An integrated clinico-metabolomic model improves prediction of death in sepsis.

Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and ß-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Design and validation of a data simulation model for longitudinal healthcare data.

Evaluating performance characteristics of analytic methods developed to identify treatment effects in longitudinal healthcare data has been hindered by lack of an objective benchmark to measure performance. Relationships between drugs and subsequent treatment effects are not precisely quantified in real-world data, and simulated data offer potential to augment method development by providing data with known, measurable characteristics. However, the use of simulated data has been limited due to its inability to adequately reflect the complexities inherent in real-world databases that are necessary for effective method development. The goal of this study was to develop and evaluate a model for simulating longitudinal healthcare data that adequately captures these complexities. An empiric design was chosen that utilizes the characteristics of a real healthcare database as simulation input. This model demonstrates the potential for simulated data with known characteristics to adequately reflect complex relationships among diseases and treatments as recorded in healthcare databases.

Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases.

OBJECTIVE: Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines. DESIGN: The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence. Drug eras, constructed to represent periods of persistent drug use, are derived from available elements from pharmacy dispensings, prescriptions written, and other medication history. Condition eras aggregate diagnoses that occur within a single episode of care. Drugs and conditions from source data are mapped to biomedical ontologies to standardize terminologies and enable analyses of higher-order effects. MEASUREMENTS: The CDM was applied to two source types: an administrative claims and an electronic medical record database. Descriptive statistics were used to evaluate transformation rules. Two case studies demonstrate the ability of the CDM to enable standard analyses across disparate sources: analyses of persons exposed to rofecoxib and persons with an acute myocardial infarction. RESULTS: Over 43 million persons, with nearly 1 billion drug exposures and 3.7 billion condition occurrences from both databases were successfully transformed into the CDM. An analysis routine applied to transformed data from each database produced consistent, comparable results. CONCLUSION: A CDM can normalize the structure and content of disparate observational data, enabling standardized analyses that are meaningfully comparable when assessing the effects of medicines.

An evaluation of three signal-detection algorithms using a highly inclusive reference event database.

BACKGROUND: Pharmacovigilance data-mining algorithms (DMAs) are known to generate significant numbers of false-positive signals of disproportionate reporting (SDRs), using various standards to define the terms 'true positive' and 'false positive'. OBJECTIVE: To construct a highly inclusive reference event database of reported adverse events for a limited set of drugs, and to utilize that database to evaluate three DMAs for their overall yield of scientifically supported adverse drug effects, with an emphasis on ascertaining false-positive rates as defined by matching to the database, and to assess the overlap among SDRs detected by various DMAs. METHODS: A sample of 35 drugs approved by the US FDA between 2000 and 2004 was selected, including three drugs added to cover therapeutic categories not included in the original sample. We compiled a reference event database of adverse event information for these drugs from historical and current US prescribing information, from peer-reviewed literature covering 1999 through March 2006, from regulatory actions announced by the FDA and from adverse event listings in the British National Formulary. Every adverse event mentioned in these sources was entered into the database, even those with minimal evidence for causality. To provide some selectivity regarding causality, each entry was assigned a level of evidence based on the source of the information, using rules developed by the authors. Using the FDA adverse event reporting system data for 2002 through 2005, SDRs were identified for each drug using three DMAs: an urn-model based algorithm, the Gamma Poisson Shrinker (GPS) and proportional reporting ratio (PRR), using previously published signalling thresholds. The absolute number and fraction of SDRs matching the reference event database at each level of evidence was determined for each report source and the data-mining method. Overlap of the SDR lists among the various methods and report sources was tabulated as well. RESULTS: The GPS algorithm had the lowest overall yield of SDRs (763), with the highest fraction of events matching the reference event database (89 SDRs, 11.7%), excluding events described in the prescribing information at the time of drug approval. The urn model yielded more SDRs (1562), with a non-significantly lower fraction matching (175 SDRs, 11.2%). PRR detected still more SDRs (3616), but with a lower fraction matching (296 SDRs, 8.2%). In terms of overlap of SDRs among algorithms, PRR uniquely detected the highest number of SDRs (2231, with 144, or 6.5%, matching), followed by the urn model (212, with 26, or 12.3%, matching) and then GPS (0 SDRs uniquely detected). CONCLUSIONS: The three DMAs studied offer significantly different tradeoffs between the number of SDRs detected and the degree to which those SDRs are supported by external evidence. Those differences may reflect choices of detection thresholds as well as features of the algorithms themselves. For all three algorithms, there is a substantial fraction of SDRs for which no external supporting evidence can be found, even when a highly inclusive search for such evidence is conducted.

Systematic investigation of time windows for adverse event data mining for recently approved drugs.

The optimum timing of drug safety data mining for a new drug is uncertain. The objective of this study was to compare cumulative data mining versus mining with sliding time windows. Adverse Event Reporting System data (2001-2005) were studied for 27 drugs. A literature database was used to evaluate signals of disproportionate reporting (SDRs) from an urn model data-mining algorithm. Data mining was applied cumulatively and with sliding time windows from 1 to 4 years in width. Time from SDR generation to the appearance of a publication describing the corresponding adverse event was calculated. Cumulative data mining and 1- to 2-year sliding windows produced the most SDRs for recently approved drugs. In the first postmarketing year, data mining produced SDRs an average of 800 days in advance of publications regarding the corresponding drug-event combination. However, this timing advantage reduced to zero by year 4. The optimum window width for sliding windows should increase with time on the market. Data mining may be most useful for early signal detection during the first 3 years of a drug's postmarketing life. Beyond that, it may be most useful for supporting or weakening hypotheses.

Drug-versus-drug adverse event rate comparisons: a pilot study based on data from the US FDA Adverse Event Reporting System.

BACKGROUND: A number of published studies compare adverse event rates for drugs on the basis of reports in the US FDA Adverse Event Reporting System (AERS). While the AERS data have the advantage of timely availability and a large capture population, the database is subject to many significant biases, and lacks complete patient information that would allow for correction of those biases. The accuracy of comparative AERS-based data mining has been questioned, but has not been systematically studied. OBJECTIVE: To determine whether AERS could be used as a data source to accurately compare the adverse event rates for pairs of drugs, using pre-defined, stringent criteria to dictate whether a given pair of drugs was considered eligible for such a comparison. METHODS: The Fisher's Exact test was utilized to detect differences in adverse event rates between such pairs of drugs. Concordance was determined between statistically significant AERS-based adverse event rate differences, and adverse event rate differences published in the literature from clinical trials and case-control studies. The conditions for validity included (i) data that are free of 'extreme duplication' in AERS reports; (ii) drugs used in similar patient populations; (iii) drugs used for similar indications; (iv) drugs used with the same spectrum of concomitant medications; and (v) drugs not widely disparate in time on the market. RESULTS: For 19 drugs studied, a total of 36 evaluable adverse event rate comparisons were identified. Comparisons were classified as favouring 'drug A', favouring 'drug B' or detecting no difference. Concordance for the resulting 3x3 table (AERS vs literature) gave a kappa statistic of 0.654, indicating moderately good agreement. In only two cases was there absolute discordance, with AERS designating one drug as having a lower rate, while the published study designated the other drug as having a lower rate, with respect to a given adverse event. CONCLUSIONS: This pilot study encourages further research regarding the use of spontaneous report databases such as AERS, under stringently defined conditions, to compare adverse event rates for drugs. While not hypothesis proving, such estimates can be used for purposes such as generating hypotheses for controlled studies, and for designing those studies.

Influence of the MedDRA hierarchy on pharmacovigilance data mining results.

PURPOSE: To compare the results of drug safety data mining with three different algorithms, when adverse events are identified using MedDRA Preferred Terms (PT) vs. High Level Terms (HLT) vs. Standardised MedDRA Queries (SMQ). METHODS: For a representative set of 26 drugs, data from the FDA Adverse Event Reporting System (AERS) database from 2001 through 2005 was mined for signals of disproportionate reporting (SDRs) using three different data mining algorithms (DMAs): the Gamma Poisson Shrinker (GPS), the urn-model algorithm (URN), and the proportional reporting rate (PRR) algorithm. Results were evaluated using a previously described Reference Event Database (RED) which contains documented drug-event associations for the 26 drugs. Analysis emphasized the percentage of SDRs in the "unlabeled supported" category, corresponding to those adverse events that were not described in the U.S. prescribing information for the drug at the time of its approval, but which were supported by some published evidence for an association with the drug. RESULTS: Based on a logistic regression analysis, the percentage of unlabeled supported SDRs was smallest at the PT level, intermediate at the HLT level, and largest at the SMQ level, for all three algorithms. The GPS and URN methods detected comparable percentages of unlabeled supported SDRs while the PRR method detected a smaller percentage, at all three MedDRA levels. No evidence of a method/level interaction was seen. CONCLUSIONS: Use of HLT and SMQ groupings can improve the percentage of unlabeled supported SDRs in data mining results. The trade-off for this gain is the medically less-specific language of HLTs and SMQs compared to PTs, and the need for the added step in data mining of examining the component PTs of each HLT or SMQ that results in a signal of disproportionate reporting.