Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ADFICE_IT).

BACKGROUND: Falls are the leading cause of injury-related mortality and hospitalization among adults aged ≥ 65 years. An important modifiable fall-risk factor is use of fall-risk increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs. METHODS: A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged ≥ 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression. DISCUSSION: The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries. TRIAL REGISTRATION: ClinicalTrials.gov NCT05449470 (7-7-2022).

Validation of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients.

OBJECTIVES: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone. DESIGN: Retrospective, combined analysis of 2 prospective cohorts. SETTING AND PARTICIPANTS: Data were included of 1125 patients (aged ≥65 years) who visited the geriatrics department or the emergency department. METHODS: We evaluated the models' discrimination using the C-statistic. Models were updated using logistic regression if calibration intercept or slope values deviated significantly from their ideal values. Decision curve analysis was applied to compare the models' clinical value (ie, net benefit) against that of falls history for different decision thresholds. RESULTS: During the 1-year follow-up, 428 participants (42.7%) endured 1 or more falls, and 224 participants (23.1%) endured a recurrent fall (≥2 falls). C-statistic values were 0.66 (95% CI 0.63-0.69) and 0.69 (95% CI 0.65-0.72) for the Any_fall and Recur_fall models, respectively. Any_fall overestimated the fall risk and we therefore updated only its intercept whereas Recur_fall showed good calibration and required no update. Compared with falls history, Any_fall and Recur_fall showed greater net benefit for decision thresholds of 35% to 60% and 15% to 45%, respectively. CONCLUSIONS AND IMPLICATIONS: The models performed similarly in this data set of geriatric outpatients as in the development sample. This suggests that fall-risk assessment tools that were developed in community-dwelling older adults may perform well in geriatric outpatients. We found that in geriatric outpatients the models have greater clinical value across a wide range of decision thresholds compared with screening for falls history alone.

Clinical Decision Support Systems in Hospitalized Older Patients: An Exploratory Analysis in a Real-Life Clinical Setting.

BACKGROUND: Inappropriate prescribing is associated with negative patient outcomes. In hospitalized patients, the use of Clinical Decision Support Systems (CDSSs) may reduce inappropriate prescribing and thereby improve patient-related outcomes. However, recently published large clinical trials (OPERAM and SENATOR) have shown negative results on the use of CDSSs and patient outcomes and strikingly low acceptance of recommendations. OBJECTIVE: The purpose of the present study was to investigate the use of a CDSS in a real-life clinical setting of hospitalized older patients. As such, we report on the real-life pattern of this in-hospital implemented CDSS, including (i) whether generated alerts were resolved; (ii) whether a recorded action by the pharmacist led to an improved number of resolved alerts; and (iii) the natural course of generated alerts, in particular of those in the non-intervention group; as these data are largely lacking in current studies. METHODS: Hospitalized patients, aged 60 years and older, admitted to Zuyderland Medical Centre, the Netherlands, in 2018 were included. The evaluation of the CDSS was investigated using a database used for standard care. Alongside demographic and clinical data, we also collected the total numbers of CDSS alerts, the number of alerts 'handled' by the pharmacist, those that resulted in an action by the pharmacist, and finally the outcome of the alerts at day 1 and day 3 after the alert was generated. RESULTS: A total of 3574 unique hospitalized patients, mean age 76.7 (SD 8.3) years and 53% female, were included. From these patients, 8073 alerts were generated, of which 7907 (97.9% of total) were handled by the pharmacist (day 1). In 51.6% of the alerts handled by the pharmacist, an action was initiated, resulting in 36.1% of the alerts resolved after day 1, compared with 27.3% if the pharmacist did not perform an action (p < 0.001). On day 3, in 52.6% of the alerts an action by the pharmacist was initiated, resulting in 62.4% resolved alerts, compared with 48.0% when no action was performed (p < 0.001). In the category renal function, the percentages differed significantly between an action versus no action of the pharmacist at day 1 and at day 3 (16.6% vs 10.6%, p < 0.001 [day 1]; 29.8% vs 19.4%, p < 0.001 [day 3]). CONCLUSION: This study demonstrates the pattern and natural course of clinical alerts of an in-hospital implemented CDSS in a real-life clinical setting of hospitalized older patients. Besides the already known beneficial effect of actions by pharmacists, we have also shown that many alerts become resolved without any specific intervention. As such, our study provides an important insight into the spontaneous course of resolved alerts, since these data are currently lacking in the literature.

Development of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Community-Dwelling Older Adults: Pooled Analyses of European Cohorts With Special Attention to Medication.

BACKGROUND: Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications. METHODS: Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal-external cross-validation. RESULTS: Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls. CONCLUSION: Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted.

Control in the Hospital by Extensive Clinical rules for Unplanned hospitalizations in older Patients (CHECkUP); study design of a multicentre randomized study.

BACKGROUND: Due to ageing of the population the incidence of multimorbidity and polypharmacy is rising. Polypharmacy is a risk factor for medication-related (re)admission and therefore places a significant burden on the healthcare system. The reported incidence of medication-related (re)admissions varies widely due to the lack of a clear definition. Some medications are known to increase the risk for medication-related admission and are therefore published in the triggerlist of the Dutch guideline for Polypharmacy in older patients. Different interventions to support medication optimization have been studied to reduce medication-related (re)admissions. However, the optimal template of medication optimization is still unknown, which contributes to the large heterogeneity of their effect on hospital readmissions. Therefore, we implemented a clinical decision support system (CDSS) to optimize medication lists and investigate whether continuous use of a CDSS reduces the number of hospital readmissions in older patients, who previously have had an unplanned probably medication-related hospitalization. METHODS: The CHECkUP study is a multicentre randomized study in older (≥60 years) patients with an unplanned hospitalization, polypharmacy (≥5 medications) and using at least two medications from the triggerlist, from Zuyderland Medical Centre and Maastricht University Medical Centre+ in the Netherlands. Patients will be randomized. The intervention consists of continuous (weekly) use of a CDSS, which generates a Medication Optimization Profile, which will be sent to the patient's general practitioner and pharmacist. The control group will receive standard care. The primary outcome is hospital readmission within 1 year after study inclusion. Secondary outcomes are one-year mortality, number of emergency department visits, nursing home admissions, time to hospital readmissions and we will evaluate the quality of life and socio-economic status. DISCUSSION: This study is expected to add evidence on the knowledge of medication optimization and whether use of a continuous CDSS ameliorates the risk of adverse outcomes in older patients, already at an increased risk of medication-related (re)admission. To our knowledge, this is the first large study, providing one-year follow-up data and reporting not only on quality of care indicators, but also on quality-of-life. TRIAL REGISTRATION: The trial was registered in the Netherlands Trial Register on October 14, 2018, identifier: NL7449 (NTR7691). https://www.trialregister.nl/trial/7449 .

Association between Clinical Frailty Scale score and hospital mortality in adult patients with COVID-19 (COMET): an international, multicentre, retrospective, observational cohort study.

BACKGROUND: During the COVID-19 pandemic, the scarcity of resources has necessitated triage of critical care for patients with the disease. In patients aged 65 years and older, triage decisions are regularly based on degree of frailty measured by the Clinical Frailty Scale (CFS). However, the CFS could also be useful in patients younger than 65 years. We aimed to examine the association between CFS score and hospital mortality and between CFS score and admission to intensive care in adult patients of all ages with COVID-19 across Europe. METHODS: This analysis was part of the COVID Medication (COMET) study, an international, multicentre, retrospective observational cohort study in 63 hospitals in 11 countries in Europe. Eligible patients were aged 18 years and older, had been admitted to hospital, and either tested positive by PCR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or were judged to have a high clinical likelihood of having SARS-CoV-2 infection by the local COVID-19 expert team. CFS was used to assess level of frailty: fit (CFS1-3), mildly frail (CFS4-5), or frail (CFS6-9). The primary outcome was hospital mortality. The secondary outcome was admission to intensive care. Data were analysed using a multivariable binary logistic regression model adjusted for covariates (age, sex, number of drugs prescribed, and type of drug class as a proxy for comorbidities). FINDINGS: Between March 30 and July 15, 2020, 2434 patients (median age 68 years [IQR 55-77]; 1480 [61%] men, 954 [30%] women) had CFS scores available and were included in the analyses. In the total sample and in patients aged 65 years and older, frail patients and mildly frail patients had a significantly higher risk of hospital mortality than fit patients (total sample: CFS6-9 vs CFS1-3 odds ratio [OR] 2·71 [95% CI 2·04-3·60], p<0·0001 and CFS4-5 vs CFS1-3 OR 1·54 [1·16-2·06], p=0·0030; age ≥65 years: CFS6-9 vs CFS1-3 OR 2·90 [2·12-3·97], p<0·0001 and CFS4-5 vs CFS1-3 OR 1·64 [1·20-2·25], p=0·0020). In patients younger than 65 years, an increased hospital mortality risk was only observed in frail patients (CFS6-9 vs CFS1-3 OR 2·22 [1·08-4·57], p=0·030; CFS4-5 vs CFS1-3 OR 1·08 [0·48-2·39], p=0·86). Frail patients had a higher incidence of admission to intensive care than fit patients (CFS6-9 vs CFS1-3 OR 1·54 [1·21-1·97], p=0·0010), whereas mildly frail patients had a lower incidence than fit patients (CFS4-5 vs CFS1-3 OR 0·71 [0·55-0·92], p=0·0090). Among patients younger than 65 years, frail patients had an increased incidence of admission to intensive care (CFS6-9 vs CFS1-3 OR 2·96 [1·98-4·43], p<0·0001), whereas mildly frail patients had no significant difference in incidence compared with fit patients (CFS4-5 vs CFS1-3 OR 0·93 [0·63-1·38], p=0·72). Among patients aged 65 years and older, frail patients had no significant difference in the incidence of admission to intensive care compared with fit patients (CFS6-9 vs CFS1-3 OR 1·27 [0·92-1·75], p=0·14), whereas mildly frail patients had a lower incidence than fit patients (CFS4-5 vs CFS1-3 OR 0·66 [0·47-0·93], p=0·018). INTERPRETATION: The results of this study suggest that CFS score is a suitable risk marker for hospital mortality in adult patients with COVID-19. However, treatment decisions based on the CFS in patients younger than 65 years should be made with caution. FUNDING: LOEY Foundation.

No association between use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers prior to hospital admission and clinical course of COVID-19 in the COvid MEdicaTion (COMET) study.

Since the outbreak of SARS-CoV-2, also known as COVID-19, conflicting theories have circulated on the influence of angiotensin-converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB) on incidence and clinical course of COVID-19, but data are scarce. The COvid MEdicaTion (COMET) study is an observational, multinational study that focused on the clinical course of COVID-19 (i.e. hospital mortality and intensive care unit [ICU] admission), and included COVID-19 patients who were registered at the emergency department or admitted to clinical wards of 63 participating hospitals. Pharmacists, clinical pharmacologists or treating physicians collected data on medication prescribed prior to admission. The association between the medication and composite clinical endpoint, including mortality and ICU admission, was analysed by multivariable logistic regression models to adjust for potential confounders. A total of 4870 patients were enrolled. ACEi were used by 847 (17.4%) patients and ARB by 761 (15.6%) patients. No significant association was seen with ACEi and the composite endpoint (adjusted odds ratio [OR] 0.94; 95% confidence interval [CI] 0.79 to 1.12), mortality (OR 1.03; 95%CI 0.84 to 1.27) or ICU admission (OR 0.96; 95%CI 0.78 to 1.19) after adjustment for covariates. Similarly, no association was observed between ARB and the composite endpoint (OR 1.09; 95%CI 0.90 to 1.30), mortality (OR 1.12; OR 0.90 to 1.39) or ICU admission (OR 1.21; 95%CI 0.98 to 1.49). In conclusion, we found no evidence of a harmful or beneficial effect of ACEi or ARB use prior to hospital admission on ICU admission or hospital mortality.

COvid MEdicaTion (COMET) study: protocol for a cohort study.

Various theories about drugs such as ACE inhibitors or angiotensin II receptor blockers (ARBs) in relation to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and clinical outcomes of COVID-19 are circulating in both mainstream media and medical literature. These are based on the fact that ACE2 facilitates SARS-CoV-2 cell invasion via binding of a viral spike protein to ACE2. However, the effect of ACE inhibitors, ARBs and other drugs on ACE2 is unclear and all theories are based on conflicting evidence mainly from animal studies. Therefore, clinical evidence is urgently needed. The aim of this study is to investigate the relationship between use of these drugs on clinical outcome of patients with COVID-19. Patients will be included from several hospitals in Europe. Data will be collected in a user-friendly database (Digitalis) on an external server. Analyses will be adjusted for sex, age and presence of cardiovascular disease, hypertension and diabetes. These results will enable more rational choices for randomised controlled trials for preventive and therapeutic strategies in COVID-19.