I would also like to thank my PhD committee – Cindy Chen, Josh Denny, Kevin Johnson and Dan Roden – for their invaluable assistance to my PhD and my future career. There are too many individuals who have helped me over the years to name them all, but I especially want to thank Cindy Gadd for her guidance throughout my time at. I would like to thank all the DBMI staff, especially Joyce Green and Rischelle Jenkins, without whom I would have been lost.
I also want to thank my fellow students, past and present, for their friendship and all our deep discussions over the years. For their assistance in evaluating the DEB2 knowledge base, I would like to thank Robert Cronin, Dara Eckerle Mize, Jacob Eichenberger, Travis Osterman, Matthew Rioth, and Michael Temple.
CHAPTER I
CHAPTER II
After successful Phase 1-‐3 studies, the research sponsor, usually a pharmaceutical company, may submit a New Drug Application (NDA) to the FDA.8 Based on the results of the trials included in the NDA, the FDA will either approve or reject the application. These include the FDA adverse event reporting system (FAERS)19 in the United States and the European Medicines Agency's SRS known as EudraVigilance20,21. The CDC estimates that ADEs result in 700,000 emergency department visits and 120,00 hospitalizations in the US in the US each year.33.
The Side Effects Resource (SIDER) database in the public domain contains drug indications and ADEs.69,70 The original SIDER database, developed by Kuhn et al., used simple text mining methods to retrieve indications and ADEs from the FDA SPL and presented using the COSTART vocabulary. In addition, rapid detection of other ADEs has resulted in a significant reduction in the number of severe ADEs.
CHAPTER III
The specific relationships used to map each clinical drug to the corresponding generic compounds were a function of term type. The project achieved this by removing drug concepts with unwanted semantic types and drug names indicating that they were not the desired type. First, custom Perl scripts extracted drug-CM pairs from each of the five compound sources.
The DEB2 construction algorithms stored the drug-CM pairs from each of the five sources in five separate tables in a MySQL database. Second, the scripts combined the data from each source table into two merged tables: the DEB2_full table, which contains all drug-CM pairs from each source, and.
DEB2
Percentage of drug-‐CM pairs from each source that agree with the consensus of other sources (when present in at least two sources). Of the 17,515 DEB2-‐CM drug pairs from multiple sources, most came from only two sources. After excluding the ties in the DEB2_final table, between 98% and 100% of the drug-‐CM pairs agree with the consensus.
A total of 121 drug-‐CM pairs from the DEB2_final table were present in the available DEB1 expert review set. The DEB2 requirement that drug-CM pairs appear in more than one source improved the precision of the knowledge base.
CHAPTER IV
For each extracted drug-‐CM ADE pair, the algorithm calculated the relative risk of the drug being associated with the CM in the H&P test corpus. For each of the 16 drug classes determined to be within the set of antihypertensives, the project team attempted to identify class-specific side effects using DEB2. The spreadsheet included the RxNorm RxCUI, drug name, drug class, CM CUI, CM name, and the relative risk score of the drug causing the ADE in the H&P test corpus.
After consultation with peer reviewers, the project team produced the final version of the AAS. With the help of the methods explained above, the project group determined the drugs and classes of drugs that belong to the set of "antihypertensive drugs". The set comprised 59 drugs and 16 drug classes (including two separate classes for cardioselective and non-cardioselective beta-blockers). The drugs, their classes and drug prevalence in the H&P test corpus are shown in Table 20.
The table shows two percentages - the first is the percentage of antihypertensive drugs in DEB2, where the relevant ADE had a relative risk score greater than 1.2; the second is the percentage of drugs that mention an ADE in DEB2 regardless of the relative risk score. The task force concluded that this is because most sources either omit it (since lowering blood pressure is an intended effect of the drug) or mention it instead. At the suggestion of pharmacist reviewers, the project group divided the CCB into two sub-‐.
Beta-blockers (non-selective) DRUG Propranolol Hallucinations Beta-blockers (non-selective) DRUG Propranolol Mental depression Beta-blockers (non-selective) DRUG Propranolol Raynaud's disease Calcium channel blockers (DHP) CLASS -‐ Constipation. Calcium channel blockers (DHP) CLASS -‐ Gingival Hyperplasia Calcium channel blockers (DHP) CLASS -‐ Peripheral edema Calcium channel blockers (non-‐DHP) CLASS -‐ Bradycardia Calcium channel blockers (non‐‐‐DHP) DHP) CLASS -‐ Constipation. Calcium channel blockers (non-‐DHP) CLASS -‐ Gingival Hyperplasia Calcium channel blockers (non-‐DHP) CLASS -- Headache.
CHAPTER V
By using a broader range for “normal,” the project eliminated false positive alerts due to high or low borderline laboratory results. The system retrieves notes from the Portfolio Database server using the ADER Database Monitor component. The ADER Status Monitor components ensure that the system operates normally and enable the project team to monitor the preliminary research results of the pilot implementation.
If the transfer to StarPanel is successful, the ADER alert variables are stored in the patient records. The file with metadata about the discharge note is used to link the discharge note with the receipt in the ADER database. The system checks that each CUI is negated using KMCI's implementation of the Negex137 algorithm; examples of potential denial include "no fever" or "patient has no headache." Then the system checks that the CUI has the CM semantic type and appears in the appropriate H&P; section.
To identify such results, the system loads the lab results file sent by the ADER database monitor to the server. To identify potential confounding diseases—that is, diseases or syndromes that may cause or contribute to the CM—the system checks the potential confounding diseases in the Confounding List. The ADER Status Monitor component continuously monitors the status updates of each of the ADER components.
The website contains a number of panels used by project team members to monitor the status of the ADER system as well as the status of the pilot evaluation. The fourth panel shows the status of ADER component scripts running on the server. The code described above was used for the ADER pilot evaluation described in the next chapter.
CHAPTER VI
Finally, the analysis compared this rate to the rate of change in the pilot study for clinicians who received ADER alerts. Because of this complexity, all systems will sometimes fail or "crash". The ADER Status Monitor component (see Chapter V) alerted project team members if any part of the system was not functioning properly due to software errors or other technical problems. Mediations" in the note as "discontinued" or "D/C". In addition, some inpatient medications are not listed in the discharge summary; it is unclear whether the medications omitted from the discharge summary were continued, held, or discontinued (e.g., the patient may .already purchased a 90-day supply so no refill prescription was given; or the patient may have been told not to take the medication but this was not recorded).
The results compared to the control group are shown in Table 29 in the next section. The project team reviewed 100 H&P notes where potential ADEs were detected in a pilot study to assess the accuracy of the NLP ADER. In one case, the provider commented, "The patient does not have acute kidney injury or hypovolemia or hyperkalemia." However, on admission, the note stated that the patient was hypovolemic, and laboratory results showed a serum potassium of 6.5, creatinine of 11.9, and BUN of 72.
Using the medication order parameters “start time”, “interruption time”, “hold start time”, and “resumption time”, the team identified whether there was an active order for each inpatient medication during each 12-hour period from admission to discharge for each patient. There was also no significant difference between groups in the rate at which suspected ADE-causing drug doses were reduced. Suspected ADE-causing inpatient medication compared with inpatient medication in the first 12, 24, and 48 hours after admission, for both the pilot and control groups.
In the pilot group, 262 ADE-causing medications had an active order at the time providers responded to the alert. When comparing admission medications with discharge medications in the pilot study group, the team found that 23% of suspected ADEs--cause antihypertensives. Based on discharge summaries, the rate of withholding or discontinuing suspected ADE-causing medications was 27% when providers received an ADER alert compared with only 9.5% in the control group.
The project team believes this is due to the location of the ADER alert in the daily progress recording form; daily progress notes. This could possibly explain why hospital medication orders did not reveal a difference in retention or change rates between the pilot study group and the control group.
CHAPTER VII
A systematic review of the performance characteristics of clinical event monitoring signals used to detect adverse drug events in the hospital setting. Comb signals from spontaneous reports and electronic health records for the detection of adverse drug reactions.
