Literature DB >> 30169413

Use of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator During Preoperative Risk Discussion: The Patient Perspective.

Britany L Raymond1, Jonathan P Wanderer1, Alexander T Hawkins2, Timothy M Geiger2, Jesse M Ehrenfeld1, John W Stokes2, Matthew D McEvoy1.   

Abstract

BACKGROUND: The American College of Surgeons (ACS) National Surgical Quality Improvement Program Surgical Risk Calculator (ACS Calculator) provides empirically derived, patient-specific risks for common adverse perioperative outcomes. The ACS Calculator is promoted as a tool to improve shared decision-making and informed consent for patients undergoing elective operations. However, to our knowledge, no data exist regarding the use of this tool in actual preoperative risk discussions with patients. Accordingly, we performed a survey to assess (1) whether patients find the tool easy to interpret, (2) how accurately patients can predict their surgical risks, and (3) the impact of risk disclosure on levels of anxiety and future motivations to decrease personal risk.
METHODS: Patients (N = 150) recruited from a preoperative clinic completed an initial survey where they estimated their hospital length of stay and personal perioperative risks of the 12 clinical complications analyzed by the ACS Calculator. Next, risk calculation was performed by entering participants' demographics into the ACS Calculator. Participants reviewed their individualized risk reports in detail and then completed a follow-up survey to evaluate their perceptions.
RESULTS: Nearly 90% of participants desire to review their ACS Calculator report before future surgical consents. High-risk patients were 3 times more likely to underestimate their risk of any complication, serious complication, and length of stay compared to low-risk patients (P < .001). After reviewing their calculated risks, 70% stated that they would consider participating in prehabilitation to decrease perioperative risk, and nearly 40% would delay their surgery to do so. Knowledge of personal ACS risk calculations had no effect on anxiety in 20% and decreased anxiety in 71% of participants.
CONCLUSIONS: The ACS Calculator may be of particular benefit to high-risk surgical populations by providing realistic expectations of outcomes and recovery. Use of this tool may also provide motivation for patients to participate in risk reduction strategies.

Entities:  

Year:  2019        PMID: 30169413     DOI: 10.1213/ANE.0000000000003718

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  9 in total

Review 1.  Non-cardiac surgery in patients with coronary artery disease: risk evaluation and periprocedural management.

Authors:  Davide Cao; Rishi Chandiramani; Davide Capodanno; Jeffrey S Berger; Matthew A Levin; Mary T Hawn; Dominick J Angiolillo; Roxana Mehran
Journal:  Nat Rev Cardiol       Date:  2020-08-05       Impact factor: 32.419

2.  Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.

Authors:  Yuanfang Ren; Tyler J Loftus; Shounak Datta; Matthew M Ruppert; Ziyuan Guan; Shunshun Miao; Benjamin Shickel; Zheng Feng; Chris Giordano; Gilbert R Upchurch; Parisa Rashidi; Tezcan Ozrazgat-Baslanti; Azra Bihorac
Journal:  JAMA Netw Open       Date:  2022-05-02

3.  Intelligent, Autonomous Machines in Surgery.

Authors:  Tyler J Loftus; Amanda C Filiberto; Jeremy Balch; Alexander L Ayzengart; Patrick J Tighe; Parisa Rashidi; Azra Bihorac; Gilbert R Upchurch
Journal:  J Surg Res       Date:  2020-04-24       Impact factor: 2.192

Review 4.  Artificial Intelligence and Surgical Decision-making.

Authors:  Tyler J Loftus; Patrick J Tighe; Amanda C Filiberto; Philip A Efron; Scott C Brakenridge; Alicia M Mohr; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  JAMA Surg       Date:  2020-02-01       Impact factor: 14.766

5.  Optimizing predictive strategies for acute kidney injury after major vascular surgery.

Authors:  Amanda C Filiberto; Tezcan Ozrazgat-Baslanti; Tyler J Loftus; Ying-Chih Peng; Shounak Datta; Philip Efron; Gilbert R Upchurch; Azra Bihorac; Michol A Cooper
Journal:  Surgery       Date:  2021-02-27       Impact factor: 4.348

Review 6.  The false dichotomy of surgical futility in the emergency laparotomy setting: scoping review.

Authors:  Hannah Javanmard-Emamghissi; Sonia Lockwood; Sarah Hare; Jon N Lund; Gillian M Tierney; Susan J Moug
Journal:  BJS Open       Date:  2022-03-08

7.  Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery.

Authors:  Gabriel Brat; Nils Gehlenborg; Undina Gisladottir; Drashko Nakikj; Rashi Jhunjhunwala; Jasmine Panton
Journal:  JMIR Hum Factors       Date:  2022-04-29

8.  Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms.

Authors:  Lan Lan; Fangwei Chen; Jiawei Luo; Mengjiao Li; Xuechao Hao; Yao Hu; Jin Yin; Tao Zhu; Xiaobo Zhou
Journal:  Digit Health       Date:  2022-07-25

9.  Association between Preoperative Medication Lists and Postoperative Hospital Length of Stay after Endoscopic Transsphenoidal Pituitary Surgery.

Authors:  Mary Saad; Benjamin Salze; Bernard Trillat; Olivier Corniou; Alexandre Vallée; Morgan Le Guen; Aurélien Latouche; Marc Fischler
Journal:  J Clin Med       Date:  2022-09-30       Impact factor: 4.964

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.