A smart algorithm for the prevention and risk management of QTc prolongation based on the optimized RISQ-PATH model

The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

Determining sensitivity and specificity of risk scores for QTc interval prolongation in hemato-oncology patients prescribed systemic antifungal therapy: a retrospective cross-sectional study

BACKGROUND

QTc interval prolongation can result in potentially lethal arrhythmias. One risk factor is QTc-prolonging drugs, including some antifungals often used in hemato-oncology patients. Screening tools for patients at risk have not yet been investigated in this patient population.

AIM

Our aim was to evaluate the sensitivity and specificity of five QTc risk scores in hemato-oncology patients receiving systemic antifungal therapy.

METHOD

Data were retrieved from an internal study database including adult hemato-oncology patients prescribed systemic antifungal therapy. Data on QTc-prolonging medication, risk factors for QTc prolongation, and electrocardiograms (ECG) were collected retrospectively for a period of 12 months. The QTc risk scores according to Tisdale, Vandael, Berger, Bindraban, and Aboujaoude as well as their sensitivity and specificity were calculated.

RESULTS

During the evaluated period, 77 patients were prescribed systemic antifungals resulting in 187 therapy episodes. Regarding therapy episodes, median age was 56 years (IQR 44-68), 41% (77) were female, and a median of 3 QTc-prolonging drugs were prescribed (range 0-6). ECGs were available for 45 (24%) of the therapy episodes 3-11 days after initiation of the antifungal therapy, 22 of which showed QTc prolongation. Regarding these 45 therapy episodes, sensitivity and specificity of the risk scores were calculated as follows: Tisdale 86%/22%, Vandael 91%/35%, Berger 32%/83%, Bindraban 50%/78%, Aboujaoude 14%/87%.

CONCLUSION

The QTc risk scores according to Tisdale and Vandael showed sufficient sensitivity for risk stratification in the studied patient population. In contrast, risk scores according to Berger, Bindraban, and Aboujaoude cannot be considered suitable due to poor sensitivity.

A Calculated Risk: Evaluation of QTc Drug-Drug Interaction (DDI) Clinical Decision Support (CDS) Alerts and Performance of the Tisdale Risk Score Calculator

INTRODUCTION

A risk factor for a potentially fatal ventricular arrhythmia Torsade de Pointes is a prolongation in the heart rate-corrected QT interval (QTc) ≥ 500 milliseconds (ms) or an increase of ≥ 60 ms from a patient's baseline value, which can cause sudden cardiac death. The Tisdale risk score calculator uses clinical variables to predict which hospitalized patients are at the highest risk for QTc prolongation.

OBJECTIVE

To determine the rate of overridden QTc drug-drug interaction (DDI)-related clinical decision support (CDS) alerts per patient admission and the prevalence by Tisdale risk score category of these overridden alerts. Secondary outcome was to determine the rate of drug-induced QTc prolongation (diQTP) associated with overrides.

METHODS

Our organization's enterprise data warehouse was used to retrospectively access QTc DDI alerts presented for patients aged ≥ 18 years who were admitted to Brigham and Women's Hospital during 2022. The QTc DDI CDS alerts were included if shown to a physician, fellow, resident, physician assistant, or nurse practitioner when entering the order in inpatient areas for patients with a length of stay of at least 2 days. Variables collected for the Tisdale calculator included age, sex, whether patient was on a loop diuretic, potassium level, admission QTc value, admitting diagnosis of acute myocardial infarction, sepsis, or heart failure, and number of QTc-prolonging drugs given to the patient.

RESULTS

A total of 2649 patients with 3033 patient admissions had 18,432 QTc DDI alerts presented that were overridden. An average of 3 unique QTc DDI alerts were presented per patient admission and the alerts were overridden an average of 6 times per patient admission. Overall, 6% of patient admissions were low risk (score ≤ 6), 64% moderate risk (score 7-10), and 30% high risk (score ≥ 11) of QTc prolongation. The most common QTc DDI alerts overridden resulting in an diQTP were quetiapine and propofol (11%) and amiodarone and haloperidol (7%). The diQTP occurred in 883 of patient admissions (29%) and was more frequent in those with higher risk score, with 46% of patient admissions with diQTP in high risk, 23% in moderate risk, and 8% in low risk.

CONCLUSION

Use of the Tisdale calculator to assess patient-specific risk of QT prolongation combined with CDS may improve overall alert quality and acceptance rate, which may decrease the diQTP rate.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms

BACKGROUND

Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

OBJECTIVES

This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population.

METHODS

We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS.

RESULTS

Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI: 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI: 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance.

CONCLUSIONS

An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.

Added value of patient- and drug-related factors to stratify drug-drug interaction alerts for risk of QT prolongation: Development and validation of a risk prediction model

AIMS

Many clinical decision support systems trigger warning alerts for drug-drug interactions potentially leading to QT prolongation and torsades de pointes (QT-DDIs). Unfortunately, there is overalerting and underalerting because stratification is only based on a fixed QT-DDI severity level. We aimed to improve QT-DDI alerting by developing and validating a risk prediction model considering patient- and drug-related factors.

METHODS

We fitted 31 predictor candidates to a stepwise linear regression for 1000 bootstrap samples and selected the predictors present in 95% of the 1000 models. A final linear regression model with those variables was fitted on the original development sample (350 QT-DDIs). This model was validated on an external dataset (143 QT-DDIs). Both true QTc and predicted QTc were stratified into three risk levels (low, moderate and high). Stratification of QT-DDIs could be appropriate (predicted risk = true risk), acceptable (one risk level difference) or inappropriate (two risk levels difference).

RESULTS

The final model included 11 predictors with the three most important being use of antiarrhythmics, age and baseline QTc. Comparing current practice to the prediction model, appropriate stratification increased significantly from 37% to 54% appropriate QT-DDIs (increase of 17.5% on average [95% CI +5.4% to +29.6%], p_adj  = 0.006) and inappropriate stratification decreased significantly from 13% to 1% inappropriate QT-DDIs (decrease of 11.2% on average [95% CI -17.7% to -4.7%], p_adj  ≤ 0.001).

CONCLUSION

The prediction model including patient- and drug-related factors outperformed QT alerting based on QT-DDI severity alone and therefore is a promising strategy to improve DDI alerting.

A QTc risk score in patients with obstructive sleep apnea

INTRODUCTION

Patients with obstructive sleep apnea (OSA) are at risk for QTc prolongation, a known risk factor for increased mortality. The pro-QTc score can help identify individuals at increased risk for mortality associated with increased QTc however, it has not been evaluated in patients with OSA. The goal of this study was to evaluate the pro-QTc score in patients with OSA.

METHODS

Medical records of patients undergoing a sleep study at our sleep center from February 2012 to August 2020 were analyzed. Presence or absence of OSA was determined by polysomnography. The pro-QTc score was calculated with 1 point assigned for each of the following: female sex, QT-prolonging diagnoses and conditions, QT-prolonging electrolyte abnormalities, and medications with known risk for QT-prolongation. Mortality was determined from the electronic medical record of an integrated healthcare system.

RESULTS

There were 2246 patients (age 58 ± 15 years, 54% male, 82 dead) with OSA and 421 patients (age 54 ± 18 years, 43% male, 18 dead) without OSA. Of those with OSA, 1628 (72.5%) had at least one risk factor for QTc prolongation. A higher pro-QTc score was associated with greater mortality in patients with OSA (HR 1.48 per pro-QTc score, p < 0.001, 95% CI 1.3-1.7) but not in patients without OSA (HR 1.25 per pro-QTc score, p = 0.30, 95% CI 0.82-1.9), after adjusting for age, body mass index (BMI), and smoking status.

CONCLUSION

In patients with OSA, a higher pro-QTc score was associated with greater mortality.

Interpretable Machine Learning Prediction of Drug-Induced QT Prolongation: Electronic Health Record Analysis

BACKGROUND

Drug-induced long-QT syndrome (diLQTS) is a major concern among patients who are hospitalized, for whom prediction models capable of identifying individualized risk could be useful to guide monitoring. We have previously demonstrated the feasibility of machine learning to predict the risk of diLQTS, in which deep learning models provided superior accuracy for risk prediction, although these models were limited by a lack of interpretability.

OBJECTIVE

In this investigation, we sought to examine the potential trade-off between interpretability and predictive accuracy with the use of more complex models to identify patients at risk for diLQTS. We planned to compare a deep learning algorithm to predict diLQTS with a more interpretable algorithm based on cluster analysis that would allow medication- and subpopulation-specific evaluation of risk.

METHODS

We examined the risk of diLQTS among 35,639 inpatients treated between 2003 and 2018 with at least 1 of 39 medications associated with risk of diLQTS and who had an electrocardiogram in the system performed within 24 hours of medication administration. Predictors included over 22,000 diagnoses and medications at the time of medication administration, with cases of diLQTS defined as a corrected QT interval over 500 milliseconds after treatment with a culprit medication. The interpretable model was developed using cluster analysis (K=4 clusters), and risk was assessed for specific medications and classes of medications. The deep learning model was created using all predictors within a 6-layer neural network, based on previously identified hyperparameters.

RESULTS

Among the medications, we found that class III antiarrhythmic medications were associated with increased risk across all clusters, and that in patients who are noncritically ill without cardiovascular disease, propofol was associated with increased risk, whereas ondansetron was associated with decreased risk. Compared with deep learning, the interpretable approach was less accurate (area under the receiver operating characteristic curve: 0.65 vs 0.78), with comparable calibration.

CONCLUSIONS

In summary, we found that an interpretable modeling approach was less accurate, but more clinically applicable, than deep learning for the prediction of diLQTS. Future investigations should consider this trade-off in the development of methods for clinical prediction.

Prolonged QT Interval in Cirrhosis: Twisting Time?

Approximately 30% to 70% of patients with cirrhosis have QT interval prolongation. In patients without cirrhosis, QT prolongation is associated with an increased risk of ventricular arrhythmias, such as torsade de pointes (TdP). In cirrhotic patients, there is likely a significant association between the corrected QT (QTc) interval and the severity of liver disease, and possibly with increased mortality. We present a stepwise overview of the pathophysiology and management of acquired long QT syndrome in cirrhosis. The QT interval is mainly determined by ventricular repolarization. To compare the QT interval in time it should be corrected for heart rate (QTc), preferably by the Fridericia method. A QTc interval >450 ms in males and >470 ms in females is considered prolonged. The pathophysiological mechanism remains incompletely understood, but may include metabolic, autonomic or hormonal imbalances, cirrhotic heart failure and/or genetic predisposition. Additional external risk factors for QTc prolongation include medication (IKr blockade and altered cytochrome P450 activity), bradycardia, electrolyte abnormalities, underlying cardiomyopathy and acute illness. In patients with cirrhosis, multiple hits and cardiac-hepatic interactions are often required to sufficiently erode the repolarization reserve before long QT syndrome and TdP can occur. While some risk factors are unavoidable, overall risk can be mitigated by electrocardiogram monitoring and avoiding drug interactions and electrolyte and acidbase disturbances. In cirrhotic patients with prolonged QTc interval, a joint effort by cardiologists and hepatologists may be useful and significantly improve the clinical course and outcome.

Clinician Satisfaction With Advanced Clinical Decision Support to Reduce the Risk of Torsades de Pointes

OBJECTIVES

Clinical decision support (CDS) can potentially help clinicians identify and manage patients who are at risk for torsades de pointes (TdP). However, computer alerts are often ignored and might contribute to alert fatigue. The goals of this project were to create an advanced TdP CDS advisory that presents patient-specific, relevant information, including 1-click management options, and to determine clinician satisfaction with the CDS.

METHODS

The advanced TdP CDS was developed and implemented across a health system comprising 29 hospitals. The advisory presents patient-specific information including relevant risk factors, laboratory values, and 1-click options to help manage the condition in high-risk patients. A short electronic survey was created to gather clinician feedback on the advisory.

RESULTS

After implementation, an email invitation to complete the anonymous advisory-related survey was sent to 442 clinicians who received the advisory. Among the 38 respondents, feedback was generally positive, with 79% of respondents reporting that the advisory helps them care for their patients and 87% responding that alternative actions for them to consider were clearly specified. However, 46% of respondents indicated the alert appeared too frequently.

CONCLUSIONS

Advanced TdP risk CDS that provides relevant, patient-specific information and 1-click management options can be generally viewed favorably by clinicians who receive the advisory.

Overall performance of a drug-drug interaction clinical decision support system: quantitative evaluation and end-user survey

BACKGROUND

Clinical decision support systems are implemented in many hospitals to prevent medication errors and associated harm. They are however associated with a high burden of false positive alerts and alert fatigue. The aim of this study was to evaluate a drug-drug interaction (DDI) clinical decision support system in terms of its performance, uptake and user satisfaction and to identify barriers and opportunities for improvement.

METHODS

A quantitative evaluation and end-user survey were performed in a large teaching hospital. First, very severe DDI alerts generated between 2019 and 2021 were evaluated retrospectively. Data collection comprised alert burden, override rates, the number of alert overrides reviewed by pharmacists and the resulting pharmacist recommendations as well as their acceptance rate. Second, an e-survey was carried out among prescribers to assess satisfaction, usefulness and relevance of DDI alerts as well as reasons for overriding.

RESULTS

A total of 38,409 very severe DDI alerts were generated, of which 88.2% were overridden by the prescriber. In 3.2% of reviewed overrides, a recommendation by the pharmacist was provided, of which 79.2% was accepted. False positive alerts were caused by a too broad screening interval and lack of incorporation of patient-specific characteristics, such as QTc values. Co-prescribing of a non-vitamin K oral anticoagulant and a low molecular weight heparin accounted for 49.8% of alerts, of which 92.2% were overridden. In 88 (1.1%) of these overridden alerts, concurrent therapy was still present. Despite the high override rate, the e-survey revealed that the DDI clinical decision support system was found useful by prescribers.

CONCLUSIONS

Identified barriers were the lack of DDI-specific screening intervals and inclusion of patient-specific characteristics, both leading to a high number of false positive alerts and risk for alert fatigue. Despite these barriers, the added value of the DDI clinical decision support system was recognized by prescribers. Hence, integration of DDI-specific screening intervals and patient-specific characteristics is warranted to improve the performance of the DDI software.

Risk assessment tools for QT prolonging pharmacotherapy in older adults: a systematic review

Purpose

Many drugs are associated with the risk of QT prolongation and torsades de pointes (TdP), and different risk assessment tools (RATs) are developed to help clinicians to manage related risk. The aim of this systematic review was to summarize the evidence of different RATs for QT prolonging pharmacotherapy.

Methods

A systematic review was conducted using PubMed and Scopus databases. Studies concerning risk assessment tools for QT prolonging pharmacotherapy, including older adults, were included. Screening and selection of the studies, data extraction, and risk of bias assessment were undertaken.

Results

A total of 21 studies were included, involving different risk assessment tools. Most commonly used tools were risk scores (n = 9), computerized physician order entry systems (n = 3), and clinical decision support systems (n = 6). The tools were developed mainly for physicians and pharmacists. Risk scores included a high number of risk factors, both pharmacological and non-pharmacological, for QT prolongation and TdP. The inclusion of patients’ risk factors in computerized physician order entry and clinical decision support systems varied.

Conclusion

Most of the risk assessment tools for QT prolonging pharmacotherapy give a comprehensive overview of patient-specific risks of QT prolongation and TdP and reduce modifiable risk factors and actual events. The risk assessment tools could be better adapted to different health information systems to help in clinical decision-making. Further studies on clinical validation of risk assessment tools with randomized controlled trials are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-022-03285-3.

Cost-effectiveness of check of medication appropriateness: methodological approach

Background Adverse drug events following inappropriate prescribing in the hospital cause a substantial and avoidable medical and economic burden to hospitals, payers and patients alike. A clinical rule-based, pharmacist-led medication-review service, the 'Check of Medication Appropriateness' (CMA) was implemented in the University Hospitals Leuven. The CMA is shown to be effective in reducing potentially inappropriate prescriptions. Aim This study investigated whether this centralised clinical pharmacy service is cost-effective. Method We performed a cost-effectiveness analysis of three clinical rules of the CMA, targeting adverse drug events at three levels of severity: A) persistent opioid-induced constipation, B) ketorolac-induced gastrointestinal bleeding and C) drug-induced Torsade de Pointes. A decision tree was developed for each clinical rule. Both intervention costs as well as total costs associated with the occurrence of an adverse drug event were considered. The outcomes were reported in the form of an incremental cost-effectiveness ratio, expressed as an incremental cost per adverse drug event avoided. Results Applying clinical rules to avoid persistent opioid-induced constipation and ketorolac-induced gastrointestinal bleeding were cost-saving. Implementation of a medication check to avoid drug-induced Torsade de Pointes costed €8,846 per Torsade de Pointes avoided. Conclusion Our study provides strong indications that the CMA is worth its investment for clinical rules targeting (very) common adverse drug events, that can be avoided with limited expenses. Further research is required to assess the full CMA. The proposed model may be useful to perform cost-effectiveness analyses of other centralised clinical pharmacy services targeting inappropriate prescribing, at the level of individual adverse drug events.

Drug-Induced QTc Prolongation: What We Know and Where We Are Going

Drug-induced QTc prolongation is a concerning electrocardiogram (ECG) abnormality. This cardiac disturbance carries a 10% risk of sudden cardiac death due to the malignant arrhythmia, Torsades de Pointes. The Arizona Center for Education and Research on Therapeutics (AzCERT) has classified QTc prolonging therapeutic classes such as antiarrhythmics, antipsychotics, anti-infectives, and others. AzCERT criteria categorizes medications into three risk categories: "known," "possible," and "conditional risk" of QTc prolongation and Torsades de Pointes. The list of QTc prolonging medications continues to expand as new drug classes are approved and studied. Risk factors for QTc prolongation can be delineated into modifiable or non-modifiable. A validated risk scoring tool may be utilized to predict the likelihood of prolongation in patients receiving AzCERT classified medication. The resultant risk score may be applied to a clinical decision support system which offers mitigation strategies. Mitigation strategies including discontinuation of possible offending agents with selection of an alternative agent, assessment of potential drug interactions or dose adjustments through pharmacokinetic and pharmacodynamic monitoring, and initiation of both ECG and electrolyte monitoring are essential to prevent a drug-induced arrhythmia. The challenges presented by the COVID-19 pandemic have led to the development of innovative continuous monitoring technology, increasing protection for both patients and healthcare workers. Early intervention strategies may reduce adverse events and improve clinical outcomes in patients identified to be at risk of QTc prolongation.

The CredibleMeds® list: Usage of QT interval prolonging drugs in Germany and discordances with prescribing information

AIMS

A substantial number of Summaries of Product Characteristics (SmPCs)/Prescribing Information (PI) have warnings or contraindications on QT interval prolongation. The goal of this work was to quantify usage of QT interval prolonging drugs according to the CredibleMeds^® database of the German outpatient drug prescription market and to evaluate discrepancies between German SmPCs/US PI and CredibleMeds^® .

METHODS

Drugs listed on CredibleMeds^® with known, possible or conditional risk for torsade de pointes were evaluated from 2000 to 2020. The German drug prescription report was used as source for defined daily dose- (DDD-) based prescriptions of the German outpatient drug prescription market of the public health insurance system. German SmPCs and US PI of 253 CredibleMeds^® -listed drugs were evaluated for contents regarding QT interval prolongation.

RESULTS

Of the drugs currently listed on CredibleMeds^® , 59.7% (95% confidence interval [CI] 53.5-65.5%) were listed after 2012. Due to newly listed drugs, the proportion of DDDs of CredibleMeds^® drugs among all prescriptions increased from 4.6% in 2013 to 21.1% in 2019. DDD-based usage of the CredibleMeds^® drugs already listed in 2013 was similar in 2019. Among the drugs with known QT risk according to CredibleMeds^® , 7.5% (95% CI 2.6-19.9%) of German SmPCs and 21.1% (95% CI 11.1-36.3%) of US PI had no mention of QT issues whatsoever.

CONCLUSION

A significant proportion of all drugs prescribed in the outpatient sector is associated with QT risks according to CredibleMeds^® . SmPCs and PI should systematically be evaluated for concordance with the widely used CredibleMeds^® database to increase medication safety.

Assisted prescribing: Clinical decision support with MedSafety Scan now available

Too often, adverse events due to prescription medications are a cause of death and disability. Many of these events could be prevented, but most efforts to do so have had limited success, mainly due to the challenges of having the information that is necessary for safe prescribing available at the time when prescriptions are being written. Hospital-based Clinical Decision Support (CDS) systems are being developed to manage this information, identify at- risk patients, and help mitigate their risk of medication-induced harm. AZCERT, a non-profit created in 1999 with federal funding has helped hospitals develop these systems and has released an internet-based CDS program to assist in the safe prescribing of medications. This CDS program, MedSafety Scan, can be customized for any clinical venue and is available as an open-source program for all healthcare providers at www.medsafetyscan.org.

Prolonged QT Interval in Patients Receiving Psychotropic Medications

INTRODUCTION: Prolonged QT interval (PQTI) is a cardiac condition widely documented in the mental health literature and linked to psychotropic medication use. Medications notable for contributing to the condition are antipsychotics, antidepressants, and some mood stabilizers. Although additional medication classes and other contributing risk factors are often present, the prudent mental health provider benefits from having a basic understanding of this condition and how to prevent and manage it with safe prescribing practices. AIMS: This guide seeks to provide mental health prescribers with a basic understanding of the risk factors, pathophysiology, identification, and management of PQTI. METHOD: Relevant literature and practice guidelines were reviewed and summarized with a focus on practical interventions for the psychiatric mental health nurse practitioner (PMHNP). RESULTS: One of the primary contributing factors to PQTI development and complications is polypharmacy. Patients with co-occurring medical, mental health, and/or substance use disorders may receive medications from multiple providers. Anticancer drugs, antiarrhythmic medications, and even a number of common antibiotics can increase the QT interval, making it a challenge for even the most experienced mental health provider to monitor medication interactions and side effects that contribute to PQTI. Having a sound knowledge base of these factors can guide safe PMHNP practice. CONCLUSIONS: Decision-making trees grounded in evidence-based research were developed in order to direct thorough assessment and safe treatment of patients requiring psychotropic medications.

[Antipsychotic pharmacotherapy for delirious syndromes - only temporary, symptom-oriented and considering QTc time]

BACKGROUND

QTc prolongation is a common and serious side effect of antipsychotics in the treatment of delirium. Nevertheless, the occurrence of ventricular tachycardia is rarely reported, so that the clinical relevance of a QTc prolongation triggered in this way remains unclear.

METHOD

The focus of this review is on the antipsychotic pharmacotherapy of delirium.

RESULTS AND CONCLUSIONS

In individual cases, before the prescription of an antipsychotic due to a delirium, a risk-benefit assessment must be made for the patient. For this purpose, patient and substance-specific risk factors for QTc prolongation must be checked and, if possible, reduced. A specific recommendation for an antipsychotic with assured low QTc interference can not be given because all antipsychotics for delirium treatment are potentially QTc-prolonging. Antipsychotic delirium treatment should be monitored, especially in patients with a high risk profile, for QTc prolongation by regular ECG controls.

A smart algorithm for the prevention and risk management of QTc prolongation based on the optimized RISQ-PATH model

AIMS

QTc prolongation is a complex problem linked with multiple risk factors. The RISQ-PATH score was previously developed to identify high-risk patients for QTc prolongation. The aim of this study was to optimize and validate this risk score in a large patient cohort, and to propose an algorithm to generate smart QT signals in the electronic medical record.

METHODS

A retrospective study was performed in the Nexus hospital network (n = 17) in Belgium. All electrocardiograms performed in 2015 in both ambulatory and hospitalized patients were collected together with risk factors for QTc prolongation (training database). Multiple logistic regression was performed to obtain the optimal prediction (RISQ-PATH) model. The model was tested in a validation database (electrocardiograms between January and April 2016).

RESULTS

In total, 60 208 patients (52.8% males, mean age 63 ± 18 years) were included; 3543 patients (5.9%) had a QTc ≥ 450(♂)/470(♀) ms and 453 (0.8%) a QTc ≥ 500 ms. The optimized RISQ-PATH model has an area under the ROC-curve of 0.772 [95% CI 0.763-0.780] to predict QTc ≥ 450(♂)/470(♀)ms. A predicted probability of ≥0.035 was set as cutoff for a high risk of QTc prolongation. This cutoff resulted in a sensitivity of 87.4% [95% CI 86.2-88.5] and a specificity of 46.2% [95% CI 45.8-46.6]. These results could be confirmed for QTc ≥ 500 ms and in the validation database (n = 28 400).

CONCLUSIONS

The RISQ-PATH model, with a cutoff probability of 0.035, predicted a prolonged QTc interval ≥ 450/470 ms or ≥500 ms with a sensitivity of ±87% and a specificity of ±45%. This RISQ-PATH model can be used in clinical decision support systems to create smart QT alerts.