A new nomogram for the evaluation of detrusor underactivity and bladder outlet obstruction in nonneurogenic female patients with lower urinary tract symptoms who undergo urodynamic studies.

INTRODUCTION: Micturition physiology differs in men and women. However, the results in standard urodynamic studies in women with lower urinary tract symptoms (LUTS) were extrapolated from studies in men. Nowadays, the only validated nomogram for females is Solomon-Greenwell's. However, it only evaluated bladder outlet obstruction (BOO) without considering detrusor underactivity (DU). This study aims to create a nomogram that includes an evaluation of DU and BOO in nonneurogenic women and validate it against videourodynamic studies along with other nomograms. MATERIALS AND METHODS: For the first analysis (creation cohort), we included 183 women with LUTS who underwent videourodynamic study between 2022 and 2023. Exclusion criteria were females with neurologic diseases, renal transplantation, and trouble performing the flow-pressure study. Baseline characteristics of the patients, urodynamic parameters, and classifications on different nomograms and indexes were evaluated. A logistic regression found Qmax and PdetQmax as predictors for DU and BOO. The Barco-Castillo nomogram was created by clustering analysis and adjusted by the results of the logistic regression. A second (test) cohort was evaluated from 2023 to 2024, including 142 patients for the validation of the nomogram. A p < 0.05 was considered significant. RESULTS: All urodynamic parameters were compared between both cohorts, with no significant differences. The median age of the creation cohort was 50 years old (interquartile range [IQR] 39-63). All patients had LUTS and a previous standard urodynamic study without a clear diagnosis. The cluster analysis had a p < 0.05 for two groups of BOO (yes/no) and two of DU (yes/no). We created the graph based on the logistic regression results and adjusted it according to the data. The median age of the test cohort was 44 years old (IQR 33.75-59) and had the same indication for the videourodynamic study. The receiver operating characteristic (ROC) curve for BOO showed an accuracy of 85.4% for Barco-Castillo nomogram, 68.5% for Blaivas-Groutz, 58.1% for Solomon-Greenwell, 57.1% for BOOI, and 50% for LinPURR. For DU, accuracy was 80.5% for PIP-1, 80.2% for Barco-Castillo, 76.6% for BCI, and 70.1% for LinPURR. CONCLUSIONS: When evaluating women's urodynamic studies, it is important to focus on female physiology and discourage the use of parameters previously standardized in men. We encourage using the new Barco-Castillo nomogram to determine BOO and DU in women as the currently easiest and more accurate tool.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. PATIENTS AND METHODS: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. RESULTS: Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 µmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. CONCLUSIONS: The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Hueso nasal como marcador de aneuploidías y resultado perinatal / Nasal bone as a marker of aneuploidies and perinatal outcome

Objetivo: Describir el resultado perinatal de los embarazos en función de la evaluación del hueso nasal como marcador de aneuploidía. Métodos: De 1006 embarazadas, 607 cumplieron con los criterios de inclusión para este estudio prospectivo, descriptivo, correlacional no causal donde se correlacionó la ausencia/presencia de hueso nasal con la presencia de síndrome de Down a través de cariotipo fetal prenatal y/o posnatal, así como examen clínico neonatal. Los datos fueron analizados mediantes frecuencias absolutas, porcentajes, capacidad diagnóstica del hueso nasal (índice de Youden), sensibilidad, especificidad, valor predictivo positivo, valor predictivo negativo y cocientes de probabilidad, positivo y negativo. Resultados: La prevalencia de síndrome de Down fue de 1,48 %, la ausencia del hueso nasal como marcador aislado, obtuvo un índice de Youden de 0,55 (0,23 - 0,88), sensibilidad de 55,56 %, especificidad de 99,50 %, valor predictivo positivo de 62,5 %, valor predictivo negativo de 99,33 %, cocientes de probabilidad positivo (hueso nasal ausente) 111 (IC 95 % 31 - 394) y cocientes de probabilidad negativo (hueso nasal presente) de 0,45 (IC 95 % 0,22 -0,93). Conclusión: La ausencia de hueso nasal en primer trimestre aumenta el riesgo de síndrome de Down en 111 veces y la presencia del mismo lo disminuye, sin valor como prueba diagnóstica sino de pesquisa debe considerarse como un marcador secundario(AU)

Objective: To know the perinatal outcome based on nasal bone evaluation as an aneuploidy marker. Methods: From 1006 pregnant women, 607 met the inclusion criteria for this prospective, descriptive, correlational not causal research correlating nasal bone absence / presence with Down syndrome through prenatal / postnatal fetal karyotype and neonatal clinical examination. Absolute frequencies and percentages, nasal bone performance as a diagnostic test (Youden índex), sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratios positive and negative, were calculated. Results: 1.48 % was the Down syndrome prevalence on the sample. The nasal bone absence as an isolated marker obtained an 0,55 Youden index (0.23 to 0.88 ), sensitivity 55,56%, specificity 99,50%, positive predictive value 62,5%, negative predictive value 99,33%, likelihood ratios positive (absent nasal bone) 111, (95% CI 31-394) and likelihood ratios negative (nasal bone present ) 0,45 (95% CI 0 22 -0.93 ). Conclusion: The nasal bone absence in first trimester increases Down syndrome risk 111 times and nasal bone presence decreases it with poor performance as a diagnostic test, so it should be considered a screening test and a secondary marker. Recommendations correlate these results with other markers to improve detection rates and quantify nasal bone measurements in order to make nasal bone nomograms in first trimester pregnancies(AU)

Albumin corrected anion gap for predicting in-hospital death among patients with acute myocardial infarction: A retrospective cohort study.

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.

Construction of prognostic nomogram based on the SEER database for esophageal cancer patients.

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.

Sonographic characteristics and clinical characteristics combined with nomogram for predicting the aggressiveness of papillary thyroid carcinoma coexisted with Hashimoto's thyroiditis.

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.

A nomogram model for the occurrence of bladder spasm after TURP in patients with prostate enlargement based on serum prostacyclin and 5-hydroxytryptamine and clinical characteristics.

OBJECTIVE: With the development of analytical methods, mathematical models based on humoral biomarkers have become more widely used in the medical field. This study aims to investigate the risk factors associated with the occurrence of bladder spasm after transurethral resection of the prostate (TURP) in patients with prostate enlargement, and then construct a nomogram model. MATERIALS AND METHODS: Two hundred and forty-two patients with prostate enlargement who underwent TURP were included. Patients were divided into Spasm group (n=65) and non-spasm group (n=177) according to whether they had bladder spasm after surgery. Serum prostacyclin (PGI2) and 5-hydroxytryptamine (5-HT) levels were measured by enzyme-linked immunoassay. Univariate and multivariate logistic regression were used to analyze the risk factors. RESULTS: Postoperative serum PGI2 and 5-HT levels were higher in patients in the Spasm group compared with the Non-spasm group (P<0.05). Preoperative anxiety, drainage tube obstruction, and elevated postoperative levels of PGI2 and 5-HT were independent risk factors for bladder spasm after TURP (P<0.05). The C-index of the model was 0.978 (0.959-0.997), with a χ2 = 4.438 (p = 0.816) for Hosmer-Lemeshow goodness-of-fit test. The ROC curve to assess the discrimination of the nomogram model showed an AUC of 0.978 (0.959-0.997). CONCLUSION: Preoperative anxiety, drainage tube obstruction, and elevated postoperative serum PGI2 and 5-HT levels are independent risk factors for bladder spasm after TURP. The nomogram model based on the aforementioned independent risk factors had good discrimination and predictive abilities, which may provide a high guidance value for predicting the occurrence of bladder spasm in clinical practice.

Development and validation of a nomogram to predicting the efficacy of PD-1/PD-L1 inhibitors in patients with nasopharyngeal carcinoma.

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.

The 2012 Briganti nomogram not only predicts lymph node involvement but also disease progression in surgically treated intermediate-risk prostate cancer patients with PSA <10 ng/mL, ISUP grade group 3, and clinical stage up to cT2b.

PURPOSE: We assessed the prognostic impact of the 2012 Briganti nomogram on prostate cancer (PCa) progression in intermediate-risk (IR) patients presenting with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b treated with robot assisted radical prostatectomy eventually associated with extended pelvic lymph node dissection. MATERIALS AND METHODS: From January 2013 to December 2021, data of surgically treated IR PCa patients were retrospectively evaluated. Only patients presenting with the above-mentioned features were considered. The 2012 Briganti nomogram was assessed either as a continuous and a categorical variable (up to the median, which was detected as 6%, vs. above the median). The association with PCa progression, defined as biochemical recurrence, and/or metastatic progression, was evaluated by Cox proportional hazard regression models. RESULTS: Overall, 147 patients were included. Compared to subjects with a nomogram score up to 6%, those presenting with a score above 6% were more likely to be younger, had larger/palpable tumors, presented with higher PSA, underwent tumor upgrading, harbored non-organ confined disease, and had positive surgical margins at final pathology. PCa progression, which occurred in 32 (21.7%) cases, was independently predicted by the 2012 Briganti nomogram both considered as a continuous (Hazard Ratio [HR]:1.04, 95% Confidence Interval [CI]:1.01-1.08;p=0.021), and a categorical variable (HR:2.32; 95%CI:1.11-4.87;p=0.026), even after adjustment for tumor upgrading. CONCLUSIONS: In IR PCa patients with PSA <10ng/mL, ISUP grade group 3, and clinical stage up to cT2b, the 2012 Briganti nomogram independently predicts PCa progression. In this challenging subset of patients, this tool can identify prognostic subgroups, independently by upgrading issues.

The Predictive Value of the Inflammatory Prognostic Index for Detecting No-Reflow in ST-Elevation Myocardial Infarction Patients. / O Valor Preditivo do Índice Prognóstico Inflamatório para Detecção de No-Reflow em Pacientes com Infarto do Miocárdio com Supradesnivelamento do Segmento ST.

BACKGROUND: No-reflow (NR) is characterized by an acute reduction in coronary flow that is not accompanied by coronary spasm, thrombosis, or dissection. Inflammatory prognostic index (IPI) is a novel marker that was reported to have a prognostic role in cancer patients and is calculated by neutrophil/lymphocyte ratio (NLR) multiplied by C-reactive protein/albumin ratio. OBJECTIVE: We aimed to investigate the relationship between IPI and NR in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). METHODS: A total of 1541 patients were enrolled in this study (178 with NR and 1363 with reflow). Lasso panelized shrinkage was used for variable selection. A nomogram was created based on IPI for detecting the risk of NR development. Internal validation with Bootstrap resampling was used for model reproducibility. A two-sided p-value <0.05 was accepted as a significance level for statistical analyses. RESULTS: IPI was higher in patients with NR than in patients with reflow. IPI was non-linearly associated with NR. IPI had a higher discriminative ability than the systemic immune-inflammation index, NLR, and CRP/albumin ratio. Adding IPI to the baseline multivariable logistic regression model improved the discrimination and net-clinical benefit effect of the model for detecting NR patients, and IPI was the most prominent variable in the full model. A nomogram was created based on IPI to predict the risk of NR. Bootstrap internal validation of nomogram showed a good calibration and discrimination ability. CONCLUSION: This is the first study that shows the association of IPI with NR in STEMI patients who undergo pPCI.

FUNDAMENTO: O no-reflow (NR) é caracterizado por uma redução aguda no fluxo coronário que não é acompanhada por espasmo coronário, trombose ou dissecção. O índice prognóstico inflamatório (IPI) é um novo marcador que foi relatado como tendo um papel prognóstico em pacientes com câncer e é calculado pela razão neutrófilos/linfócitos (NLR) multiplicada pela razão proteína C reativa/albumina. OBJETIVO: Nosso objetivo foi investigar a relação entre IPI e NR em pacientes com infarto do miocárdio com supradesnivelamento do segmento ST (IAMCSST) submetidos a intervenção coronária percutânea primária (ICPp). MÉTODOS: Um total de 1.541 pacientes foram incluídos neste estudo (178 com NR e 1.363 com refluxo). A regressão penalizada LASSO (Least Absolute Shrinkage and Select Operator) foi usada para seleção de variáveis. Foi criado um nomograma baseado no IPI para detecção do risco de desenvolvimento de NR. A validação interna com reamostragem Bootstrap foi utilizada para reprodutibilidade do modelo. Um valor de p bilateral <0,05 foi aceito como nível de significância para análises estatísticas. RESULTADOS: O IPI foi maior em pacientes com NR do que em pacientes com refluxo. O IPI esteve associado de forma não linear com a NR. O IPI apresentou maior capacidade discriminativa do que o índice de imunoinflamação sistêmica, NLR e relação PCR/albumina. A adição do IPI ao modelo de regressão logística multivariável de base melhorou a discriminação e o efeito do benefício clínico líquido do modelo para detecção de pacientes com NR, e o IPI foi a variável mais proeminente no modelo completo. Foi criado um nomograma baseado no IPI para prever o risco de NR. A validação interna do nomograma Bootstrap mostrou uma boa capacidade de calibração e discriminação. CONCLUSÃO: Este é o primeiro estudo que mostra a associação de IPI com NR em pacientes com IAMCSST submetidos a ICPp.

Development and validation of prognostic nomograms for adult with papillary renal cell carcinoma: A retrospective study.

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.

Prognostic nomogram based on pre-treatment HALP score for patients with advanced non-small cell lung cancer.

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.

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage.

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.

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma.

OBJECTIVES: Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated. METHODS: Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immune-derived multiple programmed cell death index (MPCDI) based on machine learning methods. RESULTS: Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients. CONCLUSION: Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.

Development and validation of a novel scoring system based on a nomogram for predicting inadequate bowel preparation.

BACKGROUND AND AIMS: Adequate bowel preparation (BP) is crucial for the diagnosis of colorectal diseases. Identifying patients at risk of inadequate BP allows for targeted interventions and improved outcomes. We aimed to develop a model for predicting inadequate BP based on preparation-related factors. METHODS: Adult outpatients scheduled for colonoscopy between May 2022 and October 2022 were enrolled. One set (N = 913) was used to develop and internally validate the predictive model. The primary predictive model was displayed as a nomogram and then modified into a novel scoring system, which was externally validated in an independent set (N = 177). Inadequate BP was defined as a Boston Bowel Preparedness Scale (BBPS) score of less than 2 for any colonic segment. The model was evaluated by the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: Independent factors included in the prediction model were stool frequency ≤ 5 (15 points), preparation-to-colonoscopy interval ≥ 5 h (15 points), incomplete dosage (100 points), non-split dose (90 points), unrestricted diet (88 points), no additional water intake (15 points), and last stool appearance as an opaque liquid (0-80 points). The training set exhibited the following performance metrics for identifying BP failure: area under the curve (AUC) of 0.818, accuracy (ACC) of 0.818, positive likelihood ratio (PLR) of 2.397, negative likelihood ratio (NLR) of 0.162, positive predictive value (PPV) of 0.850, and negative predictive value (NPV) of 0.723. In the internal validation set, these metrics were 0.747, 0.776, 2.099, 0.278, 0.866, and 0.538, respectively. The external validation set showed values of 0.728, 0.757, 2.10, 0.247, 0.782, and 0.704, respectively, indicating strong discriminative ability. Calibration curves demonstrated close agreement, and DCA indicated superior clinical benefits at a threshold probability of 0.73 in the training cohort and 0.75 in the validation cohort for this model. CONCLUSIONS: This novel scoring system was developed from a prospective study and externally validated in an independent set based on 7 easily accessible variables, demonstrating robust performance in predicting inadequate BP.

Can neoadjuvant systemic therapy provide additional benefits for T1 HER2+ breast cancer patients: a subgroup analysis based on different high-risk signatures.

INTRODUCTION: Neoadjuvant systemic therapy (NAST) is vital in the management of HER2-positive (HER2+) breast cancer. Nevertheless, the indications for NAST in tumors <2 cm remain controversial. METHOD: A total of 7961 patients were screened from the Surveillance, Epidemiology, and End Result database. Independent prognostic factors were identified using multivariate Cox analysis. Subgroup analyses and Kaplan-Meier analyses were used to simulate whether NAST would provide a survival benefit with different high-risk characteristics. Nomograms were constructed, and an internal validation cohort was employed. RESULTS: Of the 7961 included patients, 1137 (14.3%) underwent NAST. In the total population, NAST was associated with poorer overall survival (OS) and breast cancer-specific survival (BCSS) (OS: P = 0.00093; BCSS: P  <  0.0001). Multivariate Cox analysis confirmed that NAST markedly affected the prognosis of enrolled patients. Besides, a direct association between T, N, age, subtype, and prognosis was observed. Subgroup analyses yielded in these three subgroups, T1c, hormone receptor-negative, and 61-69 years of age, NAST and AST had comparable OS, while NAST possessed worse BCSS. Notably, even in the N3, we still did not observe any additional benefit of NAST. The calculated C-index of 0.72 and 0.73 confirmed the predictability of the nomograms. The AUCs exhibit consistency in the training and validation cohorts. CONCLUSION: Our findings suggest that NAST does not provide additional benefit to patients with T1 HER2+ breast cancer, even in the presence of lymph node metastasis, T1c, or hormone receptor negativity. This study facilitates the implementation of individualized management strategies.

Prognostic value of dynamic changes of pre- and post-operative tumor markers in colorectal cancer.

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.

Computed tomography-based radiomics nomogram for predicting therapeutic response to neoadjuvant chemotherapy in locally advanced gastric cancer : A scale for treatment predicting.

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.

Integrated analysis reveals an aspartate metabolism-related gene signature for predicting the overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Deregulating cellular metabolism is one of the prominent hallmarks of malignancy, with a critical role in tumor survival and growth. However, the role of reprogramming aspartate metabolism in hepatocellular carcinoma (HCC) are largely unknown. METHODS: The multi-omics data of HCC patients were downloaded from public databases. Univariate and multivariate stepwise Cox regression were used to establish an aspartate metabolism-related gene signature (AMGS) in HCC. The Kaplan-Meier and receiver operating characteristic curve analyses were performed to evaluate the predictive ability for overall survival (OS) in HCC patients. Gene set enrichment analysis and immune infiltration analysis were operated to determine the potential mechanisms underlying the AMGS. Single-cell RNA sequencing (scRNA-seq) data of liver cancer stem cells were visualized by t-SNE algorithm. In vivo and in vitro experiments were implemented to investigate the biological function of CAD in HCC. In addition, a nomogram based on the AMGS and clinicopathologic characteristics was constructed by univariate and multivariate Cox regression analyses. RESULTS: Patients in the high-AMGS subgroup exerted advanced tumor status and poor prognosis. Mechanistically, the high-AMGS subgroup patients had significantly enhanced proliferation and stemness-related pathways, increased infiltration of regulatory T cells and upregulated expression levels of suppressive immune checkpoints in the tumor immune microenvironment. Notably, scRNA-seq data revealed CAD, one of the aspartate metabolism-related gene, is significantly upregulated in liver cancer stem cells. Silencing CAD inhibited proliferative capacity and stemness properties of HCC cells in vitro and in vivo. Finally, a novel nomogram based on the AMGS showed an accurate prediction in HCC patients. CONCLUSIONS: The AMGS represents a promising prognostic value for HCC patients, providing a perspective for finding novel biomarkers and therapeutic targets for HCC.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database.

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.