A Nomogram for Predicting Postoperative Pulmonary Complications in Critical Patients Transferred to ICU After Abdominal Surgery.

OBJECTIVE: The purpose of this study was to investigate the risk factors associated with postoperative pulmonary complications(PPCs) in critically ill patients transferred to intensive care unit(ICU) after abdominal surgery and develop a predictive model for this disease. METHODS: Data for 3716 patients who were admitted to ICU after abdominal surgery in Peking University People's Hospital between January 2015 and December 2020 were retrospectively collected and analyzed to identify the risk factors and develop a nomogram prediction model. Data for patients admitted to ICU following abdominal surgery at Peking University People's Hospital from March 2021 to December 2022 were prospectively collected as a validation set to validate and assess the model. RESULTS: 10 independent risk factors for PPCs in critically ill patients transferred to ICU after abdominal surgery were identified. A nomogram prediction model was constructed for PPCs in this group patients, the area under ROC curve was 0.771[95%CI: 0.756,0.786] and 0.759[95%CI: 0.726,0.792] in the training set and validation set, respectively. CONCLUSIONS: In this study, independent risk factors for PPCs in critically ill patients transferred to ICU after abdominal surgery were identified. A nomogram prediction model for PPCs in critically ill surgical population was constructed using these factors, demonstrating a good predictive value.

Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne infection with a high case fatality rate. Significant gaps remain in studies analyzing the clinical characteristics of fatal cases. METHODS: From January 2017 to June 2023, 427 SFTS cases were included in this study. A total of 67 variables about their demographic, clinical, and laboratory data were collected. Univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) method was used to screen predictors from the cohort. Multivariate logistic regression was used to identify independent predictors and nomograms were developed. Calibration, decision curves and area under the curve (AUC) were used to assess model performance. RESULTS: The multivariate logistic regression analysis screened out the four most significant factors, including age > 70 years (p = 0.001, OR = 2.516, 95% CI 1.452-4.360), elevated serum PT (p < 0.001, OR = 1.383, 95% CI 1.143-1.673), high viral load (p < 0. 001, OR = 1.496, 95% CI 1.290-1.735) and high level of serum urea (> 8.0 µmol/L) (p < 0.001, OR = 4.433, 95% CI 1.888-10.409). The AUC of the nomogram based on these four factors was 0.813 (95% CI, 0.758-0.868). The bootstrap resampling internal validation model performed well, and decision curve analysis indicated a high net benefit. CONCLUSIONS: The nomogram based on age, elevated PT, high serum urea level, and high viral load can be used to help early identification of SFTS patients at risk of fatality.

The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 - 2N1M0 breast cancer.

OBJECTIVE: To explore the high-risk factors affecting the prognosis of pT1 - 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population. METHOD: The clinical data of 936 patients with pT1 - 2N1M0 who underwent mastectomy in the fourth hospital of Hebei Medical University from 2010 to 2016 were retrospectively analyzed. There were 583 patients received postmastectomy radiotherapy(PMRT), and 325 patients without PMRT. Group imbalances were mitigated using the propensity score matching (PSM) method, and the log-rank test was employed to compare overall survival (OS) and disease-free survival (DFS) between the cohorts. The efficacy of PMRT across various risk groups was evaluated using a nomogram model. RESULT: The median follow-up period was 98 months, Patients who received PMRT demonstrated significantly improved 5-year and 8-year OS and DFS compared to those who did not (P < 0.001). Multivariate analysis revealed that age, primary tumor site, positive lymph node, stage, and Ki-67 level independently influenced OS, while age, primary tumor site, and stage independently affected DFS. PMRT drastically enhanced OS in the high-risk group (P = 0.001), but did not confer benefits in the low-risk and intermediate risk groups (P = 0.057, P = 0.099). PMRT led to a significant improvement in disease-free survival (DFS) among patients in the intermediate and high-risk groups (P = 0.036, P = 0.001), whereas the low-risk group did not experience a significant benefit (P = 0.475). CONCLUSION: Age ≤ 40 years, tumor located in the inner quadrant or central area, T2 stage, 2-3 lymph nodes metastasis, and Ki67 > 30% were the high-risk factors affecting the prognosis of this cohort of patients. In OS nomogram, patients with a risk score of 149 or higher who received PMRT exhibited improved OS. Similarly, in DFS nomogram, patients with a risk score of 123 or higher who received PMRT demonstrated enhanced DFS.

The efficacy of neoadjuvant immunotherapy and lymphocyte subset predictors in locally advanced esophageal squamous cell carcinoma: A retrospective study.

BACKGROUND: Despite the recognized therapeutic potential of programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors in advanced esophageal squamous cell carcinoma (ESCC), their role in neoadjuvant therapy and reliable efficacy biomarkers remain elusive. MATERIALS AND METHODS: We retrospectively analyzed locally advanced ESCC patients who underwent surgery following a 2-cycle platinum and paclitaxel-based treatment, with or without PD-1 inhibitors (January 2020-March 2023). We assessed peripheral blood indexes and tertiary lymphoid structures (TLS) density to evaluate their impact on pathological response and prognosis, leading to a clinical prediction model for treatment efficacy and survival. RESULTS: Of the 157 patients recruited, 106 received immunochemotherapy (ICT) and 51 received chemotherapy (CT) alone. The ICT group demonstrated a superior pathological response rate (PRR) (47.2% vs. 29.4%, p = 0.034) with comparable adverse events and postoperative complications. The ICT group also showed a median disease-free survival (DFS) of 39.8 months, unattained by the CT group. The 1-year DFS and overall survival (OS) rates were 73% and 91% for the ICT group, and 68% and 81% for the CT group, respectively. We found higher baseline activated T cells, lower baseline Treg cells, and a decreased posttreatment total lymphocyte and CD4+/CD8+ ratio predicted an enhanced PRR. Reduced posttreatment CD4+/CD8+ ratio and increased NK cells were associated with prolonged survival, while higher TLS density indicated poorer prognosis. Among ICT group, a lower posttreatment CD4+/CD8+ ratio indicated longer DFS and reduced posttreatment B cells indicated longer OS. A nomogram integrating these predictors was developed to forecast treatment efficacy and survival. CONCLUSION: The combination of PD-1 inhibitors and chemotherapy appears promising for locally advanced ESCC. Evaluating the differentiation status and dynamic changes of peripheral blood immune cells may provide valuable predictive insights into treatment efficacy and prognosis.

Development and Validation of a Nomogram for Predicting Postoperative Recurrence-Free Survival in Patients With Nonmetastatic Pathological T3a Stage Renal Cell Carcinoma.

BACKGROUND: To establish a nomogram predicting postoperative recurrence-free survival (RFS) in patients with nonmetastatic renal cell carcinoma (RCC) of pathological T3a (pT3a) stage undergoing nephrectomy. MATERIALS AND METHODS: A retrospective review included 668 patients with pT3a RCC between 2008 and 2019, randomly divided into training and validation groups (7:3 ratio). Cox regression analysis established the RFS-predicting nomogram in the training group. Nomogram performance was assessed using Harrell's concordance index (C-index), time-dependent receiver operating characteristic curve, decision curve analysis, and Kaplan-Meier survival analysis. RESULTS: Of the 668 patients with pT3a RCC, 167 patients experienced local recurrence or distant metastasis. Using multivariable Cox regression analysis, tumor size, ISUP grade, necrosis, capsular invasion, pT3a invasion pattern were identified as the significant predictors for RFS to establish the nomogram. The C-index of the nomogram was 0.753 (95% CI, 0.710-0.796) and 0.762 (95% CI, 0.701-0.822) for the training and validating group, respectively. The areas under the 1-year, 3-year and 5-year RFS receiver operating characteristic curves were 0.814, 0.769 and 0.768, respectively. Decision curve analysis showed the optimal application of the model in clinical decision-making. Patients with low risk T3a RCC have better RFS than those with high risk T3a RCC. CONCLUSION: Tumor size, ISUP grade, necrosis, capsular invasion and T3a invasion patterns were independent risk factors for worse RFS in patients with nonmetastatic pT3a RCC. The current nomogram could effectively predict the RFS of patients with nonmetastatic pT3a RCC.

Development of a nomogram for postoperative surgical site infections in patients undergoing bowel resection for Crohn's disease.

BACKGROUND: Surgical site infection (SSI) is a significant concern due to its potential to cause delayed wound healing and prolonged hospital stays. This study aims to develop a predictive model in patients with Crohn's disease. METHODS: We conducted single-factor and multi-factor logistic regression analyses to identify risk factors, resulting in the development of a logistic regression model and the creation of a nomogram. The model's effect was validated by employing enhanced bootstrap resampling techniques, calibration curves, and DCA curves. Finally, we investigated the risk factors for wall and intra-abdominal infections separately. RESULTS: 90 of 675 patients (13.3%) developed SSI. Several independent risk factors for SSI were identified, including higher postoperative day one neutrophil count (p=0.033), higher relative blood loss (p=0.018), female gender (p=0.021), preoperative corticosteroid use (p=0.007), Montreal classification A1 and L2 (p<0.05), previous intestinal resection (p=0.017), and remaining lesions (p=0.015). Additionally, undergoing strictureplasty (p=0.041) is a protective factor against SSI. These nine variables were used to develop an SSI prediction model presented as a nomogram. The model demonstrated strong discrimination (adjusted C-statistic=0.709, 95% CI: 0.659∼0.757) and precise calibration. The decision curve showed that the nomogram was clinically effective within a probability threshold range of 3% to 54%. Further subgroup analysis revealed distinct risk factors for wall infections and intra-abdominal infections. CONCLUSION: We established a new predictive model, which can guide the prevention and postoperative care of SSI after Crohn's disease bowel resection surgery to minimize its occurrence rate.

Development and validation of a prognostic nomogram for esophageal cancer patients based on SEER Asian population.

This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification.

Developing a prediction model for preoperative acute heart failure in elderly hip fracture patients: a retrospective analysis.

BACKGROUND: Hip fractures in the elderly are a common traumatic injury. Due to factors such as age and underlying diseases, these patients exhibit a high incidence of acute heart failure prior to surgery, severely impacting surgical outcomes and prognosis. OBJECTIVE: This study aims to explore the potential risk factors for acute heart failure before surgery in elderly patients with hip fractures and to establish an effective clinical prediction model. METHODS: This study employed a retrospective cohort study design and collected baseline and preoperative variables of elderly patients with hip fractures. Strict inclusion and exclusion criteria were adopted to ensure sample consistency. Statistical analyses were carried out using SPSS 24.0 and R software. A prediction model was developed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. The accuracy of the model was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and a calibration curve was plotted to assess the model's calibration. RESULTS: Between 2018 and 2019, 1962 elderly fracture patients were included in the study. After filtering, 1273 were analyzed. Approximately 25.7% of the patients experienced acute heart failure preoperatively. Through LASSO and logistic regression analyses, predictors for preoperative acute heart failure in elderly patients with hip fractures were identified as Gender was male (OR = 0.529, 95% CI: 0.381-0.734, P < 0.001), Age (OR = 1.760, 95% CI: 1.251-2.479, P = 0.001), Coronary Heart Disease (OR = 1.977, 95% CI: 1.454-2.687, P < 0.001), Chronic Obstructive Pulmonary Disease (COPD) (OR = 2.484, 95% CI: 1.154-5.346, P = 0.020), Complications (OR = 1.516, 95% CI: 1.033-2.226, P = 0.033), Anemia (OR = 2.668, 95% CI: 1.850-3.847, P < 0.001), and Hypoalbuminemia (OR 2.442, 95% CI: 1.682-3.544, P < 0.001). The linear prediction model of acute heart failure was Logit(P) = -2.167-0.637×partial regression coefficient for Gender was male + 0.566×partial regression coefficient for Age + 0.682×partial regression coefficient for Coronary heart disease + 0.910×partial regression coefficient for COPD + 0.416×partial regression coefficient for Complications + 0.981×partial regression coefficient for Anemia + 0.893×partial regression coefficient for Hypoalbuminemia, and the nomogram prediction model was established. The AUC of the predictive model was 0.763, indicating good predictive performance. Decision curve analysis revealed that the prediction model offers the greatest net benefit when the threshold probability ranges from 4 to 62%. CONCLUSION: The prediction model we developed exhibits excellent accuracy in predicting the onset of acute heart failure preoperatively in elderly patients with hip fractures. It could potentially serve as an effective and useful clinical tool for physicians in conducting clinical assessments and individualized treatments.

The potential of a nomogram risk assessment model for the diagnosis of abdominal aortic aneurysm: a multicenter retrospective study.

The incidence of abdominal aortic aneurysm (AAA) is very high, but there is no risk assessment model for early identification of AAA in clinic. The aim of this study was to develop a nomogram risk assessment model for predicting AAA. The data of 280 patients diagnosed as AAA and 385 controls in The Affiliated Suzhou Hospital of Nanjing Medical University were retrospectively reviewed. The LASSO regression method was applied to filter variables, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The calibration capability of the model is evaluated by using bootstrap (resampling = 1000) internal validation and Hosmer-Lemeshow test. The clinical utility and clinical application value were evaluated by decision curve analysis (DCA) and clinical impact curve (CIC). In addition, a retrospective review of 133 AAA patients and 262 controls from The First Affiliated Hospital of Soochow University was performed as an external validation cohort. Eight variables are selected to construct the nomogram of AAA risk assessment model. The nomogram predicted AAA with AUC values of 0.928 (95%CI, 0.907-0.950) in the training cohort, and 0.902 (95%CI, 0.865-0.940) in the external validation cohort, the risk prediction model has excellent discriminative ability. The calibration curve and Hosmer-Lemeshow test proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, the DCA curve and CIC curve showed that patients could benefit. This finding was also confirmed in the external validation cohort. In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.

Diagnostic Nomogram Model for ACR TI-RADS 4 Nodules Based on Clinical, Biochemical Data and Sonographic Patterns.

OBJECTIVES: The objective of this study was to develop and validate a nomogram model integrating clinical, biochemical and ultrasound features to predict the malignancy rates of Thyroid Imaging Reporting and Data System 4 (TR4) thyroid nodules. METHODS: A total of 1557 cases with confirmed pathological diagnoses via fine-needle aspiration (FNA) were retrospectively included. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of malignancy. These predictors were incorporated into the nomogram model, and its predictive performance was evaluated using receiver-operating characteristic curve (AUC), calibration plots, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA). RESULTS: Eight out of 22 variables-age, margin, extrathyroidal extension, halo, calcification, suspicious lymph node metastasis, aspect ratio and thyroid peroxidase antibody-were identified as independent predictors of malignancy. The calibration curve demonstrated excellent performance, and DCA indicated favourable clinical utility. Additionally, our nomogram exhibited superior predictive ability compared to the current American College of Radiology (ACR) score model, as indicated by higher AUC, NRI, IDI, negative likelihood ratio (NLR) and positive likelihood ratio (PLR) values. CONCLUSIONS: The developed nomogram model effectively predicts the malignancy rate of TR4 thyroid nodules, demonstrating promising clinical applicability.

Development and validation of a nomogram to predict cancer-specific survival of patients with large hepatocellular carcinoma accepting surgical resection: a real-world analysis based on the SEER database.

Background: Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR. Methods: The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups. Results: In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram. Conclusions: The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.

A novel model based on protein post-translational modifications comprising the immune landscape and prediction of colorectal cancer prognosis.

Background: Phosphorylation is a critical post-translational modification (PTM) type contributing to colorectal cancer (CRC). The study aimed to construct a nomogram model to predict colon adenocarcinoma (COAD) prognosis based on PTM signatures. Methods: The Cancer Genome Atlas (TCGA) database has been indexed for COAD patients' RNA sequencing, proteomic data, and clinical details. To find potential PTM prognostic signatures, the least absolute shrinkage and selection operator (LASSO) was deployed. Model validation procedures included the use of the Kaplan-Meier (K-M) method, the receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the decision curve analysis (DCA). Additionally, biological enrichment, tumor immune microenvironment, and chemotherapy were also assessed. To validate the model, CRC cells were used in in vitro experiments using western blotting, proliferation assay, colony formation assay, and flow cytometry. Results: The LASSO regression analysis identified 8 PTM sites. Based on the median PTM score, patients were classified into low- and high-risk groups. K-M results showed that high-risk patients had worse prognoses (P<0.001). Our model demonstrated powerful effectiveness and predictive value (TCGA whole group: 1-year AUC =0.611, 2-year AUC =0.574, 3-year AUC =0.627). Additionally, high-risk CRC patients were enriched in KRAS signaling pathways (P=0.01), possessed more robust immune escape capacity (P=0.001, and induced cell-cycle arrest of CRC cells (P<0.01). Conclusions: We established and validated a novel nomogram model related to PTM that can predict prognosis and guide the treatment of COAD.

YAP-based nomogram predicts poor prognosis in patients with hepatocellular carcinoma after curative surgery.

Background: Hepatocellular carcinoma (HCC) ranks prominently in cancer-related mortality globally. Surgery remains the main therapeutic option for the treatment of HCC, but high post-operative recurrence rate makes prognostic prediction challenging. The quest for a reliable model to predict HCC recurrence continues to enhance prognosis. We aim to develop a nomogram with multiple factors to accurately estimate the risk of post-operative recurrence in patients with HCC. Methods: A single-center retrospective study on 262 patients who underwent partial hepatectomy for HCC at the Eastern Hepatobiliary Surgery Hospital from May 2010 to April 2013 was conducted where immunohistochemistry assessed Yes-associated protein (YAP) expression in HCC. In the training cohort, a nomogram that incorporated YAP expression and clinicopathological features was constructed to predict 2-, 3-, and 5-year recurrence-free survival (RFS). The performance of the nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. Results: A total of 262 patients who underwent partial hepatectomy for HCC at the Eastern Hepatobiliary Surgery Hospital were included in our study. HCC patients with high YAP expression exhibited significantly higher recurrence and reduced overall survival (OS) rates compared to those with low YAP expression (P<0.001). YAP was significantly associated with alpha-fetoprotein (AFP) (P=0.03), microvascular invasion (MVI) (P<0.001), and tumor differentiation grade (P<0.001). In the training cohort, factors like YAP expression, hepatitis B surface antigen (HBsAg), hepatitis B virus deoxyribonucleic acid (HBV-DNA), Child-Pugh stage, tumor size, MVI, and tumor differentiation were identified as key elements for the predictive model. Two YAP-centric Nomograms were developed, with one focused on predicting postoperative OS and the other on RFS. The calibration curve further confirmed the model's accuracy in the training cohort. The validation cohort confirmed the model's predictive accuracy. Conclusions: The proposed nomogram combining the YAP, a predictor of HCC progression, and clinical features achieved more-accurate prognostic prediction for patients with HCC after partial hepatectomy, which may help clinicians implement more appropriate interventions.

Clinical application and immune infiltration landscape of stemness-related genes in heart failure.

BACKGROUND: Heart failure (HF) is the leading cause of morbidity and mortality worldwide. Stemness refers to the self-renewal and differentiation ability of cells. However, little is known about the heart's stemness properties. Thus, the current study aims to identify putative stemness-related biomarkers to construct a viable prediction model of HF and characterize the immune infiltration features of HF. METHODS: HF datasets from the Gene Expression Omnibus (GEO) database were adopted as the training and validation cohorts while stemness-related genes were obtained from GeneCards and previously published papers. Feature selection was performed using two machine learning algorithms. Nomogram models were then constructed to predict HF risk based on the selected key genes. Moreover, the biological functions of the key genes were evaluated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) pathway analyses, and gene set variation analysis (GSVA) and enrichment analysis (GSEA) were performed between the high- and low-risk groups. The immune infiltration landscape in HF was investigated, and the interaction network of key genes was analysed to predict potential targets and molecular mechanisms. RESULTS: Seven key genes, namely SMOC2, LUM, FNDC1, SCUBE2, CD163, BLM and S1PR3, were included in the proposed nomogram. This nomogram showed good predictive performance for HF diagnosis in the training and validation sets. GO and KEGG analyses revealed that the key genes were primarily associated with ageing, inflammatory processes and DNA oxidation. GSEA and GSVA identified various inflammatory and immune signalling pathways that were enriched between the high- and low-risk groups. The infiltration of 15 immune cell subsets suggests that adaptive immunity has an important role in HF. CONCLUSIONS: Our study identified a clinically significant stemness-related signature for predicting HF risk, with the potential to improve early disease diagnosis, optimize risk stratification and provide new strategies for treating patients with HF.

Construction and Analysis of Risk Prediction Model of Eosinophilic Chronic Rhinosinusitis With Nasal Polyps: A Cross-Sectional Study in Northwest China.

OBJECTIVE: To provide guidance for clinical endotypes by constructing a risk-predictive model of eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP). DESIGN: A cross-sectional study. SETTING: Single-centre trial at tertiary medical institutions. PARTICIPANTS: A cross-sectional study included 343 CRSwNP patients divided into ECRSwNP (n = 237) and non-ECRSwNP (n = 106) groups using surgical pathology. MAIN OUTCOME MEASURES: Single-factor and multivariate analysis were used to identify statistically significant variables for constructing a nomogram, including the history of AR, hyposmia score, ethmoid sinus score, BEP and BEC. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS: Allergic rhinitis, hyposmia score, ethmoid sinus score, peripheral blood eosinophil percentage (BEP) and eosinophil count (BEC) were retained for the construction nomogram of ECRSwNP. The nomogram exhibited a certain accuracy, with an AUC of 0.897 (95% CI: 0.864-0.930), good agreement in the calibration curve and a 0.891 C-index of internal validation. Moreover, the DCA with a threshold probability between 0.0167 and 1.00 indicated a higher net benefit and greater clinical utility. CONCLUSION: The construction of a predictive risk model of ECRSwNP based on easily accessible factors could assist clinicians in more conveniently defining endotypes to make optimal diagnoses and treatment choices.

Developing a predictive nomogram for mortality in patients with extrapulmonary acute respiratory distress syndrome: the prognostic value of serum soluble thrombomodulin, lung ultrasound score, and lactate.

Objective: This study aimed to elucidate the prognostic significance of serum soluble thrombomodulin (sTM), lung ultrasound score (LUS), and lactate levels in patients with extrapulmonary acute respiratory distress syndrome (ARDS), with the goal of refining mortality risk prediction in this cohort. Methods: In a prospective cohort of 95 patients with extrapulmonary ARDS admitted to the intensive care unit, we investigated the primary endpoint of 28-day mortality. Utilizing Lasso-Cox regression analysis, we identified independent prognostic factors for mortality. A predictive nomogram was developed incorporating these factors, and its performance was validated through several statistical measures, including the consistency index, calibration plot, internal validation curve, decision curve analysis, interventions avoided analysis, receiver operating characteristic curve analysis, and Kaplan-Meier survival analysis. We further conducted a subgroup analysis to examine the impact of prone positioning on patient outcomes. Results: The study identified baseline serum sTM, LUS, and lactate levels as independent predictors of 28-day mortality in extrapulmonary ARDS patients. The predictive nomogram demonstrated superior prognostic accuracy compared to the use of sTM, LUS, or lactate levels alone, and outperformed traditional prognostic tools such as the Acute Physiology and Chronic Health Evaluation II score and the partial pressure of arterial oxygen to fractional inspired oxygen ratio. The subgroup analysis did not show a significant impact of prone positioning on the predictive value of the identified biomarkers. Conclusion: Our study results support the development and validation of a novel prognostic nomogram that integrates key clinical biomarkers and ultrasound imaging scores to predict mortality in patients with extrapulmonary ARDS. While our research is preliminary, further studies and validation are required.

Clinical Features and a Prediction Model for Early Prediction of Composite Outcome in Chlamydia psittaci Pneumonia: A Multi-Centre Retrospective Study in China.

Introduction: C. psittaci pneumonia has atypical clinical manifestations and is often ignored by clinicians. This study analyzed the clinical characteristics, explored the risk factors for composite outcome and established a prediction model for early prediction of composite outcome among C. psittaci pneumonia patients. Methods: A multicenter, retrospective, observational cohort study was conducted in ten Chinese tertiary hospitals. Patients diagnosed with C. psittaci pneumonia were included, and their clinical data were collected and analyzed. The composite outcome of C. psittaci pneumonia included death during hospitalization, ICU admission, and mechanical ventilation. Univariate and multivariable logistic regression analyses were conducted to determine the significant variables. A ten-fold cross-validation was performed to internally validate the model. The model performance was evaluated using various methods, including receiver operating characteristics (ROC), C-index, sensitivity, specificity, positive/negative predictive value (PPV/NPV), decision curve analysis (DCA), and clinical impact curve analysis (CICA). Results: In total, 83 patients comprised training cohorts and 36 patients comprised validation cohorts. CURB-65 was used to establish predictive Model 1. Multivariate logistic regression analysis identified three independent prognostic factors, including serum albumin, CURB-65, and white blood cells. These factors were employed to construct model 2. Model 2 had acceptable discrimination (AUC of 0.898 and 0.825 for the training and validation sets, respectively) and robust internal validity. The specificity, sensitivity, NPV, and PPV for predicting composite outcome in the nomogram model were 91.7%, 84.5%, 50.0%, and 98.4% in the training sets, and 100.0%, 64.7%, 14.2%, and 100.0% in the validation sets. DCA and CICA showed that the nomogram model was clinically practical. Conclusion: This study constructs a refined nomogram model for predicting the composite outcome in C. psittaci pneumonia patients. This nomogram model enables early and accurate C. psittaci pneumonia patients' evaluation, which may improve clinical outcomes.

Clinical Significance and Molecular Annotation for PD-L1 Negative Advanced Non-Small Cell Lung Cancer with Sensitivity to Responsive to Dual PD-1/CTLA-4 Blockade.

Background: Immunotherapy has become the standard treatment for driving gene-negative advanced non-small cell lung cancer (NSCLC). However, compared to PD-L1-positive patients, the efficacy of Anti-PD-(L)1 monotherapy is suboptimal in PD-L1-negative advanced NSCLC. In this study, we aim to analyze the optimal immunotherapy approach for PD-L1-negative NSCLC patients and develop a new nomogram to enhance the clinical predictability of immunotherapy for NSCLC patients. Methods: In this study, we retrieved clinical information and genomic data from cBioPortal for NSCLC patients undergoing immunotherapy. Cox regression analyses were utilized to screen the clinical information and genomic data that related to survival. The prognostic-relate genes function was studied by comprehensive bioinformatics analyses. The Kaplan-Meier plot method was employed for survival analysis. Results: A total of 199 PD-L1-negative NSCLC patients were included in this study. Among them, 165 patients received Anti-PD-(L)1 monotherapy, while 34 patients received Anti-PD-(L)1+Anti-CTLA-4 combination therapy. The Anti-PD-(L)1+Anti-CTLA-4 combination therapy demonstrated significantly higher PFS compared to the Anti-PD-(L)1 monotherapy. The mutation status of KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 were found to correlate with PFS. Utilizing the clinicopathological parameters and genomic data of the patients, a novel nomogram was developed to predict the prognosis of Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Conclusion: Our study revealed that KRAS, ANO1, COL14A1, LTBP1. ERBB4 and PCSK5 mutation could serve as predictive biomarkers for patients with Anti-PD-(L)1+Anti-CTLA-4 combination therapy. Our systematic nomogram demonstrates significant potential in predicting the prognosis for NSCLC patients with responsive to dual PD-1/CTLA-4 blockade.

Deciphering prognostic indicators in non-HIV cryptococcal meningitis: Constructing and validating a predictive Nomogram model.

Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592-0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes.

Cryptococcal meningitis (CM) is a serious fungal infection that can affect the brain and spinal cord. It is well known to occur in people with HIV, but it can also affect those without HIV, although this is less common. This study focuses on understanding how CM affects non-HIV patients, which is not as well understood as its effects on HIV patients. We analyzed data from 64 non-HIV patients with CM to identify factors that might influence their recovery or lead to poor outcomes, such as severe disability or death. Using advanced statistical methods, we developed a predictive tool called a nomogram. This tool helps doctors estimate the likelihood of a poor outcome in non-HIV Cryptococcal meningitis (CM) patients based on specific factors like age, brain imaging results, and the use of certain medications. Our findings suggest that older patients and those with specific brain imaging abnormalities may be at higher risk. On the other hand, patients who have previously used immunosuppressive drugs might have a better prognosis, which is a surprising and new insight. This research is important because it provides new knowledge that could help doctors better manage CM in non-HIV patients, leading to more personalized and effective treatments. The predictive tool we developed could be used in hospitals to improve decision-making and patient care, ultimately improving outcomes for those suffering from this serious condition.

A nomogram for predicting short-term mortality in ICU patients with coexisting chronic obstructive pulmonary disease and congestive heart failure.

OBJECTIVE: This study aimed to develop and validate a nomogram for predicting 28-day and 90-day mortality in intensive care unit (ICU) patients who have chronic obstructive pulmonary disease (COPD) coexisting with congestive heart failure (CHF). METHODS: An extensive analysis was conducted on clinical data from the Medical Information Mart for Intensive Care IV database, covering patients over 18 years old with both COPD and CHF, who were were first-time ICU admissions between 2008 and 2019. The least absolute shrinkage and selection operator (LASSO) regression method was employed to screen clinical features, with the final model being optimized using backward stepwise regression guided by the Akaike Information Criterion (AIC) to construct the nomogram. The predictive model's discrimination and clinical applicability were evaluated via receiver operating characteristic (ROC) curves, calibration curves, the C-index, and decision curve analysi s (DCA). RESULTS: This analysis was comprised of a total of 1948 patients. Patients were separated into developing and validation cohorts in a 7:3 ratio, with similar baseline characteristics between the two groups. The ICU mortality rates for the developing and verification cohorts were 20.8 % and 19.5 % at 28 days, respectively, and 29.4 % and 28.3 % at 90 days, respectively. The clinical characteristics retained by the backward stepwise regression include age, weight, systolic blood pressure (SBP), respiratory rate (RR), oxygen saturation (SpO2), red blood cell distribution width (RDW), lactate, partial thrombosis time (PTT), race, marital status, type 2 diabetes mellitus (T2DM), malignant cancer, acute kidney failure (AKF), pneumonia, immunosuppressive drugs, antiplatelet agents, vasoactive agents, acute physiology score III (APS III), Oxford acute severity of illness score (OASIS), and Charlson comorbidity index (CCI). We developed two separate models by assigning weighted scores to each independent risk factor: nomogram A excludes CCI but includes age, T2DM, and malignant cancer, while nomogram B includes only CCI, without age, T2DM, and malignant cancer. Based on the results of the AUC and C-index, this study selected nomogram A, which demonstrated better predictive performance, for subsequent validation. The calibration curve, C-index, and DCA results indicate that nomogram A has good accuracy in predicting short-term mortality and demonstrates better discriminative ability than commonly used clinical scoring systems, making it more suitable for clinical application. CONCLUSION: The nomogram developed in this study offers an effective assessment of short-term mortality risk for ICU patients with COPD and CHF, proving to be a superior tool for predicting their short-term prognosis.