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.

scholarly journals Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions

2017 ◽  
Vol 56 (05) ◽  
pp. 391-400 ◽  
Author(s):  
Carlos A. Jaramillo ◽  
Syed H. A. Faruqui ◽  
Mary J. Pugh ◽  
Adel Alaeddini
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Chronic Conditions ◽  
Clinical Decision Making ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Clinical Decision ◽  
Multiple Chronic Conditions ◽  
Post Traumatic Stress ◽  
Major Transitions

SummaryObjectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation’s healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process.Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post- Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm.Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of co- morbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV.Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision- making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.

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 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.

Belt and road initiative (BRI) supply chain risks: propositions and model development

2020 ◽  
Vol 31 (4) ◽  
pp. 777-799
Author(s):  
Jiwat Ram ◽  
Zeyang Zhang
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Risk Management ◽  
Cyber Security ◽  
Risk Sharing ◽  
Risk Model ◽  
Model Development ◽  
Belt And Road Initiative ◽  
Content Type ◽  
Belt And Road

PurposeBelt and road initiative (BRI) is a transcontinental endeavor strategically connecting supply chains (SCs) and economic infrastructures to ignite business activities and achieve trade benefits. However, the rising global SC failure costs and risks associated with this initiative (owing to unique geopolitical, economic and mega-connectivity involving over 70 countries) necessitate examining BRI SC risks. Yet, research on the subject remains limited, and the purpose of this paper is to address this gap in knowledge.Design/methodology/approachA two-pronged approach was taken. First, a data sample of 554 articles was analyzed and 178 articles found relevant were used to present a systematic, structured framework of risk factors along operational, economic, financial, social and security dimensions. Then informed by the theory of risk management and supplemented by literature evidence, we have built a BRI SC risk model.FindingsThe results presented through the model show that BRI SCs face a combination of risks triggered by operational processes, informational and environmental (PIE) deficiencies. Findings show that lack of risk and liability management, unbalanced risk-sharing partnerships, lack of transparency, inadequate project evaluation, incompatible corporate governance structures and cyber security all pose threats to BRI SCs specifically and SCs in general.Research limitations/implicationsAcademically, the results facilitate theory development by identifying and proposing seven risk factors and modeling relationship among them and BRI SC risks outcome. The results also extend application of theory of risk management to SC context.Practical implicationsThe findings provide a decision-making tool for managers to assess risk factors in their SCs, thus enabling improved decision making to avoid, mitigate, transfer or accept risks.Originality/valueIdentifies and proposes a set of seven risk factors that drive BRI SC risks. Develops a model of BRI SC risks which help build theory of SC risk management.

Making defensible decisions in the era of recovery and rights

2015 ◽  
Vol 26 (5) ◽  
pp. 474-477 ◽  
Author(s):  
Andrew Carroll ◽  
Bernadette McSherry
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Clinical Decision Making ◽  
Civil Liability ◽  
Clinical Decision ◽  
Liability Law ◽  
Essential Components ◽  
Information And Communication ◽  
Available Information

Objectives: Our aim was to develop a framework for clinical decision-making that can be used to take into account risk in an era of recovery and rights. Conclusion: We developed a framework influenced by civil liability law to develop a guide for clinical decision-making which emphasises collaboration, clarification of the available information and communication of decisions as essential components of recovery-oriented risk management.

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.

A structural design of clinical decision support system for chronic diseases risk management

Open Medicine ◽  
2007 ◽  
Vol 2 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Chi-Chang Chang ◽  
Chuen-Sheng Cheng
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Structural Design ◽  
Clinical Decision Support System ◽  
Clinical Decision Making ◽  
Clinical Decision

AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.

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