BACKGROUND: To develop and validate a support tool for healthcare providers, enabling them to make precise and critical decisions regarding intensive care unit (ICU) admissions for high-risk pregnant women, thus enhancing maternal outcomes.
METHODS: This retrospective study involves secondary data analysis of information gathered from 9550 pregnant women, who had severe maternal morbidity (any unexpected complication during labor and delivery that leads to substantial short-term or long-term health issues for the mother), collected between 2009 and 2010 from the Brazilian Network for Surveillance of Severe Maternal Morbidity, encompassing 27 obstetric reference centers in Brazil. Machine-learning models, including decision trees, Random Forest, Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost), were employed to create a risk prediction tool for ICU admission. Subsequently, sensitivity analysis was conducted to compare the accuracy, predictive power, sensitivity, and specificity of these models, with differences analyzed using the Wilcoxon test.
RESULTS: The XGBoost algorithm demonstrated superior efficiency, achieving an accuracy rate of 85%, sensitivity of 42%, specificity of 97%, and an area under the receiver operating characteristic curve of 86.7%. Notably, the estimated prevalence of ICU utilization by the model (11.6%) differed from the prevalence of ICU use from the study (21.52%).
CONCLUSIONS: The developed risk engine yielded positive results, emphasizing the need to optimize intensive care bed utilization and objectively identify high-risk pregnant women requiring these services. This approach promises to enhance the effective and efficient management of pregnant women, particularly in resource-constrained regions worldwide. By streamlining ICU admissions for high-risk cases, healthcare providers can better allocate critical resources, ultimately contributing to improved maternal health outcomes.

BACKGROUND: Various risk classification systems (RCSs) are used globally to stratify newly diagnosed patients with prostate cancer (PCa) into prognostic groups.
OBJECTIVE: To compare the predictive value of different prognostic subgroups (low-, intermediate-, and high-risk disease) within the RCSs for detecting metastatic disease on prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) for primary staging, and to assess whether further subdivision of subgroups would be beneficial.
METHODS: Patients with newly diagnosed PCa, in whom PSMA-PET/CT was performed between 2017 and 2022, were studied retrospectively. Patients were stratified into risk groups based on four RCSs: European Association of Urology, National Comprehensive Cancer Network (NCCN), Cambridge Prognostic Group (CPG), and Cancer of the Prostate Risk Assessment.
METHODS: The prevalence of metastatic disease on PSMA-PET/CT was compared among the subgroups within the four RCSs.
CONCLUSIONS: In total, 2630 men with newly diagnosed PCa were studied. Any metastatic disease was observed in 35% (931/2630) of patients. Among patients classified as having intermediate- and high-risk disease, the prevalence of metastases ranged from approximately 12% to 46%. Two RCSs further subdivided these groups. According to the NCCN, metastatic disease was observed in 5.8%, 13%, 22%, and 62% for favorable intermediate-, unfavorable intermediate-, high-, and very-high-risk PCa, respectively. Regarding the CPG, these values were 6.9%, 13%, 21%, and 60% for the corresponding risk groups.
CONCLUSIONS: This study underlines the importance of nuanced risk stratification, recommending the further subdivision of intermediate- and high-risk disease given the notable variation in the prevalence of metastatic disease. PSMA-PET/CT for primary staging should be reserved for patients with unfavorable intermediate- or higher-risk disease.
RESULTS: The use of various risk classification systems in patients with prostate cancer helps identify those at a higher risk of having metastatic disease on prostate-specific membrane antigen positron emission tomography/computed tomography for primary staging.

BACKGROUND: Primary malignant cardiac tumors (PMCTs) are fatal, but up to now, there is still a lack of survival prediction model for prognosis evaluation. We developed nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for PMCTs by the Surveillance, Epidemiology, and End Result (SEER) database.
METHODS: A total of 506 PMCTs participants were identified in the SEER database from 1973 to 2014 and were randomly assigned into the training cohort (N = 354) and the validation cohort (N = 152). The prognostic factors for PMCTs were identified by Kaplan-Meier and multivariate Cox analysis and further incorporated to build OS and CSS nomograms. The nomograms were internally and externally validated via concordance indexes (C-index) and calibration curves.
RESULTS: The independent prognostic factors for OS and CSS in PMCTs were associated with age at diagnosis, histopathology, tumor stage, cancer-directed surgery, and chemotherapy (all P < .05). In the internal validation, the C-index values were 0.71 (95% confidence interval [CI]: 0.68-0.75) for OS nomogram, and 0.70 (95% CI: 0.67-0.74) for CSS nomogram. In the external validation, the C-index values were 0.71 (95% CI: 0.66-0.77) for OS nomogram, and 0.71 (95% CI: 0.65-0.77) for CSS nomogram. The calibration curves of internal and external validation showed consistency between the nomograms and the actual observation. The risk stratification of PMCTs was significant distinction (P < .05).
CONCLUSIONS: We developed and validated credible nomograms to predict OS and CSS in PMCTs. These nomograms can be offered to clinicians to more precisely estimate the survival and identify risk stratification of PMCTs.