Can the publication of guidelines change the management of early rheumatoid arthritis? An interrupted time series analysis from the United Kingdom

An improved method for analysis of interrupted time series (ITS) data: accounting for patient heterogeneity using weighted analysis

Interrupted time series (ITS) design is commonly used to evaluate the impact of interventions in healthcare settings. Segmented regression (SR) is the most commonly used statistical method and has been shown to be useful in practical applications involving ITS designs. Nevertheless, SR is prone to aggregation bias, which leads to imprecision and loss of power to detect clinically meaningful differences. The objective of this article is to present a weighted SR method, where variability across patients within the healthcare facility and across time points is incorporated through weights. We present the methodological framework, provide optimal weights associated with data at each time point and discuss relevant statistical inference. We conduct extensive simulations to evaluate performance of our method and provide comparative analysis with the traditional SR using established performance criteria such as bias, mean square error and statistical power. Illustrations using real data is also provided. In most simulation scenarios considered, the weighted SR method produced estimators that are uniformly more precise and relatively less biased compared to the traditional SR. The weighted approach also associated with higher statistical power in the scenarios considered. The performance difference is much larger for data with high variability across patients within healthcare facilities. The weighted method proposed here allows us to account for the heterogeneity in the patient population, leading to increased accuracy and power across all scenarios. We recommend researchers to carefully design their studies and determine their sample size by incorporating heterogeneity in the patient population.

Intensive therapy for moderate established rheumatoid arthritis: the TITRATE research programme

Background

Rheumatoid arthritis is a major inflammatory disorder and causes substantial disability. Treatment goals span minimising disease activity, achieving remission and decreasing disability. In active rheumatoid arthritis, intensive management achieves these goals. As many patients with established rheumatoid arthritis have moderate disease activity, the TITRATE (Treatment Intensities and Targets in Rheumatoid Arthritis ThErapy) programme assessed the benefits of intensive management.

Objectives

To (1) define how to deliver intensive therapy in moderate established rheumatoid arthritis; (2) establish its clinical effectiveness and cost-effectiveness in a trial; and (3) evaluate evidence supporting intensive management in observational studies and completed trials.

Design

Observational studies, secondary analyses of completed trials and systematic reviews assessed existing evidence about intensive management. Qualitative research, patient workshops and systematic reviews defined how to deliver it. The trial assessed its clinical effectiveness and cost-effectiveness in moderate established rheumatoid arthritis.

Setting

Observational studies (in three London centres) involved 3167 patients. These were supplemented by secondary analyses of three previously completed trials (in centres across all English regions), involving 668 patients. Qualitative studies assessed expectations (nine patients in four London centres) and experiences of intensive management (15 patients in 10 centres across England). The main clinical trial enrolled 335 patients with diverse socioeconomic deprivation and ethnicity (in 39 centres across all English regions).

Participants

Patients with established moderately active rheumatoid arthritis receiving conventional disease-modifying drugs.

Interventions

Intensive management used combinations of conventional disease-modifying drugs, biologics (particularly tumour necrosis factor inhibitors) and depot steroid injections; nurses saw patients monthly, adjusted treatment and provided supportive person-centred psychoeducation. Control patients received standard care.

Main outcome measures

Disease Activity Score for 28 joints based on the erythrocyte sedimentation rate (DAS28-ESR)-categorised patients (active to remission). Remission (DAS28-ESR < 2.60) was the treatment target. Other outcomes included fatigue (measured on a 100-mm visual analogue scale), disability (as measured on the Health Assessment Questionnaire), harms and resource use for economic assessments.

Results

Evaluation of existing evidence for intensive rheumatoid arthritis management showed the following. First, in observational studies, DAS28-ESR scores decreased over 10–20 years, whereas remissions and treatment intensities increased. Second, in systematic reviews of published trials, all intensive management strategies increased remissions. Finally, patients with high disability scores had fewer remissions. Qualitative studies of rheumatoid arthritis patients, workshops and systematic reviews helped develop an intensive management pathway. A 2-day training session for rheumatology practitioners explained its use, including motivational interviewing techniques and patient handbooks. The trial screened 459 patients and randomised 335 patients (168 patients received intensive management and 167 patients received standard care). A total of 303 patients provided 12-month outcome data. Intention-to-treat analysis showed intensive management increased DAS28-ESR 12-month remissions, compared with standard care (32% vs. 18%, odds ratio 2.17, 95% confidence interval 1.28 to 3.68; p = 0.004), and reduced fatigue [mean difference –18, 95% confidence interval –24 to –11 (scale 0–100); p < 0.001]. Disability (as measured on the Health Assessment Questionnaire) decreased when intensive management patients achieved remission (difference –0.40, 95% confidence interval –0.57 to –0.22) and these differences were considered clinically relevant. However, in all intensive management patients reductions in the Health Assessment Questionnaire scores were less marked (difference –0.1, 95% confidence interval –0.2 to 0.0). The numbers of serious adverse events (intensive management n = 15 vs. standard care n = 11) and other adverse events (intensive management n = 114 vs. standard care n = 151) were similar. Economic analysis showed that the base-case incremental cost-effectiveness ratio was £43,972 from NHS and Personal Social Services cost perspectives. The probability of meeting a willingness-to-pay threshold of £30,000 was 17%. The incremental cost-effectiveness ratio decreased to £29,363 after including patients’ personal costs and lost working time, corresponding to a 50% probability that intensive management is cost-effective at English willingness-to-pay thresholds. Analysing trial baseline predictors showed that remission predictors comprised baseline DAS28-ESR, disability scores and body mass index. A 6-month extension study (involving 95 intensive management patients) showed fewer remissions by 18 months, although more sustained remissions were more likley to persist. Qualitative research in trial completers showed that intensive management was acceptable and treatment support from specialist nurses was beneficial.

Limitations

The main limitations comprised (1) using single time point remissions rather than sustained responses, (2) uncertainty about benefits of different aspects of intensive management and differences in its delivery across centres, (3) doubts about optimal treatment of patients unresponsive to intensive management and (4) the lack of formal international definitions of ‘intensive management’.

Conclusion

The benefits of intensive management need to be set against its additional costs. These were relatively high. Not all patients benefited. Patients with high pretreatment physical disability or who were substantially overweight usually did not achieve remission.

Future work

Further research should (1) identify the most effective components of the intervention, (2) consider its most cost-effective delivery and (3) identify alternative strategies for patients not responding to intensive management.

Trial registration

Current Controlled Trials ISRCTN70160382.

Funding

This project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 9, No. 8. See the NIHR Journals Library website for further project information.

A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events

BACKGROUND

An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner.

METHODS

To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction.

RESULTS

Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention.

CONCLUSIONS

Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.

First-year drug therapy of new-onset rheumatoid and undifferentiated arthritis: a nationwide register-based study

Background

In this retrospective cohort study, we evaluated the drug therapies used for early rheumatoid (RA) and undifferentiated (UA) arthritis patients.

Methods

From a nationwide register maintained by the Social Insurance Institution, information on sex, date of birth, and date of special medicine reimbursement decision for all new Finnish RA and UA patients between 2011 and 14 were collected, and their DMARD (Disease Modifying Antirheumatic Drug) purchases during the first year after the diagnosis were analyzed.

Results

A total of 7338 patients with early RA (67.3% female, 68.1% seropositive) and 2433 with early UA (67.8% female) were identified. DMARDs were initiated during the first month after the diagnosis to 92.0% of the patients with seropositive RA, 90.3% with seronegative RA and to 87.7% with UA (p < 0.001). Respectively, 72.1, 63.4, and 42.9% of the patients (p < 0.001) purchased methotrexate; 49.8, 35.9, and 16.0% (p < 0.001) as part of a DMARD combination during the first month. By the end of the first year after the diagnosis, self-injected biologics were purchased by 2.6, 5.3 and 3.1% (p < 0.001) of them. Only 1.4, 2.6 and 3.0% (p < 0.001) of the patients were not receiving any DMARDs. During the first year, 83.4% of the seropositive RA patients had purchased methotrexate, 50.4% sulfasalazine, 72.1% hydroxychloroquine, and 72.6% prednisolone.

Conclusions

Currently, combination therapy including methotrexate is a common treatment strategy for early seropositive RA in Finland. Despite an easy access to biologics, these drugs are seldom needed during the first year after diagnosis.

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes

PURPOSE OF REVIEW

The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowing assessment of the patient disease outcome in the future.

RECENT FINDINGS

In recent years, the time series (TS) concept has become the center of attention as a predictive model for making forecast of unseen data values. TS and one of its technologies, the interrupted TS (ITS) analysis (TS with one or more interventions), predict the next period(s) value(s) of a given patient based on their past and current information. Traditional TS/ITS methods involve segmented regression-based technologies (linear and nonlinear), while stochastic (linear modeling) and artificial intelligence approaches, including machine learning (complex nonlinear relationships between variables), are also used; however, each have limitations. We will briefly describe TS/ITS, provide examples of their application in arthritic diseases; describe their methods, challenges, and limitations; and propose a combined (stochastic and artificial intelligence) procedure in post-intervention that will optimize ITS modeling. This combined method will increase the accuracy of ITS modeling by profiting from the advantages of both stochastic and nonlinear models to capture all ITS deterministic and stochastic components. In addition, this combined method will allow ITS outcomes to be predicted as continuous variables without having to consider the time lag produced between the pre- and post-intervention periods, thus minimizing the prediction error not only for the given data but also for all possible future patterns in ITS. The use of reliable prediction methodologies for arthritis patients will permit treatment of not only the disease, but also the patient with the disease, ensuring the best outcome prediction for the patient.

Association between NICE guidance on biologic therapies with rates of hip and knee replacement among rheumatoid arthritis patients in England and Wales: An interrupted time-series analysis

OBJECTIVE

To estimate the impact of NICE approval of tumor necrosis factor inhibitor (TNFi) therapies on the incidence of total hip replacement (THR) and total knee replacement (TKR) among rheumatoid arthritis (RA) patients in England and Wales.

METHODS

Primary care data [Clinical Practice Research Datalink (CPRD)] for the study period (1995-2014) were used to identify incident adult RA patients. The age and sex-standardised 5-year incidence of THR and TKR was calculated separately for RA patients diagnosed in each six-months between 1995-2009. We took a natural experimental approach, using segmented linear regression to estimate changes in level and trend following the publication of NICE TA 36 in March 2002, incorporating a 1-year lag. Regression coefficients were used to calculate average change in rates, adjusted for prior level and trend.

RESULTS

We identified 17,505 incident RA patients of whom 465 and 650 underwent THR and TKR surgery, respectively. The modeled average incidence of THR and TKR over the biologic-era was 6.57/1000 person years (PYs) and 8.51/1000 PYs, respectively, with projected (had pre-NICE TA 36 level and trend continued uninterrupted) figures of 5.63/1000 PYs and 12.92 PYs, respectively. NICE guidance was associated with a significant average decrease in TKR incidence of -4.41/1000 PYs (95% C.I. -6.88 to -1.94), equating to a relative 34% reduction. Overall, no effect was seen on THR rates.

CONCLUSIONS

Among incident RA patients in England and Wales, NICE guidance on TNFi therapies for RA management was temporally associated with reduced rates of TKR but not THR.

Presentation of SLE in UK primary care using the Clinical Practice Research Datalink

OBJECTIVES

To describe the presenting symptoms of SLE in primary care using the Clinical Practice Research Database (CPRD) and to calculate the time from symptom presentation to SLE diagnosis.

METHODS

Incident cases of SLE were identified from the CPRD between 2000 and 2012. Presenting symptoms were identified from the medical records of cases in the 5 years before diagnosis and grouped using the British Isles Lupus Activity Group (BILAG) symptom domains. The time from the accumulation of one, two and three BILAG domains to SLE diagnosis was investigated, stratified by age at diagnosis (<30, 30-49 and ≥50 years).

RESULTS

We identified 1426 incident cases (170 males and 1256 females) of SLE. The most frequently recorded symptoms and signs prior to diagnosis were musculoskeletal, mucocutaneous and neurological. The median time from first musculoskeletal symptom to SLE diagnosis was 26.4 months (IQR 9.3-43.6). There was a significant difference in the time to diagnosis (log rank p<0.01) when stratified by age and disease severity at baseline, with younger patients <30 years and those with severe disease having the shortest times and patients aged ≥50 years and those with mild disease having the longest (6.4 years (IQR 5.8-6.8)).

CONCLUSIONS

The time from symptom onset to SLE diagnosis is long, especially in older patients. SLE should be considered in patients presenting with flaring or chronic musculoskeletal, mucocutaneous and neurological symptoms.

What evidence is there for a delay in diagnostic coding of RA in UK general practice records? An observational study of free text

OBJECTIVES

Much research with electronic health records (EHRs) uses coded or structured data only; important information captured in the free text remains unused. One dimension of EHR data quality assessment is 'currency' or timeliness, that is, data are representative of the patient state at the time of measurement. We explored the use of free text in UK general practice patient records to evaluate delays in recording of rheumatoid arthritis (RA) diagnosis. We also aimed to locate and quantify disease and diagnostic information recorded only in text.

SETTING

UK general practice patient records from the Clinical Practice Research Datalink.

PARTICIPANTS

294 individuals with incident diagnosis of RA between 2005 and 2008; 204 women and 85 men, median age 63 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

Assessment of (1) quantity and timing of text entries for disease-modifying antirheumatic drugs (DMARDs) as a proxy for the RA disease code, and (2) quantity, location and timing of free text information relating to RA onset and diagnosis.

RESULTS

Inflammatory markers, pain and DMARDs were the most common categories of disease information in text prior to RA diagnostic code; 10-37% of patients had such information only in text. Read codes associated with RA-related text included correspondence, general consultation and arthritis codes. 64 patients (22%) had DMARD text entries >14 days prior to RA code; these patients had more and earlier referrals to rheumatology, tests, swelling, pain and DMARD prescriptions, suggestive of an earlier implicit diagnosis than was recorded by the diagnostic code.

CONCLUSIONS

RA-related symptoms, tests, referrals and prescriptions were recorded in free text with 22% of patients showing strong evidence of delay in coding of diagnosis. Researchers using EHRs may need to mitigate for delayed codes by incorporating text into their case-ascertainment strategies. Natural language processing techniques have the capability to do this at scale.