scholarly journals Risk of Injury to Others: The Development of an Algorithm to Identify Children and Youth at High-Risk of Aggressive Behaviours

2022 ◽  
Vol 12 ◽  
Author(s):  
Shannon L. Stewart ◽  
Angela Celebre ◽  
John P. Hirdes ◽  
Jeffrey W. Poss
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Youth Violence ◽  
Strong Predictor ◽  
Premature Mortality ◽  
Decision Support Tool ◽  
Children And Youth ◽  
Support Tool ◽  
Increased Risk ◽  
Aggressive Behaviours

Youth violence is considered one of the most preventable causes of morbidity and premature mortality. Various risk factors have previously been identified, however, there is presently a crucial need to develop effective decision-support tools in order to identify children and youth at increased risk for violence. The current study utilised data collected from the interRAI Child and Youth Mental Health Screener (ChYMH-S), within the province of Ontario, to develop and validate a methodology for the purpose of identifying young persons who were at greater risk of harm to others. Additional data from 59 mental health agencies validated the algorithm, and it was found to be a strong predictor of harmful behaviour toward others. The RIO algorithm provides a valuable decision-support tool with strong psychometric properties that may be used to identify young persons who exhibit signs or symptoms associated with increased likelihood of harm toward others, in order to provide early intervention efforts for these vulnerable youth, thereby reducing the likelihood of future aggressive behaviours.

scholarly journals Risk of Suicide and Self-harm in Kids: The Development of an Algorithm to Identify High-Risk Individuals Within the Children’s Mental Health System

2020 ◽  
Vol 51 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Shannon L. Stewart ◽  
Angela Celebre ◽  
John P. Hirdes ◽  
Jeffrey W. Poss
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Additional Data ◽  
Strong Predictor ◽  
Signs And Symptoms ◽  
Decision Support Tool ◽  
Support Tool ◽  
Increased Risk ◽  
Support Tools ◽  
Self Harm

Abstract Suicide is the second leading cause of death in adolescents within Canada. While several risk factors have been found to be associated with increased risk, appropriate decision-support tools are needed to identify children who are at highest risk for suicide and self-harm. The aim of the present study was to develop and validate a methodology for identifying children at heightened risk for self-harm and suicide. Ontario data based on the interRAI Child and Youth Mental Health Screener (ChYMH-S) were analyzed to develop a decision-support algorithm to identify young persons at risk for suicide or self-harm. The algorithm was validated with additional data from 59 agencies and found to be a strong predictor of suicidal ideation and self-harm. The RiSsK algorithm provides a psychometrically sound decision-support tool that may be used to identify children and youth who exhibit signs and symptoms noted to increase the likelihood of suicide and self-harm.

A decision support tool to inform local suicide prevention activity in Greater Western Sydney (Australia)

2018 ◽  
Vol 52 (10) ◽  
pp. 983-993 ◽  
Author(s):  
Andrew Page ◽  
Jo-An Atkinson ◽  
William Campos ◽  
Mark Heffernan ◽  
Shahana Ferdousi ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Support ◽  
Suicide Prevention ◽  
Attempted Suicide ◽  
Decision Support Tool ◽  
Support Tool ◽  
Health Network ◽  
Primary Health ◽  
Prevention Activities ◽  
Sydney Australia

Objectives: This study describes the development of a decision support tool to identify the combination of suicide prevention activities and service priorities likely to deliver the greatest reductions in suicidal behaviour in Western Sydney (Australia) over the period 2018–2028. Methods: A dynamic simulation model for the WentWest – Western Sydney Primary Health Network population-catchment was developed in partnership with primary health network stakeholders based on defined pathways to mental health care and suicidal behaviour, and which represented the current incidence of suicide and attempted suicide in Western Sydney. A series of scenarios relating to potential suicide prevention activities and service priorities identified by primary health network stakeholders were investigated to identify the combination of interventions associated with the largest reductions in the forecast number of attempted suicide and suicide cases for a 10-year follow-up period. Results: The largest number of cases averted for both suicide and attempted suicide was associated with (1) post-suicide attempt assertive aftercare (6.1% for both attempted suicide and suicide), (2) improved community support and reductions in psychological distress in the community (5.1% for attempted suicide and 14.8% for suicide), and (3) reductions in the proportion of those lost to services following a mental health service contact (10.5% for both attempted suicide and suicide). In combination, these interventions were forecast to avert approximately 29.7% of attempted suicides and 37.1% of suicides in the primary health network catchment over the 10-year period. Conclusion: This study demonstrates the utility of dynamic simulation models, co-designed with multi-disciplinary stakeholder groups, to capture and analyse complex mental health and suicide prevention regional planning problems. The model can be used by WentWest – Western Sydney Primary Health Network as a decision support tool to guide the commissioning of future service activity, and more efficiently frame the monitoring and evaluation of interventions as they are implemented in Western Sydney.

scholarly journals MTXPK.org: A clinical decision support tool evaluating high-dose methotrexate pharmacokinetics to inform post-infusion care and use of glucarpidase

2020 ◽  
Author(s):  
Zachary L. Taylor ◽  
Tomoyuki Mizuno ◽  
Nieko C. Punt ◽  
Balaji Baskaran ◽  
Adriana Navarro Sainz ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Lymphoblastic Leukemia ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
High Dose ◽  
Support Tool ◽  
Drug Concentrations ◽  
Increased Risk ◽  
Clinical Decision Support Tool

AbstractMethotrexate (MTX), an anti-folate, is administered at high-doses to treat malignancies in children and adults. However, there is considerable interpatient variability in clearance of high-dose (HD) MTX. Patients with delayed clearance are at an increased risk for severe nephrotoxicity and life-threatening systemic MTX exposure. Glucarpidase is a rescue agent for severe MTX toxicity that reduces plasma MTX levels via hydrolysis of MTX into inactive metabolites, but is only indicated when MTX concentrations are > 2 standard deviations above the mean excretion curve specific for the given dose together with a significant creatinine increase (> 50%). Appropriate administration of glucarpidase is challenging due to the ambiguity in the labeled indication. A recent consensus guideline was published with an algorithm to provide clarity in when to administer glucarpidase, yet clinical interpretation of lab results that do not directly correspond to the algorithm prove to be a limitation of its use.The goal of our study was to develop a clinical decision support tool to optimize the administration of glucarpidase for patients receiving HD MTX. Here, we describe the development of a novel three-compartment MTX population PK model using 31,672 MTX plasma concentrations from 772 pediatric patients receiving HD MTX for the treatment of acute lymphoblastic leukemia and its integration into the online clinical decision support tool, MTXPK.org. This web-based tool has the functionality to utilize individualized demographics, serum creatinine, and real-time drug concentrations to predict the elimination profile and facilitate model-informed administration of glucarpidase.

scholarly journals Prevention of AcuTe admIssioN algorithm (PATINA): study protocol of a stepped wedge randomized controlled trial

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Anders Fournaise ◽  
Jørgen T. Lauridsen ◽  
Mickael Bech ◽  
Uffe K. Wiil ◽  
Jesper B. Rasmussen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Home Care ◽  
Controlled Trial ◽  
Decision Support Tool ◽  
Community Dwelling ◽  
Support Tool ◽  
Acute Admission ◽  
Increased Risk ◽  
Older Citizens

Abstract Background The challenges imposed by ageing populations will confront health care systems in the years to come. Hospital owners are concerned about the increasing number of acute admissions of older citizens and preventive measures such as integrated care models have been introduced in primary care. Yet, acute admission can be appropriate and lifesaving, but may also in itself lead to adverse health outcome, such as patient anxiety, functional loss and hospital-acquired infections. Timely identification of older citizens at increased risk of acute admission is therefore needed. We present the protocol for the PATINA study, which aims at assessing the effect of the ‘PATINA algorithm and decision support tool’, designed to alert community nurses of older citizens showing subtle signs of declining health and at increased risk of acute admission. This paper describes the methods, design and intervention of the study. Methods We use a stepped-wedge cluster randomized controlled trial (SW-RCT). The PATINA algorithm and decision support tool will be implemented in 20 individual area home care teams across three Danish municipalities (Kerteminde, Odense and Svendborg). The study population includes all home care receiving community-dwelling citizens aged 65 years and above (around 6500 citizens). An algorithm based on home care use triggers an alert based on relative increase in home care use. Community nurses will use the decision support tool to systematically assess health related changes for citizens with increased risk of acute hospital admission. The primary outcome is acute admission. Secondary outcomes are readmissions, preventable admissions, death, and costs of health care utilization. Barriers and facilitators for community nurse’s acceptance and use of the algorithm will be explored too. Discussion This ‘PATINA algorithm and decision support tool’ is expected to positively influence the care for older community-dwelling citizens, by improving nurses’ awareness of citizens at increased risk, and by supporting their clinical decision-making. This may increase preventive measures in primary care and reduce use of secondary health care. Further, the study will increase our knowledge of barriers and facilitators to implementing algorithms and decision support in a community care setup. Trial registration ClinicalTrials.gov, identifier: NCT04398797. Registered 13 May 2020.

scholarly journals Resource Intensity for Children and Youth: The Development of an Algorithm to Identify High Service Users in Children’s Mental Health

2019 ◽  
Vol 12 ◽  
pp. 117863291982793 ◽  
Author(s):  
Shannon L Stewart ◽  
Jeff W Poss ◽  
Elizabeth Thornley ◽  
John P Hirdes
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Health Resources ◽  
Decision Support Tool ◽  
Children’S Mental Health ◽  
Children And Youth ◽  
Children's Mental Health ◽  
Support Tool ◽  
Resource Intensity

Children’s mental health care plays a vital role in many social, health care, and education systems, but there is evidence that appropriate targeting strategies are needed to allocate limited mental health care resources effectively. The aim of this study was to develop and validate a methodology for identifying children who require access to more intense facility-based or community resources. Ontario data based on the interRAI Child and Youth Mental Health instruments were analysed to identify predictors of service complexity in children’s mental health. The Resource Intensity for Children and Youth (RIChY) algorithm was a good predictor of service complexity in the derivation sample. The algorithm was validated with additional data from 61 agencies. The RIChY algorithm provides a psychometrically sound decision-support tool that may be used to inform the choices related to allocation of children’s mental health resources and prioritisation of clients needing community- and facility-based resources.

scholarly journals iCCareD: The Development of an Algorithm to Identify Factors Associated With Distress Among Caregivers of Children and Youth Referred for Mental Health Services

2021 ◽  
Vol 12 ◽  
Author(s):  
Shannon L. Stewart ◽  
Ashley Toohey ◽  
Jeffrey W. Poss
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Assessment Tool ◽  
Health Assessment ◽  
Decision Support Tool ◽  
Well Being ◽  
Children And Youth ◽  
Caregiver Distress ◽  
Support Tool

Caregiver well-being plays an important role in children's development and a number of factors have been found to impact distress levels among caregivers of children and youth referred for mental health services. Further, caregiver distress impacts youth psychopathology, its acuity as well as related mental health interventions. The purpose of this study was to develop and validate an algorithm for identifying caregivers who are at greatest risk of experiencing caregiver distress. This algorithm was derived from, and will be embedded in, existing comprehensive interRAI child and youth instruments. Ontario data based on the interRAI Child and Youth Mental Health assessment instruments (ChYMH and ChYMH-DD) were analyzed to identify predictors of distress among caregivers of children and youth ages 4–18 years. Starting with proactive aggression, the algorithm uses 40 assessment items to assign one of 30 nodes that are grouped into five levels of risk. The interRAI ChYMH Caregiver Distress (iCCareD) algorithm was validated using longitudinal data from mental health agencies across Ontario and was found to be a good predictor among this sample with a c-statistic of 0.71 for predicting new or ongoing caregiver distress and 65% for both sensitivity and specificity using algorithm values of 3 or greater. This algorithm provides an evidence-based decision-support tool embedded within a comprehensive assessment tool that may be used by clinicians to inform their selection of supports and services for families.

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