Development and pilot testing of PHARAO—a decision support system for pharmacological risk assessment in the elderly

Böttiger, Ylva; Laine, Kari; Korhonen, Tuomas; Lähdesmäki, Janne; Shemeikka, Tero; Julander, Margaretha; Edlert, Maria; Andersson, Marine L.

doi:10.1007/s00228-017-2391-3

Cited by 22 publications

(22 citation statements)

References 23 publications

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“…The Pharmacological Risk Assessment Online System (Pharao ® , currently RiskBase) database was used to investigate the risk for drug adverse effects. The database is developed by the Sfinx ® working group . There are nine different common and/or severe adverse effects in the database including bleeding, constipation, anticholinergic side effects, orthostatic hypotension, sedation, QT prolongation, seizures, serotonergic side effects and nephrotoxicity.…”

Section: Methodsmentioning

confidence: 99%

Clinically relevant drug‐drug interactions and the risk for drug adverse effects among home‐dwelling older persons with and without type 2 diabetes

et al. 2019

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What is known and objective Polypharmacy and age are known to increase the risk for potential drug interactions. Type 2 diabetes has been associated with polypharmacy and several comorbidities. Currently, there is no information on whether the frequency of clinically relevant drug‐drug interactions and the risk for drug adverse effects differ between older persons with and without diabetes. The aim of this study was to investigate the frequency of drug‐drug interactions and the risk for drug adverse effects in these two groups in primary care. Methods The basic study population consisted of Finnish home‐dwelling primary care patients aged ≥ 65 years (N = 3039). For each person with diabetes, two controls were selected with adjusted age and gender. To collect data, electronic primary care patient records, a structured health questionnaire and a structured health examination conducted by a physician were utilized. Using the SFINX‐PHARAO® database, drug‐drug interactions and the risk for drug adverse effects were evaluated in 182 persons with type 2 diabetes and 176 persons without diabetes. Results and discussion There were no significant differences in the frequency of drug‐drug interactions or the risk for drug adverse effects in persons with and without diabetes. At least one clinically relevant interaction was found in 81 (44.5%) persons with diabetes and 73 (41.5%) persons without diabetes. The most common drugs causing interactions included non‐steroidal anti‐inflammatory drugs (NSAIDs) and warfarin. What is new and conclusion There is no difference in the frequency of drug‐drug interactions or risk for drug adverse effects in older home‐dwelling persons with and without diabetes. Due to common comorbidities and commonly used drugs among persons with diabetes, drug‐drug interactions involving warfarin or NSAIDs in particular should be carefully monitored to avoid drug adverse effects.

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Section: Methodsmentioning

confidence: 99%

Clinically relevant drug‐drug interactions and the risk for drug adverse effects among home‐dwelling older persons with and without type 2 diabetes

et al. 2019

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“…Interaction X-referencing, formerly known as SFINX), a drug-drug interaction database developed by Medbase Ltd 16 that contains concise evidence-based information about the harms and benefits of approximately 18 000 drug-drug interactions and adverse events, 17,18 and from Renbase, a database of drug dosing recommendations in patients with renal failure. 19 The CDSS was translated and adapted to the Italian health care setting by Medilogy Srl in collaboration with the University of Milan and was renamed Medilogy Decision Support System (MediDSS).…”

Section: Developed By Duodecim Medical Publications Ltd An Internatimentioning

confidence: 99%

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

et al. 2019

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IMPORTANCE Sophisticated evidence-based information resources can filter medical evidence from the literature, integrate it into electronic health records, and generate recommendations tailored to individual patients. OBJECTIVE To assess the effectiveness of a computerized clinical decision support system (CDSS) that preappraises evidence and provides health professionals with actionable, patient-specific recommendations at the point of care. DESIGN, SETTING, AND PARTICIPANTS Open-label, parallel-group, randomized clinical trial among internal medicine wards of a large Italian general hospital. All analyses in this randomized clinical trial followed the intent-to-treat principle. Between November 1, 2015, and December 31, 2016, patients were randomly assigned to the intervention group, in which CDSS-generated reminders were displayed to physicians, or to the control group, in which reminders were generated but not shown. Data were analyzed between February 1 and July 31, 2018. INTERVENTIONS Evidence-Based Medicine Electronic Decision Support (EBMEDS), a commercial CDSS covering a wide array of health conditions across specialties, was integrated into the hospital electronic health records to generate patient-specific recommendations. MAIN OUTCOMES AND MEASURES The primary outcome was the resolution rate, the rate at which medical problems identified and alerted by the CDSS were addressed by a change in practice. Secondary outcomes included the length of hospital stay and in-hospital all-cause mortality. RESULTS In this randomized clinical trial, 20 563 patients were admitted to the hospital. Of these, 6480 (31.5%) were admitted to the internal medicine wards (study population) and randomized (3242 to CDSS and 3238 to control). The mean (SD) age of patients was 70.5 (17.3) years, and 54.5% were men. In total, 28 394 reminders were generated throughout the course of the trial (median, 3 reminders per patient per hospital stay; interquartile range [IQR], 1-6). These messages led to a change in practice in approximately 4 of 100 patients. The resolution rate was 38.0% (95% CI, 37.2%-38.8%) in the intervention group and 33.7% (95% CI, 32.9%-34.4%) in the control group, corresponding to an odds ratio of 1.21 (95% CI, 1.11-1.32; P < .001). The length of hospital stay did not differ between the groups, with a median time of 8 days (IQR, 5-13 days) for the intervention group and a median time of 8 days (IQR, 5-14 days) for the control group (P = .36). In-hospital all-cause mortality also did not differ between groups (odds ratio, 0.95; 95% CI, 0.77-1.17; P = .59). Alert fatigue did not differ between early and late study periods. (continued) Key Points Question Can a multispecialty computerized clinical decision support system (CDSS) reduce inappropriate prescribing in a general hospital? Findings In this randomized clinical trial of 6480 patients, the CDSS alerted to a median of 3 reminders for prescription problems per patient per hospital stay. The CDSS led to a change in practice in approximately 4 of 100 patients, an...

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“…The calculation of the pharmacological risk scores of patients’ medications was based on the PHARAO decision support system. This system consists of a database with risk scores for nine common and/or potentially serious adverse events related to known pharmacological properties of approximately 1200 different substances [15]. An algorithm for the summation and categorisation of each risk into no, low, intermediate or high risk, as well as clinical advice on how to handle specific risks (such as; dose reduction, discontinuation, change of medication) has been developed [15].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the PHARAO (Pharmacological Risk Assessment Online) system was developed as a complement to the SFINX database in relation to potential pharmacodynamic interactions, taking into account the combinations of more than two drugs, and yet minimising the risk of alert fatigue. By adding information on pre-specified pharmacodynamic properties of substances that may give additive or synergistic effects, a risk profile for the individual patient’s drug list can be created, without compromising the specificity of the drug pair alerts [15]. The risk profile should preferably be generated on demand by the user, such as during the medication review, and not appear as automated alerts.…”

Section: Introductionmentioning

confidence: 99%

Clinical relevance of alerts from a decision support system, PHARAO, for drug safety assessment in the older adults

et al. 2019

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Background PHARAO is a decision support system developed to evaluate the risk for a set of either common or serious side-effects resulting from a combination of pharmacodynamic effects from a patient’s medications. The objective of this study was to investigate the validity of the risk scores for the common side-effects generated by PHARAO in older patients. Methods Side-effects included were sedation, constipation, orthostatic symptoms, anticholinergic and serotonergic effects. The alerts generated by PHARAO were tested in 745 persons ≥65 years old. Dispensed prescriptions retrieved from the Swedish prescribed drug register were used to generate the pharmacological risk scores of patients’ medications. Symptoms possibly related to side-effects were extracted from medical records data. Results The PHARAO system generated 776 alerts, most often for the risk of anticholinergic symptoms. The total specificity estimates of the PHARAO system were 0.95, 0.89 and 0.78 for high, intermediate and low risk alerts, respectively. The corresponding sensitivity estimates were between 0.12 and 0.37. The negative predictive value was 0.90 and the positive predictive value ranged between 0.20–0.25. Conclusions The PHARAO system had a high specificity and negative predictive value to detect symptoms possibly associated with the of patients’ medications, while the sensitivity and positive predictive value were low. The PHARAO system has the potential to minimise the risk of over-alerts in combination with a drug-drug interaction alert system, but should be used in connection with a medical evaluation of the patient. Electronic supplementary material The online version of this article (10.1186/s12877-019-1179-y) contains supplementary material, which is available to authorized users.

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Development and pilot testing of PHARAO—a decision support system for pharmacological risk assessment in the elderly

Cited by 22 publications

References 23 publications

Clinically relevant drug‐drug interactions and the risk for drug adverse effects among home‐dwelling older persons with and without type 2 diabetes

Clinically relevant drug‐drug interactions and the risk for drug adverse effects among home‐dwelling older persons with and without type 2 diabetes

Effectiveness of a Hospital-Based Computerized Decision Support System on Clinician Recommendations and Patient Outcomes

Clinical relevance of alerts from a decision support system, PHARAO, for drug safety assessment in the older adults

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