Mortality and morbidity related to hepatitis C virus infection in hospitalized adults-A propensity score matched analysis.

The World Health Organization (WHO) aims to reduce HCV mortality, but estimates are difficult to obtain. We aimed to identify electronic health records of individuals with HCV infection, and assess mortality and morbidity. We applied electronic phenotyping strategies on routinely collected data from patients hospitalized at a tertiary referral hospital in Switzerland between 2009 and 2017. Individuals with HCV infection were identified using International Classification of Disease (ICD)-10 codes, prescribed medications and laboratory results (antibody, PCR, antigen or genotype test). Controls were selected using propensity score methods (matching by age, sex, intravenous drug use, alcohol abuse and HIV co-infection). Main outcomes were in-hospital mortality and attributable mortality (in HCV cases and study population). The non-matched dataset included records from 165,972 individuals (287,255 hospital stays). Electronic phenotyping identified 2285 stays with evidence of HCV infection (1677 individuals). Propensity score matching yielded 6855 stays (2285 with HCV, 4570 controls). In-hospital mortality was higher in HCV cases (RR 2.10, 95%CI 1.64 to 2.70). Among those infected, 52.5% of the deaths were attributable to HCV (95%CI 38.9 to 63.1). When cases were matched, the fraction of deaths attributable to HCV was 26.9% (HCV prevalence: 33%), whilst in the non-matched dataset, it was 0.92% (HCV prevalence: 0.8%). In this study, HCV infection was strongly associated with increased mortality. Our methodology may be used to monitor the efforts towards meeting the WHO elimination targets and underline the importance of electronic cohorts as a basis for national longitudinal surveillance.

Frequency-based rare diagnoses as a novel and accessible approach for studying rare diseases in large datasets: a cross-sectional study.

BACKGROUND: Up to 8% of the general population have a rare disease, however, for lack of ICD-10 codes for many rare diseases, this population cannot be generically identified in large medical datasets. We aimed to explore frequency-based rare diagnoses (FB-RDx) as a novel method exploring rare diseases by comparing characteristics and outcomes of inpatient populations with FB-RDx to those with rare diseases based on a previously published reference list. METHODS: Retrospective, cross-sectional, nationwide, multicenter study including 830,114 adult inpatients. We used the national inpatient cohort dataset of the year 2018 provided by the Swiss Federal Statistical Office, which routinely collects data from all inpatients treated in any Swiss hospital. Exposure: FB-RDx, according to 10% of inpatients with the least frequent diagnoses (i.e.1.decile) vs. those with more frequent diagnoses (deciles 2-10). Results were compared to patients having 1 of 628 ICD-10 coded rare diseases. PRIMARY OUTCOME: In-hospital death. SECONDARY OUTCOMES: 30-day readmission, admission to intensive care unit (ICU), length of stay, and ICU length of stay. Multivariable regression analyzed associations of FB-RDx and rare diseases with these outcomes. RESULTS: 464,968 (56%) of patients were female, median age was 59 years (IQR: 40-74). Compared with patients in deciles 2-10, patients in the 1. were at increased risk of in-hospital death (OR 1.44; 95% CI: 1.38, 1.50), 30-day readmission (OR 1.29; 95% CI 1.25, 1.34), ICU admission (OR 1.50; 95% CI 1.46, 1.54), increased length of stay (Exp(B) 1.03; 95% CI 1.03, 1.04) and ICU length of stay (1.15; 95% CI 1.12, 1.18). ICD-10 based rare diseases groups showed similar results: in-hospital death (OR 1.82; 95% CI 1.75, 1.89), 30-day readmission (OR 1.37; 95% CI 1.32, 1.42), ICU admission (OR 1.40; 95% CI 1.36, 1.44) and increased length of stay (OR 1.07; 95% CI 1.07, 1.08) and ICU length of stay (OR 1.19; 95% CI 1.16, 1.22). CONCLUSION(S): This study suggests that FB-RDx may not only act as a surrogate for rare diseases but may also help to identify patients with rare disease more comprehensively. FB-RDx associate with in-hospital death, 30-day readmission, intensive care unit admission, and increased length of stay and intensive care unit length of stay, as has been reported for rare diseases.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

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.

Asthma Exacerbations in Patients with Type 2 Diabetes and Asthma on Glucagon-like Peptide-1 Receptor Agonists.

Rationale: GLP-1R (glucagon-like peptide-1 receptor) agonists are approved to treat type 2 diabetes mellitus and obesity. GLP-1R agonists reduce airway inflammation and hyperresponsiveness in preclinical models.Objectives: To compare rates of asthma exacerbations and symptoms between adults with type 2 diabetes and asthma prescribed GLP-1R agonists and those prescribed SGLT-2 (sodium-glucose cotransporter-2) inhibitors, DPP-4 (dipeptidyl peptidase-4) inhibitors, sulfonylureas, or basal insulin for diabetes treatment intensification.Methods: This study was an electronic health records-based new-user, active-comparator, retrospective cohort study of patients with type 2 diabetes and asthma newly prescribed GLP-1R agonists or comparator drugs at an academic healthcare system from January 2000 to March 2018. The primary outcome was asthma exacerbations; the secondary outcome was encounters for asthma symptoms. Propensity scores were calculated for GLP-1R agonist and non-GLP-1R agonist use. Zero-inflated Poisson regression models included adjustment for multiple covariates.Measurements and Main Results: Patients initiating GLP-1R agonists (n = 448), SGLT-2 inhibitors (n = 112), DPP-4 inhibitors (n = 435), sulfonylureas (n = 2,253), or basal insulin (n = 2,692) were identified. At 6 months, asthma exacerbation counts were lower in persons initiating GLP-1R agonists (reference) compared with SGLT-2 inhibitors (incidence rate ratio [IRR], 2.98; 95% confidence interval [CI], 1.30-6.80), DPP-4 inhibitors (IRR, 2.45; 95% CI, 1.54-3.89), sulfonylureas (IRR, 1.83; 95% CI, 1.20-2.77), and basal insulin (IRR, 2.58; 95% CI, 1.72-3.88). Healthcare encounters for asthma symptoms were also lower among GLP-1R agonist users.Conclusions: Adult patients with asthma prescribed GLP-1R agonists for type 2 diabetes had lower counts of asthma exacerbations compared with other drugs initiated for treatment intensification. GLP-1R agonists may represent a novel treatment for asthma associated with metabolic dysfunction.

Risk Factors of In-Hospital Mortality in Patients Treated for Pneumonia at a Tertiary Care Centre in Switzerland.

BACKGROUND: Little is known about risk factors upon hospital admission that are associated with in-hospital death of patients hospitalized for bacterial pneumonia. Identifying such factors may help to optimize the treatment and lower the mortality of these patients. OBJECTIVES: The aim of the study was to characterize baseline characteristics of patients hospitalized for bacterial pneumonia in Switzerland and to identify risk factors associated with all-cause in-hospital mortality. METHODS: Routinely collected electronic health record data of patients discharged from a large Swiss tertiary care hospital between August 2009 and 2017 were analysed. Potential risk factors such as patient demographics, physical examination findings, vital signs, laboratory results, and comorbidities were considered within ±24 h of admission. Univariable and multivariable logistic regression models identified risk factors for in-hospital death. The area under the receiver operating characteristic (ROC) curve was used to compare the identified factors to existing pneumonia scoring systems. RESULTS: Out of 1,781 hospital stays with initial and main diagnosis of bacterial pneumonia, 85 patients (4.85%) died (33.9% female, median age 62.3 years [interquartile range, 52-75]). Age, low systolic blood pressure, underweight, a missing value for body mass index, decreased haemoglobin level, raised C-reactive protein, high urea, high lactate dehydrogenase, concomitant pleural effusion, and cancer were independently associated with in-hospital death. The area under the ROC curve was 0.89 for the multivariable model containing the identified predictors. CONCLUSIONS: Our data are consistent with previous trials characterizing patients hospitalized for pneumonia. Additionally, we identified new and independent risk factors associated with in-hospital death among patients treated for bacterial pneumonia. Findings need to be further validated in larger multicentre cohorts.

A RCT evaluating a pragmatic in-hospital service to increase the quality of discharge prescriptions.

OBJECTIVE: To improve discharge prescription quality and information transfer to improve post-hospital care with a pragmatic in-hospital service. DESIGN: A single-centre, randomized controlled trial. SETTING: Internal medicine wards in a Swiss teaching hospital. PARTICIPANTS: Adult patients discharged to their homes, 76 each in the intervention and control group. INTERVENTION: Medication reconciliation at discharge by a clinical pharmacist, a prescription check for formal flaws, interactions and missing therapy durations. Important information was annotated on the prescription. MAIN OUTCOME MEASURES: At the time of medication dispensing, community pharmacy documented their pharmaceutical interventions when filling the prescription. A Poisson regression model was used to compare the number of interventions (primary outcome). The significance of the pharmaceutical interventions was categorized by the study team. Comparative analysis was used for the significance of interventions (secondary outcome). RESULTS: The community pharmacy staff performed 183 interventions in the control group, and 169 in the intervention group. The regression model revealed a relative risk for an intervention of 0.78 (95% CI 0.62-0.99, p = 0.04) in the intervention group. The rate of clinically significant interventions was lower in the intervention group than in the control group (72 of 169 (42%) vs. 108 of 183 (59%), p < 0.01), but more economically significant interventions were performed (98, 58% vs. 80, 44%, p < 0.01). CONCLUSIONS: The pragmatic in-hospital service increased the quality of prescriptions. The intervention group had a lower risk for the need for pharmaceutical interventions, and clinically significant interventions were less frequent. Overall, our pragmatic approach showed promising results to optimize post-discharge care.

Automated identification of an aspirin-exacerbated respiratory disease cohort.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) is characterized by 3 clinical features: asthma, nasal polyposis, and respiratory reactions to cyclooxygenase-1 inhibitors (nonsteroidal anti-inflammatory drugs). Electronic health records (EHRs) contain information on each feature of this triad. OBJECTIVE: We sought to determine whether an informatics algorithm applied to the EHR could electronically identify patients with AERD. METHODS: We developed an informatics algorithm to search the EHRs of patients aged 18 years and older from the Partners Healthcare system over a 10-year period (2004-2014). Charts with search terms for asthma, nasal polyps, and record of respiratory (cohort A) or unspecified (cohort B) reactions to nonsteroidal anti-inflammatory drugs were identified as "possible AERD." Two clinical experts reviewed all charts to confirm a diagnosis of "clinical AERD" and classify cases as "diagnosed AERD" or "undiagnosed AERD" on the basis of physician-documented AERD-specific terms in patient notes. RESULTS: Our algorithm identified 731 "possible AERD" cases, of which 638 were not in our AERD patient registry. Chart review of cohorts A (n = 511) and B (n = 127) demonstrated a positive predictive value of 78.4% for "clinical AERD," which rose to 88.7% when unspecified reactions were excluded. Of those with clinical AERD, 12.4% had no mention of AERD by any treating caregiver and were classified as "undiagnosed AERD." "Undiagnosed AERD" cases were less likely than "diagnosed AERD" cases to have been seen by an allergist/immunologist (38.7% vs 93.2%; P < .0001). CONCLUSIONS: An informatics algorithm can successfully identify both known and previously undiagnosed cases of AERD with a high positive predictive value. Involvement of an allergist/immunologist significantly increases the likelihood of an AERD diagnosis.

Patient- and physician-related risk factors for hyperkalaemia in potassium-increasing drug-drug interactions.

PURPOSE: Hyperkalaemia due to potassium-increasing drug-drug interactions (DDIs) is a clinically important adverse drug event. The purpose of this study was to identify patient- and physician-related risk factors for the development of hyperkalaemia. METHODS: The risk for adult patients hospitalised in the University Hospital Zurich between 1 December 2009 and 31 December 2011 of developing hyperkalaemia was correlated with patient characteristics, number, type and duration of potassium-increasing DDIs and frequency of serum potassium monitoring. RESULTS: The 76,467 patients included in this study were prescribed 8,413 potentially severe potassium-increasing DDIs. Patient-related characteristics associated with the development of hyperkalaemia were pulmonary allograft [relative risk (RR) 5.1; p < 0.0001), impaired renal function (RR 2.7; p < 0.0001), diabetes mellitus (RR 1.6; p = 0.002) and female gender (RR 1.5; p = 0.007). Risk factors associated with medication were number of concurrently administered potassium-increasing drugs (RR 3.3 per additional drug; p < 0.0001) and longer duration of the DDI (RR 4.9 for duration ≥6 days; p < 0.0001). Physician-related factors associated with the development of hyperkalaemia were undetermined or elevated serum potassium level before treatment initiation (RR 2.2; p < 0.001) and infrequent monitoring of serum potassium during a DDI (interval >48 h: RR 1.6; p < 0.01). CONCLUSION: Strategies for reducing the risk of hyperkalaemia during potassium-increasing DDIs should consider both patient- and physician-related risk factors.

Palliative care inpatients in Switzerland (2012-2021): characteristics, in-hospital mortality and avoidable admissions.

OBJECTIVES: Palliative patients generally prefer to be cared for and die at home. Overly aggressive treatments place additional strain on already burdened patients and healthcare services, contributing to decreased quality of life and increased healthcare costs. This study characterises palliative inpatients, quantifies in-hospital mortality and potentially avoidable hospitalisations. METHODS: We conducted a multicentre retrospective analysis using the national inpatient cohort. The extracted data encompassed all inpatients for palliative care spanning the years 2012-2021. The dataset comprised information on demographics, diagnoses, comorbidities, treatments and clinical outcomes. Content experts reviewed a list of treatments for which no hospitalisation was required. RESULTS: 120 396 hospitalisation records indicated palliative patients. Almost half were women (n=59 297, 49%). Most patients were ≥65 years old. 66% had an oncologic primary diagnosis. The majority were admitted from home (82 443; 69%). The patients stayed a median of 12 days (6-20). All treatments for 25 188 patients (21%) could have been performed at home. In-hospital deaths ended 64 739 stays (54%); of note, 10% (n=6357/64 739) of in-hospital deaths occurred within 24 hours. CONCLUSIONS: In this nationwide study of palliative inpatients, two-thirds were 65 years old and older. Regarding the performed treatments alone, a fifth of these hospitalisations can be considered as avoidable. More than half of the patients died during their hospital stay, and 1 in 10 of those within 24 hours.

An overview of reviews and synthesis across 440 studies examines the importance of hospital readmission predictors across various patient populations.

OBJECTIVES: The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations. STUDY DESIGN AND SETTING: An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations. RESULTS: Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes. CONCLUSION: Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

Hospitalisations Related to Adverse Drug Reactions in Switzerland in 2012-2019: Characteristics, In-Hospital Mortality, and Spontaneous Reporting Rate.

INTRODUCTION: Adverse drug reactions (ADRs) contribute to morbidity, and serious ADRs may cause hospitalisation and death. This study characterises and quantifies ADR-related hospitalisations and subsequent in-hospital deaths, and estimates the spontaneous reporting rate to regulatory authorities in Switzerland, where healthcare professionals are legally obliged to report ADRs. METHODS: This retrospective cohort study from 2012 to 2019 analysed nationwide data from the Federal Statistical Office. ICD-10 coding rules identified ADR-related hospitalisations. To estimate the reporting rate, individual case safety reports (ICSRs) collected in the Swiss spontaneous reporting system during the same period were considered. RESULTS: Among 11,240,562 inpatients, 256,550 (2.3%) were admitted for ADRs, 132,320 (51.6%) were female, 120,405 (46.9%) were aged ≥ 65 (median of three comorbidities, interquartile range [IQR] 2-4), and 16,754 (6.5%) were children/teenagers (0 comorbidities, IQR 0-1). Frequent comorbidities were hypertension (89,938 [35.1%]), fluid/electrolyte disorders (54,447 [21.2%]), renal failure (45,866 [17.9%]), cardiac arrhythmias (37,906 [14.8%]), and depression (35,759 [13.9%]). Physicians initiated 113,028 (44.1%) of hospital referrals, and patients/relatives 73,494 (28.6%). Frequently ADR-affected were the digestive system (48,219 [18.8%], e.g. noninfective gastroenteritis and colitis), the genitourinary system (39,727 [15.5%], e.g. acute renal failure), and the mental/behavioural state (39,578 [15.4%], e.g. opioid dependence). In-hospital mortality was 2.2% (5669). Since ICSRs indicated 14,109 hospitalisations and 700 in-hospital deaths, estimated reporting rates were 5% and 12%, respectively. CONCLUSIONS: This 8-year observation in Switzerland revealed that 2.3%, or roughly 32,000 admissions per year, were caused by ADRs. The majority of ADR-related admissions were not reported to the regulatory authorities, despite legal obligations.

Impact of primary care physicians working part-time on patient care: A scoping review.

BACKGROUND: Increasing numbers of primary care physicians (PCPs) are reducing their working hours. This decline may affect the workforce and the care provided to patients. OBJECTIVES: This scoping review aims to determine the impact of PCPs working part-time on quality of patient care. METHODS: A systematic search was conducted using the databases PubMed, CINAHL, Embase, and the Cochrane Library. Peer-reviewed, original articles with either quantitative, qualitative or mixed methods designs, published after 2000 and written in any language were considered. The search strings combined the two concepts: part-time work and primary care. Studies were included if they examined any effect of PCPs working part-time on quality of patient care. RESULTS: The initial search resulted in 2,323 unique studies. Abstracts were screened, and information from full texts on the study design, part-time and quality of patient care was extracted. The final dataset included 14 studies utilising data from 1996 onward. The studies suggest that PCPs working part-time may negatively affect patient care, particularly the access and continuity of care domains. Clinical outcomes and patient satisfaction seem mostly unaffected or even improved. CONCLUSION: There is evidence of both negative and positive effects of PCPs working part-time on quality of patient care. Approaches that mitigate negative effects of part-time work while maintaining positive effects should be implemented.

The impact of pharmacist-led medication reconciliation and interprofessional ward rounds on drug-related problems at hospital discharge.

BACKGROUND: During transitions of care, including hospital discharge, patients are at risk of drug-related problems (DRPs). AIM: To investigate the impact of pharmacist-led services, specifically medication reconciliation at admission and/or interprofessional ward rounds on the number of DRPs at discharge. METHOD: In this retrospective, single-center cohort study, we analyzed routinely collected data of patients discharged from internal medicine wards of a regional Swiss hospital that filled their discharge prescriptions in the hospital's community pharmacy between June 2016 and May 2019. Patients receiving one of the two or both pharmacist-led services (Study groups: Best Care = both services; MedRec = medication reconciliation at admission; Ward Round = interprofessional ward round), were compared to patients receiving standard care (Standard Care group). Standard care included medication history taken by a physician and regular ward rounds (physicians and nurses). At discharge, pharmacists reviewed discharge prescriptions filled at the hospital's community pharmacy and documented all DRPs. Multivariable Poisson regression analyzed the independent effects of medication reconciliation and interprofessional ward rounds as single or combined service on the frequency of DRPs. RESULTS: Overall, 4545 patients with 6072 hospital stays were included in the analysis (Best Care n = 72 hospital stays, MedRec n = 232, Ward Round n = 1262, and Standard Care n = 4506). In 1352 stays (22.3%) one or more DRPs were detected at hospital discharge. The combination of the two pharmacist-led services was associated with statistically significantly less DRPs compared to standard care (relative risk: 0.33; 95% confidence interval: 0.16, 0.65). Pharmacist-led medication reconciliation alone showed a trend towards fewer DRPs (relative risk: 0.75; 95% confidence interval: 0.54, 1.03). CONCLUSION: Our results support the implementation of pharmacist-led medication reconciliation at admission in combination with interprofessional ward rounds to reduce the number of DRPs at hospital discharge.

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 ß-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.

Clinical decision support for monitoring drug-drug-interactions and potassium-increasing drug combinations: need for specific alerts.

Computer-triggered reminders alerting physicians on every potentially harmful drug-drug-interaction (DDI) induce alert fatigue due to frequent messages of limited clinical relevance. On demand DDI-checks, however, are not commonly used by physicians. Optimal strategies for sustained quality assurance have to consider patients' risk factors and focus on the most significant DDIs only. An approach is proposed based on the analysis of concurrent prescription of potassium-sparing diuretics and potassium supplements (CPPP), which are the most frequent DDIs classified as contraindicated. Although the frequency of monitoring potassium serum levels declined during prolonged periods of CPPP, the likelihood of observing a hyperkalaemia increased. The median treatment period of CPPP was 3.3 days, whereas hyperkalaemia occurred after a median observation time of 4.5 days of CPPP. Thus, computer-triggered reminders for ordering potassium serum levels may be indicated if monitoring has been discontinued after 48h of CPPP.

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.

30-Day Potentially Avoidable Readmissions Due to Adverse Drug Events.

OBJECTIVE: To analyze the patterns of potentially avoidable readmissions due to adverse drug events (ADEs) to identify the most appropriate risk reduction interventions. METHODS: In this observational study, we analyzed a random sample of 534 potentially avoidable 30-day readmissions from 10,275 consecutive discharges from the medical department of an academic hospital. Readmissions due to ADEs were reviewed to identify the causative drugs and the severity and interventions to prevent them. RESULTS: Seventy cases (13.1%) of readmission were partially or predominantly due to ADEs, of which, 58 (82.9%) were serious ADEs. Overall, 65 (92.9%) of the ADEs have been confirmed to be preventable. Inappropriate prescribing was identified as the cause of ADE in 34 cases (48.6%) mainly involving diuretics, analgesics, or antithrombotics: misprescribing n = 19 (27.1%), underprescribing n = 8 (11.4%), and overprescribing n = 7 (10.0%). The remaining half of preventable ADEs (n = 36; 51.4%) were related to suboptimal patient monitoring/education, such as adherence issues (n = 6; 8.6%) or lack of monitoring (n = 31; 44.3%). In 64 cases (91.4%), the readmission could have been potentially prevented by better monitoring for drug efficacy/disease control, or for predictable side effect. Thirty-three (97.1%) of the 34 ADEs due to inappropriate prescribing could have also been prevented by better monitoring. CONCLUSIONS: Adverse drug events accounted for approximately 13% of 30-day preventable readmissions. A half were due to prescription errors involving mainly diuretics, analgesics, or antithrombotics, and the other half were due to suboptimal patient monitoring/education, most frequently with antineoplastics. Both these avoidable causes may represent opportunities to reduce the total drug-related adverse events.