Literature DB >> 26909516

Lung cancer interval times from point of referral to the acute health sector to the start of first treatment.

Geraldine Largey1, Eli Ristevski2, Helen Chambers2, Heather Davis1, Peter Briggs1.   

Abstract

Objective The aim of the present study was to compare lung cancer diagnostic and treatment intervals with agreed target measures across three large public health services in Victoria and assess any differences in interval times by treatment type and health service. Methods A retrospective medical record audit of 78 patients admitted with a new diagnosis of lung cancer was conducted. Interval times from referral to diagnosis, diagnosis to first treatment and referral to first treatment were recorded in three treatment types: surgery, chemotherapy and radiotherapy. Results There was a significant difference in the mean number of days from referral to diagnosis by treatment type. Patients who underwent surgery waited significantly longer (mean (± s.d.) 41.6±38.4 days) to obtain a diagnosis than those who received radiotherapy (15.1±18.6 days). Only 47% of surgical patients obtained a diagnosis within the recommended 28 days. Moreover, only 45% and 44% of patients, respectively, met the diagnosis-to-treatment target of 14 days and referral-to-treatment target of 42 days. Conclusion The present study highlights the effect of treatment type on lung cancer referral interval times. It demonstrates the benefits of using evidenced-based interval target times to benchmark and compare performance outcomes in lung cancer. What is known about the topic? Lung cancer is the leading cause of cancer mortality in Australia and has the lowest 5-year survival rate of all cancer types. Delays in the diagnosis of lung cancer can change the prognosis from potentially curable to incurable, particularly in faster-growing tumours. What does this paper add? This study reveals treatment type was a greater factor in explaining variations in diagnosis and treatment than health service. Surgical patients were consistently lower in meeting the recommended interval targets across referral to diagnosis, diagnosis to treatment and referral to treatment. What are the implications for practitioners? This study demonstrates the value of using evidenced-based interval target times to benchmark and compare performance outcomes in lung cancer. Such measures may further improve prognostic outcomes in lung cancer by reducing unwanted delays.

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Year:  2016        PMID: 26909516     DOI: 10.1071/AH15220

Source DB:  PubMed          Journal:  Aust Health Rev        ISSN: 0156-5788            Impact factor:   1.990


  5 in total

Review 1.  Defining timeliness in care for patients with lung cancer: a scoping review.

Authors:  Adnan Ansar; Virginia Lewis; Christine Faye McDonald; Chaojie Liu; Muhammad Aziz Rahman
Journal:  BMJ Open       Date:  2022-04-07       Impact factor: 2.692

Review 2.  Wait Times and Survival in Lung Cancer Patients across the Province of Quebec, Canada.

Authors:  Marie-Hélène Denault; Catherine Labbé; Carolle St-Pierre; Brigitte Fournier; Andréanne Gagné; Claudia Morillon; Philippe Joubert; Serge Simard; Simon Martel
Journal:  Curr Oncol       Date:  2022-04-29       Impact factor: 3.109

3.  Pathways to diagnosis of non-small cell lung cancer: a descriptive cohort study.

Authors:  Stuart Purdie; Nicola Creighton; Kahren Maree White; Deborah Baker; Dan Ewald; Chee Khoon Lee; Alison Lyon; Johnathan Man; David Michail; Alexis Andrew Miller; Lawrence Tan; David Currow; Jane M Young
Journal:  NPJ Prim Care Respir Med       Date:  2019-02-08       Impact factor: 2.871

4.  Time intervals and routes to diagnosis for lung cancer in 10 jurisdictions: cross-sectional study findings from the International Cancer Benchmarking Partnership (ICBP).

Authors:  Usha Menon; Peter Vedsted; David Weller; Alina Zalounina Falborg; Henry Jensen; Samantha Harrison; Irene Reguilon; Andriana Barisic; Rebecca J Bergin; David H Brewster; John Butler; Odd Terje Brustugun; Oliver Bucher; Victoria Cairnduff; Anna Gavin; Eva Grunfeld; Elizabeth Harland; Jatinderpal Kalsi; Anne Kari Knudsen; Mats Lambe; Rebecca-Jane Law; Yulan Lin; Martin Malmberg; Donna Turner; Richard D Neal; Victoria White
Journal:  BMJ Open       Date:  2019-11-27       Impact factor: 2.692

5.  The LEAD study protocol: a mixed-method cohort study evaluating the lung cancer diagnostic and pre-treatment pathways of patients from Culturally and Linguistically Diverse (CALD) backgrounds compared to patients from Anglo-Australian backgrounds.

Authors:  Danielle Mazza; Xiaoping Lin; Fiona M Walter; Jane M Young; David J Barnes; Paul Mitchell; Bianca Brijnath; Andrew Martin; Jon D Emery
Journal:  BMC Cancer       Date:  2018-07-21       Impact factor: 4.430

  5 in total

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