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Risk factors for intensive care unit admission after cesarean hysterectomy for placenta accreta spectrum
Jessian L Munoz1, CeCe Cheng2, Georgia A McCann2
1Divisions of Maternal Fetal Medicine and Fetal Intervention, Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA.
Insights
Identifying preoperative risk factors for intensive care unit (ICU) admission in placenta accreta spectrum (PAS) cases can aid in patient counseling and resource allocation. Early gestational age, vaginal bleeding, and emergent delivery predict ICU needs.
Area of Science:
- Obstetrics and Gynecology
- Maternal-Fetal Medicine
- Surgical Critical Care
Background:
- Placenta accreta spectrum (PAS) is a severe obstetric complication associated with significant maternal morbidity and mortality.
- Effective management of PAS requires careful planning, especially concerning intensive care unit (ICU) resource allocation.
- Predicting ICU admission is crucial for optimizing patient care and hospital resource utilization.
Purpose of the Study:
- To identify preoperative risk factors associated with ICU admission in patients with PAS.
- To develop a predictive model for ICU admission to guide patient counseling and resource management.
- To provide an evidence-based approach for preoperative decision-making in PAS cases.
Main Methods:
- A case-control study involving 145 patients undergoing cesarean hysterectomy for histopathologically confirmed PAS.
- Data on pre-, intra-, and postoperative variables were collected from electronic medical records.
- Uni- and multivariate logistic regression analyses were used to identify independent predictors of ICU admission.
Main Results:
- Of 145 patients, 63 (43%) required ICU admission post-surgery.
- Preoperative factors predicting ICU admission included earlier gestational age, more than two episodes of vaginal bleeding, and emergent delivery.
- A model using these three variables achieved an area under the curve of 0.73 for predicting ICU admission; patients with all three factors had a 100% ICU admission rate.
Conclusions:
- Preoperative identification of risk factors is essential for managing ICU resource allocation in PAS.
- This predictive model can enhance patient counseling and facilitate proactive resource planning for staffing and utilization.
- Evidence-based prediction of ICU needs improves management strategies for placenta accreta spectrum disorders.
Objective:
Placenta accreta spectrum (PAS) is a complex disorder of uterine wall disruption with significant morbidity and mortality, particularly at time of delivery. Both physician and physical hospital resource allocation/utilization remains a challenge in PAS cases including intensive care unit (ICU) beds. The primary objective of the present study was to identify preoperative risk factors for ICU admission and create an ICU admission prediction model for patient counseling and resource utilization decision making in an evidence-based manner.
Methods:
This was a case-control study of 145 patients at our PAS referral center undergoing cesarean hysterectomy for PAS. Final confirmation by histopathology was required for inclusion. Patient disposition after surgery (ICU vs post-anesthesia care unit) was our primary outcome and pre-/intra-/postoperative variables were obtained via electronic medical records with an emphasis on the predictive capabilities of the preoperative variables. Uni- and multivariate analysis was performed to identify independent predictive factors for ICU admission.
Results:
In this large cohort of 145 patients who underwent cesarean hysterectomy for PAS, with histopathologic confirmation, 63 (43%) were admitted to the ICU following delivery. These patients were more likely to be delivered at an earlier gestational age (34 vs 35 weeks, P < 0.001), have had >2 episodes of vaginal bleeding and emergent delivery compared to patients admitted to patients with routine recovery care (44% vs 18.3%, P = 0.009). Uni- and multivariate logistic regression showed an area under the curve of 0.73 (95% CI: [0.63, 0.81], P < 0.001) for prediction of ICU admission with these three variables. Patients with all three predictors had 100% ICU admission rate.
Conclusion:
Resource prediction, utilization and allocation remains a challenge in PAS management. By identifying patients with preoperative risk factors for ICU admission, not only can patients be counseled but this resource can be requested preoperatively for staffing and utilization purposes.
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