Assessing Acute Risk of Violence in Military Veterans

2015 ◽  
Author(s):  
Eric B. Elbogen ◽  
Robert Graziano
Keyword(s):  
Risk Assessment ◽  
Systematic Approach ◽  
Clinical Decision Making ◽  
Military Veterans ◽  
Clinical Decision ◽  
Assessment Tools ◽  
Clinical Settings ◽  
Empirically Supported ◽  
Risk Assessment Tools ◽  
Scientific Knowledge Base

Research has shown aggression toward others is a problem in a subset of military veterans. Predicting this kind of aggression would be immensily helpful in clinical settings. To our knowledge, there currently are no risk assessment tools or screens that have been validated to specifically evaluate acute violence among veterans. This chapter reviews what we do and do not know about violence in veterans so that clinicians who are making decisions about acute violence can be informed by the existing scientific knowledge base. Examining these empirically supported risk and protective factors using a systematic approach may optimize clinical decision making when assessing acute violence in veterans.

scholarly journals Improving risk assessment of violence among military Veterans: An evidence-based approach for clinical decision-making

2010 ◽  
Vol 30 (6) ◽  
pp. 595-607 ◽  
Author(s):  
Eric B. Elbogen ◽  
Sara Fuller ◽  
Sally C. Johnson ◽  
Stephanie Brooks ◽  
Patricia Kinneer ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Military Veterans ◽  
Clinical Decision ◽  
Evidence Based

Predicting Adverse Perioperative Events in Patients Undergoing Primary Cleft Palate Repair

2018 ◽  
Vol 55 (4) ◽  
pp. 574-581 ◽  
Author(s):  
Marten N. Basta ◽  
John E. Fiadjoe ◽  
Albert S. Woo ◽  
Kenneth N. Peeples ◽  
Oksana A. Jackson
Keyword(s):  
Risk Assessment ◽  
Cleft Palate ◽  
Clinical Decision Making ◽  
Assessment Tool ◽  
Clinical Decision ◽  
Assessment Tools ◽  
C Statistic ◽  
Extreme Risk ◽  
Risk Patients ◽  
Cleft Palate Repair

Objective: This study aimed to identify risk factors for adverse perioperative events (APEs) after cleft palatoplasty to develop an individualized risk assessment tool. Design: Retrospective cohort. Setting: Tertiary institutional. Patients: Patients younger than 2 years with cleft palate. Interventions: Primary Furlow palatoplasty between 2008 and 2011. Main Outcome Measure(s): Adverse perioperative event, defined as laryngo- or bronchospasm, accidental extubation, reintubation, obstruction, hypoxia, or unplanned intensive care unit admission. Results: Three hundred patients averaging 12.3 months old were included. Cleft distribution included submucous, 1%; Veau 1, 17.3%; Veau 2, 38.3%; Veau 3, 30.3%; and Veau 4, 13.0%. Pierre Robin (n = 43) was the most prevalent syndrome/anomaly. Eighty-three percent of patients received reversal of neuromuscular blockade, and total morphine equivalent narcotic dose averaged 0.19 mg/kg. Sixty-nine patients (23.0%) had an APE, most commonly hypoventilation (10%) and airway obstruction (8%). Other APEs included reintubation (4.7%) and laryngobronchospasm (3.3%). APE was associated with multiple intubation attempts (odds ratio [OR] = 6.6, P = .001), structural or functional airway anomaly (OR = 4.5, P < .001), operation >160 minutes (OR = 2.2, P = .04), narcotic dose >0.3 mg/kg (OR = 2.3, P = .03), inexperienced provider (OR = 2.1, P = .02), and no paralytic reversal administration (OR = 2.0, P = .049); weight between 9 and 13 kg was protective (OR = 0.5, P = .04). Patients were risk-stratified according to individual profiles as low, average, high, or extreme risk (APE 2.5%-91.7%) with excellent risk discrimination (C-statistic = 0.79). Conclusions: APE incidence was 23.0% after palatoplasty, with a 37-fold higher incidence in extreme-risk patients. Individualized risk assessment tools may enhance perioperative clinical decision making to mitigate complications.

IMPACT ASSESSMENT ON STORM SURGE RISKS TO CONTAINERS CONSIDERING GLOBAL WARMING

2017 ◽  
Author(s):  
Rikito Hisamatsu ◽  
Rikito Hisamatsu ◽  
Kei Horie ◽  
Kei Horie
Keyword(s):  
Risk Assessment ◽  
Global Warming ◽  
Storm Surge ◽  
Storm Surges ◽  
Assessment Tools ◽  
Inundation Depth ◽  
Risk Assessment Tools ◽  
Risk Maps ◽  
The Impact ◽  
Vulnerability Functions

Container yards tend to be located along waterfronts that are exposed to high risk of storm surges. However, risk assessment tools such as vulnerability functions and risk maps for containers have not been sufficiently developed. In addition, damage due to storm surges is expected to increase owing to global warming. This paper aims to assess storm surge impact due to global warming for containers located at three major bays in Japan. First, we developed vulnerability functions for containers against storm surges using an engineering approach. Second, we simulated storm surges at three major bays using the SuWAT model and taking global warming into account. Finally, we developed storm surge risk maps for containers based on current and future situations using the vulnerability function and simulated inundation depth. As a result, we revealed the impact of global warming on storm surge risks for containers quantitatively.

scholarly journals The Benefits of Risk Assessment Tools for Prostate Cancer

2012 ◽  
Vol 61 (4) ◽  
pp. 662-663 ◽  
Author(s):  
Ian M. Thompson ◽  
Donna P. Ankerst
Keyword(s):  
Prostate Cancer ◽  
Risk Assessment ◽  
Assessment Tools ◽  
Risk Assessment Tools

Suicide risk assessment in a large public mental health service: do suicide risk classifications identify those at risk?

2021 ◽  
pp. 103985622098403
Author(s):  
Marianne Wyder ◽  
Manaan Kar Ray ◽  
Samara Russell ◽  
Kieran Kinsella ◽  
David Crompton ◽  
...  
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Risk Assessment ◽  
High Risk ◽  
Suicide Risk ◽  
Public Mental Health ◽  
Assessment Tools ◽  
Suicide Risk Assessment ◽  
Public Mental Health Service ◽  
Risk Assessment Tools

Introduction: Risk assessment tools are routinely used to identify patients at high risk. There is increasing evidence that these tools may not be sufficiently accurate to determine the risk of suicide of people, particularly those being treated in community mental health settings. Methods: An outcome analysis for case serials of people who died by suicide between January 2014 and December 2016 and had contact with a public mental health service within 31 days prior to their death. Results: Of the 68 people who had contact, 70.5% had a formal risk assessment. Seventy-five per cent were classified as low risk of suicide. None were identified as being at high risk. While individual risk factors were identified, these did not allow to differentiate between patients classified as low or medium. Discussion: Risk categorisation contributes little to patient safety. Given the dynamic nature of suicide risk, a risk assessment should focus on modifiable risk factors and safety planning rather than risk prediction. Conclusion: The prediction value of suicide risk assessment tools is limited. The risk classifications of high, medium or low could become the basis of denying necessary treatment to many and delivering unnecessary treatment to some and should not be used for care allocation.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

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