Prognostic factors for the occurrence of post-operative shoulder stiffness after arthroscopic rotator cuff repair: a systematic review

Stojanov, Thomas; Modler, Linda; Müller, A; Aghlmandi, Soheila; Appenzeller-Herzog, Christian; Loucas, Rafael; Loucas, Marios; Audigé, Laurent

doi:10.1186/s12891-022-05030-4

Cited by 11 publications

(7 citation statements)

References 53 publications

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“…leaves us with a lack of information for comparison. In a recent review, we identi ed the need for researchers to use adequate reporting guidelines when reporting multivariable prediction models [39].…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a model predicting patient-reported shoulder function after arthroscopic rotator cuff repair in a Swiss setting

Stojanov

Aghlmandi

Müller

et al. 2023

Preprint

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Background. Prediction models for outcomes after orthopedic surgery provide patients with evidence-based post-operative outcome expectations. Our objectives were (1) to identify prognostic factors associated with the post-operative shoulder function outcome of the Oxford Shoulder Score (OSS) and (2) to develop and validate a prediction model for post-operative OSS. Methods. Patients undergoing arthroscopic rotator cuff repair (ARCR) were prospectively documented at a Swiss orthopedic tertiary care center. The first primary ARCR in adult patients with a partial or complete rotator cuff tear were included between October 2013 and June 2021. Twenty-two potential prognostic factors were used for prediction model development. Three sets of factors were compared in terms of overall performance (using Akaike’s Information Criterion and model bias) and discriminative ability (using R squared and Root Mean Squared Error). Results. A complete-case analysis of 1,310 patients was performed. Set 3 retained the following factors: age, sex, American Society of Anesthesiologists (ASA) classification, number of pre-operative steroid infiltrations, baseline OSS, baseline level of depression and anxiety, infraspinatus tear, tendon delamination and acromioclavicular joint resection. Linear regression and Tobit models had similar performance on both the training (R squared = 0.201 and 0.201, respectively) and test sets (R squared = 0.119 and 0.123, respectively). The Tobit model was slightly more consistent than the linear regression model after bootstrap validation (R squared = 0.189 compared to 0.161). Conclusion. A prediction model for patients undergoing ARCR was developed to provide patients and surgeons with individualized expectations for post-operative shoulder function outcomes.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a model predicting patient-reported shoulder function after arthroscopic rotator cuff repair in a Swiss setting

Stojanov

Aghlmandi

Müller

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Several clinical studies have used linear and logistic regression methods to develop prognostic models for identifying risk factors and supporting treatment decisions [2, 5, 26]. A more recent model was built to predict postoperative shoulder stiffness (POSS) and evaluate the potential personalized risk factors [8].…”

Section: Introductionmentioning

confidence: 99%

Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears

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Hou

et al. 2023

Knee Surg Sports Traumatol Arthrosc

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Purpose The aim of this study is to develop a machine learning model to identify important clinical features related to rotator cuf tears (RCTs) using explainable artiicial intelligence (XAI) for eiciently predicting outpatients with RCTs. Methods A retrospective review of a local clinical registry dataset was performed to include patients with shoulder pain and dysfunction who underwent questionnaires and physical examinations between 2019 and 2022. RCTs were diagnosed by shoulder arthroscopy. Six machine-learning algorithms (Stacking, Gradient Boosting Machine, Bagging, Random Forest, Extreme Gradient Boost (XGBoost), and Adaptive Boosting) were developed for the prediction. The performance of the models was assessed by the area under the receiver operating characteristic curve (AUC), Brier scores, and Decision curve. The interpretability of the predicted outcomes was evaluated using Shapley additive explanation (SHAP) values. Results A total of 1684 patients who completed questionnaires and clinical tests were included, and 417 patients with RCTs underwent shoulder arthroscopy. In six machining learning algorithms for predicting RCTs, the accuracy, AUC values, and Brier scores were in the range of 0.81-0.86, 0.75-0.92, and 0.15-0.19, respectively. The XGBoost model showed superior performance with accuracy, AUC, and Brier scores of 0.85(95% conidence interval, 0.82-0.87), 0.92 (95% conidence interval,0.90-0.94), and 0.15 (95% conidence interval,0.14-0.16), respectively. The Shapley plot showed the impact of the clinical features on predicting RCTs. The most important variables were Jobe test, Bear hug test, and age for prediction, with mean SHAP values of 1.458, 0.950, and 0.790, respectively. Conclusion The machine learning model successfully identiied important clinical variables for predicting patients with RCTs. In addition, the best algorithm was also integrated into a digital application to provide predictions in outpatient settings. This tool may assist patients in reducing their pain experience and providing prompt treatments. Level of Evidence Level III.

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“…Postoperative complications from ARCR cause a significant burden on patients and clinicians alike [1]. A common postoperative complication after ARCR is postoperative shoulder stiffness (POSS), which can result in increased healthcare costs and unfavorable patient outcomes [6][7][8][9]. The incidence of POSS is highly variable and ranges from 2% to 28% in the literature [4,10,11].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Postoperative Rehabilitation Therapy Utilization After Arthroscopic Rotator Cuff Repair: An Epidemiological Analysis

Baumann¹,

Indermuhle²,

Curtis³

et al. 2023

Cureus

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Background and objectiveRotator cuff tear (RCT) is an orthopedic shoulder pathology commonly managed via arthroscopic rotator cuff repair (ARCR) after the failure of conservative treatment options. Physical therapy (PT) after ARCR is an important component of patient recovery. Postoperative complications, such as postoperative shoulder stiffness (POSS), are frequent among these patients and place a significant burden on patients and clinicians. The purpose of this study is to analyze temporal PT utilization among patients after ARCR and its potential to improve patient outcomes and examine possible factors affecting postoperative complication rates. MethodsAn epidemiological study was performed by using a large de-identified national health research network (TriNetX) within the United States to search for patients with a diagnosis of partial or complete RCT and subsequent ARCR. Data were collected on patient demographics, number of postoperative PT visits, and PT visits distribution in the early postoperative period. Statistical analysis was performed to analyze factors that impacted the utilization of postoperative PT after ARCR. ResultsA total of 21,540 patients underwent ARCR with 11,312 receiving ARCR for partial RCT and 10,228 for complete RCT. Of all ARCR patients, 6,923 (32.1%) received postoperative PT within one year of ARCR. Patients with partial RCT had a greater number of PT visits (mean ±SD: 3.85 ± 8.33; min-max: 0-110; t = 15.2) compared to patients with complete RCT (2.90 ± 7.97; min-max: 0-125) after ARCR (p<0.001). Patients with ARCR for partial RCT also had more visits within the first 12 weeks after ARCR as compared to patients with ARCR for complete RCT (p<0.001). Female patients had more visits than male patients after ARCR, regardless of the RCT extent (p<0.001). ConclusionPartial RCT and female sex are associated with increased postoperative PT usage after ARCR. Postoperative PT utilization has high variability after ARCR, regardless of the RCT extent. More research is needed to further explore the impact of PT utilization on postoperative complications after ARCR.

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Prognostic factors for the occurrence of post-operative shoulder stiffness after arthroscopic rotator cuff repair: a systematic review

Cited by 11 publications

References 53 publications

Development and validation of a model predicting patient-reported shoulder function after arthroscopic rotator cuff repair in a Swiss setting

Development and validation of a model predicting patient-reported shoulder function after arthroscopic rotator cuff repair in a Swiss setting

Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears

Factors Affecting Postoperative Rehabilitation Therapy Utilization After Arthroscopic Rotator Cuff Repair: An Epidemiological Analysis

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