Computerized detection of adverse drug reactions in the medical intensive care unit. Kane-Gill, S. L., Visweswaran, S., Saul, M. I., Wong, A. I., Penrod, L. E., & Handler, S. M. International Journal of Medical Informatics, 80(8):570–578, August, 2011. ![Computerized detection of adverse drug reactions in the medical intensive care unit [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Objective Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit. Method This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed. Results 145 patients were eligible for evaluation. For the 48 patients (50% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation. Conclusions Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.
@article{kane-gill_computerized_2011,
series = {Special {Issue}: {Supporting} {Collaboration} in {Healthcare} {Settings}: {The} {Role} of {Informatics}},
title = {Computerized detection of adverse drug reactions in the medical intensive care unit},
volume = {80},
issn = {1386-5056},
url = {http://www.sciencedirect.com/science/article/pii/S1386505611001079},
doi = {10.1016/j.ijmedinf.2011.04.005},
abstract = {Objective
Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit.
Method
This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed.
Results
145 patients were eligible for evaluation. For the 48 patients (50\% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation.
Conclusions
Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.},
number = {8},
urldate = {2018-03-19TZ},
journal = {International Journal of Medical Informatics},
author = {Kane-Gill, Sandra L. and Visweswaran, Shyam and Saul, Melissa I. and Wong, An-Kwok Ian and Penrod, Louis E. and Handler, Steven M.},
month = aug,
year = {2011},
keywords = {Adverse drug event, Adverse drug reaction reporting systems, Clinical, Critical care, Decision support systems, Drug toxicity, Information systems, Intensive care units, Safety},
pages = {570--578}
}
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M."],"year":2011,"bibtype":"article","biburl":"https://bibbase.org/zotero/Robert Laurine","bibdata":{"bibtype":"article","type":"article","series":"Special Issue: Supporting Collaboration in Healthcare Settings: The Role of Informatics","title":"Computerized detection of adverse drug reactions in the medical intensive care unit","volume":"80","issn":"1386-5056","url":"http://www.sciencedirect.com/science/article/pii/S1386505611001079","doi":"10.1016/j.ijmedinf.2011.04.005","abstract":"Objective Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit. Method This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed. Results 145 patients were eligible for evaluation. For the 48 patients (50% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation. Conclusions Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.","number":"8","urldate":"2018-03-19TZ","journal":"International Journal of Medical Informatics","author":[{"propositions":[],"lastnames":["Kane-Gill"],"firstnames":["Sandra","L."],"suffixes":[]},{"propositions":[],"lastnames":["Visweswaran"],"firstnames":["Shyam"],"suffixes":[]},{"propositions":[],"lastnames":["Saul"],"firstnames":["Melissa","I."],"suffixes":[]},{"propositions":[],"lastnames":["Wong"],"firstnames":["An-Kwok","Ian"],"suffixes":[]},{"propositions":[],"lastnames":["Penrod"],"firstnames":["Louis","E."],"suffixes":[]},{"propositions":[],"lastnames":["Handler"],"firstnames":["Steven","M."],"suffixes":[]}],"month":"August","year":"2011","keywords":"Adverse drug event, Adverse drug reaction reporting systems, Clinical, Critical care, Decision support systems, Drug toxicity, Information systems, Intensive care units, Safety","pages":"570–578","bibtex":"@article{kane-gill_computerized_2011,\n\tseries = {Special {Issue}: {Supporting} {Collaboration} in {Healthcare} {Settings}: {The} {Role} of {Informatics}},\n\ttitle = {Computerized detection of adverse drug reactions in the medical intensive care unit},\n\tvolume = {80},\n\tissn = {1386-5056},\n\turl = {http://www.sciencedirect.com/science/article/pii/S1386505611001079},\n\tdoi = {10.1016/j.ijmedinf.2011.04.005},\n\tabstract = {Objective\nClinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit.\nMethod\nThis is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed.\nResults\n145 patients were eligible for evaluation. For the 48 patients (50\\% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation.\nConclusions\nComputerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.},\n\tnumber = {8},\n\turldate = {2018-03-19TZ},\n\tjournal = {International Journal of Medical Informatics},\n\tauthor = {Kane-Gill, Sandra L. and Visweswaran, Shyam and Saul, Melissa I. and Wong, An-Kwok Ian and Penrod, Louis E. and Handler, Steven M.},\n\tmonth = aug,\n\tyear = {2011},\n\tkeywords = {Adverse drug event, Adverse drug reaction reporting systems, Clinical, Critical care, Decision support systems, Drug toxicity, Information systems, Intensive care units, Safety},\n\tpages = {570--578}\n}\n\n","author_short":["Kane-Gill, S. 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