Cost-utility analysis of alemtuzumab in comparison with interferon beta, fingolimod, and natalizumab treatment for relapsing-remitting multiple sclerosis in Austria

Figures & data

Figure 1. Markov model. Markov process with cycle length of 3 months and 18 defined states (10 EDSS stages; 4 treatment lines; relapse; discontinuation of treatment, followed by treatment switch or no-treatment; adverse events; and death).

Table 1. Clinical input data.

EDSS distribution^a
EDSS	0	1–1.5	2	2.5–3	3.5–4	4.5–5	5.5–6	6.5–7	7.5–8	9.5
untreated	4%	34%	23%	37%	2%	0%	0%	0%	0%	0%
pretreated	3%	19%	16%	30%	22%	9%	1%	0%	0%	0%
Mortality multiplier by EDSS state^b
EDSS	0	1–1.5	2	2.5–3	3.5–4	4.5–5	5.5–6	6.5–7	7.5–8	9.5
multiplier	1	1.3	1.6	1.68	1.76	1.84	2.71	3.57	4.44	5.31
Discontinuation rate 24 months
	%	Acceptability RR	Discontinuation RR	No further treatment	No source
Alemtuzumab untreated	3.7%				^7
Alemtuzumab pre-treated	6.3%				8
Interferon beta-1a (same assumed for 1b)		0.27	1.37	(18%)	^11
Dimethyl fumarate		0.58	2.38	(18%)
Glatiramer acetate		0.60	2.50	(16.7%)
Teriflunomide		0.56	2.27	(17.7%)
Fingolimod		0.43	1.75	(14.5%)
Natalizumab		0.47	1.89	(11.3%)
Relapse rate
	% 12 month	% 24 month	Source
Alemtuzumab	26%	33%	^11
Interferon beta-1a (same assumed for 1b)	68%	64%
Dimethyl fumarate	51%	64%
Glatiramer acetate	52%	59%
Teriflunomide	55%	63%
Fingolimod	41%	51%
Natalizumab	37%	40%
Disability worsening
	%	RR	Source
Alemtuzumab untreated	8%		^7
Alemtuzumab pre-treated	12.71%		^8
Alemtuzumab long-term	22% / 4.8% per year		^15
Interferon beta-1a (same assumed for 1b)		1.5	^11
Dimethyl fumarate		1.6
Glatiramer acetate		1.5
Teriflunomide		1.7
Fingolimod		1.7
Natalizumab		1.3
Percentage transfer to SPMS^c
EDSS	1-1.5	2	2.5-3	3.5-4	4.5-5	5.5-6	6.5-7	7.5-8	9.5
	0.2%	2.9%	10.2%	19.9%	25.6%	18.4%	23.7%	6.6%	16.7%

^a Source: Cohen et al.⁷, Coles et al.⁸

^b Source: Pokorski¹⁷.

^c Source: Tran et al.¹⁶

Table 2. Model inputs: costs.

MS medication costs
Comparator	Product name	per unit (€)	Source
Alemtuzumab	Lemtrada	7,921.87	Warenverzeichnis 1/2017
Interferon beta-1a IM	Avonex	824.70
Interferon beta-1b SC	Betaferon	934.65
Peginterferon beta-1a SC	Plegridy	789.05
Interferon beta-1a SC	Rebif	1037.70
Glatiramer acetate	Copaxone	863.02
Fingolimod	Gilenya	1,755.75
Natalizumab	Tysabri	1,613.55
Teriflunomide	Aubagio	789.05
Dimethyl fumarate	Tecfidera	1,451.35
Outpatient costs
Services	Unit cost (€)	Source
GP	18.44	Tariff catalog of sickness funds 2017
Neurologist	20.85
i.v. application	5.22
i.m. application	2.34
Complete blood count	5.73
Serum creatinine	4,11
Urinanalysis	2.42
TFT	8.93
LFT	21.98
MRT brain	178.83
ECG	24.72
JC Virus Test	185.08
Monitoring costs
Products	1^st year	2^nd year	Source
Lemtrada	275.47	254.28	Tariff catalog of sickness funds 2017
Avonex	63.05	63.05	Calculations based on information concerning monitoring from the product information
Betaferon	114.97	114.97
Plegridy	63.05	63.05
Rebif	124.82	124.82
Copaxone	41.69	41.69
Gilenya	525.53	99.59
Tysabri	733.55	363.91
Aubagio	157.30	104.86
Tecfidera	418.68	24.52
Adverse event costs
Event	Cost per month (€)	Source
Influenza-like symptoms	18.89	Tariff catalog of sickness funds 2017
Fatigue	112.95	LKF 2017^30
Flushing	12.01
Infection	1,164.30
Depression	10.13
Infusion reaction	21.44
Injection side reactions	42.92
Hypersensitivity	25.34
Liver enzyme elevation	61.88
Thyroid disorder	48.67
Gastrointestinal disorders	25.06
Cardiovascular disorders (bradycardia and atrioventricular block)	82.08
MS exacerbation costs
EDSS score	Costs per year (€)	Source
Inpatient costs	2,281.00	LKF 2017^30
Outpatient costs	231.96	Tariff catalog of sickness funds 2017, Warenverzeichnis 1/2017
Direct costs per EDSS-stage (1 year)
EDSS score	Costs per year (€)	Source
0–2	3,451.65	^22
2.5–6	6,119.13	Calculations for 2017 based on Tariff catalog of sickness funds and LKF 2017^30
6.5–9.5	7,374.42

Table 3. Model inputs: utilities.

Utilities EDSS score
EDSS	Annual utility	Source
EDSS 0	0.887	^3
EDSS 1–1.5	0.810	adjusted for this grouping of EDSS
EDSS 2	0.751
EDSS 2.5–3	0.717
EDSS 3.5–4	0.676
EDSS 4.5–5	0.572
EDSS 5.5–6	0.590
EDSS 6.5–7	0.462
EDSS 7.5–8	0.238
EDSS 8.5–9	−0.179
EDSS 9.5	−0.303
Disutility - relapse
Event	Disutility	Source
relapse	0.094	Lilja et al. 2005^31
Disutility – adverse events
Event	Disutility	Duration of AE	Source
Influenza-like symptoms	0.21	1 day per week	^32
Fatigue	0.125	6 months	^33
Flushing	0.1244	24 hours	^10
Infection	0.005	1.5 weeks	^25^,^34
Depression	0.56	365 days	^25^,^35
Infusion reaction	0.066	24 hours	assumed to be identical like injection side reaction
Injection side reactions	0.066	24 hours	^36
Hypersensitivity	0	6 months
Liver enzyme elevation	0	365 days
Thyroid disorder	0	365 days
Gastrointestinal disorders	0.071	1.5 weeks	^37
Cardiovascular disorders (bradycardia and atrioventricular block)	0.1244	14 days	^10

Table 4. Cost-effectiveness results.

Results for previously untreated patients
Cost components	Alemtuzumab	Interferon beta*
Medication costs	82,513	96,873
Monitoring costs	1,031	1,784
Relapse costs	1,421	3,169
EDSS stage costs	35,663	39,138
AE costs	12,036	23,195
Total costs	132,663	164,159
95% CI total costs	124,260−139,703	154,676−173,422
Cost difference		31,496
QALYs
QALYs	5.25	4.85
95% CI QALYs	4.56−5.50	4.56−5.10
QALY difference		−0.39
ICER /QALY		Dominated by alemtuzumab
Results for pre-treated patients
Cost components	Alemtuzumab	Interferon beta**	Fingolimod	Natalizumab
Medication costs	83,250	127,673	174,501	171,029
Monitoring costs	1,047	1,960	1,676	2,747
Relapse costs	1,380	3,112	2,046	1,774
EDSS stage costs	40,242	43,481	43,642	42,606
AE costs	11,490	23,908	19,039	29,601
Total costs	137,409	200,133	240,903	247,758
95% CI total costs	129,118−145,014	189,451−212,438	224,988−258,148,	232.979−262.833
Cost difference		62,725	103,495	110,349
QALYs
QALYs	4.88	4.38	4.64	4.40
95% CI QALYs	4.58−5.13	4.05−4.66	4.34−4.90	4.19−4.72
QALY difference		−0.50	−0.25	−0.48
ICER /QALY		Dominated by alemtuzumab	Dominated by alemtuzumab	Dominated by alemtuzumab

* Interferons beta (mean of IM interferon beta-1a, SC interferon beta-1a, SC interferon beta-1b, and SC pegylated interferon beta-1a).

** SC interferon beta-1a.

Figure 2. Deterministic sensitivity analysis was used to identify the critical variables. Results are displayed as Tornado diagrams, where each bar represents a one-way sensitivity analysis, and width of bars represents impact on model results. The ICER per patient is plotted on the x-axis.

Figure 3. Probabilistic sensitivity analysis: the scatterplot shows results of the Monte Carlo probabilistic sensitivity analysis for 500 patients. Incremental costs are plotted on the y-axis, incremental effectiveness is plotted on the x-axis. The location of majority of points in the lower right quadrant shows that the simulations yields cost savings and improved health outcomes. Alemtuzumab dominates comparators. The base case results attest to the model’s low level of uncertainty. (a) Untreated patient population; (b) pre-treated patient population.

Cohen JA1, Coles AJ, Arnold DL, et al. Alemtuzumab versus interferon beta 1a as first-line treatment for patients with relapsing-remitting multiple sclerosis: A randomised controlled phase 3 trial. Lancet. 2012;380(9856):1819-1828. DOI:10.1016/S0140-6736(12)61769-3 Epub 2012 Nov 1.

 PubMed Web of Science ®Google Scholar

Tramacere I1, Del Giovane C, Salanti G, et al. Immunomodulators and immunosuppressants for relapsing-remitting multiple sclerosis: a network meta-analysis. Cochrane Database Syst Rev. 2015;(9):CD011381. DOI:10.1002/14651858.CD011381.pub2

 PubMed Web of Science ®Google Scholar

Coles AJ, Twyman CL, Arnold DL, et al. Alemtuzumab for patients with relapsing multiple sclerosis after disease-modifying therapy: A randomised controlled phase 3 trial. Mult Scler. 2012;380(9856):1829-1839. DOI:10.1016/S0140-6736(12)61768-1 Epub 2012 Nov 1.

 Google Scholar

Coles AJ1, Fox E, Vladic A, et al. Alemtuzumab more effective than interferon β-1a at 5-year follow-up of CAMMS223 clinical trial. Neurology. 2012;78(14):1069-1078. DOI:10.1212/WNL.0b013e31824e8ee7 Epub 2012 Mar 21. 2012a

 PubMed Web of Science ®Google Scholar

Pokorski RJ. Long-term survival experience of patients with multiple sclerosis. J Insur Med. 1997;29(2):101-106.

 PubMedGoogle Scholar

Tran K, Milev S, Jabr MF, et al. Canadian Agency for Drugs and Technologies in Health. CADTH therapeutic review. Comparative clinical and cost-effectiveness of drug therapies for relapsing-remitting multiple sclerosis [Internet]. Ottawa: The Agency; 2013. (CADTH Therapeutic Review vol.1, no. 2b). Available from: http://www.cadth.ca/media/pdf/TR0004_RRMS_ScienceReport_e.pdf

 Google Scholar

Bundesministerium für Gesundheit und Frauen (BMGF). Austrian DRG System (LKF); 2017. Available from: https://www.sozialministerium.at/site/Gesundheit/Gesundheitssystem/Krankenanstalten/LKF_Modell_2017/ Accessed March 2017.

 Google Scholar

Kobelt G, Berg J, Lindgren P, et al. Costs and quality of life of multiple sclerosis in Austria. Eur J Health Econ. 2006;7(suppl. 2):S14-S23.

 PubMedGoogle Scholar

Berger T, Kobelt G, Berg J, et al. New insights into the burden and costs of multiple sclerosis in Europe: Results for Austria. Mult Scler. 2017;23(2_suppl):17-28. DOI:10.1177/1352458517708099

 PubMed Web of Science ®Google Scholar

Lilja U, Berg J, Plesnilla C, Lindgren P, Kobelt G, Jönsson B. Costs and quality of life in Multiple Sclerosis: A cross-sectional study in Austria. Stockholm, October 2005.

 Google Scholar

Matza LS, Sapra SJ, Dillon JF, et al. Health state utilities associated with attributes of treatments for hepatitis C. Eur J Health Econ. 2015;16(9):1005-1018. DOI:10.1007/s10198-014-0649-6 Epub 2014 Dec 7.

 PubMed Web of Science ®Google Scholar

Vera-Llonch M, Brandenburg NA, Oster G. Cost-effectiveness of addon therapy with pregabalin in patients with refractory partial epilepsy. Epilepsia. 2008;49(3):431-437.

 PubMed Web of Science ®Google Scholar

Soini E, Joutseno J, Sumelahti ML. Cost-utility of first-line disease-modifying treatments for relapsing-remitting multiple sclerosis. Clin Ther. 2017;39(3):537-557.e10. DOI:10.1016/j.clinthera.2017.01.028 Epub 2017 Feb 14.

 PubMed Web of Science ®Google Scholar

Institute for Clinical and Economic Review. Disease-modifying therapies for relapsing-remitting and primary-progressive multiple sclerosis: effectiveness and value. 2016. Available from: https://icer-review.org/wp-content/uploads/2016/08/CTAF_MS_Final_Report_030617.pdf Accessed April. 2017.

 Google Scholar

Jakubowiak AJ, Campioni M, Benedict Á, et al Cost-effectiveness of adding carfilzomib to lenalidomide and dexamethasone in relapsed multiple myeloma from a US perspective. J Med Econ. 2016;19(11):1061-1074. Epub 2016 Jun 16.

 PubMed Web of Science ®Google Scholar

Mauskopf J, Fay M, Iyer R, et al. Cost-effectiveness of delayed-release dimethyl fumarate for the treatment of relapsing forms of multiple sclerosis in the United States. J Med Econ. 2016;19(4):432-442. DOI:10.3111/13696998.2015.1135805 Epub 2016 Jan 20.

 PubMed Web of Science ®Google Scholar

Bergenheim K, Williams SA, Bergeson JG, et al. US cost-effectiveness of saxagliptin in type 2 diabetes mellitus. Am J Pharm Benef 2012;4(1):20-28.

 Google Scholar

NICE. Natalizumab for the treatment of adults with highly active relapsing-remitting multiple sclerosis. 2007. Available from: http://www.nice.org.uk/TA127. Accessed March 2014.

 Google Scholar