Precision medicine in stroke and other related neurological diseases
Debmalya Barh in Precision Medicine in Cancers and Non-Communicable Diseases, 2018
Electronic health records contain huge amounts of disease-related useful information that could potentially be used for building models for predicting onset of diseases and its models for predicting treatment. In a study, a stroke-prediction model applied advanced machine-learning techniques to aggregated health records in patients with atrial fibrillation. The model outperformed the CHADS2 score (a method or rule used for clinical evaluation of stroke patients with nonrheumatic atrial fibrillation) and helped in predicting stroke, thus providing the useful information for clinical interpretation (Shahn et al., 2015). Therefore, both the medical claims and large health record data keep the potential to improve prediction accuracy when compared to individual patient-level prediction models.
Clinical Reasoning and Diagnostic Errors
Paul Cerrato, John Halamka in Reinventing Clinical Decision Support, 2020
Several debiasing strategies have been developed over the decades to combat these cognitive errors, though most of these tools have never been formally labeled as “debiasing.” Even the simple act of taking a detailed medical history using a well-documented assessment form gives the diagnostic process structure and discourages snap judgments. So does a thorough physical examination that covers all the organ systems. Similarly, there are many clinical prediction rules that can help clinicians more accurately evaluate a patient’s condition and shift the diagnostic process from the subjective to the objective end of the continuum. The CHADS2 score, for example, collects patient data to help clinicians diagnose the risk of stroke with atrial fibrillation; the APGAR score lets clinicians evaluate the health status of a newborn. They join diagnostic and assessment rules such as the Pneumonia Severity Index (PSI) and the Wells criteria for pulmonary embolism, which helps facilitate a diagnosis by assigning a numeric probability to the existence of a suspected disorder.
Evidence-based management
Christos Tziotzios, Jesse Dawson, Matthew Walters, Kennedy R Lees in Stroke in Practice, 2017
Presence of atrial fibrillation increased risk of both first stroke and recurrent stroke, and those with concurrent increased age, diabetes, congestive cardiac failure, previous stroke, and hypertension are at the highest risk. Scoring algorithms are now commonly employed to help predict stroke risk and identify those with atrial fibrillation most likely to benefit from anticoagulant treatment. An example is the CHADS2 score, where the variables of presence of recent congestive cardiac failure, hypertension, age > 75 years, and diabetes mellitus are assigned one point and history of stroke or TIA two points. Those with a score of two or more (assuming no treatment) have a stroke risk of approximately 4% per annum, rising to 8.5% per annum in those with a score of four.
Comparison of all-cause costs and healthcare resource use among patients with newly-diagnosed non-valvular atrial fibrillation newly treated with oral anticoagulants
Published in Current Medical Research and Opinion, 2018
Adrienne M. Gilligan, Jessica Franchino-Elder, Xue Song, Cheng Wang, Caroline Henriques, Amy Sainski-Nguyen, Kathleen Wilson, David M. Smith, Stephen Sander
Clinical characteristics, baseline HCRU, and baseline costs were measured in the 12-month pre-index period. Baseline clinical characteristics included the Deyo-Charlson Comorbidity Index (DCI), an aggregate measure of comorbidity based on weighted values for select diagnoses32, and the presence of specific comorbidities, including chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), pneumonia, liver disease (cirrhosis or hepatitis), coronary artery disease, diabetes mellitus, gastrointestinal bleeding, heart failure, hip fracture, home oxygen use, ischemic stroke/transient ischemic attack (TIA), hemorrhagic stroke, post-traumatic intracranial bleed, extracranial bleed, psychiatric disorders, and venous thromboembolism. In addition, stroke and bleeding risk were assessed using the CHADS2 score (Congestive heart failure, Hypertension, Age, Diabetes, prior Stroke) and its successor CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ≥75 years doubled, Diabetes mellitus, prior Stroke/TIA/thromboembolism doubled, Vascular disease, Age 65–74 years, Sex category), as well as the HAS-BLED score (Hypertension, Abnormal renal/liver function, Stroke, Bleeding history/predisposition, Labile INR, Elderly, Drugs/alcohol concomitantly), and ATRIA score (Anticoagulation and Risk Factors in Atrial Fibrillation)6,12,33–36.
Prognostic value of GRACE and CHA2DS2-VASc score among patients with atrial fibrillation undergoing percutaneous coronary intervention
Published in Annals of Medicine, 2021
Tingting Guo, Ziwei Xi, Hong Qiu, Yong Wang, Jianfeng Zheng, Kefei Dou, Bo Xu, Shubin Qiao, Weixian Yang, Runlin Gao
The CHA2DS2-VASc score showed marginally better ability in predicting the appearance of MCCEs over the GRACE score in patients with angioplasty in a study from Trantalis et al. [36] On the contrary, the CHA2DS2-VASc score only achieved suboptimal discrimination for ischaemic stroke with an AUC of 0.580 which indicate modest discriminatory power and poor specificity in our study, while the GRACE was shown to be relatively predictive for stroke after PCI with an AUC of 0.715. The renal function parameter was included as an element in the GRACE score but not in the CHA2DS2-VASc score, which might be an explanation of the better performance of the former. A study from Piccini et al. found that a modified CHADS2 score by adding 2 points for creatinine clearance <60 mL/min improved net stroke risk reclassification over the CHADS2 and CHA2DS2-VASc score [37]. Interestingly, a study from Shuvy et al. suggested that the addition of the CHA2DS2-VASc score to the GRACE score in ACS patients could significantly improve risk stratification of patients with low and intermediate risk [38]. In terms of bleeding, our findings are in line with a previous study that validated the CHA2DS2-VASc score in non-AF patients undergoing PCI from Capodanno et al. and suggested that the CHA2DS2-VASc score had modest discrimination for major bleeding [39]. However, the ENTRUST-AF PCI subgroup analysis from Goette et al. demonstrated that a high CHA2DS2-VASc score was a marker for occurrence of major bleeding [33].
Long-term effect of catheter ablation on tachycardia-bradycardia syndrome: evidenced by 10 years follow up
Published in Acta Cardiologica, 2020
Shushan Zhang, Yanzong Yang, Yunlong Xia, Lianjun Gao, Xuanhe Zhang, Gary Tse, Xiaomeng Yin, Shiyu Dai, Dong Chang
During follow-up, some parameters were recorded. They were AF recurrence, AADs administration, progression to persistent AF, death, ischaemic stroke, and intracranial haemorrhage or other severe haemorrhage. In the CA group, the patients underwent anticoagulation for at least 3 months after the procedures. Patients with CHADS2 or CHA2DS2-VASc score ≥ 1 points were encouraged to continue with long-term anticoagulation no matter AF recurred or not. Anticoagulants included warfarin and non-vitamin K antagonist oral anticoagulants. Patients with oral administration of warfarin were required to regularly monitor international normalised ratio (INR), and regularly to maintain it at 2.0–3.0. All patients were encouraged to report events of bleeding and neurologic syndrome. AADs were only given to patients with recurrent AF without long pauses during follow-up. All patients were followed up in the outpatient clinic or through telephone interviews. All patients were encouraged to report any symptoms indicative of tachycardia or long pauses, and underwent 12-lead electrocardiography (ECG) or 24-hour Holter monitoring every 3 to 6 months after procedures. AF recurrence was defined as atrial tachyarrhythmia sustained for more than 30 s, and occurring for more than 3 months after the procedure.
Related Knowledge Centers
- Anticoagulant
- Arrhythmia
- Atrial Fibrillation
- Clinical Prediction Rule
- Rheumatic Fever
- Stroke
- Thrombus
- Heart
- Medical Scoring
- Prothrombin Time