Comparison of risk factors associated with sepsis between road traffic injuries and non-road traffic injuries in ICU patients with severe trauma

Over 50% of road traffic injury (RTI) patients experience post-traumatic acute lung injury (ALI) and it is, therefore, extremely important to identify the risk factors related to the poor outcomes associated with ALI in RTI populations. This study evaluated 19 potential risk factors associated with the outcomes of ALI in 366 RTI patients. They were divided into two groups: a ‘favourable outcomes group’ and an ‘unfavourable outcomes group’. The results indicated that the Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the presence of gastrointestinal haemorrhage may help predict the outcomes of ALI in the early post-trauma phase of treatment. The duration of trauma and sepsis were shown to impact strongly on both the short- and long-term outcomes of ALI. Age (≥ 65 years) and disseminated intravascular coagulation in the early RTI phase were also independent risk factors for a poorer short- and long-term outcome in ALI.

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Road use pattern and risk factors for non-fatal road traffic injuries among children in urban India

Injury ◽

10.1016/j.injury.2009.10.048 ◽

2011 ◽

Vol 42 (1) ◽

pp. 97-103 ◽

Cited By ~ 19

Author(s):

Rakhi Dandona ◽

G. Anil Kumar ◽

Shanthi Ameratunga ◽

Lalit Dandona

Keyword(s):

Risk Factors ◽

Road Traffic ◽

Road Traffic Injuries ◽

Urban India ◽

Use Pattern

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Prevalence of behavioural risk factors for road-traffic injuries among the Iranian population: findings from STEPs 2016

International Journal of Epidemiology ◽

10.1093/ije/dyz021 ◽

2019 ◽

Vol 48 (4) ◽

pp. 1187-1196 ◽

Cited By ~ 1

Author(s):

Soraya Fathollahi ◽

Sahar Saeedi Moghaddam ◽

Nazila Rezaei ◽

Ayyoob Jafari ◽

Niloofar Peykari ◽

...

Keyword(s):

Risk Factors ◽

Road Traffic ◽

Cross Sectional ◽

Road Traffic Injuries ◽

The Road ◽

Human Risk ◽

Behavioural Risk Factors ◽

Risky Behaviours ◽

Drunk Driver ◽

Seatbelt Compliance

Abstract Background To achieve Sustainable Development Goal 3.6 in Iran, we need to have a comprehensive understanding of the distribution of risky behaviours regarding road-traffic injuries at national and sub-national levels. Little is known about the road-use vulnerability patterns of road-traffic injuries in Iran. The aim of this study is to describe the prevalence of self-reported human risk factors in road-traffic injuries using the findings from a large-scale cross-sectional study based on the World Health Organization’s stepwise approach to surveillance of non-communicable diseases (STEPs). Methods A cross-sectional survey study in 2016 assessed the road-use pattern and prevalence of risky behaviours of people more than 18 years old. In this study, we planned to recruit 31 050 individuals as a representative sample at national and provincial levels. In practice, 30 541 individuals (3105 clusters) from urban and rural areas of Iran were selected. Basic socio-demographic data, major behavioural risk factors such as seatbelt and helmet non-compliance, drunk driving and occupant in a car with a drunk driver were assessed through baseline interviews gathered through an Android tablet-based questionnaire. Results The overall prevalence of seatbelt and helmet compliance was 75.2% (95% confidence interval: 74.7–75.7) and 13.9% (13.4–14.5), respectively, at the national level. The prevalence of risk-taking behaviours such as drink driving was 0.5% (0.4–0.6) and for being an occupant in a car with a drunk driver was 3.5% (3.2–3.8). At the provincial level, the highest age-standardized prevalence of seatbelt compliance (89.6%) was almost 1.5 times higher than the lowest provincial prevalence (58.5%). In 63% of provinces, the lowest prevalence of seatbelt compliance was observed among people aged 18–24 years old. Conclusions In Iran, existing disease-prevention and health-promotion programmes should be expanded to target vulnerable subgroups that have more prevalent human risk factors for road-traffic injuries. Further research is required to investigate the context-specific proximal human risk factors and vulnerability patterns in Iran.

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Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17114111 ◽

2020 ◽

Vol 17 (11) ◽

pp. 4111

Author(s):

Ali J. Ghandour ◽

Huda Hammoud ◽

Samar Al-Hajj

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Injury Severity ◽

Road Traffic ◽

Road Traffic Injury ◽

Outcome Variable ◽

Contributing Factors ◽

Road Crashes ◽

Factors Associated ◽

Ensemble Machine Learning

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.

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Risk Factors Associated With Resistance to Ciprofloxacin in Clinical Bacterial Isolates From Intensive Care Unit Patients

Infection Control and Hospital Epidemiology ◽

10.1086/511701 ◽

2007 ◽

Vol 28 (3) ◽

pp. 331-336 ◽

Cited By ~ 10

Author(s):

Phillip D. Levin ◽

Robert A. Fowler ◽

Cameron Guest ◽

William J. Sibbald ◽

Alex Kiss ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care Unit ◽

Intensive Care ◽

Prior Treatment ◽

Bacterial Isolates ◽

Icu Patients ◽

Gram Negative ◽

Factors Associated ◽

Icu Admission ◽

Ciprofloxacin Resistance

Objective.To determine risk factors and outcomes associated with ciprofloxacin resistance in clinical bacterial isolates from intensive care unit (ICU) patients.Design.Prospective cohort study.Setting.Twenty-bed medical-surgical ICU in a Canadian tertiary care teaching hospital.Patients.All patients admitted to the ICU with a stay of at least 72 hours between January 1 and December 31, 2003.Methods.Prospective surveillance to determine patient comorbidities, use of medical devices, nosocomial infections, use of antimicrobials, and outcomes. Characteristics of patients with a ciprofloxacin-resistant gram-negative bacterial organism were compared with characteristics of patients without these pathogens.Results.Ciprofloxacin-resistant organisms were recovered from 20 (6%) of 338 ICU patients, representing 38 (21%) of 178 nonduplicate isolates of gram-negative bacilli. Forty-nine percent ofPseudomonas aeruginosaisolates and 29% ofEscherichia coliisolates were resistant to ciprofloxacin. In a multivariate analysis, independent risk factors associated with the recovery of a ciprofloxacin-resistant organism included duration of prior treatment with ciprofloxacin (relative risk [RR], 1.15 per day [95% confidence interval {CI}, 1.08-1.23];P< .001), duration of prior treatment with levofloxacin (RR, 1.39 per day [95% CI, 1.01-1.91];P= .04), and length of hospital stay prior to ICU admission (RR, 1.02 per day [95% CI, 1.01-1.03];P= .005). Neither ICU mortality (15% of patients with a ciprofloxacin-resistant isolate vs 23% of patients with a ciprofloxacin-susceptible isolate;P= .58 ) nor in-hospital mortality (30% vs 34%;P= .81 ) were statistically significantly associated with ciprofloxacin resistance.Conclusions.ICU patients are at risk of developing infections due to ciprofloxacin-resistant organisms. Variables associated with ciprofloxacin resistance include prior use of fluoroquinolones and duration of hospitalization prior to ICU admission. Recognition of these risk factors may influence antibiotic treatment decisions.

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