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BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

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OBJECTIVE: The association between Papillary Thyroid Carcinoma (PTC) and coexistent Hashimoto's Thyroiditis (HT) was controversial. The purpose of this study was to evaluate the presence of HT exerts any influence on the aggressiveness of PTC, and to establish a nomogram for predicting the possibility of aggressiveness in PTC. METHODS: 373 consecutive PTC patients with/without coexistent HT from January 2017 to December 2020 were retrospective reviewed. Patients' clinicopathologic and sonographic characteristics were collected for univariate and multivariate analyses. A nomogram was established based on the risk factors for aggressiveness in PTC. RESULTS: Male (p = 0.001), tumor size >1.0 cm (p = 0.046) and lymph node metastasis (p = 0.018) were negatively associated with PTC coexisted with HT, while it was significantly positively associated with the frequence of multifocality (p = 0.010). Univariate and multivariate analyses suggested that age ≥55 years (p = 0.000), male (p = 0.027), HT (p = 0.017), tumor size >1.0 cm (p = 0.015), multifocality (p = 0.041), distance to capsular ≤0 cm (p = 0.050) and blood flow (Grade I: p = 0.044) were independent risk factors for predicting the aggressiveness in PTC. A nomogram according to these predictors was further developed and validated. The receiver operating characteristic curve (AUC = 0.734 and 0.809 for training and validation cohorts, respectively) and decision curve analyses indicated that the nomogram model was clinically useful. The calibration curve revealed that the nomogram exhibited an excellent consistency. CONCLUSIONS: In this study, the coexistent HT might play a protective role in preventing the proliferation of PTC. Dispensable aggressive treatment may be reduced in PTC by pre-operative identification of sonographic and clinical characteristics and incorporating with the predicted nomogram model.

Assuntos

Doença de Hashimoto , Nomogramas , Câncer Papilífero da Tireoide , Neoplasias da Glândula Tireoide , Ultrassonografia , Humanos , Masculino , Doença de Hashimoto/complicações , Doença de Hashimoto/diagnóstico por imagem , Doença de Hashimoto/patologia , Pessoa de Meia-Idade , Feminino , Estudos Retrospectivos , Adulto , Câncer Papilífero da Tireoide/diagnóstico por imagem , Câncer Papilífero da Tireoide/patologia , Câncer Papilífero da Tireoide/complicações , Neoplasias da Glândula Tireoide/diagnóstico por imagem , Neoplasias da Glândula Tireoide/patologia , Neoplasias da Glândula Tireoide/complicações , Fatores de Risco , Idoso , Adulto Jovem , Metástase Linfática/diagnóstico por imagem

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OBJECTIVE: To explore the relationship between Anion Gap (AG), Albumin Corrected AG (ACAG), and in-hospital mortality of Acute Myocardial Infarction (AMI) patients and develop a prediction model for predicting the mortality in AMI patients. METHODS: This was a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC)-Ⅲ, MIMIC-IV, and eICU Collaborative Study Database (eICU). A total of 9767 AMI patients who were admitted to the intensive care unit were included. The authors employed univariate and multivariable cox proportional hazards analyses to investigate the association between AG, ACAG, and in-hospital mortality; p < 0.05 was considered statistically significant. A nomogram incorporating ACAG and clinical indicators was developed and validated for predicting mortality among AMI patients. RESULTS: Both ACAG and AG exhibited a significant association with an elevated risk of in-hospital mortality in AMI patients. The C-index of ACAG (C-index = 0.606) was significantly higher than AG (C-index = 0.589). A nomogram (ACAG combined model) was developed to predict the in-hospital mortality for AMI patients. The nomogram demonstrated a good predictive performance by Area Under the Curve (AUC) of 0.763 in the training set, 0.744 and 0.681 in the external validation cohort. The C-index of the nomogram was 0.759 in the training set, 0.756 and 0.762 in the validation cohorts. Additionally, the C-index of the nomogram was obviously higher than the ACAG and age shock index in three databases. CONCLUSION: ACAG was related to in-hospital mortality among AMI patients. The authors developed a nomogram incorporating ACAG and clinical indicators, demonstrating good performance for predicting in-hospital mortality of AMI patients.

Assuntos

Equilíbrio Ácido-Base , Mortalidade Hospitalar , Infarto do Miocárdio , Nomogramas , Humanos , Estudos Retrospectivos , Masculino , Feminino , Infarto do Miocárdio/mortalidade , Pessoa de Meia-Idade , Idoso , Albumina Sérica/análise , Valor Preditivo dos Testes , Fatores de Risco , Medição de Risco/métodos , Modelos de Riscos Proporcionais , Unidades de Terapia Intensiva/estatística & dados numéricos , Idoso de 80 Anos ou mais , Prognóstico

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Currently, the incidence of esophageal cancer continues to rise around the world. Because of its good early prognosis, it is of great significance to establish an effective model for predicting the survival of EC patients. The purpose of this study was to predict survival after diagnosis in Esophageal Cancer (EC) patients by constructing a valid clinical nomogram. In this study, 5037 EC patient samples diagnosed from 2010 to 2015 were screened by accessing the SEER database, and 8 independent prognostic factors were screened by various methods, and Cox multivariate regression was included to construct a prognostic model and nomogram for esophageal cancer. to estimate esophageal cancer recurrence and overall survival. Calibration of the nomogram predicted probabilities of 1-year, 3-year and 5-year survival probability, which were closely related to actual survival. In conclusion, this study validated that the column-line graphical model can be considered an individualized quantitative tool for predicting the prognosis of patients with EC in order to assist clinicians in making therapeutic decisions.

Assuntos

Neoplasias Esofágicas , Nomogramas , Programa de SEER , Humanos , Neoplasias Esofágicas/mortalidade , Masculino , Feminino , Pessoa de Meia-Idade , Prognóstico , Idoso , Modelos de Riscos Proporcionais , Estadiamento de Neoplasias , Adulto , Recidiva Local de Neoplasia , Fatores de Risco , Estimativa de Kaplan-Meier , Fatores de Tempo

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Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

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Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

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Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.

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BACKGROUND: Few studies have been designed to predict the survival of Chinese patients initially diagnosed with metastatic gastric cancer (mGC). Therefore, the objective of this study was to construct and validate a new nomogram model to predict cancer-specific survival (CSS) in Chinese patients. METHODS: We collected 328 patients with mGC from Northern Jiangsu People's Hospital as the training cohort and 60 patients from Xinyuan County People's Hospital as the external validation cohort. Multivariate Cox regression was used to identify risk factors, and a nomogram was created to predict CSS. The predictive performance of the nomogram was evaluated using the consistency index (C-index), the calibration curve, and the decision curve analysis (DCA) in the training cohort and the validation cohort. RESULTS: Multivariate Cox regression identified differentiation grade (P < 0.001), T-stage (P < 0.05), N-stage (P < 0.001), surgery (P < 0.05), and chemotherapy (P < 0.001) as independent predictors of CSS. Nomogram of chemotherapy regimens and cycles was also designed by us for the prediction of mGC. Thus, these factors are integrated into the nomogram model: the C-index value was 0.72 (95% CI 0.70-0.85) for the nomogram model and 0.82 (95% CI 0.79-0.89) and 0.73 (95% CI 0.70-0.86) for the internal and external validation cohorts, respectively. Calibration curves and DCA also demonstrated adequate fit and ideal net benefit in prediction and clinical applications. CONCLUSIONS: We established a practical nomogram to predict CSS in Chinese patients initially diagnosed with mGC. Nomograms can be used to individualize survival predictions and guide clinicians in making therapeutic decisions.

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PURPOSE: With the treatment of nasopharyngeal carcinoma (NPC) by PD-1/PD-L1 inhibitors used widely in clinic, it becomes very necessary to anticipate whether patients would benefit from it. We aimed to develop a nomogram to evaluate the efficacy of anti-PD-1/PD-L1 in NPC patients. METHODS: Totally 160 NPC patients were enrolled in the study. Patients were measured before the first PD-1/PD-L1 inhibitors treatment and after 8-12 weeks of immunotherapy by radiological examinations to estimate the effect. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to screen hematological markers and establish a predictive model. The nomogram was internally validated by bootstrap resampling and externally validated. Performance of the model was evaluated using concordance index, calibration curve, decision curve analysis and receiver operation characteristic curve. RESULTS: Patients involved were randomly split into training cohort ang validation cohort. Based on Lasso logistic regression, systemic immune-inflammation index (SII) and ALT to AST ratio (LSR) were selected to establish a predictive model. The C-index of training cohort and validating cohort was 0.745 and 0.760. The calibration curves and decision curves showed the precise predictive ability of this nomogram. The benefit of the model showed in decision curve was better than TNM stage. The area under the curve (AUC) value of training cohort and validation cohort was 0.745 and 0.878, respectively. CONCLUSION: The predictive model helped evaluating efficacy with high accuracy in NPC patients treated with PD-1/PD-L1 inhibitors.

Assuntos

Antígeno B7-H1 , Inibidores de Checkpoint Imunológico , Carcinoma Nasofaríngeo , Neoplasias Nasofaríngeas , Nomogramas , Humanos , Masculino , Feminino , Carcinoma Nasofaríngeo/tratamento farmacológico , Carcinoma Nasofaríngeo/patologia , Pessoa de Meia-Idade , Neoplasias Nasofaríngeas/tratamento farmacológico , Neoplasias Nasofaríngeas/patologia , Inibidores de Checkpoint Imunológico/uso terapêutico , Antígeno B7-H1/antagonistas & inibidores , Adulto , Curva ROC , Receptor de Morte Celular Programada 1/antagonistas & inibidores , Idoso , Modelos Logísticos

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OBJECTIVE: The aim of the study was to create two consensus nomograms for predicting Overall Survival (OS) and Cancer-Specific Survival (CSS) in adults with papillary Renal Cell Carcinoma (pRCC). METHODS: Using the Surveillance, Epidemiology, and End Results databases, a retrospective analysis of 1,074 adults with pRCC from 2004 to 2015 was performed. These patients were then randomly divided into two independent cohorts with a ratio of 7:3 (training cohort: 752; validation cohort: 322). In a retrospective analysis of 752 patients from the training cohort, independent prognostic variables affecting OS and CSS were found. R software was used to create prognostic nomograms based on the findings of Cox regression analysis. The performance of the nomograms was assessed using the Concordance Index (C-index), the Area Under Curve (AUC), a calibration curve, and Decision Curve Analysis (DCA). Data from the 107 postoperative pRCC patients at the Affiliated Hospital of Xuzhou Medical University were used for external validation of the nomogram. RESULTS: For OS and CSS, the C-indices and AUCs of the training cohort and the validation cohort indicated that the model had excellent discrimination. The DCA demonstrated that the model was clinically applicable, and the calibration curves in the internal and external validations showed that the model's accuracy was high. CONCLUSION: The authors developed and validated a prognostic nomogram that accurately predicted the 3-, 5-, and 8-year OS and CSS of adults with pRCC. Clinicians can use this knowledge to direct the clinical management and counseling of patients with pRCC.

Assuntos

Carcinoma de Células Renais , Neoplasias Renais , Nomogramas , Humanos , Masculino , Carcinoma de Células Renais/mortalidade , Carcinoma de Células Renais/patologia , Carcinoma de Células Renais/cirurgia , Estudos Retrospectivos , Feminino , Pessoa de Meia-Idade , Neoplasias Renais/mortalidade , Neoplasias Renais/patologia , Neoplasias Renais/cirurgia , Prognóstico , Adulto , Idoso , Reprodutibilidade dos Testes , Estadiamento de Neoplasias , Programa de SEER

RESUMO

BACKGROUND: To explore the correlation of pre-treatment Hemoglobin-Albumin-Lymphocyte-Platelet (HALP) score with the prognosis of patients with advanced Non-Small Cell Lung Cancer (NSCLC) undergoing first-line conventional platinum-based chemotherapy. METHODS: In this retrospective cohort study, 203 patients with advanced NSCLC were recruited from January 2017 to December 2021. The cut-off value for the HALP score was determined by Receiver Operating Characteristic (ROC) curve analysis. The baseline characteristics and blood parameters were recorded, and the Log-rank test and Kaplan-Meier curves were applied for the survival analysis. In the univariate and multivariate analyses, the Cox regression analysis was carried out. The predictive accuracy and discriminative ability of the nomogram were determined by the Concordance index (C-index) and calibration curve and compared with a single HALP score by ROC curve analysis. RESULTS: The optimal cut-off value for the HALP score was 28.02. The lower HALP score was closely associated with poorer Progression-Free Survival (PFS) and Overall Survival (OS). The male gender and other pathological types were associated with shorter OS. Disease progression and low HALP were correlated with shorter OS and PFS. In addition, nomograms were established based on HALP scores, gender, pathology type and efficacy rating, and used to predict OS. The C-index for OS prediction was 0.7036 (95% CI 0.643 to 0.7643), which was significantly higher than the C-index of HALP at 6-, 12-, and 24-months. CONCLUSION: The HALP score is associated with the prognosis of advanced NSCLC patients receiving conventional platinum-based chemotherapy, and the nomogram established based on the HALP score has a better predictive capability for OS.

Assuntos

Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Nomogramas , Humanos , Carcinoma Pulmonar de Células não Pequenas/mortalidade , Carcinoma Pulmonar de Células não Pequenas/patologia , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Masculino , Feminino , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/mortalidade , Neoplasias Pulmonares/tratamento farmacológico , Estudos Retrospectivos , Pessoa de Meia-Idade , Prognóstico , Idoso , Hemoglobinas/análise , Curva ROC , Adulto , Estimativa de Kaplan-Meier , Contagem de Plaquetas , Plaquetas/patologia , Estadiamento de Neoplasias , Contagem de Linfócitos , Albumina Sérica/análise

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BACKGROUND: Colorectal cancer (CRC) prognosis assessment is vital for personalized treatment plans. This study investigates the prognostic value of dynamic changes of tumor markers CEA, CA19-9, CA125, and AFP before and after surgery and constructs prediction models based on these indicators. METHODS: A retrospective clinical study of 2599 CRC patients who underwent radical surgery was conducted. Patients were randomly divided into training (70%) and validation (30%) datasets. Univariate and multivariate Cox regression analyses identified independent prognostic factors, and nomograms were constructed. RESULTS: A total of 2599 CRC patients were included in the study. Patients were divided into training (70%, n = 1819) and validation (30%, n = 780) sets. Univariate and multivariate Cox regression analyses identified age, total number of resected lymph nodes, T stage, N stage, the preoperative and postoperative changes in the levels of CEA, CA19-9, and CA125 as independent prognostic factors. When their postoperative levels are normal, patients with elevated preoperative levels have significantly worse overall survival. However, when the postoperative levels of CEA/CA19-9/CA125 are elevated, whether their preoperative levels are elevated or not has no significance for prognosis. Two nomogram models were developed, and Model I, which included CEA, CA19-9, and CA125 groups, demonstrated the best performance in both training and validation sets. CONCLUSION: This study highlights the significant predictive value of dynamic changes in tumor markers CEA, CA19-9, and CA125 before and after CRC surgery. Incorporating these markers into a nomogram prediction model improves prognostic accuracy, enabling clinicians to better assess patients' conditions and develop personalized treatment plans.

Assuntos

Biomarcadores Tumorais , Antígeno Ca-125 , Antígeno CA-19-9 , Antígeno Carcinoembrionário , Neoplasias Colorretais , Nomogramas , Humanos , Neoplasias Colorretais/cirurgia , Neoplasias Colorretais/sangue , Neoplasias Colorretais/patologia , Neoplasias Colorretais/mortalidade , Masculino , Feminino , Estudos Retrospectivos , Prognóstico , Biomarcadores Tumorais/sangue , Pessoa de Meia-Idade , Idoso , Antígeno Carcinoembrionário/sangue , Antígeno Ca-125/sangue , Antígeno CA-19-9/sangue , Período Pós-Operatório , Idoso de 80 Anos ou mais , Período Pré-Operatório , Adulto , Taxa de Sobrevida

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BACKGROUND AND OBJECTIVE: Neoadjuvant chemotherapy results in various responses when used to treat locally advanced gastric cancer, we aimed to develop and validate a predictive model of the response to neoadjuvant chemotherapy in patients with gastric cancer. METHODS: A total of 128 patients with locally advanced gastric cancer who underwent pre-treatment computed tomography (CT) scanning followed by neoadjuvant chemoradiotherapy were included (training cohort: n = 64; validation cohort: n = 64). We built a radiomics score combined with laboratory parameters to create a nomogram for predicting the efficacy of neoadjuvant chemotherapy and calculating scores for risk factors. RESULTS: The radiomics score system demonstrated good stability and prediction performance for the response to neoadjuvant chemotherapy, with the area under the curve of the training and validation cohorts being 0.8 and 0.64, respectively. The radiomics score proved to be an independent risk factor affecting the efficacy of neoadjuvant chemotherapy. In addition to the radiomics score, four other risk factors were included in the nomogram, namely the platelet-to-lymphocyte ratio, total bilirubin, ALT/AST, and CA199. The model had a C-index of 0.8. CONCLUSIONS: Our results indicated that radiomics features could be potential biomarkers for the early prediction of the response to neoadjuvant treatment.

Assuntos

Terapia Neoadjuvante , Nomogramas , Neoplasias Gástricas , Tomografia Computadorizada por Raios X , Humanos , Neoplasias Gástricas/patologia , Neoplasias Gástricas/diagnóstico por imagem , Neoplasias Gástricas/terapia , Neoplasias Gástricas/tratamento farmacológico , Masculino , Feminino , Pessoa de Meia-Idade , Tomografia Computadorizada por Raios X/métodos , Idoso , Adulto , Estudos Retrospectivos , Fatores de Risco , Bilirrubina/sangue , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Área Sob a Curva , Contagem de Plaquetas , Radiômica , Antígenos Glicosídicos Associados a Tumores

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OBJECTIVE: To validate the Cancer of the Bladder Risk Assessment (COBRA) score in patients with urothelial variants. METHODS: Epidemiological, clinical, radiological, and anatomopathological data were collected from patients with urothelial carcinoma who underwent radical cystectomy at the Institute of Cancer of São Paulo between May 2008 and December 2022. Patients with the presence of at least 10% of any urothelial variants in the radical cystectomy specimens' anatomopathological exam were included in the study. The COBRA score and derivatives were applied and correlated with oncological outcomes. RESULTS: A total of 680 patients [482 men (70.9%) and 198 women (29.1%)]; 66 years (IQR 59-73) underwent radical cystectomy for bladder tumor, and of these patients, a total of 167 patients presented any type of urothelial variant. The median follow-up time was 28.77 months (IQR 12-85). The three most prevalent UV were squamous differentiation (50.8%), glandular differentiation (31.3%), and micropapillary differentiation (11.3%). The subtypes with the worst prognosis were sarcomatoid with a median survival of 8 months (HR 1.161; 95% CI 0.555-2.432) and plasmacytoid with 14 months (HR 1.466; 95% CI 0.528-4.070). The COBRA score for patients with micropapillary variants demonstrated good predictive accuracy for OS (log-rank P = 0.009; 95% IC 6.78-29.21) and CSS (log-rank P = 0.002; 95% IC 13.06-26.93). CONCLUSIONS: In our study, the COBRA score proved an effective risk stratification tool for urothelial histological variants, especially for the micropapillary urothelial variant. It may be helpful in the prognosis evaluation of UV patients after radical cystectomy.

Assuntos

Carcinoma de Células de Transição , Neoplasias da Bexiga Urinária , Masculino , Humanos , Feminino , Neoplasias da Bexiga Urinária/patologia , Carcinoma de Células de Transição/patologia , Cistectomia , Estudos Retrospectivos , Brasil , Medição de Risco

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Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.

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OBJECTIVE: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. METHOD: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. RESULTS: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. CONCLUSIONS: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

Assuntos

Hemorragia Cerebral Intraventricular , Nomogramas , Humanos , Recém-Nascido , Estudos Retrospectivos , Feminino , Masculino , Hemorragia Cerebral Intraventricular/diagnóstico , Índice de Gravidade de Doença , Bases de Dados Factuais , Hemorragia Cerebral/diagnóstico , Valor Preditivo dos Testes , Contagem de Plaquetas

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OBJECTIVE: Reliably prediction models for coronary artery abnormalities (CAA) in children aged >5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. METHODS: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. RESULTS: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. CONCLUSIONS: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decision-making.

Assuntos

Síndrome de Linfonodos Mucocutâneos , Nomogramas , Humanos , Síndrome de Linfonodos Mucocutâneos/complicações , Síndrome de Linfonodos Mucocutâneos/diagnóstico , Masculino , Feminino , Criança , Pré-Escolar , Anomalias dos Vasos Coronários , Curva ROC , Modelos Logísticos , Medição de Risco/métodos

RESUMO

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.

Assuntos

Carcinoma Hepatocelular , Neoplasias Hepáticas , RNA Longo não Codificante , Humanos , Carcinoma Hepatocelular/patologia , Prognóstico , Neoplasias Hepáticas/patologia , RNA Longo não Codificante/genética , RNA Longo não Codificante/metabolismo , Nomogramas , Estimativa de Kaplan-Meier

RESUMO

PURPOSE: To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. METHODS: Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. RESULTS: Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. CONCLUSION: Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.

Assuntos

Neoplasias do Colo , Nomogramas , Humanos , Prognóstico , Neoplasias do Colo/cirurgia , Bases de Dados Factuais , Análise Multivariada