Automatic calculation and visualization of comorbidity scores for decision-making in tumor boards].

[Automatic calculation and visualization of comorbidity scores for decision-making in tumor boards].:

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[Automatic calculation and visualization of comorbidity scores for decision-making in tumor boards].

Laryngorhinootologie. 2019 Dec 02;:

Authors: Wald T, Birnbaum K, Wiegand S, Dietz A, Zebralla V, Wichmann G

Abstract

OBJECTIVE:  Comorbidity reduces treatment options for patients with head and neck cancer (HNC). Utilization of ICD-10 codes instead of manual research may facilitate estimation of comorbidity relevant for decision-making in the interdisciplinary tumor board (TB). Providing this information immediately in an intuitively ascertainable way whenever registering a patient for the TB would trigger awareness for comorbidities and shows potentially missing information.

MATERIAL AND METHODS:  Administrative data was extracted of four databases at our clinic (hospital information system (HIS*-MED), the clinic's tumor database, OncoFlow® and OncoFunction®). After data extraction and record linkage facilitated by python libraries Pandas and Record linkage, ICD-10 codes were rated applying the Charlson Score and prepared for visualization within OncoFlow®. Coding quality was tested assessing the imported and manually researched diabetes status of a 1:1 matched cohort of 240 patients.

RESULTS:  29 073 ICD-10 codes of 2087 HNC patients were extracted. Matched data are immediately made available whenever registering a patient for the TB and are visualized in a pictogram within OncoFlow® providing information about comorbidities and missing diagnostics. The precision of diagnostic coding at our clinic was 95.0 %.

CONCLUSIONS:  The high prevalence of comorbidities in HNC patients with impact on their eligibility for particular treatment indicates the usefulness of our algorithm for providing automatic comorbidity assessment from administrative data for clinical routine and requires high quality of coding diagnoses.

PMID: 31791084 [PubMed - as supplied by publisher]