Evidence map›Paper›PMID 35177936›Full record

ArticleClinical epidemiology2022

Identifying Valid Algorithms for Number of Lines of Anti-Neoplastic Therapy in the Danish National Patient Registry Among Patients with Advanced Ovarian, Gastric, Renal Cell, Urothelial, and Non-Small Cell Lung Cancer Attending a Danish University Hospital.

Signe Sørup, Bianka Darvalics, Jakob Schöllhammer Knudsen, Anne Staub Rasmussen, Cathrine Fonnesbech Hjorth, Søren Viborg Vestergaard, Azza Ahmed Khalil, Leo Russo, Dina Oksen, Emmanuelle Boutmy and 3 more

Abstract read
In one paragraph

Article in Clinical epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Signe SørupDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-1942-683X
Bianka DarvalicsDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-9074-2574
Jakob Schöllhammer KnudsenDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.
Anne Staub RasmussenDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-6562-0572
Cathrine Fonnesbech HjorthDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0003-4378-8531
Søren Viborg VestergaardDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-8445-7758
Azza Ahmed KhalilDepartment of Oncology, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-9568-6871
Leo RussoWorldwide Medical and Safety, Pfizer, Collegeville, PA, USA.
Dina OksenGlobal Epidemiology, Merck Healthcare KGaA, Darmstadt, Germany.ORCID 0000-0003-2120-7716
Emmanuelle BoutmyGlobal Epidemiology, Merck Healthcare KGaA, Darmstadt, Germany.ORCID 0000-0001-9657-9376
Patrice VerpillatGlobal Epidemiology, Merck Healthcare KGaA, Darmstadt, Germany.ORCID 0000-0003-0010-1699
Mikael RørthDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.
Deirdre Cronin-FentonDepartment of Clinical Epidemiology, Department of Clinical Medicine, Aarhus University & Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0001-9738-2284

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop algorithms to identify number of lines of anti-neoplastic therapy per patient based on the Danish National Patient Registry (DNPR) and identify which algorithm has the highest percentage agreement with a reference standard of documentation in medical records. PATIENTS AND

methodsWe included 179 patients diagnosed between January 1, 2012, and December 31, 2016, with stage II, III, or IV urothelial cell carcinoma or stage III or IV epithelial ovarian cancer, gastric adenocarcinoma, renal cell carcinoma, or non-small cell lung cancer (NSCLC). We developed two algorithms for number of lines of anti-neoplastic therapy based on dates and treatment codes (eg, "treatment with cisplatin" or "cytostatic treatment") in the DNPR. First, to denote a change in line of therapy the "Time-based algorithm" used the number of days between consecutive administrations. Second, the "Drug-based algorithm" used information on drug names if available or the number of days between consecutive administrations if no drug names were specified. We calculated the percentage agreement between the algorithms setting the number of allowed days between consecutive administrations from 28 to 50 and the reference standard - information on anti-neoplastic therapy drugs abstracted from medical records and subsequently coded according to lines of anti-neoplastic therapy.

resultsFor the "Time-based algorithm", the highest percentage agreement with the reference standard was found when using <45 days between consecutive administrations (67.6%; 95% CI: 60.1-73.8%). However, the percentage agreement was higher for the "Drug-based algorithm" using <45 days between consecutive administrations for registrations where the drug name was unspecified (90.5%; 95% CI: 85.0-93.7%).

conclusionThe algorithm for number of lines of anti-neoplastic therapy that had the highest percentage agreement with the reference standard (medical records) incorporated both registration of specific drug names and <45 days between consecutive administrations if the drug name was unspecified in routinely recorded data from DNPR.

Indexed as

Denmarkduration of chemotherapymedical oncologic treatmentsmedical records reviewpositive predictive value

Identifiers

PMID35177936
PMCPMC8846560

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.