Design, construction, and validation of obstetric risk classification systems to predict intensive care unit admission.

INTRODUCTION: 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%). CONCLUSION: 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.

Evaluation of the complexity of postoperative care following breast and gynecologic cancer surgery.

The purpose of this study was to evaluate the complexity of postoperative care required by women who had undergone breast or gynecologic cancer surgery, using the Therapeutic Intervention Scoring System-28. An observational, longitudinal study was carried out on 83 women admitted postoperatively to the Intensive Care Unit of the Center for Women's Integrated Healthcare. The results of this study show that women diagnosed with gynecologic cancer had a significantly higher mean Therapeutic Intervention Scoring System-28 score compared with women who had been diagnosed with breast cancer (P = .01). Women who underwent gynecologic surgery had a significantly higher mean Therapeutic Intervention Scoring System-28 score than women who had breast surgery (P = .03). Most of the activities performed by staff during the postoperative intensive care unit period involved only basic care procedures. Only advanced age and hypertension were related to death during the postoperative period. In conclusion, from the nursing standpoint, the complexity of care required in the intensive care unit during the postoperative period was moderate. Women aged 80 years or older were at the highest risk of death during this period.