A preoperative risk score to predict red blood cell transfusion in patients undergoing hysterectomy for ovarian cancer

The impact of perioperative transfusions on the oncologic outcomes of patients with ovarian cancer: A population-based study

Perioperative blood transfusion in ovarian cancer patients was associated with a 28% increase in all-cause mortality. The negative impact of perioperative blood transfusion extends beyond the immediate postoperative period.

OBJECTIVES

The effect of perioperative blood transfusions on long-term oncologic outcomes of patients with advanced ovarian cancer undergoing cytoreductive surgery remains uncertain. Our study aims to determine the association between perioperative blood transfusion and all-cause mortality in this population.

METHODS

Using province-wide administrative databases, patients with advanced ovarian cancer who underwent surgery between 2007 and 2021 as part of first-line treatment were identified. Perioperative transfusion was defined as any transfusion from date of surgery to discharge from hospital. Multivariable Cox proportional hazards regression models were used to determine if there was an independent association of transfusion with all-cause mortality, accounting significant confounders.

RESULTS

A total of 5891 patients had cytoreductive surgery for advanced ovarian cancer between 2007 and 2021, of which 2898 (49.2%) had interval cytoreductive surgery (ICS) and 2993 (50.8%) had primary cytoreductive surgery (PCS). Perioperative blood transfusion was given to 37.3% of patients (40.5% ICS and 34.2% PCS). On multivariable analysis, there was an increased hazard of all-cause mortality for patients receiving perioperative transfusion compared to those who did not (hazard ratio: 1.28; 95% CI: 1.20-1.37). The association of increased all-cause mortality was observed starting 1 year after surgery, was sustained thereafter, and seen in both ICS and PCS groups.

CONCLUSION

Perioperative blood transfusion after cytoreductive surgery for ovarian cancer is common in Ontario, Canada and was significantly associated with an increase in all-cause mortality. Blood transfusion is a poor prognostic factor, and the negative impact of blood transfusion persists beyond the immediate postoperative period.

Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery

BACKGROUND

Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice.

STUDY DESIGN AND SETTING

We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST).

RESULTS

We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.

CONCLUSION

Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.

A Machine Learning-Modified Novel Nomogram to Predict Perioperative Blood Transfusion of Total Gastrectomy for Gastric Cancer

Background

Perioperative blood transfusion reserves are limited, and the outcome of blood transfusion remains unclear. Therefore, it is important to prepare plans for perioperative blood transfusions. This study aimed to establish a risk assessment model to guide clinical patient management.

Methods

This retrospective comparative study involving 513 patients who had total gastrectomy (TG) between January 2018 and January 2021 was conducted using propensity score matching (PSM). The influencing factors were explored by logistic regression, correlation analysis, and machine learning; then, a nomogram was established.

Results

After assessment of the importance of factors through machine learning, blood loss, preoperative controlling nutritional status (CONUT), hemoglobin (Hb), and the triglyceride–glucose (TyG) index were considered as the modified transfusion-related factors. The modified model was not considered to be different from the original model in terms of performance, but is simpler. A nomogram was created, with a C-index of 0.834, and the decision curve analysis (DCA) demonstrated good clinical benefit.

Conclusions

A nomogram was established and modified with machine learning, which suggests the importance of the patient’s integral condition. This emphasizes that caution should be exercised regarding transfusions, and, if necessary, preoperative nutritional interventions or delayed surgery should be implemented for safety.

Morbidity and mortality of surgically treated pathologic humerus fractures compared to native humerus fractures

BACKGROUND

Despite an increasing prevalence of patients sustaining pathologic fractures of neoplastic origin, few studies have investigated 30-day postoperative complication profiles after surgical treatment of pathologic humerus fractures. The purposes of this study were to use a large nationally representative database to determine short-term complication profiles after surgical treatment of pathologic humerus fractures and assess how these complications compared with more commonly studied native humerus fractures.

METHODS

Using the National Surgical Quality Improvement Program database, we identified 30,866 patients who underwent surgical treatment for either pathologic (n = 449) or native humerus fractures (n = 30,417) from 2007 to 2017. Thirty-day postoperative complication profiles were ascertained and compared between the 2 groups using χ² analyses. Three logistic regression models were then performed to determine which complications were primarily attributable to the pathologic fracture itself vs. the increased comorbidity burden faced by these patients.

RESULTS

Patients with pathologic humerus fractures experienced significantly higher rates of death (6.0% vs. 0.3%, P < .001), serious adverse events (12.2% vs. 3.7%, P < .001), minor complications (15.8% vs. 4.8%, P < .001), extended postoperative lengths of stay (42.3% vs. 21.3%, P < .001), discharge to facilities (22.3% vs. 13.5%, P < .001), and readmissions (14.8% vs. 3.4%, P < .001) compared with patients with native humerus fractures. With respect to specific complications, patients with pathologic fractures were at significantly higher risk of pulmonary complications (1.3% vs. 0.3%, P < .001), renal complications (0.7% vs. 0.2%, P = .007), thromboembolic complications (1.6% vs. 0.6%, P = .01), and transfusions (15.1% vs. 4.1%, P < .001).

CONCLUSION

After surgical treatment, patients with pathologic humerus fractures had significantly higher complication rates compared with native humerus fractures, suggesting that guidelines and treatment algorithms for native humerus fractures may not be generalizable for those of pathologic origin. These findings have significant implications for preoperative patient counseling and may be used to negotiate higher reimbursement rates for these patients given a significantly higher morbidity and mortality than was previously described in literature. Postoperatively, orthopedic surgeons should closely monitor patients with pathologic humerus fractures for deep vein thrombosis, renal complications, and pulmonary complications, use blood-sparing techniques, and employ a multidisciplinary approach to help manage and prevent a more heterogeneous profile of postsurgical complications.

A Clinically Applicable Prediction Model for the Risk of Transfusion in Women Undergoing Myomectomy

STUDY OBJECTIVE

We sought to identify the variables independently associated with intra/postoperative blood transfusion at the time of myomectomy. We further hoped to develop an accurate prediction model using preoperative variables to categorize an individual's risk of blood transfusion during myomectomy.

DESIGN

Case-control study.

SETTING

Not applicable to this study, which used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database.

PATIENTS

Women who underwent an open/abdominal or laparoscopic (robotic or conventional) myomectomy between 2014 and 2017 at participating ACS-NSQIP sites.

INTERVENTION

The primary dependent variable was occurrence of intra/postoperative bleeding requiring blood transfusion. Patient demographics, clinical characteristics, preoperative comorbidities, intraoperative variables, and additional 30-day postoperative outcomes were compared at the bivariable level. For the prediction-model development, only variables that can be reasonably known before surgery were included. Variables associated with intra/postoperative bleeding were entered into 2 separate multivariable logistic regression models. Validation of our prediction model was performed internally using 250 bootstrapped iterations of 50% subsamples drawn from the overall population of myomectomy cases from the ACS-NSQIP database.

MEASUREMENTS AND MAIN RESULTS

We identified 6387 myomectomies performed during the defined study period. The most common race in our population was black/African American (45.7%), and most of the patients (57.5%) received an open/abdominal route of myomectomy. A total of 623 patients who underwent myomectomy (9.8%) experienced intraoperative/postoperative bleeding with a need for blood transfusion. At the bivariable level, we identified several variables independently associated with the need for blood transfusion at the time of myomectomy. In using only those variables that can be reasonably known before surgery to develop our prediction model, additional multivariable logistic regression elucidated black race, need for preoperative blood transfusion, planned abdominal/open route of surgery, and preoperative hematocrit value as independently associated with blood transfusion.

CONCLUSION

We identified a number of perioperative variables associated with intraoperative or postoperative bleeding requiring blood transfusion at the time of myomectomy. We subsequently created a model that accurately predicts individual bleeding risk from myomectomy, using variables that are reasonably apparent preoperatively. Making this prediction model clinically available to gynecologic surgeons will serve to improve the care of women undergoing myomectomy.

Predicting postoperative day 1 hematocrit levels after hysterectomy for malignant indication: Validating a previously published model

OBJECTIVE

To determine whether the Swenson model of postoperative day 1 (POD1) hematocrit after benign hysterectomy is applicable to gynecologic oncology hysterectomies.

METHODS

Data were retrospectively collected from cases of hysterectomy with malignant pathology in Hartford, USA, from 2014 to 2016. Predicted POD1 hematocrit was compared with actual hematocrit. ROC curve analysis was used to determine the optimal cut-off point for predicting hematocrit levels of 30% or less.

RESULTS

Among 107 women, mean age was 62.9 years and body mass index was 34.0. Most underwent robotic (44.9%) or abdominal (43.9%) hysterectomy. The published equation correctly predicted hematocrit to within ±5% for 83.2% of women, which was less accurate than observed in the original validation set. The equation was more likely to underestimate lower hematocrit levels, adding safety to its use. By ROC curve analysis, the best cut-off point for predicting actual hematocrit above 30% was predicted hematocrit 32.3% (100% specificity).

CONCLUSION

The Swenson equation predicted POD1 hematocrit less accurately in the current dataset. As a screening tool for hematocrit below 30%, however, ordering postoperative hematocrit is probably unnecessary if the predicted value is 32.3% or higher. This equation should be used as a screening tool to reduce unnecessary laboratory tests.

Pathologic Versus Native Hip Fractures: Comparing 30-day Mortality and Short-term Complication Profiles

BACKGROUND

A large body of research on native hip fractures has resulted in several evidence-based guidelines aimed at improving postsurgical care for these patients. In contrast, there is a paucity of data on pathologic hip fractures, and whether native hip fracture protocols are generalizable to this population is unknown. The purpose of this study was to compare mortality rates and complication profiles between patients with pathologic and native hip fractures.

METHODS

Using the American College of Surgeons-National Surgical Quality Improvement Program (NSQIP) database, we identified patients who underwent surgical treatment for pathologic and native hip fractures from 2007 to 2017 and 2601 matched pairs were identified using propensity scoring. Baseline covariates were controlled for, and rates of 30-day postoperative complications and mortality were compared using McNemar's test.

RESULTS

Pathologic hip fracture patients experienced significantly higher rates of death (6.3% vs 4.3%, P < .001), serious adverse events (17.3% vs 13.5%, P < .001), minor complications (34.3% vs 29.1%, P < .001), extended postoperative lengths of stay (30.2% vs 25.9%, P < .001), readmissions (11.9% vs 8.4%, P < .001), thromboembolic complications (3.0% vs 1.6%, P < .001), and perioperative transfusions (31.5% vs 26.4%, P < .001) compared to native hip fracture patients.

CONCLUSION

Pathologic hip fractures result in significantly higher complication rates than native hip fractures after surgical treatment, suggesting that guidelines for native hip fractures may not be generalizable for pathologic hip fractures. Orthopedic surgeons should closely monitor these patients for deep vein thrombosis, utilize blood sparing techniques, and employ a multidisciplinary approach to help manage and prevent a more heterogenous profile of postsurgical complications.

Impact of perioperative red blood cell transfusion on postoperative recovery and long-term outcome in patients undergoing surgery for ovarian cancer: A propensity score-matched analysis

BACKGROUND

The impact of perioperative red blood cell transfusion (PRBCT) on cancer survival has remained controversial.

METHODS

We conducted a retrospective study in patients undergoing primary debulking surgery (PDS) for ovarian cancer between January 2013 and December 2017. The patients were divided into two groups based on whether they received PRBCT. Clinical characteristics were compared between groups. After propensity score matching, perioperative systemic inflammation-based scores, quality of recovery, postoperative outcomes, disease-free survival (DFS), and overall survival (OS) were compared between groups. Univariate and multivariable Cox proportional hazard models were used to evaluate the association between covariates and survival outcomes.

RESULTS

A total of 1037 patients were enrolled in this study, and 31.7% of patients received PRBCT. After propensity matching, there was no significant difference in the clinical characteristics between groups. Patients receiving PRBCT had more postoperative fluctuations in systemic inflammatory response-related indicators (P < 0.001), a higher incidence of postoperative grade II complications (28.4% vs. 14.8%), a longer length of stay (10.6 d vs. 6.2 d) and higher 30-day and total readmission rates (7.1% vs. 4.4% and 11.2% vs. 8.1%, P < 0.001, respectively) than patients who did not receive PRBCT. The OS and DFS rates 3 years after surgery were significantly lower in the patients receiving PRBCT than in patients not receiving PRBCT (58.9% vs. 74.5%, 39.6% vs. 52.3%).

CONCLUSIONS

PRBCT was significantly associated with more fluctuations in systemic inflammatory indicators, a prolonged length of stay, higher postoperative complication rates and increased cancer recurrence and overall mortality in ovarian cancer patients undergoing PDS.