Maria Iachina, Anders Green, Erik Jakobsen, Anders Mellemgaard, Mark Krasnik, Margreet Lüchtenborg, Henrik Møller

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1 The effect of different comorbidity groups on the survival of patients with non-small cell lung cancer Maria Iachina, Anders Green, Erik Jakobsen, Anders Mellemgaard, Mark Krasnik, Margreet Lüchtenborg, Henrik Møller

2 Comorbidity comorbidity is the presence of one or more diseases in addition to a primary disease Comorbidity index the Cumulative Illness Rating Scale (CIRS) the Index of Co-Existent Disease (ICED) the Kaplan Feinstein Classification (KFC) the Charlson Comorbidity Index (CCI)

3 Charlson Index ShortName Name Weight AMI Acute Myocardial Infarction 1 HF Congestive Heart Failure 1 VASC Peripheral Vascular Disease 1 CVD Cerebrovascular Disease 1 DEMENS Dementia 1 COL Chronic Obstructive Pulmonary Disease CONNECT Connective Tissue Disease 1 ULCUS Peptic Ulcer Disease 1 LIVER_MILD Mild liver disease 1 DM_UNCOMP DM without complications 1 HEMIPLEG Hemiplegia 2 KIDNEY Mod-Severe Renal Disease 2 DM_COMP DM with complications 2 CA_SOLID Any tumor (Danish: Solide kræftformer) LEUC Leukemia 2 LYMPH Lymphoma 2 LIVER_SEVERE Mod-Severe Liver Disease 3 CA_METAST Metastatic solid tumor (Dansk: Metastaserende cancer) AIDS AIDS

4 Comorbidity groups ShortName Name Group AMI Acute Myocardial Infarction cardiovascular disorders HF Congestive Heart Failure cardiovascular disorders VASC Peripheral Vascular Disease peripheral vascular disorders including cerebrovascular disorders CVD Cerebrovascular Disease peripheral vascular disorders including cerebrovascular disorders HEMIPLEG Hemiplegia peripheral vascular disorders including cerebrovascular disorders LEUC Leukemia Cancer LYMPH Lymphoma Cancer LIVER_SEVERE Mod-Severe Liver Disease Cancer CA_METAST Metastatic solid tumour Cancer CA_SOLID Any tumor Cancer COL Chronic Obstructive Pulmonary Disease Other complicated diseases CONNECT Connective Tissue Disease Other complicated diseases KIDNEY Mod-Severe Renal Disease Other complicated diseases DM_COMP DM with complications Other complicated diseases DEMENS Dementia Other complicated diseases AIDS AIDS Other complicated diseases ULCUS Peptic Ulcer Disease Other uncomplicated condition LIVER MILD Mild liver disease Other uncomplicated condition DM UNCOMP DM without complications Other uncomplicated condition

5 Study population The analysis is based on lung cancer patients who have a diagnosed NSCLC in and are registered in DLCR We included information on comorbidity for each patient up to 10 years before lung cancer diagnosis, using the Danish National Patient Register

6 Methods There will be performed six separate analyses, one for each disease group and one for multiple comorbidity control: no records in CRS in 10 years case: at least one record from only one disease group in CRS in 10 years

7 Number of patients with comorbidity in one disease group group Group name Number 0 None 4,872 1 Uncomplicated disorders Cardio-vascular disorders peripheral vascular disorders including cerebrovascular disorders 4 Other complicated diseases 1,190 5 Cancer Multiple 2,

8 Kaplan-Meier survivor functions days days days none uncomplicated none complicated none cardiovascular days analysis time days none peripheral vascular none cancer none multiple

9 Cox regression analysis HR 95% CI Uncomplicated condition ; 1.36 Other complicated diseases ; 1.35 Cardio-vascular disorders ; 1.49 Peripheral vascular disorders including cerebrovascular disorders ; 1.20 Cancer ; 1.03 Multiple disorders ; 1.35 adjusted by age, sex, stage and resection

10 symptoms GP referral to the hospital diagnostic process treatment death Impact of comorbidity time impact quality impact

11 What do we have? Unfortunately, we do not know when the first symptoms appear We have no information from the GP From the time of referral to the hospital for all lung cancer patients all clinical relevant information is reported to the DLCR

12 referral to the hospital diagnostic process treatment death Impact of comorbidy time impact quality impact

13 age, sex clinical stage resection true stage mortality comorbidity

14 Models Staging model: clinical stage f ( true stage, age, sex, comorbidity ) Resection model: Survival model: mortality resection g( clinical stage, age, sex, comorbidity) h( true stage, age, sex, comorbidity, resection)

15 Estimation methods Maximum likelihood STATA, SAS, SPCC MCMC WinBugs ( gives the possibility to estimate three models at the same time)

16 Results of estimation using MCMC Staging model Resection model Survival model Uncomplicated condition 0.97 (0.65; 1.40) 1.00 (0.66; 1.58) 1.08 (0.95; 1.24) Other complicated 1.13 (0.87; 1.46) 0.57 (0.44; 0.74) 1.17 (1.07; 1.28) diseases Cardio-vascular disorders 0.86 (0.57; 1.28) 0.54 ( 0.34; 0.86) 1.32 (1.14; 1.52) Peripheral vascular and cerebrovascular disorders 1.00 (0.69; 1,49) 0.94 (0.64; 1.46) 1.07 (0.92; 1.22) Cancer 1.13 (0.83; 1.49) 0.78 (0.56; 1.07) 0.95 (0.85; 1.05)

17 Conclusions We could not find an effect of any comorbidity groups on staging process We found that the presence of some burdensome diseases or cardio-vascular disorders have a negative effect on the chance of resection The presence of some burdensome diseases or cardio-vascular disorders has a negative effect on survival

18 Future Research a similar analysis will be performed on English patients data