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

Perioperative Glycemic Variability Influences Infection Rates Differently Following Revision Hip and Knee Arthroplasty

BACKGROUND

Recent investigations have determined that abnormal postoperative glycemia following primary total joint arthroplasty is associated with adverse events. Our study aimed to determine if hyperglycemia and glycemic variability following aseptic revision total joint arthroplasty were associated with periprosthetic joint infection (PJI) within two years postoperatively.

METHODS

A retrospective review was performed of 2,208 patients within a single institution undergoing aseptic revision total joint arthroplasty from 2012 to 2019. Postoperative glucose values were recorded. Glycemic variability was measured via three parameters: coefficient of variation, mean amplitude of glycemic excursions, and J-index. Logistic regression analyses were performed to examine associations with PJI at 90-day, 1-, and 2-year follow-up.

RESULTS

In revision hips, all glycemic measures were not associated with PJI at any time point in logistic regression analyses, except for the mean amplitude of glycemic excursions, which predicted PJI at one year (P = 0.045); body mass index was the only factor associated with PJI at all timepoints in all models. In revision knees, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses; however, PJI rates differed between diabetics and nondiabetics at all time points (P < 0.05).

CONCLUSIONS

Our findings illustrate that decreasing preoperative body mass index and postoperative glycemic variability may be critical in reducing PJI rates in revision hips. Furthermore, patients who have diabetes should be counseled that they remain at higher risk of PJI regardless of perioperative glucose control after revision knee surgery.

A Comparison of the Rates of Postoperative Infection Following Distal Radius Fixation Between Pediatric and Young Adult Populations: An Analysis of 32 368 Patients

BACKGROUND

Infection following surgical fixation of a distal radius fracture can markedly compromise a patient's functional outcome. This study aimed to compare infection rates in pediatric (5-14 years) and adolescent (15-17 years) patients undergoing fixation of a distal radius fracture to a cohort of young adult (18-30 years) patients.

METHODS

A matched retrospective study was performed using PearlDiver to determine the rates of postoperative infection following distal radius fixation. χ2 and logistic regression were used to assess differences in rates, while linear regression was used to analyze rates of infection over time.

RESULTS

In 32 368 patients, young adults experienced postoperative infection at a significantly increased rate (odds ratio [OR] = 1.81; 95% confidence interval [CI], 1.45-2.27). This trend was consistent among the male (OR = 1.96; 1.49-2.57) and female (OR = 2.11, 1.37-3.27) cohorts. In the multivariate model, the adult cohort remained at increased risk (OR = 1.40; 95% CI, 1.04-1.89), with open fracture (OR = 4.99; 3.55-6.87), smoking (OR = 1.76; 1.22-2.48), hypertension (OR = 1.69; 1.20-2.33), and obesity (OR = 1.37; 1.02, 1.80) identified as other significant risk factors. There was no significant change in the rate of postoperative infections over the 11-year study period.

CONCLUSION

This study demonstrated that although surgical site infections following distal radius fixation are low in patients aged 30 years or younger (0.97%), young adults develop infections at a significantly increased rate. This is important for surgeons to recognize when counseling patients on the risks of surgical fixation.

Postoperative Communication Volume Following Total Joint Arthroplasty Can Be a Precursor for Emergency Department Visits

Background

Unplanned calls, messages, and visits to the clinic can occur at a higher rate as newer technologies allow patients more accessibility and connectivity to clinicians. By reviewing postoperative patient phone calls and electronic portal messages, we compared the methods and frequency of communications between conventional and robotic joint arthroplasty cases.

Methods

A retrospective review of total hip, total knee, and unicompartmental knee arthroplasty procedures by fellowship-trained adult reconstruction surgeons at our hospitals between 2017 and 2022 was performed. Any unplanned postoperative communication within 30 days of the postoperative period and unplanned emergency department visits were collected.

Results

There were 12,300 robotic and manual consecutive primary total hip, total knee, and unicompartmental knee arthroplasty procedures performed on 10,908 patients over the study period. A total of 905 (40.4%) patients and 2012 (23.2%) patients sent an electronic text message (ETM) in the robotic and manual arthroplasty cohorts (P < .0001), respectively. Overall, 1942 (86.6%) patients in the robotic arthroplasty group and 6417 (74%) patients in the manual arthroplasty group had at least one phone call within the first month after their joint arthroplasty.

Conclusions

Robotic arthroplasty patients place an increased demand on the orthopaedic surgery department in terms of unplanned patient contacts. Robotic arthroplasty patients had a significantly increased rate of unplanned postoperative ETMs and phone calls when compared to manual arthroplasty patients. An increased number of postoperative phone calls, but not ETMs, can also be indicative of an emergency department visit. These findings can be used in the perioperative setting to counsel and educate patients about expectations.

The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection Following Aseptic Revision Total Knee Arthroplasty

Periprosthetic joint infection (PJI) following revision total knee arthroplasty (TKA) for aseptic failure is associated with poor outcomes, patient morbidity, and high health care expenditures. The aim of this study was to develop novel machine learning algorithms for the prediction of PJI following revision TKA for patients with aseptic indications for revision surgery. A single-institution database consisting of 1,432 consecutive revision TKA patients with aseptic etiologies was retrospectively identified. The patient cohort included 208 patients (14.5%) who underwent re-revision surgery for PJI. Three machine learning algorithms (artificial neural networks, support vector machines, k-nearest neighbors) were developed to predict this outcome and these models were assessed by discrimination, calibration, and decision curve analysis. This is a retrospective study. Among the three machine learning models, the neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.78), calibration, and decision curve analysis. The strongest predictors for PJI following revision TKA for aseptic reasons were prior open procedure prior to revision surgery, drug abuse, obesity, and diabetes. This study utilized machine learning as a tool for the prediction of PJI following revision TKA for aseptic failure with excellent performance. The validated machine learning models can aid surgeons in patient-specific risk stratifying to assist in preoperative counseling and clinical decision making for patients undergoing aseptic revision TKA.

The Same Day Discharges following Primary Total Knee Arthroplasty: A Single Surgeon, Propensity Score-Matched Cohort Analysis

This is a retrospective study. As new surgical techniques and improved perioperative care approaches have become available, the same-day discharge in selected total knee arthroplasty (TKA) patients was introduced to decrease health care costs without compromising outcomes. This study aimed to compare clinical and functional outcomes between same-day discharge TKA patients and inpatient-discharge TKA patients. A retrospective review of 100 consecutive patients with same-day discharge matched to a cohort of 300 patients with inpatient discharge that underwent TKA by a single surgeon at a tertiary referral center was conducted. Propensity-score matching was performed to adjust for baseline differences in preoperative patient demographics, medical comorbidities, and patient-reported outcome measures (PROMs) between both cohorts. All patients had a minimum of 1-year follow-up (range: 1.2-2.8 years). In terms of clinical outcomes for the propensity score-matched cohorts, there was no significant difference in terms of revision rates (1.0 vs. 1.3%, p = 0.76), 90-day emergency department visits (3.0 vs. 3.3%, p = 0.35), 30-day readmission rates (1.0 vs. 1.3%, p = 0.45), and 90-day readmission rates (3.0 vs. 3.6%, p = 0.69). Patients with same-day discharge demonstrated significantly higher postoperative PROM scores, at both 3-month and 1-year follow-up, for PROMIS-10 Physical Score (50 vs. 46, p = 0.028), PROMIS-10 Mental Score (56 vs. 53, p = 0.039), and Physical SF10A (57 vs. 52, p = 0.013). This study showed that patients with same-day discharge had similar clinical outcomes and superior functional outcomes, when compared with patients that had a standard inpatient protocol. This suggests that same-day discharge following TKA may be a safe, viable option in selected total knee joint arthroplasty patients.

Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

BACKGROUND

Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.

RESULTS

Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.

CONCLUSION

Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.

Complete blood platelet and lymphocyte ratios increase diagnostic accuracy of periprosthetic joint infection following total hip arthroplasty

INTRODUCTION

Systemically, changes in serum platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte (MLR) represent primary responses to early inflammation and infection. This study aimed to determine whether PLR, PVR, NLR, and MLR can be useful in diagnosing periprosthetic joint infection (PJI) in total hip arthroplasty (THA) patients.

METHODS

A total of 464 patients that underwent revision THA with calculable PLR, PVR, NLR, and MLR in 2 groups was evaluated: 1) 191 patients with a pre-operative diagnosis of PJI, and 2) 273 matched patients treated for revision THA for aseptic complications.

RESULTS

The sensitivity and specificity of PLR combined with erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), synovial white blood cell count (WBC) and synovial polymorphonuclear leukocytes (PMN) (97.9%; 98.5%) is significantly higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01). The sensitivity and specificity of PVR combined with ESR, CRP and synovial WBC, and synovial PMN (98.4%; 98.2%) is higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01).

CONCLUSION

The study results demonstrate that both PLR and PVR calculated from complete blood counts when combined with serum and synovial fluid markers have increased diagnostic sensitivity and specificity in diagnosing periprosthetic joint infection in THA patients.

LEVEL OF EVIDENCE

III, case-control retrospective analysis.

Elevated ESR and CRP Prior to Second-Stage Reimplantation Knee Revision Surgery for Periprosthetic Joint Infection Are Associated with Increased Reinfection Rates

Although two-stage revision surgery is considered as the most effective treatment for managing chronic periprosthetic joint infection (PJI), there is no current consensus on the predictors of optimal timing to second-stage reimplantation. This study aimed to compare clinical outcomes between patients with elevated erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) prior to second-stage reimplantation and those with normalized ESR and CRP prior to second-stage reimplantation. We retrospectively reviewed 198 patients treated with two-stage revision total knee arthroplasty for chronic PJI. Cohorts included patients with: (1) normal level of serum ESR and CRP (n = 96) and (2) elevated level of serum ESR and CRP prior to second-stage reimplantation (n = 102). Outcomes including reinfection rates and readmission rates were compared between both cohorts. At a mean follow-up of 4.4 years (2.8-6.5 years), the elevated ESR and CRP cohort demonstrated significantly higher reinfection rates compared with patients with normalized ESR and CRP prior to second-stage reimplantation (33.3% vs. 14.5%, p < 0.01). Patients with both elevated ESR and CRP demonstrated significantly higher reinfection rates, when compared with patients with elevated ESR and normalized CRP (33.3% vs. 27.6%, p = 0.02) as well as normalized ESR and elevated CRP (33.3% vs. 26.3%, p < 0.01). This study demonstrates that elevated serum ESR and/or CRP levels prior to reimplantation in two-stage knee revision surgery for chronic PJI are associated with increased reinfection rate after surgery. Elevation of both ESR and CRP were associated with a higher risk of reinfection compared with elevation of either ESR or CRP, suggesting the potential benefits of normalizing ESR and CRP prior to reimplantation in treatment of chronic PJI.

External Validation of Prediction Models for Surgical Complications in People Considering Total Hip or Knee Arthroplasty Was Successful for Delirium but Not for Surgical Site Infection, Postoperative Bleeding, and Nerve Damage: A Retrospective Cohort Study

Although several models for the prediction of surgical complications after primary total hip or total knee replacement (THA and TKA, respectively) are available, only a few models have been externally validated. The aim of this study was to externally validate four previously developed models for the prediction of surgical complications in people considering primary THA or TKA. We included 2614 patients who underwent primary THA or TKA in secondary care between 2017 and 2020. Individual predicted probabilities of the risk for surgical complication per outcome (i.e., surgical site infection, postoperative bleeding, delirium, and nerve damage) were calculated for each model. The discriminative performance of patients with and without the outcome was assessed with the area under the receiver operating characteristic curve (AUC), and predictive performance was assessed with calibration plots. The predicted risk for all models varied between <0.01 and 33.5%. Good discriminative performance was found for the model for delirium with an AUC of 84% (95% CI of 0.82-0.87). For all other outcomes, poor discriminative performance was found; 55% (95% CI of 0.52-0.58) for the model for surgical site infection, 61% (95% CI of 0.59-0.64) for the model for postoperative bleeding, and 57% (95% CI of 0.53-0.61) for the model for nerve damage. Calibration of the model for delirium was moderate, resulting in an underestimation of the actual probability between 2 and 6%, and exceeding 8%. Calibration of all other models was poor. Our external validation of four internally validated prediction models for surgical complications after THA and TKA demonstrated a lack of predictive accuracy when applied in another Dutch hospital population, with the exception of the model for delirium. This model included age, the presence of a heart disease, and the presence of a disease of the central nervous system as predictor variables. We recommend that clinicians use this simple and straightforward delirium model during preoperative counselling, shared decision-making, and early delirium precautionary interventions.

Periprosthetic joint infection is the main reason for failure in patients following periprosthetic fracture treated with revision arthroplasty

INTRODUCTION

Periprosthetic fracture after primary total hip and knee arthroplasty (THA; TKA) can be challenging, requiring open reduction internal fixation (ORIF), revision, or both. The aim of this study was to evaluate the outcomes and risk factors associated with re-revision surgery following failed revision arthroplasty for periprosthetic fracture.

METHODS

A total of 316 consecutive THA patients and 79 consecutive TKA patients underwent a revision for periprosthetic fracture, of which 68 THA patients (21.5%) and 15 TKA patients (18.9%) underwent re-revision surgery. The most common indication for hip and knee re-revision was periprosthetic joint infection (PJI) in 28 THA patients (46.6%) and 11 TKA patients (47.8%).

RESULTS

The complication rates of THA and TKA revision were 24.3% and 25.3% respectively, and 35.0% and 39.1% respectively for re-revision surgery at an average follow-up of 4.5 years. Periprosthetic joint infection was the most common indication for THA and TKA re-revision (46.7%; 47.8%) and third revision surgery (15.0%; 13.0%). Factors significantly contributing to an increased risk of THA and TKA re-revision included revision with plate fixation and revision with combined ORIF.

CONCLUSION

The overall complication rate of THA and TKA re-revision surgery following failed revision surgery for periprosthetic fracture was higher than of revision surgery. The most common indication for re-revision and third revision was periprosthetic joint infection. These findings may assist surgeons in the management and preoperative counseling of patients undergoing THA and TKA revision surgery for a periprosthetic fracture to optimize the outcomes for these patients.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models

BACKGROUND

Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty.

METHODS

A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN).

RESULTS

We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m²) were the strongest predictors associated with surgical operative time.

CONCLUSIONS

This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

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

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.

RESULTS

The 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.

Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty

PURPOSE

Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA.

METHODS

A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis.

RESULTS

The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients.

CONCLUSION

The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use.

LEVEL OF EVIDENCE

IV.

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

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.

Outcome and Predictors of Septic Failure Following Total Joint Arthroplasty for Prior Septic Arthritis of Hip and Knee Joint

BACKGROUND

Arthroplasty patients with prior septic arthritis are at a high risk of developing periprosthetic joint infection (PJI). The aims of this study are to investigate the outcome and predictors of septic failure following total joint arthroplasty (TJA) for prior septic arthritis. In addition, the optimal timing of TJA is also discussed.

METHODS

A retrospective review of 105 TJA patients with prior septic arthritis between January 2000 and December 2019 was performed. Patient-specific and surgery-related factors, organism profiles, and other relevant variables were recorded.

RESULTS

At a mean follow-up of 10.3 years, the PJI rate was 16.2%. The adjusted Cox proportional hazards model showed that male gender (HR, 9.95; P < .01), end-stage renal disease (HR, 37.34; P < .01), debridement surgery ≥3 times (HR,4.75; P = .04) and polymicrobial infection in primary septic arthritis (HR, 10.02; P = .02) were independent risk factors for PJI. Neither the types of initial debridement, nor one-stage vs two-stage arthroplasty was related to the risk of PJI. While delaying the timing of TJA did not correlate with a reduction of PJI rate, there was a higher risk of PJI re-infection by the same microorganisms isolated in prior septic arthritis if TJA was performed within 6 months after septic arthritis.

CONCLUSIONS

Our study demonstrated that male gender, end-stage renal disease (ESRD), multiple debridement surgeries and polymicrobial septic arthritis predisposed septic failure of TJA following prior septic arthritis. Surgeons should counsel patients with the potential complications, and be cognizant about the risk factors pertaining to septic failure when considering TJA.

Can machine learning models predict failure of revision total hip arthroplasty?

INTRODUCTION

Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty.

METHODS

A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis.

RESULTS

The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m²) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients.

CONCLUSION

This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

Survival and Outcomes of 1.5-Stage vs 2-Stage Exchange Total Knee Arthroplasty Following Prosthetic Joint Infection

BACKGROUND

Traditional management of prosthetic joint infection following total knee arthroplasty (TKA) consists of a 2-stage approach. However, 1.5-stage exchange has seen preliminary success, whereby metal femoral and all-polyethylene tibia components are placed without intention for subsequent second stage. We sought to examine all patients who underwent a 1.5-stage exchange TKA at a single institution compared to historical 2-stage controls. We assessed the following: (1) infection-free survivorship and risk factors for reinfection; (2) 1-year surgical/medical outcomes; (3) patient-reported outcomes (ie, Knee Injury and Osteoarthritis Outcome Score for Joint Replacement [KOOS JR]); and (4) radiographic outcomes.

METHODS

We reviewed all patients undergoing a 1.5-stage (between 2015 and 2019) and 2-stage exchange TKA (between 2011 and 2016) at a single institution. A total of 162 knees were included (1.5-stage: 114; 2-stage: 48) with mean clinical follow-up of 2.6 years. KOOS JR scores and radiographic outcomes were evaluated at last clinical follow-up.

RESULTS

The 1.5-stage exchange TKA resulted in a 10.1% difference in infection-free survival (85.1% vs 75.0%, P = .158), compared to 2-stage exchange. Prior prosthetic joint infection was found to be an independent risk factor for reinfection (P = .030). Overall, postoperative complications were lower among 1.5-stage exchanges (8.8% vs 31.3%, P < .001). KOOS JR scores improved more from baseline among 1.5-staged (Δ24.7 vs Δ16.6, P < .001). Radiographic review did not demonstrate any progressive radiolucent lines, subsidences, or failures in either group.

CONCLUSION

A 1.5-stage exchange TKA is an effective alternative to the traditional 2-stage protocols with noninferior infection eradication and absence of radiographic complications at over 2 years of mean follow-up.

How Does a "Dry Tap" Impact the Accuracy of Preoperative Aspiration Results in Predicting Chronic Periprosthetic Joint Infection?

BACKGROUND

Periprosthetic joint infection (PJI) after total hip arthroplasty (THA) is challenging to diagnose. We aimed to evaluate the impact of dry taps requiring saline lavage during preoperative intra-articular hip aspiration on the accuracy of diagnosing PJI before revision surgery.

METHODS

A retrospective review was conducted for THA patients with suspected PJI who received an image-guided hip aspiration from May 2016 to February 2020. Musculoskeletal Infection Society (MSIS) diagnostic criteria for PJI were compared between patients who had dry tap (DT) vs successful tap (ST). Sensitivity and specificity of synovial markers were compared between the DT and ST groups. Concordance between preoperative and intraoperative cultures was determined for the 2 groups.

RESULTS

In total, 335 THA patients met inclusion criteria. A greater proportion of patients in the ST group met MSIS criteria preoperatively (30.2% vs 8.3%, P < .001). Patients in the ST group had higher rates of revision for PJI (28.4% vs 17.5%, P = .026) and for any indication (48.4% vs 36.7%, P = .039). MSIS synovial white blood cell count thresholds were more sensitive in the ST group (90.0% vs 66.7%). There was no difference in culture concordance (67.9% vs 65.9%, P = .709), though the DT group had a higher rate of negative preoperative cultures followed by positive intraoperative cultures (85.7% vs 41.1%, P = .047).

CONCLUSION

Our results indicate that approximately one third of patients have dry hip aspiration, and in these patients cultures are less predictive of intraoperative findings. This suggests that surgeons considering potential PJI after THA should apply extra scrutiny when interpreting negative results in patients who require saline lavage for hip joint aspiration.

Factors predicting hip joint aspiration yield or “dry taps” in patients with total hip arthroplasty

Background

Image-guided joint aspirations used to assist the diagnosis of periprosthetic joint infection (PJI) may commonly result in a dry tap–or insufficient fluid for culture and cell count analysis. Dry tap aspirations are painful and invasive for patients and often utilize a subsequent saline lavage to obtain a microbiology sample. Currently, there is a paucity of the literature addressing predictors that could suggest whether a dry tap will occur. The purpose of this study was to examine the effects of various factors on “dry tap” occurrence in patients with suspected PJI following total hip arthroplasty (THA).

Methods

A retrospective review was performed among THA patients suspected for PJI who received image-guided joint aspiration procedures at our institution from May 2016 to February 2020. The procedural factors included the imaging modality used for aspiration, anatomic approach, needle gauge size used, and the presence of a trainee. The patient-specific factors included number of prior ipsilateral hip surgeries, femoral head size, ESR/CRP values, and BMI.

Results

In total, 336 patients met our inclusion criteria. One hundred and twenty hip aspirations resulted in a dry tap (35.7%) where the patients underwent a saline lavage. Among the procedural and patient-specific factors, none of the factors were found to be statistically different between the two cohorts nor conferred any greater odds of a dry tap occurring.

Conclusion

No associations with dry tap occurrence were found among the procedural and patient-specific factors studied. Further research is needed to identify additional factors that may be more predictive of dry taps.

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

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.

Outcomes and Risk Factors Associated With Failures of Debridement, Antibiotics, and Implant Retention in Patients With Acute Hematogenous Periprosthetic Joint Infection

BACKGROUND

Acute hematogenous periprosthetic joint infections (PJI) accounts for 20% to 35% of all PJI cases. Treatment options include débridement, antibiotics, and implant retention (DAIR) or implant revision (single-stage/two-stage revision). Because the reported success rates of DAIR for acute PJIs as reported in the literature varies widely, this study aimed to investigate (1) the outcome of DAIR as revision surgery procedure and (2) the potential risk factors for treatment failure of DAIR in patients with acute hematogenous PJI.

METHODS

We reviewed 106 consecutive cases of total joint arthroplasty patients who underwent DAIR for the diagnosis of acute hematogenous PJI. Outcomes of the cohort including infection free survival was investigated. Mean follow-up was 4.9 years. Demographics, case data, comorbidities, and extremity score were analyzed by univariate and multivariate regressions to identify risk factors for failure of DAIR.

RESULTS

The failure rate of patients who underwent DAIR was 23.6% (25 of 106 patients). Univariate regression demonstrated that diabetes mellitus (P = 0.01) and polymicrobial infections (P < 0.01) are associated with failure of DAIR. Multivariate regression confirmed diabetes mellitus and polymicrobial infections as independent risk factors for failure of DAIR.

DISCUSSION

Debridement, antibiotics, and implant retention may be a viable treatment option with moderate failure rates at the midterm follow-up in cases of acute hematogenous PJI. The study also identified diabetes mellitus and polymicrobial infections as independent risk factors for failure of DAIR. The findings of this study provide clinically useful information for surgeons in treatment of patients with acute hematogenous PJI.

Plasma levels of D-dimer and fibrin degradation product are unreliable for diagnosing periprosthetic joint infection in patients undergoing re-revision arthroplasty

Background

The preoperative diagnosis of periprosthetic joint infection (PJI) in patients undergoing re-revision arthroplasty is crucial, so we evaluated whether plasma levels of D-dimer and fibrin degradation product (FDP) could aid such diagnosis.

Methods

We retrospectively analyzed data on patients who underwent re-revision hip or knee arthroplasty at our institute during 2008–2020. Patients were stratified into those who experienced PJI or not, based on 2013 International Consensus Meeting Criteria. Plasma levels of D-dimer and FDP as well as levels of the traditional inflammatory biomarkers C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and interleukin-6 were compared between the groups. The ability of these biomarkers to diagnose PJI was assessed based on the area under the receiver operating characteristic (AUC) curve, for which predictive cut-offs were optimized based on the Youden index.

Results

Based on a cut-off of 0.80 mg/L, D-dimer gave an AUC of 0.595, high sensitivity of 85.7% but poor specificity of 47.8%. Based on a cut-off of 2.80 mg/L, FDP gave an AUC of 0.550, poor sensitivity of 56.5% and poor specificity of 52.9%. CRP, ESR and interleukin-6 showed much better diagnostic ability, with AUCs > 0.82. The combination of CRP and interleukin-6 gave an AUC of 0.877, high sensitivity of 91.7% and acceptable specificity of 78.3%.

Conclusions

Plasma levels of D-dimer and FDP may be inappropriate for diagnosing PJI in patients undergoing re-revision arthroplasty, whereas the combination of serum CRP and interleukin-6 may be effective.

Comparison of patient reported outcome measures after single versus two-stage revision for chronic infection of total hip arthroplasty: a retrospective propensity score matched cohort study

INTRODUCTION

Two-stage revision is the current gold standard treatment for infected total hip arthroplasties (THA) with good clinical outcomes. Single-stage revision THA offers the advantage of only a single surgical intervention, potentially leading to improved functional outcomes. This study aimed to compare the differences in patient-reported outcome measures (PROMs) and complications between single and two-stage revision THA for chronic periprosthetic joint infection (PJI).

METHODS

A total of 159 consecutive revision THA patients for chronic PJI with complete pre-and post-operative patient-reported outcome measures (PROM) was investigated. A total of 46 patients with single-stage revision THA was matched to 92 patients following two-stage revision THA using propensity score matching, yielding a total of 136 propensity score-matched patients for analysis.

RESULTS

Single and two-stage revision THA improved PROM scores post-operatively, with significantly higher PROMs for single-stage revision THA (HOOS-PS: 50.7 vs 46.4, p = 0.04; Physical SF 10A: 42.1 vs 36.6, p < 0.001; PROMIS SF Physical: 41.4 vs 37.4, p < 0.001; PROMIS SF Mental: 52.8 vs 47.6, p < 0.001). There was no significant difference between both cohorts for reinfection rates (p = 0.81) and 90-day mortality rates (p = 1.0).

CONCLUSION

This study found a demonstrable functional benefit of single-stage revision compared to two-stage revision for THA with chronic periprosthetic joint infection, suggesting that single-stage revision THA may provide an effective alternative to two-stage revision in selected patients with chronic PJI.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

One-stage revision is as effective as two-stage revision for chronic culture-negative periprosthetic joint infection after total hip and knee arthroplasty

AIMS

Removal of infected components and culture-directed antibiotics are important for the successful treatment of chronic periprosthetic joint infection (PJI). However, as many as 27% of chronic PJI patients yield negative culture results. Although culture negativity has been thought of as a contraindication to one-stage revision, data supporting this assertion are limited. The aim of our study was to report on the clinical outcomes for one-stage and two-stage exchange arthroplasty performed in patients with chronic culture-negative PJI.

METHODS

A total of 105 consecutive patients who underwent revision arthroplasty for chronic culture-negative PJI were retrospectively evaluated. One-stage revision arthroplasty was performed in 30 patients, while 75 patients underwent two-stage exchange, with a minimum of one year's follow-up. Reinfection, re-revision for septic and aseptic reasons, amputation, readmission, mortality, and length of stay were compared between the two treatment strategies.

RESULTS

The patient demographic characteristics did not differ significantly between the groups. At a mean follow-up of 4.2 years, the treatment failure for reinfection for one-stage and two-stage revision was five (16.7%) and 15 patients (20.0%) (p = 0.691), and for septic re-revision was four (13.3%) and 11 patients (14.7%) (p = 0.863), respectively. No significant differences were observed between one-stage and two-stage revision for 30- 60- and 90-day readmissions (10.0% vs 8.0%; p = 0.714; 16.7% vs 9.3%; p = 0.325; and 26.7% vs 10.7%; p = 0.074), one-year mortality (3.3% vs 4.0%; p > 0.999), and amputation (3.3% vs 1.3%; p = 0.496).

CONCLUSION

In this non-randomized study, one-stage revision arthroplasty demonstrated similar outcomes including reinfection, re-revision, and readmission rates for the treatment of chronic culture-negative PJI after TKA and THA compared to two-stage revision. This suggests culture negativity may not be a contraindication to one-stage revision arthroplasty for chronic culture-negative PJI in selected patients. Cite this article: Bone Joint J 2021;103-B(3):515-521.