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Risk factors for 30-day mortality after resection of lung cancer and prediction of their magnitude
  1. Trond-Eirik Strand (trond-eirik.strand{at}
  1. Cancer Registry of Norway, Norway
    1. Hans Rostad (hans.rostad{at}
    1. Cancer Registry of Norway, Norway
      1. Ronald AM Damhuis (damhuis{at}
      1. Rotterdam Cancer Registry, Netherlands
        1. Jarle Norstein (jnorstein{at}
        1. Private, Norway


          Introduction: Considerable variability in reported operative mortality rates and risk factors for mortality after surgery for lung cancer exists. Population-based data provide unbiased estimates and may aid in treatment selection.

          Methods: All lung cancer patients in Norway diagnosed from 1993 through 2005 were reported to the Cancer Registry of Norway (n=26,665). A total of 4,395 patients underwent surgical resection and were included for analysis. Data on demographics, tumours and treatment were registered. A subset of patients (n=1,844) was scored according to Charlson co-morbidity index. Potential factors influencing 30-day mortality were analyzed by logistic regression.

          Results: The overall postoperative mortality was 4.4% within 30 days with a declining trend in the period. Male sex (odds ratio 1.76), older age (age-band 70-79, odds ratio 3.38), right-sided tumour (odds ratio 1.73) and extensive procedures (pneumonectomy, odds ratio 4.54) were identified as risk factors for postoperative mortality in multivariate analysis. Postoperative mortality at high volume hospitals (³20 procedures/year)was lower, odds ratio 0.76 (p-value 0.076). Adjusted odds ratios for postoperative mortality at individual hospitals ranged from 0.32 to 2.28. The Charlson co-morbidity index was identified as an independent risk factor (p=0.017) for postoperative mortality. A prediction model for postoperative mortality is presented.

          Conclusions: Even though improvements have been observed in recent years with regard to postoperative mortality these findings indicate a further potential to optimize the surgical treatment of lung cancer. Hospital treatment results varied but a significant volume effect was not observed. Prognostic models may identify patients requiring intensive postoperative care.

          • co-morbidity
          • hospital volume
          • prognostic models
          • thoracic surgery

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