scholarly journals ABC of intensive care: Outcome data and scoring systems

BMJ ◽  
1999 ◽  
Vol 319 (7204) ◽  
pp. 241-244 ◽  
Author(s):  
K. Gunning ◽  
K. Rowan
Keyword(s):  
Intensive Care ◽  
Scoring Systems ◽  
Outcome Data ◽  
Care Outcome

Scoring systems in intensive care

1998 ◽  
Vol 9 (1) ◽  
pp. 8-15 ◽  
Author(s):  
A. Steele ◽  
G.A. Bocconi ◽  
R. Oggioni ◽  
G. Tulli
Keyword(s):  
Intensive Care ◽  
Scoring Systems

scholarly journals Sepsis Diagnostics: Intensive Care Scoring Systems Superior to MicroRNA Biomarker Testing

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 701
Author(s):  
Fabian Link ◽  
Knut Krohn ◽  
Anna-Maria Burgdorff ◽  
Annett Christel ◽  
Julia Schumann
Keyword(s):  
Intensive Care ◽  
Scoring Systems ◽  
Digital Pcr ◽  
Absolute Quantification ◽  
Medical Problem ◽  
Control Group ◽  
Multiple Parameters ◽  
Sepsis Diagnosis ◽  
Intensive Care Patients ◽  
Healthy Control

Sepsis represents a serious medical problem accounting for numerous deaths of critically ill patients in intensive care units (ICUs). An early, sensitive, and specific diagnosis is considered a key element for improving the outcome of sepsis patients. In addition to classical laboratory markers, ICU scoring systems and serum miRNAs are discussed as potential sepsis biomarkers. In the present prospective observational study, the suitability of miRNAs in sepsis diagnosis was tested based on proper validated and normalized data (i.e., absolute quantification by means of Droplet Digital PCR (ddPCR)) in direct comparison to classical sepsis markers and ICU scores within the same patient cohort. Therefore, blood samples of septic intensive care patients (n = 12) taken at day of admission at ICU were compared to non-septic intensive care patients (n = 12) and a healthy control group (n = 12). Our analysis indicates that all tested biomarkers have only a moderate informative power and do not allow an unequivocal differentiation between septic and non-septic ICU patients. In conclusion, there is no standalone laboratory parameter that enables a reliable diagnosis of sepsis. miRNAs are not superior to classical parameters in this respect. It seems recommendable to measure multiple parameters and scores and to interpret them with regard to the clinical presentation.

scholarly journals The use of score for neonatal acute physiology perinatal extention II (SNAPPE II) in predicting neonatal outcome in neonatal intensive care unit

2016 ◽  
Vol 45 (6) ◽  
pp. 241
Author(s):  
Mia R A ◽  
Risa Etika ◽  
Agus Harianto ◽  
Fatimah Indarso ◽  
Sylviati M Damanik
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Characteristic Curve ◽  
Newborn Care ◽  
Scoring Systems ◽  
Laboratory Observation ◽  
The Mean ◽  
Overall Mortality

Background Scoring systems which quantify initial risks have animportant role in aiding execution of optimum health services by pre-dicting morbidity and mortality. One of these is the score for neonatalacute physiology perinatal extention (SNAPPE), developed byRichardson in 1993 and simplified in 2001. It is derived of 6 variablesfrom the physical and laboratory observation within the first 12 hoursof admission, and 3 variables of perinatal risks of mortality.Objectives To assess the validity of SNAPPE II in predicting mor-tality at neonatal intensive care unit (NICU), Soetomo Hospital,Surabaya. The study was also undertaken to evolve the best cut-offscore for predicting mortality.Methods Eighty newborns were admitted during a four-month periodand were evaluated with the investigations as required for the specifi-cations of SNAPPE II. Neonates admitted >48 hours of age or afterhaving been discharged, who were moved to lower newborn care <24hours and those who were discharged on request were excluded. Re-ceiver operating characteristic curve (ROC) were constructed to derivethe best cut-off score with Kappa and McNemar Test.Results Twenty eight (35%) neonates died during the study, 22(82%) of them died within the first six days. The mean SNAPPE IIscore was 26.3+19.84 (range 0-81). SNAPPE II score of thenonsurvivors was significantly higher than the survivors(42.75+18.59 vs 17.4+14.05; P=0.0001). SNAPPE II had a goodperformance in predicting overall mortality and the first-6-daysmortality, with area under the ROC 0.863 and 0.889. The best cut-off score for predicting mortality was 30 with sensitivity 81.8%,specificity 76.9%, positive predictive value 60.0% and negativepredictive value 90.0%.Conclusions SNAPPE II is a measurement of illness severity whichcorrelates well with neonatal mortality at NICU, Soetomo Hospital.The score of more than 30 is associated with higher mortality

scholarly journals Application of different scoring systems and their value in pediatric intensive care unit

2014 ◽  
Vol 62 (3-4) ◽  
pp. 59-64 ◽  
Author(s):  
Hanaa I. Rady ◽  
Shereen A. Mohamed ◽  
Nabil A. Mohssen ◽  
Mohamed ElBaz
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Pediatric Intensive Care ◽  
Scoring Systems

P69 Moderate resuscitative hypothermia after brain trauma (simulated epidural hematoma) in new intensive care outcome model in dogs

Resuscitation ◽  
1994 ◽  
Vol 28 (2) ◽  
pp. S13 ◽  
Author(s):  
U. Ebmeyer ◽  
P. Safar ◽  
S. Pomeranz ◽  
S. Tisherman ◽  
A. Radovsky ◽  
...  
Keyword(s):  
Intensive Care ◽  
Epidural Hematoma ◽  
Brain Trauma ◽  
Care Outcome

The Impact of Implementing an Intensivist Model With Nighttime In-Hospital Nocturnist and Effect on ICU Outcomes

2018 ◽  
Vol 35 (5) ◽  
pp. 461-467
Author(s):  
Maged A. Tanios ◽  
Daniel Teres ◽  
Hyunsoon Park ◽  
Antonio Beltran ◽  
Arunpal Sehgal ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
High Intensity ◽  
Outcome Data ◽  
House Staff ◽  
First Year ◽  
Implementation Phase ◽  
Large Teaching Hospital ◽  
Multidisciplinary Rounds ◽  
The Impact

Background: Various intensivist staffing models have been suggested, but the long-term sustainability and outcomes vary and may not be sustained. We examined the impact of implementing a high-intensity intensivist coverage model with a nighttime in-house nocturnist (non-intensivist) and its effect on intensive care unit (ICU) outcomes. Methods: We obtained historical control baseline data from 2007 to 2011 and compared the same data from 2011 to 2015. The Acute Physiological and Chronic Health Evaluation outcomes system was utilized to collect clinical, physiological, and outcome data on all adult patients in the medical ICU and to provide severity-adjusted outcome predictions. The model consists of a mandatory in-house daytime intensivist service that leads multidisciplinary rounds, and an in-house nighttime coverage is provided by nocturnist (nonintensivists) with current procedural skills in airways management, vascular access, and commitment to supervise house staff as needed. The intensivist continues to be available remotely at nighttime for house staff and consultation with the nocturnist. A backup intensivist is available for surge management. Results: First year yielded improved throughput (2428 patients/year to 2627 then 2724 at fifth year). Case mix stable at 53.7 versus 55.2. The ICU length of stay decreased from 4.7 days (predicted 4.25 days) to 3.8 days (4.15) in first year; second year: 3.63 days (4.29 days); third year: 3.24 days (4.37), fourth year: 3.34 days (4.45), and fifth year: 3.61 days (4.42). Intensive care unit <24 hours readmission remained at 1%; >24 hours increased from 4% to 6%. Low-risk monitoring admissions remained at an average 17% (benchmark 17.18%). Intensive care unit mortality improved with standardized mortality ration averaging at 0.84. Resident satisfaction surveys improved. Conclusions: Implementing an intensivist service with nighttime nocturnist staffing in a high-intensity large teaching hospital is feasible and improved ICU outcomes in a sustained manner that persisted after the initial implementation phase. The model resulted in reduced and sustained observed-to-predicted length of ICU stay.

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