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A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections.

ABSTRACT: BACKGROUND:Healthcare-associated infections impose a significant burden on the healthcare system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times and imperfect accuracy, reducing their effectiveness. METHODS:This study examined whether the amount of time a patient spends on a ward with other patients clinically suspected of infection, termed 'co-presence', can be used as a tool to predict subsequent healthcare-associated infection. Compared with contact tracing, this leverages passively collected electronic data rather than manually collected data, allowing for improved monitoring. All 133,304 inpatient records between 2011 and 2015 were abstracted from a healthcare system in the UK. The area under the receiver-operator curve (AUROC) for each of five pathogens was calculated based on co-presence time, sensitivity and specificity of the test, and how much earlier co-presence would have predicted infection for the true-positive cases. FINDINGS:For the five pathogens, AUROC ranged from 0.92 to 0.99, and was 0.52 for the negative control. Optimal cut-points of co-presence ranged from 25 to 59 h, and would have led to detection of true-positive cases up to an average of 1 day earlier. INTERPRETATION:These findings show that co-presence time would help to predict healthcare-acquired infection, and would do so earlier than the current standard of care. Using this measure prospectively in hospitals based on real-time data could limit the consequences of infection, both by being able to treat individual infected patients earlier, and by preventing potential secondary infections stemming from the original infected patient.

SUBMITTER: Rewley J

PROVIDER: S-EPMC7395302 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections.

Rewley J J Koehly L L Marcum C S CS Reed-Tsochas F F

The Journal of hospital infection 20200801 3

<h4>Background</h4>Healthcare-associated infections impose a significant burden on the healthcare system. Current methods for detecting these infections are constrained by combinations of high cost, long processing times and imperfect accuracy, reducing their effectiveness.<h4>Methods</h4>This study examined whether the amount of time a patient spends on a ward with other patients clinically suspected of infection, termed 'co-presence', can be used as a tool to predict subsequent healthcare-asso ...[more]

PMID: 32745591

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Project description:BackgroundFew small studies have described hospital-acquired infections (HAIs) occurring in patients with COVID-19.Research questionWhat characteristics in critically ill patients with COVID-19 are associated with HAIs and how are HAIs associated with outcomes in these patients?Study design and methodsMulticenter retrospective analysis of prospectively collected data including adult patients with severe COVID-19 admitted to eight Italian hub hospitals from February 20, 2020, through May 20, 2020. Descriptive statistics and univariate and multivariate Weibull regression models were used to assess incidence, microbial cause, resistance patterns, risk factors (ie, demographics, comorbidities, exposure to medication), and impact on outcomes (ie, ICU discharge, length of ICU and hospital stays, and duration of mechanical ventilation) of microbiologically confirmed HAIs.ResultsOf the 774 included patients, 359 patients (46%) demonstrated 759 HAIs (44.7 infections/1,000 ICU patient-days; 35% multidrug-resistant [MDR] bacteria). Ventilator-associated pneumonia (VAP; n = 389 [50%]), bloodstream infections (BSIs; n = 183 [34%]), and catheter-related BSIs (n = 74 [10%]) were the most frequent HAIs, with 26.0 (95% CI, 23.6-28.8) VAPs per 1,000 intubation-days, 11.7 (95% CI, 10.1-13.5) BSIs per 1,000 ICU patient-days, and 4.7 (95% CI, 3.8-5.9) catheter-related BSIs per 1,000 ICU patient-days. Gram-negative bacteria (especially Enterobacterales) and Staphylococcus aureus caused 64% and 28% of cases of VAP, respectively. Variables independently associated with infection were age, positive end expiratory pressure, and treatment with broad-spectrum antibiotics at admission. Two hundred thirty-four patients (30%) died in the ICU (15.3 deaths/1,000 ICU patient-days). Patients with HAIs complicated by septic shock showed an almost doubled mortality rate (52% vs 29%), whereas noncomplicated infections did not affect mortality. HAIs prolonged mechanical ventilation (median, 24 days [interquartile range (IQR), 14-39 days] vs 9 days [IQR, 5-13 days]; P < .001), ICU stay (24 days [IQR, 16-41 days] vs 9 days [IQR, 6-14 days]; P = .003), and hospital stay (42 days [IQR, 25-59 days] vs 23 days [IQR, 13-34 days]; P < .001).InterpretationCritically ill patients with COVID-19 are at high risk for HAIs, especially VAPs and BSIs resulting from MDR organisms. HAIs prolong mechanical ventilation and hospitalization, and HAIs complicated by septic shock almost double mortality.Trial registryClinicalTrials.gov; No.: NCT04388670; URL: www.clinicaltrials.gov.

Project description:IntroductionHealthcare-associated (HCA) infections represent a growing public health problem. The aim of this study was to compare community-onset healthcare associated (CO-HCA) bacteremic urinary tract infections (BUTI) and hospital-acquired (HA)-BUTI with special focus on multidrug resistances (MDR) and outcomes.MethodsITUBRAS-project is a prospective multicenter cohort study of patients with HCA-BUTI. All consecutive hospitalized adult patients with CO-HCA-BUTI or HA-BUTI episode were included in the study. Exclusion criteria were: patients < 18 years old, non-hospitalized patients, bacteremia from another source or primary bacteremia, non-healthcare-related infections and infections caused by unusual pathogens of the urinary tract. The main outcome variable was 30-day all-cause mortality with day 1 as the first day of positive blood culture. Logistic regression was used to analyze factors associated with clinical cure at hospital discharge and with receiving inappropriate initial antibiotic treatment. Cox regression was used to evaluate 30-day all-cause mortality.ResultsFour hundred forty-three episodes were included, 223 CO-HCA-BUTI. Patients with CO-HCA-BUTI were older (p < 0.001) and had more underlying diseases (p = 0.029) than those with HA-BUTI. The severity of the acute illness (Pitt score) was also higher in CO-HCA-BUTI (p = 0.026). Overall, a very high rate of MDR profiles (271/443, 61.2%) was observed, with no statistical differences between groups. In multivariable analysis, inadequate empirical treatment was associated with MDR profile (aOR 3.35; 95% CI 1.77-6.35), Pseudomonas aeruginosa (aOR 2.86; 95% CI 1.27-6.44) and Charlson index (aOR 1.11; 95% CI 1.01-1.23). Mortality was not associated with the site of acquisition of the infection or the presence of MDR profile. However, in the logistic regression analyses patients with CO-HCA-BUTI (aOR 0.61; 95% CI 0.40-0.93) were less likely to present clinical cure.ConclusionThe rate of MDR infections was worryingly high in our study. No differences in MDR rates were found between CO-HCA-BUTI and HA-BUTI, in the probability of receiving inappropriate empirical treatment or in 30-day mortality. However, CO-HCA-BUTIs were associated with worse clinical cure.

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A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections.

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A passive monitoring tool using hospital administrative data enables earlier specific detection of healthcare-acquired infections.

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