Deriving common comorbidity indices from the MedDRA classification and exploring their performance on key outcomes in patients with rheumatoid arthritis

A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose Trial of AWZ1066S, an Anti-Wolbachia Candidate Macrofilaricide

AWZ1066S has been developed as a potential treatment for the neglected tropical diseases lymphatic filariasis and onchocerciasis. AWZ1066S targets the Wolbachia bacterial endosymbiont present in the causative nematode parasites. This phase 1, first-in-human study aimed to assess the safety and pharmacokinetics of AWZ1066S in healthy human participants. In a randomized double-blind, placebo-controlled, single ascending dose study, healthy adults received a single oral dose of AWZ1066S (or placebo) and were followed up for 10 days. The planned single doses of AWZ1066S ranged from 100 to 1600 mg, and each dose was administered to a cohort of 8 participants (6 AWZ1066S and 2 placebo). In total 30 people participated, 18 (60%) female, median age 30.0 years (minimum 20, maximum 61). The cohorts administered 100, 200, 300, and 400 mg of AWZ1066S progressed unremarkably. After single 700-mg doses all 4 participants developed symptoms of acute gastritis and transient increases in liver enzymes. The severity of these adverse events ranged from mild to severe, with 1 participant needing hospital admission. Pharmacokinetic analysis indicated that AWZ1066S is rapidly absorbed with predictable pharmacokinetics. In conclusion, safety concerns prevented this study from reaching the human exposures needed for AWZ1066S to be clinically effective against lymphatic filariasis and onchocerciasis.

Investigating the Prevalence of Comorbidity in Multiple Sclerosis Clinical Trial Populations

BACKGROUND AND OBJECTIVES

Comorbidity is common in multiple sclerosis (MS) with the most prevalent conditions being depression, anxiety, hypertension, and hyperlipidemia. Limited information regarding the representation of comorbidity status is available from phase III clinical trials in MS leading to concern about the potential underrepresentation of individuals with comorbidity in clinical trials. The objective was to estimate the prevalence of comorbidities in MS clinical trial populations.

METHODS

Individual-level data from multiple sponsors were requested for a 2-stage meta-analysis of phase III clinical trials of MS disease-modifying therapies. To ensure consistency of our approach across trials, we followed the Maelstrom retrospective harmonization guidelines. Chronic comorbidities at clinical trial enrollment recommended by the International Advisory Committee on Clinical Trials in MS were considered (depression, anxiety, hypertension, hyperlipidemia, migraine, diabetes, chronic lung disease). Additional comorbidities were also classified. Classification was based on medical history data. Individual comorbidities were summed and categorized as 0, 1, 2, or ≥3. We report the pooled prevalence (95% confidence interval [95% CI]) of comorbidity. The pooled prevalence and prevalence ratios across age, sex, race, disability level, and treatment were also reported. Heterogeneity was assessed using the I2 statistic.

RESULTS

Seventeen trials involving 17,926 participants were included. Fourteen trials enrolled participants with relapsing MS (RMS) while 3 enrolled participants with progressive MS (PMS). The distributions of sex, age, and disability level were generally consistent within RMS and PMS trials. When pooled, almost half of trial participants (46.5%) had ≥1 comorbidity (1: 25.0%, 95% CI 23.0-27.0, I2 = 89.9; 2: 11.4% [9.3-14.0], I2 = 96.3; ≥3: 6.0% [4.2-8.4], I2 = 97.7). Depression (16.45% [12.96-20.88], I2 = 98.3) was the most prevalent comorbidity reported, followed by hypertension (10.16% [8.61-11.98], I2 = 93.2). Heterogeneity was high across trials. Older age and female participants were associated with increased number of comorbidities. Older individuals and male participants had a higher prevalence of hyperlipidemia, while older individuals and female participants had a higher prevalence of depression and anxiety.

DISCUSSION

Individuals with comorbidities are included in clinical trials, although they may still be underrepresented compared with the general MS population. Given the comorbidity prevalence in the trial populations and studies suggesting an association of comorbidities with disease activity, comorbidity may influence outcomes in clinical trials.

Using the Functional Comorbidity Index with administrative workers' compensation data: Utility, validity, and caveats

BACKGROUND

Chronic health conditions impact worker outcomes but are challenging to measure using administrative workers' compensation (WC) data. The Functional Comorbidity Index (FCI) was developed to predict functional outcomes in community-based adult populations, but has not been validated for WC settings. We assessed a WC-based FCI (additive index of 18 conditions) for identifying chronic conditions and predicting work outcomes.

METHODS

WC data were linked to a prospective survey in Ohio (N = 512) and Washington (N = 2,839). Workers were interviewed 6 weeks and 6 months after work-related injury. Observed prevalence and concordance were calculated; survey data provided the reference standard for WC data. Predictive validity and utility for control of confounding were assessed using 6-month work-related outcomes.

RESULTS

The WC-based FCI had high specificity but low sensitivity and was weakly associated with work-related outcomes. The survey-based FCI suggested more comorbidity in the Ohio sample (Ohio mean = 1.38; Washington mean = 1.14), whereas the WC-based FCI suggested more comorbidity in the Washington sample (Ohio mean = 0.10; Washington mean = 0.33). In the confounding assessment, adding the survey-based FCI to the base model moved the state effect estimates slightly toward null (<1% change). However, substituting the WC-based FCI moved the estimate away from null (8.95% change).

CONCLUSIONS

The WC-based FCI may be useful for identifying specific subsets of workers with chronic conditions, but less useful for chronic condition prevalence. Using the WC-based FCI cross-state appeared to introduce substantial confounding. We strongly advise caution-including state-specific analyses with a reliable reference standard-before using a WC-based FCI in studies involving multiple states.

Discontinuation rate of sulfasalazine, leflunomide and methotrexate due to adverse events in a real-life setting (NOR-DMARD)

Objectives

MTX, LEF and SSZ are conventional synthetic DMARDs (csDMARDs) with a well-established role in the treatment of RA. We aimed to estimate and compare the relative risks for adverse events (AEs) and the discontinuation of these drugs owing to AEs.

Methods

We included all 3339 patients from the NOR-DMARD study treated with MTX, LEF or SSZ in monotherapy. All reported AEs were compared between treatment groups using quasi-Poisson regression. In addition, drug retention rates were analysed using Kaplan-Meier estimates with Cox regression to control for possible confounders. We analysed drug retention rates and cumulative risk of discontinuation attributable to AEs using the Kaplan-Meier estimator. We assessed age, sex, baseline DAS in 28 joints with ESR (DAS28-ESR), seropositivity, prednisolone use, previous DMARD use, year of inclusion and co-morbidity as possible cofounders.

Results

We found that the discontinuation rate attributable to AEs was significantly higher for LEF and SSZ than for MTX. After the first year, it was 13.7% (95% CI 12.2, 15.2), 39.6% (95% CI 34.8, 44) and 43.4% (95% CI 38.2, 48.1) for MTX, SSZ and LEF, respectively. Similar results were found when adjusting for confounders. The overall AEs were comparable across the treatment groups. The AE profile was as expected for each drug.

Conclusion

Our work has shown a similar AE profile of csDMARDs to previous data. However, higher discontinuation rates for SSZ and LEF cannot be explained easily from AE profiles.

Association between Rheumatic Disease Comorbidity Index and factors of poor prognosis in a cohort of 280 patients with rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is commonly associated with higher rates of comorbidities. Recent recommendations highlight screening comorbidities during the disease course because of their impact on patients' ability to function, on disease outcome, but also on treatment choices. Hence the interest of our study that aimed to quantify the impact of comorbidities among RA patients using a validated tool the Rheumatic Disease Comorbidity Index (RDCI) and to explore the association between comorbidities and disease characteristics.

METHODS

We conducted a cross-sectional study over 12 months period, including patients followed for an established RA according to the ACR/EULAR 2010 criteria and hospitalized in our rheumatology department. Patients' characteristics and disease features were collected for each patient. Comorbidities were quantified using the RDCI. We looked for the association between RDCI and patients characteristics and RA parameters. Univariable and multivariable analysis were made.

RESULTS

They were 280 patients: 233 female (83.2%) and 47 male (16.8%) with a mean age of 58.07 (SD 11.12) years. The mean follow-up period was 14.74 (SD 1.63) years. Comorbidities were noted in 133 patients (47.5%). The mean comorbidity score measured by the RDCI was 1.05 (SD 1.23). RDCI was positively correlated with age (p < 0.001, r = 0.359). RA patients whose age of disease onset exceeds 40 years have significantly higher RDCI (1.8 (SD 1.3) [CI 95%: 1.36-1.88] vs. 1.5 (SD 1.2), p = 0.007). Moreover, RDCI was significantly associated with the presence pulmonary involvement (p < 0.001) and ocular involvement (p = 0.002). RDCI was also associated with erosive RA (p = 0.006), the presence of atlanto-axial dislocation (p = 0.014), and coxitis (p = 0.029). Regarding therapy regimen, RDCI was statistically increased in patients receiving bDMARDs compared to patients under csDMARDs (2.8 (SD 1.6) vs. 1.0 (SD 1.0), p = 0.021).

CONCLUSION

In this study, comorbidity index was associated with signs of poor prognosis such as erosions, coxitis, and atlanto-axial dislocation. This confirmed the hypothesis that comorbidity can be a threat to the improvement in the long-term prognosis in RA patients.

Comparison of Indexes to Measure Comorbidity Burden and Predict All-Cause Mortality in Rheumatoid Arthritis

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.

Lapses in Care Among Patients Assigned to Ranibizumab for Proliferative Diabetic Retinopathy: A Post Hoc Analysis of a Randomized Clinical Trial

Importance

The follow-up schedule for individuals with eyes treated with anti-vascular endothelial growth factor agents for proliferative diabetic retinopathy (PDR) requires that patients return frequently for monitoring and repeated treatment. The likelihood that a patient will comply should be a consideration in choosing a treatment approach.

Objective

To describe completion of scheduled examinations among participants assigned to intravitreous injections of ranibizumab for PDR in a multicenter randomized clinical trial.

Design, Setting, and Participants

This post hoc analysis evaluates data from a randomized clinical trial conducted at 55 US sites among 305 adults with proliferative diabetic retinopathy enrolled between February and December 2012. Both eyes were enrolled for 89 participants (1 eye to each study group), with a total of 394 study eyes. The final 2-year visit was completed in January 2015. Data were analyzed from April 2019 to July 2021.

Interventions

Ranibizumab injections for PDR or macular edema.

Main Outcomes and Measures

A long lapse in care of 8 or more weeks past a scheduled examination, dropout from follow-up, visual acuity at 5 years.

Results

Among 170 participants, the median age was 51 years, and 44.7% were female. Through 5 years of follow-up, 94 of 170 participants (55.3%) had 1 or more long lapse in care. Median time to the first long lapse was 210 weeks, and 69 of 94 participants (73.4%) returned for examination after the first long lapse. Fifty of 170 participants (29.4%) dropped out of follow-up by 5 years. Among the 120 participants who completed the 5-year examination, median change from baseline in visual acuity was -2 letters for participants who had 1 or more long lapse compared with +5 letters for those without a long lapse (P = .02). After multivariable adjustment, the odds ratio (95% CI) for baseline associations with 1 or more long lapse was 1.21 (1.03-1.43) for each 5-letter decrement in visual acuity score, 2.19 (1.09-4.38) for neovascularization of the disc and elsewhere, and 3.48 (1.38-8.78) for no prior laser treatment for diabetic macular edema.

Conclusions and Relevance

Over 5 years, approximately half of the participants assigned to ranibizumab for PDR had a long lapse in care despite substantial effort by the DRCR Retina Network to facilitate timely completion of examinations. The likelihood of a long lapse in care during long-term follow-up needs to be considered when choosing treatment for PDR.

Trial Registration

ClinicalTrials.gov Identifier: NCT01489189.

Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics

BACKGROUND

Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies.

OBJECTIVE

We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson-Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality.

MATERIALS AND METHODS

We used data including 163,983 observations of patients aged 65 and older who experienced discharge delay in Ontario, Canada, during 2004 - 2017. First, we utilized various classification ML algorithms, including classification and regression trees, random forests, bagging trees, extreme gradient boosting, and logistic regression, to predict the multimorbidity status based on CDCI, ECI, and FCI. Second, we used adjusted multinomial logistic regression to assess the association between multimorbidity indices and the patient-important outcomes, including 30-day mortality and readmission.

RESULTS

For all ML algorithms and regardless of the predictive performance criteria, better predictions were established for the CDCI compared with the ECI and FCI. Remarkably, the most predictable multimorbidity index (i.e., CDCI with Area Under the Receiver Operating Characteristic Curve = 0.80, 95% CI = 0.79 - 0.81) also offered the highest prognostications regarding adverse events (RRR_mortality = 3.44, 95% CI = 3.21 - 3.68 and RRR_readmission = 1.36, 95% CI = 1.31 - 1.40).

CONCLUSIONS

Our findings highlight the feasibility and utility of predicting multimorbidity status using ML algorithms, resulting in the early detection of patients at risk of mortality and readmission. This can support proactive triage and decision-making about staffing and resource allocation, with the goal of optimizing patient outcomes and facilitating an upstream and informed discharge process through prioritizing complex patients for discharge and providing patient-centered care.

Comorbidity burden in the first three years after diagnosis in patients with rheumatoid arthritis, psoriatic arthritis or spondyloarthritis: a general practice registry-based study

OBJECTIVES

Rheumatoid arthritis (RA), psoriatic arthritis (PsA) and spondyloarthritis (SpA) are chronic inflammatory rheumatic conditions with high levels of comorbidity requiring additional therapeutic attention. We aimed to compare the 3-year comorbidity incidence and pain medication prescription in patients diagnosed with RA, PsA or SpA versus controls.

METHODS

Data between 1999 and 2012 were obtained from Intego, a general practitioner (GP) morbidity registry in Flanders, Belgium. Cases were identified by International Classification of Primary Care (ICPC-2) codes representing 'rheumatoid/seropositive arthritis (L88)' or 'musculoskeletal disease other (L99)'. The registered keywords mapped to these ICPC-2 codes were further verified and mapped to a RA/SpA/PsA diagnosis. Controls were matched on age, gender, GP practice and diagnosis date. We analysed the 3-year comorbidity burden in cases and controls, measured by the Rheumatic Diseases Comorbidity Index (RDCI). All electronically GP-prescribed drugs were registered.

RESULTS

In total, 738, 229 and 167 patients were included with a diagnosis of RA, SpA or PsA, respectively. Patients with RA or PsA had comparable median RDCI scores at baseline, but higher scores at year 3 compared with controls (RA: p=0.010; PsA: p=0.008). At baseline, depression was more prevalent in PsA patients vs controls (p<0.003). RA patients had a higher 3-year incidence of cardiovascular disease including myocardial infarction than controls (p<0.035). All disease population were given more prescriptions than controls for any pain medication type, even opioids excluding tramadol.

CONCLUSIONS

This study highlights the increasing comorbidity burden of patients with chronic inflammatory rheumatic conditions, especially for individuals with RA or PsA. The high opioid use in all populations was remarkable.

Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis

Introduction

Comorbidities influence the prognosis, clinical outcomes, disease activity, and treatment response in rheumatoid arthritis (RA). RA patients have a high-comorbidity burden necessitating their study. Comorbidity indices are used to measure comorbidities and to study their impacts on different outcomes. A large number of such indices are used in clinical research. Some indices have been specifically developed in RA patients.

Aim

This review aims to provide an overview of generic and specific comorbidity indices commonly used in RA research.

Methods

We performed a critical literature review of comorbidity indices in RA using the PubMed database.

Results/discussion

This non-systematic literature review provides an overview of generic and specific comorbidity indices commonly used in RA studies. Some of the older but commonly used comorbidity indices like the Charlson comorbidity index and the Elixhauser comorbidity measure were primarily developed to estimate mortality risk from comorbid diseases. They were not specifically developed for RA patients but have been widely used in rheumatology comorbidity measurement. Of the many comorbidity indices available, only the rheumatic disease comorbidity index (RDCI) and the multimorbidity index have been specifically developed in RA patients. The functional comorbidity index was developed to look at functional disability and has been used in RA patients considering that morbidity is more important than mortality in such patients. While there is limited data comparing these indices, available evidence seems to favor the use of RDCI as it predicts mortality, hospitalization, disability, and healthcare utilization. The choice of the index, however, depends on several factors such as the population under study, outcome of interest, and sources of data. More research is needed to study the RA-specific comorbidity measures to make evidence-based recommendations for the choice of a comorbidity measure.