A Prediction Nomogram for No-Reflow in Acute Myocardial Infarction Patients after Primary Percutaneous Coronary Intervention.

Background: The coronary no-reflow (NR) phenomenon is an independent predictor of major adverse cardiac events (MACEs). This study aimed to establish a clinical and comprehensive nomogram for predicting NR in acute myocardial infarction (AMI) patients after primary percutaneous coronary intervention (pPCI). Methods: The multivariable logistic regression analysis was performed to determine the NR-related factors. A nomogram was established via several clinical and biochemical factors, and the performance was evaluated via discrimination, calibration, and clinical factors. Results: The study consisted of 3041 AMI patients after pPCI, including 2129 patients in the training set (70%) and 912 patients in the validation set (30%). The NR event was 238 in the training set and 87 in the validation set. The level of N-terminal prohormone B-type natriuretic peptide (NT-proBNP), basophil count (BASO), neutrophil count (NEUBC), D-dimer, hemoglobin (Hb), and red blood cell distribution width (RDW.CV) in NR patients showed statistically significant differences. In the training set, the C-index was 0.712, 95% CI 0.677 to 0.748. In the validation set, the C-index was 0.663, 95% CI 0.604 to 0.722. Conclusions: A nomogram that may predict NR in AMI patients undergoing pPCI was established and validated. We hope this nomogram can be used for NR risk assessment and clinical decision-making and significantly prevent potentially impaired reperfusion associated with NR.

Development and validation of a prediction nomogram for sleep disorders in hospitalized patients with acute myocardial infarction.

PURPOSE: Sleep disorders are becoming more prevalent in hospitalized patients with acute myocardial infarction (AMI). We aimed to investigate the risk factors for sleep disorders in hospitalized patients with AMI, then develop and validate a prediction nomogram for the risk of sleep disorders. METHODS: Clinical data were collected from patients with AMI hospitalized in our hospital from January 2020 to June 2023. All patients were divided into the training group and the validation group with a ratio of 7:3 in sequential order. The LASSO regression analysis and multivariate logistic regression analysis were used to screen potential risk factors for sleep disorders. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were plotted. RESULTS: A total of 256 hospitalized patients with AMI were enrolled. Patients were divided into the training group (180) and the validation group (76) according to a scale of 7:3. Of the 256 patients, 90 patients (35.16%) suffered from sleep disorders, and 33 patients (12.89%) needed hypnotics. The variables screened by LASSO regression included age, smoking, NYHA class, anxiety status at admission, depression status at admission, and strangeness of environment. A nomogram model was established by incorporating the risk factors selected. The C-index, calibration curve, and DCA showed good predictive performance. CONCLUSIONS: We identified six clinical characteristics as predictors of sleep disorders in hospitalized patients with AMI. It helps nurses make appropriate decisions in clinical practice.

Construction and application of fetal loss risk model in systemic lupus erythematosus patients with mild disease severity.

BACKGROUND: This dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception. METHODS: An analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: Lupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787-0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation. CONCLUSION: The predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception.

Development and validation of a prenatal predictive nomogram for the risk of NICU admission in infants born to Chinese mothers over 35 years of age: a retrospective cohort study.

BACKGROUND: The rising number of women giving birth at advanced maternal age has posed significant challenges in obstetric care in recent years, resulting in increased incidence of neonatal transfer to the Neonatal Intensive Care Unit (NICU). Therefore, identifying fetuses requiring NICU transfer before delivery is essential for guiding targeted preventive measures. OBJECTIVE: This study aims to construct and validate a nomogram for predicting the prenatal risk of NICU admission in neonates born to mothers over 35 years of age. STUDY DESIGN: Clinical data of 4218 mothers aged ≥ 35 years who gave birth at the Department of Obstetrics of the Second Hospital of Shandong University between January 1, 2017 and December 31, 2021 were reviewed. Independent predictors were identified by multivariable logistic regression, and a predictive nomogram was subsequently constructed for the risk of neonatal NICU admission. RESULTS: Multivariate logistic regression demonstrated that the method of prenatal screening, number of implanted embryos, preterm premature rupture of the membranes, preeclampsia, HELLP syndrome, fetal distress, premature birth, and cause of preterm birth are independent predictors of neonatal NICU admission. Analysis of the nomogram decision curve based on these 8 independent predictors showed that the prediction model has good net benefit and clinical utility. CONCLUSION: The nomogram demonstrates favorable performance in predicting the risk of neonatal NICU transfer after delivery by mothers older than 35 years. The model serves as an accurate and effective tool for clinicians to predict NICU admission in a timely manner.

Predictive nomograms and an algorithm for managing patients with probable Meniere's disease.

PURPOSE: To explore the efficacy of diagnostic tests in accurately reclassifying patients initially diagnosed with probable Meniere's disease (MD) into either definite or non-MD categories. MATERIALS AND METHODS: A retrospective cohort study was conducted at a neurotology clinic between 1/2016 and 5/2022. Patients underwent a battery of tests, from which sensitivity, specificity, positive and negative predictive values, as well as positive and negative likelihood ratios, were calculated. Additionally, prediction nomograms were developed. RESULTS: Of the 69 patients, 25 (36.2 %) were initially classified as definite MD, 21 (30.4 %), probable MD, and 23 (33.4 %) non-MD. The mean follow-up was 3.5 years. The sensitivity of electrocochleography (ECochG) was the highest (92 %), with a negative likelihood ratio of 15 %. Magnetic resonance imaging (MRI) with MD-protocol had the highest specificity (100 %), with a positive likelihood ratio of 100 %. Videonystagmography, video head impulse test, and cervical vestibular-evoked myogenic potentials, had lower sensitivity and specificity. We were able to reclassify 18 (86 %) patients with probable MD: 12 (57 %) were diagnosed with definite MD, and 6 (29 %) were diagnosed with non-MD, consistent with their clinical course. CONCLUSIONS: The combination of ECochG and MRI with MD-protocol provides the most reliable approach to reclassify patients with Probable MD, ensuring a precise and accurate diagnosis. Vestibular tests express the functional status of the labyrinth and may not be reliable. Our findings provide valuable insights into clinical decision-making for patients with Probable MD and raise the consideration of additional diagnostic tests as supplementary to the existing clinical-only diagnosis criteria.

Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model.

BACKGROUND: The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI) . METHODS: This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell's C-index and decision curve analysis (DCA). RESULTS: In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell's C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721-0.823) in the development cohort and 0.736 (95%CI: 0.656-0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. CONCLUSIONS: Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI.

Tumor residue in patients with stage II-IVA nasopharyngeal carcinoma who received intensity-modulated radiation therapy: development and validation of a prediction nomogram integrating postradiotherapy plasma Epstein-Barr virus deoxyribonucleic acid, clinical stage, and radiotherapy dose.

BACKGROUND: To develop and validate a predictive nomogram for tumor residue 3-6 months after treatment based on postradiotherapy plasma Epstein-Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II-IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). METHODS: In this retrospective study, 1050 eligible patients with stage II-IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3-6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). RESULTS: Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P<0.001). A prediction nomogram based on postradiotherapy plasma EBV DNA level (0 vs. 1-499 vs. ≥500 copies/ml), clinical stage (II vs. III vs. IVA), and RT dose (68.00-69.96 vs. 70.00-74.00 Gy) estimated the probability of residue development. The nomogram showed better discrimination (area under the curve (AUC): 0.752) than either the clinical stage (0.659) or postradiotherapy EBV DNA level (0.627) alone in the development and validation cohorts (AUC: 0.728). CONCLUSIONS: We developed and validated a nomogram model integrating clinical characteristics at the end of IMRT for predicting whether tumor will residue or not after 3-6 months. Thus, high-risk NPC patients who might benefit from immediate additional intervention could be identified by the model, and the probability of residue can be reduced in the future.

New scoring system combining computed tomography body composition analysis and inflammatory-nutritional indicators to predict postoperative complications in stage II-III colon cancer.

BACKGROUND AND AIM: Postoperative complications are important clinical outcomes for colon cancer patients. This study aimed to investigate the predictive value of inflammatory-nutritional indicators combined with computed tomography body composition on postoperative complications in patients with stage II-III colon cancer. METHODS: We retrospectively collected data from patients with stage II-III colon cancer admitted to our hospital from 2017 to 2021, including 198 patients in the training cohort and 50 patients in the validation cohort. Inflammatory-nutritional indicators and body composition were included in the univariate and multivariate analyses. Binary regression was used to develop a nomogram and evaluate its predictive value. RESULTS: In the multivariate analysis, the monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), nutritional risk score (NRS), skeletal muscle index (SMI), and visceral fat index (VFI) were independent risk factors for postoperative complications of stage II-III colon cancer. In the training cohort, the area under the receiver operating characteristic curve of the predictive model was 0.825 (95% confidence interval [CI] 0.764-0.886). In the validation cohort, it was 0.901 (95% CI 0.816-0.986). The calibration curve showed that the prediction results were in good agreement with the observational results. Decision curve analysis showed that colon cancer patients could benefit from the predictive model. CONCLUSIONS: A nomogram combining MLR, SII, NRS, SMI, and VFI with good accuracy and reliability in predicting postoperative complications in patients with stage II-III colon cancer was established, which can help guide treatment decisions.

The predictive value of baseline symptom score and the peripheral CD4CD8 double-positive T cells in patients with AECOPD.

BACKGROUND: Accurate prediction of acute exacerbation helps select patients with chronic obstructive pulmonary disease (COPD) for individualized therapy. The potential of lymphocyte subsets to function as clinical predictive factors for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains uncertain. METHODS: In this single-center prospective cohort study with a 2-year follow-up, 137 patients aged 51 to 79 with AECOPD were enrolled. We examined the prognostic indicators of AECOPD by analyzing lymphocyte subsets and baseline symptom score. Furthermore, a predictive model was constructed to anticipate the occurrence of respiratory failure in patients experiencing AECOPD. RESULTS: The COPD Assessment Test (CAT) score combined with home oxygen therapy and CD4+CD8+ T cells% to predict respiratory failure in AECOPD patients were the best (the area under the curves [AUC] = 0.77, 95% CI: 0.70-0.86, P < 0.0001, sensitivity: 60.4%, specificity: 86.8%). The nomogram model, the C index, calibration plot, decision curve analysis, and clinical impact curve all indicate the model's good predictive performance. The observed decrease in the proportions of CD4+CD8+ T cells appears to be correlated with more unfavorable outcomes. CONCLUSIONS: The nomogram model, developed to forecast respiratory failure in patients with AECOPD, utilizing variables such as home oxygen therapy, CAT score, and CD4+CD8+ T cells%, demonstrated a high level of practicality in clinical settings. CD4+CD8+ T cells serve as a reliable and readily accessible predictor of AECOPD, exhibiting greater stability compared to other indices. It is less susceptible to subjective influences from patients or physicians. This model facilitated personalized estimations, enabling healthcare professionals to make informed decisions regarding preventive interventions.

Development and validation of a nomogram for predicting pulmonary infections after Intracerebral hemorrhage in elderly people.

OBJECTIVES: The purpose of this study was to develop and validate a nomogram for the prediction of pulmonary infections in elderly patients with intracerebral hemorrhage (ICH) during hospitalization in the intensive care unit (ICU). METHODS: A total of 1183 elderly patients diagnosed with ICH were included from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly grouped into training (n=831) and validation (n=352) cohorts. Candidate predictors were identified using the least absolute shrinkage and selection operator (LASSO) regression. Meanwhile, the variables derived from the LASSO regression were included in the multivariate logistic regression analysis, the variables with P < 0.05 were included in the final model and the nomogram was constructed. The discriminatory ability was assessed by plotting the receiver operating curve (ROC) and calculating the area under the curve (AUC). The Performance of the model was assessed by calibration plots and the Hosmer-Lemeshow goodness-of-fit test (HL test). In addition, clinical decision curves assess the net clinical benefit. RESULTS: The nomogram included chronic lung disease, dysphagia, mechanical ventilation, use of antibiotics, Glasgow Coma Scale (GCS), Logical Organ Dysfunction System (LODS), blood oxygen saturation (SpO2), white blood cell count (WBC) and prothrombin time (PT). The AUC of the predictive model was 0.905 (95 % CI: 0.877, 0.764) in the training cohort and 0.888 (95 % CI: 0.754, 0.838) in the validation cohort, which showed satisfactory discriminative ability. Second, the nomogram showed good calibration. Decision curve analysis showed that the predictive nomogram was clinically useful. CONCLUSION: A prediction model for predicting pulmonary infections in elderly ICH patients was constructed. The model can help clinicians to identify high-risk patients as soon as possible and prevent the occurrence of pulmonary infections.

Can urodynamic diagnosis of dysfunctional voiding/external sphincter nonrelaxation be made on two-channel pressure-flow study without video-urodynamics, electromyography, or urethral pressure profilometry? Plateau detrusor pattern in perspective.

AIMS: urodynamic diagnosis of dysfunctional voiding/external-sphincter nonrelaxation (DV/EUSD) needs assistance of specialized testing namely urethral pressure profilometry (UPP), electromyography (EMG), and/or videofluoroscopy (VUDS). We aimed to find a predictive model based on standard pressure-flow study without need for specialized testing. MATERIAL AND METHODS: In this retrospective study (2017-2021), clinical and urodynamic data of adult men and women presenting with voiding dysfunction was collected. Mandatory inclusion criteria were availability of all-(1) findings of clinical examination and neurological status, (2) a valid filling cystometry and pressure-flow study (with active detrusor contraction), (3) a final clinic-urodynamic diagnosis. Voiding cystourethrography (VCUG) was performed to confirm the location of obstruction. RESULTS: Data of 218 participants (178♂, 40♀) was eligible. Plateau detrusor contraction pattern was observed in 89.0% of men and 86% of women with DV/EUSD; whereas only 7.5% men and no women with other obstructions demonstrated this pattern. Forward likelihood Logistic regression analysis revealed presence of plateau pattern, lower bladder outlet obstruction index (BOOI), and smaller difference between Pdetmax and PdetQmax highly predictive of presence of DV/EUSD in men as per the following equation-Y = -9.900 + (0.085 × BOOI) + (0.123 × pdetmax - pdetQmax) + (4.061 × detrusor pattern). A kattan-type nomogram was constructed based on the above equation. In women, presence of plateau pattern alone was highly predictive of DV/EUSD. CONCLUSION: Diagnosis of DV/EUSD can be accurately predicted using parameters of three-channel urodynamics (plateau pattern, BOOI, Pdetmax-pdetQmax) minimizing need for specialized testing.

Daily variations and levels of human chorionic gonadotropin before methotrexate treatment as predictors of treatment success.

AIM: The aim of this study is to investigate the role of human chorionic gonadotropin (hCG) daily variations and levels prior to methotrexate treatment as predictors for treatment outcome. METHODS: This retrospective study included patients who had a sonographically confirmed ectopic pregnancy at the International Peace Maternity and Child Health Hospital between November 2015 and June 2020. The associations of hCG levels and daily variations with the treatment success were evaluated by multivariable logistic regression and receiver operator characteristic (ROC) curve. Establish a nomogram that predicts how methotrexate (MTX) therapy will turn out. The performance of the model was assessed utilizing concordance index, receiver operating characteristic curves, and calibration plots. RESULTS: The median serum hCG levels before treatment and hCG daily variation in the failure group were higher than those in the success group (487.8 vs. 270.7 IU/L, -1.86% vs. 7.29%, both p < 0.01). According to the ROC curve analysis, the cutoff values of serum hCG level before treatment and daily variations were 617.35 IU/L and 1.76%/day. By multivariable logistic regression analysis, serum hCG levels before treatment (odds ratio [OR]: 1.001, 95% confidence interval [CI]: 1.000 ~ 1.001) and hCG daily variations were independently associated with the treatment success (OR: 1.033, 95% CI: 1.015 ~ 1.052). The nomogram was effective at predicting the outcome of MTX treatment with a receiver operating characteristic area under the curve of 0.717 (p < 0.001). The nomogram's calibration curve was almost parallel to the ideal diagonal line. CONCLUSION: We successfully created a nomogram based on serum hCG levels before treatment and hCG daily changes to anticipate the result of MTX therapy, which could assist medical professionals in selecting therapeutic schedule for patients with tubal pregnancies.

Survival prediction for non-small cell lung cancer patients treated with CT-guided microwave ablation: development of a prognostic nomogram.

OBJECTIVES: To explore the outcomes of CT-guided percutaneous microwave ablation (MWA) in non-small cell lung cancer (NSCLC) patients, and then develop an effective nomogram to predict the survival. METHODS: NSCLC patients treated with MWA were randomly allocated to either the training cohort or the validation cohort (3:1). The primary outcome measurement was overall survival (OS), whose predictors were identified by univariate and multivariate analyses in the training cohort. Then, a predictive nomogram was developed to predict the OS, with the predictive accuracy evaluated by C-statistic and receiver operating characteristic in both the training and validation cohorts. RESULTS: A total of 234 patients (training cohort: n = 176; validation cohort: n = 58) and 271 tumors with a median OS of 17.0 ± 12.2 months were included. The predictors selected into the nomogram included tumor diameter (hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.37-3.30; p < 0.001), extrapulmonary metastases (HR, 1.77; 95% CI, 1.06-2.95; p = 0.030), tumor stage (HR, 1.38; 95% CI, 1.07-1.79; p = 0.013), tumor type (HR, 2.00; 95% CI, 1.48-2.72; p < 0.001) and post-MWA TKIs (HR, 0.55; 95% CI, 0.34-0.89; p < 0.001), based on the results of univariate and multivariate analyses. The C-statistic showed good predictive performance, with a C-statistic of 0.838 (95% CI, 0.779-0.897) internally and 0.808 (95% CI, 0.695-0.920) externally (training cohort and validation cohort, respectively). CONCLUSIONS: The nomogram was effective in predicting the OS in NSCLC patients treated with MWA, and could be applied to identify patients who may benefit most from MWA and be helpful for clinical decision making.

Development and validation of a nomogram for predicting acute kidney injury after orthotopic liver transplantation.

BACKGROUND: We aim to develop and validate a nomogram model for predicting severe acute kidney injury (AKI) after orthotopic liver transplantation (OLT). METHODS: A total of 576 patients who received OLT in our center were enrolled. They were assigned to the development and validation cohort according to the time of inclusion. Univariable and multivariable logistic regression using the forward variable selection routine were applied to find risk factors for post-OLT severe AKI. Based on the results of multivariable analysis, a nomogram was developed and validated. Patients were followed up to assess the long-term mortality and development of chronic kidney disease (CKD). RESULTS: Overall, 35.9% of patients were diagnosed with severe AKI. Multivariable logistic regression analysis revealed that recipients' BMI (OR 1.10, 95% CI 1.04-1.17, p = 0.012), hypertension (OR 2.32, 95% CI 1.22-4.45, p = 0.010), preoperative serum creatine (sCr) (OR 0.96, 95% CI 0.95-0.97, p < 0.001), and intraoperative fresh frozen plasm (FFP) transfusion (OR for each 1000 ml increase 1.34, 95% CI 1.03-1.75, p = 0.031) were independent risk factors for post-OLT severe AKI. They were all incorporated into the nomogram. The area under the ROC curve (AUC) was 0.73 (p < 0.05) and 0.81 (p < 0.05) in the development and validation cohort. The calibration curve demonstrated the predicted probabilities of severe AKI agreed with the observed probabilities (p > 0.05). Kaplan-Meier survival analysis showed that patients in the high-risk group stratified by the nomogram suffered significantly poorer long-term survival than the low-risk group (HR 1.92, p < 0.01). The cumulative risk of CKD was higher in the severe AKI group than no severe AKI group after competitive risk analysis (HR 1.48, p < 0.05). CONCLUSIONS: With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for severe AKI and poor long-term prognosis after OLT.

Risk prediction tool for grade re-classification in men with favourable-risk prostate cancer on active surveillance.

OBJECTIVE: To create a nomogram for men on active surveillance (AS) for prediction of grade re-classification (GR) above Gleason score 6 (Grade group >2) at surveillance biopsy. PATIENTS AND METHODS: From a cohort of men enrolled in an AS programme, a multivariable model was used to identify clinical and pathological parameters predictive of GR. Nomogram performance was assessed using receiver operating characteristic curves, calibration, and decision curve analysis. RESULTS: Of 1 374 men, 254 (18.50%) were re-classified to Gleason ≥7 on surveillance prostate biopsy. Variables predictive of GR were earlier year of diagnosis [≤2004 vs ≥2005; odds ratio (OR) 2.16, P < 0.001], older age (OR 1.05, P < 0.001), higher prostate-specific antigen density [OR 1.19 (per 0.1 unit increase), P = 0.04], bilateral disease (OR 2.86, P < 0.001), risk strata (low-risk vs very-low-risk, OR 1.79, P < 0.001), and total number of biopsies without GR (OR 0.68, P < 0.001). On internal validation, a nomogram created using the multivariable model had an area under the curve of 0.757 (95% confidence interval 0.730-0.797) for predicting GR at the time of next surveillance biopsy. CONCLUSION: The nomogram described is currently being used at each return visit to assess the need for a surveillance biopsy, and could increase retention in AS.

A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma.

PURPOSE: Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5. METHODS: To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs). RESULTS: Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients). CONCLUSIONS: A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.

A nomogram for predicting hemorrhagic shock in pediatric patients with multiple trauma.

The timely detection and management of hemorrhagic shock hold paramount importance in clinical practice. This study was designed to establish a nomogram that may facilitate early identification of hemorrhagic shock in pediatric patients with multiple-trauma. A retrospective study was conducted utilizing a cohort comprising 325 pediatric patients diagnosed with multiple-trauma, who received treatment at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China. For external validation, an additional cohort of 144 patients from a children's hospital in Taizhou was included. The model's predictor selection was optimized through the application of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Subsequently, a prediction nomogram was constructed using multivariable logistic regression analysis. The performance and clinical utility of the developed model were comprehensively assessed utilizing various statistical metrics, including Harrell's Concordance Index (C-index), receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). Multivariate logistic regression analysis identified systolic blood pressure (ΔSBP), platelet count, activated partial thromboplastin time (APTT), and injury severity score (ISS) as independent predictors for hemorrhagic shock. The nomogram constructed using these predictors demonstrated robust predictive capabilities, as evidenced by an impressive area under the curve (AUC) value of 0.963. The model's goodness-of-fit was assessed using the Hosmer-Lemeshow test (χ2 = 10.023, P = 0.209). Furthermore, decision curve analysis revealed significantly improved net benefits with the model. External validation further confirmed the reliability of the proposed predictive nomogram. This study successfully developed a nomogram for predicting the occurrence of hemorrhagic shock in pediatric patients with multiple trauma. This nomogram may serve as an accurate and effective tool for timely and efficient management of children with multiple trauma.

Predicting central lymph node metastasis in papillary thyroid cancer: A nomogram based on clinical, ultrasound and contrast­enhanced computed tomography characteristics.

Central lymph node (CLN) status is considered to be an important risk factor in patients with papillary thyroid carcinoma (PTC). The aim of the present study was to identify risk factors associated with CLN metastasis (CLNM) for patients with PTC based on preoperative clinical, ultrasound (US) and contrast-enhanced computed tomography (CT) characteristics, and establish a prediction model for treatment plans. A total of 786 patients with a confirmed pathological diagnosis of PTC between January 2021 to December 2022 were included in the present retrospective study, with 550 patients included in the training group and 236 patients enrolled in the validation group (ratio of 7:3). Based on the preoperative clinical, US and contrast-enhanced CT features, univariate and multivariate logistic regression analyses were used to determine the independent predictive factors of CLNM, and a personalized nomogram was constructed. Calibration curve, receiver operating characteristic (ROC) curve and decision curve analyses were used to assess discrimination, calibration and clinical application of the prediction model. As a result, 38.9% (306/786) of patients with PTC and CLNM(-) status before surgery had confirmed CLNM using postoperative pathology. In multivariate analysis, a young age (≤45 years), the male sex, no presence of Hashimoto thyroiditis, isthmic location, microcalcification, inhomogeneous enhancement and capsule invasion were independent predictors of CLNM in patients with PTC. The nomogram integrating these 7 factors exhibited strong discrimination in both the training group [Area under the curve (AUC)=0.826] and the validation group (AUC=0.818). Furthermore, the area under the ROC curve for predicting CLNM based on clinical, US and contrast-enhanced CT features was higher than that without contrast-enhanced CT features (AUC=0.818 and AUC=0.712, respectively). In addition, the calibration curve was appropriately fitted and decision curve analysis confirmed the clinical utility of the nomogram. In conclusion, the present study developed a novel nomogram for preoperative prediction of CLNM, which could provide a basis for prophylactic central lymph node dissection in patients with PTC.

Reclassified the phenotypes of cancer types and construct a nomogram for predicting bone metastasis risk: A pan-cancer analysis.

BACKGROUND: Numerous of models have been developed to predict the bone metastasis (BM) risk; however, due to the variety of cancer types, it is difficult for clinicians to use these models efficiently. We aimed to perform the pan-cancer analysis to create the cancer classification system for BM, and construct the nomogram for predicting the BM risk. METHODS: Cancer patients diagnosed between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included. Unsupervised hierarchical clustering analysis was performed to create the BM prevalence-based cancer classification system (BM-CCS). Multivariable logistic regression was applied to investigate the possible associated factors for BM and construct a nomogram for BM risk prediction. The patients diagnosed between 2017 and 2018 were selected for validating the performance of the BM-CCS and the nomogram, respectively. RESULTS: A total of 50 cancer types with 2,438,680 patients were included in the construction model. Unsupervised hierarchical clustering analysis classified the 50 cancer types into three main phenotypes, namely, categories A, B, and C. The pooled BM prevalence in category A (17.7%; 95% CI: 17.5%-17.8%) was significantly higher than that in category B (5.0%; 95% CI: 4.5%-5.6%), and category C (1.2%; 95% CI: 1.1%-1.4%) (p < 0.001). Advanced age, male gender, race, poorly differentiated grade, higher T, N stage, and brain, lung, liver metastasis were significantly associated with BM risk, but the results were not consistent across all cancers. Based on these factors and BM-CCS, we constructed a nomogram for predicting the BM risk. The nomogram showed good calibration and discrimination ability (AUC in validation cohort = 88%,95% CI: 87.4%-88.5%; AUC in construction cohort = 86.9%,95% CI: 86.8%-87.1%). The decision curve analysis also demonstrated the clinical usefulness. CONCLUSION: The classification system and prediction nomogram may guide the cancer management and individualized BM screening, thus allocating the medical resources to cancer patients. Moreover, it may also have important implications for studying the etiology of BM.

Development and validation of a model for the early prediction of progression from essential thrombocythemia to post-essential thrombocythemia myelofibrosis: a multicentre retrospective study.

Background: Essential thrombocythemia (ET), a myeloproliferative neoplasm (MPN), has a substantial risk of evolving into post-essential thrombocythemia myelofibrosis (post-ET MF). This study aims to establish a prediction nomogram for early prediction of post-ET MF in ET patients. Methods: The training cohort comprised 558 patients from 8 haematology centres between January 1, 2010, and May 1, 2023, while the external validation cohort consisted of 165 patients from 6 additional haematology centres between January 1, 2010, and May 1, 2023. Univariable and multivariable Cox regression analysis was performed to identified independent risk factors and establish a nomogram to predict the post-ET MF free survival. Both bias-corrected area under the curve (AUC), calibration curves and concordance index (C-index) were employed to assess the predictive accuracy of the nomogram. Findings: Multivariate Cox regression demonstrated that elevated red blood cell distribution width (RDW), elevated levels of lactate dehydrogenase (LDH) and the level of haemoglobin (Hb), a history of smoking and the presence of splenomegaly were independent risk factors for post-ET MF. The C-index displayed of the training and validation cohorts were 0.877 and 0.853. The 5 years, 10 years AUC values in training and external validation cohorts were 0.948, 0.769 and 0.978, 0.804 respectively. Bias-corrected curve is close to the ideal curve and revealed a strong consistency between actual observation and prediction. Interpretation: We developed a nomogram capable of predicting the post-ET MF free survival probability at 5 years and 10 years in ET patients. This tool helps doctors identify patients who need close monitoring and appropriate counselling. Funding: This research was funded by the Key R&D Program of Zhejiang (No. 2022C03137); the Public Technology Application Research Program of Zhejiang, China (No. LGF21H080003); and the Zhejiang Medical Association Clinical Medical Research special fund project (No. 2022ZYC-D09).