Feasibility of automatic evaluation of clinical rules in general practice.

PURPOSE: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluation of quality of care in general practice. METHODS: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders (ACOVE) clinical rules, against Dutch College of General Practitioners (NHG) data model. Each CR was analyzed using the Logical Elements Rule METHOD: (LERM). LERM is a stepwise method of assessing and formalizing clinical rules for decision support. Clinical rules that satisfied the criteria outlined in the LERM method were judged to be implementable in automatic evaluation in general practice. RESULTS: Thirty-three out of 81 (40.7%) Dutch-translated ACOVE clinical rules can be automatically evaluated in electronic medical record systems. Seven out of 7 CRs (100%) in the domain of diabetes can be automatically evaluated, 9/17 (52.9%) in medication use, 5/10 (50%) in depression care, 3/6 (50%) in nutrition care, 6/13 (46.1%) in dementia care, 1/6 (16.6%) in end of life care, 2/13 (15.3%) in continuity of care, and 0/9 (0%) in the fall-related care. Lack of documentation of care activities between primary and secondary health facilities and ambiguous formulation of clinical rules were the main reasons for the inability to automate the clinical rules. CONCLUSION: Approximately two-fifths of the primary care Dutch ACOVE-based clinical rules can be automatically evaluated. Clear definition of clinical rules, improved GP database design and electronic linkage of primary and secondary healthcare facilities can improve prospects of automatic assessment of quality of care. These findings are relevant especially because the Netherlands has very high automation of primary care.

Quality of Co-Prescribing NSAID and Gastroprotective Medications for Elders in The Netherlands and Its Association with the Electronic Medical Record.

OBJECTIVE: To assess guideline adherence of co-prescribing NSAID and gastroprotective medications for elders in general practice over time, and investigate its potential association with the electronic medical record (EMR) system brand used. METHODS: We included patients 65 years and older who received NSAIDs between 2005 and 2010. Prescription data were extracted from EMR systems of GP practices participating in the Dutch NIVEL Primary Care Database. We calculated the proportion of NSAID prescriptions with co-prescription of gastroprotective medication for each GP practice at intervals of three months. Association between proportion of gastroprotection, brand of electronic medical record (EMR), and type of GP practice were explored. Temporal trends in proportion of gastroprotection between electronic medical records systems were analyzed using a random effects linear regression model. RESULTS: We included 91,521 patient visits with NSAID prescriptions from 77 general practices between 2005 and 2010. Overall proportion of NSAID prescriptions to the elderly with co-prescription of gastroprotective medication was 43%. Mean proportion of gastroprotection increased from 27% (CI 25-29%) in the first quarter of 2005 with a rate of 1.2% every 3 months to 55%(CI 52-58%) at the end of 2010. Brand of EMR and type of GP practice were independently associated with co-prescription of gastroprotection. CONCLUSION: Although prescription of gastroprotective medications to elderly patients who receive NSAIDs increased in The Netherlands, they are not co-prescribed in about half of the indicated cases. Brand of EMR system is associated with differences in prescription of gastroprotective medication. Optimal design and utilization of EMRs is a potential area of intervention to improve quality of prescription.

Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity.

OBJECTIVES: Although the course of single diseases can be studied using traditional epidemiologic techniques, these methods cannot capture the complex joint evolutionary course of multiple disorders. In this study, multilevel temporal Bayesian networks were adopted to study the course of multimorbidity in the expectation that this would yield new clinical insight. STUDY DESIGN AND SETTING: Clinical data of patients were extracted from 90 general practice registries in the Netherlands. One and half million patient-years were used for analysis. The simultaneous progression of six chronic cardiovascular conditions was investigated, correcting for both patient and practice-related variables. RESULTS: Cumulative incidence rates of one or more new morbidities rapidly increase with the number of morbidities present at baseline, ranging up to 47% and 76% for 3- and 5-year follow-ups, respectively. Hypertension and lipid disorders, as health risk factors, increase the cumulative incidence rates of both individual and multiple disorders. Moreover, in their presence, the observed cumulative incidence rates of combinations of cardiovascular disorders, that is, multimorbidity differs significantly from the expected rates. CONCLUSION: There are clear synergies between health risks and chronic diseases when multimorbidity within a patient progresses over time. The method used here supports a more comprehensive analysis of such synergies compared with what can be obtained by traditional statistics.

Frequency and risk factors for under- and over-treatment in stroke prevention for patients with non-valvular atrial fibrillation in general practice.

OBJECTIVE: To determine adequacy of antithrombotic treatment in patients with non-valvular atrial fibrillation. To determine risk factors for under- and over-treatment. DESIGN: Retrospective, cross-sectional study of electronic health records from 36 general practitioners in 2008. SETTING: General practice in the Netherlands. SUBJECTS: Primary care physicians (n = 36) and patients (n = 981) aged 65 years and over. MAIN OUTCOME MEASURES: Rates of adequate, under and over-treatment, risk factors for under and over-treatment. RESULTS: Of the 981 included patients with a mean of age 78, 18% received no antithrombotic treatment (under-treatment), 13% received antiplatelet drugs and 69% received oral anticoagulation (OAC). Further, 43% of the included patients were treated adequately, 26% were under-treated, and 31% were over-treated. Patients with a previous ischaemic stroke were at high risk for under-treatment (OR 2.4, CI 1.6-3.5), whereas those with contraindications for OAC were at high risk for over-treatment (OR 37.0, CI 18.1-79.9). Age over 75 (OR 0.2, CI: 0.1-0.3]), diabetes (OR 0.1, CI: 0.1-0.3), heart failure (OR 0.2, CI: 0.1-0.3), hypertension (OR 0.1, CI: 0.1-0.2) and previous ischaemic stroke (OR 0.04, CI: 0.02-0.11) protected against over-treatment. CONCLUSIONS: In general practice, CHADS2-criteria are being used, but the antithrombotic treatment of patients with atrial fibrillation frequently deviates from guidelines on this topic. Patients with previous stroke are at high risk of not being prescribed OAC. Contraindications for OAC, however, seem to be frequently overlooked.

Multilevel Bayesian networks for the analysis of hierarchical health care data.

OBJECTIVE: Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel data. However, for the statistical analysis of interactions between entities from a domain, multilevel regression yields little to no insight. While Bayesian networks have proved to be useful for analysis of interactions, they do not have the capability to deal with hierarchical data. In this paper, we describe a new formalism, which we call multilevel Bayesian networks; its effectiveness for the analysis of hierarchically structured health care data is studied from the perspective of multimorbidity. METHODS: Multilevel Bayesian networks are formally defined and applied to analyze clinical data from family practices in The Netherlands with the aim to predict interactions between heart failure and diabetes mellitus. We compare the results obtained with multilevel regression. RESULTS: The results obtained by multilevel Bayesian networks closely resembled those obtained by multilevel regression. For both diseases, the area under the curve of the prediction model improved, and the net reclassification improvements were significantly positive. In addition, the models offered considerable more insight, through its internal structure, into the interactions between the diseases. CONCLUSIONS: Multilevel Bayesian networks offer a suitable alternative to multilevel regression when analyzing hierarchical health care data. They provide more insight into the interactions between multiple diseases. Moreover, a multilevel Bayesian network model can be used for the prediction of the occurrence of multiple diseases, even when some of the predictors are unknown, which is typically the case in medicine.

Pandemic influenza A(H1N1)pdm09 improves vaccination routine in subsequent years: a cohort study from 2009 to 2011.

BACKGROUND: In 2009 the pandemic influenza virus A(H1N1)pdm09 emerged with guidance that people at risk should be vaccinated. It is unclear how this event affected the underlying seasonal vaccination rate in subsequent years. PURPOSE: To investigate the association of pandemic influenza A(H1N1)pdm09 and seasonal flu vaccination status in 2009 with vaccination rates in 2010 and 2011. METHODS: Data were collected in 40 Dutch family practices on patients at risk for influenza during 2009-2011; data analysis was conducted in 2012. RESULTS: A multilevel logistic regression model (n=41,843 patients) adjusted for practice and patient characteristics (age and gender, as well as those patient groups at risk), showed that people who were vaccinated against A(H1N1)pdm09 in 2009 were more likely to have been vaccinated in 2010 (OR 6.02; 95%CI 5.62-6.45, p<.0001). This likelihood was even more for people who were vaccinated against seasonal flu in 2009 (OR 13.83; 95%CI 12.93-14.78, p<.0001). A second analysis on the uptake rate in 2011 (n=39,468 patients) showed that the influence of the vaccination state in 2009 declined after two years, but the diminishing effect was smaller for people vaccinated against A(H1N1)pdm09 than for seasonal flu (OR 5.50; 95%CI 5.13-5.90, p<.0001; OR 10.98; 95%CI 10.26-11.75, p<.0001, respectively). CONCLUSION: Being vaccinated against A(H1N1)pdm09 and seasonal influenza in the pandemic year 2009 enhanced the probability of vaccination in the next year and this was still effective in 2011. This suggests that peoples' vaccination routines were not changed by the rumor around the outbreak of A(H1N1)pdm09, but rather confirmed underlying behavior.

Inappropriateness of medication prescriptions to elderly patients in the primary care setting: a systematic review.

BACKGROUND: Inappropriate medication prescription is a common cause of preventable adverse drug events among elderly persons in the primary care setting. OBJECTIVE: The aim of this systematic review is to quantify the extent of inappropriate prescription to elderly persons in the primary care setting. METHODS: We systematically searched Ovid-Medline and Ovid-EMBASE from 1950 and 1980 respectively to March 2012. Two independent reviewers screened and selected primary studies published in English that measured (in)appropriate medication prescription among elderly persons (>65 years) in the primary care setting. We extracted data sources, instruments for assessing medication prescription appropriateness, and the rate of inappropriate medication prescriptions. We grouped the reported individual medications according to the Anatomical Therapeutic and Chemical (ATC) classification and compared the median rate of inappropriate medication prescription and its range within each therapeutic class. RESULTS: We included 19 studies, 14 of which used the Beers criteria as the instrument for assessing appropriateness of prescriptions. The median rate of inappropriate medication prescriptions (IMP) was 20.5% [IQR 18.1 to 25.6%.]. Medications with largest median rate of inappropriate medication prescriptions were propoxyphene 4.52 (0.10-23.30)%, doxazosin 3.96 (0.32 15.70)%, diphenhydramine 3.30 (0.02-4.40)% and amitriptiline 3.20 (0.05-20.5)% in a decreasing order of IMP rate. Available studies described unequal sets of medications and different measurement tools to estimate the overall prevalence of inappropriate prescription. CONCLUSIONS: Approximately one in five prescriptions to elderly persons in primary care is inappropropriate despite the attention that has been directed to quality of prescription. Diphenhydramine and amitriptiline are the most common inappropriately prescribed medications with high risk adverse events while propoxyphene and doxazoxin are the most commonly prescribed medications with low risk adverse events. These medications are good candidates for being targeted for improvement e.g. by computerized clinical decision support.

Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions.

OBJECTIVE: Appropriate antimicrobial treatment of infections in critically ill patients should be started as soon as possible, as delay in treatment may reduce a patient's prognostic outlook considerably. Ventilator-associated pneumonia (VAP) occurs in patients in intensive care units who are mechanically ventilated and is almost always preceded by colonisation of the respiratory tract by the causative microorganisms. It is very difficult to clinically diagnose VAP and, therefore, some form of computer-based decision support might be helpful for the clinician. MATERIALS AND METHODS: As diagnosing and treating VAP involves reasoning with uncertainty, we have used a Bayesian network as the primary tool for building a decision-support system. The effects of usage of antibiotics on the colonisation of the respiratory tract by various pathogens and the subsequent antibiotic choices in case of VAP were modelled using the notion of causal independence. In particular, the conditional probability distribution of the random variable that represents the overall coverage of pathogens by antibiotics was modelled in terms of the conjunctive effect of the seven different pathogens, usually referred to as the noisy-AND model. In this paper, we investigate different coverage models, as well as generalisations of the noisy-AND, called noisy-threshold models, and test them on clinical data of intensive care unit (ICU) patients who are mechanically ventilated. RESULTS: Some of the constructed noisy-threshold models offered further improvement of the performance of the Bayesian network in covering present causative pathogens by advising appropriate antimicrobial treatment. CONCLUSIONS: By reconsidering the modelling of interactions between the random variables in a Bayesian network using the theory of causal independence, it is possible to refine its performance. This was clearly shown for our Bayesian network concerning VAP, indicating that only specific noisy-threshold models might be appropriate for the modelling of the interaction between pathogens and antimicrobial treatment with respect to susceptibility. The results obtained also provide evidence that the noisy-OR and noisy-AND might not always be the best functions to model interactions among random variables.

Predicting pathogens causing ventilator-associated pneumonia using a Bayesian network model.

BACKGROUND: We previously validated a Bayesian network (BN) model for diagnosing ventilator-associated pneumonia (VAP). Here, we report on the performance of the model to predict microbial causes of VAP and to select antibiotics. METHODS: Pathogens were grouped into seven categories based upon the antibiotic susceptibility and epidemiological characteristics. Colonization of the upper respiratory tract was modelled in the BN and depended--in additional steps--on (i) duration of admission and ventilation, (ii) previous culture results and (iii) previous antibiotic use. A database with 153 VAP episodes and their microbial causes was used as reference standard. Appropriateness of antibiotic prescription, with fixed choices for pathogens predicted, was determined. RESULTS: One hundred and seven VAP episodes were monobacterial and 46 were caused by two pathogens. Using duration of admission and ventilation only, areas under the receiver operating curve (AUC) ranged from 0.511 to 0.772 for different pathogen groups, and model predictions significantly improved when adding information on culture results, but not when adding information on antibiotic use. The best performing model (with all information) had AUC values ranging from 0.859 for Acinetobacter spp. to 0.929 for Streptococcus pneumoniae. With this model, 91 (85%) and 29 (63%) of all pathogen groups were correctly predicted for monobacterial and polymicrobial VAP, respectively. With fixed antibiotic choices linked to pathogen groups, 92% of all episodes would have been treated appropriately. CONCLUSIONS: The BN models' performance to predict pathogens causing VAP improved markedly with information on colonization, resulting in excellent pathogen prediction and antibiotic selection. Prospective external validation is needed.

Effects of systemic antibiotic therapy on bacterial persistence in the respiratory tract of mechanically ventilated patients.

OBJECTIVE: Bacterial respiratory tract colonization predisposes critically ill patients to intensive care unit (ICU)-acquired infections. It is unclear to what extent systemic antibiotics affect colonization persistence. Persistence of respiratory tract colonization, and the effects of systemic antibiotics hereon, were determined in a cohort of ICU patients. DESIGN: Clinical and microbiological data were collected from 715 admitted mechanically ventilated ICU patients with bacterial growth documented in respiratory tract samples. First day of colonization, persistence of colonization and antibiotic effects hereon were analyzed for six groups of pathogens: Pseudomonas aeruginosa, Acinetobacter species, Enterobacteriaceae, Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae. Systemic antibiotics were grouped into 'effective' and 'ineffective' antibiotics, based on in-vitro susceptibility data for the relevant bacteria. The effects of antibiotics were quantified as relative risk (RR) of bacterial persistence in the absence of effective antibiotics. MEASUREMENTS AND RESULTS: Persistence of colonization differed significantly between pathogens, ranging from 4 days (median) for H. influenzae and Strep. pneumoniae to 8 days for P. aeruginosa. Systemic antibiotics were administered on 7,102 (61%) of patient days. Antibiotic use was associated with non-persistence for all pathogens, except Acinetobacter species and P. aeruginosa. RR for non-persistence (as compared to ineffective or no antibiotics) ranged from 3.1 (95% CI 1.4-6.6) for H. influenzae to 0.5 (0.3-1.0) for Acinetobacter species. CONCLUSIONS: In mechanically ventilated patients, persistence dynamics of bacterial respiratory tract colonization, and the effects of (in-vitro) effective antibiotics hereon, are pathogen-specific.

A Bayesian decision-support system for diagnosing ventilator-associated pneumonia.

OBJECTIVE: To determine the diagnostic performance of a Bayesian Decision-Support System (BDSS) for ventilator-associated pneumonia (VAP). DESIGN: A previously developed BDSS, automatically obtaining patient data from patient information systems, provides likelihood predictions of VAP. In a prospectively studied cohort of 872 ICU patients, VAP was diagnosed by two infectious-disease specialists using a decision tree (reference diagnosis). After internal validation daily BDSS predictions were compared with the reference diagnosis. For data analysis two approaches were pursued: using BDSS predictions (a) for all 9422 patient days, and (b) only for the 238 days with presumed respiratory tract infections (RTI) according to the responsible physicians. MEASUREMENTS AND RESULTS: 157 (66%) of 238 days with presumed RTI fulfilled criteria for VAP. In approach (a), median daily BDSS likelihood predictions for days with and without VAP were 77% [Interquartile range (IQR) = 56-91%] and 14% [IQR 5-42%, p < 0.001, Mann-Whitney U-test (MWU)], respectively. In receiver operating characteristics (ROC) analysis, optimal BDSS cut-off point for VAP was 46%, and with this cut-off point positive predictive value (PPV) and negative predictive value (NPV) were 6.1 and 99.6%, respectively [AUC = 0.857 (95% CI 0.827-0.888)]. In approach (b), optimal cut-off for VAP was 78%, and with this cut-off point PPV and NPV were 86 and 66%, respectively [AUC = 0.846 (95% CI 0.794-0.899)]. CONCLUSIONS: As compared with the reference diagnosis, the BDSS had good test characteristics for diagnosing VAP, and might become a useful tool for assisting ICU physicians, both for routinely daily assessment and in patients clinically suspected of having VAP. Empirical validation of its performance is now warranted.

Incorporating evaluation into the design of a decision-support system.

Medical decision support systems will only be accepted by the medical community if properly evaluated. However, little attention has been given in the scientific literature to the topic of how to incorporate evaluation issues into the design of a decision-support system. In this paper, we describe work in developing a decision-support system that is intended to support the management (diagnosis and treatment selection) of ventilator-associated pneumonia in patients. From the beginning of the development of this system, we have taken care to incorporate evaluation issues into the design of the system. In the paper, we analyse the problems that need be taken into account when evaluating a system. Next, we describe the consequences for the functionality of the system.