scholarly journals 399. Epidemiology of Laboratory-identified Late-onset SARS-CoV-2 Positivity in Two Large, Urban, Acute-Care Hospitals: Implications for Surveillance of Hospital-Acquired COVID-19

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S301-S302
Author(s):  
William Trick ◽  
Michael Y Lin ◽  
Sharon F Welbel ◽  
Onfofre T Donceras ◽  
Huiyuan Zhang ◽  
...  
Keyword(s):  
Acute Care ◽  
Chart Review ◽  
Late Onset ◽  
Delayed Diagnosis ◽  
Acute Care Hospitals ◽  
Healthcare Facility ◽  
Clinical Criteria ◽  
Number Of Patients ◽  
Time Of Presentation ◽  
Hospital Acquired

Abstract Background Laboratory identification (Lab-ID) of late-onset SARS-CoV-2 positive tests during a hospital stay is a potential public health surveillance approach for hospital-acquired COVID-19. However, prolonged RNA fragment shedding and intermittent detection of SARS-CoV-2 virus via PCR testing among infected patients may hamper interpretation of laboratory-identified events. We aimed to describe the epidemiology of late-onset SARS-CoV-2 laboratory events using clinical criteria, to evaluate the feasibility of a Lab-ID approach to detection of nosocomial SARS-COV-2 infection. Methods We evaluated all SARS-CoV-2 RT-PCR positive results recovered from patients at two acute-care hospitals in Chicago, IL, during March 1 — November 30, 2020. Each hospital maintained stringent infection control policies through-out the study period. Through chart review (WT & CS), we categorized all initial SARS-CoV-2 positive tests collected > Hospital Day 5 (defined as ‘late-onset’ based on the 5-day mean incubation period for COVID-19) into the following clinical categories: Community Acquired; Unlikely Hospital Acquired; Possible Hospital Acquired; and Probable Hospital Acquired. Categorizations were made using hospital day, symptoms, alternative diagnoses, and clinical notes (Figure 1). Results Of 2,671 SARS-CoV-2-positive patients, most positive tests (n=2,551; 96%) were recovered pre-admit or by Hospital Day 2; first positive tests were uncommon during Hospital Days 6 to 14 (n=40; 1.5%); and rare after Hospital Day 14 (n=15; 0.6%). By chart review, of the 55 late-onset records reviewed, categorizations in descending order were: Prior positive at outside facility (n=23); Possible Hospital Acquired (n=16); Community Acquired (n=12); Probable Hospital Acquired (n=4). Less than half of the late-onset cases were categorized as a possible or probable hospital acquisition (Figure 2). Conclusion Hospital-acquired SARS-CoV-2 infection was uncommon. Most late-onset episodes of SARS-CoV-2 were explained by detection at an outside healthcare facility or by delayed diagnosis of patients with symptoms at time of presentation. A Lab-ID approach to nosocomial COVID-19 surveillance would potentially misclassify a substantial number of patients. Disclosures All Authors: No reported disclosures

Hospital-acquired infections in Belgian acute-care hospitals: an estimation of their global impact on mortality, length of stay and healthcare costs

2011 ◽  
Vol 140 (1) ◽  
pp. 126-136 ◽  
Author(s):  
F. VRIJENS ◽  
F. HULSTAERT ◽  
S. DEVRIESE ◽  
S. VAN DE SANDE
Keyword(s):  
Length Of Stay ◽  
Acute Care ◽  
Excess Mortality ◽  
National Level ◽  
Acute Care Hospitals ◽  
Public Healthcare ◽  
Hospital Acquired Infections ◽  
Number Of Patients ◽  
Hospital Acquired ◽  
The Impact

SUMMARYAssessing the overall burden of disease which can be attributed to hospital-acquired infections (HAIs) remains a challenge. A matched cohort study was performed to estimate excess mortality, length of stay and costs attributable to HAIs in Belgian acute-care hospitals, using six matching factors (hospital, diagnosis-related group, age, ward, Charlson score, estimated length of stay prior to infection). Information was combined from different sources on the epidemiology and burden of HAIs to estimate the impact at national level. The total number of patients affected by a HAI each year was 125 000 (per 10·9 million inhabitants). The excess mortality was 2·8% and excess length of stay was 7·3 days, corresponding to a public healthcare cost of €290 million. A large burden was observed outside the intensive-care unit setting (87% of patients infected and extra costs, 73% of excess deaths).

Unit-level time trends and seasonality in the rate of hospital-acquired pressure ulcers in US acute care hospitals

2013 ◽  
Vol 36 (2) ◽  
pp. 171-180 ◽  
Author(s):  
Jianghua He ◽  
Vincent S. Staggs ◽  
Sandra Bergquist-Beringer ◽  
Nancy Dunton
Keyword(s):  
Acute Care ◽  
Time Trends ◽  
Pressure Ulcers ◽  
Acute Care Hospitals ◽  
Unit Level ◽  
Hospital Acquired

scholarly journals Changes in hospital safety following penalties in the US Hospital Acquired Condition Reduction Program: retrospective cohort study

BMJ ◽  
2019 ◽  
pp. l4109 ◽  
Author(s):  
Roshun Sankaran ◽  
Devraj Sukul ◽  
Ushapoorna Nuliyalu ◽  
Baris Gulseren ◽  
Tedi A Engler ◽  
...  
Keyword(s):  
Acute Care ◽  
Retrospective Cohort ◽  
Acute Care Hospitals ◽  
The United States ◽  
Fiscal Year ◽  
Percentage Points ◽  
Hospital Acquired Conditions ◽  
The Us ◽  
Reduction Program ◽  
Hospital Acquired

Abstract Objective To evaluate the association between hospital penalization in the US Hospital Acquired Condition Reduction Program (HACRP) and subsequent changes in clinical outcomes. Design Regression discontinuity design applied to a retrospective cohort from inpatient Medicare claims. Setting 3238 acute care hospitals in the United States. Participants Medicare fee-for-service beneficiaries discharged from acute care hospitals between 23 July 2014 and 30 November 2016 and eligible for at least one targeted hospital acquired condition (n=15 470 334). Intervention Hospital receipt of a penalty in the first year of the HACRP. Main outcome measures Episode level count of targeted hospital acquired conditions per 1000 episodes, 30 day readmissions, and 30 day mortality. Results Of 724 hospitals penalized under the HACRP in fiscal year 2015, 708 were represented in the study. Mean counts of hospital acquired conditions were 2.72 per 1000 episodes for penalized hospitals and 2.06 per 1000 episodes for non-penalized hospitals; 30 day readmissions were 14.4% and 14.0%, respectively, and 30 day mortality was 9.0% for both hospital groups. Penalized hospitals were more likely to be large, teaching institutions, and have a greater share of patients with low socioeconomic status than non-penalized hospitals. HACRP penalties were associated with a non-significant change of −0.16 hospital acquired conditions per 1000 episodes (95% confidence interval −0.53 to 0.20), −0.36 percentage points in 30 day readmission (−1.06 to 0.33), and −0.04 percentage points in 30 day mortality (−0.59 to 0.52). No clear patterns of clinical improvement were observed across hospital characteristics. Conclusions Penalization was not associated with significant changes in rates of hospital acquired conditions, 30 day readmission, or 30 day mortality, and does not appear to drive meaningful clinical improvements. By disproportionately penalizing hospitals caring for more disadvantaged patients, the HACRP could exacerbate inequities in care.

Has Improved Hand Hygiene Compliance Reduced the Risk of Hospital-Acquired Infections among Hospitalized Patients in Ontario? Analysis of Publicly Reported Patient Safety Data from 2008 to 2011

2013 ◽  
Vol 34 (6) ◽  
pp. 605-610 ◽  
Author(s):  
Giulio DiDiodato
Keyword(s):  
Patient Safety ◽  
Hand Hygiene ◽  
Acute Care ◽  
Poisson Regression ◽  
Hand Hygiene Compliance ◽  
Acute Care Hospitals ◽  
Ordinary Least Squares ◽  
Hospital Acquired Infections ◽  
Hygiene Compliance ◽  
Hospital Acquired

Design.Prospective, observational, ecological, time series, cross-sectional study examining the association between hand hygiene compliance (HHC) rates and the incidence of hospital-acquired infections.Setting.Acute care hospitals (N = 166) located in the province of Ontario, Canada.Methods.All data were extracted from the Ontario patient safety indicator database (http://www.hqontario.ca/public-reporting/patient-safety). Complete data were available for 166 acute care hospitals from October 1, 2008, to December 31, 2011. The rates of Clostridium difficile infection (CDI) are reported monthly, methicillin-resistant Staphylococcus aureus (MRSA) bacteremia quarterly, and HHC rates yearly. Trends and associations for each indicator were evaluated by ordinary least squares regression (HHC), zero-inflated Poisson regression (MRSA bacteremia), or Poisson regression (CDI). Dependent variables included in the regression analyses were extracted from the same database and included year, healthcare region, and type of hospital (teaching or small or large community).Results.Compared to those in 2008, reported HHC rates improved every year both before and after environment/patient contact (range, 10.6%–25.3%). Compared to those in 2008, there was no corresponding change in the rates of MRSA bacteremia; however, the rates of CDI decreased in 2009 but were not statistically significantly decreased from baseline in either 2010 or 2011. No consistent association was demonstrated between changes in the rates of HHC and these two healthcare-associated infections (HAIs).Conclusions.Despite significant improvements in reported rates of HHC among healthcare personnel in Ontario's hospitals, we could not demonstrate a positive ecological impact on rates of these two HAIs.

scholarly journals 210. Vancomycin Infusion Frequency and Intensity: Analysis of Real-World Data Generated from Automated Infusion Devices

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S108-S108
Author(s):  
Cynthia Yamaga ◽  
David L Bostick ◽  
Ying P Tabak ◽  
Ann Liu-Ferrara ◽  
Didier Morel ◽  
...  
Keyword(s):  
Acute Care ◽  
Real World ◽  
Acute Care Hospitals ◽  
Data Driven ◽  
Stopping Times ◽  
Becton Dickinson ◽  
Infusion Device ◽  
Session Duration ◽  
Number Of Patients ◽  
Infusion Devices

Abstract Background Automated infusion devices captures actual infused medication administration data in real-time. Vancomycin use is now recommended to be driven by AUC (area under the curve) dosing. We evaluated automated infusion device data to depict vancomycin administration practices in acute care hospitals. Figure 1. Distribution of vancomycin infusion dosing Figure 2. Distribution of time intervals between each vancomycin infusion session (mostly around 8 or 12 hours) Methods We analyzed archived vancomycin infusion data from 2,417 patients captured by automated infusion systems from 3 acute care hospitals. The infusion device informatics software recorded a variety of events during infusion – starting and stopping times, alarms and alerts, vancomycin dose, and other forms of timestamped usage information. We evaluated infusion session duration and dosing, using data-driven clustering algorithms. Results A total of 13,339 vancomycin infusion sessions from 2,417 unique adult patients were analyzed. Approximately 26.1% of patients had just one infusion of vancomycin. For the rest of the patients, the median number of infusion sessions per patient was 4; the interquartile range was 3 and 8. The most common dose was 1.0 gram (53.7%) or 1.5 gram (24.6%) (see Figure 1). The distribution of infusion session duration (hours) was 4.2% (≤1.0 hh); 40.1% (1.01–1.5 hh); 29.1% (1.51–2.0 hh); and 26.6% (>2.0 hh). The dosing frequency was 39.5% (q8 hh), 42.9% (q12 hh), 11.1% (q24 hh), and 6.5% (>q24 hh) (Figure 2), demonstrating clinical interpretability. Conclusion A considerable number of patients received just one vancomycin infusion during their hospital stay, suggesting a potential overuse of empiric vancomycin. The majority of infusion doses were between 1 to 1.5 grams and most infusion sessions were administered every 8 or 12 hours. The actual infusion duration for each dose often exceeds the prescribed 1- or 2-hour infusion orders, which may be due to known instances of infusion interruptions due to patient movement, procedures or IV access compromise. The data generated by infusion devices can augment insights on actual antimicrobial administration practices and duration. As vancomycin AUC dosing becomes more prevalent, real world infusion data may aid timely data-driven antimicrobial stewardship and patient safety interventions for vancomycin and other AUC dosed drugs. Disclosures Cynthia Yamaga, PharmD, BD (Employee) David L. Bostick, PhD, Becton, Dickinson and Co. (Employee) Ying P. Tabak, PhD, Becton, Dickinson and Co. (Employee) Ann Liu-Ferrara, PhD, Becton, Dickinson and Co. (Employee) Didier Morel, PhD, Becton, Dickinson and Co. (Employee) Kalvin Yu, MD, Becton, Dickinson and Company (Employee)GlaxoSmithKline plc. (Other Financial or Material Support, Funding)

scholarly journals Physical restraint of dementia patients in acute care hospitals during the COVID-19 pandemic: A cohort analysis in Japan

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260446
Author(s):  
Takuya Okuno ◽  
Hisashi Itoshima ◽  
Jung-ho Shin ◽  
Tetsuji Morishita ◽  
Susumu Kunisawa ◽  
...  
Keyword(s):  
Acute Care ◽  
Hospital Admissions ◽  
Cohort Analysis ◽  
Physical Restraint ◽  
Acute Care Hospitals ◽  
Physical Restraints ◽  
Restraint Use ◽  
Dementia Patients ◽  
Number Of Patients ◽  
The Impact

Introduction The coronavirus disease (COVID-19) pandemic has caused unprecedented challenges for the medical staff worldwide, especially for those in hospitals where COVID-19-positive patients are hospitalized. The announcement of COVID-19 hospital restrictions by the Japanese government has led to several limitations in hospital care, including an increased use of physical restraints, which could affect the care of elderly dementia patients. However, few studies have empirically validated the impact of physical restraint use during the COVID-19 pandemic. We aimed to evaluate the impact of regulatory changes, consequent to the pandemic, on physical restraint use among elderly dementia patients in acute care hospitals. Methods In this retrospective study, we extracted the data of elderly patients (aged > 64 years) who received dementia care in acute care hospitals between January 6, 2019, and July 4, 2020. We divided patients into two groups depending on whether they were admitted to hospitals that received COVID-19-positive patients. We calculated descriptive statistics to compare the trend in 2-week intervals and conducted an interrupted time-series analysis to validate the changes in the use of physical restraint. Results In hospitals that received COVID-19-positive patients, the number of patients who were physically restrained per 1,000 hospital admissions increased after the government’s announcement, with a maximum incidence of 501.4 per 1,000 hospital admissions between the 73rd and 74th week after the announcement. Additionally, a significant increase in the use of physical restraints for elderly dementia patients was noted (p = 0.004) in hospitals that received COVID-19-positive patients. Elderly dementia patients who required personal care experienced a significant increase in the use of physical restraints during the COVID-19 pandemic. Conclusion Understanding the causes and mechanisms underlying an increased use of physical restraints for dementia patients can help design more effective care protocols for similar future situations.

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