R. Michael Massanari

Cost of nosocomial infection: Relative contributions of laboratory, antibiotic, and per diem costs in serious Staphylococcus aureus infections

This study reports an analysis of the relative importance of laboratory antibiotic, and per diem costs of caring for 58 patients with serious Staphylococcus aureus nosocomial infections. Laboratory costs accounted for 2%, antibiotics for 21%, and per diem costs for 77% of total infection-related costs. Only 45% of patients were hospitalized for additional days specifically because of infection, but these patients stayed an average of 18 extra days. Nosocomial infections with S. aureus resistant to penicillinase-resistant penicillins (PRP) were more frequently associated with additional infection-related days of hospitalization than were PRP-susceptible infections. The cost of PRP-resistant infections was also significantly greater than PRP-susceptible infections, primarily because of the costs of additional days of hospitalization. Rational strategies to control costs of nosocomial infection should focus on two approaches: (1) prevention and (2) reduction of acute hospital days attributable to infections.

A Potential Causal Association Mining Algorithm for Screening Adverse Drug Reactions in Postmarketing Surveillance

Early detection of unknown adverse drug reactions (ADRs) in postmarketing surveillance saves lives and prevents harmful consequences. We propose a novel data mining approach to signaling potential ADRs from electronic health databases. More specifically, we introduce potential causal association rules (PCARs) to represent the potential causal relationship between a drug and ICD-9 (CDC. (2010). International Classification of Diseases, Ninth Revision (ICD-9). [Online]. Available: http://www.cdc.gov/nchs/icd/icd9.html) coded signs or symptoms representing potential ADRs. Due to the infrequent nature of ADRs, the existing frequency-based data mining methods cannot effectively discover PCARs. We introduce a new interestingness measure, potential causal leverage, to quantify the degree of association of a PCAR. This measure is based on the computational, experience-based fuzzy recognition-primed decision (RPD) model that we developed previously (Y. Ji, R. M. Massanari, J. Ager, J. Yen, R. E. Miller, and H. Ying, “A fuzzy logic-based computational recognition-primed decision model,” Inf. Sci., vol. 177, pp. 4338-4353, 2007) on the basis of the well-known, psychology-originated qualitative RPD model (G. A. Klein, “A recognition-primed decision making model of rapid decision making,” in Decision Making in Action: Models and Methods, 1993, pp. 138-147). The potential causal leverage assesses the strength of the association of a drug-symptom pair given a collection of patient cases. To test our data mining approach, we retrieved electronic medical data for 16 206 patients treated by one or more than eight drugs of our interest at the Veterans Affairs Medical Center in Detroit between 2007 and 2009. We selected enalapril as the target drug for this ADR signal generation study. We used our algorithm to preliminarily evaluate the associations between enalapril and all the ICD-9 codes associated with it. The experimental results indicate that our approach has a potential to better signal potential ADRs than risk ratio and leverage, two traditional frequency-based measures. Among the top 50 signal pairs (i.e., enalapril versus symptoms) ranked by the potential causal-leverage measure, the physicians on the project determined that eight of them probably represent true causal associations.

A fuzzy logic-based computational recognition-primed decision model

The recognition-primed decision (RPD) model is a primary naturalistic decision-making approach which seeks to explicitly recognize how human decision makers handle complex tasks and environment based on their experience. Motivated by the need for quantitative computer modeling and simulation of human decision processes in various application domains, including medicine, we have developed a general-purpose computational fuzzy RPD model that utilizes fuzzy sets, fuzzy rules, and fuzzy reasoning to represent, interpret, and compute imprecise and subjective information in every aspect of the model. Experiences acquired by solicitation with experts are stored in experience knowledge bases. New local and global similarity measures have been developed to identify the experience that is most applicable to the current situation in a specific decision-making context. Furthermore, an action evaluation strategy has been developed to select the workable course of action. The proposed fuzzy RPD model has been preliminarily validated by using it to calculate the extent of causality between a drug (Cisapride, withdrawn by the FDA from the market in 2000) and some of its adverse effects for 100 hypothetical patients. The simulated patients were created based on the profiles of over 1000 actual patients treated with the drug at our medical center before its withdrawal. The model validity was demonstrated by comparing the decisions made by the proposed model and those by two independent internists. The levels of agreement were established by the weighted Kappa statistic and the results suggested good to excellent agreement.

A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs

In many real-world applications, it is important to mine causal relationships where an event or event pattern causes certain outcomes with low probability. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The retrieved data included 16,206 patients (15,605 male, 601 female). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin, and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin, and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

A Distributed, Collaborative Intelligent Agent System Approach for Proactive Postmarketing Drug Safety Surveillance

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275 000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.

An evaluation of the cost effectiveness of drotrecogin alfa (activated) relative to the number of organ system failures

AbstractBackground: While drotrecogin alfa (activated) was shown to decrease absolute 28-day mortality by 6.1% in patients with severe sepsis in the Recombinant Human Protein C Worldwide Evaluation in Severe Sepsis (PROWESS) study, no mortality benefit was observed in the subset of patients with only one organ system failure. Consequently, some institutions restrict drotrecogin alfa (activated) use to patients with severe sepsis with ≥ organ system failures. Objective: To measure the cost effectiveness of drotrecogin alfa (activated) for treatment of severe sepsis in relation to the number of organ system failures and determine the economic impact of restricting drotrecogin alfa (activated) use based on the number of organ system failures. Perspective: Policy perspective specific to our 340-bed, level I trauma centre. Methods: A Monte Carlo simulation analysis was conducted to evaluate a hypothetical cohort of 10 000 patients with severe sepsis in four scenarios restricting treatment with drotrecogin alfa (activated) to patients with ≥1, ≥2, ≥3 or ≥4 organ system failures. The primary outcomes of 28-day all-cause mortality and serious bleeding were obtained from the PROWESS study. Costs (year 2002 values) were obtained from institutional financial records and literature estimates. The incremental cost per life saved at 28 days with drotrecogin alfa (activated) plus best standard care versus best standard care alone (placebo) was calculated. The incidence of severe sepsis and number of drotrecogin alfa (activated) candidates were estimated through chart review, and projected annual institutional expenditures were derived according to these data. Results: With increasing number of organ system failures, the proportion of lives saved with drotrecogin alfa (activated) increased, and consequently the ICER decreased. Restriction of drotrecogin alfa (activated) to patients with ≥4 organ system failures was the most cost-effective scenario (0.11 lives saved;

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition-primed decision model

US56 727 per life saved). For the nine patients that would be treated annually by our institution under this policy, one life would be saved at a total additional cost of

INFECTION CONTROL IN AMBULATORY CARE

US56 160 per year. Use of the drug in patients with ≥1 or ≥2 organ system failures would save the greatest number of lives per year (4–5); however, restricting drotrecogin alfa (activated) to patients with ≥ organ system failures would be the cheaper alternative (total additional cost

A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance

US356 022 vs

Everyday Spirituality in Central City Elders

US462 204). Conclusion: While restriction of drotrecogin alfa (activated) use to patients with sepsis with ≥4 organ system failures is the most cost-effective alternative, restriction to those with ≥2 organ system failures is the preferred alternative for our institution according to the number of lives saved and available financial resources.