Clinical trial NCT04676321: Key Performance Indicators for the Assessment of NSCLC Patients Pathway
Official title: Key Performance Indicators for the Assessment of Diagnostic and Therapeutic Pathway of NSCLC Patients: a Multicenter Study (KIND NSCLC)
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Non Small Cell Lung Cancer
Keywords: lung cancer
NSCLC
Administrative databases
Algorithm
Lead sponsor: Istituto Romagnolo per lo Studio dei Tumori Dino Amadori IRST S.r.l. IRCCS
Sponsor class: OTHER
Collaborators: name: Azienda USL Reggio Emilia - IRCCS
name: Azienda Ospedaliera Universitaria Policlinico
First posted: 2020-12-21
Enrollment: 430
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The study population consisted of all adult patients (aged ≥ 18 years) residing in Emilia-Romagna region, identified in the Hospital discharge card, who has been discharged in any one of the participating sites (hospitals of Modena, Reggio-Emilia and Forlì-Cesena provinces) with a newly diagnosis of Non small cell lung cancer between January and December 2017.

Brief summary: This is a multicenter, observational study involving a retrospective collection of data. A total of potential 16 key performance indicators (KPIs) had been developed from a panel of experts (clinicians, IT experts, etc..) to investigate the appropriateness of care in NSCLC patients, with a special focus on the use of immunotherapy. The eligible population and data will be gathered retrospectively using an algorithm. Administrative databases will be used as unique resource: to identify target population and to collect patient's data with which measure KPIs.

Detailed description: This is a multi-center, observational study involving retrospective collection of NSCLC patients information. All consecutive patients who had a newly diagnosis of NSCLC in 2017 from healthcare administrative database between January 2017 and December 2017, identified through the proposed algorithm, will be considered. All data needed for KPI calculation, even if they fall outside January-December 2017 period, will be collected. The end of data collection will be defined as patient death or 30 June 2018, whichever came first.

A set of potential KPIs had been developed from a panel of experts (clinicians, IT experts etc..) to investigate the appropriateness of activities within NSCLC care pathway, with a special focus on the use of immunotherapy. The following KPIs will be measured to investigate the appropriateness of NSCLC patient care pathways, with a special focus on the use of immunotherapy:

1. Percentage of patients who performed biopsy within 7 days from the date of examination prescription;
2. Percentage of metastatic NSCLC patients at diagnosis;
3. Percentage of major surgical resections within the year of analysis;
4. Percentage of surgical patients on the total number of patients with exploratory thoracotomy in inpatient setting;
5. Percentage of patients undergoing neoadjuvant therapy ( CT or CT+RT within 6 months prior to the first major resection date;
6. 30-day mortality rate from major surgery;
7. Percentage of patients discussed in the multidisciplinary team (MDT) meeting at least once in the year of incidence;
8. Percentage of patients who performed imaging exams such as MRI, TC and PET within 7 days from MDT meeting;
9. Average and range of length of stay;
10. Average time between biopsy and the first administration of systematic anti-cancer drug;
11. Percentage of patients treated with immunotherapies;
12. Percentage of patients who underwent immunotherapy for more than 6 months after first dose;
13. Percentage of patients who underwent anti-cancer drug treatment in the last 30 days of life;
14. Percentage of patients who underwent the first administration of systematic anticancer drug within 30 days from discussed in MDT meeting;
15. Percentage of patients who underwent RT in the last 30 days of life ( excluded palliative treatments single fraction);
16. Percentage of patients who underwent at least one Integrated Home Care (IHC);
17. Percentage of patients who underwent at least one hospice care;

The eligible population and data will be gathered retrospectively through an algorithm from administrative databases (Hospital discharge cards, pharmaceutical databases as FED and AFT - direct and territorial distribution, the regional register of mortality REM, the regional register of outpatient specialist medical procedure ASA, the integrated home care IHC, and SDHS-hospice). Administrative data will be used as unique resource to identify patients cohort and to measure KPIs along care pathway. An evaluation of the NSCLC patients selection algorithm will be performed on a sub-population using the electronic health record EHR as gold standard.

Eligibility criteria: Inclusion Criteria:

- patients identified from hospital administrative databases of Emilia-Romagna region using a case selection algorithm for identifying Non small cell lung cancer

Exclusion Criteria:

* patients not residing in Emilia Romagna region (due to the difficulty in obtaining the medical data).

Primary outcomes:
measure: Key Performance indicators
timeFrame: six months
Secondary outcomes:
measure: Algorithm evaluatation
timeFrame: two month
Other outcomes:
Locations:
city: Meldola
country: Italy
facility: IRST
state: Forlì-Cesena
city: Reggio Emilia
country: Italy
facility: IRCCS Arcispedale S.Maria Nuova
state: RE
city: Modena
country: Italy
facility: Policlinico of Modena
Study officials:
affiliation: IRST
name: Mattia Altini, Master
role: PRINCIPAL_INVESTIGATOR
References:
citation: Balzi W, Roncadori A, Danesi V, Massa I, Manunta S, Gentili N, Delmonte A, Crino L, Altini M. How to discriminate non-small cell lung cancer (NSCLC) cases from an Italian administrative database? A retrospective, secondary data use study for evaluating a novel algorithm performance. BMJ Open. 2021 Sep 24;11(9):e048188. doi: 10.1136/bmjopen-2020-048188.