Application of an algorithm for staging small-cell lung cancer can save one third of the initial evaluation costs

Arch Intern Med. 1993 Feb 8;153(3):329-37.


Objective: Design of a cost-effective algorithm for staging disease in patients with small-cell lung cancer.

Design: An algorithm was constructed by analyzing all permutations of a sequence of procedures required to stage disease in patients with small-cell lung cancer. Procedural costs were determined, and the model was applied to the small-cell lung cancer patient population treated at the National Cancer Institute, Bethesda, Md, from 1973 to 1989. The final algorithm was derived from the permutation with the lowest cost per accurately staged patient.

Setting: A single government institute, the National Cancer Institute.

Patients: Four hundred fifty-one patients with previously untreated, consecutive histologically documented small-cell lung cancer entered into therapeutic protocols at the National Cancer Institute from April 1973 through July 1989. Data were obtained from small-cell lung cancer protocol databases and patients' medical records.

Main outcome measure: The cost per patient of each sequence of staging procedures when applied to the patient population.

Results: The least expensive sequence of procedures saved $1418 per patient when compared with application of a standard set of staging procedures to all patients. The major factor in reducing costs was the concept of stopping the staging procedures after a site of distant metastatic disease had been identified.

Conclusions: An algorithm consisting of a set of sequential staging procedures can accurately stage disease in patients with small-cell lung cancer and save more than one third of the costs of an inclusive standard set of staging procedures.

MeSH terms

  • Algorithms*
  • Carcinoma, Small Cell / economics*
  • Carcinoma, Small Cell / pathology
  • Cost-Benefit Analysis
  • Humans
  • Lung Neoplasms / economics*
  • Lung Neoplasms / pathology
  • Neoplasm Staging / economics
  • Neoplasm Staging / methods
  • Sensitivity and Specificity