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Back to Journal »Neuropsychiatric Diseases and Treatment» Volume 17
Author Zolezzi M, Elhakim A, Elamin WM, Homs S, Mahmoud DE, Qubaiah IA
Published on November 19, 2021, the 2021 volume: 17 pages 3395-3405
Single anonymous peer review
Editor approved for publication: Dr. Roger Pinder
Video abstract of "QTc Extension Algorithm in Mental Illness" [ID 334350].
Monica Zolezzi, 1 Athar Elhakim, 2 Waad M Elamin, 1 Shorouk Homs, 1 Doaa E Mahmoud, 1 Iman A Qubaiah 1 1 QU Health School of Pharmacy, University of Qatar, Doha, Qatar; 2 Newsletter from the School of Health Sciences, North Atlantic College, Doha, Qatar: Doha, Qatar University QU Health Monica Zolezzi School of Pharmacy Phone +974 4403-5623 Email [email protected] Background: QTc interval (QTcI) prolongation leads to serious complications, making it a concern for clinicians. It is important to assess the risk of prolonged QTcI in patients with psychosis because they are exposed to a variety of drugs that are known to increase the risk of life-threatening arrhythmias. Purpose: The purpose of this study is to validate the content of algorithms used to assess, manage, and monitor drug-induced QTc prolongation in people with mental illness. Method: Conduct qualitative semi-structured interviews with cardiologists to collect information on their methods of assessing the risk of prolonged QTc caused by drugs at the time of prescription. After the interview, an introduction to the algorithm was provided and a link to the cross-sectional anonymous survey was provided. Online surveys include quantitative and qualitative parts to collect feedback on the relevance and appropriateness of each step in the algorithm. Results: Interview answers were included in 4 topics. The response indicated the lack of a unified agreement when assessing QTcI extension, which supports the need for algorithms that include validated risk scoring tools. The quantitative survey results show that the appropriateness of the algorithm steps is 3.08 to 3.67, the total score is 4, the safety is 3.08 to 3.58, and the reliability of the references used is 3.17 to 3.75. Additional analysis using the modified kappa and I-CVI statistical measurements showed high content validity and high agreement among raters. According to open-ended questions, cardiologists support the implementation of the algorithm; however, they recommend simplifying these steps because they seem cumbersome. Conclusion: The results show that implementing this algorithm after minor changes can prove useful as a tool for QTc prolonged risk assessment. Further validation of the algorithm with mental health pharmacists and clinicians will be conducted as a separate phase of the research. Keywords: drug-induced arrhythmia, QTc interval prolongation, algorithm, psychiatric population
People with severe mental illness (SMI), that is, people with mental disorders such as schizophrenia and mood disorders such as bipolar depression and major depression, face a higher overall risk of mortality. According to reports, compared with the general population, the mortality rate of SMI patients is two to three times higher. 1 There are many reasons for the shorter life expectancy of SMI patients, among which cardiovascular diseases account for about 40% to 50%. 2 Although most sudden cardiac death (SCD) in the psychiatric population is caused by ischemic heart disease and related risk factors Yes, but about 10% are unexplainable and are thought to be caused by arrhythmia. 3,4
Since psychotropic drugs may prolong the QT (QTc) interval for heart rate correction, psychotropic drugs are associated with an increased risk of SCD in psychiatric patients. 5,6 In addition to psychotropic drugs, other commonly used drugs affected by SMI are also implicated in prolonging the QTc interval, such as antiarrhythmic drugs, antibiotics, antifungals and antiemetics, some of which have been removed from the market. Because they are related to torsade de pointes (TdP), TdP is a life-threatening polymorphic ventricular arrhythmia and is also related to SCD.7
Studies have shown that people diagnosed with SMI often use multiple drugs, which makes them more susceptible to drug interactions that prolong QTc. 8-10 In addition, as the number of available drugs continues to increase, clinicians may face difficulties in how to evaluate, manage, monitor, and refer patients at risk of QTc prolongation. Difficulty in accessing health care facilities may also increase the risk of adverse consequences from the use of QT-prolonging drug combinations, further contributing to the high premature mortality rates observed in the SMI population. 8
Although there are several guidelines to help clinicians assess, manage, and monitor patients at risk of prolonged QTc interval (QTcI) caused by drugs, only a few guidelines are specifically designed to guide mental health practitioners. 11-16 However, it has been reported that identifying individuals who may be at risk of QTcI prolongation is challenging, especially for mental health clinicians, especially when prescribing. 17,18 In order to partially solve these challenges, a ladder-based algorithm was developed (refer to QTcI extension algorithm). 19 The overall purpose of creating the QTcI extension algorithm is to help mental health clinicians obtain reliable information about QTcI extension drugs to assess and record the overall risk of someone experiencing drug-induced QTcI extension, and to prescribe drugs known to increase QTcI Provide safety and monitoring recommendations from time to time.
Although the literature review that established the algorithm is robust, determining the validity of the content before recommending it for wider use is crucial to ensuring its scientific rationality. 20 Therefore, the main purpose of this study is to determine the content validity of the QTcI extension algorithm derived from the subject matter expert panel.
Most currently available QTcI prolongation guidelines recommend consulting cardiology at certain key stages of the decision-making process, 11, 15, 16, 21-23, especially when assessing individuals at risk of QTcI prolongation or interpreting the electrocardiogram, so the expert selected Cardiologist included in the group. The study included a qualitative phase, using semi-structured interviews to gather information about the methods used by cardiologists to assess the risk of prolonged QTcI caused by drugs. After the interview, participants were given a QTcI extension algorithm (Appendix 1) and a brief introduction describing the steps required to assess the risk of QTcI extension. Participants are then required to complete an online survey that includes quantitative and qualitative parts. Figure 1 provides a schematic diagram of the research phases and methods. Figure 1 Schematic diagram of the research design.
Figure 1 Schematic diagram of the research design.
Cardiologists from Qatar and the United Kingdom (UK) were recruited using purposeful and snowball sampling techniques. Cardiologists working in various health centers in Qatar and the United Kingdom provide professional cardiac care and are able to speak and read English and are eligible to participate. Recruitment emails have been sent to potential participants. The participant information form and consent form are distributed electronically to all potential participants so that they can be read and understood before agreeing to participate.
Face-to-face semi-structured interviews lasting 30 to 40 minutes are conducted in person with participants, if any, or via Skype voice/phone conversation. The subject guide for conducting the interview (Appendix 2) was used. The guide was developed by identifying key areas of investigation based on a comprehensive literature review, and further pilot testing and revision by the research team. Telephone interviews were recorded and transcribed immediately after each interview, and face-to-face interviews were handwritten by researchers.
Surveys containing quantitative and qualitative components are managed through SurveyMonkey® (Appendix 3). Use the Likert 4-point scale to score participants’ opinions on each decision statement/step of the QTcI extension algorithm as follows: 1 = irrelevant/appropriate, 2 = unable to assess relevance/appropriateness, 3 = Relevant/appropriate, but requires slight changes, and 4 = very relevant and appropriate. In addition, it also includes open-ended questions related to participants’ perceptions of specific decision points and the overall process described in the step-based algorithm.
The analysis of the content of the cardiologist interview transcripts is carried out independently by each researcher. The coding framework is developed through an iterative process. After completing the independent analysis, the researchers discuss any differences between their codes and reach a consensus on derived topics and subtopics.
The survey data collected by SurveyMonkey® is exported to an Excel data sheet for analysis. The average score of the appropriateness of each decision step in the algorithm, the reliability and safety of the references is used to calculate the content validity index (CVI) of these three attributes. Item-CVI (I-CVI) is used to evaluate the effectiveness of each item in the algorithm. It is calculated by dividing the number of experts (cardiologists) with a Likert score of 3 or above by the total number of experts. The recommended cut-off point for 5 or more experts is 0.78.24. If the I-CVI of any item is less than 0.5, it indicates rejection. The average CVI (Ave-CVI) for evaluating the effectiveness of the tool is also determined for each attribute and calculated by adding up the I-CVI scores of each item and dividing by the number of items. Ave-CVI ≥ 0.9 has excellent content validity. 25 Although CVI is widely used to estimate content validity, it does not consider the possibility of overstating. Therefore, the improved statistical measurement of kappa (k*) is used to reduce the likelihood of this risk because it has the advantage of measuring consistency beyond chance. 26 It is calculated by subtracting the likelihood of agreement between evaluators. These items are correlated (Pc; probability of random correlation coefficient) from I-CVI; then the resulting number is divided by the maximum possible agreement beyond chance . 27 In this study, a cut-off point of 0.78 was used to reflect excellent content validity.
Demographic information and participant characteristics are presented in the form of frequency and used for correlation testing where applicable. Use word cloud to analyze participants' open comments, which is a method of visually analyzing text data. This method is easy to use and reduces the risk of bias. In the word cloud, the font size reflects the frequency of word usage. Therefore, the most frequently used words are presented in the largest font size. The font size of the remaining words is automatically adjusted and calculated by the free software used to create the word cloud, which can be accessed on the website: https://www.wordclouds.com/.
In the process of word cloud analysis, try to avoid changing the participants' own words. However, in some cases, it is necessary to combine terms with similar meanings.
As shown in Figure 1, a total of 54 cardiologists were invited to participate, 21 of whom agreed, but only 17 were interviewed (15 men and 2 women). Among the interviewees, only 12 completed the post-targeting survey (10 men and 2 women). Their median age is 43.5 years, and most of them graduated between 1990 and 2000. Of the 12 cardiologists who completed the survey, 11 were from Qatar and 1 was from the United Kingdom.
As described in Table 1 and described below, regarding cardiologists’ assessment of drug-induced QTc prolongation, four main themes emerged in the interview: Table 1 New themes in the interview
Table 1 Emerging themes in the interview
This topic focuses on the degree to which cardiologists rely on ECG when managing patients on QTcI prolonging drugs. Several cardiologists said that after evaluating the ECG results, other patient-specific factors such as medical and family history, medication history, physical examination, clinical manifestations and laboratory data will be considered to make treatment decisions. Cardiologists pointed out that the baseline QTcI can be obtained manually (using the formula: QTcI=QT/√RR), or it can be obtained automatically using the readings calculated by the ECG machine. Considering that the cut-off value for QTcI extension varies from cardiologist to cardiologist, it ranges from ≥ 460 to 500 milliseconds (ms). Some cardiologists also pointed out that when considering the QTcI cut-off point, there are age and gender differences; for example, QTcIs of adult women are normal between 460-480ms, and QTcIs of adult men should not exceed 470ms. Children before puberty do not. Should exceed 460ms. The cardiologist also emphasized the importance of obtaining a baseline ECG before starting QTc prolongation drugs. Some people say that they remeasure QTcI 4 to 8 hours after starting to use QTc prolonging drugs to ensure tolerance. They also pointed out that an interval of 500 ms or more is considered a serious QTc prolongation.
Cardiologists said that when prescribing drugs, they mainly consider the patient’s characteristics and clinical and medication history to avoid any potential drug-drug or drug-disease interactions that may directly increase the risk of QTcI prolongation (when a known drug is prescribed) Medications that may cause QTcI prolongation, such as haloperidol) or indirectly (when the prescribed medication makes the patient vulnerable to QTcI prolongation events, such as electrolyte imbalance caused by diuretics). Cardiologists also emphasized the use of various drug information resources, such as Lexicomp, UpToDate, British National Formulary (BNF) and Medscape, but mainly for information purposes, not for QTcI extension management. However, most cardiologists are basically unaware of specific sources of information about drug-induced QTcI prolongation, such as CredibleMeds®. Some cardiologists said that the prescription is also carried out with the assistance of a clinical pharmacist.
Cardiologists describe a variety of assessments used to assess the risk of QTcI prolongation when drugs known to prolong QTcI are prescribed, including the patient’s QTc prolongation or family history of SCD, chronic or congenital heart disease, renal and liver dysfunction . Some people also said that the genotype and phenotype of the patient should be considered. Cardiologists further described how they use physical examinations to identify signs and symptoms of prolonged QTcI, such as dizziness, loss of consciousness, syncope, bradycardia, palpitations, and arrhythmias, such as ventricular tachycardia or ventricular fibrillation. Cardiologists also emphasized the importance of monitoring electrolyte disturbances, especially the serum levels of potassium, sodium, magnesium, and calcium, because they may cause drug-induced QTcI prolongation.
Cardiologists commented that in practice, the availability of guidelines and protocols for assessing and managing QTcI extension is limited. Cardiologists also agreed that risk assessment is mainly based on ECG results and clinical manifestations, rather than using validated risk scoring tools. There is a general lack of knowledge of specific tools used to assess or quantify the risk of QTcI prolongation in specific patients. Some people stated that they did not use or have time to perform risk scoring in practice. Some people mentioned using specific hospital-based ECG protocols or referring to published guidelines.
In general, the scores indicate that most of the steps in the algorithm are very reliable or reliable and need to be changed slightly. The references used to support the decision steps in the algorithm give the highest reliability scores.
As summarized in Table 2 and shown in Figure 2A, the average average score of the appropriateness of each step of the algorithm evaluated by the cardiologist ranges from 3.08 to 3.67. The appropriateness decision statement with the highest overall average score (3.67) is related to assessing the need for ECG monitoring based on the QTc risk score and avoiding treatment and considering cardiac counseling when the ECG readings indicate QTcI ≥ 500 ms. The k* range for I-CVI and the appropriateness of different decision statements/steps ranges from 0.83 to 1. There is no refusal to evaluate the suitability of QTcI extension algorithm steps (ie, I-CVI <0.5). The Ave-CVI of the adequacy of the algorithm steps is 0.95. Table 2 Applicability evaluation of each step of QTcI extension algorithm Figure 2 CVI score of reliability of QTcI extension algorithm. (A) In addition to I-CVI, it also shows the average score of the appropriateness rating of each step of the algorithm. (B) Represents the average score of the safety of each step of the algorithm except I-CVI. (C) shows the average score of the reliability of the reference used in each step of the algorithm except I-CVI. Abbreviations: CVI, content validity index; QTcI, QTc interval extension; I-CVI, item-level content validity index.
Table 2 Applicability evaluation of each step of QTcI extension algorithm
Figure 2 The reliability CVI score of the QTcI extension algorithm. (A) In addition to I-CVI, it also shows the average score of the appropriateness rating of each step of the algorithm. (B) Represents the average score of the safety of each step of the algorithm except I-CVI. (C) shows the average score of the reliability of the reference used in each step of the algorithm except I-CVI.
Abbreviations: CVI, content validity index; QTcI, QTc interval extension; I-CVI, item-level content validity index.
As shown in Table 3 and Figure 2B, cardiologists rate the safety of decision-making steps in the algorithm, with an average score ranging from 3.08 to 3.58. If the ECG reading shows QTcI ≥ 500 ms, the safety decision statement with the highest overall average score (3.58) is related to avoiding treatment and considering cardiac counseling. The I-CVI and k* values for different safety-related decision statements/steps range from 0.83 to 1. There is no refusal to evaluate the safety of QTcI extended algorithm steps (ie, I-CVI <0.5). The Ave-CVI of algorithm step security is 0.92. Table 3 Safety evaluation of each step of QTcI extension algorithm
Table 3 Safety evaluation of each step of QTcI extension algorithm
As shown in Table 4 and Figure 2C, cardiologists rated the reliability of the references used in each step of the algorithm, with an average score ranging from 3.17 to 3.75. If the ECG reading shows QTcI ≥ 500 ms, the reference reliability decision statement with the highest overall average score (3.75) is related to avoiding treatment and considering cardiac counseling. The k* of I-CVI and reference decision statement/step reliability ranges from 0.83 to 1. Projects that did not evaluate the reliability of the references of the QTcI extension algorithm steps were rejected (ie, I-CVI <0.5). The Ave-CVI of the reliability of the reference used in the algorithm is 0.94. Table 4 Reliability evaluation of references used in each step of QTcI extension algorithm
Table 4 Reliability evaluation of references used in each step of QTcI extension algorithm
Figure 3 provides a word cloud representation of word frequency. When asked to describe their overall assessment of the QTc prolongation algorithm, cardiologists’ open-ended responses were the most prominent. More frequent words include: "pharmacists should use" and "cumbersome/complex". Figure 3 The word cloud representation of the open-ended answer to the survey.
Figure 3 The word cloud representation of the open-ended answer to the survey.
Emerging topics arising from cardiologist interviews provide insights into how experts deal with QTcI prolongation in clinical practice. The first topic, the reliance on ECG readings, suggests that cardiologists use this measurement as a starting point when making decisions related to the prescription of drugs that are known to increase QTcI. Although this is reasonable, because ECG has always been the traditional and most advocated method of assessing QTcI prolongation, it may also be impractical to perform an ECG every time a drug that prolongs QTcI is prescribed. 21 In addition, the QTcI reading data reported by automatic standard measurement shows that the most commonly used 12-lead ECG in clinical practice is inaccurate. Even the heart rate correction formulas used, such as those developed by Bazett, can cause over or under correction. This is because they do not completely eliminate the dependence of QTcI on heart rate, and are based on the assumption that the QT/heart rate relationship remains consistent between different individuals. The QT nomogram described by Isbister and Page28 solves this problem and is reported to be more specific than the Bazett formula. In addition, a survey designed to characterize trends in the practice of ECG monitoring by psychiatric residents showed that outpatient providers are unlikely to order ECGs when prescribing antipsychotic drugs. 29 The authors attribute this finding to financial problems and the lack of access to ECG resource settings for outpatients. The American Heart Association's updated hospital environmental ECG monitoring guidelines provide recommendations on which patient populations are most likely to benefit from ECG-based QTc monitoring during hospitalization. 30 These controversial algorithms regarding the use of ECG should be reviewed and considered when revising the QTcI extension.
The second theme, the clinically guided QTc extension of drug prescriptions, also shows that cardiologists follow other methods that can support ECG-based assessments, including the patient's clinical/drug history, and the use of drug information resources to assess drug interactions. However, cardiologists are basically ignorant of specific drug information resources for drug-induced QTcI prolongation, such as CredibleMeds®, which is considered by many to be the most reliable QTcI prolongation drug database. 6,31 This resource developed a risk stratification process to classify drugs based on their relative likelihood of QTc prolongation and/or life-threatening ventricular arrhythmia. Cardiologists also pointed out that clinical pharmacists can serve as a valuable resource for evaluating drug-induced QTcI prolongation, and this role has been supported in the literature. 32-34 Consultation about drug factors, such as dosage, route of administration, renal elimination, and drug interactions are important considerations, among which pharmacist’s advice may be particularly useful when prescribing.
The third topic is the clinical evaluation of QTc prolongation. Cardiologists have identified risk factors that are routinely evaluated when prescribing drugs that may increase QTcI, such as the history, signs and symptoms of QTcI prolongation, and other factors such as kidney damage and electrolyte imbalances. However, similar to the findings of the above-mentioned psychiatric resident survey, 29 cardiologists did not mention the well-known susceptibility causes of QTc prolongation, such as a family history of long QT syndrome or sudden death, or a personal history of syncope. 35 As the QTcI extension algorithm includes a specific question about the history of long QT syndrome, it may help to address this generally overlooked risk during the evaluation process.
The fourth theme, the limited availability of agreements, may be related to the limited regular use of risk scoring tools in practice. Although a variety of QTcI prolonged risk scoring tools have been published in the literature, the 14,36-38 cardiologists in our study either did not know or thought they had limited time to use them in practice. Some studies have shown that risk stratification and the implementation of standardized QTcI monitoring programs can provide relevant clinical guidance for treatment decisions and lead to positive patient outcomes. 29,37,39
The results of the second part of this study evaluated the effectiveness of the QTcI extension algorithm, including the overall appropriateness, safety, and reference materials for each decision point to support its reliability. The values of I-CVI, Ave-CVI, and k* reflect positive inter-rater reliability because they are all above the recommended cut-off point. For the overall appropriateness and reliability of the references used in each decision step of the algorithm, two steps have the lowest average score. These steps are: "Using CredibleMeds® to evaluate the drug" and "If the risk score ≤ 7 points, then recommend treatment". Since most cardiologists do not understand CredibleMeds® and do not guide prescription decisions based on risk stratification scores, these steps may have the lowest scores. Interestingly, both of these questions became the subject of interviews with cardiologists. Although there are various guidelines available, our research results indicate that they may not provide practical risk assessment methods, or that healthcare providers may have limited knowledge or availability of risk stratification protocols. The use of care maps such as the QTcI extension algorithm may help fill these gaps in practice.
Regarding the overall adequacy and safety of the algorithm, the highest average rating is "If ECG shows QTc ≥ 500 ms, avoid treatment and consider cardiac counseling". This finding was unexpected because the guidelines recommend consulting a cardiologist as an important safety step before prescribing drugs that may increase QTcI. 11,15,16,21-23 However, it is important to note that the cut-off point for when to avoid using QTc prolonging drugs varies in the literature. Some guidelines determine the prolongation of male QTc values ≥ 450 ms and female QTc values ≥ 460 ms, but there is no universally accepted measurement method. 31 Nonetheless, QTc ≥ 500 ms is generally considered to be severely prolonged, and with two to three times the risk of TdP. 40 Therefore, the cut-off point used in the QTcI extension algorithm seems to be supported by the current literature.
Finally, a word cloud analysis of the overall opinion of cardiologists on the QTcI extension algorithm shows that the use of this algorithm by healthcare providers can improve decision-making at the time of prescription. However, it is considered to be potentially time-consuming. The use of electronic decision support systems can facilitate the evaluation and monitoring process, and alerts can be added when prescribing drugs with known or associated risks that prolong QTcI. At this point, the pharmacist can use the algorithm, and the pharmacist can notify the doctor of their assessment and recommendations. Another possibility is to integrate the algorithm as part of the electronic prescription process. Using algorithms when prescribing can facilitate the flow of steps by extracting existing patient information and reduce the time required to reach recommendations. The verification of other content of the algorithm will be carried out with mental health clinicians as the second phase of this research. Before being integrated into the electronic prescription process, feedback and suggestions collected in the two content verification phases will be considered and integrated into the revised version of the algorithm.
Participants were initially recruited from a list of cardiologists participating in the meeting, and then snowballed sampling. Although this method helps to capture interested candidates, it cannot guarantee the representativeness of the sample because the true distribution of the population and the sample is unknown. Sampling deviations may also occur when using this sampling technique. The initial themes tend to nominate people they know well. In addition, most cardiologists are based in Qatar. This may affect the generality of the results in clinical practice outside of Qatar. In order to partially solve this problem, we specially invited British cardiologists to participate and included the results obtained. In addition to the above limitations, although CVI is widely used by researchers to estimate content validity, the index does not consider the possibility of exaggerating the value due to chance consistency. Therefore, we tried to solve this problem by using I-CVI and k*. These can be used to measure the validity of content and the consistency between raters, which helps to improve the validity of representativeness and support for small sample size results. 27,41 Another important limitation in the data collection process is the COVID-19 pandemic, which limits the participation of cardiologists in research. However, this restriction provides further support for authorizing other healthcare professionals (such as pharmacists) to assess the risk of QTc prolongation when prescribing, especially when access to cardiology counseling (such as mental health services) is limited .
Algorithms developed based on a systematic review of the literature to assess the risk of drug-induced QTcI prolongation in people with psychosis have shown relatively strong content validity. Although cardiologists mainly rely on baseline ECG readings to assess the risk of drug-induced QTc prolongation, their evaluation of the algorithm supports the use of the algorithm to assist the decision-making process without cardiology consultation. The algorithm also has the potential to address current practice gaps and has proven to be a useful tool for risk assessment in psychiatric settings or community mental health services where cardiology consultation is limited or difficult to reach. Consideration should be given to implementing the algorithm in the future using decision support systems that can be integrated into patient assessment and health management systems in mental health.
The corresponding author may be contacted for further data sharing.
The research was conducted in accordance with the ethical principles described in the Declaration of Helsinki. The Qatar University Institutional Review Board (QU-IRB) received ethical approval on February 25, 2019 (approval number: QU-IRB 1026-EA/19).
The authors would like to thank the cardiologists who participated in the interview for sharing their experience in the evaluation of drug-induced QTc prolongation and reviewing the algorithm. We also thank Ms. Enge Tawfik for helping interview a cardiologist. This research was realized by the Qatar National Research Foundation's Undergraduate Research Experience Program (UREP) grant (UREP24-041-3-016).
All authors have contributed to the concept and design of this study and/or data interpretation; participated in drafting articles or critically revised important knowledge content; agreed to submit to the current journal; finally approved the version to be published; and agreed to Responsible for all aspects of the work.
The authors report no conflicts of interest in this work.
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