Comparison of the Glasgow Coma Scale with Full Outline of Unresponsiveness in Predicting In-Hospital Mortality of Patients with Cerebrovascular Accident in Intensive Care Units

2019 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Elham Sepahvand ◽  
Ahmadreza Baghernezhad ◽  
Mahin Adeli
Keyword(s):  
Intensive Care ◽  
Glasgow Coma Scale ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebrovascular Accident ◽  
Coma Scale

Comparison of the Full Outline of Unresponsiveness (FOUR) Score with the Glasgow Coma Scale (GCS) as a Coma Assessment Scale in Pediatric Intensive Care

2021 ◽  
Author(s):  
Merve Misirlioglu ◽  
Dincer Yildizdas ◽  
Faruk Ekinci ◽  
Ozden Ozgur Horoz ◽  
Gulen Gul Mert
Keyword(s):  
Mechanical Ventilation ◽  
Intensive Care ◽  
Glasgow Coma Scale ◽  
Intensive Care Units ◽  
Rapid Assessment ◽  
Pediatric Intensive Care ◽  
Coma Scale ◽  
Additional Information ◽  
Ventilation Monitoring ◽  
Four Score

AbstractRapid assessment of cerebral dysfunction is crucial for the management of patients in intensive care units. The Glasgow Coma Scale (GCS) evaluates eye, verbal, and motor responses, but is insufficient to effectively evaluate patients on mechanical ventilation, or who are unable to speak. The Full Outline of Unresponsiveness (FOUR) score includes additional information such as brainstem reflexes and respiratory status to provide a more complete clinical assessment. In this study, we aimed to compare the FOUR score with GCS in the assessment of patients with coma. This prospective study included patients between 1 month and under 18 years of age, who were hospitalized in the pediatric intensive care unit due to risk of coma or ongoing impairment of consciousness between May 2018 and June 2019. Information regarding FOUR scores, GCS values, patient demographics, duration of hospitalization, requirement for mechanical ventilation, and patient comorbidities were recorded and analyzed. Among the 80 patients included in the study, a statistically significant correlation was found between (low) GCS and FOUR scores during admission, and mortality and requirement for mechanical ventilation. Monitoring the level of consciousness is important in pediatric intensive care units and may be predictive of the course and disease outcome. Similar to the GCS, the FOUR score is a good indicator for predicting mortality and requirement for mechanical ventilation.

scholarly journals Tradução e Validação da Escala FOUR para a Pediatria e o seu Uso Como Indicador Prognóstico: Um Estudo Piloto

2017 ◽  
Vol 30 (9) ◽  
pp. 599 ◽  
Author(s):  
Sofia Simões Ferreira ◽  
Daniel Meireles ◽  
Alexandra Pinto ◽  
Francisco Abecasis
Keyword(s):  
Intensive Care ◽  
Glasgow Coma Scale ◽  
Intensive Care Units ◽  
Adult Population ◽  
Strong Association ◽  
Pediatric Intensive Care ◽  
Impaired Consciousness ◽  
Pediatric Intensive Care Units ◽  
Coma Scale ◽  
Minimum Score

Introduction: The Full Outline of UnResponsiveness - FOUR scale has been previously validated to assess impaired consciousness in the adult population. The aim of this study is the translation into Portuguese and validation of the FOUR scale in the pediatric population. The study also compares the FOUR scale and Glasgow coma scale score ratings and the clinical outcome of patients hospitalized in Pediatric Intensive Care Units.Material and Methods: This study prospectively rated patients admitted to the Pediatric Intensive Care Units with impaired consciousness during one year. Both scales were applied daily to patients by three types of examiners: intensivists, residents and nurses, from the moment of admission until clinical discharge. Neurological sequelae was evaluated using the King’s Outcome Scale for Childhood Head Injury - KOSCHI.Results: Twenty seven patients between one and 17 years of age were included. Both scales are reliable and inter-rater reliability was greater for the FOUR score. Glasgow coma scale showed a minimum score in eight evaluations, whereas the FOUR scale obtained the minimum score in only two of these evaluations. In both scales there was a strong association between the admission score and the patient’s outcome (area under curve FOUR = 0.939, versus Glasgow coma scale = 0.925).Discussion: The FOUR scale provides more neurological information than Glasgow coma scale in patients with impaired consciousness and has prognostic interest.Conclusion: The FOUR scale can be applied in patients admitted with impaired consciousness in Pediatric Intensive Care Units. We think that a multicenter study would be very beneficial for confirming and generalizing these results.

Score for Neonatal Acute Physiology: Validation in Three Kaiser Permanente Neonatal Intensive Care Units

PEDIATRICS ◽  
1995 ◽  
Vol 96 (5) ◽  
pp. 918-922 ◽  
Author(s):  
Gabriel J. Escobar ◽  
Allen Fischer ◽  
De Kun Li ◽  
Robert Kremers ◽  
Mary Anne Armstrong
Keyword(s):  
Intensive Care ◽  
Birth Weight ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Neonatal Intensive Care ◽  
Severity Of Illness ◽  
Predictive Ability ◽  
Neonatal Intensive Care Units ◽  
Total Hospital ◽  
Total Hospital Stay

Background. Measurement of the severity of illness is a research area of growing importance in neonatal intensive care. Most severity of illness scales have been developed in tertiary care settings. Their applicability in community neonatal intensive care units has not been tested. Objectives. Our goal was to assess the operational characteristics of the score for neonatal acute physiology (SNAP): the relationship to birth weight, the length of total hospital stay, and in-hospital mortality. Methods. We assigned SNAP scores prospectively to all inborn admissions at three community neonatal intensive care units during an 11-month period. Data on other neonatal predictors (eg, birth weight and the presence of congenital heart disease) were also collected. We measured in-hospital mortality, the experience of interhospital transport to a higher level of care, and total hospital stay. Results. We found that the SNAP's relationship to birth weight was similar to previous reports. The SNAP's perinatal extension is a reliable predictor of newborn in-hospital mortality, with an area under the receiver operator characteristic curve of 0.95. The SNAP is also a good predictor of total hospital length of stay, whether by itself (by which it can explain 31% of the total stay) or in combination with other variables. Its predictive ability is better among infants of low birth weight (<2500 g) than among those of normal birth weight (≥2500 g). The SNAP's predictive power was most limited among infants admitted to rule out sepsis. The predictive ability of a model containing birth weight, the SNAP, and transport status was not improved by the inclusion of two major diagnostic categories, the presence of congenital heart disease or complex illness. Conclusion. Although it has definite limitations among infants who weigh 2500 g or more, the SNAP is a potent tool for outcomes research. Modification of some of its parameters could result in a multifunctional scale suitable for use with all birth weights.

Effects of Organizational Characteristics on Outcomes and Resource Use in Patients With Cancer Admitted to Intensive Care Units

2016 ◽  
Vol 34 (27) ◽  
pp. 3315-3324 ◽  
Author(s):  
Marcio Soares ◽  
Fernando A. Bozza ◽  
Luciano C.P. Azevedo ◽  
Ulysses V.A. Silva ◽  
Thiago D. Corrêa ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Resource Use ◽  
Organizational Characteristics ◽  
General Hospitals ◽  
Cancer Centers ◽  
Processes Of Care ◽  
Patients With Cancer ◽  
Efficient Resource

Purpose To investigate the impact of organizational characteristics and processes of care on hospital mortality and resource use in patients with cancer admitted to intensive care units (ICUs). Patients and Methods We performed a retrospective cohort study of 9,946 patients with cancer (solid, n = 8,956; hematologic, n = 990) admitted to 70 ICUs (51 located in general hospitals and 19 in cancer centers) during 2013. We retrieved patients’ clinical and outcome data from an electronic ICU quality registry. We surveyed ICUs regarding structure, organization, staffing patterns, and processes of care. We used mixed multivariable logistic regression analysis to identify characteristics associated with hospital mortality and efficient resource use in the ICU. Results Median number of patients with cancer per center was 110 (interquartile range, 58 to 154), corresponding to 17.9% of all ICU admissions. ICU and hospital mortality rates were 15.9% and 25.4%, respectively. After adjusting for relevant patient characteristics, presence of clinical pharmacists in the ICU (odds ratio [OR], 0.67; 95% CI, 0.49 to 0.90), number of protocols (OR, 0.92; 95% CI, 0.87 to 0.98), and daily meetings between oncologists and intensivists for care planning (OR, 0.69; 95% CI, 0.52 to 0.91) were associated with lower mortality. Implementation of protocols (OR, 1.52; 95% CI, 1.11 to 2.07) and meetings between oncologists and intensivists (OR, 4.70; 95% CI, 1.15 to 19.22) were also independently associated with more efficient resource use. Neither admission to ICUs in cancer centers compared with general hospitals nor annual case volume had an impact on mortality or resource use. Conclusion Organizational aspects, namely the implementation of protocols and presence of clinical pharmacists in the ICU, and close collaboration between oncologists and ICU teams are targets to improve mortality and resource use in critically ill patients with cancer.

scholarly journals P082: Predictive ability of the quick Sepsis-related Organ Failure Assessment score among patients with infection transported by paramedics: a Bayesian analysis

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S93
Author(s):  
S. Alex Love ◽  
D. Lane
Keyword(s):  
Glasgow Coma Scale ◽  
Hospital Mortality ◽  
Bayesian Analysis ◽  
Organ Failure ◽  
Predictive Ability ◽  
Decision Threshold ◽  
Coma Scale ◽  
C Statistic ◽  
Failure Assessment ◽  
Qsofa Score

Introduction: The quick Sepsis-related Organ Failure Assessment (qSOFA) score was developed to provide clinicians with a quick assessment for patients with latent organ failure possibly consistent with sepsis at high-risk for mortality. With the clinical heterogeneity of patients presenting with sepsis, a Bayesian validation approach may provide a better understanding of its clinical utility. This study used a Bayesian analysis to assess the prediction of hospital mortality by the qSOFA score among patients with infection transported by paramedics. Methods: A one-year cohort of adult patients transported by paramedics in a large, provincial EMS system was linked to Emergency Department (ED) and hospital administrative databases, then restricted to those patients with an ED diagnosed infection. A Bayesian binomial regression model was constructed using Hamiltonian Markov-Chain Monte-Carlo sampling, normal priors for each parameter, the calculated score, age and sex as the predictors, and hospital mortality as the outcome. Discrimination was assessed using posterior predictions to calculate a “Bayesian” C statistic, and calibration was assessed with calibration plots of the observed and predicted probability distributions. The independent predictive ability of each measure was tested by including each component measure (respiratory rate, Glasgow Coma Scale, and systolic blood pressure) as continuous predictors in a second model. Results: A total of 9,920 patients with ED diagnosed infection were included. 264 (2.7%) patients were admitted directly to the ICU, and 955 (9.6%) patients died in-hospital. As independent predictors, the probability of mortality increased as each measure became more extreme, with the Glasgow Coma Scale predicting the greatest change in mortality risk from a high to low score; however, no dramatic change in the probability supporting a single decision threshold was seen for any measure. For the calculated score, the C statistic for predicting mortality was 0.728. The calibration curve had no overlap of predictions, with a probability of 0.5 (50% credible interval 0.47-0.53) for patients with a qSOFA score of 3. Conclusion: Although no single decision threshold was identified for each component measure, a calculated qSOFA score provides good prediction of mortality for patients with ED diagnosed infection. When validating clinical prediction scores, a Bayesian approach may be used to assess probabilities of interest for clinicians to support better clinical decision making. Character count 2494

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