BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure.
OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage.
METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient\'s EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients\' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better.
RESULTS: We found that the penalized logistic regression performs better than other methods, and this model\'s performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient\'s comorbidity conditions, and network features. Among these, network features add the most value and improve the model\'s performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations.
CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model\'s performance.

BACKGROUND: Catheter-Associated Urinary Tract Infections (CAUTIs) frequently occur in the intensive care unit (ICU) and are correlated with a significant burden.
METHODS: We implemented a strategy involving a 9-element bundle, education, surveillance of CAUTI rates and clinical outcomes, monitoring compliance with bundle components, feedback of CAUTI rates and performance feedback. This was executed in 299 ICUs across 32 low- and middle-income countries. The dependent variable was CAUTI per 1,000 UC days, assessed at baseline and throughout the intervention, in the second month, third month, 4 to 15 months, 16 to 27 months, and 28 to 39 months. Comparisons were made using a 2-sample t test, and the exposure-outcome relationship was explored using a generalized linear mixed model with a Poisson distribution.
RESULTS: Over the course of 978,364 patient days, 150,258 patients utilized 652,053 UC-days. The rates of CAUTI per 1,000 UC days were measured. The rates decreased from 14.89 during the baseline period to 5.51 in the second month (risk ratio [RR] = 0.37; 95% confidence interval [CI] = 0.34-0.39; P < .001), 3.79 in the third month (RR = 0.25; 95% CI = 0.23-0.28; P < .001), 2.98 in the 4 to 15 months (RR = 0.21; 95% CI = 0.18-0.22; P < .001), 1.86 in the 16 to 27 months (RR = 0.12; 95% CI = 0.11-0.14; P < .001), and 1.71 in the 28 to 39 months (RR = 0.11; 95% CI = 0.09-0.13; P < .001).
CONCLUSIONS: Our intervention, without substantial costs or additional staffing, achieved an 89% reduction in CAUTI incidence in ICUs across 32 countries, demonstrating feasibility in ICUs of low- and middle-income countries.

暂无摘要。

An increase in hospital-onset Clostridioides difficile prompted an interprofessional team to implement enhanced environmental service cleaning practices and adopt a routine cleaning program undertaken by clinical staff. These interventions resulted in a reduction of hospital-onset Clostridioides difficile from 7.27 cases per 10,000 patient days to 1.54 cases per 10,000 patient days.

BACKGROUND: Central line (CL)-associated bloodstream infections (CLABSIs) occurring in the intensive care unit (ICU) are common and associated with a high burden.
METHODS: We implemented a multidimensional approach, incorporating an 11-element bundle, education, surveillance of CLABSI rates and clinical outcomes, monitoring compliance with bundle components, feedback of CLABSI rates and clinical outcomes, and performance feedback in 316 ICUs across 30 low- and middle-income countries. Our dependent variables were CLABSI per 1,000-CL-days and in-ICU all-cause mortality rates. These variables were measured at baseline and during the intervention, specifically during the second month, third month, 4 to 16 months, and 17 to 29 months. Comparisons were conducted using a two-sample t test. To explore the exposure-outcome relationship, we used a generalized linear mixed model with a Poisson distribution to model the number of CLABSIs.
RESULTS: During 1,837,750 patient-days, 283,087 patients, used 1,218,882 CL-days. CLABSI per 1,000 CL-days rates decreased from 15.34 at the baseline period to 7.97 in the 2nd month (relative risk (RR) = 0.52; 95% confidence interval [CI] = 0.48-0.56; P < .001), 5.34 in the 3rd month (RR = 0.35; 95% CI = 0.32-0.38; P < .001), and 2.23 in the 17 to 29 months (RR = 0.15; 95% CI = 0.13-0.17; P < .001). In-ICU all-cause mortality rate decreased from 16.17% at baseline to 13.68% (RR = 0.84; P = .0013) at 17 to 29 months.
CONCLUSIONS: The implemented approach was effective, and a similar intervention could be applied in other ICUs of low- and middle-income countries to reduce CLABSI and in-ICU all-cause mortality rates.

BACKGROUND: Following a 200% increase in the central line-associated bloodstream infection (CLABSI) standardized infection ratio in a private teaching hospital between 2019 and 2020, a program with the utilization of a CLABSI Prevention Registered Nurse was implemented to reduce CLABSIs through a variety of focused prevention efforts and education.
METHODS: The CLABSI P-RN project consisted of a dedicated team of RNs that performed interventions that included an audit of all central lines daily, real-time education and remediation, bi-weekly educational handouts, and assistance with 2-person dressing changes. Audit data were utilized to identify trends in gaps in compliance with the CLABSI bundle (ie, daily bathing, dressing integrity). The CLABSI rate/1,000 central line days were tracked in preintervention, intervention, and postintervention time periods.
RESULTS: During the intervention, the CLABSI rate decreased from 1.4 during the preintervention time period, to 0.4 during the intervention time period (P-value .04). Additionally, daily bathing compliance improved from 84.3% during the preintervention time period, to 90.8% during the intervention time period (P-value .004).
CONCLUSIONS: The implementation of the CLABSI P-RN was beneficial in reducing CLABSIs.
CONCLUSIONS: An expert-trained role dedicated to central line management reduces CLABSI occurrences, reduces health care costs, and improves patient outcomes.

A systematic approach to contact investigations has long been a cornerstone of interrupting the transmission of tuberculosis in community settings. This paper describes the implementation of a systematic 10-step contact investigation within an acute care setting during a multistate outbreak of healthcare-associated tuberculosis. A systematic approach to contact investigations might have applicability to the prevention of other communicable infections within healthcare settings.

We aim to evaluate the role of frailty and inflammatory markers in predicting the short-term outcomes after catheter-associated urinary tract infections (CAUTI) and central line-associated bloodstream infections (CLABSI).
Data regarding the patients\' characteristics, isolates on CAUTI and CLABSI, antibiotic susceptibility, frailty (11-point Modified Frailty Index), and inflammatory markers were retrospectively collected. Their impact on the short-term outcomes was assessed using regression modeling response.
One hundred and one patients with CAUTI (n = 71) and CLABSI (n = 30) between January 2018 and December 2019 were included in this study. The pooled incidence rates for CAUTI were 5.50 and for CLABSI 3.58 episodes/1000 catheter-days. We observed 74.7% drug resistance in our CAUTI isolates and 93.3% in CLABSI. In the multivariate analysis, frailty (P = 0.006), neutrophil/lymphocyte ratio (NLR) (P = 0.007) and the presence of sepsis (P = 0.029) were found to be significant predictors of in-hospital mortality in CAUTI. In patients with CLABSI, frailty (P = 0.029) and NLR (P = 0.029) were found significant and along with sepsis (P = 0.069) resulted in a regression model with good accuracy in predicting mortality. The receiver operating characteristic curve showed that 11-point Modified Frailty Index and NLR as well as the regression model significantly predicted mortality with an area under the curve of 86.1%, 81.4%, and 95.4%, respectively, in CAUTI, and 70.9%, 77.8%, and 95.2%, respectively, in CLABSI.

BACKGROUND: This study examines the impact of the COVID-19 pandemic on health care-associated infection (HAI) incidence in low- and middle-income countries (LMICs).
METHODS: Patients from 7 LMICs were followed up during hospital intensive care unit (ICU) stays from January 2019 to May 2020. HAI rates were calculated using the International Nosocomial Infection Control Consortium (INICC) Surveillance Online System applying the Centers for Disease Control and Prevention\'s National Healthcare Safety Network (CDC-NHSN) criteria. Pre-COVID-19 rates for 2019 were compared with COVID-19 era rates for 2020 for central line-associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), ventilator-associated events (VAEs), mortality, and length of stay (LOS).
RESULTS: A total of 7,775 patients were followed up for 49,506 bed days. The 2019 to 2020 rate comparisons were 2.54 and 4.73 CLABSIs per 1,000 central line days (risk ratio [RR] = 1.85, p = .0006), 9.71 and 12.58 VAEs per 1,000 mechanical ventilator days (RR = 1.29, p = .10), and 1.64 and 1.43 CAUTIs per 1,000 urinary catheter days (RR = 1.14; p = .69). Mortality rates were 15.2% and 23.2% for 2019 and 2020 (RR = 1.42; p < .0001), respectively. Mean LOS for 2019 and 2020 were 6.02 and 7.54 days (RR = 1.21, p < .0001), respectively.
CONCLUSIONS: This study documents an increase in HAI rates in 7 LMICs during the first 5 months of the COVID-19 pandemic and highlights the need to reprioritize and return to conventional infection prevention practices.

OBJECTIVE: Since 2002, France has adopted the Patients\' Rights Law, an alternative malpractice scheme creating a faster, less expensive out-of-court settlement ensuring compensation even in the absence of fault. We aimed to describe the implications of this system by analyzing 5 years of claims for infections related to spinal surgeries collected by the main insurer of French spine surgeons.
METHODS: We retrospectively analyzed 98 anonymized malpractice claims from 2015 to 2019 (20% of overall claims), including anonymized medical records of the patients, reports of the independent experts, final judgments, and entities supporting the compensation if any.
RESULTS: Claims included 8 deaths and 17 newly acquired neurological sequelae. The conclusions identified 22 faulty cases. The most frequent fault was a delay in diagnosis (10 cases), followed by inadequate surgical management (6 cases), inadequate antibiotic therapy (5 cases), and inadequate follow-up (1 case). Among the 67 cases (68.4%) proved not to be at fault, 10 were covered by the national solidarity fund because of their severity, and the remaining 57 were covered by hospitals.
CONCLUSIONS: Since the 2002 Patients\' Rights Law, patients with postoperative infections have always received compensation. The out-of-court settlement offers the patients incurring morbidities the assurance of faster compensation. Although certainly subject to selection criteria, this procedure is free and does not necessitate the presence of a lawyer. The analysis of expert reports and the resulting court decisions imply prevention, anticipation, and collaboration of all health care providers and open an opportunity to improve their practices to limit these crucial followings.