scholarly journals Changes in the Number of Intensive Care Unit Beds in U.S. Hospitals During the Early Months of the COVID-19 Pandemic, as reported to the National Healthcare Safety Network’s COVID-19 Module

2021 ◽  
pp. 1-12
Author(s):  
Lindsey M. Weiner-Lastinger ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
Raymund Dantes ◽  
Cindy Gross ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Type ◽  
National Healthcare ◽  
National Healthcare Safety Network ◽  
Bed Capacity ◽  
Using Data

Abstract Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

Device-Associated Infections among Neonatal Intensive Care Unit Patients: Incidence and Associated Pathogens Reported to the National Healthcare Safety Network, 2006–2008

2012 ◽  
Vol 33 (12) ◽  
pp. 1200-1206 ◽  
Author(s):  
Susan N. Hocevar ◽  
Jonathan R. Edwards ◽  
Teresa C. Horan ◽  
Gloria C. Morrell ◽  
Martha Iwamoto ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Bloodstream Infections ◽  
Incidence Rates ◽  
General Hospitals ◽  
Weight Category ◽  
National Healthcare ◽  
National Healthcare Safety Network

Objective.To describe rates and pathogen distribution of device-associated infections (DAIs) in neonatal intensive care unit (NICU) patients and compare differences in infection rates by hospital type (children's vs general hospitals).Patients and Setting.Neonates in NICUs participating in the National Healthcare Safety Network from 2006 through 2008.Methods.We analyzed central line–associated bloodstream infections (CLABSIs), umbilical catheter–associated bloodstream infections (UCABs), and ventilator-associated pneumonia (VAP) among 304 NICUs. Differences in pooled mean incidence rates were examined using Poisson regression; nonparametric tests for comparing medians and rate distributions were used.Results.Pooled mean incidence rates by birth weight category (750 g or less, 751–1,000 g, 1,001–1,500 g, 1,501–2,500 g, and more than 2,500 g, respectively) were 3.94, 3.09, 2.25, 1.90, and 1.60 for CLABSI; 4.52, 2.77, 1.70, 0.91, and 0.92 for UCAB; and 2.36, 2.08, 1.28, 0.86, and 0.72 for VAP. When rates of infection between hospital types were compared, only pooled mean VAP rates were significantly lower in children's hospitals than in general hospitals among neonates weighing 1,000 g or less; no significant differences in medians or rate distributions were noted. Pathogen frequencies were coagulase-negative staphylococci (28%), Staphylococcus aureus (19%), and Candida species (13%) for bloodstream infections and Pseudomonas species (16%), S. aureus (15%), and Klebsiella species (14%) for VAP. Of 673 S. aureus isolates with susceptibility results, 33% were methicillin resistant.Conclusions.Neonates weighing 750 g or less had the highest DAI incidence. With the exception of VAP, pooled mean NICU incidence rates did not differ between children's and general hospitals. Pathogens associated with these infections can pose treatment challenges; continued efforts at prevention need to be applied to all NICU settings.

Parameters of bed occupancy of intensive care units in Hungary

2011 ◽  
Vol 152 (24) ◽  
pp. 946-950 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Health Insurance ◽  
Intensive Care Units ◽  
Emergency Care ◽  
Present Author ◽  
Bed Occupancy ◽  
National Network ◽  
Bed Capacity

According to the Semmelweis Plan for Saving Health Care, ”the capacity of the national network of intensive care units in Hungary is one but not the only bottleneck of emergency care at present”. Author shows on the basis of data reported to the health insurance that not on a single calendar day more than 75% of beds in intensive care units were occupied. There were about 15 to 20 thousand sick days which could be considered unnecessary because patients occupying these beds were discharged to their homes directly from the intensive care unit. The data indicate that on the whole bed capacity is not low, only in some institutions insufficient. Thus, in order to improve emergency care in Hungary, the rearrangement of existing beds, rather than an increase of bed capacity is needed. Orv. Hetil., 2011, 152, 946–950.

Correlates of Improved Influenza Vaccination of Healthcare Personnel: A Survey of Hospitals in Louisiana

2013 ◽  
Vol 34 (7) ◽  
pp. 723-729 ◽  
Author(s):  
Kayla L. Fricke ◽  
Mariella M. Gastañaduy ◽  
Renee Klos ◽  
Rodolfo E. Bégué
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Influenza Vaccination ◽  
Vaccination Campaign ◽  
Vaccination Rate ◽  
Healthcare Personnel ◽  
Patient Contact ◽  
Hospital Type ◽  
Vaccination Rates ◽  
Item Questionnaire

Objective.To describe practices for influenza vaccination of healthcare personnel (HCP) with emphasis on correlates of increased vaccination rates.Design.Survey.Participants.Volunteer sample of hospitals in Louisiana.Methods.All hospitals in Louisiana were invited to participate. A 17-item questionnaire inquired about the hospital type, patients served, characteristics of the vaccination campaign, and the resulting vaccination rate.Results.Of 254 hospitals, 153 (60%) participated and were included in the 124 responses that were received. Most programs (64%) required that HCP either receive the vaccine or sign a declination form, and the rest were exclusively voluntary (36%); no program made vaccination a condition of employment. The median vaccination rate was 67%, and the vaccination rate was higher among hospitals that were accredited by the Joint Commission; provided acute care; served children, pregnant women, oncology patients, or intensive care unit patients; required a signed declination form; or imposed consequences for unvaccinated HCP (the most common of which was to require that a mask be worn on patient contact). Hospitals that provided free vaccine, made vaccine widely available, advertised the program extensively, required a declination form, and imposed consequences had the highest vaccination rates (median, 86%; range, 81%–91%).Conclusions.The rate of influenza vaccination of HCP remains low among the hospitals surveyed. Recommended practices may not be enough to reach 90% vaccination rates unless a signed declination requirement and consequences are implemented. Wearing a mask is a strong consequence. Demanding influenza vaccination as a condition of employment was not reported as a practice by the participating hospitals.

Nosocomial Infections in a New Medical Center, Turkey

2000 ◽  
Vol 21 (8) ◽  
pp. 534-536 ◽  
Author(s):  
Bengül Durmaz ◽  
Riza Durmaz ◽  
Bariş Otlu ◽  
Emine Sönmez
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Nosocomial Infection ◽  
General Surgery ◽  
Nosocomial Infections ◽  
Medical Center ◽  
The Other ◽  
Hospital Acquired Infections ◽  
Bed Capacity ◽  
Hospital Acquired

Nosocomial infection was found in 255 (2.5%) of 10,164 inpatients in a new medical center with a 310-bed capacity. The infection rate was 12.5% in the intensive care unit, 9.5% in neurology, 5.5% in general surgery, and 4.0% in orthopedics. Rates in the other services were lower. Hospital-acquired infections in our medical center frequently involved multiply resistant Enterobacteriaceae and staphylococci.

scholarly journals The role of remdesivir in South Africa: preventing COVID-19 deaths through increasing ICU capacity

2020 ◽  
Author(s):  
Brooke E Nichols ◽  
Lise Jamieson ◽  
Sabrina RC Zhang ◽  
Sheetal Silal ◽  
Juliet R.C. Pulliam ◽  
...  
Keyword(s):  
South Africa ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Expected Number ◽  
Bed Capacity

ABSTRACTCountries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of COVID-19 patients requiring ICU care. Remdesivir can prevent deaths in countries such as South Africa by decreasing the number of days people spend in ICU, therefore freeing up ICU bed capacity.

scholarly journals Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Antonin Dauvin ◽  
Carolina Donado ◽  
Patrik Bachtiger ◽  
Ke-Chun Huang ◽  
Christopher Martin Sauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Impaired Renal Function ◽  
Kidney Injury ◽  
Learning Models ◽  
Intensive Care Patients ◽  
Using Data ◽  
Machine Learning Models ◽  
Baseline Hemoglobin

AbstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

Double Trouble: Patients With Both True and False Arrhythmia Alarms

2020 ◽  
Vol 40 (2) ◽  
pp. 14-23
Author(s):  
Stella Chiu Nguyen ◽  
Sukardi Suba ◽  
Xiao Hu ◽  
Michele M. Pelter
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Center ◽  
Academic Medical Center ◽  
Secondary Analysis ◽  
Altered Mental Status ◽  
False Alarms ◽  
Number Of False Alarms ◽  
Academic Medical ◽  
Using Data

Background Patients with both true and false arrhythmia alarms pose a challenge because true alarms might be buried among a large number of false alarms, leading to missed true events. Objective To determine (1) the frequency of patients with both true and false arrhythmia alarms; (2) patient, clinical, and electrocardiographic characteristics associated with both true and false alarms; and (3) the frequency and types of true and false arrhythmia alarms. Methods This was a secondary analysis using data from an alarm study conducted at a tertiary academic medical center. Results Of 461 intensive care unit patients, 211 (46%) had no arrhythmia alarms, 12 (3%) had only true alarms, 167 (36%) had only false alarms, and 71 (15%) had both true and false alarms. Ventricular pacemaker, altered mental status, mechanical ventilation, and cardiac intensive care unit admission were present more often in patients with both true and false alarms than among other patients (P &lt; .001). Intensive care unit stays were longer in patients with only false alarms (mean [SD], 106 [162] hours) and those with both true and false alarms (mean [SD], 208 [333] hours) than in other patients. Accelerated ventricular rhythm was the most common alarm type (37%). Conclusions An awareness of factors associated with arrhythmia alarms might aid in developing solutions to decrease alarm fatigue. To improve detection of true alarms, further research is needed to build and test electrocardiographic algorithms that adjust for clinical and electrocardiographic characteristics associated with false alarms.

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