Established and Novel Risk Factors for 30-Day Readmission Following Total Knee Arthroplasty: A Modified Delphi and Focus Group Study to Identify Clinically Important Predictors

Gould, Daniel; Dowsey, Michelle M.; Spelman, Tim; Bailey, James M.; Bunzli, Samantha; Rele, Siddharth; Choong, Peter

doi:10.3390/jcm12030747

Cited by 2 publications

(3 citation statements)

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“…There were also newly identified predictors for readmission: number of historical knee procedures, socioeconomic status, and body mass index (BMI). There was evidence from the systematic review and meta-analysis [ 14 ] that these risk factors correlated with readmission, however, BMI and low socioeconomic status only received a majority vote of moderate importance in the Delphi survey, and the number of historical knee procedures received a majority low importance vote [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…Risk factors were selected from the systematic review and meta-analysis [ 14 ] carried out by the authors of this study for which there was moderate- or high-quality evidence and which correlated with the readmission. To utilise the knowledge of clinicians [ 8 ], a modified Delphi survey and focus group study was carried out [ 20 ]. Variables selected for the model were those with a high-importance vote by a simple majority of ≥ 50%.…”

Section: Methodsmentioning

confidence: 99%

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Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

et al. 2023

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Background Thirty-day readmission is an increasingly important problem for total knee arthroplasty (TKA) patients. The aim of this study was to develop a risk prediction model using machine learning and clinical insight for 30-day readmission in primary TKA patients. Method Data used to train and internally validate a multivariable predictive model were obtained from a single tertiary referral centre for TKA located in Victoria, Australia. Hospital administrative data and clinical registry data were utilised, and predictors were selected through systematic review and subsequent consultation with clinicians caring for TKA patients. Logistic regression and random forest models were compared to one another. Calibration was evaluated by visual inspection of calibration curves and calculation of the integrated calibration index (ICI). Discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Results The models developed in this study demonstrated adequate calibration for use in the clinical setting, despite having poor discriminative performance. The best-calibrated readmission prediction model was a logistic regression model trained on administrative data using risk factors identified from systematic review and meta-analysis, which are available at the initial consultation (ICI = 0.012, AUC-ROC = 0.589). Models developed to predict complications associated with readmission also had reasonable calibration (ICI = 0.012, AUC-ROC = 0.658). Conclusion Discriminative performance of the prediction models was poor, although machine learning provided a slight improvement. The models were reasonably well calibrated, meaning they provide accurate patient-specific probabilities of these outcomes. This information can be used in shared clinical decision-making for discharge planning and post-discharge follow up.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

et al. 2023

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“…MGH helped by observing the pilot test of the focus group and providing feedback on its structure and conduct. This study has also been published [ 64 ]. Level = Involve Phase = Design and preparation, dissemination Develop and internally validate the model.…”

Section: Methodsmentioning

confidence: 99%

Research Buddy partnership in a MD–PhD program: lessons learned

et al. 2023

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Background and aims There is increasing recognition of the importance of patient involvement in research. In recent years, there has also been growing interest in patient partnerships with doctoral studies students. However, it can be difficult to know where to start and how to go about such involvement activities. The purpose of this perspective piece was to share experiential insight of the experience of a patient involvement program such that others can learn from this experience. Body This is a co-authored perspective piece centred on the experience of MGH, a patient who has had hip replacement surgery, and DG, a medical student completing a PhD, participating in a Research Buddy partnership over the course of over 3 years. The context in which this partnership took place was also described to facilitate comparison with readers’ own circumstances and contexts. DG and MGH met regularly to discuss, and work together on, various aspects of DG’s PhD research project. Reflexive thematic analysis was conducted on reflections from DG and MGH regarding their experience in the Research Buddy program to synthesise nine lessons which were then corroborated with reference to published literature on patient involvement in research. These lessons were: learn from experience; tailor the program; get involved early; embrace uniqueness; meet regularly; build rapport; ensure mutual benefit; broad involvement; regularly reflect and review. Conclusions In this perspective piece, a patient and a medical student completing a PhD reflected upon their experience co-designing a Research Buddy partnership within a patient involvement program. A series of nine lessons was identified and presented to inform readers seeking to develop or enhance their own patient involvement programs. Researcher-patient rapport is foundational to all other aspects of the patient’s involvement.

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Established and Novel Risk Factors for 30-Day Readmission Following Total Knee Arthroplasty: A Modified Delphi and Focus Group Study to Identify Clinically Important Predictors

Cited by 2 publications

References 53 publications

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

Research Buddy partnership in a MD–PhD program: lessons learned

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