Identification of prosthetic hip and knee joint infections using administrative databases-A validation study

A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data

Patient safety is a key quality issue for health systems. Healthcare acquired adverse events (AEs) compromise safety and quality; therefore, their reporting and monitoring is a patient safety priority. Although administrative datasets are potentially efficient tools for monitoring rates of AEs, concerns remain over the accuracy of their data. Chart review validation studies are required to explore the potential of administrative data to inform research and health policy. This review aims to present an overview of the methodological approaches and strategies used to validate rates of AEs in administrative data through chart review. This review was conducted in line with the Joanna Briggs Institute methodological framework for scoping reviews. Through database searches, 1054 sources were identified, imported into Covidence, and screened against the inclusion criteria. Articles that validated rates of AEs in administrative data through chart review were included. Data were extracted, exported to Microsoft Excel, arranged into a charting table, and presented in a tabular and descriptive format. Fifty-six studies were included. Most sources reported on surgical AEs; however, other medical specialties were also explored. Chart reviews were used in all studies; however, few agreed on terminology for the study design. Various methodological approaches and sampling strategies were used. Some studies used the Global Trigger Tool, a two-stage chart review method, whilst others used alternative single-, two-stage, or unclear approaches. The sources used samples of flagged charts (n = 24), flagged and random charts (n = 11), and random charts (n = 21). Most studies reported poor or moderate accuracy of AE rates. Some studies reported good accuracy of AE recording which highlights the potential of using administrative data for research purposes. This review highlights the potential for administrative data to provide information on AE rates and improve patient safety and healthcare quality. Nonetheless, further work is warranted to ensure that administrative data are accurate. The variation of methodological approaches taken, and sampling techniques used demonstrate a lack of consensus on best practice; therefore, further clarity and consensus are necessary to develop a more systematic approach to chart reviewing.

The Impact of Surgical Approach on 90-Day Prosthetic Joint Infection After Total Hip Replacement - A Population-Based, Propensity Score-Matched Cohort Study

BACKGROUND

Prosthetic joint infection (PJI) is a devastating complication of total hip arthroplasty (THA). This study aimed to determine if the anterior approach (AP) influenced the incidence of early PJI in THA compared to posterior approach (PP).

METHODS

Record linkage was performed between state-wide hospitalization data and a national joint replacement registry to identify unilateral THA performed via the AP or PP. Complete data on 12,605 AP and 25,569 PP THAs were obtained. Propensity score matching (PSM) was undertaken to match covariates between the approaches. Outcomes were the 90-day PJI hospital readmission rate(using narrow and broad definitions) and 90-day PJI revision rate (defined as component removal or exchange).

RESULTS

The raw PJI readmission rate for AP was lower than PP (0.8% versus 1.1%, respectively). In the PSM analysis, there was no statistically significant difference in PJI readmission rate between approaches using narrow or broad definition of PJI readmission. In terms of revision for infection, both methods showed AP had a significantly lower rate than PP, with an adjusted odds ratio (OR) of 0.47 (95% confidence interval (CI) 0.30, 0.75) for the 1:1 nearest neighbor method and 0.50 (95% CI 0.32, 0.77) for the subclassification method.

CONCLUSION

After addressing known confounders, there was no significant difference in the 90-day hospital readmission rate for hip PJI between approaches. There was a significantly reduced 90-day PJI revision rate for AP. The difference in revision may reflect differences in the surgical management of PJI between hip approaches rather than a difference in the underlying rate of infection.

Validation of the first computerized indicator for orthopaedic surgical site infections in France: ISO-ORTHO

BACKGROUND

The French national authority for health (HAS) develops in-hospital indicators for improving quality of care, safety and patient outcome. Since 2017, it has developed a measurement of surgical site infections (SSI) after total hip or knee arthroplasty (TH/KA) by using a computerized indicator, called ISO-ORTHO, based on a hospital discharge database (HDD) algorithm. The aim of the study was to assess the performance of this new indicator .

METHODS

The ISO-ORTHO performance was estimated via its positive predictive value (PPV) among adult patients having undergone a TH/KA between January 1st and September 30th 2018, based on the orthopaedic procedure codes. Patients at very high risk of SSI and/or with SSI not related to the in-hospital care were excluded. SSI were detected from the date of admission up to 90 days after the TH/KA using the ISO-ORTHO algorithm, based on 15 combinations of ICD-10 and procedure codes. Its PPV was estimated by a chart review in volunteer healthcare organisations (HCO).

RESULTS

Over the study period, 777 HCO including 143,227 TH/KA stays were selected, providing 1,279 SSI according to the ISO-ORTHO indicator. The 90-day SSI rate was 0.89 per 100 TH/KA stays (0.98% for THA and 0.80% for TKA). Among the 448 HCO with at least 1 SSI, 250 HCO participated in reviewing 725 SSI charts; 665 were confirmed, giving a PPV of 90.3% [88.2-92.5%], 89.9% [87.1-92.8%] in THA and 90.9% [87.7-94.2%] in TKA.

CONCLUSIONS

The PPV of ISO-ORTHO over 90% confirms its validity for any use according to the HAS method. ISO-ORTHO and detailed information were provided in 2020 to HCO and used for quality assessment and in-hospital risk management.

Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: a comparison of traditional methods and machine learning

Background

Cardiac implantable electronic device (CIED) surgical site infections (SSIs) have been outpacing the increases in implantation of these devices. While traditional surveillance of these SSIs by infection prevention and control would likely be the most accurate, this is not practical in many centers where resources are constrained. Therefore, we explored the validity of administrative data at identifying these SSIs.

Methods

We used a cohort of all patients with CIED implantation in Calgary, Alberta where traditional surveillance was done for infections from Jan 1, 2013 to December 31, 2019. We used this infection subgroup as our “gold standard” and then utilized various combinations of administrative data to determine which best optimized the sensitivity and specificity at identifying infection. We evaluated six approaches to identifying CIED infection using administrative data, which included four algorithms using International Classification of Diseases codes and/or Canadian Classification of Health Intervention codes, and two machine learning models. A secondary objective of our study was to assess if machine learning techniques with training of logistic regression models would outperform our pre-selected codes.

Results

We determined that all of the pre-selected algorithms performed well at identifying CIED infections but the machine learning model was able to produce the optimal method of identification with an area under the receiver operating characteristic curve (AUC) of 96.8%. The best performing pre-selected algorithm yielded an AUC of 94.6%.

Conclusions

Our findings suggest that administrative data can be used to effectively identify CIED infections. While machine learning performed the most optimally, in centers with limited analytic capabilities a simpler algorithm of pre-selected codes also has excellent yield. This can be valuable for centers without traditional surveillance to follow trends in SSIs over time and identify when rates of infection are increasing. This can lead to enhanced interventions for prevention of SSIs.

Is Prior Hip Arthroscopy Associated With Higher Complication Rates or Prolonged Opioid Claims After Total Hip Arthroplasty? A Matched Cohort Study

Background:

Hip arthroscopy (HA) procedures have increased exponentially in recent years. Their effect on outcomes after subsequent total hip arthroplasty (THA) remains unclear.

Purpose:

To compare rates of complications and opioid claims after elective THA among patients with prior HA versus controls.

Study Design:

Cohort study; Level of evidence, 3.

Methods:

Patients who underwent THA were identified in the PearlDiver database. Arthroplasty performed for hip fractures and hip avascular necrosis were excluded. Within this population, patients with HA before arthroplasty (n = 3156) were propensity score matched 1:1 with controls on age, sex, US region, and several comorbidities. Rates of medical complications within 90 days and prosthesis-related complications within 2 years were queried. The number of patients with an opioid claim within 0 to 30 days and subsequent opioid claim(s) during the 90-day global period were obtained to assess rates of prolonged opioid use after arthroplasty. Rates of postoperative complications and opioid claims were compared using logistic regression.

Results:

Patients with prior HA exhibited significantly lower rates of readmission (5.6% vs 7.3%; odds ratio [OR], 0.72), pulmonary embolism (0.2% vs 0.6%; OR, 0.45), urinary tract infection (3.1% vs 4.0%; OR, 0.75), and blood transfusion (3.6% vs 6.1%; OR, 0.55). The prior HA cohort also exhibited a significantly lower rate of prosthetic joint infection at 1 year postoperatively (0.6% vs 1.3%; OR, 0.50). Rates of dislocation, periprosthetic fracture, mechanical complications, and aseptic revision arthroplasty were statistically comparable between the cohorts within 2 years. The prior HA cohort was significantly less likely to file persistent opioid claims after 30 days postoperatively, including between 31 and 60 days (27.2% vs 33.1%; OR, 0.74) and 61 to 90 days (16.2% vs 20.9%; OR, 0.71).

Conclusion:

After elective THA, patients with prior HA exhibited significantly lower rates of medical complications and prolonged opioid claims within 90 days and prosthetic joint infection at 1 year. Rates of all other prosthesis-related complications within 2 years were statistically comparable.

Estimating incidence rates of periprosthetic joint infection after hip and knee arthroplasty for osteoarthritis using linked registry and administrative health data

AIMS

The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA).

METHODS

This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC.

RESULTS

The mean 90-day revision rate for infection was 0.1% (0.1% to 0.2%) for TKA and 0.3% (0.1% to 0.5%) for THA. The mean 90-day PJI rates defined by T84.5 were 1.3% (1.1% to 1.7%) for TKA and 1.1% (0.8% to 1.3%) for THA. The mean 90-day PJI rates using the extended definition were 1.9% (1.5% to 2.2%) and 1.5% (1.3% to 1.7%) following TKA and THA, respectively.

CONCLUSION

When reporting the revision arthroplasty for infection, the AOANJRR substantially underestimates the rate of PJI at 90 days. Using combinations of infection codes and PJI-related surgical procedure codes in linked hospital administrative databases could be an alternative way to monitor PJI rates.Cite this article: Bone Joint J 2022;104-B(9):1060-1066.

Latex Allergy as an Independent Risk Factor for Prosthetic Joint Infection

In response to increasing rates of self-reported latex allergies, changes have been made to prevent anaphylaxis in the operating room, including the use of latex-free gloves. However, the impact of these changes on the risk of prosthetic joint infection (PJI) after arthroplasty is unclear. This study evaluated whether documented latex allergy is an independent risk factor for PJI and aseptic revision surgery after total hip arthroplasty (THA) and total knee arthroplasty (TKA). A retrospective matched cohort study was conducted with an administrative claims database. A total of 17,501 patients who underwent TKA and had documented latex allergy were matched 1:4 with 70,004 control subjects, and 8221 patients who underwent THA and had documented latex allergy were matched 1:4 with 32,884 control subjects. Multivariable logistic regression showed that patients who had TKA and had a latex allergy showed significantly higher risk of PJI at both 90 days (odds ratio [OR], 1.26) and 1 year (OR, 1.22) and significantly higher risk of aseptic revision TKA at 1 year (OR, 1.21) after surgery compared with control subjects. Patients who had THA and had a latex allergy had significantly higher risk of PJI at 1 year (OR, 1.19) compared with control subjects. Rates of aseptic revision THA were higher in the latex allergy cohort but statistically comparable (P>.05). Latex allergy was associated with significantly increased risk of PJI and aseptic revision after TKA and significantly increased risk of PJI after THA. More work is needed to determine whether these risks can be mitigated or if latex allergy is an inherent, nonmodifiable risk factor requiring modification to typical arthroplasty pathways. [Orthopedics. 2022;45(4):244-250.].

Development and validation of case-finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data

PURPOSE

To determine the positive predictive values (PPVs) of ICD-9, ICD-10, and current procedural terminology (CPT)-based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration.

METHODS

We identified patients with: (1) hospital discharge ICD-9 or ICD-10 diagnosis of PJI, (2) ICD-9, ICD-10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X-ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD-9 and ICD-10-based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD-9 and ICD-10 PJI algorithms were calculated.

RESULTS

Among a sample of 80 patients meeting the ICD-9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%-84.0%]) had confirmed PJI. Among 80 patients who met the ICD-10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%-92.0%]) had a confirmed diagnosis.

CONCLUSIONS

An algorithm consisting of an ICD-9 or ICD-10 PJI diagnosis following a TKA code combined with CPT codes for a knee X-ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD-9) and 85.0% (ICD-10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.