Clinical decision making and cardiovascular risk factors: What about the Heart Team?

2014 ◽  
Vol 214 (4) ◽  
pp. 200-201 ◽  
Author(s):  
M.-J. de Boer ◽  
H.J.G. Bilo
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Heart Team

Reversibility of Delirium in Palliative Care (DRAFT)

2018 ◽  
Author(s):  
Tiffany Shaw ◽  
Eric Prommer
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Palliative Care ◽  
Clinical Case ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Factors Associated ◽  
Frequent Event ◽  
Study Intervention

Delirium is a frequent event in patients with advanced cancer. Untreated delirium affects assessment of symptoms, impairs communication including participation in clinical decision-making. This study used specific diagnostic criteria for delirium and prospectively identified precipitating causes of delirium. The study identified factors associated with reversible and irreversible delirium. Impact of delirium on prognosis was evaluated. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case. Topics covered include delirium, neoplasms, palliative care, polypharmacy, risk factors, and therapeutics.

scholarly journals Updates on the Latest Surgical Approach of the Aortic Stenosis

2021 ◽  
Vol 10 (21) ◽  
pp. 5140
Author(s):  
Lucian Geicu ◽  
Olivier Busuttil ◽  
Nicolas D’Ostrevy ◽  
Mathieu Pernot ◽  
Walid Benali ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Daily Practice ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Heart Team ◽  
Surgical Indications ◽  
Therapeutic Decisions ◽  
Clinical Scenarios ◽  
Technical Advances

Over the last twenty years, we marked significant progresses in the field of tissue engineering and the development of new aortic valve structural and delivery systems. These continuous iterations on the field, have completely changed the surgical indications and approaches for AVR. Nowadays, therapeutic decisions are endorsed by international guidelines; however, new technical advances need a new integrated approach. The clinical scenarios issued from the interaction between the Guidelines and the newest approaches and technologies are regularly on debate by the Heart Team. We will present some of our most encountered situations and the pattern of our therapeutic decisions. To easily navigate through Guidelines and clinical scenarios, we reported in this review a simplified and easy to use Clinical decision-making algorithm that may be a valuable tool in our daily practice.

scholarly journals Assessment of violence risk in 440 psychiatric patients in China: examining the feasibility and acceptability of a novel and scalable approach (FoVOx)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shaoling Zhong ◽  
◽  
Rongqin Yu ◽  
Robert Cornish ◽  
Xiaoping Wang ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Psychiatric Patients ◽  
Clinical Decision ◽  
Violence Risk ◽  
Violence Risk Assessment ◽  
Risk Categories

Abstract Background Violence risk assessment is a routine part of clinical services in mental health, and in particular secure psychiatric hospitals. The use of prediction models and risk tools can assist clinical decision-making on risk management, including decisions about further assessments, referral, hospitalization and treatment. In recent years, scalable evidence-based tools, such as Forensic Psychiatry and Violent Oxford (FoVOx), have been developed and validated for patients with mental illness. However, their acceptability and utility in clinical settings is not known. Therefore, we conducted a clinical impact study in multiple institutions that provided specialist mental health service. Methods We followed a two-step mixed-methods design. In phase one, we examined baseline risk factors on 330 psychiatric patients from seven forensic psychiatric institutes in China. In phase two, we conducted semi-structured interviews with 11 clinicians regarding violence risk assessment from ten mental health centres. We compared the FoVOx score on each admission (n = 110) to unstructured clinical risk assessment and used a thematic analysis to assess clinician views on the accuracy and utility of this tool. Results The median estimated probability of violent reoffending (FoVOx score) within 1 year was 7% (range 1–40%). There was fair agreement (72/99, 73% agreement) on the risk categories between FoVOx and clinicians’ assessment on risk categories, and moderate agreement (10/12, 83% agreement) when examining low and high risk categories. In a majority of cases (56/101, 55%), clinicians thought the FoVOx score was an accurate representation of the violent risk of an individual patient. Clinicians suggested some additional clinical, social and criminal risk factors should be considered during any comprehensive assessment. In addition, FoVOx was considered to be helpful in assisting clinical decision-making and individual risk assessment. Ten out of 11 clinicians reported that FoVOx was easy to use, eight out of 11 was practical, and all clinicians would consider using it in the future. Conclusions Clinicians found that violence risk assessment could be improved by using a simple, scalable tool, and that FoVOx was feasible and practical to use.

scholarly journals Predicting Cardiovascular Risk in Athletes: Resampling Improves Classification Performance

2020 ◽  
Vol 17 (21) ◽  
pp. 7923
Author(s):  
Davide Barbieri ◽  
Nitesh Chawla ◽  
Luciana Zaccagni ◽  
Tonći Grgurinović ◽  
Jelena Šarac ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Classification Performance ◽  
Biomedical Data ◽  
Sport Practice

Cardiovascular diseases are the main cause of death worldwide. The aim of the present study is to verify the performances of a data mining methodology in the evaluation of cardiovascular risk in athletes, and whether the results may be used to support clinical decision making. Anthropometric (height and weight), demographic (age and sex) and biomedical (blood pressure and pulse rate) data of 26,002 athletes were collected in 2012 during routine sport medical examinations, which included electrocardiography at rest. Subjects were involved in competitive sport practice, for which medical clearance was needed. Outcomes were negative for the largest majority, as expected in an active population. Resampling was applied to balance positive/negative class ratio. A decision tree and logistic regression were used to classify individuals as either at risk or not. The receiver operating characteristic curve was used to assess classification performances. Data mining and resampling improved cardiovascular risk assessment in terms of increased area under the curve. The proposed methodology can be effectively applied to biomedical data in order to optimize clinical decision making, and—at the same time—minimize the amount of unnecessary examinations.

scholarly journals Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System

2020 ◽  
Author(s):  
Timothy S Chang ◽  
Yi Ding ◽  
Malika K Freund ◽  
Ruth Johnson ◽  
Tommer Schwarz ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Los Angeles ◽  
Renal Disease ◽  
Health System ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Inpatient Admission ◽  
Government Policies ◽  
Prior Use

SummaryWith the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9th, 2020 and June 14th, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical co-morbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 × 10−10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 × 10−6) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 × 10−6). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2×10−4) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7×10−4). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3×10−4), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8×10−5). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.

Using Intensive Longitudinal Data to Identify Early Predictors of Suicide-Related Outcomes in High-Risk Adolescents: Practical and Conceptual Considerations

Assessment ◽  
2020 ◽  
pp. 107319112093916
Author(s):  
Ewa K. Czyz ◽  
Jamie R.T. Yap ◽  
Cheryl A. King ◽  
Inbal Nahum-Shani
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Longitudinal Data ◽  
Roc Curve ◽  
Clinical Decision Making ◽  
Predictive Accuracy ◽  
Clinical Decision ◽  
Future Research ◽  
Intensive Longitudinal Data ◽  
Suicidal Crisis

Mobile technology offers new possibilities for assessing suicidal ideation and behavior in real- or near-real-time. It remains unclear how intensive longitudinal data can be used to identify proximal risk and inform clinical decision making. In this study of adolescent psychiatric inpatients ( N = 32, aged 13-17 years, 75% female), we illustrate the application of a three-step process to identify early signs of suicide-related crises using daily diaries. Using receiver operating characteristic (ROC) curve analyses, we considered the utility of 12 features—constructed using means and variances of daily ratings for six risk factors over the first 2 weeks postdischarge (observations = 360)—in identifying a suicidal crisis 2 weeks later. Models derived from single risk factors had modest predictive accuracy (area under the ROC curve [AUC] 0.46-0.80) while nearly all models derived from combinations of risk factors produced higher accuracy (AUCs 0.80-0.91). Based on this illustration, we discuss implications for clinical decision making and future research.

scholarly journals Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach

2021 ◽  
Author(s):  
Abhinav Vepa ◽  
Amer Saleem ◽  
Kambiz Rakhshan ◽  
Amr Omar ◽  
Diana Dharmaraj ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Pulmonary Embolism ◽  
Bayesian Networks ◽  
Predictive Models ◽  
Inpatient Treatment ◽  
Clinical Decision Making ◽  
Ventilatory Support ◽  
Clinical Decision ◽  
Inpatient Mortality

AbstractIntroductionWithin the UK, COVID-19 has contributed towards over 103,000 deaths. Multiple risk factors for COVID-19 have been identified including various demographics, co-morbidities, biochemical parameters, and physical assessment findings. However, using this vast data to improve clinical care has proven challenging.Aimsto develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, to aid risk-stratification and earlier clinical decision-making.MethodsAnonymized data regarding 44 independent predictor variables of 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-controlled analysis. Primary outcomes included inpatient mortality, level of ventilatory support and oxygen therapy required, and duration of inpatient treatment. Secondary pulmonary embolism was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were created using Bayesian Networks, and cross-validated.ResultsOur multivariable models were able to predict, using feature selected risk factors, the probability of inpatient mortality (F1 score 83.7%, PPV 82%, NPV 67.9%); level of ventilatory support required (F1 score varies from 55.8% “High-flow Oxygen level” to 71.5% “ITU-Admission level”); duration of inpatient treatment (varies from 46.7% for “≥ 2 days but < 3 days” to 69.8% “≤ 1 day”); and risk of pulmonary embolism sequelae (F1 score 85.8%, PPV of 83.7%, and NPV of 80.9%).ConclusionOverall, our findings demonstrate reliable, multivariable predictive models for 4 outcomes, that utilize readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as clinical decision-making tools.HighlightsUsing COVID-19 risk-factor data to assist clinical decision making is a challengeAnonymous data from 355 COVID-19 inpatients was collected & balancedKey independent variables were feature selected for 4 different outcomesAccurate, multi-variable predictive models were computed, using Bayesian NetworksFuture research should externally validate our models & demonstrate clinical utility

Cardiovascular risk in clinical decision making

2007 ◽  
Vol 9 (5) ◽  
pp. 339-341 ◽  
Author(s):  
Fernando Rodríguez-Artalejo ◽  
José R. Banegas
Keyword(s):  
Decision Making ◽  
Cardiovascular Risk ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Prediction of the Rehabilitation Duration and Risk Management for Mild-Moderate COVID-19

2020 ◽  
Vol 14 (5) ◽  
pp. 652-657 ◽  
Author(s):  
Qiong-Na Zheng ◽  
Mei-Yan Xu ◽  
Yong-Le Zheng ◽  
Xiu-Ying Wang ◽  
Hui Zhao
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Risk Management ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Linear Regression Analysis ◽  
Significant Risk ◽  
Clinical Decision ◽  
Clinical Practices

ABSTRACTObjectives:More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.Methods:A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.Results:Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.Conclusions:The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

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