Dataset Information

Predicting the Exception-CRP and Primary Hip Arthroplasty.

ABSTRACT:

Background

While primary hip arthroplasty is the most common operative procedure in orthopedic surgery, a periprosthetic joint infection is its most severe complication. Early detection and prediction are crucial. In this study, we aimed to determine the value of postoperative C-reactive protein (CRP) and develop a formula to predict this rare, but devastating complication.

Methods

We retrospectively evaluated 708 patients with primary hip arthroplasty. CRP, white blood cell count (WBC), and several patient characteristics were assessed for 20 days following the operative procedure.

Results

Eight patients suffered an early acute periprosthetic infection. The maximum CRP predicted an infection with a sensitivity and specificity of 75% and 56.9%, respectively, while a binary logistic regression reached values of 75% and 80%. A multinominal logistic regression, however, was able to predict an early infection with a sensitivity and specificity of 87.5% and 78.9%. With a one-phase decay, 71.6% of the postoperative CRP-variance could be predicted.

Conclusion

To predict early acute periprosthetic joint infection after primary hip arthroplasty, a multinominal logistic regression is the most promising approach. Including five parameters, an early infection can be predicted on day 5 after the operative procedure with 87.5% sensitivity, while it can be excluded with 78.9% specificity.

SUBMITTER: Meier MP

PROVIDER: S-EPMC8584609 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.