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Polypharmacy PatientsNCT05589870

Assessment of a Novel Algorithm System for Polypharmacy Patient Medication Safety and Consultation

An observational study of Polypharmacy Patients, run by MDI Health at 1 site in Israel.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record

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Trial summary

The aim of this study is to determine if insights based on the analysis of historical data of multidrug patients' electronic health records, by a novel system and algorithm, is noninferior to a clinical pharmacist analysis and insights. Read more about this study

Who is it for?
Ages 45 to 100 years
Conditions the study is for
Polypharmacy Patients
What is being tested?
Multi-drug analysis of polypharmacy treatment
How many people
91 participants
Where
1 siteIsrael
Study dates
13 Feb 2021to 7 Jul 2021
Phase
Observational

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 45 to 100 years
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Study population: Patients aged ≥45 years, that were prescribed at least six (6) medications on hospital admission for one of the following medical conditions: type 2 diabetes mellitus, hypertension, hyperlipidemia, ischemic heart disease, s/p cerebrovascular accident (CVA), depression, dementia, neuropathy, and related symptoms and disease states [sleep disorder, pain, diabetic neuropathy, gastrointestinal (GI) discomfort]. Patients were excluded from the study if they had one of the following disease states: oncologic disease, dialysis / end-stage renal disease (ESRD), epilepsy, ventilated and sedated, hyperparathyroidism or were breastfeeding or pregnant.

Who may be able to join

  • Men or women aged 45 and over (inclusive) at the time of admission to hospital. Hospitalized at Hadassah Hospital from 2010 to 2017 in one of the following departments: Internal Medicine, Cardiology, Orthopedics, Neurology, Rehabilitation.
  • Patients who have available hospitalization summary information available that contains the content required to complete the indicated fields (according to the fields listed in Appendix 1 - Patient Sheet).
  • The patient's chronic medications at admission to hospital are from the following pharmacological families / medications: angiotensin converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), Beta Blockers, calcium channel blockers (CCB), Alpha blockers, Potassium-sparing diuretics, diuretics loop, diuretics thiazide, Anti platelets agents anticoagulants, Metformin Insulin, sulfonylurea (SU) / repaglinide glucagon like peptide 1 (GLP1) analogues, dipeptidyl peptidase IV (DPP-4) inhibitors, sodium-glucose transport protein 2 (SGLT2) inhibitor, statin Fibrates, benzodiazapines / Z-drugs, Mirtazapine, selective serotonin reuptake inhibitors (SSRIs) / norepinephrine reuptake inhibitors (SNRIs), tricyclic antidepressants (TCA), Antispasmodic / Anticholinergic, Antiepileplic agents) Gabapentin, Pregabalin, Primidone) , Opioids, proton-pump inhibitors (PPIs), histamine 2 (H2) blockers.
  • Take at least six (6) chronic medications from the above list at the time of admission to the hospital (the list may also include medications that have been discontinued on admission to the hospital).
  • -

Who cannot join

  • Oncology patients
  • Dialysis patients
  • Epileptic patients
  • Respiratory and anesthetized
  • Breastfeeding patients
  • Pregnant patients
  • Patients with hyperparathyroidism

Trial locations and contacts

1 site in 1 country.

Israel1 site
  • Hadassah Ein Kerem

    Jerusalem, 9112001

Other trials in Israel

About this study

Official title: A Retrospective, Comparative Study, for the Assessment of a Novel System, an Algorithm for Medication Safety and Consultation for Chronic Polypharmacy Patients

The aim of this study is to determine if insights based on the analysis of historical data of multidrug patients' electronic health records, by a novel system and algorithm, is noninferior to a clinical pharmacist analysis and insights.

Multidrug patients, also known as polypharmacy patients often suffer from adverse drug reactions (ADRs). In routine practice the clinical pharmacist helps prevent ADRs by comprehensive medication review, identifying drug related risks and problems and providing recommendations. The analysis of multidrug patients is highly complex and time consuming due to the large amount of multifactorial data of chronic multidrug patients' medications, symptoms, comorbidities and age-related issues. The MDI system is a tool for analyzing and providing insights on polypharmacy data [including electronic health records (EHRs) and claims data] to help clinicians evaluate complex medical records and ensure optimal and personal treatment recommendations.

Read more

After initial training of the MDI system on historical real-life patient EHRs, the MDI system and the clinical pharmacist reviewed patient EHR data from another patient cohort according to five categories: 1) duplication of therapy, 2) age-related issues, 3) incorrect dose, 4) current side effects and 5) future side effects risks. The insights of this assessment were recorded on patient conclusion sheets and adjudicated by an external judging committee, comprised of two senior academic clinical pharmacists. The judging committee were blinded to the source of the conclusion sheets.

Diagnostic accuracy parameters: agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the MDI system and the clinical pharmacist were assessed. The gold standard of the diagnostic accuracy analysis was the judging committee.

Assuming that the total agreement is 5% higher for the MDI System, with a non-inferiority margin of 5%, α level of 5%, statistical power of 90%, and an expected standard deviation of 15%, the minimum sample size is about 20 cases. The achieved recruitment level was more than twice as much in the actual clinical trial.

Read the detailed description

This is an observational, retrospective study performed on deidentified electronic health records (EHRs). The study includes two stages: A pilot study for training and verification of a novel system; and a clinical validation study for evaluating the MDI system performance in comparison to the clinical pharmacist.

The main aim of the study is to determine the noninferiority of a novel system performance in comparison to the clinical pharmacist, for the creation of insights based on the analysis of multidrug patients' EHRs. The quality and accuracy of the analysis and insights is adjudicated by an external judging committee (the gold standard), comprised of two (2) senior academic clinical pharmacists (Pharm.D, Ph.D).

The clinical site's Information systems division scanned for EHRs of multidrug patients according to extraction criteria. The raw data of the patients' records were reviewed by the principal investigator (PI). Medical coding definitions of the extraction criteria were modified according to discrepancies found by the PI. The patients' deidentified EHRs were randomized to a training cohort for the training phase and to the clinical validation cohort.

Stage 1 of the trial was for training the MDI system on real-life patient EHRs. Deidentified EHRs of the training cohort were uploaded to the MDI System platform, which calculates each patient's risk for drug induced problems according to five categories: 1) duplication of therapy, 2) age-related issues, 3) incorrect dose, 4) current side effects and 5) future side effects risks. The MDI System output report is reviewed by the clinical team. The algorithms are modified based on the system-wide conclusions and the risk calculation model.

Stage 2 of the trial was for the clinical validation of the MDI system. The MDI system and clinical pharmacists independently evaluated the clinical cohort EHRs and reported insights on conclusion sheets according to the five categories: 1) duplication of therapy, 2) age-related issues, 3) incorrect dose, 4) current side effects and 5) future side effects risks. An external judging committee comprised of two (2) senior clinical pharmacy doctors, who were blinded to the source of the conclusion sheets, adjudicated the MDI system and clinical pharmacist insights.

Agreement is defined as an identical conclusion between the MDI system/clinical pharmacist's conclusion on any of the categories / problems documented on the patient's conclusion sheet and the judging committee conclusion (the gold standard). The judges were also instructed to add any finding that was missing in their professional opinion from the documented patient conclusion sheets. Agreement of a conclusion with the judging committee's conclusion is marked as true positive. Nonagreement of a conclusion and the judging committee's conclusion is marked as false positive. A conclusion variable found missing by the judging committee is marked as false negative. True negatives are defined as agreement between a conclusion regarding a missing finding in one of the categories.

The sample size for the clinical validation of the MDI System was based on the primary endpoint of non-inferiority of the diagnostic total agreement of the MDI System compared to the diagnostic total agreement of the Clinical Pharmacists for the total of the Combined Categories. The gold standard was the Judging Committee. Assuming that the total agreement is 5% higher for the MDI System, with a non-inferiority margin of 5%, α level of 5%, statistical power of 90%, and an expected standard deviation of 15%, the minimum sample size is about 20 cases. The achieved recruitment level was more than twice as much in the actual clinical trial.

Confidence intervals of 95% (95%CI) were calculated according to the T distribution under the non-inferiority assumption that the total agreement of the MDI algorithm system will not be inferior than the agreement accuracy of the clinical pharmacist. If the 95% CI limit of the percentage differences did not exceed 5% in favor of the clinical pharmacist, the non-inferiority hypothesis was confirmed. The calculation was performed for the primary endpoint (total of the five categories) and the secondary endpoints for each individual category.

The discrete variables assessed in the categories: duplication of therapy, age-related issues and incorrect dose were medications. The discrete variables assessed in the categories: current side effects and future side effects were the individual side effects. Diagnostic accuracy of the MDI System and the Clinical Pharmacists were assessed by the Judging Committee (the "gold standard").

For each subject, total agreement, PPV, NPV, sensitivity and specificity were calculated for all the combined categories together and for each separate category. The diagnostic accuracy of the total subjects is presented as mean, 95%CI of the mean, standard deviation (Std), median, minimum and maximum.

In addition, overall by-line (regardless of subject) validity parameters (overall agreement, PPV, NPV, Sensitivity and Specificity) were analyzed using frequency, percentages and 95% CI.

Being tested
Multi-drug analysis of polypharmacy treatment
Keywords
Polypharmacy, Adverse Drug Reaction, Drug related problems, Drug induced disease, Multi-morbidity, Multi-drug treatments, Drug safety, Comprehensive medication reviews, Drug interactions, Side effects, Clinical pharmacists, Drug effects, Drug related risks
Medical subject headings
Drug-Related Side Effects and Adverse Reactions

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Multi-drug analysis of polypharmacy treatmentOther

    Multi-drug analysis to assess and provide insights for lowering the risk of ADRs and increasing the benefit of polypharmacy treatment

Study groups in NCT05589870
Study groupRoleWhat they receive
Clinical pharmacists groupA group of patient files that were evaluated by the clinical pharmacists, Insights Insights were categorized according to five categories: 1) duplication of therapy, 2) age-related issues, 3) incorrect dose, 4) current side effects and 5) future side effects risks. An external judging committee comprised of two (2) senior clinical pharmacy doctors, who were blinded to the source of the conclusion sheets, adjudicated the MDI system and clinical pharmacist insights.
Receives: Other: Multi-drug analysis of polypharmacy treatment
MDI system groupA group of patient files that were evaluated by the MDI system. Insights were categorized according to five categories: 1) duplication of therapy, 2) age-related issues, 3) incorrect dose, 4) current side effects and 5) future side effects risks. An external judging committee comprised of two (2) senior clinical pharmacy doctors, who were blinded to the source of the conclusion sheets, adjudicated the MDI system and clinical pharmacist insights.
Receives: Other: Multi-drug analysis of polypharmacy treatment

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Primary Outcome Measure: Noninferiority of agreement for combined categories

    The overall mean percentage of agreement between the MDI system/clinical pharmacists and the judging committee (gold standard) for the combined categories

    Measured: 90 days

Secondary outcomes (3)
  1. Secondary Outcome: Noninferiority of agreement for separate categories

    The mean percentage of agreement between the MDI system/clinical pharmacists and the judging committee (gold standard) for the separate categories

    Measured: 90 days

  2. Secondary Outcome: Noninferiority of PPV, NPV, sensitivity and specificity for the combined categories

    PPV, NPV, sensitivity and specificity for the MDI system/clinical pharmacists according to the judging committee (gold standard) for the combined categories

    Measured: 90 days

  3. Secondary Outcome: Noninferiority of PPV, NPV, sensitivity and specificity for the separate categories

    PPV, NPV, sensitivity and specificity for the MDI system/clinical pharmacists according to the judging committee (gold standard) for the separate categories

    Measured: 90 days

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted21 Oct 2022
  2. Study start13 Feb 2021
  3. Primary completion7 Jul 2021
  4. Study completion7 Jul 2021

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Other
Time perspective
Retrospective
Enrolment
91 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT05589870
Sponsor's protocol ID
HMO-0353-19
Status last verified
Oct 2023
First submitted
18 Oct 2022
Last updated
23 Oct 2023
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE. What is polypharmacy? A systematic review of definitions. BMC Geriatr. 2017 Oct 10;17(1):230. doi: 10.1186/s12877-017-0621-2.

    PubMed 29017448
  • Parameswaran Nair N, Chalmers L, Peterson GM, Bereznicki BJ, Castelino RL, Bereznicki LR. Hospitalization in older patients due to adverse drug reactions -the need for a prediction tool. Clin Interv Aging. 2016 May 2;11:497-505. doi: 10.2147/CIA.S99097. eCollection 2016.

    PubMed 27194906
  • Fabbri E, Zoli M, Gonzalez-Freire M, Salive ME, Studenski SA, Ferrucci L. Aging and Multimorbidity: New Tasks, Priorities, and Frontiers for Integrated Gerontological and Clinical Research. J Am Med Dir Assoc. 2015 Aug 1;16(8):640-7. doi: 10.1016/j.jamda.2015.03.013. Epub 2015 May 7.

    PubMed 25958334
  • Johnell K, Klarin I. The relationship between number of drugs and potential drug-drug interactions in the elderly: a study of over 600,000 elderly patients from the Swedish Prescribed Drug Register. Drug Saf. 2007;30(10):911-8. doi: 10.2165/00002018-200730100-00009.

    PubMed 17867728
  • Syrowatka A, Song W, Amato MG, Foer D, Edrees H, Co Z, Kuznetsova M, Dulgarian S, Seger DL, Simona A, Bain PA, Purcell Jackson G, Rhee K, Bates DW. Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review. Lancet Digit Health. 2022 Feb;4(2):e137-e148. doi: 10.1016/S2589-7500(21)00229-6. Epub 2021 Nov 23.

    PubMed 34836823

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