Opioid use, postoperative complications, and implant survival after unicompartmental versus total knee replacement: a population-based network study

Reporting of Observational Studies Explicitly Aiming to Emulate Randomized Trials: A Systematic Review

Importance

Observational (nonexperimental) studies that aim to emulate a randomized trial (ie, the target trial) are increasingly informing medical and policy decision-making, but it is unclear how these studies are reported in the literature. Consistent reporting is essential for quality appraisal, evidence synthesis, and translation of evidence to policy and practice.

Objective

To assess the reporting of observational studies that explicitly aimed to emulate a target trial.

Evidence Review

We searched Medline, Embase, PsycINFO, and Web of Science for observational studies published between March 2012 and October 2022 that explicitly aimed to emulate a target trial of a health or medical intervention. Two reviewers double-screened and -extracted data on study characteristics, key predefined components of the target trial protocol and its emulation (eligibility criteria, treatment strategies, treatment assignment, outcome[s], follow-up, causal contrast[s], and analysis plan), and other items related to the target trial emulation.

Findings

A total of 200 studies that explicitly aimed to emulate a target trial were included. These studies included 26 subfields of medicine, and 168 (84%) were published from January 2020 to October 2022. The aim to emulate a target trial was explicit in 70 study titles (35%). Forty-three studies (22%) reported use of a published reporting guideline (eg, Strengthening the Reporting of Observational Studies in Epidemiology). Eighty-five studies (43%) did not describe all key items of how the target trial was emulated and 113 (57%) did not describe the protocol of the target trial and its emulation.

Conclusion and Relevance

In this systematic review of 200 studies that explicitly aimed to emulate a target trial, reporting of how the target trial was emulated was inconsistent. A reporting guideline for studies explicitly aiming to emulate a target trial may improve the reporting of the target trial protocols and other aspects of these emulation attempts.

Management of Bilateral Quadriceps Tendon Ruptures Post Total Knee Arthroplasty by Kesler Technique Using Fiber Tape

Total knee arthroplasty is an effective way to manage osteoarthritis patients surgically. However, patients may encounter post-surgical complications, such as quadriceps rupture in rare instances, in addition to surgical complications. In our clinical practice, we encountered a 67-year-old Saudi male patient with a rare bilateral quadriceps rupture two weeks post-total knee arthroplasty. The cause of the bilateral rupture was a history of falls in both knees. The patient was reported to our clinic with clinical features like pain in the knee joint, immobility, and bilateral swelling in the knees. The X-ray did not show any periprosthetic fracture, but an ultrasound of the anterior thigh revealed a complete cut of the quadriceps tendon on both sides. The repair of the bilateral quadriceps tendon was done by direct repair using the Kessler technique and was reinforced with fiber tape. Following knee immobilization for six weeks, the patient began intensive physical therapy management to decrease pain, enhance muscle strength, and increase range of motion. After rehabilitation, the patient regained a complete range of motion in the knee and improved functionality, and he could walk independently without crutches.

Evaluating a novel approach to stimulate open science collaborations: a case series of "study-a-thon" events within the OHDSI and European IMI communities

OBJECTIVE

We introduce and review the concept of a study-a-thon as a catalyst for open science in medicine, utilizing harmonized real world, observation health data, tools, skills, and methods to conduct network studies, generating insights for those wishing to use study-a-thons for future research.

MATERIALS AND METHODS

A series of historical study-a-thons since 2017 to present were reviewed for thematic insights as to the opportunity to accelerate the research method to conduct studies across therapeutic areas. Review of publications and experience of the authors generated insights to illustrate the conduct of study-a-thons, key learning, and direction for those wishing to conduct future such study-a-thons.

RESULTS

A review of six study-a-thons have provided insights into their scientific impact, and 13 areas of insights for those wishing to conduct future study-a-thons. Defining aspects of the study-a-thon method for rapid, collaborative research through network studies reinforce the need to clear scientific rationale, tools, skills, and methods being collaboratively to conduct a focused study. Well-characterized preparatory, execution and postevent phases, coalescing skills, experience, data, clinical input (ensuring representative clinical context to the research query), and well-defined, logical steps in conducting research via the study-a-thon method are critical.

CONCLUSIONS

A study-a-thon is a focused multiday research event generating reliable evidence on a specific medical topic across different countries and health systems. In a study-a-thon, a multidisciplinary team collaborate to create an accelerated contribution to scientific evidence and clinical practice. It critically accelerates the research process, without inhibiting the quality of the research output and evidence generation, through a reproducible process.

Adjusting for indirectly measured confounding using large-scale propensity score

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs) and administrative claims. Modern medical data typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.

Unicompartmental compared with total knee replacement for patients with multimorbidities: a cohort study using propensity score stratification and inverse probability weighting

BACKGROUND

Although routine NHS data potentially include all patients, confounding limits their use for causal inference. Methods to minimise confounding in observational studies of implantable devices are required to enable the evaluation of patients with severe systemic morbidity who are excluded from many randomised controlled trials.

OBJECTIVES

Stage 1 - replicate the Total or Partial Knee Arthroplasty Trial (TOPKAT), a surgical randomised controlled trial comparing unicompartmental knee replacement with total knee replacement using propensity score and instrumental variable methods. Stage 2 - compare the risk benefits and cost-effectiveness of unicompartmental knee replacement with total knee replacement surgery in patients with severe systemic morbidity who would have been ineligible for TOPKAT using the validated methods from stage 1.

DESIGN

This was a cohort study.

SETTING

Data were obtained from the National Joint Registry database and linked to hospital inpatient (Hospital Episode Statistics) and patient-reported outcome data.

PARTICIPANTS

Stage 1 - people undergoing unicompartmental knee replacement surgery or total knee replacement surgery who met the TOPKAT eligibility criteria. Stage 2 - participants with an American Society of Anesthesiologists grade of ≥ 3.

INTERVENTION

The patients were exposed to either unicompartmental knee replacement surgery or total knee replacement surgery.

MAIN OUTCOME MEASURES

The primary outcome measure was the postoperative Oxford Knee Score. The secondary outcome measures were 90-day postoperative complications (venous thromboembolism, myocardial infarction and prosthetic joint infection) and 5-year revision risk and mortality. The main outcome measures for the health economic analysis were health-related quality of life (EuroQol-5 Dimensions) and NHS hospital costs.

RESULTS

In stage 1, propensity score stratification and inverse probability weighting replicated the results of TOPKAT. Propensity score adjustment, propensity score matching and instrumental variables did not. Stage 2 included 2256 unicompartmental knee replacement patients and 57,682 total knee replacement patients who had severe comorbidities, of whom 145 and 23,344 had linked Oxford Knee Scores, respectively. A statistically significant but clinically irrelevant difference favouring unicompartmental knee replacement was observed, with a mean postoperative Oxford Knee Score difference of < 2 points using propensity score stratification; no significant difference was observed using inverse probability weighting. Unicompartmental knee replacement more than halved the risk of venous thromboembolism [relative risk 0.33 (95% confidence interval 0.15 to 0.74) using propensity score stratification; relative risk 0.39 (95% confidence interval 0.16 to 0.96) using inverse probability weighting]. Unicompartmental knee replacement was not associated with myocardial infarction or prosthetic joint infection using either method. In the long term, unicompartmental knee replacement had double the revision risk of total knee replacement [hazard ratio 2.70 (95% confidence interval 2.15 to 3.38) using propensity score stratification; hazard ratio 2.60 (95% confidence interval 1.94 to 3.47) using inverse probability weighting], but half of the mortality [hazard ratio 0.52 (95% confidence interval 0.36 to 0.74) using propensity score stratification; insignificant effect using inverse probability weighting]. Unicompartmental knee replacement had lower costs and higher quality-adjusted life-year gains than total knee replacement for stage 2 participants.

LIMITATIONS

Although some propensity score methods successfully replicated TOPKAT, unresolved confounding may have affected stage 2. Missing Oxford Knee Scores may have led to information bias.

CONCLUSIONS

Propensity score stratification and inverse probability weighting successfully replicated TOPKAT, implying that some (but not all) propensity score methods can be used to evaluate surgical innovations and implantable medical devices using routine NHS data. Unicompartmental knee replacement was safer and more cost-effective than total knee replacement for patients with severe comorbidity and should be considered the first option for suitable patients.

FUTURE WORK

Further research is required to understand the performance of propensity score methods for evaluating surgical innovations and implantable devices.

TRIAL REGISTRATION

This trial is registered as EUPAS17435.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 66. See the NIHR Journals Library website for further project information.

Unicompartmental knee arthroplasty: A PearlDiver study evaluating complications rates, opioid use and utilization in the Medicare population

PURPOSE

Despite increased utilization of unicompartmental knee arthroplasty (UKA) for unicompartmental knee osteoarthritis, outcomes in Medicare patients are not well-reported. The purpose of this study is to analyze practice patterns and outcome differences between UKA and TKA in the Medicare population. It is hypothesized that UKA utilization will have increased over the course of the study period and that UKA will be associated with reduced opioid use and lower complication rates compared to TKA.

METHODS

Using PearlDiver, the Humana Claims dataset and the Medicare Standard Analytic File (SAF) were analyzed. Patients who underwent UKA and TKA were identified by CPT codes. Postoperative complications were identified by ICD-9/ICD-10 codes. Opioid use was analyzed by the number of days patients were prescribed opioids postoperatively. Survivorship was defined as conversion to TKA.

RESULTS

In the Humana dataset, 7,808 UKA and 150,680 TKA patients were identified. 8-year survivorship was 87.7% (95% CI [0.861,0.894]). Postoperative opioid use was significantly higher after TKA (186.1 days) compared to UKA (144.7 days) (p < 0.01, Δ = 41.1, 95% CI = [30.41, 52.39]). In the SAF dataset, 20,592 UKA patients and 110,562 TKA patients were identified. Survivorship was highest in patients > 80 years old and lowest in patients < 70 years old. In both datasets, postoperative complication rates were higher in TKA patients compared to UKA patients in nearly all categories.

CONCLUSIONS

UKA represents an increasingly utilized treatment for osteoarthritis in the Medicare population and may be comparatively advantageous to TKA due to reduced opioid use and complication rates after surgery.

LEVEL OF EVIDENCE

Level III.

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

BACKGROUND AND OBJECTIVE

As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code).

METHODS

We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA.

RESULTS

Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated.

CONCLUSION

Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.

Persistent postoperative opioid use after total hip or knee arthroplasty: A systematic review and meta-analysis

Disclaimer

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Purpose

To identify the proportion of patients with continued opioid use after total hip or knee arthroplasty.

Methods

This systematic review and meta-analysis searched Embase, MEDLINE, the Cochrane Central Register of Controlled Trials, and International Pharmaceutical Abstracts for articles published from January 1, 2009, to May 26, 2021. The search terms (opioid, postoperative, hospital discharge, total hip or knee arthroplasty, and treatment duration) were based on 5 key concepts. We included studies of adults who underwent total hip or knee arthroplasty, with at least 3 months postoperative follow-up.

Results

There were 30 studies included. Of these, 17 reported on outcomes of total hip arthroplasty and 19 reported on outcomes of total knee arthroplasty, with some reporting on outcomes of both procedures. In patients having total hip arthroplasty, rates of postoperative opioid use at various time points were as follows: at 3 months, 20% (95% CI, 13%-26%); at 6 months, 17% (95% CI, 12%-21%); at 9 months, 19% (95% CI, 13%-24%); and at 12 months, 16% (95% CI, 15%-16%). In patients who underwent total knee arthroplasty, rates of postoperative opioid use were as follows: at 3 months, 26% (95% CI, 19%-33%); at 6 months, 20% (95% CI, 17%-24%); at 9 months, 23% (95% CI, 17%-28%); and at 12 months, 21% (95% CI, 12%-29%). Opioid naïve patients were less likely to have continued postoperative opioid use than those who were opioid tolerant preoperatively.

Conclusion

Over 1 in 5 patients continued opioid use for longer than 3 months after total hip or knee arthroplasty. Clinicians should be aware of this trajectory of opioid consumption after surgery.

Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

BACKGROUND

Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches.

METHODS

The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes.

RESULTS

Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility.

CONCLUSIONS

There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

Physician induced demand for knee replacement surgery in Iran

BACKGROUND

The structure of the Iranian health system has raised this hypothesis that a part of the Knee Replacement Surgery (KRS) services are provided due to Physician-Induced Demand (PID).

METHODS

This paper used an unbalanced individual panel data covering the steady-state 15,729 KRSs performed by 995 surgeons provided by the Armed Forces Insurance Organization at the provincial level over the 60 months (2014-2018). We use a generalized method of moment's system (GMM-SYS) to obtain consistent and asymptotically efficient estimates, which provide a vital instrument for our dynamic panel data.

RESULTS

The outcomes show that with unequal increasing orthopedic surgeons to population ratio, both the number and size of KRS services were increased significantly at a 1 % level. Given that the positive elasticity obtained for the service size was significantly larger than the number of services, the findings give strong support for the existence of PID in the Iran system for KRS care. Also, the raw and population-adjusted number of KRS, cost, and the surgery per active physician increased significantly at the monthly province level.

CONCLUSIONS

This is the first time that the existence of PID in the Iranian health system is investigated using approved econometric models. The findings indicate that the health system structure has been provided the conditions for aggressive, costly, and high-risk services such as KRS to be exposed to PID.

Evaluation of State Cannabis Laws and Rates of Self-harm and Assault

Importance

State cannabis laws are changing rapidly. Research is inconclusive about their association with rates of self-harm and assault. Existing studies have not considered variations in cannabis commercialization across states over time.

Objective

To evaluate the association of state medical and recreational cannabis laws with self-harm and assault, overall and by age and sex, while considering varying degrees of commercialization.

Design, Setting, and Participants

Using a cohort design with panel fixed-effects analysis, within-state changes in claims for self-harm and assault injuries before and after changes in cannabis laws were quantified in all 50 US states and the District of Columbia. Comprehensive claims data on commercial and Medicare Advantage health plan beneficiaries from January 1, 2003, to December 31, 2017, grouped by state and month, were evaluated. Data analysis was conducted from January 31, 2020, to January 21, 2021.

Exposures

Categorical variable that indexed the degree of cannabis legalization in each state and month based on law type (medical or recreational) and operational status of dispensaries (commercialization).

Main Outcomes and Measures

Claims for self-harm and assault injuries based on International Classification of Diseases codes.

Results

The analysis included 75 395 344 beneficiaries (mean [SD] age, 47 [22] years; 50% female; and median follow-up, 17 months [interquartile range, 8-36 months]). During the study period, 29 states permitted use of medical cannabis and 11 permitted recreational cannabis. Point estimates for populationwide rates of self-harm and assault injuries were higher in states legalizing recreational cannabis compared with states with no cannabis laws, but these results were not statistically significant (adjusted rate ratio [aRR] assault, recreational dispensaries: 1.27; 95% CI, 0.79-2.03;self-harm, recreational dispensaries aRR: 1.15; 95% CI, 0.89-1.50). Results varied by age and sex with no associations found except for states with recreational policies and self-harm among males younger than 40 years (aRR <21 years, recreational without dispensaries: 1.70; 95% CI, 1.11-2.61; aRR aged 21-39 years, recreational dispensaries: 1.46; 95% CI, 1.01-2.12). Medical cannabis was generally not associated with self-harm or assault injuries populationwide or among age and sex subgroups.

Conclusions and Relevance

Recreational cannabis legalization appears to be associated with relative increases in rates of claims for self-harm among male health plan beneficiaries younger than 40 years. There was no association between cannabis legalization and self-harm or assault, for any other age and sex group or for medical cannabis. States that legalize but still constrain commercialization may be better positioned to protect younger male populations from unintended harms.

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.

Improving rheumatoid arthritis comparative effectiveness research through causal inference principles: systematic review using a target trial emulation framework

OBJECTIVES

Target trial emulation is an intuitive design framework that encourages investigators to formulate their comparative effectiveness research (CER) question as a hypothetical randomised controlled trial (RCT). Our aim was to systematically review CER studies in rheumatoid arthritis (RA) to provide examples of design limitations that could be avoided using target trial emulation, and how these limitations might introduce bias.

METHODS

We searched for head-to-head CER studies of biologic disease modifying anti-rheumatic drugs (DMARDs) in RA. Study designs were reviewed for seven components of the target trial emulation framework: eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrasts of interest (ie, intention-to-treat (ITT) or per-protocol effect) and analysis plan. Hypothetical trials corresponding to the reported methods were assessed to identify design limitations that would have been avoided with an explicit target trial protocol. Analysis of the primary effectiveness outcome was chosen where multiple analyses were performed.

RESULTS

We found 31 CER studies, of which 29 (94%) had at least one design limitation belonging to seven components. The most common limitations related to: (1) eligibility criteria: 19/31 (61%) studies used post-baseline information to define baseline eligibility; (2) causal contrasts: 25 (81%) did not define whether ITT or per-protocol effects were estimated and (3) assignment procedures: 13 (42%) studies did not account for confounding by indication or relied solely on statistical confounder selection.

CONCLUSIONS

Design limitations were found in 94% of observational CER studies in RA. Target trial emulation is a structured approach for designing observational CER studies that helps to avoid potential sources of bias.