A model to predict risk of blood transfusion after gynecologic surgery

Stanhiser, Jamie; Chagin, Kevin; Jelovsek, J. Eric

doi:10.1016/j.ajog.2017.01.004

Cited by 17 publications

(14 citation statements)

References 26 publications

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“…Conventionally, there are mainly several diagnostic test indicators such as sensitivity, speci city and AUC as demonstrated below and these indicators only measure the diagnostic accuracy of the prediction model, but fail to consider the clinical availability of it. DCA (Decision Curve Analysis) is such a novel tool which can be used to evaluate whether a prediction model has clinical usage by calculating the value of net bene t within certain range of threshold probabilities (20,21). This net bene t is produced by comparing the difference between expected bene t and expected harm related to each proposed testing and treatment method (22).…”

Section: Test Of Clinical Usementioning

confidence: 99%

Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

Zhang¹,

Xu²,

Liu³

et al. 2020

Preprint

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Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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Section: Test Of Clinical Usementioning

confidence: 99%

Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

Zhang¹,

Xu²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Consistent with the extant literature, to compute the direct cost of blood loss, we assume a linear risk of blood transfusion of 2% at the lower first quartile (30 ml) and 5% at the upper third quartile (120 ml) of blood loss (Shander et al, 2010;Stanhiser, Chagin, & Jelovsek, 2017). Consistent with the extant literature, to compute the direct cost of blood loss, we assume a linear risk of blood transfusion of 2% at the lower first quartile (30 ml) and 5% at the upper third quartile (120 ml) of blood loss (Shander et al, 2010;Stanhiser, Chagin, & Jelovsek, 2017).…”

Section: An Overview Of Surgical Costs For the Simulationmentioning

confidence: 99%

“…The cost of blood transfusion is estimated at an average of $750 per unit of blood transfused. Consistent with the extant literature, to compute the direct cost of blood loss, we assume a linear risk of blood transfusion of 2% at the lower first quartile (30 ml) and 5% at the upper third quartile (120 ml) of blood loss (Shander et al, 2010;Stanhiser, Chagin, & Jelovsek, 2017).…”

Section: An Overview Of Surgical Costs For the Simulationmentioning

confidence: 99%

Robot‐assisted surgical care delivery at a hospital: Policies for maximizing clinical outcome benefits and minimizing costs

Mukherjee

Sinha

2019

J of Ops Management

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Robot-assisted surgery is a major advancement in minimally invasive surgical care delivery. It allows surgeons to gain additional dexterity, improved precision in surgical tasks, enhanced three-dimensional vision of the surgical field, and superior accessibility to the surgical field. Notwithstanding the clinical outcome benefits (such as reduced blood loss and surgical duration, and shorter length-of-stay at a hospital), the cost of performing robot-assisted surgery is significantly high, due to the investments needed for robot acquisition, maintenance, and procurement of surgical accessories. In this study, we address the twin objectives of a hospital where robot-assisted surgery is performed: maximize the clinical outcome benefits and minimize the total cost of robot-assisted surgery. The surgical robot that is the focus of the study is a da Vinci surgical system. The surgical procedure that serves as the context of this study is hysterectomy. We demonstrate the application of an integrated methodological approach-combining empirical analysis involving an on-site, prospective investigation at a hospital and retrospective analysis of archival data from the hospital for robustness checks, analytical modeling, and discrete event simulation-to identify and analyze actionable policies that a hospital can implement. The specific policies we analyze are related to: (a) patient triaging for robot-assisted surgery based on the criticality of disease condition of a patient; (b) the optimal size of surgeon pool to facilitate the development of surgeon experience and learning; and (c) the minimum experience level of a surgeon with a robot needed to be included in the surgeon pool for robot-assisted surgery. The key contributions of this article are in demonstrating the following: First, hospital-level policies can help to realize both the clinical outcome and cost benefits of a surgical robot. Second, the criticality of patient condition is a significant determinant of surgeon learning in robot-assisted surgery. Third, application of the proposed integrated methodological approach can yield nuanced and actionable insights into an operational setting where data availability is limited and generalizability of study insights is a concern, as is the case in this study. K E Y W O R D Scost of healthcare, hospital policy, hysterectomy, multi-method study, surgeon learning, robot-assisted surgery

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“…The modality of myomectomy correlates with risk of transfusion. Stanhiser et al [5] found that laparotomy carried 4.8 times (95% confidence interval [CI], 3.8−5.8) the risk of transfusion over a laparoscopic approach [5]. A large database study using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) found the rate of transfusion to be 6.7% with hysteroscopic, 2.7% with laparoscopic, and 16.4% with open/abdominal myomectomies when both emergent and elective cases were included [3].…”

mentioning

confidence: 99%

“…Preoperative calculators to aid in the estimation of transfusion risk in gynecologic surgery do exist, but they focus on hysterectomy and are not optimized to consider nuances within myomectomy [5,8]. Theoretically, on the basis of the aforementioned identified risk factors and potentially others yet unidentified, one could calculate an individualized assessment of the risk of transfusion with myomectomy.…”

mentioning

confidence: 99%

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

Klebanoff

Marfori²,

Sparks

et al. 2021

Journal of Minimally Invasive Gynecology

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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. Journal of Minimally Invasive Gynecology (2021) 00, 1−9.

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A model to predict risk of blood transfusion after gynecologic surgery

Cited by 17 publications

References 26 publications

Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

Robot‐assisted surgical care delivery at a hospital: Policies for maximizing clinical outcome benefits and minimizing costs

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

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