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Violation of expectations is correlated with satisfaction following hip arthroscopy

  • HIP
  • Published:
Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Purpose

The mechanism by which preoperative expectations may be associated with patient satisfaction and procedural outcomes following hip preservation surgery (HPS) is far from simple or linear. The purpose of this study is to better understand patient expectations regarding HPS and their relationship with patient-reported outcomes (PROs) and satisfaction using machine learning (ML) algorithms.

Methods

Patients scheduled for hip arthroscopy completed the Hip Preservation Surgery Expectations Survey (HPSES) and the pre- and a minimum 2 year postoperative International Hip Outcome Tool (iHOT-33). Patient demographics, including age, gender, occupation, and body mass index (BMI), were also collected. At the latest follow-up, patients were evaluated for subjective satisfaction and postoperative complications. ML algorithms and standard statistics were used.

Results

A total of 69 patients were included in this study (mean age 33.7 ± 13.1 years, 62.3% males). The mean follow-up period was 27 months. The mean HPSES score, patient satisfaction, preoperative, and postoperative iHOT-33 were 83.8 ± 16.5, 75.9 ± 26.9, 31.6 ± 15.8, and 73 ± 25.9, respectively. Fifty-nine patients (86%) reported that they would undergo the surgery again, with no significant difference with regards to expectations. A significant difference was found with regards to expectation violation (p < 0.001). Expectation violation scores were also found to be significantly correlated with satisfaction.

Conclusion

ML algorithms utilized in this study demonstrate that violation of expectations plays an important predictive role in postoperative outcomes and patient satisfaction and is associated with patients’ willingness to undergo surgery again.

Level of evidence

IV.

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Abbreviations

HPS:

Hip preservation surgery

PROs:

Patient-reported outcomes

ML:

Machine learning

HPSES:

Hip preservation surgery expectations survey

i-HOT-33:

International hip outcome tool

BMI:

Body mass index

mHHS:

Modified harris hip score

HOS-ADL:

Hip outcome score-activities of daily living subscale

FAIS:

Femoral acetabular impingement syndrome

EXPVIO:

Expectations violation

AGAIN:

Patients who reported having the operation again to those regretting the operation

VS-MPR:

Vovk-sellke maximum p-ratio

MCID:

Minimal clinically important difference

References

  1. Alpaydın E (2020) Clustering. Introduction to machine learning, 4th edn. The MIT Press Cambridge, Massachusetts, pp 155–173

    Google Scholar 

  2. Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, Shilo N, Epstein A, Mor-Cohen R, Biber A, Rahav G, Levy I, Tirosh A (2020) Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med 15(8):1435–1443

    Article  PubMed  PubMed Central  Google Scholar 

  3. Beck EC, Nwachukwu BU, Kunze KN, Chahla J, Nho SJ (2019) How can we define clinically important improvement in pain scores after hip arthroscopy for femoroacetabular impingement syndrome? minimum 2 year follow-up study. Am J Sports Med 47:3133–3140

    Article  PubMed  Google Scholar 

  4. Chahla J, Beck EC, Nwachukwu BU, Alter T, Harris JD, Nho SJ (2019) Is there an association between preoperative expectations and patient-reported outcome after hip arthroscopy for femoroacetabular impingement syndrome? Arthroscopy 35:3250-3258.e1

    Article  PubMed  Google Scholar 

  5. Choi ES, Sim JA, Na YG, Seon JK, Shin HD (2021) Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee. Knee Surg Sports Traumatol Arthrosc 29:3142–3148

    Article  PubMed  PubMed Central  Google Scholar 

  6. Factor S, Vidra M, Shalom M, Clyman S, Roth Y, Amar E, Rath E (2021) Preoperative expectations do not correlate with postoperative ihot-33 scores and patient satisfaction following hip arthroscopy for the treatment of femoroacetabular impingement syndrome. Arthroscopy 38(6):1869–1875

    Article  PubMed  Google Scholar 

  7. Hagger MS, Orbell S (2003) A meta-analytic review of the common-sense model of illness representations. Psychol Bull 143(11):1117–1154

    Article  Google Scholar 

  8. Helm JM, Swiergosz AM, Haeberle HS, Karnuta JM, Schaffer JL, Krebs VE, Spitzer AI, Ramkumar PN (2020) Machine learning and artificial intelligence: definitions, applications, and future directions. Curr Rev Musculoskelet Med 13(1):69–76

    Article  PubMed  PubMed Central  Google Scholar 

  9. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recogn 38(12):2270–2285

    Article  Google Scholar 

  10. Kunze KN, Krivicich LM, Clapp IM, Bodendorfer BM, Nwachukwu BU, Chahla J, Nho SJ (2021) Machine learning algorithms predict achievement of clinically significant outcomes after orthopaedic surgery: a systematic review. Arthroscopy 8(6):2090–2105

    Article  Google Scholar 

  11. Kunze KN, Polce EM, Clapp I, Nwachukwu BU, Chahla J, Nho SJ (2021) Machine learning algorithms predict functional improvement after hip arthroscopy for femoroacetabular impingement syndrome in athletes. J Bone Joint Surg Am 103(12):1055–1062

    Article  PubMed  Google Scholar 

  12. Laferton JAC, Oeltjen L, Neubauer K, Ebert DD, Munder T (2022) The effects of patients’ expectations on surgery outcome in total hip and knee arthroplasty: a prognostic factor meta-analysis. Health Psychol Rev 6(1):50–66

    Article  Google Scholar 

  13. Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Kanada K, de Oliveira MG, Gallegos J, Gabriele S, Gupta V, Singh N, Natarajan V, Hofmann-Wellenhof R, Corrado GS, Peng LH, Webster DR, Ai D, Huang SJ, Liu Y, Dunn RC, Coz D (2020) A deep learning system for differential diagnosis of skin diseases. Nat Med 26(6):900–908

    Article  CAS  PubMed  Google Scholar 

  14. Lu Y, Forlenza E, Cohn MR, Lavoie-Gagne O, Wilbur RR, Song BM, Krych AJ, Forsythe B (2021) Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc 29:2958–2966

    Article  PubMed  Google Scholar 

  15. Mancuso CA (2022) Editorial commentary: assessing outcomes in terms of fulfillment of patient expectations is complementary to traditional measures including satisfaction. Arthroscopy 38:1876–1878

    Article  PubMed  Google Scholar 

  16. Mancuso CA, Wentzel CH, Ghomrawi HMK, Kelly BT (2017) Hip preservation surgery expectations survey: a new method to measure patients’ preoperative expectations. Arthroscopy 33(5):959–968

    Article  PubMed  Google Scholar 

  17. Martin RL, Kivlan BR, Christoforetti JJ, Wolff AB, Nho SJ, Salvo JP, Ellis TJ, Van TG, Matsuda DK, Carreira DS (2019) Minimal clinically important difference and substantial clinical benefit values for the 12-item international hip outcome tool. Arthroscopy 35(2):411–416

    Article  PubMed  Google Scholar 

  18. McCarthy SC, Lyons AC, Weinman J, Talbot R, Purnell D (2003) Do expectations influence recovery from oral surgery? An illness representation approach. Psychol Heal 18:109–126

    Article  Google Scholar 

  19. Mohtadi NGH, Griffin DR, Pedersen ME, Chan D, Safran MR, Parsons N, Sekiya JK, Kelly BT, Werle JR, Leunig M, McCarthy JC, Martin HD, Byrd JWT, Philippon MJ, Martin RL, Guanche CA, Clohisy JC, Sampson TG, Kocher MS, Larson CM (2012) The development and validation of a self-administered quality-of-life outcome measure for young, active patients with symptomatic hip disease: the International Hip Outcome Tool (iHOT-33). Arthroscopy 28(5):595–605

    Article  PubMed  Google Scholar 

  20. Naylor CD (2018) On the prospects for a (Deep) learning health care system. JAMA 320(11):1099–1100

    Article  PubMed  Google Scholar 

  21. Nwachukwu BU, Chang B, Adjei J, Schairer WW, Ranawat AS, Kelly BT, Nawabi DH (2018) Time required to achieve minimal clinically important difference and substantial clinical benefit after arthroscopic treatment of femoroacetabular impingement. Am J Sports Med 46(11):2601–2606

    Article  PubMed  Google Scholar 

  22. Pinquart M, Rothers A, Gollwitzer M, Khosrowtaj Z, Pietzsch M, Panitz C (2021) Predictors of coping with expectation violation: an integrative review. Rev Gen Psychol 25(3):321–333

    Article  Google Scholar 

  23. Polce EM, Kunze KN, Fu MC, Garrigues GE, Forsythe B, Nicholson GP, Cole BJ, Verma NN (2021) Development of supervised machine learning algorithms for prediction of satisfaction at 2 years following total shoulder arthroplasty. J Shoulder Elbow Surg 30(6):e290–e299

    Article  PubMed  Google Scholar 

  24. Ramkumar PN, Kunze KN, Haeberle HS, Karnuta JM, Luu BC, Nwachukwu BU, Williams RJ (2021) Clinical and research medical applications of artificial intelligence. Arthroscopy 37(5):1694–1697

    Article  PubMed  Google Scholar 

  25. Schapire RE. (2003) The Boosting Approach to Machine Learning: An Overview. In: Denison DD, Hansen MH, Holmes CC, Mallick B, Yu B. Nonlinear Estimation and Classification. Lecture Notes in Statistics. Springer, NY. 149–171

  26. Zhang Z (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4(11):218–225

    Article  PubMed  PubMed Central  Google Scholar 

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No funding was received for this project.

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Contributions

ER and EA: are equally contributing last authors to this article. All authors that have contributed to this manuscript have agreed on the final revised version of this manuscript. Data are available at reasonable request from the corresponding author.

Corresponding author

Correspondence to Shai Factor.

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The authors declare no conflicts of interest.

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0402–13-TLV.

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Factor, S., Neuman, Y., Vidra, M. et al. Violation of expectations is correlated with satisfaction following hip arthroscopy. Knee Surg Sports Traumatol Arthrosc 31, 2023–2029 (2023). https://doi.org/10.1007/s00167-022-07182-1

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  • DOI: https://doi.org/10.1007/s00167-022-07182-1

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