Evaluation of the association of anticholinergic burden and delirium in older hospitalised patients - A cohort study comparing 19 anticholinergic burden scales

AIMS A recent review identified 19 anticholinergic burden scales (ABSs) but no study has yet compared the impact of all 19 ABSs on delirium. We evaluated whether a high anticholinergic burden as classified by each ABS is associated with incident delirium. METHOD We performed a retrospective cohort study in a Swiss tertiary teaching hospital using data from 2015-2018. Included were patients aged ≥65, hospitalised ≥48 hours with no stay >24 hours in intensive care. Delirium was defined twofold: (i) ICD-10 or CAM and (ii) ICD-10 or CAM or DOSS. Patients' cumulative anticholinergic burden score, calculated within 24 hours after admission, was classified using a binary (<3: low, ≥3: high burden) and a categorical approach (0: no, 0.5-3: low, ≥3: high burden). Association was analysed using multivariable logistic regression. RESULTS Over 25 000 patients (mean age 77.9 ± 7.6 years) were included. Of these, (i) 864 (3.3%) and (ii) 2770 (11.0%) developed delirium. Depending on the evaluated ABS, 4-63% of the patients were exposed to at least one anticholinergic drug. Out of 19 ABSs, (i) 14 and (ii) 16 showed a significant association with the outcomes. A patient with a high anticholinergic burden score had odds ratios (ORs) of 1.21 (95% confidence interval [CI]: 1.03-1.42) to 2.63 (95% CI: 2.28-3.03) for incident delirium compared to those with low or no burden. CONCLUSION A high anticholinergic burden within 24 hours after admission was significantly associated with incident delirium. Although prospective studies need to confirm these results, discontinuing or substituting drugs with a score of ≥3 at admission might be a targeted intervention to reduce incident delirium

Predicting delirium in older non-intensive care unit inpatients: development and validation of the DELIrium risK Tool (DELIKT)

BACKGROUND Effective delirium prevention could benefit from automatic risk stratification of older inpatients using routinely collected clinical data. AIM Primary aim was to develop and validate a delirium prediction model (DELIKT) suitable for implementation in hospitals. Secondary aim was to select an anticholinergic burden scale as a predictor. METHOD We used one cohort for model development and another for validation with electronically available data collected within the first 24 h of admission. Included were patients aged ≥ 65, hospitalised ≥ 48 h with no stay > 24 h in an intensive care unit. Predictors, such as administrative and laboratory variables or an anticholinergic burden scale, were selected using a combination of feature selection filter method and forward/backward selection. The final model was based on logistic regression and the DELIKT was derived from the β-coefficients. We report the following performance measures: area under the curve, sensitivity, specificity and odds ratio. RESULTS Both cohorts were similar and included over 10,000 patients each (mean age 77.6 ± 7.6 years) with 11% experiencing delirium. The model included nine variables: age, medical department, dementia, hemi-/paraplegia, catheterisation, potassium, creatinine, polypharmacy and the anticholinergic burden measured with the Clinician-rated Anticholinergic Scale (CrAS). The external validation yielded an AUC of 0.795. With a cut-off at 20 points in the DELIKT, we received a sensitivity of 79.7%, specificity of 62.3% and an odds ratio of 5.9 (95% CI 5.2, 6.7). CONCLUSION The DELIKT is a potentially automatic tool with predictors from standard care including the CrAS to identify patients at high risk for delirium

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them.Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency.Results: This study revealed 2’496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss’ kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss’ kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references.Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions

Development and Validation of Automatic Tools to Improve Adverse Drug Event Management in Hospitalised Older Patients

This thesis aims to contribute to improving the management of adverse drug events of hospitalised older patients, especially for drugs with anticholinergic properties, by identifying risk factors to develop and validate automatic prediction models. The thesis consists of four subprojects (Chapters II to IV) and is framed by a general introduction (Chapter I) and an overall discussion, conclusion and outlook (Chapter V). In the first subproject, we identified all published scales that score drugs from 0 to 3 depending on their anticholinergic activity. Further, we assessed their quality and impact on clinical outcomes. In the next projects, we used data from patients’ electronic health records from a Swiss tertiary teaching hospital to evaluate the association of all published scales with in-hospital mortality, length of stay and delirium during hospitalisation. Finally, we developed and validated prediction models to study their potential usefulness in clinics for tailored decision-making

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland.

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2’496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss’ kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss’ kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions

High anticholinergic burden at admission associated with in-hospital mortality in older patients: A comparison of 19 different anticholinergic burden scales

Although no gold standard exists to assess a patient's anticholinergic burden, a review identified 19 anticholinergic burden scales (ABSs). No study has yet evaluated whether a high anticholinergic burden measured with all 19 ABSs is associated with in-hospital mortality and length of stay (LOS). We conducted a cohort study at a Swiss tertiary teaching hospital using patients' electronic health record data from 2015-2018. Included were patients aged ≥65 years, hospitalised ≥48 h without stays >24 h in intensive care. Patients' cumulative anticholinergic burden score was classified using a binary (<3: low, ≥3: high) and categorical approach (0: no, 0.5-3: low, ≥3: high). In-hospital mortality and LOS were analysed using multivariable logistic and linear regression, respectively. We included 27,092 patients (mean age 78.0±7.5 years, median LOS 6 days). Of them, 913 died. Depending on the evaluated ABS, 1,370 to 17,035 patients were exposed to anticholinergics. Patients with a high burden measured by all 19 ABSs were associated with a 1.32- to 3.03-fold increase in in-hospital mortality compared to those with no/low burden. We obtained similar results for LOS. To conclude, discontinuing drugs with anticholinergic properties (score ≥3) at admission might be a targeted intervention to decrease in-hospital mortality and LOS

External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients

BACKGROUND Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort. METHODS A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression. RESULTS The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort. CONCLUSION This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models

High anticholinergic burden at admission associated with in-hospital mortality in older patients: A comparison of 19 different anticholinergic burden scales

Although no gold standard exists to assess a patient's anticholinergic burden, a review identified 19 anticholinergic burden scales (ABSs). No study has yet evaluated whether a high anticholinergic burden measured with all 19 ABSs is associated with in-hospital mortality and length of stay (LOS). We conducted a cohort study at a Swiss tertiary teaching hospital using patients' electronic health record data from 2015–2018. Included were patients aged ≥65 years, hospitalised ≥48 h without stays and >24 h in intensive care. Patients' cumulative anticholinergic burden score was classified using a binary (<3: low, ≥3: high) and categorical approach (0: no, 0.5–3: low, ≥3: high). In-hospital mortality and LOS were analysed using multivariable logistic and linear regression, respectively. We included 27,092 patients (mean age 78.0 ± 7.5 years, median LOS 6 days). Of them, 913 died. Depending on the evaluated ABS, 1370 to 17,035 patients were exposed to anticholinergics. Patients with a high burden measured by all 19 ABSs were associated with a 1.32- to 3.03-fold increase in in-hospital mortality compared with those with no/low burden. We obtained similar results for LOS. To conclude, discontinuing drugs with anticholinergic properties (score ≥3) at admission might be a targeted intervention to decrease in-hospital mortality and LOS.ISSN:1742-7835ISSN:1742-784

External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients

Background Readmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort. Methods A cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression. Results The final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575–0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33–5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort. Conclusion This external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.ISSN:1932-620

Evaluation of the association of anticholinergic burden and delirium in older hospitalised patients – A cohort study comparing 19 anticholinergic burden scales

Aims A recent review identified 19 anticholinergic burden scales (ABSs) but no study has yet compared the impact of all 19 ABSs on delirium. We evaluated whether a high anticholinergic burden as classified by each ABS is associated with incident delirium. Method We performed a retrospective cohort study in a Swiss tertiary teaching hospital using data from 2015–2018. Included were patients aged ≥65, hospitalised ≥48 hours with no stay >24 hours in intensive care. Delirium was defined twofold: (i) ICD-10 or CAM and (ii) ICD-10 or CAM or DOSS. Patients' cumulative anticholinergic burden score, calculated within 24 hours after admission, was classified using a binary (<3: low, ≥3: high burden) and a categorical approach (0: no, 0.5–3: low, ≥3: high burden). Association was analysed using multivariable logistic regression. Results Over 25 000 patients (mean age 77.9 ± 7.6 years) were included. Of these, (i) 864 (3.3%) and (ii) 2770 (11.0%) developed delirium. Depending on the evaluated ABS, 4–63% of the patients were exposed to at least one anticholinergic drug. Out of 19 ABSs, (i) 14 and (ii) 16 showed a significant association with the outcomes. A patient with a high anticholinergic burden score had odds ratios (ORs) of 1.21 (95% confidence interval [CI]: 1.03–1.42) to 2.63 (95% CI: 2.28–3.03) for incident delirium compared to those with low or no burden. Conclusion A high anticholinergic burden within 24 hours after admission was significantly associated with incident delirium. Although prospective studies need to confirm these results, discontinuing or substituting drugs with a score of ≥3 at admission might be a targeted intervention to reduce incident delirium