Unplanned postoperative intensive care unit admission for ovarian cancer cytoreduction is associated with significant decrease in overall survival

Prerequisites to improve surgical cytoreduction in FIGO stage III/IV epithelial ovarian cancer and subsequent clinical ramifications

BACKGROUND

No residual disease (CC 0) following cytoreductive surgery is pivotal for the prognosis of women with advanced stage epithelial ovarian cancer (EOC). Improving CC 0 resection rates without increasing morbidity and no delay in subsequent chemotherapy favors a better outcome in these women. Prerequisites to facilitate this surgical paradigm shift and subsequent ramifications need to be addressed. This quality improvement study assessed 559 women with advanced EOC who had cytoreductive surgery between January 2014 and December 2019 in our tertiary referral centre. Following implementation of the Enhanced Recovery After Surgery (ERAS) pathway and prehabilitation protocols, the surgical management paradigm in advanced EOC patients shifted towards maximal surgical effort cytoreduction in 2016. Surgical outcome parameters before, during, and after this paradigm shift were compared. The primary outcome measure was residual disease (RD). The secondary outcome parameters were postoperative morbidity, operative time (OT), length of stay (LOS) and progression-free-survival (PFS).

RESULTS

R0 resection rate in patients with advanced EOC increased from 57.3% to 74.4% after the paradigm shift in surgical management whilst peri-operative morbidity and delays in adjuvant chemotherapy were unchanged. The mean OT increased from 133 + 55 min to 197 + 85 min, and postoperative high dependency/intensive care unit (HDU/ICU) admissions increased from 8.1% to 33.1%. The subsequent mean LOS increased from 7.0 + 2.6 to 8.4 + 4.9 days. The median PFS was 33 months. There was no difference for PFS in the three time frames but a trend towards improvement was observed.

CONCLUSIONS

Improved CC 0 surgical cytoreduction rates without compromising morbidity in advanced EOC is achievable owing to the right conditions. Maximal effort cytoreductive surgery should solely be carried out in high output tertiary referral centres due to the associated substantial prerequisites and ramifications.

Common obstetrics and gynecologic topics in critical care: A narrative review

The fields of Obstetrics and Gynecology and Critical Care often share medically and surgically complex patients. Peripartum anatomic and physiologic changes can predispose or exacerbate certain conditions and rapid action is often needed. This review discusses some of the most common conditions responsible for the admission of obstetrical and gynecological patients to the critical care unit. We will consider both obstetrical and gynecologic concepts including postpartum hemorrhage, antepartum hemorrhage, abnormal uterine bleeding, preeclampsia and eclampsia, venous thromboembolism, amniotic fluid embolism, sepsis and septic shock, obstetrical trauma, acute abdomen, malignancies, peripartum cardiomyopathy, and substance abuse. This article aims to be a primer for the Critical Care provider.

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70-98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3-5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS.

Incidence of unplanned intensive care unit admission following surgery and associated factors in Amhara regional state hospitals

Unplanned postoperative critical care admission poses a potential risk to patients and places unanticipated pressure on clinical services and it has become an important parameter to assess patient safety in perioperative services. This study was aimed to determine the incidence of unplanned intensive care unit admission following surgery and the associated factors. A multi-center cross-sectional study was conducted on postoperative patients admitted to the ICU of three hospitals located in the Amhara region. Data were collected via a structured survey tool and analyzed using SPSS version 23 software with binary logistic regression analysis. The statistical significance to identify patient, anesthetic and surgical related factors in the preoperative, intraoperative, and postoperative period was < 0.05 for multivariable regression with a 95% confidence interval. Predominantly patients were admitted to the ICU in an unplanned manner. ASA status, preoperative hemoglobin (Hgb) level, intraoperative estimated blood loss, and adverse events occurring in the operating room were significantly associated with intensive care unit admission following surgery. Patients who had a low preoperative Hgb value were 35.1 times more likely to be admitted to the intensive care unit in an unplanned manner compared with their counterparts [(Adjust odds ratio (AOR) 35.16; CI 12.82, 96.44)]. Patients with ASA II and III were 19.4 and 16.2 times more likely to be admitted to ICU in an unplanned way compared to patients who had ASA I physical status [(AOR 51.79; CI 8.28, 323.94) (AOR 67.8 CI 14.68, 313.53)]. Unplanned ICU admission after surgery was high in this study, suggesting poor perioperative planning, risk stratification, and optimization of patients.

Development and Validation of a Multivariable Prediction Model for Postoperative Intensive Care Unit Stay in a Broad Surgical Population

IMPORTANCE

Despite limited capacity and expensive cost, there are minimal objective data to guide postoperative allocation of intensive care unit (ICU) beds. The Surgical Risk Preoperative Assessment System (SURPAS) uses 8 preoperative variables to predict many common postoperative complications, but it has not yet been evaluated in predicting postoperative ICU admission.

OBJECTIVE

To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population.

DESIGN, SETTING, AND PARTICIPANTS

This decision analytical model was a retrospective, observational analysis of prospectively collected patient data from the 2012 to 2018 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, which were merged with individual patients' electronic health record data to capture postoperative ICU use. Multivariable logistic regression modeling was used to determine how the 8 preoperative variables of the SURPAS model predicted ICU use compared with a model inputting all 28 preoperatively available NSQIP variables. Data included in the analysis were collected for the ACS NSQIP at 5 hospitals (1 tertiary academic center, 4 academic affiliated hospitals) within the University of Colorado Health System between January 1, 2012, and December 31, 2018. Included patients were those undergoing surgery in 9 surgical specialties during the 2012 to 2018 period. Data were analyzed from May 29 to July 30, 2021.

EXPOSURE

Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery.

MAIN OUTCOMES AND MEASURES

Use of ICU care up to 30 days after surgery.

RESULTS

A total of 34 568 patients were included in the analytical data set: 32 032 (92.7%) in the cohort without postoperative ICU use and 2545 (7.4%) in the cohort with postoperative ICU use (no ICU use: mean [SD] age, 54.9 [16.6] years; 18 188 women [56.8%]; ICU use: mean [SD] age, 60.3 [15.3] years; 1333 men [52.4%]). For the internal chronologic validation of the 7-variable SURPAS model, data from 2012 to 2016 were used as the training data set (n = 24 250, 70.2% of the total sample size of 34 568) and data from 2017 to 2018 were used as the test data set (n = 10 318, 29.8% of the total sample size of 34 568). The C statistic improved in the test data set compared with the training data set (0.933; 95% CI, 0.924-0.941 vs 0.922; 95% CI, 0.917-0.928), whereas the Brier score was slightly worse in the test data set compared with the training data set (0.045; 95% CI, 0.042-0.048 vs 0.045; 95% CI, 0.043-0.047). The SURPAS model compared favorably with the model inputting all 28 NSQIP variables, with both having good calibration between observed and expected outcomes in the Hosmer-Lemeshow graphs and similar Brier scores (model inputting all variables, 0.044; 95% CI, 0.043-0.048; SURPAS model, 0.045; 95% CI, 0.042-0.046) and C statistics (model inputting all variables, 0.929; 95% CI, 0.925-0.934; SURPAS model, 0.925; 95% CI, 0.921-0.930).

CONCLUSIONS AND RELEVANCE

Results of this decision analytical model study revealed that the SURPAS prediction model accurately predicted postoperative ICU use across a diverse surgical population. These results suggest that the SURPAS prediction model can be used to help with preoperative planning and resource allocation of limited ICU beds.

Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

Research Progress of PCNA in Reproductive System Diseases

Reproductive system diseases have become a public health problem that endangers human physical and mental health. The causes of reproductive diseases are complex and diverse. From a biological point of view, abnormal cell proliferation may affect important physiological functions of reproductive organs and cause various gynecological or andrological diseases. Proliferating cell nuclear antigen (PCNA) is the most commonly used indicator for detecting cell proliferation activity. The up- or downregulation of its expression is of great significance in reproductive system diseases. This review summarizes the significance of the latest research on PCNA expression in reproductive system diseases.

Postoperative Admission in Critical Care Units Following Gynecologic Oncology Surgery: Outcomes Based on a Systematic Review and Authors' Recommendations

BACKGROUND/AIM

The present study aimed to evaluate the predictors of admission to the Critical Care Units (CCUs) and factors predisposing to prolonged stay in CCUs after gynecological oncology surgery.

PATIENTS AND METHODS

Studies which addressed cases of women who underwent surgery for gynecological malignancies and required postoperative CCU admission were included.

RESULTS

Seven studies with 3820 patients were included. Among them, 1680 required admission to CCU. Advanced age, higher Charlson Comorbidity Index (CCI) score, longer operative times, protracted blood loss and intestinal resection were associated with higher probability of CCU admission. Patients' age, operative times, blood loos and intestinal resection were significant predictors of prolonged stay to CCUs.

CONCLUSION

Admission to CCU and length of stay following surgery for gynecologic malignancies is driven by specific patient characteristics as well as intraoperative values. Further studies are needed to validate high risk patients who will benefit from postoperative care to CCUs to ensure favorable postoperative outcomes and cost-effectiveness.

Surgical Apgar score is strongly associated with postoperative ICU admission

Immediate postoperative intensive care unit (ICU) admission can increase the survival rate in patients undergoing high-risk surgeries. Nevertheless, less than 15% of such patients are immediately admitted to the ICU due to no reliable criteria for admission. The surgical Apgar score (SAS) (0–10) can be used to predict postoperative complications, mortality rates, and ICU admission after high-risk intra-abdominal surgery. Our study was performed to determine the relationship between the SAS and postoperative ICU transfer after all surgeries. All patients undergoing operative anesthesia were retrospectively enrolled. Among 13,139 patients, 68.4% and < 9% of whom had a SASs of 7–10 and 0–4. Patients transferred to the ICU immediately after surgery was 7.8%. Age, sex, American Society of Anesthesiologists (ASA) class, emergency surgery, and the SAS were associated with ICU admission. The odds ratios for ICU admission in patients with SASs of 0–2, 3–4, and 5–6 were 5.2, 2.26, and 1.73, respectively (P < 0.001). In general, a higher ASA classification and a lower SAS were associated with higher rates of postoperative ICU admission after all surgeries. Although the SAS is calculated intraoperatively, it is a powerful tool for clinical decision-making regarding the immediate postoperative ICU transfer.

Clinical Factors Associated with Longer Hospital Stay Following Ovarian Cancer Surgery

Background: Ovarian cancer (OC) is the leading cause of death from gynecologic malignancy and is treated with a combination of cytoreductive surgery and platinum-based chemotherapy. Extended length of stay (LOS) after surgery can affect patient morbidity, overall costs, and hospital resource utilization. The primary objective of this study was to identify factors contributing to prolonged LOS for women undergoing surgery for ovarian cancer. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried to identify women from 2012-2016 who underwent hysterectomy for ovarian, fallopian tube and peritoneal cancer. The primary outcome was LOS >50th percentile. Preoperative and intraoperative variables were examined to determine which were associated with prolonged LOS. Results: From 2012-2016, 1771 women underwent elective abdominal surgery for OC and were entered in the ACS-NSQIP database. The mean and median LOS was 4.6 and 4.0 days (IQR 0-38), respectively. On multivariate analysis, factors associated with prolonged LOS included: American Society of Anesthesiologists (ASA) Classification III (aOR 1.71, 95% CI 1.38-2.13) or IV (aOR 1.88, 95% CI 1.44-2.46), presence of ascites (aOR 1.88, 95% CI 1.44-2.46), older age (aOR 1.23, 95% CI 1.13-1.35), platelet count >400,000/mm3 (aOR 1.74, 95% CI 1.29-2.35), preoperative blood transfusion (aOR 11.00, 95% CI 1.28-94.77), disseminated cancer (aOR 1.28, 95% CI 1.03-1.60), increased length of operation (121-180 min, aOR 1.47, 95% CI 1.13-1.91; >180 min, aOR 2.78, 95% CI 2.13-3.64), and postoperative blood transfusion within 72 h of incision (aOR 2.04, 95% CI 1.59-2.62) (p < 0.05 for all). Conclusions: Longer length of hospital stay following surgery for OC is associated with many patient, disease, and treatment-related factors. The extent of surgery, as evidenced by perioperative blood transfusion and length of surgical procedure, is a factor that can potentially be modified to shorten LOS, improve patient outcomes, and reduce hospital costs.