C-index Analysis Research Articles

Analysis of risk factors associated with incision complications in modified "L" approach for calcaneal fracture.

This study aimed to identify risk factors associated with incision complications following the modified "L" approach for calcaneal fractures. Data from 100 patients treated with the modified "L" approach for calcaneal fractures between January 2018 and December 2021 were analyzed. These included 52 cases in the poorly healing group and 48 in the well-healing group. Variables such as patient age, sex, body mass index, fracture type (Sanders classification), smoking history, alcohol consumption, diabetes status, timing of surgery, tourniquet use, bone grafting, suture method, and postoperative incision care were evaluated. A nomogram was developed using R software to predict the risk of incision complications, validated through the area under the ROC curve, C-index, and decision curve analysis. Both univariate and multivariate regression analyses identified fracture type, smoking history, diabetes, timing of surgery, and duration of tourniquet application as significant predictors of incision complications. These factors were incorporated into a clinical predictive nomogram. The nomogram's calibration curves demonstrated high accuracy, both internally and externally. The unadjusted concordance indes (C-index) was 0.793 [95% confidence interval (CI), 0.825-0.995], and the area under the curve for the nomogram was 0.7875882. Decision curve analysis confirmed the clinical applicability of the model at a threshold probability of 20-60%. We have developed a reliable clinical nomogram to predict the risk factors for incision complications in the modified "L" approach for calcaneal fractures, enhancing decision-making in clinical settings.

EBF1 is a potential biomarker for predicting progression from mild cognitive impairment to Alzheimer's disease: an in silico study.

The prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is an important clinical challenge. This study aimed to identify the independent risk factors and develop a nomogram model that can predict progression from MCI to AD. Data of 141 patients with MCI were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We set a follow-up time of 72 months and defined patients as stable MCI (sMCI) or progressive MCI (pMCI) according to whether or not the progression of MCI to AD occurred. We identified and screened independent risk factors by utilizing weighted gene co-expression network analysis (WGCNA), where we obtained 14,893 genes after data preprocessing and selected the soft threshold β = 7 at an R 2 of 0.85 to achieve a scale-free network. A total of 14 modules were discovered, with the midnightblue module having a strong association with the prognosis of MCI. Using machine learning strategies, which included the least absolute selection and shrinkage operator and support vector machine-recursive feature elimination; and the Cox proportional-hazards model, which included univariate and multivariable analyses, we identified and screened independent risk factors. Subsequently, we developed a nomogram model for predicting the progression from MCI to AD. The performance of our nomogram was evaluated by the C-index, calibration curve, and decision curve analysis (DCA). Bioinformatics analysis and immune infiltration analysis were conducted to clarify the function of early B cell factor 1 (EBF1). First, the results showed that 40 differentially expressed genes (DEGs) related to the prognosis of MCI were generated by weighted gene co-expression network analysis. Second, five hub variables were obtained through the abovementioned machine learning strategies. Third, a low Montreal Cognitive Assessment (MoCA) score [hazard ratio (HR): 4.258, 95% confidence interval (CI): 1.994-9.091] and low EBF1 expression (hazard ratio: 3.454, 95% confidence interval: 1.813-6.579) were identified as the independent risk factors through the Cox proportional-hazards regression analysis. Finally, we developed a nomogram model including the MoCA score, EBF1, and potential confounders (age and gender). By evaluating our nomogram model and validating it in both internal and external validation sets, we demonstrated that our nomogram model exhibits excellent predictive performance. Through the Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis, and immune infiltration analysis, we found that the role of EBF1 in MCI was closely related to B cells. EBF1, as a B cell-specific transcription factor, may be a key target for predicting progression from MCI to AD. Our nomogram model was able to provide personalized risk factors for the progression from MCI to AD after evaluation and validation.

Nomograms for the prediction of decannulation in patients with neurological injury: a study based on clinical practice

BackgroundRational prediction of the probability of decannulation in tracheotomy patients is of great importance to clinicians and patients’ families. This study aimed to develop a prediction model for decannulation in tracheotomized patients with neurological injury using routine clinical data and blood tests.MethodsWe developed a prediction model based on 186 tracheotomized patients, and data were collected from January 2018 to March 2021. The least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for the decannulation risk model. The performance of the prediction model was evaluated in terms of discrimination, calibration, and clinical utility using measures such as C-index, calibration plot, and decision curve analysis (DCA). Internal validation was performed through bootstrapping validation.ResultsA total of 66.13% (123/186) of patients were decannulated. Predictors included in the prediction nomogram were age, gender, subtype of neurological injury, Glasgow Coma Scale (GCS) score, swallowing function, duration of tracheotomy, procalcitonin (PCT) level, white blood cell (WBC) count, and serum albumin (ALB) level. The predictive model showed good discrimination, with a C-index of 0.755 (95% confidence interval: 0.68–0.83). Internal validation also confirmed a satisfactory C-index of 0.690. The DCA indicated that the nomogram added substantial value in predicting decannulation risk for patients with threshold probabilities falling between >21% and <98% compared to the existing scheme.ConclusionsThis predictive model serves as a valuable instrument for clinicians to quantitatively assess the probability of decannulation in patients with neurological injury, aiding in informed decision-making and patient management.

Identification and validation of telomerase related lncRNAs signature to predict prognosis and tumor immunotherapy response in bladder cancer

Telomerase allows eukaryotic cells to proliferate indefinitely, an important characteristic of tumor cells. Telomerase-related long no coding RNAs (TERLs) are involved in prognosis and drug sensitivity prediction; however, their association with bladder cancer (BLCA) is still unreported. The objective of this research is to determine a predictive prognostic TERL signature for OS and to provide an efficient treatment option for BLCA. The RNA sequence, clinical information, and mutational data of BLCA patients were acquired from The Cancer Genome Atlas (TCGA) database. With the help of the data from least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a prognostic signature was established including 14 TERLs, which could divide BLCA patients into low-risk (L-R) and high-risk (H-R) cohorts. The time-dependent receiver operating characteristic (ROC) curve demonstrated the greater predictive power of the model. By combing the TERLs-based signature and clinical risk factors (age, sex, grade, and stage), a prognostic nomogram was constructed to forecast the survival rates of patients with BLCA at 1-, 3-, and 5-years, which was well matched by calibration plots C-index and Decision curve analysis (DCA). Furthermore, the L-R cohort showed higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) than the H-R cohort, as well as substantial variability in immune cell infiltration and immune function between the two cohorts was elucidated. As for external validation, LINC01711 and RAP2C-AS1 were identified as poor prognostic factors by survival analysis from the Kaplan–Meier Plotter database, which were validated in BLCA cell lines (EJ, 253J, T24, and 5637) and SV-HUC-1 cells as the control group using qRT-PCR. In addition, interference with the expression of RAP2C-AS1 suppresses the proliferation and migration of BLCA cells, and RAP2C-AS1 could affect the expression of CD274 and CTLA4, which could serve as prognostic markers and characterize the tumor microenvironment in BLCA. Overall, the model based on the 14-TERLs signature can efficiently predict the prognosis and drug treatment response in individuals with bladder cancer.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma

BackgroundCancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established. MethodsThe Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups. ResultsThe signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs. ConclusionWe identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

Development and external validation of dual online tools for prognostic assessment in elderly patients with high-grade glioma: a comprehensive study using SEER and Chinese cohorts.

Elderly individuals diagnosed with high-grade gliomas frequently experience unfavorable outcomes. We aimed to design two web-based instruments for prognosis to predict overall survival (OS) and cancer-specific survival (CSS), assisting clinical decision-making. We scrutinized data from the SEER database on 5,245 elderly patients diagnosed with high-grade glioma between 2000-2020, segmenting them into training (3,672) and validation (1,573) subsets. An additional external validation cohort was obtained from our institution. Prognostic determinants were pinpointed using Cox regression analyses, which facilitated the construction of the nomogram. The nomogram's predictive precision for OS and CSS was gauged using calibration and ROC curves, the C-index, and decision curve analysis (DCA). Based on risk scores, patients were stratified into high or low-risk categories, and survival disparities were explored. Using multivariate Cox regression, we identified several prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in elderly patients with high-grade gliomas, including age, tumor location, size, surgical technique, and therapies. Two digital nomograms were formulated anchored on these determinants. For OS, the C-index values in the training, internal, and external validation cohorts were 0.734, 0.729, and 0.701, respectively. We also derived AUC values for 3-, 6-, and 12-month periods. For CSS, the C-index values for the training and validation groups were 0.733 and 0.727, with analogous AUC metrics. The efficacy and clinical relevance of the nomograms were corroborated via ROC curves, calibration plots, and DCA for both cohorts. Our investigation pinpointed pivotal risk factors in elderly glioma patients, leading to the development of an instrumental prognostic nomogram for OS and CSS. This instrument offers invaluable insights to optimize treatment strategies.

Construction of disulfidptosis-related lncRNA signature for predicting the prognosis and immune escape in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most frequent types of cancer worldwide. Disulfidptosis has been identified as a new mode of cell death recently. The goal of this study was to explore the possibility of a connection between disulfidptosis and COAD. RNA sequencing data from COAD patients were retrieved from the The Cancer Genome Atlas (TCGA) database for this investigation. R software and various methods were used to identify disulfidptosis-related lncRNAs (DRLs) in COAD, and a prognostic model was created based on 6 DRLs (AP003555.1, AL683813.1, SNHG7, ZEB1-AS1, AC074212.1, RPL37A-DT). The prognostic model demonstrated a good accuracy in predicting the prognosis of COAD patients, according to receiver operating characteristic (ROC) curve and Concordance index (C-index) analyses. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed significant differences in biological functions and signaling pathways involved in differential genes in risk subgroups, including protein − DNA complex subunit organization, Hippo signaling pathway, Wnt signaling pathway. TIDE analysis was done on risk groupings in this study, and it found that patients in the high-risk group had more immune escape potential and were less probable to react to immunotherapy. Real-time quantitative pcr (qRT-PCR) was used to identify the relatively high expression of 6 DRLs in colon cancer cell lines. In summary, 6 DRLs were identified as possible novel molecular therapy targets for COAD in this investigation. This prognostic model has the potential to be a novel tool for forecasting COAD prognosis in clinical practice, as well as providing new insights on the potential function and mechanism of disulfidptosis in the COAD process.

Development and validation of a nomogram for urinary tract infection in geriatric patients with hip fracture: a retrospective study.

This study aims to develop and validate a risk nomogram for urinary tract infections (UTIs) in geriatric patients with hip fractures. A total of 900 geriatric patients who underwent hip fracture surgery at Dandong Central Hospital between June 2017 and June 2023 were systematically collected. The cohort was randomly divided into a training set (70%, n=632) and a validation set (30%, n=268) for model development and validation, respectively. Univariate and multivariate logistic regression analyses were conducted to identify the independent risk factors associated with UTIs. Based on the results of the multivariate analysis, a UTI nomogram prediction model was developed and evaluated in the training and validation sets using the C-index, ROC curve, calibration curve, and decision curve analysis to assess discrimination, calibration, and clinical utility, respectively. Out of the 900 participants, 24.6% were diagnosed with UTIs. The nomogram was developed based on 9 predictors that were found to be independently associated with UTI. The area under the curve (AUC) for predicting UTI in geriatric patients with hip fractures was 0.829 in the training set and 0.803 in the validation set. Following internal verification, the modified C-index remained at 0.829. Furthermore, the nomogram's calibration plot and decision curve analysis demonstrated good performance in both the training and validation sets. The established and validated nomogram provides a reliable and convenient tool for predicting UTI risk in geriatric patients with hip fractures. This model facilitates the early identification of high-risk patients and offers guidance for implementing targeted preventive interventions.

Individualised prediction of progression of solitary sub-solid pulmonary nodules based on CT semantic and clinical features: a 3-year follow-up study

To develop and validate a progressive prediction model for estimating the time to progression (TTP) of sub-solid pulmonary nodules (SSNs). A total of 126 cases who met inclusion and exclusion criteria were included in the study. The primary endpoint of the study was TTP of SSNs. Baseline characteristics were assessed in terms of clinical and CT semantic features. Kaplan-Meier analysis and Cox regression analysis were performed to determine the relationship between SSNs TTP and factors from the entire data set. The nomogram was constructed based on the result of multivariate analysis and internal validation was performed using the bootstrapping. The nomogram's performance was assessed with the C-index, calibration curves, and decision curve analysis. The median follow-up time of the population was 42.5 (21.5) months. On Kaplan-Meier analysis, patients with higher or positive values of the indices had higher cumulative progression rates (p<0.05). Multivariate Cox regression models identified diameter, consolidation tumour ratio (CTR), morphology, and vasodilation sign (VDS) as independent risk factors of TTP. These predictors were included in the final model to estimate individual probabilities of progression in the 3 years, which performed well in the discrimination (the C-index was 0.901 [95%CI: 0.830-0.981] and 0.875 [95%CI: 0.805-0.942] in the training and internally validation sets). The radiological semantic features nomogram is a promising and favourable prognostic biomarker for predicting progression and may aid in clinical risk stratification and decision-making for SSNs.

Nomogram model for predicting frailty of patients with hematologic malignancies – A cross-sectional survey

ObjectiveThis study aimed to develop and validate an assessment tool for predicting and mitigating the risk of frailty in patients diagnosed with hematologic malignancies. MethodsA total of 342 patients with hematologic malignancies participated in this study, providing data on various demographics, disease-related information, daily activities, nutritional status, psychological well-being, frailty assessments, and laboratory indicators. The participants were randomly divided into training and validation groups at a 7:3 ratio. We employed Lasso regression analysis and cross-validation techniques to identify predictive factors. Subsequently, a nomogram prediction model was developed using multivariable logistic regression analysis. Discrimination ability, accuracy, and clinical utility were assessed through receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA). ResultsSeven predictors, namely disease duration of 6–12 months, disease duration exceeding 12 months, Charlson Comorbidity Index (CCI), prealbumin levels, hemoglobin levels, Generalized Anxiety Disorder-7 (GAD-7) scores, and Patient Health Questionnaire-9 (PHQ-9) scores, were identified as influential factors for frailty through Lasso regression analysis. The area under the ROC curve was 0.893 for the training set and 0.891 for the validation set. The Hosmer-Lemeshow goodness-of-fit test confirmed a good model fit. The C-index values for the training and validation sets were 0.889 and 0.811, respectively. The DCA curve illustrated a higher net benefit when using the nomogram prediction model within patients threshold probabilities ranging from 10% to 98%. ConclusionsThis study has successfully developed and validated an effective nomogram model for predicting frailty in patients diagnosed with hematologic malignancies. The model incorporates disease duration (6–12 months and > 12 months), CCI, prealbumin and hemoglobin levels, GAD-7, and PHQ-9 scores as predictive variables.

A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer.

Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators. This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA). LNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer-Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set. We established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.

The long-term outcome of nephron-sparing surgery versus radical nephroureterectomy for organ-localized upper urinary tract urothelial carcinoma: a population-based study of 1969 patients.

To compare the long-term outcomes after nephron-sparing surgery (NSS) and radical nephroureterectomy (RNU) and investigate prognostic factors for organ-localized upper urinary tract urothelial carcinoma (UTUC) as the role of NSS for UTUC remains unclear. Patients diagnosed with organ-localized UTUC between 2004 and 2020 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score overlap weighting (PSOW) process, Cox regression analysis, Kaplan‒Meier analysis, competing-risks models, and subgroup analysis were employed to compare the outcomes and identify prognostic factors. The overall survival (OS) and cancer-specific survival (CSS) nomograms were developed and evaluated using the concordance index (C-index) and calibration curve. A total of 1969 patients were included. After the process of PSOW, baseline data were well balanced. RNU was associated with similar OS and CSS than NSS in the overall cohort. Age, T stage, and histologic grade were independent prognostic factors for OS and CSS, while marital status was an independent prognostic factor only for OS. Four and three predictors were identified for developing the OS and CSS nomograms, respectively. C-index (OS 0.637, CSS 0.670), calibration curve, and Kaplan-Meier analysis proved excellent predictive accuracy of nomograms. Patients accepting RNU had a comparative or better outcome in each sample group. NSS achieved a similar oncologic control for selected patients with organ-localized UTUC.

Personalised decision making to predict absolute metastatic risk in cutaneous squamous cell carcinoma: development and validation of a clinico-pathological model

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer, affecting more than 2 million people worldwide yearly and metastasising in 2-5% of patients. However, current clinical staging systems do not provide estimates of absolute metastatic risk, hence missing the opportunity for more personalised treatment advice. We aimed to develop a clinico-pathological model that predicts the probability of metastasis in patients with cSCC. Nationwide cohorts from (1) all patients with a first primary cSCC in The Netherlands in 2007-2008 and (2) all patients with a cSCC in 2013-2015 in England were used to derive nested case-control cohorts. Pathology records of primary cSCCs that originated a loco-regional or distant metastasis were identified, and these cSCCs were matched to primary cSCCs of controls without metastasis (1:1 ratio). The model was developed on the Dutch cohort (n=390) using a weighted Cox regression model with backward selection and validated on the English cohort (n=696). Model performance was assessed using weighted versions of the C-index, calibration metrics, and decision curve analysis; and compared to the Brigham and Women's Hospital (BWH) and the American Joint Committee on Cancer (AJCC) staging systems. Members of the multidisciplinary Skin Cancer Outcomes (SCOUT) consortium were surveyed to interpret metastatic risk cutoffs in a clinical context. Eight out of eleven clinico-pathological variables were selected. The model showed good discriminative ability, with an optimism-corrected C-index of 0.80 (95% Confidence interval (CI) 0.75-0.85) in the development cohort and a C-index of 0.84 (95% CI 0.81-0.87) in the validation cohort. Model predictions were well-calibrated: the calibration slope was 0.96 (95% CI 0.76-1.16) in the validation cohort. Decision curve analysis showed improved net benefit compared to current staging systems, particularly for thresholds relevant for decisions on follow-up and adjuvant treatment. The model is available as an online web-based calculator (https://emc-dermatology.shinyapps.io/cscc-abs-met-risk/). This validated model assigns personalised metastatic risk predictions to patients with cSCC, using routinely reported histological and patient-specific risk factors. The model can empower clinicians and healthcare systems in identifying patients with high-risk cSCC and offering personalised care/treatment and follow-up. Use of the model for clinical decision-making in different patient populations must be further investigated. PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.

Development and validation of a nomogram for predicting osteoporosis in prostate cancer patients: A cross-sectional study from China.

The specific risk factors contributing to the development of osteoporosis and the appropriate timing of treatment in Chinese prostate cancer (PCa) patients remain unclear. Our objective was to develop and validate a nomogram capable of predicting the occurrence of osteoporosis in PCa patients. We conducted a cross-sectional study with PCa patients attending the Second Hospital of Tianjin Medical University, collecting data from June 2021 to February 2023. The patients were divided into training and validation sets in a 7:3 ratio. The LASSO regression was used to identify the most relevant predictive variables, and the multivariable logistic regression was used to construct the nomogram. The nomogram's performance was validated through receiver operating characteristic(ROC) curves, C-index, calibration curves, and decision curve analysis (DCA) in both the training and validation sets. We collected data from a total of 596 patients and then constructed the nomogram using age, body mass index, hemoglobin, vitamin D3, testosterone, and androgen deprivation therapy duration. The C-index of the nomogram was 0.923 in the training set and 0.859 in the validation set. The nomogram showed good consistency in both sets. DCA demonstrated the clinical benefit of the nomogram across various prediction thresholds. Furthermore, a separate nomogram was constructed to predict bone loss in patients undergoing ADT, exhibiting equally favorable diagnostic performance and clinical benefit. This study constructed two reliable nomograms to predict osteoporosis and bone loss, integrating personal health information and PCa-specific treatment data. These nomograms offer an easy and individualized approach to predict the occurrence of osteoporosis and bone loss in PCa patients.

A novel nomogram based on the hematological prognosis risk scoring system can predict the overall survival of patients with hepatocellular carcinoma.

This study aimed to establish and validate a nomogram based on a hematological prognostic risk scoring system to predict the overall survival in patients with unresectable hepatocellular carcinoma (HCC). Patients diagnosed with unresectable HCC undergoing transcatheter arterial chemoembolization (TACE) in 2012-2016 and 2017-2018 were included in the development set and validation set, respectively. The clinical outcome was overall survival (OS). The LASSO regression analysis was used to construct a hematological prognostic risk scoring system (HPR) by using the 18 hematological markers of patients in the development set. Combining the features of oncology on the basis of HPR to construct a nomogram for OS. In the development set and validation sets, the C-index, calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction performance of the nomogram. Multiple markers of immunity, coagulation, liver function, and nutrition, including red blood cell distribution width-coefficient of variation (RDW-CV), platelet (PLT), aspartate transferase (AST), alkaline phosphatase (ALP), prognostic nutritional index (PNI), and fibrinogen (Fib), construct the HPR. HPR was an independent risk factor for OS in patients with HCC. The C-index of the nomogram was 0.731 (95% confidence interval (CI) 0.712-0.749) and 0.696 (95% CI 0.668-0.725) in the development set and the validation set, respectively. HPR was a complement to the clinical features of patients with unresectable HCC. The nomogram based on HPR proved to be a practical and effective method for prognosticating HCC patients who undergo TACE.

A new in-hospital mortality prediction nomogram for intensive care unit patients with acute pancreatitis.

Acute pancreatitis (AP) is a prevalent inflammatory disease that can lead to severe abdominal pain and multiple organ failure, potentially resulting in pancreatic necrosis and persistent dysfunction. A nomogram prediction model was developed to accurately evaluate the prognosis and provide therapy guidance to AP patients. Retrospective data extraction was performed using MIMIC-IV, an open-source clinical database, to obtain 1344 AP patient records, of which the primary dataset included 1030 patients after the removal of repeated hospitalizations. The prediction of in-hospital mortality (IHM) used the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. A multivariate logistic regression analysis was used to build a prediction model incorporating the selected features, and the C-index, calibration plot, and decision curve analysis (DCA) were utilized to evaluate the discrimination, calibration, and clinical applicability of the prediction model. The nomogram utilized a combination of indicators, including the SAPS II score, RDW, MBP, RR, PTT, and fluid-electrolyte disorders. Impressively, the model exhibited a satisfactory diagnostic performance, with area under the curve values of 0.892 and 0.856 for the training cohort and internal validation, respectively. Moreover, the calibration plots and the Hosmer-Lemeshow goodness-of-fit (HL) test revealed a strong correlation between the predicted and actual outcomes (p = 0.73), further confirming the reliability of our model. Notably, the results of the decision curve analysis (DCA) highlighted the superiority of our model over previously described scoring methods in terms of net clinical benefit, solidifying its value in clinical applications. Our novel nomogram is a simple tool for accurately predicting IHM in ICU patients with AP. Treatment methods that enhance the factors involved in the model may contribute to increased in-hospital survival for these ICU patients.

A practical nomogram for predicting amputation rates in acute compartment syndrome patients based on clinical factors and biochemical blood markers

BackgroundAmputation is a serious complication of acute compartment syndrome (ACS), and predicting the risk factors associated with amputation remains a challenge for surgeons. The aim of this study was to analyze the risk factors for amputation in patients with ACS and develop a nomogram to predict amputation risk more accurately.MethodsThe study population consisted of 143 patients (32 in the amputation group and 111 in the limb preservation group) diagnosed with ACS. LASSO and multivariate logistic regression were used to screen predictors and create a nomogram. The model’s accuracy was assessed by receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA).ResultsThe predictors included cause of injury, vascular damage, shock, and fibrinogen in the nomogram. The C-index of the model was 0.872 (95% confidence interval: 0.854–0.962), and the C-index calculated by internal validation was 0.838. The nomogram’s area under the curve (AUC) was 0.849, and the calibration curve demonstrated a high degree of agreement between the nomogram’s predictions and actual observations. Additionally, the DCA indicated good clinical utility for the nomogram.ConclusionThe risk of amputation in ACS patients is associated with the cause of injury, vascular damage, shock, and fibrinogen. Our nomogram integrating clinical factors and biochemical blood markers enables doctors to more conveniently predict the risk of amputation in patients with ACS.

Nomogram for predicting coronary artery lesions in patients with Kawasaki disease.

Coronary artery lesions are the most important complications of Kawasaki disease. Approximately 25-30% of untreated patients develop coronary artery disease, which can lead to long-term cardiovascular sequelae. The aim of this study is to evaluate the risk factors for coronary artery lesions in Kawasaki disease and to construct a nomogram for predicting the likelihood of developing such lesions. Data from 599 patients between January 2012 and June 2020 were reviewed retrospectively. Patients were randomly assigned to the training set (n = 450) and the validation set (n = 149). A comparison of clinical features and laboratory data was performed, followed by multivariate logistic regression analysis to identify independent risk factors and develop the nomogram. The predictive efficiency of the nomogram was evaluated using the calibration curve, area under the receiver operating characteristic curve (AUC), C-index, and decision curve analysis (DCA). Intravenous immunoglobulin (IVIG) resistance, delayed IVIG treatment, C-reactive protein, and neutrophil/lymphocyte ratio were identified as independent risk factors for the development of coronary artery lesions. The nomogram was constructed based on these four variables. The calibration curve of the nomogram showed a high degree of agreement between the predicted probability and the actual probability. The AUC of the nomogram in the training and validation set was 0.790 and 0.711, respectively. In addition, DCA revealed that the nomogram provided a significant net benefit, further supporting its clinical utility. The constructed nomogram demonstrates a strong and reliable performance in predicting coronary artery lesions, which enables clinicians to make timely and tailored clinical decisions.

Predictive and Prognostic Role of the Neutrophil-to-Lymphocyte Ratio in Muscle Invasive Bladder Cancer Treated With Neoadjuvant Chemotherapy and Radical Cystectomy.

There is a persistent lack of validated biomarkers that identify patients most likely to benefit from neoadjuvant chemotherapy (NAC) in urothelial carcinoma of the bladder (UCB). Therefore, the purpose of this study was to investigate the predictive and prognostic impact of the pretreatment neutrophil-to-lymphocyte ratio (NLR) in UCB patients treated with NAC and radical cystectomy (RC). We conducted a retrospective analysis of an international-multicenter database comprising 404 UCB patients staged cT2-4N0-3M0. The cohort was split into low and high NLR using an optimal cutoff value determined by maximizing Youden's index. Logistic and Cox regression analyses were performed with respect to several clinical endpoints. The discriminative ability of the models and the additive discriminative value of NLR was assessed by calculating the area under receiver operating characteristics curves, C-index, and decision curve analysis (DCA). A total of 169 patients (41.8%) had a high NLR, which was associated with a decreased probability of complete response (CR, OR: 0.24 [95% CI, 0.13-0.42], P < .001) and/or partial response (PR, OR: 0.33 [95% CI, 0.21-0.49], P < .001). Adding the NLR to predictive reference models significantly improved their accuracy for the prediction of both CR and PR. A high NLR was associated with poor survival outcomes in the pretreatment setting, however, it didn't meaningfully change the C-index based on the model. We confirmed that an elevated NLR is an independent and clinically significant predictor of response to NAC and adverse pathological features in UCB treated with NAC plus RC. The accuracy of this biomarker in the age of immunotherapy warrants further evaluation.

EASIX-1year and late mortality after allogeneic stem cell transplantation

AbstractPatients with hematological malignancies who survive the first year after allogeneic stem cell transplantation (allo-SCT) without relapse have a substantial risk of nonrelapse mortality (NRM) and missing predictive markers. The Endothelial Activation and Stress Index (EASIX) predicts endothelial complications and NRM early after allo-SCT. We hypothesized that EASIX assessed 1 year after allo-SCT in survivors who were disease free may predict late NRM. Survivors who were relapse-free at 1 year after allo-SCT were retrospectively studied in 2 independent cohorts (training cohort, n = 610; merged validation cohort, n = 852). EASIX determined 1 year after allo-SCT correlated with the overall survival (OS), NRM, and relapse. Serum endothelial and inflammatory markers were measured in the training cohort and correlated with EASIX-1year, which predicted OS and NRM but not relapse risk in both the training and validation cohorts in univariable and multivariable Cox regression analyses. Brier score and c-index analyses validated the univariable EASIX effects. There was no significant interaction between EASIX-1year and incidence of chronic graft-versus-host disease (GVHD) on OS. EASIX-1year predicted the outcome irrespective of preexisting comorbidities. Principal causes of NRM in both training and validation cohorts were infections with and without GVHD as well as cardiovascular complications. EASIX-1year correlated with sCD141 and interleukin-18 but not with C-reactive protein, suppressor of tumorigenicity-2, angiopoietin-2, CXCL9, or CXCL8. To our knowledge, EASIX-1year is the first validated predictor of late overall and NRM. Patients who are high risk as defined by EASIX-1year might be considered for intensified surveillance and prophylactic measures.