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BackgroundRobotic surgery for malignant uterine cancer raises issue of economic sustainability for providers. The objective of this study was to assess the value of surgical admissions for malignant uterine cancer in a University Hospital through an analysis of their costs and outcomes by comparing three different surgical approaches (laparotomy, laparoscopic, and robotic surgery).MethodsHospitalizations between 1 January 2019 and 31 October 2021 for malignant uterine cancer surgery were selected and stratified. For each surgical approach, mean values (with 95% confidence intervals, CI) were calculated for cost items. Moreover, 30-day readmission frequency was calculated for the three approaches compared to each other. ANOVA and Student's t-test and relative risk (RR) were used for statistical analysis. A break-even analysis was carried out by evaluating the volume of robotic and non-robotic surgical admissions.ResultsA total of 1,336 hospitalizations were included in the study, 366 with robotic, 591 with laparoscopic, and 379 with laparotomy surgery. Robotic surgery, compared to laparoscopic and laparotomy ones, showed a statistically significant difference (p < 0.001) in the economic margin, which was largely negative (−1069.18 €; 95%CI:−1240.44-−897.92 €) mainly due to devices cost, and a lower percentage of 30-day readmissions (1.4%; 95%CI: 0.2–2.6%), with a statistically significant difference only vs. laparotomy (p = 0.029). Laparoscopic compared to laparotomy surgery showed a significantly (p < 0,001) more profitable economic margin (1692.21 €; 95%CI: 1531.75 €−1852.66 €) without a significant difference for 30-day readmissions. Break-even analysis showed that, on average, for each malignant uterine cancer elective surgery performed laparoscopically, 1.58 elective robotic surgeries are sustainable for the hospital (95% CI: 1.23–2.06).ConclusionBreak-even analysis could be a useful tool to support hospital management in planning and governance of malignant uterine cancer surgery. Systematic application of this tool will allow defining over time right distribution of robotic, laparoscopic, and laparotomy surgeries' volumes to perform to ensure both quality and economic-financial balance and therefore value of uterine oncological surgery. Concerning research, this study paves the way for a multicentric study, the extension of outcomes of malignant uterine surgery to be considered and assessed, and the future inclusion of other therapeutic interventions in the analysis.

Immediate breast reconstruction is recommended for eligible patients undergoing mastectomy, raising the issue of economic sustainability of both mastectomy and breast reconstruction performed within the same hospitalization, as opposed to two surgical procedures in two different hospitalizations.A retrospective analysis was conducted to compare economic sustainability of mastectomies with or without immediate breast reconstruction.Economic data on hospitalizations for mastectomy in a Teaching Hospital between 1 January 2019 and 31 March 2021 were analyzed to assess their sustainability.338 admissions were selected (63.9% with immediate breast reconstruction (CI 99%: 57.2% to 70.6%). Compared to mastectomy alone, mastectomy with immediate breast reconstruction had higher cost of € 2,245 (p0.001), with operating rooms and devices as main cost drivers. Current reimbursements rates (which are the same for mastectomy alone and for mastectomy with immediate breast reconstruction) led to an average loss of € 1,719 for each mastectomy with immediate breast reconstruction.Current DRGs reimbursement rates for hospital admissions for breast cancer surgery do not guarantee immediate breast reconstruction's economic sustainability. DRGs system should be revised, or other solutions as bundled payment should be implemented in the light of the costs of innovation in healthcare, considering mastectomy and breast reconstruction steps in a path of linked actions aimed at improving patients' health.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.