A decision analysis of treatment strategies for acute periprosthetic joint infection: Early irrigation and debridement versus delayed treatment based on organism

Bedair, Hany; Katakam, Akhil; Bedeir, Yehia H.; Yeroushalmi, David; Schwarzkopf, Ran

doi:10.1016/j.jor.2020.04.003

Cited by 7 publications

(5 citation statements)

References 26 publications

(32 reference statements)

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“…This reduction in ambiguity is pivotal in clinical settings, where timely and confident decision-making is essential. The existing literature suggests that delays in the care of PJI may lead to poor patient outcomes, even when the correct treatment is eventually rendered [ 33 ]. Therefore, an ML model increasing diagnostic certainty and streamlining the diagnostic process would be expected to lead to faster physician decision-making and hence an improvement in patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm

Paranjape,

Thai-Paquette,

Miamidian

et al. 2023

Cureus

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Background and objectiveThe current periprosthetic joint infection (PJI) diagnostic guidelines require clinicians to interpret and integrate multiple criteria into a complex scoring system. Also, PJI classifications are often inconclusive, failing to provide a clinical diagnosis. Machine learning (ML) models could be leveraged to reduce reliance on these complex systems and thereby reduce diagnostic uncertainty. This study aimed to develop an ML algorithm using synovial fluid (SF) test results to establish a PJI probability score. MethodsWe used a large clinical laboratory's dataset of SF samples, aspirated from patients with hip or knee arthroplasty as part of a PJI evaluation. Patient age and SF biomarkers [white blood cell count, neutrophil percentage (%PMN), red blood cell count, absorbance at 280 nm wavelength, C-reactive protein (CRP), alpha-defensin (AD), neutrophil elastase, and microbial antigen (MID) tests] were used for model development. Data preprocessing, principal component analysis, and unsupervised clustering (K-means) revealed four clusters of samples that naturally aggregated based on biomarker results. Analysis of the characteristics of each of these four clusters revealed three clusters (n=13,133) with samples having biomarker results typical of a PJI-negative classification and one cluster (n=4,032) with samples having biomarker results typical of a PJI-positive classification. A decision tree model, trained and tested independently of external diagnostic rules, was then developed to match the classification determined by the unsupervised clustering. The performance of the model was assessed versus a modified 2018 International Consensus Meeting (ICM) criteria, in both the test cohort and an independent unlabeled validation set of 5,601 samples. The SHAP (SHapley Additive exPlanations) method was used to explore feature importance. ResultsThe ML model showed an area under the curve of 0.993, with a sensitivity of 98.8%, specificity of 97.3%, positive predictive value (PPV) of 92.9%, and negative predictive value (NPV) of 99.8% in predicting the modified 2018 ICM diagnosis among test set samples. The model maintained its diagnostic accuracy in the validation cohort, yielding 99.1% sensitivity, 97.1% specificity, 91.9% PPV, and 99.9% NPV. The model's inconclusive rate (diagnostic probability between 20-80%) in the validation cohort was only 1.3%, lower than that observed with the modified 2018 ICM PJI classification (7.4%; p<0.001).The SHAP analysis found that AD was the most important feature in the model, exhibiting dominance among >95% of "infected" and "not infected" diagnoses. Other important features were the sum of the MID test panel, %PMN, and SF-CRP.

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Section: Discussionmentioning

confidence: 99%

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm

Paranjape,

Thai-Paquette,

Miamidian

et al. 2023

Cureus

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“…Additionally, the study design was retrospective with a small sample size and interpretation may be influenced by selection bias and the composition of the study cohort. Furthermore, outcomes in PJI could be influenced by drug sensitivity, antibiotic regimen, comorbidities, and duration of the infection [ 29 , 30 ]. Furthermore, it was difficult to design a well-matched comparison group.…”

Section: Discussionmentioning

confidence: 99%

The role of fibrinogen in predicting reinfection after DAIR for periprosthetic joint infections

Zhao

Wang

et al. 2021

BMC Musculoskelet Disord

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Background Fibrinogen (FIB) has been found to be a promising marker in diagnosing periprosthetic joint infection (PJI), however, the value of FIB in predicting reinfection of PJI is unknown. The purpose of this study was to evaluate the value of FIB in predicting reinfection after debridement, antibiotics, and implant retention (DAIR) for PJI. Methods We retrospectively analyzed the clinical data of patients who were diagnosed with PJI and underwent DAIR from 2013 to 2019. The levels of the FIB, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were measured before DAIR. After DAIR, patients were followed and reinfections were identified. For both acute and chronic PJI, the predictive value of FIB was evaluated by calculating the sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve (ROC), and was compared with traditional inflammatory markers including ESR and CRP. Results The expression of FIB differed between patients reinfected and those not reinfected in both acute and chronic PJI (p < 0.05). In patients who underwent DAIR for acute PJI, the sensitivity and specificity of FIB were 81.82 and 83.33%, respectively, which were significantly higher than that of CRP (sensitivity, 72.73%; specificity, 50%; p < 0.05), while the specificity was higher than that of ESR (specificity, 41.67%; p < 0.05). In patients who underwent DAIR for chronic PJI, the sensitivity and specificity of FIB were 80.00 and 66.66%, respectively, which were significantly higher than that of CRP (sensitivity, 53.33%; specificity, 66.66%; p < 0.05) and ESR (sensitivity was 66.00%; specificity, 16.66%; p < 0.05). The ROC curves showed that FIB demonstrated the highest AUC among the biomarkers in both acute and chronic PJI. Conclusion FIB is a promising indicator in predicting reinfection after DAIR for both acute and chronic PJI, and it seems to perform better than ESR and CRP.

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“…Finally, there are many factors leading to the failure of DAIR treatment, such as the choice of surgical timing, bacterial culture results, drug sensitivity, comorbidities, etc. [30,31], which need to be homogenized.…”

Section: Discussionmentioning

confidence: 99%

The Role of Serum Fibrinogen in Predicting Reinfection after DAIR (debridement, antibiotics, and implant retention) Treatment of Periprosthetic Joint Infections

Zhao¹,

He²,

Wang³

et al. 2021

Preprint

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Background Fibrinogen (FIB) has been used to differentiate periprosthetic joint infection (PJI) from aseptic loosening. The purpose of this study was to evaluate the diagnostic value of FIB in predicting postoperative reinfection in patients with debridement, antibiotics and implant retention (DAIR). Methods We retrospectively analyzed the patients who were admitted to DAIR from January 2013 to August 2019 for consideration of PJI readmission. Subgroups were divided into subgroups based on whether there was reinfection after DAIR treatment, and the diagnostic value of serum fibrinogen, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) before DAIR treatment was analyzed by receiver operating Characteristic curve (ROC). To evaluate the diagnostic value of FIB in predicting postoperative reinfection in DAIR patients. Results FIB expression was different in acute PJI patients and chronic PJI patients treated with DAIR (4.03 VS 3.08; P < .05, 4.28 VS 3.68; P < .05). In patients with acute PJI treated with DAIR, the sensitivity and specificity of FIB were 81.82% and 83.33%, respectively, significantly higher than CRP (sensitivity, 72.73%; Specificity, 50%; P < .05), while the specificity was higher than ESR (specificity,41.67%; P < .05). In patients with chronic PJI treated with DAIR, the sensitivity and specificity of FIB were 80.00% and 66.66%, respectively, significantly higher than CRP (sensitivity, 53.33%; Specificity, 66.66%; P < .05), ESR (sensitivity was 66.00%; Specificity, 16.66 %; P < .05). Conclusion FIB can predict reinfection after DAIR treatment for acute or chronic PJI. Considering the low success rate of DAIR treatment for chronic PJI, it should be chosen carefully.

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A decision analysis of treatment strategies for acute periprosthetic joint infection: Early irrigation and debridement versus delayed treatment based on organism

Cited by 7 publications

References 26 publications

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm

The role of fibrinogen in predicting reinfection after DAIR for periprosthetic joint infections

The Role of Serum Fibrinogen in Predicting Reinfection after DAIR (debridement, antibiotics, and implant retention) Treatment of Periprosthetic Joint Infections

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