Implementation of a multiprofessional, multicomponent delirium management guideline in two intensive care units, and its effect on patient outcomes and nurse workload: a pre-post design retrospective cohort study

Background: There is little evidence on the changing prevalence, microbiological profile, and outcome of nosocomial Acinetobacter baumannii complex (ABC)-caused bloodstream infection (ABCBSI) specified in intensive care units (ICUs) in long-term studies, especially in China.Objective: We aimed to investigate changes in incidence, antibiotic resistance, therapy, and prognosis of ABCBSI in ICUs in eastern China during 2009–2018.Methods: A multicenter retrospective cohort study was conducted, and microbiological and clinical data for patients with ABCBSI acquired in nine adult ICUs in eastern China from 2009 to 2018.Results: A total of 202 cases were enrolled. For the years 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018, the incidence of ABCBSI increased significantly, as did the percentage of pan-drug-resistant isolates and resistant rates to most of antimicrobial agents; the percentage of drug-sensitive isolates decreased (all P < 0.05). The frequency of treatment with carbapenems and tigecycline increased, and that of cephalosporins decreased. Compared with those in the first years (2009–2012), ABCBSI patients in the lattermost years (2017–2018) were less often treated with appropriate empirical therapy, more often underwent pneumonia-related ABCBSI and mechanical ventilation support, and had higher 28-day mortality rates. Multivariate Cox regression indicated that increase in the degree of ABC antibiotics resistance, pneumonia-related ABCBSI, and septic shock were risk factors of 28-day mortality and associated with significant lower survival days.Conclusions: The past decade has witnessed a marked increase in the incidence of ABCBSI and in antibiotic resistance, with increasing pneumonia-related infections and worrisome mortality in ICUs in China. Controlling increasing resistance and preventing nosocomial pneumonia may play important roles in combatting these infections.

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Validation of a carbapenem-resistant Enterobacteriaceae colonization risk prediction model: A retrospective cohort study in Korean intensive care units

American Journal of Infection Control ◽

10.1016/j.ajic.2019.07.001 ◽

2019 ◽

Vol 47 (12) ◽

pp. 1436-1442

Author(s):

Ju Yeoun Song ◽

Ihn Sook Jeong

Keyword(s):

Cohort Study ◽

Intensive Care ◽

Prediction Model ◽

Intensive Care Units ◽

Risk Prediction ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Risk Prediction Model ◽

Carbapenem Resistant ◽

Carbapenem Resistant Enterobacteriaceae

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Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocab051 ◽

2021 ◽

Author(s):

Kexin Huang ◽

Tamryn F Gray ◽

Santiago Romero-Brufau ◽

James A Tulsky ◽

Charlotta Lindvall

Keyword(s):

Cohort Study ◽

Intensive Care ◽

Length Of Stay ◽

Patient Outcomes ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Medical Center ◽

Gradient Boosting ◽

Short Term ◽

Icu Length Of Stay

Abstract Objective Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods. Materials and Methods This was a retrospective cohort study with split sampling for model training and testing. We included patients ≥18 years of age admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, Massachusetts, from 2008 to 2012. Physician or nursing notes generated within the first 48 hours of admission were used with standard machine learning methods to predict outcomes. Results For the primary outcome of composite score of ICU length of stay ≥7 days or in-hospital mortality, the gradient boosting model had better performance than the logistic regression and random forest models. Nursing and physician notes achieved area under the curves (AUCs) of 0.826 and 0.796, respectively, with even better predictive power when combined (AUC, 0.839). Discussion Models using only nursing notes more accurately predicted short-term prognosis than did models using only physician notes, but in combination, the models achieved the greatest accuracy in prediction. Conclusions Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Physicians’ and nurses’ notes are both uniquely important in mortality prediction and combining these notes can produce a better predictive model.

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