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Aim: This study aimed to evaluate the mean post-test probability (PTP) of the Maturity-onset diabetes of the young (MODY) calculator in a multiethnic cohort of patients previously diagnosed with type 1 diabetes (T1DM). Materials and methods: The MODY probability calculator proposed by Shields and colleagues (2012) was applied to 117 patients from a T1DM outpatient clinic at a tertiary hospital in Brazil. Additionally, two exons of the HNF1A gene were sequenced in eight patients who hadn't received insulin treatment within six months after the diagnosis. Results: 17.1 % of patients achieved PTP >10 %; 11.1 % achieved PTP >25 % (and all patients >30 %), and 7.7 % achieved PTP >40 %. Among the patients who were selected for genetic sequencing, 100 % presented PTP >30 %, with 66.6 % achieving PTP >40 % and 41.6 % achieving PTP >75 %. These cutoffs are as suggested for the Brazilian population, according to previous investigations. No mutation was observed in the sequenced exons. Conclusion: Considering that only around 10 % of the evaluated cases achieved PTP >30 %, it is highly probable that the most suitable cutoff to select patients for genetic sequencing in a Brazilian cohort of T1DM is higher than the cutoff used in Caucasian populations.

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OBJECTIVE: To assess the risk of cognitive impairment among infants born extremely preterm using the INTERGROWTH-21st standards. STUDY DESIGN: We analyzed anthropometric data at birth and 36 weeks postmenstrual age (PMA) from infants born extremely preterm (24-26 weeks of gestation) admitted to US neonatal units between 2008 and 2018. To determine INTERGROWTH-21st z-score values that indicate an increased risk of cognitive impairment at 2 years of age (Bayley cognitive score <85), we employed classification and regression trees and redefined growth failure (weight, length, and head circumference z-scores at 36 weeks PMA) and growth faltering (weight, length, and head circumference z-score declines from birth to 36 weeks PMA). RESULTS: Among 5393 infants with a mean gestational age of 25 weeks, growth failure defined as a weight z-score of -1.8 or below at 36 weeks PMA and growth faltering defined as a weight z-score decline of 1.1 or greater from birth to 36 weeks PMA indicated a higher likelihood of cognitive impairment. A length z-score less than -1 at 36 weeks PMA had the highest sensitivity to detect cognitive impairment at 2 years (80%). A head circumference z-score decline of 2.43 or greater from birth to 36 weeks PMA had the highest specificity (86%). Standard definitions had fair to low sensitivity and specificity for risk detection of cognitive impairment. CONCLUSIONS: Length and head circumference z-scores had the highest sensitivity and specificity for risk detection of cognitive impairment. Monitoring these growth parameters could guide earlier individualized interventions with potential to reduce cognitive impairment. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID Generic Database: NCT00063063.

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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.

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Carcinoma Hepatocelular , Cirrosis Hepática , Neoplasias Hepáticas , Aprendizaje Automático , alfa-Fetoproteínas , Humanos , Carcinoma Hepatocelular/sangre , Carcinoma Hepatocelular/virología , Carcinoma Hepatocelular/epidemiología , Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/etiología , Neoplasias Hepáticas/sangre , Neoplasias Hepáticas/virología , Neoplasias Hepáticas/epidemiología , Neoplasias Hepáticas/etiología , Neoplasias Hepáticas/diagnóstico , alfa-Fetoproteínas/análisis , alfa-Fetoproteínas/metabolismo , Masculino , Femenino , Persona de Mediana Edad , Cirrosis Hepática/sangre , Cirrosis Hepática/virología , Cirrosis Hepática/diagnóstico , Medición de Riesgo , Factores de Riesgo , China/epidemiología , Hepatitis B Crónica/complicaciones , Hepatitis B Crónica/sangre , Valor Predictivo de las Pruebas , Adulto , Nomogramas , Biomarcadores de Tumor/sangre , Hepatitis B/complicaciones , Hepatitis B/sangre , Hepatitis B/diagnóstico , Anciano , Estudios Retrospectivos

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INTRODUCTION: This multi-center study aims to explore the roles of plasma exosomal microRNAs (miRNAs), ultrasound (US) radiomics, and total prostate-specific antigen (tPSA) levels in early prostate cancer detection. METHODS: We analyzed the publicly available dataset GSE112264 to identify the differentially expressed miRNAs associated with prostate cancer. Then, PyRadiomics was used to extract image features, and least absolute shrinkage and selection operator (LASSO) was used to screen the data. Subsequently, according to strict inclusion and exclusion criteria, the internal dataset (n = 199) was used to construct a diagnostic model, and the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and DeLong test were used to evaluate its diagnostic performance. Finally, we used an external dataset (n = 158) for further validation. RESULTS: The number of features extracted by PyRadiomics was 851, and the number of features screened by LASSO was 23. We combined the hsa-miR-320c, hsa-miR-944, radiomics, and tPSA features to construct a joint model. The area under the ROC curve of the combined model was 0.935. In the internal validation, the area under the curve (AUC) of the training set was 0.943, and the AUC of the test set was 0.946. The AUC of the external data set was 0.910. The calibration curve and decision curve were consistent with the performance of the combined model. There was a significant difference in the prediction ability between the combined prediction model and the single index prediction model, indicating the high credibility and accuracy of the combined model in predicting PCa. CONCLUSIONS: The combined prediction model, consisting of plasma exosomal miRNAs (hsa-miR-320c and hsa-miR-944), US radiomics, and clinical tPSA, can be utilized for the early diagnosis of prostate cancer.

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This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

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BACKGROUND: Breast cancer is a leading cause of cancer-related deaths in females, and the hormone receptor-positive subtype is the most frequent. Breast cancer is a common source of brain metastases; therefore, we aimed to generate a brain metastases prediction model in females with hormone receptor-positive breast cancer. METHODS: The primary cohort included 3,682 females with hormone receptor-positive breast cancer treated at a single center from May 2009 to May 2020. Patients were randomly divided into a training dataset (n = 2,455) and a validation dataset (n = 1,227). In the training dataset, simple logistic regression analyses were used to measure associations between variables and the diagnosis of brain metastases and to build multivariable models. The model with better calibration and discrimination capacity was tested in the validation dataset to measure its predictive performance. RESULTS: The variables incorporated in the model included age, tumor size, axillary lymph node status, clinical stage at diagnosis, HER2 expression, Ki-67 proliferation index, and the modified Scarff-Bloom-Richardson grade. The area under the curve was 0.81 (95 % CI 0.75-0.86), p < 0.001 in the validation dataset. The study presents a guide for the clinical use of the model. CONCLUSION: A brain metastases prediction model in females with hormone receptor-positive breast cancer helps assess the individual risk of brain metastases.

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Neoplasias Encefálicas , Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/patología , Neoplasias Encefálicas/secundario , Persona de Mediana Edad , Medición de Riesgo , Anciano , Receptor ErbB-2/metabolismo , Adulto , Receptores de Estrógenos/metabolismo , Receptores de Estrógenos/análisis , Receptores de Progesterona/metabolismo

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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: Surgical risk stratification is crucial for enhancing perioperative assistance and allocating resources efficiently. However, existing models may not capture the complexity of surgical care in Brazil. Using data from various healthcare settings nationwide, we developed a new risk model for 30-day in-hospital mortality (the Ex-Care BR model). METHODS: A retrospective cohort study was conducted in 10 hospitals from different geographic regions in Brazil. Data were analysed using multilevel logistic regression models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration plots. Derivation and validation cohorts were randomly assigned. RESULTS: A total of 107,372 patients were included, and 30-day in-hospital mortality was 2.1% (n=2261). The final risk model comprised four predictors related to the patient and surgery (age, ASA physical status classification, surgical urgency, and surgical size), and the random effect related to hospitals. The model showed excellent discrimination (AUROC=0.93, 95% confidence interval [CI], 0.93-0.94), calibration, and overall performance (Brier score=0.017) in the derivation cohort (n=75,094). Similar results were observed in the validation cohort (n=32,278) (AUROC=0.93, 95% CI, 0.92-0.93). CONCLUSIONS: The Ex-Care BR is the first model to consider regional and organisational peculiarities of the Brazilian surgical scene, in addition to patient and surgical factors. It is particularly useful for identifying high-risk surgical patients in situations demanding efficient allocation of limited resources. However, a thorough exploration of mortality variations among hospitals is essential for a comprehensive understanding of risk. CLINICAL TRIAL REGISTRATION: NCT05796024.

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Mortalidad Hospitalaria , Humanos , Masculino , Femenino , Brasil/epidemiología , Persona de Mediana Edad , Estudios Retrospectivos , Anciano , Medición de Riesgo/métodos , Adulto , Procedimientos Quirúrgicos Operativos/mortalidad , Estudios de Cohortes , Anciano de 80 o más Años , Curva ROC , Adulto Joven , Factores de Riesgo

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BACKGROUND: The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil. OBJECTIVE: This study aimed (1) to validate the Fetal Medicine Foundation combined algorithm for the prediction of preterm preeclampsia in the Brazilian population and (2) to describe the accuracy and calibration of the Fetal Medicine Foundation algorithm when considering the prophylactic use of aspirin by clinical criteria. STUDY DESIGN: This was a cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11 to 14 weeks of gestation, examining maternal characteristics, medical history, and biophysical markers between October 2010 and December 2018 in a university hospital in Brazil. Risks were calculated using the 2018 version of the algorithm available on the Fetal Medicine Foundation website, and cases were classified as low or high risk using a cutoff of 1/100 to evaluate predictive performance. Expected and observed cases with preeclampsia according to the Fetal Medicine Foundation-estimated risk range (≥1 in 10; 1 in 11 to 1 in 50; 1 in 51 to 1 in 100; 1 in 101 to 1 in 150; and <1 in 150) were compared. After identifying high-risk pregnant women who used aspirin, the treatment effect of 62% reduction in preterm preeclampsia identified in the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Preeclampsia Prevention trial was used to evaluate the predictive performance adjusted for the effect of aspirin. The number of potentially unpreventable cases in the group without aspirin use was estimated. RESULTS: Among 2749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cutoff of 1/100, the screen-positive rate was 25.8%. The detection rate was 71.4%, with a false positive rate of 24.4%. The area under the curve was 0.818 (95% confidence interval, 0.773-0.863). In the risk range ≥1/10, there is an agreement between the number of expected cases and the number of observed cases, and in the other ranges, the predicted risk was lower than the observed rates. Accounting for the effect of aspirin resulted in an increase in detection rate and positive predictive values and a slight decrease in the false positive rate. With 27 cases of preterm preeclampsia in the high-risk group without aspirin use, we estimated that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis. CONCLUSION: In a high-prevalence setting, the Fetal Medicine Foundation algorithm can identify women who are more likely to develop preterm preeclampsia. Not accounting for the effect of aspirin underestimates the screening performance.

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Artisanal and small-scale gold mining (ASGM) is the primary global source of anthropogenic mercury (Hg) emissions. It has impacted the Amazon rainforest in the Peruvian region of Madre de Dios. However, few studies have investigated Hg's distribution in terrestrial ecosystems in this region. We studied Hg's distribution and its predictors in soil and native plant species from artisanal mining sites. Total Hg concentrations were determined in soil samples collected at different depths (0-5 cm and 5-30 cm) and plant samples (roots, shoots, leaves) from 19 native plant species collected in different land cover categories: naked soil (L1), gravel piles (L2), natural regeneration (L3), reforestation (L4), and primary forest (L5) in the mining sites. Hg levels in air were also studied using passive air samplers. The highest Hg concentrations in soil (average 0.276 and 0.210 mg kg-1 dw.) were found in the intact primary forest (L5) at 0-5 cm depth and in the plant rooting zones at 5-30 cm depth, respectively. Moreover, the highest Hg levels in plants (average 0.64 mg kg-1 dw) were found in foliage of intact primary forest (L5). The results suggest that the forest in these sites receives Hg from the atmosphere through leaf deposition and that Hg accumulates in the soil surrounding the roots. The Hg levels found in the plant leaves of the primary forest are the highest ever recorded in this region, exceeding values found in forests impacted by Hg pollution worldwide and raising concerns about the extent of the ASGM impact in this ecosystem. Correlations between Hg concentrations in soil, bioaccumulation in plant roots, and soil physical-chemical characteristics were determined. Linear regression models showed that the soil organic matter content (SOM), pH, and electrical conductivity (EC) predict the Hg distribution and accumulation in soil and bioaccumulation in root plants.

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Monitoreo del Ambiente , Oro , Mercurio , Minería , Contaminantes del Suelo , Suelo , Mercurio/análisis , Perú , Contaminantes del Suelo/análisis , Suelo/química , Plantas/metabolismo , Ecosistema , Bosques , Bosque Lluvioso

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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.

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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Nomogramas , Humanos , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Carcinoma de Pulmón de Células no Pequeñas/patología , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Masculino , Femenino , Neoplasias Pulmonares/patología , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/tratamiento farmacológico , Estudios Retrospectivos , Persona de Mediana Edad , Pronóstico , Anciano , Hemoglobinas/análisis , Curva ROC , Adulto , Estimación de Kaplan-Meier , Recuento de Plaquetas , Plaquetas/patología , Estadificación de Neoplasias , Recuento de Linfocitos , Albúmina Sérica/análisis

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Background: Patients with type 2 diabetes are at an increased risk of chronic kidney disease (CKD) hence it is recommended that they receive annual CKD screening. The huge burden of diabetes in Mexico and limited screening resource mean that CKD screening is underperformed. Consequently, patients often have a late diagnosis of CKD. A regional minimal-resource model to support risk-tailored CKD screening in patients with type 2 diabetes has been developed and globally validated. However, population heath and care services between countries within a region are expected to differ. The aim of this study was to evaluate the performance of the model within Mexico and compare this with the performance demonstrated within the Americas in the global validation. Methods: We performed a retrospective observational study with data from primary care (Clinic Specialized in Diabetes Management in Mexico City), tertiary care (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán) and the Mexican national survey of health and nutrition (ENSANUT-MC 2016). We applied the minimal-resource model across the datasets and evaluated model performance metrics, with the primary interest in the sensitivity and increase in the positive predictive value (PPV) compared to a screen-everyone approach. Results: The model was evaluated on 2510 patients from Mexico (primary care: 1358, tertiary care: 735, ENSANUT-MC: 417). Across the Mexico data, the sensitivity was 0.730 (95% CI: 0.689 - 0.779) and the relative increase in PPV was 61.0% (95% CI: 52.1% - 70.8%). These were not statistically different to the regional performance metrics for the Americas (sensitivity: p=0.964; relative improvement: p=0.132), however considerable variability was observed across the data sources. Conclusion: The minimal-resource model performs consistently in a representative Mexican population sample compared with the Americas regional performance. In primary care settings where screening is underperformed and access to laboratory testing is limited, the model can act as a risk-tailored CKD screening solution, directing screening resources to patients who are at highest risk.

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Diabetes Mellitus Tipo 2 , Insuficiencia Renal Crónica , Humanos , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiología , México/epidemiología , Tasa de Filtración Glomerular , Insuficiencia Renal Crónica/diagnóstico , Insuficiencia Renal Crónica/epidemiología , Tamizaje Masivo

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OBJECTIVE: To clarify the composition of lesions in different magnetic resonance imaging (MRI) partitions of positive surgical margins (PSM) after laparoscopic radical prostatectomy, explore the influence of lesion location on PSM, and construct a clinical prediction model to predict the risk of PSM. MATERIALS AND METHODS: This retrospective cohort study included 309 patients who underwent laparoscopic radical prostatectomy from 2018 to 2021 in our center was performed. 129 patients who met the same criteria from January to September 2022 were external validation cohorts. RESULTS: The incidence of PSM in transition zone (TZ) lesions was higher than that in peripheral zone (PZ) lesions. The incidence of PSM in the middle PZ was lower than that in other regions. Prostate specific antigen (PSA), clinical T-stage, the number of positive cores, international society of urological pathology (ISUP) grade (biopsy), MRI lesion location, extracapsular extension, seminal vesicle invasion (SVI), pseudo-capsule invasion (PCI), long diameter of lesions, lesion volume, lesion volume ratio, PSA density were related to PSM. MRI lesion location and PCI were independent risk factors for PSM. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a clinical prediction model for PSM, including five variables: the number of positive cores, SVI, MRI lesion location, long diameter of lesions, and PSA. CONCLUSION: The positive rate of surgical margin in middle PZ was significantly lower than that in other regions, and MRI lesion location was an independent risk factor for PSM.

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Laparoscopía , Imagen por Resonancia Magnética , Márgenes de Escisión , Prostatectomía , Neoplasias de la Próstata , Humanos , Masculino , Prostatectomía/métodos , Laparoscopía/métodos , Neoplasias de la Próstata/cirugía , Neoplasias de la Próstata/patología , Neoplasias de la Próstata/diagnóstico por imagen , Estudios Retrospectivos , Imagen por Resonancia Magnética/métodos , Persona de Mediana Edad , Anciano , Antígeno Prostático Específico/sangre , Factores de Riesgo , Medición de Riesgo/métodos , Clasificación del Tumor , Estadificación de Neoplasias

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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.

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Hemorragia Cerebral Intraventricular , Nomogramas , Humanos , Recién Nacido , Estudios Retrospectivos , Femenino , Masculino , Hemorragia Cerebral Intraventricular/diagnóstico , Índice de Severidad de la Enfermedad , Bases de Datos Factuales , Hemorragia Cerebral/diagnóstico , Valor Predictivo de las Pruebas , Recuento 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.

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Síndrome Mucocutáneo Linfonodular , Nomogramas , Humanos , Síndrome Mucocutáneo Linfonodular/complicaciones , Síndrome Mucocutáneo Linfonodular/diagnóstico , Masculino , Femenino , Niño , Preescolar , Anomalías de los Vasos Coronarios , Curva ROC , Modelos Logísticos , Medición de Riesgo/métodos

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BACKGROUND: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADRs) can negatively affect adherence and treatment success rates. METHODS: We developed prediction models for TB-ADRs, considering participants with drug-susceptible pulmonary TB who initiated standard TB therapy. TB-ADRs were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM) use, human immunodeficiency virus (HIV) status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backward selection. Cox regression was used to evaluate TB-ADR risk. RESULTS: There were 156 TB-ADRs among 102 of the 945 (11%) participants included. Most TB-ADRs were hepatic (n = 82 [53%]), of moderate severity (grade 2; n = 121 [78%]), and occurred in NAT2 slow acetylators (n = 62 [61%]). The main prediction model included CM use, HbA1c, alcohol use, HIV seropositivity, BMI, and age, with robust performance (c-statistic = 0.79 [95% confidence interval {CI}, .74-.83) and fit (optimism-corrected slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV seropositivity (hazard ratio [HR], 2.68 [95% CI, 1.75-4.09]) and CM use (HR, 5.26 [95% CI, 2.63-10.52]) increased TB-ADR risk. CONCLUSIONS: The models, with clinical variables and with NAT2, were highly predictive of TB-ADRs.

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Arilamina N-Acetiltransferasa , Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos , Seropositividad para VIH , Tuberculosis Pulmonar , Humanos , Antituberculosos/efectos adversos , Brasil/epidemiología , Hemoglobina Glucada , Seropositividad para VIH/tratamiento farmacológico , Tuberculosis Pulmonar/tratamiento farmacológico , Arilamina N-Acetiltransferasa/metabolismo

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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.

Asunto(s)

Neoplasias del Colon , Nomogramas , Humanos , Pronóstico , Neoplasias del Colon/cirugía , Bases de Datos Factuales , Análisis Multivariante

RESUMEN

OBJECTIVE: To compare the predictive performance of the current clinical prediction models for predicting intravesical recurrence (IVR) after radical nephroureterectomy (RNU) in patients with upper tract urothelial carcinoma (UTUC). METHODS: We retrospectively analysed upper tract urothelial carcinoma patients who underwent radical nephroureterectomy in our centre from January 2009 to December 2019. We used the propensity score matching (PSM) method to adjust the confounders between the IVR and non-IVR groups. Additionally, Xylinas' reduce model and full model, Zhang's model, and Ishioka's risk stratification model were used to retrospectively calculate predictions for each patient. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUCs) were compared to identify the method with the highest predictive value. RESULTS: We included 217 patients with a median follow-up of 41 months, of which 57 had IVR. After PSM analysis, 52 pairs of well-matched patients were included in the comparative study. No significant difference was found in clinical indicators besides hydronephrosis. The model comparison showed that the AUCs of the reduced Xylinas' model for 12 months, 24 months, and 36 months were 0.69, 0.73, and 0.74, respectively, and those of the full Xylinas' model were 0.72, 0.75, and 0.74, respectively. The AUC of Zhang's model for 12 months, 24 months, and 36 months was 0.63, 0.71, and 0.71, respectively, the performance of Ishioka's model is that the AUC of 12 months, 24 months and 36 months was 0.66, 0.71, and 0.74, respectively. CONCLUSION: The external verification results of the four models show that more comprehensive data and a larger sample size of patients are needed to strengthen the models' derivation and updating procedure, to better apply them to different populations.

Asunto(s)

Carcinoma de Células Transicionales , Neoplasias de la Vejiga Urinaria , Humanos , Neoplasias de la Vejiga Urinaria/patología , Carcinoma de Células Transicionales/cirugía , Carcinoma de Células Transicionales/patología , Nefroureterectomía , Estudios Retrospectivos , Nefrectomía , Recurrencia Local de Neoplasia/patología

RESUMEN

Abstract Objective: This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. Methods: This prospective cohort study included 9,895 pregnant women who received prenatal care at a maternal health facility in China from January 2021 to December 2022. Data on demographics, medical history, lifestyle factors, and mental health were collected. A multivariable logistic regression analysis was performed to develop the prediction model with spontaneous abortion as the outcome. The model was internally validated using bootstrapping techniques, and its discrimination and calibration were assessed. Results: The spontaneous abortion rate was 5.95% (589/9,895) 1. The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score 1. The model showed good discrimination with a C-statistic of 0.88 (95% CI 0.87‒0.90) 1, and its calibration was adequate based on the Hosmer-Lemeshow test (p = 0.27). Conclusions: The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. Further external validation is recommended before clinical application.