Description of a clinical decision support tool with integrated dose calculator for paediatrics

Medication errors, especially dosing errors are a leading cause of preventable harm in paediatric patients. The paediatric patient population is particularly vulnerable to dosing errors due to immaturity of metabolising organs and developmental changes. Moreover, the lack of clinical trial data or suitable drug forms, and the need for weight-based dosing, does not simplify drug dosing in paediatric or neonatal patients. Consequently, paediatric pharmacotherapy often requires unlicensed and off-label use including manipulation of adult dosage forms. In practice, this results in the need to calculate individual dosages which in turn increases the likelihood of dosing errors. In the age of digitalisation, clinical decision support (CDS) tools can support healthcare professionals in their daily work. CDS tools are currently amongst the gold standards in reducing preventable errors. In this publication, we describe the development and core functionalities of the CDS tool PEDeDose, a Class IIa medical device software certified according to the European Medical Device Regulation. The CDS tool provides a drug dosing formulary with an integrated calculator to determine individual dosages for paediatric, neonatal, and preterm patients. Even a technical interface is part of the CDS tool to facilitate integration into primary systems. This enables the support of the paediatrician directly during the prescribing process without changing the user interface. Conclusion: PEDeDose is a state-of-the-art CDS tool for individualised paediatric drug dosing that includes a certified calculator

Impact of a clinical decision support system on paediatric drug dose prescribing: a randomised within-subject simulation trial

BACKGROUND Drug dosing errors are among the most frequent causes of preventable harm in paediatrics. Due to the complexity of paediatric pharmacotherapy and the working conditions in healthcare, it is not surprising that human factor is a well-described source of error. Thus, a clinical decision support system (CDSS) that supports healthcare professionals (HCP) during the dose prescribing step provides a promising strategy for error prevention. METHODS The aim of the trial was to simulate the dose derivation step during the prescribing process. HCPs were asked to derive dosages for 18 hypothetical patient cases. We compared the CDSS PEDeDose, which provides a built-in dose calculator to the Summary of Product Characteristics (SmPC) used together with a pocket calculator in a randomised within-subject trial. We assessed the number of dose calculation errors and the time needed for calculation. Additionally, the effect of PEDeDose without using the built-in calculator but with a pocket calculator instead was assessed. RESULTS A total of 52 HCPs participated in the trial. The OR for an erroneous dosage using the CDSS as compared with the SmPC with pocket calculator was 0.08 (95% CI 0.02 to 0.36, p<0.001). Thus, the odds of an error were 12 times higher while using the SmPC. Furthermore, there was a 45% (95% CI 39% to 51%, p<0.001) time reduction when the dosage was derived using the CDSS. The exploratory analysis revealed that using only PEDeDose but without the built-in calculator did not substantially reduce errors. CONCLUSION Our results provide robust evidence that the use of the CDSS is safer and more efficient than manual dose derivation in paediatrics. Interestingly, only consulting a dosing database was not sufficient to substantially reduce errors. We are confident the CDSS PEDeDose ensures a higher safety and speeds up the prescribing process in practice

PEDeDose Simulation Study

R scripts and collected data of the PEDeDose simulation stud

Incidence of Differentiation Syndrome Associated with Treatment Regimens in Acute Myeloid Leukemia: A Systematic Review of the Literature

Differentiation syndrome (DS) is a potentially fatal adverse drug reaction caused by the so-called differentiating agents such as all-trans retinoic acid (ATRA) and arsenic trioxide (ATO), used for remission induction in the treatment of the M3 subtype of acute myeloid leukemia (AML), acute promyelocytic leukemia (APL). However, recent DS reports in trials of isocitrate dehydrogenase (IDH)-inhibitor drugs in patients with IDH-mutated AML have raised concerns. Given the limited knowledge of the incidence of DS with differentiating agents, we conducted a systematic literature review of clinical trials with reports of DS to provide a comprehensive overview of the medications associated with DS. In particular, we focused on the incidence of DS reported among the IDH-inhibitors, compared to existing ATRA and ATO therapies. We identified 44 published articles, encompassing 39 clinical trials, including 6949 patients. Overall, the cumulative incidence of DS across all treatment regimens was 17.7%. Incidence of DS was notably lower in trials with IDH-inhibitors (10.4%) compared to other regimens, including ATRA and/or ATO (15.4–20.6%). Compared to other therapies, the median time to onset was four times longer with IDH-inhibitors (48 vs. 11 days). Treating oncologists should be mindful of this potentially fatal adverse drug reaction, as we expect the current trials represent an underestimation of the actual incidence.ISSN:2077-038

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

Impact of a clinical decision support system on paediatric drug dose prescribing: a randomised within-subject simulation trial

Background Drug dosing errors are among the most frequent causes of preventable harm in paediatrics. Due to the complexity of paediatric pharmacotherapy and the working conditions in healthcare, it is not surprising that human factor is a well-described source of error. Thus, a clinical decision support system (CDSS) that supports healthcare professionals (HCP) during the dose prescribing step provides a promising strategy for error prevention.Methods The aim of the trial was to simulate the dose derivation step during the prescribing process. HCPs were asked to derive dosages for 18 hypothetical patient cases. We compared the CDSS PEDeDose, which provides a built-in dose calculator to the Summary of Product Characteristics (SmPC) used together with a pocket calculator in a randomised within-subject trial. We assessed the number of dose calculation errors and the time needed for calculation. Additionally, the effect of PEDeDose without using the built-in calculator but with a pocket calculator instead was assessed.Results A total of 52 HCPs participated in the trial. The OR for an erroneous dosage using the CDSS as compared with the SmPC with pocket calculator was 0.08 (95% CI 0.02 to 0.36, p&lt;0.001). Thus, the odds of an error were 12 times higher while using the SmPC. Furthermore, there was a 45% (95% CI 39% to 51%, p&lt;0.001) time reduction when the dosage was derived using the CDSS. The exploratory analysis revealed that using only PEDeDose but without the built-in calculator did not substantially reduce errors.Conclusion Our results provide robust evidence that the use of the CDSS is safer and more efficient than manual dose derivation in paediatrics. Interestingly, only consulting a dosing database was not sufficient to substantially reduce errors. We are confident the CDSS PEDeDose ensures a higher safety and speeds up the prescribing process in practice

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