A comparison of pre ICU admission SIRS, EWS and q SOFA scores for predicting mortality and length of stay in ICU.

This research was published in the Journal of Critical Care May 2017 Volume 41 by Siddiqui et al

Introduction:  The 2015 sepsis definitions suggest using the quick SOFA score for risk stratification of sepsis patients among other changes in sepsis definition. Our aim was to validate the q sofa score for diagnosing sepsis and comparing it to traditional scores of pre ICU admission sepsis outcome prediction such as EWS and SIRS in our setting in order to predict mortality and length of stay.

Methods:  This was a retrospective cohort study. We retrospectively calculated the q sofa, SIRS and EWS scores of all ICU patients admitted with the diagnosis of sepsis at our center in 2015. This was analysed using STATA 12. Logistic regression and ROC curves were used for analysis in addition to descriptive analysis.

Results:  58 patients were included in the study. Based on our one year results we have shown that although q SOFA is more sensitive in predicting LOS in ICU of sepsis patients, the EWS score is more sensitive and specific in predicting mortality in the ICU of such patients when compared to q SOFA and SIRS scores.

Conclusion:  We find that in our setting, EWS is better than SIRS and q SOFA for predicting mortality and perhaps length of stay as well. The q Sofa score remains validated for diagnosis of sepsis.

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Removal of sinks and introduction of ‘water-free’ patient care

Sinks in patient rooms are associated with hospital-acquired infections | Antimicrobial Resistance & Infection Control


Background: The aim of this study was to evaluate the effect of removal of sinks from the Intensive Care Unit (ICU) patient rooms and the introduction of ‘water-free’ patient care on gram-negative bacilli colonization rates.

Conclusions: Removal of sinks from patient rooms and introduction of a method of ‘water-free’ patient care is associated with a significant reduction of patient colonization with GNB, especially in patients with a longer ICU length of stay.

Full reference: Hopman, J. et al. (2017) Reduced rate of intensive care unit acquired gram-negative bacilli after removal of sinks and introduction of ‘water-free’ patient care. Antimicrobial Resistance & Infection Control. 6:59

An Official American Thoracic Society Systematic Review: The Effect of Night time Intensivist Staffing on Mortality and Length of Stay among Intensive Care Unit Patients

This systematic review by Kerlin et al was published in the American Journal of Respiratory and Critical Care Medicine in 2017.  The fullajrccm-2016-194-issue-6-cover
text of the article is available to subscribers to this journal via this link
.  The Library and Knowledge Service can obtain the full text of the article for registered members by requesting it via the library website document request form.

Background:  Studies of night time intensivist staffing have yielded mixed results.

Goals:  To review the association of night time intensivist staffing with outcomes of intensive care unit (ICU) patients.

Methods:  We searched five databases (2000–2016) for studies comparing in-hospital night time intensivist staffing with other night time staffing models in adult ICUs and reporting mortality or length of stay. We abstracted data on staffing models, outcomes, and study characteristics and assessed study quality, using standardized tools. Meta-analyses used random effects models.

Results:  Eighteen studies met inclusion criteria: one randomized controlled trial and 17 observational studies. Overall methodologic quality was high. Studies included academic hospitals (n = 10), community hospitals (n = 2), or both (n = 6). Baseline clinician staffing included residents (n = 9), fellows (n = 4), and nurse practitioners or physician assistants (n = 2). Studies included both general and specialty ICUs and were geographically diverse. Meta-analysis (one randomized controlled trial; three nonrandomized studies with exposure limited to night time intensivist staffing with adjusted estimates of effect) demonstrated no association with mortality (odds ratio, 0.99; 95% confidence interval, 0.75–1.29). Secondary analyses including studies without risk adjustment, with a composite exposure of organizational factors, stratified by intensity of daytime staffing and by ICU type, yielded similar results. Minimal or no differences were observed in ICU and hospital length of stay and several other secondary outcomes.

Conclusions:  Notwithstanding limitations of the predominantly observational evidence, our systematic review and meta-analysis suggests night time intensivist staffing is not associated with reduced ICU patient mortality. Other outcomes and alternative staffing models should be evaluated to further guide staffing decisions.

Newborn Individualized Developmental Care and Assessment Program Reduces Length of Stay

Moody, C. et al. Journal of Pediatric Nursing. Published online: 3 December 2016


Infants born at ≤32 weeks gestation are at risk of developmental delays. Review of the literature indicates NIDCAP improves parental satisfaction, minimizes developmental delays, and decreases length of stay, thus reducing cost of hospitalization.


  • NIDCAP is a proven framework for providing developmentally supportive care in the NICU, and can mitigate risks of prematurity
  • Earlier initiation of NIDCAP led to discharge at a younger post-menstrual age
  • Quality improvement investigations are effective in addressing critical healthcare needs

Read the full abstract here

Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review.

Verburg, I. et al. Critical Care Medicine. Published online: October 20 2016


Objective: We systematically reviewed models to predict adult ICU length of stay.

Data Sources: We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models.

Study Selection: We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models.

Data Extraction: Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R2 across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.

Data Synthesis: The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R2 was 0.05-0.28 across patients and 0.01-0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22.

Conclusion: No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation.

Read the abstract here