Development of a Preoperative Risk Calculator for Reinfection Following Revision Surgery for Periprosthetic Joint Infection

Klemt, Christian; Tirumala, Venkatsaiakhil; Smith, Evan J.; Padmanabha, Anand; Kwon, Young‐Min

doi:10.1016/j.arth.2020.08.004

Cited by 26 publications

(34 citation statements)

References 31 publications

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“…The findings of this present study demonstrate that surgical variables (previous irrigation and debridement with or without modular component exchange; > 4 prior open surgeries) and microbiology (Enterococcus, MRSA) were strong predictors for recurrent infections in patients following revision TKA for PJI. Similar observations were made in previous non-machine learning, retrospective studies [20,31]. Shohat et al demonstrated increased failure rates for DAIR patients, when compared to patients treated with either single or two-stage revision TJA, in a retrospective with 199 patients following revision TJA [27].…”

Section: Discussionsupporting

confidence: 80%

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

Klemt

Laurencin

Uzosike

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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Purpose This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. Methods A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. ResultsThe factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84). Conclusion This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes. Level of evidence IV.

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Section: Discussionsupporting

confidence: 80%

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

Klemt

Laurencin

Uzosike

et al. 2021

Knee Surg Sports Traumatol Arthrosc

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“…26 28 However, the present study identified increased importance of obesity than previously reported. 8 32 With previous studies reporting a risk of 1–2% for the development of PJI solely due to obesity, 32 this present ML study shows a greater significance of a high BMI (3.8%). This may be based on the increased accuracy of data analysis as provided by ML algorithms, which possess the ability to accurately identify complex relationships between clinical variables, even in noisy and incomplete datasets.…”

Section: Discussionsupporting

confidence: 65%

“…An AUC of 1 represents a perfect ML model, while ML models no better than chance have an AUC of 0.5. 8 ML model calibration was achieved through the use of a calibration plot. Overall model performance was assessed using the Brier score.…”

Section: Methodsmentioning

confidence: 99%

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA

et al. 2022

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This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1–4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.

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“…Over the past decade, a number of tools that aim to predict the likelihood of success or failure of a given treatment strategy have been created to help clinicians and patients make informed decisions about the preferred strategy for surgical management of a given infection (10)(11)(12)(13)(14)(15)(16)(17). However, the process of developing a clinical tool that can be reliably implemented remains challenging.…”

Section: Introductionmentioning

confidence: 99%

Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review

Naufal¹,

Wouthuyzen-Bakker²,

Babazadeh³

et al. 2022

Front. Rehabilit. Sci.

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The management of periprosthetic joint infection (PJI) generally requires both surgical intervention and targeted antimicrobial therapy. Decisions regarding surgical management–whether it be irrigation and debridement, one-stage revision, or two-stage revision–must take into consideration an array of factors. These include the timing and duration of symptoms, clinical characteristics of the patient, and antimicrobial susceptibilities of the microorganism(s) involved. Moreover, decisions relating to surgical management must consider clinical factors associated with the health of the patient, alongside the patient's preferences. These decisions are further complicated by concerns beyond mere eradication of the infection, such as the level of improvement in quality of life related to management strategies. To better understand the probability of successful surgical treatment of a PJI, several predictive tools have been developed over the past decade. This narrative review provides an overview of available clinical prediction models that aim to guide treatment decisions for patients with periprosthetic joint infection, and highlights key challenges to reliably implementing these tools in clinical practice.

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Development of a Preoperative Risk Calculator for Reinfection Following Revision Surgery for Periprosthetic Joint Infection

Cited by 26 publications

References 31 publications

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA

Methodological Challenges in Predicting Periprosthetic Joint Infection Treatment Outcomes: A Narrative Review

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