (95%CI: 0.775-0.903) and 0.856 (95%CI: 0.806-0.905) in the training set, and 0.808 (95%CI: 0.719-0.896) and 0.874 (95%CI: 0.804-0.943) in the validation set. Subsequent calibration curves, DCA analysis and Kaplan-Meier survival curves demonstrated the high accuracy and efficacy of the nomograms for clinical application. CONCLUSIONS: The nomograms we constructed could effectively predict MVI and early recurrence in SHCC patients, providing a basis for clinical decision-making.

Based on 3D-PDU and clinical characteristics nomogram for prediction of lymph node metastasis and lymph-vascular space invasion of early cervical cancer preoperatively.

PURPOSE: To develop and validate a nomogram based on 3D-PDU parameters and clinical characteristics to predict LNM and LVSI in early-stage cervical cancer preoperatively. MATERIALS AND METHODS: A total of first diagnosis 138 patients with cervical cancer who had undergone 3D-PDU examination before radical hysterectomy plus lymph dissection between 2014 and 2019 were enrolled for this study. Multivariate logistic regression analyses were performed to analyze the 3D-PDU parameters and selected clinicopathologic features and develop a nomogram to predict the probability of LNM and LVSI in the early stage. ROC curve was used to evaluate model differentiation, calibration curve and Hosmer-Lemeshow test were used to evaluate calibration, and DCA was used to evaluate clinical practicability. RESULTS: Menopause status, FIGO stage and VI were independent predictors of LNM. BMI and maximum tumor diameter were independent predictors of LVSI. The predicted AUC of the LNM and LSVI models were 0.845 (95%CI,0.765-0.926) and 0.714 (95%CI,0.615-0.813). Calibration curve and H-L test (LNM groups P = 0.478; LVSI P = 0.783) all showed that the predicted value of the model had a good fit with the actual observed value, and DCA indicated that the model had a good clinical net benefit. CONCLUSION: The proposed nomogram based on 3D-PDU parameters and clinical characteristics has been proposed to predict LNM and LVSI with high accuracy, demonstrating for the first time the potential of non-invasive prediction. The probability derived from this nomogram may have the potential to provide valuable guidance for physicians to develop clinical individualized treatment plans of FIGO patients with early cervical cancer.

Nomogram for predicting survival after first-line anti-PD-1-based immunotherapy in unresectable stage IV melanoma: a multicenter international study.

BACKGROUND: The introduction of anti-programmed cell death protein 1 (PD-1) immunotherapy has revolutionized the treatment landscape for melanoma, enhancing both response rates and survival outcomes in patients with advanced stages of the disease. Despite these remarkable advances, a noteworthy subset of patients (40%-60%) does not derive advantage from this therapeutic approach. This study aims to identify key predictive factors and create a user-friendly predictive nomogram for stage IV melanoma patients receiving first-line anti-PD-1-based immunotherapy, improving treatment decisions. MATERIALS AND METHODS: In this retrospective study, we included patients with unresectable stage IV melanoma who received first-line treatment with either anti-PD-1 monotherapy or anti-PD-1 plus anti-cytotoxic T-lymphocyte associated protein 4 between 2014 and 2018. We documented clinicopathological features and blood markers upon therapy initiation. By employing the random survival forest model and backward variable selection of the Cox model, we identified variables associated with progression-free survival (PFS) after the first-line anti-PD-1-based treatment. We developed and validated a predictive nomogram for PFS utilizing the identified variables. We assessed calibration and discrimination performance metrics as part of the evaluation process. RESULTS: The study involved 719 patients, divided into a training cohort of 405 (56%) patients and a validation cohort of 314 (44%) patients. We combined findings from the random survival forest and the Cox model to create a nomogram that incorporates the following factors: lactate dehydrogenase (LDH), S100, melanoma subtype, neutrophil-to-lymphocyte ratio (NLR), body mass index, type of immune checkpoint inhibitor, and presence of liver or brain metastasis. The resultant model had a C-index of 0.67 in the training cohort and 0.66 in the validation cohort. Performance remained in different patient subgroups. Calibration analysis revealed a favorable correlation between predicted and actual PFS rates. CONCLUSIONS: We developed and validated a predictive nomogram for long-term PFS in patients with unresectable stage IV melanoma undergoing first-line anti-PD-1-based immunotherapy.

A Hybrid Unsupervised Clustering Method for Predicting the Risk of Dental Implant Loss.

OBJECTIVES: The aim of this study was to predict the risk of dental implant loss by clustering features associated with implant survival rates. MATERIALS AND METHODS: Multiple clinical features from 8513 patients who underwent single implant placement were retrospectively analysed. A hybrid method integrating unsupervised learning algorithms with survival analysis was employed for data mining. Two-step cluster, univariate Cox regression, and Kaplan‒Meier survival analyses were performed to identify the clustering features associated with implant survival rates. To predict the risk of dental implant loss, nomograms were constructed on the basis of time-stratified multivariate Cox regression. RESULTS: Six clusters with distinct features and prognoses were identified using two-step cluster analysis and Kaplan‒Meier survival analysis. Compared with the other clusters, only one cluster presented significantly lower implant survival rates, and six specific clustering features within this cluster were identified as high-risk factors, including age, smoking history, implant diameter, implant length, implant position, and surgical procedure. Nomograms were created to assess the impact of the six high-risk factors on implant loss for three periods: 1) 0-120 days, 2) 120-310 days, and 3) more than 310 days after implant placement. The concordance indices of the models were 0.642, 0.781, and 0.715, respectively. CONCLUSIONS: The hybrid unsupervised clustering method, which clusters and identifies high-risk clinical features associated with implant loss without relying on predefined labels or target variables, represents an effective approach for developing a visual model for predicting implant prognosis. However, further validation with a multimodal, multicentre, prospective cohort is needed. CLINICAL SIGNIFICANCE: Visual prognosis prediction utilizing this nomogram that predicts the risk of implant loss on the basis of clustering features can assist dentists in preoperative assessments and clinical decision-making, potentially improving dental implant prognosis.

Identification of the risk factors for insomnia in nurses with long COVID-19.

PURPOSE: To investigate the prevalence of insomnia among nurses with long COVID-19, analyze the potential risk factors and establish a nomogram model. METHODS: Nurses in Ningbo, China, were recruited for this study. General demographic information and insomnia, burnout, and stress assessment scores were collected through a face-to face questionnaire survey administered at a single center from March to May 2023. We used LASSO regression to identify potential factors contributing to insomnia. Then, a nomogram was plotted based on the model chosen to visualize the results and evaluated by receiver operating characteristic curves and calibration curves. RESULTS: A total of 437 nurses were recruited. 54% of the nurses had insomnia according to the Insomnia Severity Index (ISI) score. Eleven variables, including family structure, years of work experience, relaxation time, respiratory system sequelae, nervous system sequelae, others sequelae, attitudes toward COVID-19, sleep duration before infection, previous sleep problems, stress, and job burnout, were independently associated with insomnia. The R-squared value was 0.464, and the area under the curve was 0.866. The derived nomogram showed that neurological sequelae, stress, job burnout, sleep duration before infection, and previous sleep problems contributed the most to insomnia. The calibration curves showed significant agreement between the nomogram models and actual observations. CONCLUSION: This study focused on insomnia among nurses with long COVID-19 and identified eleven risk factors related to nurses' insomnia. A nomogram model was established to illustrate and visualize these factors, which will be instrumental in future research for identifying nurses with insomnia amid pandemic normalization and may increase awareness of the health status of healthcare workers with long COVID-19.

A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students' grade and their perceptions of the learning environment.

BACKGROUND: Medical school learning environment (MSLE) has a holistic impact on students' psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student's grade in the perception of MSLE. METHODS: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student's grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch's ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student's grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. RESULTS: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265-1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326-1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student's low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). CONCLUSION: The student's grade was a significant influencing factor in medical students' perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students' perceptions are urgently needed.

A nomogram predicting distant metastasis risk for gastric cancer patients with preoperative anemia: a multicenter retrospective study.

BACKGROUND: Anemia represents a well-established risk factor for patients diagnosed with gastric cancer, and is often associated with an unfavorable prognosis. In this context, the timely prediction of distant metastasis risk in patients with anemic gastric cancer assumes paramount importance. METHODS: Information of gastric cancer patients complicated with preoperative anemia in the First Affiliated Hospital of Sun Yat-sen University was collected. The cohort from the First Affiliated Hospital of Guangxi Medical University was used as an external validation set. A Nomogram was established based on the risk factors screened by univariate and multivariate logistic regression analyses. RESULTS: A total of 848 gastric cancer patients with preoperative anemia were enrolled. Pyloric obstruction, carcinoma antigen 125, T stage, N stage, tumor size, and preoperative weight loss were independent predictors of distant metastasis in gastric cancer patients with anemia (p < 0.05), based on which a nomogram was constructed. The accuracy, reliability and clinical value of the nomogram were evaluated by concordance index, receiver operating characteristic curve, decision curve analysis, calibration curve and showed good stability and clinical predictive value. CONCLUSIONS: Preoperative anemic gastric cancer patients, complicated with pyloric obstruction, elevated CA125, advanced T and N stage, larger tumor size, and preoperative weight loss, should be paid more attention to distant metastasis.

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

PURPOSE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration. METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs. RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting. CONCLUSION: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.