Infect Control Hosp Epidemiol

ObjectiveTo predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admissionDesignRetrospective data analysisSettingSix US acute care hospitalsPatientsAdult inpatientsMethodsWe used clinical data present at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a non-duplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.ResultsAmong 78,080 adult admissions, 323 HO-CDI cases were identified (4.1/1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age 6565, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin 643 g/dL, creatinine >2.0 mg/dL, bands > 32%, platelets 64150 or >420 109/L, and WBC >11,000 mm3. The model had a c-statistic of 0.78 (95% CI: 0.76, 0.81) with good calibration. For 79% patients with risk score of 0-7, there were 19 HO-CDIs per 10,000 admissions; for patients with risk score of 20+, there were 623 HO-CDIs per 10, 000 admissions (P<0.0001).ConclusionUsing clinical parameters available at the time of admission, this HO-CDI model displayed a good predictive ability. It may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.20152018-01-15T00:00:00ZCC999999/Intramural CDC HHS/United States25753106PMC5768429809

Data Quality: Integral to CAUTI Surveillance and Improvement in Non-Critical Care Units

Background: Urinary tract infections (UTIs) are the most common type of healthcare-acquired infection (HAI), with 75% approximately associated with urinary catheter use. The key to preventing UTIs is to avoid the use of indwelling urinary catheters (IUCs). This study explores denominator data extract logic modifications to increase IUC data capture and accuracy. It is set in a 249-bed acute care, teaching hospital in the Diablo Service Area in Northern California. Problem: The electronic system used to extract the CAUTI denominator data is inconsistently capturing the IUC device days from the electronic medical record (EMR). This has regulatory reporting ramifications and negatively impacts CAUTI metrics, specifically the Standardized Infection Ratio (SIR) and the Urinary Catheter Standardized Utilization Ratio (SUR). Interventions: Enhancing the Infoview Foley Days report aims to maximize device capture and increase data accuracy. The three-pronged approach involves modifying the extract logic to focus on individual inpatient encounters, applying inpatient admission status as the date of admission, and modifying the data extract time. Outcome Measures: Two hundred forty Infoview cases validated against the EMR from January to June 2023 yielded 100% data capture and accuracy, exceeding intervention targets. Results: Data extract logic modification using the set criteria and applying additional exclusion criteria improved the CAUTI denominator data quality by 40%. Conclusion: Infoview modification increased the data quality for CAUTI surveillance and reporting, also improving the CAUTI SUR. The improved SUR is utilized as an adjunct to the CAUTI SIR for tailored data-driven infection prevention initiatives. The project’s success led to the implementation of the revised logic across the Northern California hospital system and will be rolled out as the enterprise-wide model for standardized CAUTI denominator data extract

Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored