Use of a claims-based algorithm to estimate disease severity in the multiple sclerosis Medicare population

Factors associated with the initiation of high-efficacy disease-modifying agents over moderate-efficacy disease-modifying agents in multiple sclerosis

BACKGROUND

With multiple treatment options, choosing the initial disease-modifying agent (DMA) could be crucial to managing multiple sclerosis (MS). Common treatment strategies recommend starting patients with moderate-efficacy disease-modifying agents (meDMAs), while others advocate initiating high-efficacy disease-modifying agents (heDMAs). However, limited real-world evidence exists regarding the factors associated with utilizing differing treatment strategies in the MS.

OBJECTIVE

This study evaluated the factors associated with the initiation of heDMAs in comparison to meDMAs among patients with MS.

METHODS

A retrospective cohort study was conducted using the Merative MarketScan Commercial Claims Database. Adult (18-64 years) MS patients with ≥1 DMA prescription were identified from 2016 to 2019. Patients were classified as incident heDMA or meDMA users based on their earliest DMA prescription, with a 12-month washout period. All covariates were measured during the 12-month baseline before the index DMA date. A multivariable logistic regression model, guided by the Andersen Behavioral Model, was applied to examine the predisposing, enabling, and need factors associated with using heDMAs over meDMAs.

RESULTS

There were 10,003 eligible MS patients, with the majority of users being female (74.92 %), middle-aged adults (35-54 years, 58.97 %), and enrolled in the Preferred Provider Organization (PPO, 53.10 %) healthcare plan. Overall, 2293 (22.92 %) MS patients initiated heDMAs. The multivariable logistic regression model revealed that male patients (adjusted odds ratio [aOR]: 1.46, 95 % Confidence Interval [CI]: 1.30-1.64) had higher odds of initiating heDMAs. Meanwhile, patients with bladder dysfunction medications (aOR: 1.39, 95% CI: 1.21-1.61), fatigue medications (aOR: 1.77, 95 %CI: 1.44-2.17), and impaired walking (aOR: 1.62, 95 %CI: 1.42-1.86) were more likely to initiate treatment with heDMAs. In contrast, patients with higher Elixhauser comorbidities scores, sensory symptoms (aOR: 0.47, 95 %CI: 0.42-0.53), visual symptoms (aOR: 0.63, 95 %CI: 0.54-0.73), and brainstem symptoms (aOR: 0.81, 95 %CI: 0.67-0.97) were less likely to be prescribed with heDMAs.

CONCLUSION

The study found that approximately one in four MS patients initiated heDMAs. Both demographic and clinical factors influenced the selection of heDMA. More work is needed to understand the differential value of selecting heDMAs over meDMAs for personalizing DMA treatment.

Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records

Background

The Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases.

Objectives

To develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records.

Methods

We built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS3D). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GM codes within 3 months before and after recorded true EDSS measures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0-7.0, ≥8.0), three-fold (EDSS 1.0-3.0, 4.0-5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis.

Results

We recruited 100 people with MS insured by AOK PLUS who had ≥1 EDSS measure in MSDS3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS.

Conclusion

Generally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance.

Real-world treatment patterns and effectiveness of cladribine tablets in patients with relapsing forms of multiple sclerosis in the United States

BACKGROUND

Real-world evidence on the use of cladribine tablets (CladT) for relapsing forms of multiple sclerosis (RMS) in the United States is emerging. The objective of this study was to assess the real-world treatment patterns and effectiveness of CladT in RMS.

METHODS

Adults with RMS initiating CladT were selected from the Symphony Integrated Dataverse. Baseline and follow-up periods were the 12 months before and 24 months after CladT initiation (index date). Switching to another disease-modifying therapy (DMT) and number of CladT courses were described during follow-up. Annualized relapse rate (ARR), MS disease severity, Expanded Disability Status Scale-Derived Disability Indicators (EDSS-DDI), corticosteroid use, and healthcare resource utilization (HRU) were described during Years 1 and 2 of follow-up and compared with baseline.

RESULTS

A total of 539 CladT-treated patients were included (mean age: 49.9 years; 77.6 % female). Over the 2-year follow-up, 91 % and 59 % of patients had one and two CladT courses, respectively, and 7 % of patients had evidence of switching to another DMT. ARR, MS disease severity score, and corticosteroid use decreased significantly during follow-up compared with baseline, while EDSS-DDI remained stable. All-cause and MS-related HRU decreased during follow-up.

CONCLUSION

CladT-treated patients with RMS had low switch rates, reduced ARR, disease severity, corticosteroid use, and HRU.

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

BACKGROUND

Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients.

METHODS

This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance.

RESULTS

In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index.

CONCLUSIONS

Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data.

Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders.

Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was −0.0644 (95%CI −0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection.

Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

COVID-19 severity and outcome in multiple sclerosis: Results of a national, registry-based, matched cohort study

Background

Risk factors associated with coronavirus disease 2019 (COVID-19) severity in patients with multiple sclerosis (MS) have been described. Recent improvements in supportive care measures and increased testing capacity may modify the risk of severe COVID-19 outcome in MS patients. This retrospective study evaluates the severity and outcome of COVID-19 in MS and characterizes temporal trends over the course of the pandemic in the United States.

Methods

We conducted a comparative cohort study using de-identified electronic health record (EHR) claims-based data. MS patients diagnosed with COVID-19 between February 2, 2020 and October 13, 2020 were matched (1:2) to a control group using propensity score analysis. The primary outcome was a composite of intensive care unit (ICU) admission, mechanical ventilation, and/or death.

Results

A total of 2,529 patients (843 MS and 1,686 matched controls) were included. Non-ambulatory and pre-existing comorbidities were independent risk factors for COVID-19 severity. The risk for the severe composite outcome was lower in the late cohorts compared with the early cohorts.

Conclusions

The majority of MS patients actively treated with a disease-modifying therapy (DMT) had mild disease. The observed trend toward a reduction in severity risk in recent months suggests an improvement in COVID-19 outcome.

Association between multiple sclerosis disease severity and adherence to disease-modifying therapies

BACKGROUND: For multiple sclerosis (MS) patients taking disease-modifying therapies (DMTs), adherence to treatment is a key component of achieving beneficial outcomes, such as delayed disease progression and the reduction and prevention of symptoms and relapses. OBJECTIVES: The aim of this study was to assess the impact of a claims-based measure of MS disease severity on DMT adherence in a one-year study period. METHODS: Patients were identified from Humana Medicare Advantage claims data from January 1, 2013 to December 31, 2015. Patients over the age of 18 with at least 12 months of continuous enrollment and > 1 outpatient MS visit with DMT use prior to the index date were included. Patients who switched DMT type (oral, platform, IV) during the study period were excluded. Medication possession ratios (MPR) for DMTs were calculated from pharmacy and medical claims over 12 months of claims data, and a previously developed claims algorithm was used to determine MS disease severity. Patients with MPRs of 0.8 or higher were considered adherent to DMT treatment. Multivariable logistic regression was used to evaluate the association of MS disease severity, gender, DMT type, and age category with DMT adherence. RESULTS: The study population of 3,347 patients had an average MPR of 84.7 (75% were classified as adherent). Multivariable logistic analysis demonstrated that compared to the 18-45 age group, the 46-64 and 65+ age groups were 1.33 (OR: 1.33 [95% CI 1.08-1.64]) and 1.55 (OR: 1.55, [95% CI 1.18-2.05]) times more likely to be adherent. Patients with a high level of MS disease severity were 53% (OR: 0.47, [95% CI 0.36-0.62]) less likely to be adherent compared to those with low MS disease severity. No significant difference was identified for gender or DMT type (oral, platform, or IV). CONCLUSIONS: Increased age and lower MS disease severity were associated with better DMT adherence. MS disease severity should be considered when assessing risk for low DMT adherence. DISCLOSURES: No funding supported this project. The authors have nothing to disclose. Preliminary results were previously presented virtually at AMCP Annual 2020 in April 2020.