The ventricular tachycardia prediction model: Derivation and validation data.
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ABSTRACT:
SUBMITTER: Kashou AH
PROVIDER: S-EPMC7200856 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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The ventricular tachycardia prediction model: Derivation and validation data.
Kashou Anthony H AH DeSimone Christopher V CV Hodge David O DO Carter Rickey R Lin Grace G Asirvatham Samuel J SJ Noseworthy Peter A PA Deshmukh Abhishek J AJ May Adam M AM
Data in brief 20200421
In a recent publication [1], we introduced and described a novel means (i.e. VT Prediction Model) to correctly categorize wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) using routine measurements shown on electrocardiogram (ECG) paper recordings. In this article, we summarize data components relating to the derivation and validation of the VT Prediction Model. ...[more]
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Project description:A recent publication (May et al., 2019) introduced a novel means (i.e. WCT Formula) to automatically distinguish ventricular tachycardia and supraventricular wide complex tachycardia using modern-day computerized electrocardiogram software measurements. In this article, a summary of data components relating to the derivation and validation of the WCT Formula is presented.
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Project description:BackgroundPediatric hand fractures are common, and most can be managed by a period of immobilization. However, it remains challenging to identify those more complex fractures requiring the expertise of a hand surgeon to ensure a good outcome. The purpose of this study was to develop a prediction model for identification of complex pediatric hand fractures requiring care by a hand surgeon.MethodsA 2-year retrospective cohort study of consecutively referred pediatric (<18 years) hand fracture patients was used to derive and internally validate a prediction model for identification of complex fractures requiring the expertise of a hand surgeon. These complex fractures were defined as those that required surgery, closed reduction, or four or more appointments with a hand surgeon. The model, derived by multivariable logistic regression analysis, was internally validated using bootstrapping and then translated into a risk index.ResultsOf 1170 fractures, 416 (35.6%) met criteria for a complex fracture. Multivariable regression analysis identified six significant predictors of complex fracture: open fracture, rotational deformity, angulation, condylar involvement, dislocation or subluxation, and displacement. Internal validation demonstrated good performance of the model (C-statistic = 0.88, calibration curve p = 0.935). A threshold of ≥1 point (ie, any one of the predictors) resulted in a simple, easy-to-use tool with 96.4% sensitivity and 45.5% specificity.ConclusionsA high-performing and clinically useful decision support tool was developed for emergency and urgent care physicians providing initial assessment for children with hand fractures. This tool will provide the basis for development of a clinical care pathway for pediatric hand fractures.
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Project description:ObjectiveUsing primary care data, develop and validate sex-specific prognostic models that estimate the 10-year risk of people with non-diabetic hyperglycaemia developing type 2 diabetes.DesignRetrospective cohort study.SettingPrimary care.Participants154 705 adult patients with non-diabetic hyperglycaemia.Primary outcomeDevelopment of type 2 diabetes.MethodsThis study used data routinely collected in UK primary care from general practices contributing to the Clinical Practice Research Datalink. Patients were split into development (n=109 077) and validation datasets (n=45 628). Potential predictor variables, including demographic and lifestyle factors, medical and family history, prescribed medications and clinical measures, were included in survival models following the imputation of missing data. Measures of calibration at 10 years and discrimination were determined using the validation dataset.ResultsIn the development dataset, 9332 patients developed type 2 diabetes during 293 238 person-years of follow-up (31.8 (95% CI 31.2 to 32.5) per 1000 person-years). In the validation dataset, 3783 patients developed type 2 diabetes during 115 113 person-years of follow-up (32.9 (95% CI 31.8 to 33.9) per 1000 person-years). The final prognostic models comprised 14 and 16 predictor variables for males and females, respectively. Both models had good calibration and high levels of discrimination. The performance statistics for the male model were: Harrell's C statistic of 0.700 in the development and 0.701 in the validation dataset, with a calibration slope of 0.974 (95% CI 0.905 to 1.042) in the validation dataset. For the female model, Harrell's C statistics were 0.720 and 0.718, respectively, while the calibration slope was 0.994 (95% CI 0.931 to 1.057) in the validation dataset.ConclusionThese models could be used in primary care to identify those with non-diabetic hyperglycaemia most at risk of developing type 2 diabetes for targeted referral to the National Health Service Diabetes Prevention Programme.
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Project description:This scenario was developed to educate emergency medicine residents on the diagnosis and management of ventricular tachycardia (VT) that is refractory to single dose anti-arrhythmic management. Electrical storm, defined as three or more episodes of sustained VT, ventricular fibrillation, or appropriate shocks from an implantable cardioverter defibrillator within 24 hours,1 has a mortality rate up to 14% in the first 48 hours.2 Ventricular tachycardia may present in a heterogenous fashion, not only with stable versus unstable clinical presentations, but also with different electrocardiographic morphologies and etiologies.1 Understanding how to rapidly diagnose, treat, and utilize second or third-line treatments is vital in the setting of refractory ventricular tachycardia rather than relying on the success of first-line agents. Appreciation for what medications are readily available in your crash cart and medication dispensing cabinet is critical for timely management for refractory ventricular tachycardia. At the conclusion of the simulation session, learners will be able to: 1) identify the different etiologies of VT, including structural heart disease, acute ischemia, and acquired or congenital QT syndrome; 2) describe confounding factors of VT, such as electrolyte abnormalities and sympathetic surge; 3) describe how to troubleshoot an unsuccessful synchronized cardioversion, including checking equipment connections, increasing delivered energy, and changing pad placement; 4) compare and contrast treatments of VT based on suspected underlying etiology; 5) describe reasons to activate the cardiac catheterization lab other than occlusive myocardial infarction; and 6) identify appropriate disposition of the patient to the cardiac catheterization lab. This session was conducted using high-fidelity simulation, followed by a debriefing session and lecture on the diagnosis, differential diagnosis, and management of VT. Debriefing methods may be left to the discretion of participants, but the authors have utilized advocacy-inquiry techniques. This scenario may also be run as an oral board case. Our residents are provided a survey at the completion of the debriefing session so they may rate different aspects of the simulation, as well as provide qualitative feedback on the scenario. The local institution's simulation center's electronic feedback form is based on the Center of Medical Simulation's Debriefing Assessment for Simulation in Healthcare (DASH) Student Version Short Form3 with the inclusion of required qualitative feedback if an element was scored less than a 6 or 7. Twelve learners completed a feedback form. This session received 6 and 7 scores (consistently effective/very good and extremely effective/outstanding, respectively) other than three isolated 5 scores. The lowest average score was 6.67 for "Before the simulation, the instructor set the stage for an engaging learning experience." The highest average score was 7 for "The instructor helped me see how to improve or how to sustain good performance." The form also includes an area for general feedback about the case at the end. Illustrative examples of feedback include: "Excellent care and debrief." Specific scores are available upon request. This is a cost-effective method for reviewing VT diagnosis and management. The case may be modified for appropriate audiences, such as describing what medications may be readily available in a free-standing emergency department or pre-hospital setting. Medical simulation, ventricular tachycardia, cardiac emergencies, dysrhythmias, cardiology, emergency medicine.
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Project description:Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation. Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.
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Project description:Accurate weight predictions are essential for weight management program patients. The freely available National Institutes of Health Body Weight Planner (NIH-BWP) returns expected weights over time but overestimates weight when patients consume a low-calorie diet. This study sought to increase the accuracy of NIH-BWP predicted weights for people on low-calorie diets. People enrolled in a weight management program were included if they received meal replacements with defined caloric content for the 3 months of the weight loss phase of the program. The Ottawa Weight Loss Prediction Model (OWL-PM) modelled the relative difference between observed and NIH-BWP predicted weights using longitudinal analysis methods based on patient factors. OWL-PM was externally validated. 1761 people were included (mean age 46 years, 73.3% women) with a mean (SD) baseline weight in pounds and body mass index of 271.9 (55.6) and 43.9 (7.4), respectively. At the end of the program's weight loss phase, people lost a median (IQR) of 17.1% (14.8-19.5) of their baseline weight. Observed weight relative to NIH-BWP predicted weights had a median value of - 4.9% but ranged from - 32.1% to + 28.5%. After adjustment, weight overestimation by NIH-BWP was most pronounced in male patients, people without diabetes and with increased observation time. OWL-PM returned expected weights at 3 months that were significantly more accurate than those from NIH-BWP alone (mean difference observed vs. expected [95% CI] 6.7lbs [6.4-7.0] vs. 12.6lbs [12.1-13.0]). In the external validation cohort (n = 106), OWL-PM was significantly more accurate than NIH-BWP (mean squared error 24.3 vs. 40.0, p = 0.0018). OWL-PM incorporated patient-level covariates to significantly increase weight prediction accuracy of NIH-BWP in patients consuming a low-calorie diet.
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Project description:BackgroundUnplanned readmissions after colorectal cancer (CRC) surgery are common, expensive, and result from failure to progress in postoperative recovery. The context of their preventability and extent of predictability remains undefined. This study aimed to define the 30-day unplanned readmission (UR) rate after CRC surgery, identify risk factors, and develop a prediction model with external validation.MethodsConsecutive patients who underwent CRC surgery between 2012 and 2017 at Christchurch Hospital were retrospectively identified. The primary outcome was UR within 30 days after index discharge. Statistically significant risk factors were identified and incorporated into a predictive model. The model was then externally evaluated on a prospectively recruited dataset from 2018 to 2019.ResultsOf the 701 patients identified, 15.1% were readmitted within 30 days of discharge. Stoma formation (OR 2.45, 95% CI 1.59-3.81), any postoperative complications (PoCs) (OR 2.27, 95% CI 1.48-3.52), high-grade PoCs (OR 2.52, 95% CI 1.18-5.11), and rectal cancer (OR 2.11, 95% CI 1.48-3.52) were statistically significant risk factors for UR. A clinical prediction model comprised of rectal cancer and high-grade PoCs predicted UR with an AUC of 0.64 and 0.62 on internal and external validation, respectively.ConclusionsURs after CRC surgery are predictable and occur within 2 weeks of discharge. They are driven by PoCs, most of which are of low severity and develop after discharge. Atleast 16% of readmissions are preventable by management in an outpatient setting with appropriate surgical expertise. Targeted outpatient follow-up within two weeks of discharge is therefore the most effective transitional-care strategy for prevention.
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Project description:AbstractMost studies on the prediction of venous thromboembolism (VTE) focused on hospitalized, surgery, and cancer patients or women receiving hormonal contraceptives or menopausal hormone therapy. No study considered diabetic and general populations to establish a VTE prediction model, especially in Asia. We developed a predictive model for VTE among type 2 diabetic patients and the general population.This study considered 2 nationwide retrospective cohort studies consisting of 52,427 diabetic participants and 508,664 participants from the general population aged 30 to 85 years during 2001 to 2004 in Taiwan. All participants were followed up until VTE event, death, or December 2011. The outcome event was VTE, including deep venous thrombosis and pulmonary embolism. Candidate predictors consisted of socio-demographic factors, diabetes-related factors and biomarkers, comorbidities, and medicine use. Our study followed the procedures proposed by the Framingham Heart Study to develop prediction models by using a Cox regression model. The predictive accuracy and performance characteristics were assessed using the area under curve of receiver operating characteristics curve and calibration of a risk score were performed by Hosmer-Lemeshow goodness-of-fit test.The common factors for persons with type 2 diabetes and general population included age, hospitalization status 1 year before the baseline, hypertension, chronic kidney disease, chronic obstructive pulmonary disease, and anti-diabetes medications; the specific factors for persons with type 2 diabetes consisted of body mass index, glycosylated hemoglobin A1C, and creatinine; and the factors for general population included gender, peripheral vascular disease, cancer, hypertension medication, cardiovascular medication, and non-steroidal anti-inflammatory drug. The area under curve of 3-, 5-, and 8-year VTE prediction models were 0.74, 0.71, and 0.69 in the diabetic population and 0.77, 0.76, and 0.75 in the general population, respectively.The new clinical prediction models can help identify a high risk of VTE and provide medical intervention in diabetic and general populations.
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Project description:Catecholaminergic polymorphic ventricular tachycardia (VT) is a lethal familial disease characterized by bidirectional VT, polymorphic VT, and ventricular fibrillation. Catecholaminergic polymorphic VT is caused by enhanced Ca2+ release through defective ryanodine receptor (RyR2) channels. We used epicardial and endocardial optical mapping, chemical subendocardial ablation with Lugol's solution, and patch clamping in a knockin (RyR2/RyR2(R4496C)) mouse model to investigate the arrhythmogenic mechanisms in catecholaminergic polymorphic VT. In isolated hearts, spontaneous ventricular arrhythmias occurred in 54% of 13 RyR2/RyR2(R4496C) and in 9% of 11 wild-type (P=0.03) littermates perfused with Ca2+and isoproterenol; 66% of 12 RyR2/RyR2(R4496C) and 20% of 10 wild-type hearts perfused with caffeine and epinephrine showed arrhythmias (P=0.04). Epicardial mapping showed that monomorphic VT, bidirectional VT, and polymorphic VT manifested as concentric epicardial breakthrough patterns, suggesting a focal origin in the His-Purkinje networks of either or both ventricles. Monomorphic VT was clearly unifocal, whereas bidirectional VT was bifocal. Polymorphic VT was initially multifocal but eventually became reentrant and degenerated into ventricular fibrillation. Endocardial mapping confirmed the Purkinje fiber origin of the focal arrhythmias. Chemical ablation of the right ventricular endocardial cavity with Lugol's solution induced complete right bundle branch block and converted the bidirectional VT into monomorphic VT in 4 anesthetized RyR2/RyR2(R4496C) mice. Under current clamp, single Purkinje cells from RyR2/RyR2(R4496C) mouse hearts generated delayed afterdepolarization-induced triggered activity at lower frequencies and level of adrenergic stimulation than wild-type. Overall, the data demonstrate that the His-Purkinje system is an important source of focal arrhythmias in catecholaminergic polymorphic VT.
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Project description:Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a condition of abnormal heart rhythm (arrhythmia), induced by physical activity or stress. Mutations in ryanodine receptor 2 (RyR2), a Ca2+ release channel located in the sarcoplasmic reticulum (SR), or calsequestrin 2 (CASQ2), a SR Ca2+ binding protein, are linked to CPVT. For specific drug development and to study distinct arrhythmias, simple models are required to implement and analyze such mutations. Here, we introduced CPVT inducing mutations into the pharynx of Caenorhabditis elegans, which we previously established as an optogenetically paced heart model. By electrophysiology and video-microscopy, we characterized mutations in csq-1 (CASQ2 homologue) and unc-68 (RyR2 homologue). csq-1 deletion impaired pharynx function and caused missed pumps during 3.7 Hz pacing. Deletion mutants of unc-68, and in particular the point mutant UNC-68(R4743C), analogous to the established human CPVT mutant RyR2(R4497C), were unable to follow 3.7 Hz pacing, with progressive defects during long stimulus trains. The pharynx either locked in pumping at half the pacing frequency or stopped pumping altogether, possibly due to UNC-68 leakiness and/or malfunctional SR Ca2+ homeostasis. Last, we could reverse this 'worm arrhythmia' by the benzothiazepine S107, establishing the nematode pharynx for studying specific CPVT mutations and for drug screening.
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