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CME / ABIM MOC / CE

Can Risk Tools Help Provide Better Hysterectomy Outcomes?

  • Authors: News Author: Marilynn Larkin; CME Author: Laurie Barclay, MD
  • CME / ABIM MOC / CE Released: 11/23/2022
  • Valid for credit through: 11/23/2023, 11:59 PM EST
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  • Credits Available

    Physicians - maximum of 0.25 AMA PRA Category 1 Credit(s)™

    ABIM Diplomates - maximum of 0.25 ABIM MOC points

    Nurses - 0.25 ANCC Contact Hour(s) (0 contact hours are in the area of pharmacology)

    Physician Assistant - 0.25 AAPA hour(s) of Category I credit

    IPCE - 0.25 Interprofessional Continuing Education (IPCE) credit

    You Are Eligible For

    • Letter of Completion
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Target Audience and Goal Statement

This activity is intended for obstetricians/gynecologists/women's health clinicians, family medicine/primary care clinicians, internists, nurses, physician assistants, and other members of the health care team who treat and manage patients considering or undergoing hysterectomy for benign conditions.

The goal of this activity is for learners to be better able to describe the development, validation, and performance of multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions.

Upon completion of this activity, participants will:

  • Assess the development, validation, and performance of multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions, based on an analysis of routinely collected National Health Service health administrative data from 2011 to 2018
  • Evaluate the clinical implications of the development, validation, and performance of multivariable logistic regression models to predict major complications of laparoscopic or abdominal hysterectomy for benign conditions, based on an analysis of routinely collected National Health Service health administrative data from 2011 to 2018
  • Outline implications for the healthcare team


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News Author

  • Marilynn Larkin

    Freelance writer, Medscape

    Disclosures

    Marilynn Larkin has no relevant financial relationships.

CME Author

  • Laurie Barclay, MD

    Freelance writer and reviewer
    Medscape, LLC

    Disclosures

    Laurie Barclay, MD, has the following relevant financial relationships:
    Formerly owned stocks in: AbbVie Inc.

Editor/Nurse Planner

  • Leigh Schmidt, MSN, RN, CNE, CHCP

    Associate Director, Accreditation and Compliance, Medscape, LLC

    Disclosures

    Leigh Schmidt, MSN, RN, CNE, CHCP, has no relevant financial relationships.

Compliance Reviewer

  • Yaisanet Oyola, MD

    Associate Director, Accreditation and Compliance, Medscape, LLC

    Disclosures

    Yaisanet Oyola, MD, has no relevant financial relationships.

Peer Reviewer

This activity has been peer reviewed and the reviewer has no relevant financial relationships.


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CME / ABIM MOC / CE

Can Risk Tools Help Provide Better Hysterectomy Outcomes?

Authors: News Author: Marilynn Larkin; CME Author: Laurie Barclay, MDFaculty and Disclosures

CME / ABIM MOC / CE Released: 11/23/2022

Valid for credit through: 11/23/2023, 11:59 PM EST

processing....

Clinical Context

Hysterectomy is one of the most frequently performed surgeries. The proportion of laparoscopic hysterectomies has increased substantially in many countries during the last decade, as clinicians and patients prefer minimal access approaches.

National guidelines recommend that the patient and surgeon discuss the chosen surgical approach, considering relative benefits and risks. Most surgeons can intuitively identify patient factors that may increase surgical complexity and complications, but a more standardized approach could help inform clinical decision-making.

Study Synopsis and Perspective

An online prediction tool provides personalized risk estimates to help clinicians and patients choose between laparoscopic and abdominal surgery for hysterectomy for benign disease.

The tool, which integrates 11 routinely available predictors, had "acceptable" predictive ability and "moderate" discrimination.

"The overall numbers of hysterectomies are declining, and there are also more surgeons with smaller caseloads in the United Kingdom, and we felt the tool might help surgeons advocate for things that may make surgery safer," PhD student Krupa Madhvani, consultant obstetrician and gynecologist at the Royal London Hospital, United Kingdom, told Medscape Medical News. "These things include extra [operating room] time, extra surgical expertise, referral to regional centers, and robotic surgery for more complex cases, which is usually reserved for cancer cases in the UK."

The tool, which includes models for laparoscopic and abdominal surgery, is available online.

The study was published October 3 in the Canadian Medical Association Journal.

Adhesions Strongest Predictor

Dr Madhvani and colleagues analyzed routinely collected data from the English National Health System from 2011 to 2018. They identified major complications of laparoscopic and open abdominal surgery for hysterectomy for benign disease based on postoperative outcomes. After exclusions for malignancy and other factors, data from 68,599 patients who underwent laparoscopic hysterectomy and 125,971 patients who underwent abdominal hysterectomy were included in the models.

Multivariable logistic regression prediction models were based on the following 11 predictors: age, ethnicity, obesity, diabetes, fibroids, menstrual disorders, endometriosis or pain, adenomyosis, benign adnexal mass, adhesions, and other.

Major complications occurred in 4.4% of laparoscopic and 4.9% of abdominal hysterectomies. The models showed consistent discrimination in the development cohort (C-statistic: laparoscopic, 0.61; abdominal, 0.67), and similar or better discrimination in the validation cohort (C-statistic: 0.67 for both).

Adhesions were the strongest predictor of complications in both models.

In the laparoscopic model, adhesions (adjusted odds ratio [aOR], 1.92) and adenomyosis (aOR, 1.46) were associated with increased risk for major complications. In contrast, menstrual disorders (aOR, 0.75), benign adnexal masses (aOR, 0.85), and other gynecologic diagnoses at the time of hysterectomy (aOR, 0.87) were associated with protection against major complications.

In the abdominal model, adhesions (aOR, 2.46), Asian ethnicity (aOR, 1.40), and diabetes (aOR, 1.16) were associated with increased complication risk. Protective factors included benign adnexal masses (aOR, 0.79), fibroids (aOR 0.75), menstrual disorders (aOR, 0.52), and other gynecological disorders (aOR, 0.78).

The authors write, "Although a surgeon's experience and expert opinion carries utility, it cannot be used solely to guide risk management. [O]ur models could be useful tools to stratify risk."

Dr Madhvani added, "The next steps would be to see how the individual experience of the surgeon can improve this model. Evidence from observational studies shows that this has an impact on conversations from laparoscopy to laparotomy and complications."

She suggested that clinicians "explore the tool to see if the risk prediction matches your own intuition when estimating the risk of complications for patients on an individual level." 

Clinically Useful?

Commenting on the study for Medscape Medical News, Jonathan Schaffir, MD, medical director of the Obstetrics and Gynecology Outpatient Clinic at Ohio State University Wexner Medical Center in Columbus, said that the tool will not significantly aid counseling for patients who are undergoing hysterectomy. Dr Schaffir was not involved in the research.

It is important that patients understand the risks of surgery, he said. "But it is unlikely that a numerical value for the rate of complications will significantly affect the choice to have surgery. The recommendation for surgery and the decision as to the best route is dependent on many individualized factors that are not covered by this tool, and ultimately each person undergoing hysterectomy will have to make the decision for herself whether perceived benefits outweigh the risks."

For example, he noted, "The authors do not consider thromboembolism or wound infections treated with antibiotics to be complications in this algorithm. These are some of the most common complications that we see, so I would certainly want my patients to know their risks of these issues in advance."

In addition, he said, "some factors that are examined as a risk are poorly defined. Obesity was a yes or no response, but clearly the complication rate is much higher for a woman with class III obesity compared with a woman who is slightly obese. Similarly, fibroids are placed in a single category, but the difference in doing surgery for a woman with small incidental fibroids vs one whose fibroids make her look 7 months pregnant is vast and would be expected to have very different risks."

In the study, fibroids were associated with a protective effect, he added, "which does not make sense, given the significant distortion of anatomy that may be present with large fibroids."

"I would want to see more research of patient perceptions and opinions," Dr Schaffir said. "Is there a risk value for which a particular set of patients would decline surgery? What information would they want to know that would persuade them to have one route of hysterectomy vs another? Unless there is a clear use for having a numerical prediction, I don't think the tool is practical."

Data acquisition was funded by the British Society for Gynaecological Endoscopy. Dr Madhvani has received article processing fees from East London International Women's Health Charity. Dr Schaffir has disclosed no relevant financial relationships.

CMAJ. Published online October 3, 2022.[1]

Study Highlights

  • NHS health administrative data were routinely collected from 2011 to 2018. 
  • Major postoperative complications included ureteric, gastrointestinal, and vascular injury and wound complications. 
  • Eleven predictors were specified a priori: age, ethnicity, obesity, diabetes, fibroids, menstrual disorders, endometriosis or pain, adenomyosis, benign adnexal mass, adhesions, and other.
  • In the development cohort, internal-external cross-validation assessed discrimination and calibration across seven NHS regions. 
  • Final multivariable logistic regression models were validated using data from an additional NHS region.
  • Over time, the number of laparoscopic hysterectomies increased, whereas the number of abdominal hysterectomies decreased.
  • Major complications occurred in 4.4% (3037/68,599) of laparoscopic and 4.9% (6201/125,971) of abdominal hysterectomies. 
  • The models showed consistent discrimination in the development cohort (laparoscopic: C-statistic, 0.61 [95% confidence interval (CI), 0.60-0.62[; abdominal: C-statistic, 0.67 [95% CI, 0.64-0.70]) and similar or better discrimination in the validation cohort (laparoscopic: C-statistic, 0.67 [95% CI, 0.65-0.69]; abdominal: C-statistic, 0.67 [95% CI, 0.65-0.69]). 
  • In both models, adhesions were most predictive of complications (laparoscopic: odds ratio [OR], 1.92 [95% CI, 1.73-2.13]; abdominal: OR, 2.46 [95% CI, 2.27-2.66]). 
  • In the laparoscopic model, adenomyosis was associated with increased risk for major complications (adjusted OR, 1.46), whereas menstrual disorders (adjusted OR, 0.75), benign adnexal masses (adjusted OR, 0.85), and other gynecologic diagnoses at the time of hysterectomy (adjusted OR, 0.87) had protective associations.
  • In the abdominal model, adhesions (adjusted OR, 2.46), Asian ethnicity (adjusted OR, 1.40), and diabetes (adjusted OR, 1.16) were associated with increased complication risk; protective factors included benign adnexal masses (adjusted OR, 0.79), fibroids (adjusted OR, 0.75), menstrual disorders (adjusted OR, 0.52), and other gynecological disorders (adjusted OR, 0.78).
  • The investigators concluded that personalized risk estimates from their models, which showed moderate discrimination and acceptable predictive ability, yielded simple online prediction tools to inform clinical decision-making regarding hysterectomy for benign conditions and to be used by surgeons to aid preoperative counseling. 
  • These tools do not apply to patients undergoing hysterectomy for cancer.
  • The tools integrate 11 routinely available predictors, can guide shared decision-making, and may indicate the need for consideration of nonsurgical treatment options or for interventions to make surgery safer, such as referral to centers and clinicians with greater surgical expertise, extra operating room time, and robotic surgery for more complex cases.
  • Adhesions, the leading risk factor, should be suspected with previous history of laparotomy, cesarean section, pelvic infection or endometriosis, and can be reliably diagnosed preoperatively, using ultrasonography.
  • Obesity did not significantly predict major complications for either hysterectomy approach but was assessed with a binary variable rather than exact body mass index.
  • Despite the value of a surgeon's experience and expert opinion, it cannot be used alone to guide risk management. 
  • In Canada and globally, the overall rate of hysterectomy for benign disease is decreasing, and more patients are undergoing surgery by lower-volume surgeons, who may be lack expertise in every procedure. 
  • Future studies should address improving discrimination ability of these tools by including factors other than patient characteristics, including surgeon volume, which has been shown to decrease complications.
  • Study limitations include lack of detailed clinical information on exact BMI; fibroid location, type, and size; severity of adhesions and endometriosis, and long-term outcomes.

Clinical Implications

  • Simple online prediction tools can inform clinical decision-making for people with benign conditions who may require hysterectomy.
  • Despite the value of a surgeon's experience and expert opinion, it cannot be used alone to guide risk management. 
  • Implications for the Health Care Team: It is important that patients understand the risks of surgery; therefore, along with evidence and experience, clinicians need to utilize risk prediction tools along with shared decision making conversations with the patient to ensure informed decisions have been achieved. 

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