PMID- 35042837 OWN - NLM STAT- MEDLINE DCOM- 20220620 LR - 20230815 IS - 1528-1132 (Electronic) IS - 0009-921X (Print) IS - 0009-921X (Linking) VI - 480 IP - 7 DP - 2022 Jul 1 TI - Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis. PG - 1271-1284 LID - 10.1097/CORR.0000000000002105 [doi] AB - BACKGROUND: Surgery for thumb carpometacarpal osteoarthritis is offered to patients who do not benefit from nonoperative treatment. Although surgery is generally successful in reducing symptoms, not all patients benefit. Predicting clinical improvement after surgery could provide decision support and enhance preoperative patient selection. QUESTIONS/PURPOSES: This study aimed to develop and validate prediction models for clinically important improvement in (1) pain and (2) hand function 12 months after surgery for thumb carpometacarpal osteoarthritis. METHODS: Between November 2011 and June 2020, 2653 patients were surgically treated for thumb carpometacarpal osteoarthritis. Patient-reported outcome measures were used to preoperatively assess pain, hand function, and satisfaction with hand function, as well as the general mental health of patients and mindset toward their condition. Patient characteristics, medical history, patient-reported symptom severity, and patient-reported mindset were considered as possible predictors. Patients who had incomplete Michigan Hand outcomes Questionnaires at baseline or 12 months postsurgery were excluded, as these scores were used to determine clinical improvement. The Michigan Hand outcomes Questionnaire provides subscores for pain and hand function. Scores range from 0 to 100, with higher scores indicating less pain and better hand function. An improvement of at least the minimum clinically important difference (MCID) of 14.4 for the pain score and 11.7 for the function score were considered "clinically relevant." These values were derived from previous reports that provided triangulated estimates of two anchor-based and one distribution-based MCID. Data collection resulted in a dataset of 1489 patients for the pain model and 1469 patients for the hand function model. The data were split into training (60%), validation (20%), and test (20%) dataset. The training dataset was used to select the predictive variables and to train our models. The performance of all models was evaluated in the validation dataset, after which one model was selected for further evaluation. Performance of this final model was evaluated on the test dataset. We trained the models using logistic regression, random forest, and gradient boosting machines and compared their performance. We chose these algorithms because of their relative simplicity, which makes them easier to implement and interpret. Model performance was assessed using discriminative ability and qualitative visual inspection of calibration curves. Discrimination was measured using area under the curve (AUC) and is a measure of how well the model can differentiate between the outcomes (improvement or no improvement), with an AUC of 0.5 being equal to chance. Calibration is a measure of the agreement between the predicted probabilities and the observed frequencies and was assessed by visual inspection of calibration curves. We selected the model with the most promising performance for clinical implementation (that is, good model performance and a low number of predictors) for further evaluation in the test dataset. RESULTS: For pain, the random forest model showed the most promising results based on discrimination, calibration, and number of predictors in the validation dataset. In the test dataset, this pain model had a poor AUC (0.59) and poor calibration. For function, the gradient boosting machine showed the most promising results in the validation dataset. This model had a good AUC (0.74) and good calibration in the test dataset. The baseline Michigan Hand outcomes Questionnaire hand function score was the only predictor in the model. For the hand function model, we made a web application that can be accessed via https://analyse.equipezorgbedrijven.nl/shiny/cmc1-prediction-model-Eng/. CONCLUSION: We developed a promising model that may allow clinicians to predict the chance of functional improvement in an individual patient undergoing surgery for thumb carpometacarpal osteoarthritis, which would thereby help in the decision-making process. However, caution is warranted because our model has not been externally validated. Unfortunately, the performance of the prediction model for pain is insufficient for application in clinical practice. LEVEL OF EVIDENCE: Level III, therapeutic study. CI - Copyright (c) 2022 by the Association of Bone and Joint Surgeons. FAU - Loos, Nina L AU - Loos NL AD - Department of Plastic, Reconstructive and Hand Surgery, Erasmus MC, Rotterdam, the Netherlands. AD - Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, the Netherlands. FAU - Hoogendam, Lisa AU - Hoogendam L AUID- ORCID: 0000-0001-9542-5105 AD - Department of Plastic, Reconstructive and Hand Surgery, Erasmus MC, Rotterdam, the Netherlands. AD - Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, the Netherlands. AD - Hand and Wrist Center, Xpert Clinics, the Netherlands. FAU - Souer, J Sebastiaan AU - Souer JS AD - Hand and Wrist Center, Xpert Clinics, the Netherlands. FAU - Slijper, Harm P AU - Slijper HP AD - Department of Plastic, Reconstructive and Hand Surgery, Erasmus MC, Rotterdam, the Netherlands. AD - Hand and Wrist Center, Xpert Clinics, the Netherlands. FAU - Andrinopoulou, Eleni-Rosalina AU - Andrinopoulou ER AD - Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands. AD - Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. FAU - Coppieters, Michel W AU - Coppieters MW AD - Menzies Health Institute Queensland, Griffith University, Brisbane and Gold Coast, Australia. AD - Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. FAU - Selles, Ruud W AU - Selles RW AUID- ORCID: 0000-0002-2432-6756 AD - Department of Plastic, Reconstructive and Hand Surgery, Erasmus MC, Rotterdam, the Netherlands. AD - Department of Rehabilitation Medicine, Erasmus MC, Rotterdam, the Netherlands. CN - , the Hand-Wrist Study Group LA - eng PT - Journal Article DEP - 20220118 PL - United States TA - Clin Orthop Relat Res JT - Clinical orthopaedics and related research JID - 0075674 SB - IM CIN - Clin Orthop Relat Res. 2022 Jul 1;480(7):1285-1286. PMID: 35122682 CIN - Clin Orthop Relat Res. 2022 Nov 01;480(11):2277. PMID: 36099409 MH - Humans MH - Machine Learning MH - *Osteoarthritis/surgery MH - Pain MH - Patient Reported Outcome Measures MH - *Thumb/surgery PMC - PMC9191288 COIS- Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research(R) editors and board members are on file with the publication and can be viewed on request. FIR - Arjen M Blomme, R IR - Arjen M Blomme R FIR - Sluijter, Berbel Jr IR - Sluijter BJ FIR - van der Avoort, Dirk-Jan Jc IR - van der Avoort DJ FIR - Kroeze, Alexander IR - Kroeze A FIR - Smit, Jeroen IR - Smit J FIR - Debeij, Jan IR - Debeij J FIR - Walbeehm, Erik T IR - Walbeehm ET FIR - van Couwelaar, Gijs M IR - van Couwelaar GM FIR - Vermeulen, Guus M IR - Vermeulen GM FIR - de Schipper, Hans IR - de Schipper H FIR - Temming, Hans IR - Temming H FIR - van Uchelen, Jeroen H IR - van Uchelen JH FIR - Luitzen de Boer, H IR - Luitzen de Boer H FIR - de Haas, Nicoline IR - de Haas N FIR - Harmsen, Kennard IR - Harmsen K FIR - Zophel, Oliver T IR - Zophel OT FIR - Feitz, Reinier IR - Feitz R FIR - Koch, Richard IR - Koch R FIR - Hovius, Steven Er IR - Hovius SE FIR - Moojen, Thybout M IR - Moojen TM FIR - Smit, Xander IR - Smit X FIR - van Huis, Rob IR - van Huis R FIR - Pennehouat, Pierre Y IR - Pennehouat PY FIR - Schoneveld, Karin IR - Schoneveld K FIR - van Kooij, Yara E IR - van Kooij YE FIR - Wouters, Robbert M IR - Wouters RM FIR - Zagt, Paul IR - Zagt P FIR - van Ewijk, Folkert J IR - van Ewijk FJ FIR - Veltkamp, Joris J IR - Veltkamp JJ FIR - Fink, Alexandra IR - Fink A FIR - de Ridder, Willemijn A IR - de Ridder WA FIR - Jansen, Miguel C IR - Jansen MC FIR - van der Oest, Mark Jw IR - van der Oest MJ FIR - Sun, Pepijn O IR - Sun PO FIR - Teunissen, Joris S IR - Teunissen JS FIR - Dekker, Jak IR - Dekker J FIR - Jansen-Landheer, Marlies L IR - Jansen-Landheer ML FIR - Ter Stege, Marloes Hp IR - Ter Stege MH EDAT- 2022/01/20 06:00 MHDA- 2022/06/22 06:00 PMCR- 2023/07/01 CRDT- 2022/01/19 06:47 PHST- 2021/07/21 00:00 [received] PHST- 2021/12/13 00:00 [accepted] PHST- 2022/01/20 06:00 [pubmed] PHST- 2022/06/22 06:00 [medline] PHST- 2022/01/19 06:47 [entrez] PHST- 2023/07/01 00:00 [pmc-release] AID - 00003086-202207000-00009 [pii] AID - CORR-D-21-01127 [pii] AID - 10.1097/CORR.0000000000002105 [doi] PST - ppublish SO - Clin Orthop Relat Res. 2022 Jul 1;480(7):1271-1284. doi: 10.1097/CORR.0000000000002105. Epub 2022 Jan 18.