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Timeliness of In-Hospital Journey of Suspected Lung Cancer Patients: From First Presentation-to-Start of Therapy
Authors: Akash Verma
Number of views: 364
Aim: Assess timeliness of lung cancer management, causes for delays, and whether length of delays affected the prognosis.
Method: A retrospective study of patients diagnosed with lung cancer between January 2012 - September 2014.
Results: The median (range) delay in CT scan from first presentation to health care setting, “CT scan-to-first diagnostic procedure,” “first
diagnostic procedure to confirmed diagnosis,” “confirmed diagnosis-to-start of treatment” was 13 (1-399), 7 (1-490), 3 (1-176), and 35 (1-150)
days respectively. The median length of the journey from “CT scan-to-start of treatment,” and “first presentation to healthcare-to-start of treatment”
was 56 (6-192), and 74 (2-438) days respectively. Thirty six percent waited more than 2 months to start definitive treatment from the time of their
CT scan. Less-timely care correlated with those who underwent transthoracic needle aspiration, elderly males, and had non-small cell carcinoma.
It also correlated with better survival 272 (18-965) vs. 97 (2-1615) days (p=0.01) due to more number of early stage lung cancer in this group
(43.1% vs. 27.1%, p=0.05). Common causes of less-timely care were misdiagnosis of cancer as TB, failure of first diagnostic procedure to provide
diagnosis, delay in patient`s decisions regarding initiation of therapy, and development of inter-current illness while waiting for therapy.
Conclusions: Delay in the management of early stage lung cancer patients was seen. CT guided biopsy (transthoracic needle aspiration),
advanced age, male gender, and NSCLC were the predictors of delay. Limited (twice weekly) availability of CT guided biopsy, misdiagnosis as
TB, delayed patients` decision, and development of inter-current illness were the main causes of delay. Correlation between less timely care
and better survival, attributable to the early stage indicates risk for progression of the disease and merits measures for more efficient resource
allocation.