Expected Versus Unexpected Delivery for Placenta Accreta Spectrum (PAS) Disorders with Same Team in Single Tertiary Center.

OBJECTIVES: To evaluate the maternal and neonatal outcomes of expected and unexpected pathologically proven placenta accreta spectrum (PAS) cases in a single multidisciplinary center. MATERIAL AND METHODS: This was a retrospective cohort study of 92 PAS cases from January 2011 until September 2021. Only cases with histopathologically invasive placentation were included in the study. The cases diagnosed at the time of delivery were defined as unexpected PAS (uPAS) and those diagnosed antenatally as expected PAS (ePAS). Maternal and neonatal outcomes of both groups were compared. RESULTS: Thirty-five (38%) of 92 cases were in the uPAS group. Placenta previa and high-grade PAS (percreata) were significantly higher in the ePAS group (p=0.028, p<0.001; respectively). The mean packed red blood cell transfusion was significantly higher in the uPAS group (p=0.030) but transfusions of other blood products were similar in the two groups. There was no significant difference in intraoperative complication rates between the two groups. Preterm delivery (<37 weeks) was significantly higher in the ePAS group (p<0.001), but there was no significant difference between the two groups in terms of adverse neonatal outcomes. CONCLUSIONS: Our single center data show that although ePAS cases include more highly invasive PAS cases, maternal hemorrhagic morbidity is lower than uPAS cases. Reducing maternal morbidity in PAS cases can be achieved by increasing antenatal diagnosis.

Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study.

BACKGROUND: COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. METHODS: The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. RESULTS: While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. CONCLUSION: Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.