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

Preoperative Laboratory Values Predicting Periprosthetic Joint Infection in Morbidly Obese Patients Undergoing Total Hip or Knee Arthroplasty

BACKGROUND

Morbidly obese patients are an ever-growing high-risk population undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) for end-stage osteoarthritis. This study sought to identify preoperative laboratory values that may serve as predictors of periprosthetic joint infection (PJI) in morbidly obese patients undergoing THA or TKA.

METHODS

All morbidly obese patients with preoperative laboratory data before undergoing primary elective TKA or THA were identified using the Premier Healthcare Database. Patients who developed PJI within 90 days after surgery were compared with patients without PJI. Laboratory value thresholds were defined by clinical guidelines or primary literature. Univariate and multivariable regression analyses were utilized to assess the association between PJI and preoperative laboratory values, including total lymphocyte count, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), albumin level, platelet count, albumin-globulin ratio, hemoglobin level, and hemoglobin A1c.

RESULTS

Of the 6,780 patients identified (TKA: 76.67%; THA: 23.33%), 47 (0.69%) developed PJI within 90 days after surgery. The rate of PJI was 1.69% for patients with a hemoglobin level of <12 g/dL (for females) or <13 g/dL (for males), 2.14% for those with a platelet count of <142,000/µL or >417,000/µL, 1.11% for those with an NLR of >3.31, 1.69% for those with a PLR of >182.3, and 1.05% for those with an SII of >776.2. After accounting for potential confounding factors, we observed an association between PJI and an abnormal preoperative NLR (adjusted odds ratio [aOR]: 2.38, 95% confidence interval [CI]: 1.04 to 5.44, p = 0.039), PLR (aOR: 4.86, 95% CI: 2.15 to 10.95, p < 0.001), SII (aOR: 2.44, 95% CI: 1.09 to 5.44, p = 0.029), platelet count (aOR: 3.50, 95% CI: 1.11 to 10.99, p = 0.032), and hemoglobin level (aOR: 2.62, 95% CI: 1.06 to 6.50, p = 0.038).

CONCLUSIONS

This study identified preoperative anemia, abnormal platelet count, and elevated NLR, PLR, and SII to be associated with an increased risk of PJI among patients with a body mass index of ≥40 kg/m 2 . These findings may help surgeons risk-stratify this high-risk patient population.

LEVEL OF EVIDENCE

Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Different biomarker ratios in peripheral blood have limited value in diagnosing periprosthetic joint infection after total joint arthroplasty: a single-center, retrospective study

BACKGROUND

Periprosthetic joint infection (PJI) is a severe complication that can occur after total joint arthroplasty (TJA). The timely and accurate diagnosis of PJI is the key to treatment. This study investigated the diagnostic value of platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) in PJI after total knee arthroplasty (TKA) and total hip arthroplasty (THA).

METHODS

We performed a retrospective analysis of the patients who underwent revision hip or knee arthroplasty at our Institute between June 2015 and June 2020. Of the 187 patients reviewed, 168 were included in the study. According to the diagnostic criteria of the Musculoskeletal Infection Society (MSIS), 58 patients were in the PJI group, and 110 patients were in the aseptic loosening (AL) group. We recorded and compared the preoperative peripheral blood white blood cell (WBC) count, platelet count (PLT), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), PLR, PVR, NLR, and MLR in both groups. The diagnostic performance of the WBC, PLT, PLR, PVR, NLR, and MLR individually and in combination with the ESR and CRP for PJI diagnosis was evaluated by receiver operating characteristic (ROC) curves, and the sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

RESULTS

Compared to those in the AL group, the mean WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR in the peripheral blood of the PJI group were significantly greater (P < 0.05). The analysis of the ROC curve revealed that the ESR, CRP, PLR, PVR, NLR, and MLR in peripheral blood had moderate effectiveness in diagnosing PJI, with area under the curve (AUC) values of 0.760 (95% CI: 0.688-0.823), 0.758 (95% CI: 0.687-0.821), 0.714 (95% CI: 0.639-0.781), 0.709 (95% CI: 0.634-0.777), 0.723 (95% CI: 0.649-0.789), and 0.728 (95% CI: 0.654-0.793), respectively. Conversely, the WBC and PLT counts demonstrated poor diagnostic value for PJI, with AUC values of 0.578 (95% CI: 0.499-0.653) and 0.694 (95% CI: 0.619-0.763), respectively. The results of the prediction model calculations revealed that the combined AUC of the WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR was the highest at 0.853 (95% CI, 0.790-0.909), indicating good value in the diagnosis of PJI, with a sensitivity of 82.8% and a specificity of 72.7%. Moreover, the novel composite of parameters improved the accuracy and reliability in diagnosing PJI compared to the traditional biomarkers ESR and CRP (P = 0.015).

CONCLUSION

Our study suggested that the diagnostic value of the peripheral blood biomarkers PLR, PVR, NLR, and MLR for diagnosing PJI is limited and not superior to that of the ESR or CRP. However, when the WBC, PLT, ESR, CRP, PLR, PVR, NLR, and MLR are combined, the diagnostic performance of PJI in TJA patients can be improved.

Systemic Inflammation Response Index (SIRI) and Monocyte-to-Lymphocyte Ratio (MLR) Are Predictors of Good Outcomes in Surgical Treatment of Periprosthetic Joint Infections of Lower Limbs: A Single-Center Retrospective Analysis

BACKGROUND

Periprosthetic joint infection (PJI) is a devastating complication that develops after total joint arthroplasty (TJA), whose incidence is expected to increase over the years. Traditionally, surgical treatment of PJI has been based on algorithms, where early infections are preferably treated with debridement, antibiotics, and implant retention (DAIR) and late infections with two-stage revision surgery. Two-stage revision is considered the "gold standard" for treatment of chronic prosthetic joint infection (PJI) as it enables local delivery of antibiotics, maintenance of limb-length and mobility, and easier reimplantation. Many studies have attempted to identify potential predicting factors for early diagnosis of PJI, but its management remains challenging. In this observational retrospective study, we investigated the potential role of inflammatory blood markers (neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI)) as prognostic factors in two-stage exchange arthroplasty for PJI.

METHODS

A single-center retrospective analysis was conducted, collecting clinical data and laboratory parameters from patients submitted to prosthetic explantation (EP) for chronic PJI. Laboratory parameters (PCR, NLR, MLR, PLR, SIRI, SII, and AISI) were evaluated at the explantation time; at 4, 6, and 8 weeks after surgery; and at reimplantation time. The correlation between laboratory parameters and surgery success was evaluated and defined as infection absence/resolution at the last follow-up.

RESULTS

A total of 57 patients with PJI were evaluated (62% males; average age 70 years, SD 12.14). Fifty-three patients with chronic PJI were included. Nine patients underwent DAIR revision surgery and chronic suppressive therapy; two patients died. Nineteen patients completed the two-stage revision process (prosthetic removal, spacer placement, and subsequent replanting). Among them, none showed signs of reinfection or persistence of infection at the last available follow-up. The other twenty-three patients did not replant due to persistent infection: among them, some (the most) underwent spacer retention; others (fewer in number) were submitted to resection arthroplasty and arthrodesis (Girdlestone technique) or chronic suppressive antibiotic therapy; the remaining were, over time, lost to follow-up. Of the patients who concluded the two-stage revision, the ones with high SIRI values (mean 3.08 SD 1.7 and p-value 0.04) and MLR values (mean 0.4 SD 0.2 and p-value 0.02) at the explantation time were associated with a higher probability of infection resolution. Moreover, higher variation in the SIRI and PCR, also defined, respectively, as delta-SIRI (mean -2.3 SD 1.8 and p-value 0.03) and delta-PCR (mean -46 SD 35.7 and p-value 0.03), were associated with favorable outcomes.

CONCLUSIONS

The results of our study suggest that, in patients with PJI undergoing EP, the SIRI and MLR values and delta-SIRI and delta-PCR values could be predictive of a favorable outcome. The evaluation of these laboratory indices, especially their determination at 4 weeks after removal, could therefore help to determine which patients could be successfully replanted and to identify the best time to replant. More studies analyzing a wider cohort of patients with chronic PJI are needed to validate the promising results of this study.

Limited value of platelet-related markers in diagnosing periprosthetic joint infection

OBJECTIVE

To evaluate the diagnostic values of serum platelet count (PC), mean platelet volume ratio (MPV), platelet count to mean platelet volume ratio (PVR), platelet to lymphocyte ratio (PLR), platelet to neutrophil ratio (PNR), PC/Albumin-globulin ratio (PC/AGR), and PC/C-reactive protein (PC/ CRP) in the diagnosis of periprosthetic joint infection (PJI).

METHODS

The medical records were retrospectively analyzed of the 158 patients who had undergone hip or knee revisions from January 2018 to May 2022. Of them, 79 cases were diagnosed with PJI and 79 with aseptic loosening (AL). PJI was defined using the Musculoskeletal Infection Society criteria. The plasma levels of CRP, the erythrocyte sedimentation rate (ESR), PC, MPV, PVR, PLR, PNR, PC/AGR, and PC/CRP in the 2 groups were recorded and analyzed. In addition, tests were performed according to different joint types. The receiver operating characteristic curve was used to calculate the sensitivity and specificity of each indicator. The diagnostic value for each indicator was calculated according to the area under the curve (AUC).

RESULTS

The PC, PVR, PLR and PC/AGR levels in the PJI group were significantly higher than those in the AL group, while PC/CRP levels were significantly lower (P < 0.001). The AUC for PC/CRP, and PC/AGR was 0.804 and 0.802, respectively, which were slightly lower than that of CRP (0.826) and ESR (0.846). ROC analysis for PC/CRP, and PC/AGR revealed a cut-off value of 37.80 and 160.63, respectively, which provided a sensitivity of 73.42% and 84.81% and a specificity of 75.95% and 65.82% for PJI. The area under the curve of PLR and PC was 0.738 and 0.702. The area under the curve values for PVR, PNR, and MPV were 0.672, 0.553, and 0.544, respectively.

CONCLUSIONS

The results of this study suggest that PC, PLR, PC/CRP, and PC/AGR values do not offer significant advantages over ESR or CRP values when employed for the diagnosis of PJI. PVR, PNR, and MPV were not reliable in the diagnosis of PJI.

Neutrophil-Lymphocyte Ratio and Lymphocyte-Monocyte Ratio correlate with Chronic Prosthetic Joint Infection but are not useful markers for diagnosis

PURPOSE

To investigate reported correlations between Neutrophil-to-Lymphocyte (NLR) and Lymphocyte-to-Monocyte (LMR) ratios and their value in diagnosis of chronic prosthetic joint infection (PJI) in a large cohort of patients from a single specialist hospital.

METHODS

Diagnostic aspirations of 362 patients under investigation for PJI were identified. Of the included patients 185 patients received a final diagnosis of PJI and 177 were classed as aseptic. Established criteria (ICM 2018) were employed to define PJI. Included in the analysis are differential white cell counts, C-Reactive Protein (CRP), Synovial Leukocyte Count, Synovial Alpha-defensin ELISA and Synovial Leukocyte esterase activity. Receiver-operator characteristic (ROC) curves were calculated for each of the available diagnostic tests together with the corresponding area under the curve values (AUC). Youden's index was utilized to identify the optimal diagnostic threshold point for the NLR and LMR. Other diagnostic tests were evaluated as per the threshold values previously defined in the literature and specified in the ICM criteria.

RESULTS

Using Youden's Index to identify the optimal NLR cut-off within our cohort we established a value of 2.93. This yielded a sensitivity of 0.60 and specificity of 0.64. The area under the curve (AUC) of a receiving operator characteristics (ROC) curve was 0.625. Regarding the LMR the results demonstrate similar findings; a positive correlation with a diagnosis of infection but poor sensitivity and specificity. The AUC for LMR was 0.633 and was not superior to NLR (P = 0.753).

CONCLUSIONS

There is a significant correlation between higher Neutrophil-Lymphocyte and Lymphocyte-Monocyte ratios, and a diagnosis of PJI. The sensitivity and specificity of this calculation is poor and the does not add value to the diagnostic algorithm for PJI.

LEVEL OF EVIDENCE

Level III Retrospective Cohort analysis.

What is the diagnostic accuracy of neutrophil to lymphocyte ratio and monocyte to lymphocyte ratio in detecting acute periprosthetic joint infections? A gender-specific analysis

PURPOSE

The accurate and timely diagnosis of periprosthetic joint infection (PJI) is critical for guiding optimal treatment management and success, highlighting the requirement of readily available inexpensive serum biomarkers to increase the diagnostic accuracy for PJI. Many studies have investigated the diagnostic accuracy of neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR). However, there is a lack of existing literature regarding optimal thresholds for acute PJI. The purpose of this study was to reveal the most appropriate cut-off values for MLR and NLR in detecting acute PJI with a gender specific analysis.

METHODS

Patients were classified as having an acute PJI if they met the International Consensus Meeting (ICM) 2018 modified criteria. Patients who had a negative clinical and diagnostic workup for a PJI and the presence of erythema on the index surgical area were included in the erysipelas group (control group). Data obtained from all patients included age, sex, body mass index (BMI), Charlson Comorbidity Index (CCI), procedure type (THA or TKA), serum C-reactive protein (CRP), and blood studies at the admission and culture results were retrieved from the electronic medical record.

RESULTS

ROC curve analysis was used to determine the gender-specific optimal threshold values for CRP, NLR, and MLR. Comparing the sensitivities and specificities of NLR and MLR at the identified best thresholds in males and females, the study found similar sensitivities of NLR in males and females with 0.84 and 0.84, respectively. On the other hand, an MLR of 0.67 or more reported a notably higher specificity in male patients [0.90 (95% CI 0.75-0.96) versus 0.70 (95% CI 0.56-0.80)].

CONCLUSION

NLR and MLR represent commonly ordered, low-cost, simple, and readily available complete cell count laboratory values and should be used as adjunct tests with reasonable diagnostic accuracy in detecting acute PJIs. Moreover, with its excellent specificity and PPV, MLR could provide valuable insight in diagnosing acute PJI, particularly in male patients.

LEVEL OF EVIDENCE

Level III Retrospective Cohort analysis.

Can C-Reactive Protein-Lymphocyte Ratio Be Used as a Screening Tool to Confirm the Diagnosis of Periprosthetic Joint Infection?

Background

This study aimed to investigate whether periprosthetic joint infection (PJI) can be predicted by the C-reactive protein-to-lymphocyte ratio (CLR), whether this ratio increases the accuracy of PJI diagnosis, and whether it is more sensitive than other blood values and ratios.

Methods

The patients were divided into two groups: the septic revision (SR) group and the aseptic revision (AR) group. In cases of septic revision, the diagnosis of PJI was made based on the criteria proposed by the European Bone and Joint Infection Society (EBJIS). The groups were compared in terms of age, sex, body mass index, comorbidity, and preoperative laboratory results. The sensitivity, specificity, and diagnostic performance of the values and ratios were analyzed and compared.

Results

The receiver operating characteristic (ROC) analysis for the CLR gave a diagnostic value of 15.52, which provided a sensitivity of 91.1% and a specificity of 64.2% for PJI. The CLR gave lower specificity and higher sensitivity compared to the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) values. The ROC analysis showed that the CLR had a similar area under the curve (AUC) with the ESR and CRP (0.808). The CLR had a higher specificity than other ratios (platelet volume ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio) and a higher value of the AUC. In the multivariate analysis, the CLR (hazard ratio, 1.088; 95% confidence interval, 1.063-1.113; p < 0.001) was found to be a significant risk factor. As CLR increased by one unit, the risk of PJI increased by 1.088 times, and it was statistically significant (p < 0.001).

Conclusions

The findings of this study suggest that CLR can serve as a valuable screening tool for diagnosing PJI. CLR demonstrated higher sensitivity in predicting PJI compared to ESR and CRP, and it exhibited greater specificity than other infection markers.

Diagnosing periprosthetic joint infection

Aims

The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic accuracy. The aim of the study was to further validate the effectiveness of PVR and PLR in diagnosing PJI.

Methods

A retrospective review was conducted to assess the accuracy of different marker combinations for diagnosing chronic PJI. A total of 573 patients were included in the study, of which 124 knees and 122 hips had a diagnosis of chronic PJI. Complete blood count and synovial fluid analysis were collected. Recently published blood cell ratio cut-off points were applied to receiver operating characteristic curves for all markers and combinations. The area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values were calculated.

Results

The results of the analysis showed that the combination of ESR, CRP, synovial white blood cell count (Syn. WBC), and polymorphonuclear neutrophil percentage (PMN%) with PVR had the highest AUC of 0.99 for knees, with sensitivity of 97.73% and specificity of 100%. Similarly, for hips, this combination had an AUC of 0.98, sensitivity of 96.15%, and specificity of 100.00%.

Conclusion

This study supports the use of PVR calculated from readily available complete blood counts, combined with established markers, to improve the accuracy in diagnosing chronic PJI in both total hip and knee arthroplasties.

Elevation of White Blood Cell Subtypes in Adult Trauma Patients with Stress-Induced Hyperglycemia

BACKGROUND

Blood immune cell subset alterations following trauma can indicate a patient's immune-inflammatory status. This research explored the influence of stress-induced hyperglycemia (SIH) on platelet counts and white blood cell (WBC) subtypes, including the derived indices of the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), in trauma patients.

METHODS

We studied 15,480 adult trauma patients admitted from 1 January 1998 to 31 December 2022. They were categorized into four groups: nondiabetic normoglycemia (NDN, n = 11,602), diabetic normoglycemia (DN, n = 1750), SIH (n = 716), and diabetic hyperglycemia (DH, n = 1412). A propensity score-matched cohort was formed after adjusting for age, sex, and comorbidities, allowing for comparing the WBC subtypes and platelet counts.

RESULTS

Patients with SIH exhibited significantly increased counts of monocytes, neutrophils, and lymphocytes in contrast to NDN patients. However, no significant rise in platelet counts was noted in the SIH group. There were no observed increases in these cell counts in either the DN or DH groups.

CONCLUSIONS

Our results demonstrated that trauma patients with SIH showed significantly higher counts of monocytes, neutrophils, and lymphocytes when compared to NDN patients, whereas the DN and DH groups remained unaffected. This underscores the profound association between SIH and elevated levels of specific WBC subtypes.

Can Platelets/Mean Platelet Volume Accurately Diagnose Periprosthetic Joint Infection? Revealing Their Actual Diagnostic Efficacy

Background

Currently, there is no single test indicator for diagnosing periprosthetic joint infection (PJI) with an acceptable level of sensitivity. Therefore, ratio indicators have been introduced to improve the accuracy of diagnostic algorithms. Platelet count /mean platelet volume (PMR) is reported to be a potential PJI diagnostic biomarker, but its clinical value for diagnosing PJI is still uncertain. This study aims to provide additional evidence to support the effectiveness of PMR in accurately diagnosing PJI.

Methods

This study recruited 116 patients with PJI and 137 patients with aseptic loosening, divided them into PJI group and AL group. Collect subjects' preoperative laboratory indicators such as ESR, CRP, PLT, MPV, etc. The area under the curve (AUC) was calculated by plotting the receiver operating characteristic (ROC) curve to determine the diagnostic efficacy of PMR.

Results

ESR, CRP, PLT, and PLT/MPV were significantly increased in the PJI group, while MPV levels were decreased (both P< 0.001). The AUC of the PMR was 0.752, and the optimal cut-off value for diagnosing chronic PJI was determined to be 27.8 based on the Youden index. The sensitivity and specificity for diagnosing PJI were 79.3% and 47.9%, respectively, with a positive predictive value of 68.27%, a negative predictive value of 69.80%, and a diagnostic odds ratio of 4.97. The AUC (0.752) of the ratio biomarker was lower than that of ESR (0.825) and CRP (0.900). After predictive model calculation, the combination of PMR, CRP, and ESR had an AUC value of 0.910, with a sensitivity of 84.5% and a specificity of 84.7%, showing good discriminative ability.

Conclusion

Compared with traditional biomarkers ESR and CRP, the value of the PMR for diagnosing PJI is not significant, but it can be used as an auxiliary indicator for PJI diagnosis in combination with other indicators (P<0.001).

Utility of Blood Cell Ratio Combinations for Diagnosis of Periprosthetic Joint Infection

Background

Periprosthetic joint infection (PJI) is a serious complication following joint replacement surgery, and its diagnosis can be challenging due to the similarity of symptoms to other conditions and the lack of confirmatory imaging tests. Platelet/mean platelet volume ratio (PVR), platelet/lymphocyte ratio, monocyte/lymphocyte ratio, and neutrophil/lymphocyte ratio have been proposed as potential markers to aid in the diagnosis of PJI. This study aimed to further assess the utility of these blood cell ratio combinations for the diagnosis of PJI.

Methods

A retrospective chart review was conducted on patients who presented to a university hospital for evaluation for PJI or underwent aseptic revision surgery. All patients were reviewed for inclusion in the study. Data were collected on several markers, including complete blood counts, synovial fluid white blood cell count, and polymorphonuclear percentage. Receiver operator characteristic curve analysis was used to evaluate the diagnostic capabilities of the markers and marker combinations.

Results

The combination of erythrocyte sedimentation rate, C-reactive protein, synovial white blood cell count, and synovial polymorphonuclear percentage, with PVR, had the highest area under the curve of 0.97, with a sensitivity of 94.3% and a specificity of 88.9%, and a positive predictive value of 97.1% and a negative predictive value of 80.0%.

Conclusions

This study further supports the use of PVR calculated from complete blood count commonly ordered laboratory values obtained during routine complete blood counts when combined with established serum and synovial markers to increase the diagnostic accuracy for diagnosing PJI.

Prognostic value of neutrophil and lymphocyte counts and neutrophil/lymphocyte ratio for predicting death in patients hospitalized for COVID-19

COVID-19 primarily affects the respiratory system and can cause changes in other systems. Early identification of patients with a higher potential for complications is critical to provide the best possible treatment to reduce the disease's lethality. This study aimed to analyze the behavior of hematologic biomarkers in predicting mortality in patients hospitalized with COVID-19. This retrospective cohort study used data from the medical records of patients hospitalized with COVID-19 between March and August 2020 in two referral hospitals for treatment of the disease in the city of Cuiabá (in the state of Mato Grosso, Brazil). Clinical and laboratory characteristics related to cardiovascular involvement and death during hospitalization were evaluated. Neutrophils, lymphocytes, and monocytes, as well as the neutrophil-to-lymphocyte ratio (NLR) and the monocyte-to-lymphocyte ratio (MRL), were used as potential biomarkers of death. A total of 199 patients were included (male: 113; mean age: 51.4 years). Leukocyte, neutrophil, and lymphocyte counts showed a statistically significant association with death, as did NLR and MRL. Satisfactory accuracy in predicting death was observed for leukocyte, neutrophil, lymphocyte, NLR, and MLR counts. The hematologic biomarkers studied may be useful for prognosticating hospitalized patients for the possibility of death from COVID-19.

A prospective study to identify preoperative serum parameters for spinal implant infection detected by sonication fluid culture

PURPOSE

Low-virulent microorganisms identified on pedicle screws by sonication fluid culture (SFC) are an important cause of implant loosening. While sonication of explanted material improves the detection rate, the risk of contamination exists and no standardized diagnostic criteria for chronic low-grade spinal implant-related infection (CLGSII) are stablished. Besides, the role of serum C-reactive protein (CRP) and procalcitonin (PCT) in CLGSII has not been adequately investigated.

METHODS

Blood samples were collected prior to implant removal. To increase sensitivity, the explanted screws were sonicated and processed separately. Patients exhibiting at least one positive SFC were classified in the infection group (loose criteria). To increase specificity, the strict criteria only considered multiple positive SFC (≥ 3 implants and/or ≥ 50% of explanted devices) as meaningful for CLGSII. Factors which might promote implant infection were also recorded.

RESULTS

Thirty-six patients and 200 screws were included. Among them, 18 (50%) patients had any positive SFCs (loose criteria), whereas 11 (31%) patients fulfilled the strict criteria for CLGSII. Higher serum protein level was the most accurate marker for the preoperative detection of CLGSSI, exhibiting an area under the curve of 0.702 (loose criteria) and 0.819 (strict criteria) for the diagnosis of CLGSII. CRP only exhibited a modest accuracy, whereas PCT was not a reliable biomarker. Patient history (spinal trauma, ICU hospitalization and/or previous wound-related complications) increased the likelihood of CLGSII.

CONCLUSION

Markers of systemic inflammation (serum protein level) and patient history should be employed to stratify preoperative risk of CLGSII and decide the best treatment strategy.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty

BACKGROUND

Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty.

METHODS

A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis.

RESULTS

The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis.

CONCLUSION

This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

The Diagnostic Accuracy of Neutrophils to Lymphocytes Ratio, Platelets to Lymphocytes Ratio, Monocytes to Lymphocytes Ratio, and Platelets to Mean Platelet Volume Ratio in Diagnosing Periprosthetic Knee Infections. Are Gender-Specific Cutoff Values Needed?

BACKGROUND

This study aimed to assess the most appropriate thresholds for neutrophils to lymphocytes ratio (NLR), platelets to lymphocytes ratio, monocytes to lymphocytes ratio (MLR), and platelets to mean platelet volume ratio in patients who had a suspected prosthetic knee infection. Furthermore, we evaluated the diagnostic accuracy of our proposed thresholds by men and women.

METHODS

A total of 261 consecutive patients affected by painful total knee arthroplasty were included. International Consensus Meeting diagnostic criteria were used to identify periprosthetic infections. Sensitivity, specificity, positive, and negative predictive values were calculated for each cutoff value obtained. The area under the receiver operating characteristic curve was evaluated.

RESULTS

NLR reported the best diagnostic accuracy. MLR and NLR reported higher area under the curves in men and women. We obtained an MLR value ≥0.30 (optimal cutoff value for men) and ≥0.17 (optimal cutoff value for women). In men, the sensitivity and the specificity were 0.71 and 0.77, respectively, versus 0.82 and 0.29, in women. For NLR, we obtained a value ≥2.52 (best cutoff value for men) and ≥2.46 (best cutoff value for women). These cutoffs reported a sensitivity and specificity of 0.71 and 0.88 versus 0.54 and 0.76 in men and women, respectively.

CONCLUSION

These biomarkers present a low diagnostic accuracy in periprosthetic joint infection detection. Men whose MLR and NLR were above cutoff values had a 77 and 88% probability of presenting a septic prosthetic failure. NLR of at least 2.46 was reasonably sensitive for women who have a periprosthetic knee infection.

LEVEL OF EVIDENCE

Diagnostic study, Level II.

Inflammatory Biomarkers as Prognostic Factors of Acute Deep Vein Thrombosis Following the Total Knee Arthroplasty

Background and objectives: Deep vein thrombosis (DVT) is one of the most serious post-operative complications in the case of total knee arthroplasty (TKA). This study aims to verify the predictive role of inflammatory biomarkers [monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)] in acute DVT following TKA. Materials and methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with surgical indications for TKA, admitted to the Department of Orthopedics, Regina Maria Health Network, Targu Mures, Romania, and the Department of Orthopedics, Humanitas MedLife Hospital, Cluj-Napoca, Romania between January 2017 and July 2022. The primary endpoint was the risk of acute DVT following the TKA, and the secondary endpoint was the length of hospital stay, and the outcomes were stratified for the baseline’s optimal MLR, NLR, PLR, SII, SIRI, and AISI cut-off value. Results: DVT patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [arterial hypertension (p = 0.02), atrial fibrillation (p = 0.01)], malignancy (p = 0.005), as well as risk factors [smoking (p = 0.03) and obesity (p = 0.02)]. Multivariate analysis showed a high baseline value for all hematological ratios: MLR (OR: 11.06; p < 0.001), NLR (OR: 10.15; p < 0.001), PLR (OR: 12.31; p < 0.001), SII (OR: 18.87; p < 0.001), SIRI (OR: 10.86; p < 0.001), and AISI (OR: 14.05; p < 0.001) was an independent predictor of DVT after TKA for all recruited patients. Moreover, age above 70 (OR: 2.96; p = 0.007), AH (OR: 2.93; p = 0.02), AF (OR: 2.71; p = 0.01), malignancy (OR: 3.98; p = 0.002), obesity (OR: 2.34; p = 0.04), and tobacco (OR: 2.30; p = 0.04) were all independent predictors of DVT risk. Conclusions: Higher pre-operative hematological ratios MLR, NLR, PLR, SII, SIRI, and AISI values determined before operations strongly predict acute DVT following TKA. Moreover, age over 70, malignancy, cardiovascular disease, and risk factors such as obesity and tobacco were predictive risk factors for acute DVT.

Reliable Diagnostic Tests and Thresholds for Preoperative Diagnosis of Non-Inflammatory Arthritis Periprosthetic Joint Infection: A Meta-analysis and Systematic Review

Objective

The current diagnostic criteria for periprosthetic joint infection (PJI) are diverse and controversial, leading to delayed diagnosis. This study aimed to evaluate and unify their diagnostic accuracy and the threshold selection of serum and synovial routine tests for PJI at an early stage.

Methods

We searched the MEDLINE and Embase databases for retrospective or prospective studies which reported preoperative‐available assays (serum, synovial, or culture tests) for the diagnosis of chronic PJI among inflammatory arthritis (IA) or non‐IA populations from January 1, 2000 to June 30, 2022. Threshold effective analysis was performed on synovial polymorphonuclear neutrophils (PMN%), synovial white blood cell (WBC), serum C‐reactive protein (CRP), and erythrocyte sedimentation rate (ESR) to find the relevant cut‐offs.

Results

Two hundred and sixteen studies and information from 45,316 individuals were included in the final analysis. Synovial laboratory‐based α‐defensin and calprotectin had the best comprehensive sensitivity (0.91 [0.86–0.94], 0.95 [0.88–0.98]) and specificity (0.96 [0.94‐0.97], 0.95 [0.89–0.98]) values. According to the threshold effect analysis, the recommended cut‐offs are 70% (sensitivity 0.89 [0.85–0.92], specificity 0.90 [0.87–0.93]), 4100/μL (sensitivity 0.90 [0.87–0.93], specificity 0.97 [0.93–0.98]), 13.5 mg/L (sensitivity 0.84 [0.78–0.89], specificity 0.83 [0.73–0.89]), and 30 mm/h (sensitivity 0.79 [0.74–0.83], specificity 0.78 [0.72–0.83]) for synovial PMN%, synovial WBC, serum CRP, and ESR, respectively, and tests seem to be more reliable among non‐IA patients.

Conclusions

The laboratory‐based synovial α‐defensin and synovial calprotectin are the two best independent preoperative diagnostic tests for PJI. A cut off of 70% for synovial PMN% and tighter cut‐offs for synovial WBC and serum CRP could have a better diagnostic accuracy for non‐IA patients with chronic PJI.

Diagnostic Performance of Neutrophil to Lymphocyte Ratio, Monocyte to Lymphocyte Ratio, Platelet to Lymphocyte Ratio, and Platelet to Mean Platelet Volume Ratio in Periprosthetic Hip and Knee Infections: A Systematic Review and Meta-Analysis

The current literature on the diagnosis of periprosthetic joint infection provides controversial evidence on the diagnostic accuracy of MLR, NLR, PVR, and PLR. Therefore, this critical literature search and meta-analysis was aimed to summarize the diagnostic accuracy of these biomarkers for the diagnosis of hip and knee prosthetic infection. According to the PRISMA flowchart, we searched MEDLINE, Scopus, and Web of Science, for studies on these ratios for diagnosing PJI. Sensitivity, specificity, positive and negative likelihood ratio, diagnostic odds ratio, and AUC were analyzed. We included 11 articles in our meta-analysis, including 7537 patients who underwent total hip and knee arthroplasties; among these, 1974 (26%) patients reported a joint infection. The pooled sensitivity and specificity were 0.72 and 0.74, respectively, for NLR, 0.72 and 0.77 for PVR, and 0.77 and 0.75 for PLR. The sensitivity of MLR ranges from 0.54 to 0.81, while the specificity ranges from 0.78 to 0.81. Regarding the evaluation of AUCs, the best diagnostic performance was achieved by MLR (AUC = 0.77) followed by PLR (AUC = 0.75), NLR (AUC = 0.73), and PVR (AUC = 0.70). This meta-analysis demonstrates a fair diagnostic accuracy of these ratios, thus not being useful as a screening tool.

Diagnostic Value of the Blood Neutrophil-to-Lymphocyte Ratio and Monocyte-to-Lymphocyte Ratio in Tibia Fracture-Related Infection

Objective. The diagnostic value of neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) in predicting fracture-related infection (FRI) in tibia fracture patients remains to be explored. Methods. A retrospective controlled study was carried out with 170 tibia FRI patients and 162 control subjects. The following information was evaluated at admission: age, gender, clinical features, number of white blood cells (WBCs), neutrophils, lymphocytes, monocytes, red blood cells (RBCs), platelets, level of hemoglobin, C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR), as well as NLR, MLR, and PLR. Results. The number of lymphocytes, RBCs, and platelets in the FRI group was higher than those in the control group, while the number of neutrophils and ESR level was lower ( $P<0.05$ ). The level of NLR and MLR was significantly lower in patients with tibia FRI than in control subjects ( $P<0.05$ ). Both indicators were positively correlated with WBCs, CRP level, and ESR level ( $P<0.001$ ). The results of logistic regression analysis showed that five variables including NLR, MLR, platelets, fracture pattern (closed or open fracture), and site pattern (single or multiple site) were used to construct the FRI risk predictor. The ROC curve analysis result showed that FRI risk predictor yielded the highest AUC, with a sensitivity of 91.2% and a specificity of 90.1%, and made the distinction efficiently between tibia FRI patients and non-FRI patients. Conclusion. NLR and MLR were decreased in tibia FRI patients compared to non-FRI patients. Both indicators had a positive correlation with WBCs, CRP level, and ESR level. FRI risk predictor constructed based on five variables including NLR and MLR had a high diagnostic value for tibia FRI.

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.