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VASO (Vitamin D and Arthroplasty Surgery Outcomes) study - supplementation of vitamin D deficiency to improve outcomes after total hip or knee replacement: study protocol for a randomised controlled feasibility trial.

ABSTRACT: Vitamin D deficiency has been linked to poor outcomes after total hip replacement (THR) or total knee replacement (TKR), including lower patient-reported outcome measures (PROMs), peri-prosthetic infection and longer hospital stay. We present a randomised feasibility trial protocol designed to prospectively investigate the influence of vitamin D testing, and subsequent supplementation for deficiency, prior to THR/TKR.One hundred adult patients undergoing primary THR/TKR for osteoarthritis at two NHS hospital trusts in North East England will be recruited. Exclusion criteria include lack of mental capacity, revision surgery, participants already taking vitamin D/calcium supplements, or a known contraindication to vitamin D treatment. Participants will be ineligible for the trial if they have an estimated glomerular filtration rate?

SUBMITTER: Morrison RJM

PROVIDER: S-EPMC5669000 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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VASO (Vitamin D and Arthroplasty Surgery Outcomes) study - supplementation of vitamin D deficiency to improve outcomes after total hip or knee replacement: study protocol for a randomised controlled feasibility trial.

Morrison Rory J M RJM Bunn Deborah D Gray William K WK Baker Paul N PN White Craig C Rangan Amar A Rankin Kenneth S KS Reed Mike R MR

Trials 20171102 1

<h4>Background</h4>Vitamin D deficiency has been linked to poor outcomes after total hip replacement (THR) or total knee replacement (TKR), including lower patient-reported outcome measures (PROMs), peri-prosthetic infection and longer hospital stay. We present a randomised feasibility trial protocol designed to prospectively investigate the influence of vitamin D testing, and subsequent supplementation for deficiency, prior to THR/TKR.<h4>Methods/design</h4>One hundred adult patients undergoing ...[more]

PMID: 29096686

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Project description:IntroductionThe purpose of this study is to evaluate the role of major psychiatric illness on patient outcomes after total joint arthroplasty.MethodsPatients with a diagnosis of a major psychiatric disorder undergoing total joint arthroplasty were retrospectively matched one-to-one with a cohort without such a diagnosis. Major psychiatric disorder in the registry was identified by diagnosis of anxiety, mood, or a psychotic disorder. Primary outcome of interest included perioperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Secondary outcomes included EuroQol-5D, adverse events, length of stay, 30-day readmission, and discharge destination.ResultsTotal number of patients were 1828. The total hip arthroplasty (37.80 ± 17.91, p = 0.023) and the total knee arthroplasty psychiatric group (43.38 ± 18.41, p = 0.050) had significantly lower pre-operative WOMAC scores. At 3 months, the total hip arthroplasty (76.74 ± 16.94, p = 0.036) and total knee arthroplasty psychiatric group (71.09 ± 18.64, p < 0.01) again had significantly lower 3-month post-operative WOMAC score compared to the control groups. However, outcomes at 1 year were difficult to interpret, as patients with major psychiatric conditions had an extremely high loss to follow-up. Compared to the control groups, the total hip arthroplasty and total knee arthroplasty psychiatric group had an increased length of stay by 1.43 days (p < 0.01) and 0.77 days, respectively (p = 0.05). Similarly, the psychiatric groups were discharged directly home less often (total hip arthroplasty 86.9%, p = 0.024 and total knee arthroplasty 87.6%, p = 0.022) than the control groups.ConclusionPatients with the diagnosis of a major psychiatric illness have an increased length of stay and are more likely to require a rehabilitation facility, compared to the control groups. Arguably, of utmost importance, there is a very high rate of loss to follow-up within the psychiatric groups. As such, we recommend these patients should be treated for their diagnosis prior to total joint arthroplasty. Furthermore, importance of clinical follow-up should be emphasized carefully.

Project description:BackgroundTotal joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.ObjectiveThis study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.MethodsWe performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded.ResultsThe proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability.ConclusionsMachine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

Dataset Information

VASO (Vitamin D and Arthroplasty Surgery Outcomes) study - supplementation of vitamin D deficiency to improve outcomes after total hip or knee replacement: study protocol for a randomised controlled feasibility trial.

Publications

VASO (Vitamin D and Arthroplasty Surgery Outcomes) study - supplementation of vitamin D deficiency to improve outcomes after total hip or knee replacement: study protocol for a randomised controlled feasibility trial.

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