Dataset Information

Risk of cardiac events with azithromycin-A prediction model.

ABSTRACT: Previous studies have suggested an increased risk of cardiac events with azithromycin, but the predictors of such events are unknown. We sought to develop and validate two prediction models to identify such predictors. We used data from Truven Marketscan Database (01/2009 to 06/2015). Using a split-sample approach, we developed two prediction models, which included baseline demographics, clinical conditions (Model 1), concurrent use of any drug (Model 1) and therapeutic class (Model 2) with a risk of QT-prolongation (CQT-Rx). Patients enrolled in a health plan for 365 days before and five days after dispensing of azithromycin (episodes). Cardiac events included syncope, palpitations, ventricular arrhythmias, cardiac arrest as a primary diagnosis for hospitalization including death. For each model, a backward elimination of predictors using logistic regression was applied to identify predictors in 100 random samples of the training cohort. Predictors prevalent in >50% of the models were included in the final model. A score for the Assessment of Cardiac Risk with Azithromycin (ACRA) was generated using the training cohort then tested in the validation cohort. A cohort of 20,134,659 episodes with 0.03% cardiac events were included. Over 60% included females with mean age of 40.1±21.3 years. Age, sex, history of syncope, cardiac dysrhythmias, non-specific chest pain, and presence of a CQT-Rx were included as predictors for Model-1 (c-statistic = 0.68). For Model-2 (c-statistic = 0.64), predictors included age, sex, anti-arrhythmic agents, anti-emetics, antidepressants, loop diuretics, and ACE inhibitors. ACRA score is available online (bit.ly/ACRA_2020). The ACRA score may help identify patients who are at higher risk of cardiac events following treatment with azithromycin. Providers should assess the risk-benefit of using azithromycin and consider alternative antibiotics among high-risk patients.

SUBMITTER: Patel H

PROVIDER: S-EPMC7561086 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Risk of cardiac events with azithromycin-A prediction model.

Patel Haridarshan H DiDomenico Robert J RJ Suda Katie J KJ Schumock Glen T GT Calip Gregory S GS Lee Todd A TA

PloS one 20201015 10

Previous studies have suggested an increased risk of cardiac events with azithromycin, but the predictors of such events are unknown. We sought to develop and validate two prediction models to identify such predictors. We used data from Truven Marketscan Database (01/2009 to 06/2015). Using a split-sample approach, we developed two prediction models, which included baseline demographics, clinical conditions (Model 1), concurrent use of any drug (Model 1) and therapeutic class (Model 2) with a ri ...[more]

PMID: 33057356

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Project description:ImportanceConflicting evidence exists on the association between azithromycin use and cardiac events.ObjectiveTo compare the odds of cardiac events among new users of azithromycin relative to new users of amoxicillin using real-world data.Design, setting, and participantsThis retrospective cohort study used data from Truven Health Analytics MarketScan database from January 1, 2009, to June 30, 2015. Patients receiving either amoxicillin or azithromycin and enrolled in a health care plan 365 days before (baseline period) the dispensing date (index date) were included in the study. Patients were matched 1:1 on high-dimensional propensity scores. Data were analyzed from October 1, 2018, to December 31, 2019.ExposuresNew use of azithromycin compared with new use of amoxicillin.Main outcomes and measuresThe primary outcome consisted of cardiac events, including syncope, palpitations, ventricular arrhythmias, cardiac arrest, or death as a primary diagnosis for hospitalization at 5, 10, and 30 days from the index date. Logistic regression models were used to estimate odds ratios (ORs) with 95% CIs.ResultsAfter matching, the final cohort included 2 141 285 episodes of each index therapy (N = 4 282 570) (mean [SD] age of patients, 35.7 [22.3] years; 52.6% female). Within 5 days after therapy initiation, 1474 cardiac events (0.03%) occurred (708 in the amoxicillin cohort and 766 in the azithromycin cohort). The 2 most frequent events were syncope (1032 [70.0%]) and palpitations (331 [22.5%]). The odds of cardiac events with azithromycin compared with amoxicillin were not significantly higher at 5 days (OR, 1.08; 95% CI, 0.98-1.20), 10 days (OR, 1.05; 95% CI, 0.97-1.15), and 30 days (OR, 0.98; 95% CI, 0.92-1.04). Among patients receiving any concurrent QT-prolonging drug, the odds of cardiac events with azithromycin were 1.40 (95% CI, 1.04-1.87) greater compared with amoxicillin. Among patients 65 years or older and those with a history of cardiovascular disease and other risk factors, no increased risk of cardiac events with azithromycin was noted.Conclusions and relevanceThis study found no association of cardiac events with azithromycin compared with amoxicillin except among patients using other QT-prolonging drugs concurrently. Although azithromycin is a safe therapy, clinicians should carefully consider its use among patients concurrently using other QT-prolonging drugs.

Project description:BackgroundHypertrophic cardiomyopathy is the leading cause of sudden cardiac death (SCD) in children and young adults. Our objective was to develop and validate a SCD risk prediction model in pediatric hypertrophic cardiomyopathy to guide SCD prevention strategies.MethodsIn an international multicenter observational cohort study, phenotype-positive patients with isolated hypertrophic cardiomyopathy <18 years of age at diagnosis were eligible. The primary outcome variable was the time from diagnosis to a composite of SCD events at 5-year follow-up: SCD, resuscitated sudden cardiac arrest, and aborted SCD, that is, appropriate shock following primary prevention implantable cardioverter defibrillators. Competing risk models with cause-specific hazard regression were used to identify and quantify clinical and genetic factors associated with SCD. The cause-specific regression model was implemented using boosting, and tuned with 10 repeated 4-fold cross-validations. The final model was fitted using all data with the tuned hyperparameter value that maximizes the c-statistic, and its performance was characterized by using the c-statistic for competing risk models. The final model was validated in an independent external cohort (SHaRe [Sarcomeric Human Cardiomyopathy Registry], n=285).ResultsOverall, 572 patients met eligibility criteria with 2855 patient-years of follow-up. The 5-year cumulative proportion of SCD events was 9.1% (14 SCD, 25 resuscitated sudden cardiac arrests, and 14 aborted SCD). Risk predictors included age at diagnosis, documented nonsustained ventricular tachycardia, unexplained syncope, septal diameter z-score, left ventricular posterior wall diameter z score, left atrial diameter z score, peak left ventricular outflow tract gradient, and presence of a pathogenic variant. Unlike in adults, left ventricular outflow tract gradient had an inverse association, and family history of SCD had no association with SCD. Clinical and clinical/genetic models were developed to predict 5-year freedom from SCD. Both models adequately discriminated between patients with and without SCD events with a c-statistic of 0.75 and 0.76, respectively, and demonstrated good agreement between predicted and observed events in the primary and validation cohorts (validation c-statistic 0.71 and 0.72, respectively).ConclusionOur study provides a validated SCD risk prediction model with >70% prediction accuracy and incorporates risk factors that are unique to pediatric hypertrophic cardiomyopathy. An individualized risk prediction model has the potential to improve the application of clinical practice guidelines and shared decision making for implantable cardioverter defibrillator insertion. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0403679.

Project description:Background Commonly used cardiovascular risk calculators do not provide risk estimation of stroke, a major postoperative complication with high morbidity and mortality. We developed and validated an accurate cardiovascular risk prediction tool for stroke, major cardiac complications (myocardial infarction or cardiac arrest), and mortality after non-cardiac surgery. Methods and Results This retrospective cohort study included 1 165 750 surgical patients over a 4-year period (2007-2010) from the American College of Surgeons National Surgical Quality Improvement Program Database. A predictive model was developed with the following preoperative conditions: age, history of coronary artery disease, history of stroke, emergency surgery, preoperative serum sodium (≤130 mEq/L, >146 mEq/L), creatinine >1.8 mg/dL, hematocrit ≤27%, American Society of Anesthesiologists physical status class, and type of surgery. The model was trained using American College of Surgeons National Surgical Quality Improvement Program data from 2007 to 2009 (n=809 880) and tested using data from 2010 (n=355 870). Risk models were developed using multivariate logistic regression. The outcomes were postoperative 30-day stroke, major cardiovascular events (myocardial infarction, cardiac arrest, or stroke), and 30-day mortality. Major cardiac complications occurred in 0.66% (n=5332) of patients (myocardial infarction, 0.28%; cardiac arrest, 0.41%), postoperative stroke in 0.25% (n=2005); 30-day mortality was 1.66% (n=13 484). The risk prediction model had high predictive accuracy with area under the receiver operating characteristic curve for stroke (training cohort=0.869, validation cohort=0.876), major cardiovascular events (training cohort=0.871, validation cohort=0.868), and 30-day mortality (training cohort=0.922, validation cohort=0.925). Surgery types, history of stroke, and coronary artery disease are significant risk factors for stroke and major cardiac complications. Conclusions Postoperative stroke, major cardiac complications, and 30-day mortality can be predicted with high accuracy using this web-based predictive model.

Project description:ImportanceSudden cardiac death (SCD) is the most common mode of death in childhood hypertrophic cardiomyopathy (HCM), but there is no validated algorithm to identify those at highest risk.ObjectiveTo develop and validate an SCD risk prediction model that provides individualized risk estimates.Design, setting, and participantsA prognostic model was developed from a retrospective, multicenter, longitudinal cohort study of 1024 consecutively evaluated patients aged 16 years or younger with HCM. The study was conducted from January 1, 1970, to December 31, 2017.ExposuresThe model was developed using preselected predictor variables (unexplained syncope, maximal left-ventricular wall thickness, left atrial diameter, left-ventricular outflow tract gradient, and nonsustained ventricular tachycardia) identified from the literature and internally validated using bootstrapping.Main outcomes and measuresA composite outcome of SCD or an equivalent event (aborted cardiac arrest, appropriate implantable cardioverter defibrillator therapy, or sustained ventricular tachycardia associated with hemodynamic compromise).ResultsOf the 1024 patients included in the study, 699 were boys (68.3%); mean (interquartile range [IQR]) age was 11 (7-14) years. Over a median follow-up of 5.3 years (IQR, 2.6-8.3; total patient years, 5984), 89 patients (8.7%) died suddenly or had an equivalent event (annual event rate, 1.49; 95% CI, 1.15-1.92). The pediatric model was developed using preselected variables to predict the risk of SCD. The model's ability to predict risk at 5 years was validated; the C statistic was 0.69 (95% CI, 0.66-0.72), and the calibration slope was 0.98 (95% CI, 0.59-1.38). For every 10 implantable cardioverter defibrillators implanted in patients with 6% or more of a 5-year SCD risk, 1 patient may potentially be saved from SCD at 5 years.Conclusions and relevanceThis new, validated risk stratification model for SCD in childhood HCM may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model's predictions in diverse patient populations.

Project description:BackgroundPeople with kidney failure often require surgery and experience worse postoperative outcomes compared to the general population, but existing risk prediction tools have excluded those with kidney failure during development or exhibit poor performance. Our objective was to derive, internally validate, and estimate the clinical utility of risk prediction models for people with kidney failure undergoing non-cardiac surgery.Design, setting, participants, and measuresThis study involved derivation and internal validation of prognostic risk prediction models using a retrospective, population-based cohort. We identified adults from Alberta, Canada with pre-existing kidney failure (estimated glomerular filtration rate [eGFR] < 15 mL/min/1.73m2 or receipt of maintenance dialysis) undergoing non-cardiac surgery between 2005-2019. Three nested prognostic risk prediction models were assembled using clinical and logistical rationale. Model 1 included age, sex, dialysis modality, surgery type and setting. Model 2 added comorbidities, and Model 3 added preoperative hemoglobin and albumin. Death or major cardiac events (acute myocardial infarction or nonfatal ventricular arrhythmia) within 30 days after surgery were modelled using logistic regression models.ResultsThe development cohort included 38,541 surgeries, with 1,204 outcomes (after 3.1% of surgeries); 61% were performed in males, the median age was 64 years (interquartile range [IQR]: 53, 73), and 61% were receiving hemodialysis at the time of surgery. All three internally validated models performed well, with c-statistics ranging from 0.783 (95% Confidence Interval [CI]: 0.770, 0.797) for Model 1 to 0.818 (95%CI: 0.803, 0.826) for Model 3. Calibration slopes and intercepts were excellent for all models, though Models 2 and 3 demonstrated improvement in net reclassification. Decision curve analysis estimated that use of any model to guide perioperative interventions such as cardiac monitoring would result in potential net benefit over default strategies.ConclusionsWe developed and internally validated three novel models to predict major clinical events for people with kidney failure having surgery. Models including comorbidities and laboratory variables showed improved accuracy of risk stratification and provided the greatest potential net benefit for guiding perioperative decisions. Once externally validated, these models may inform perioperative shared decision making and risk-guided strategies for this population.

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Risk of cardiac events with azithromycin-A prediction model.

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Risk of cardiac events with azithromycin-A prediction model.

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