Association between antipsychotic drug dose and length of clinical notes: a proxy of disease severity?
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Association between antipsychotic drug dose and length of clinical notes : a proxy of disease severity? / Sørup, Freja Karuna Hemmingsen; Brunak, Søren; Eriksson, Robert.
In: BMC Medical Research Methodology, Vol. 20, No. 1, 107, 2020.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Association between antipsychotic drug dose and length of clinical notes
T2 - a proxy of disease severity?
AU - Sørup, Freja Karuna Hemmingsen
AU - Brunak, Søren
AU - Eriksson, Robert
PY - 2020
Y1 - 2020
N2 - BACKGROUND: Most structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden. Text mining of clinical notes could provide a basis for detailed profiles of phenotypic traits. The objective of the current study was to determine whether drug dose, regardless of polypharmacy, is associated with the length of clinical notes, and to determine the frequency of adverse events per word in clinical notes.METHODS: In this observational study, we utilized restricted-access data from an electronic patient record system. Using three methods (defined daily dose, olanzapine equivalents, and chlorpromazine equivalents) we calculated antipsychotic dose equivalents and compared these with the number of words recorded per treatment day. For each normalization method, the frequencies of adverse events per word in manually curated samples were compared to dose intervals.RESULTS: The length of clinical notes per treatment day was positively associated with the prescribed dose for all normalization methods. The number of adverse events per word was stable over the analyzed dose spectrum.CONCLUSIONS: Assuming that drug dose increases with the severity of disease, the length of clinical notes can serve as a proxy for disease severity. Due to the near-linear relationship, correction of daily word count is unnecessary when text mining for potential adverse drug reactions.
AB - BACKGROUND: Most structured clinical data, such as diagnosis codes, are not sufficient to obtain precise phenotypes and assess disease burden. Text mining of clinical notes could provide a basis for detailed profiles of phenotypic traits. The objective of the current study was to determine whether drug dose, regardless of polypharmacy, is associated with the length of clinical notes, and to determine the frequency of adverse events per word in clinical notes.METHODS: In this observational study, we utilized restricted-access data from an electronic patient record system. Using three methods (defined daily dose, olanzapine equivalents, and chlorpromazine equivalents) we calculated antipsychotic dose equivalents and compared these with the number of words recorded per treatment day. For each normalization method, the frequencies of adverse events per word in manually curated samples were compared to dose intervals.RESULTS: The length of clinical notes per treatment day was positively associated with the prescribed dose for all normalization methods. The number of adverse events per word was stable over the analyzed dose spectrum.CONCLUSIONS: Assuming that drug dose increases with the severity of disease, the length of clinical notes can serve as a proxy for disease severity. Due to the near-linear relationship, correction of daily word count is unnecessary when text mining for potential adverse drug reactions.
U2 - 10.1186/s12874-020-00993-1
DO - 10.1186/s12874-020-00993-1
M3 - Journal article
C2 - 32381026
VL - 20
JO - B M C Medical Research Methodology
JF - B M C Medical Research Methodology
SN - 1471-2288
IS - 1
M1 - 107
ER -
ID: 243907813