Development of a claims-based flare algorithm for systemic lupus erythematosus

Figures & data

Table 1. Patient characteristics.

Patient characteristics	All patients
	N = 2472
Demographics
Age, Mean (SD)	51.4 (14.2)
Median	52.0
Gender, N (%)
Male	217 (8.8%)
Female	2255 (91.2%)
Index year, N (%)
2003–2011	516 (20.9%)
2012–2018	1956 (79.1%)
Have prior SLE encounter, N (%)	1494 (60.4%)
Time from first SLE diagnosis to index date, Mean (SD)	962.9 (948.9)
Median	658.0
Number of 30 day periods, Mean (SD)	34.0 (23.3)
Median	26.0
Average follow-up in days, Mean (SD)	1033.0 (698.2)
Median	800.5
Clinical characteristics
SLE Adjusted Deyo Charlson Comorbidity Index, Mean (SD)	1.7 (2.9)
Median	0.0
Comorbidities, N (%)
Other inflammatory polyarthropathies	827 (33.5%)
Fibromyalgia	644 (26.1%)
Myositis/Myalgia	602 (24.4%)
Hypertension	1463 (59.2%)
Renal disease (any)	690 (27.9%)
Liver disease	255 (10.3%)
Depression	784 (31.7%)
Cerebrovascular disease	421 (17.0%)
Osteoporosis/Osteopenia	467 (18.9%)
Medications prescribed, N (%)
Any steroid use	1314 (53.2%)
Oral steroid use	950 (38.4%)
Anticoagulant	405 (16.4%)
Immunosuppressants^a	421 (17.0%)
Antimalarials^b	872 (35.3%)
NSAIDs	792 (32.0%)
Leukotriene modifier	108 (4.4%)
Disease modifying antirheumatics^c	991 (40.1%)
Belimumab	50 (2.0%)
No medication prescribed	706 (28.6%)

^aMycophenolate Mofetil, Methotrexate, Cyclophosphamide, Azathioprine

^bQuinine, Pyrimethamine/Sulfadoxine, Mefloquine, Primaquine, Artemether/Lumefantrine, Pyrimethamine, Atovaquone/Proguanil, Hydroxychloroquine

^cBaracitinib, Sulfasalazine, Methotrexate, Cyclophosphamide, Hydroxychloroquine, Sarilumab, Azathioprine, Tofacitinib, Adalimumab, Upadacitinib, Leflunomide, Rituximab, Etanercept, Abatacept

Table 2. Forward selection-capped at 10-predictors model for claims based algorithm, c-statistic 0.76, Brier score 0.07 (model 2).

Predictor	Odds Ratio	95% Confidence interval (lower)	95% Confidence interval (upper)	p-Value
Any IP admission	4.76	4.10	5.53	<.001
Any ER visit	2.25	2.01	2.52	<.001
Any OP office visit	3.04	2.75	3.37	<.001
Number of OP office visits	1.19	1.15	1.22	<.001
Number of rheumatology visits	1.75	1.61	1.91	<.001
Any CBC lab	1.48	1.37	1.61	<.001
Any X-ray	1.34	1.23	1.47	<.001
Any MRI	2.26	1.93	2.64	<.001
Evidence of fibromyalgia	1.53	1.31	1.78	<.001
Evidence of hypertension	1.32	3.18	3.64	<.001

Figure 1. The impact of applying inclusion/exclusion criteria on the patient population.

Figure 2. The prevalence of each component of the proxy SLEDAI-2K and the associated weight for each component.

Table 3. Point values assigned to predictors in model 2 for total score (model 3).

Predictor	10-Variable model (from model 2) points
Any IP admission	9
Any ER visit	5
Any OP office visit	6
Number of OP office visits	1
Number of rheumatology visits	3
Any CBC lab	2
Any X-ray	2
Any MRI	5
Evidence of fibromyalgia	2
Evidence of hypertension	2
Model 3 results and performance	Values
Odds Ratio, per one point increase (95% CI)	1.19 (1.18–1.20)
p-Value	<.001
c-Statistic	0.76
Brier Score	0.07

Data availability statement

The data that support the findings of this study are available from IBM Watson Health. Restrictions apply to the availability of these data, which were used under license for this study.