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Healthcare-associated infections (HAIs) remain a major challenge. Various strategies have been tried to prevent or control HAIs. Positive deviance, a strategy that has been used in the last decade, is based on the observation that a few at-risk individuals follow uncommon, useful practices and that, consequently, they experience better outcomes than their peers who share similar risks. We performed a systematic literature review to measure the impact of positive deviance in controlling HAIs.
A systematic search strategy was used to search PubMed, CINAHL, Scopus, and Embase through May 2020 for studies evaluating positive deviance as a single intervention or as part of an initiative to prevent or control healthcare-associated infections. The risk of bias was evaluated using the Downs and Black score.
Of 542 articles potentially eligible for review, 14 articles were included for further analysis. All studies were observational, quasi-experimental (before-and-after intervention) studies. Hand hygiene was the outcome in 8 studies (57%), and an improvement was observed in association with implementation of positive deviance as a single intervention in all of them. Overall HAI rates were measured in 5 studies (36%), and positive deviance was associated with an observed reduction in 4 (80%) of them. Methicillin-resistant Staphylococcus aureus infections were evaluated in 5 studies (36%), and positive deviance containing bundles were successful in all of them.
Positive deviance may be an effective strategy to improve hand hygiene and control HAIs. Further studies are needed to confirm this effect.
There are currently no guidelines for central-line insertion site evaluation. Our study revealed an association between insertion site inflammation (ISI) and the development of central-line–associated bloodstream infections (CLABSIs). Automated surveillance for ISI is feasible and could help prevent CLABSI.
Background: Surveillance for surgical site infections (SSI) is recommended by the CDC. Currently, colon and abdominal hysterectomy SSI rates are publicly available and impact hospital reimbursement. However, the CDC NHSN allows surgical procedures to be abstracted based on International Classification of Diseases, Tenth Revision (ICD-10) or current procedural terminology (CPT) codes. We assessed the impact of using ICD and/or CPT codes on the number of cases abstracted and SSI rates. Methods: We retrieved administrative codes (ICD and/or CPT) for procedures performed at the University of Iowa Hospitals & Clinics over 1 year: October 2018–September 2019. We included 10 procedure types: colon, hysterectomy, cesarean section, breast, cardiac, craniotomy, spinal fusion, laminectomy, hip prosthesis, and knee prosthesis surgeries. We then calculated the number of procedures that would be abstracted if we used different permutations in administration codes: (1) ICD codes only, (2) CPT codes only, (3) both ICD and CPT codes, and (4) at least 1 code from either ICD or CPT. We then calculated the impact on SSI rates based on any of the 4 coding permutations. Results: In total, 9,583 surgical procedures and 180 SSIs were detected during the study period using the fourth method (ICD or CPT codes). Denominators varied according to procedure type and coding method used. The number of procedures abstracted for breast surgery had a >10-fold difference if reported based on ICD only versus ICD or CPT codes (104 vs 1,109). Hip prosthesis had the lowest variation (638 vs 767). For SSI rates, cesarean section showed almost a 3-fold increment (2.6% when using ICD only to 7.32% with both ICD & CPT), whereas abdominal hysterectomy showed nearly a 2-fold increase (1.14% when using CPT only to 2.22% with both ICD & CPT codes). However, SSI rates remained fairly similar for craniotomy (0.14% absolute difference), hip prosthesis (0.24% absolute difference), and colon (0.09% absolute difference) despite differences in the number of abstracted procedures and coding methods. Conclusions: Denominators and SSI rates vary depending on the coding method used. Variations in the number of procedures abstracted and their subsequent impact on SSI rates were not predictable. Variations in coding methods used by hospitals could impact interhospital comparisons and benchmarking, potentially leading to disparities in public reporting and hospital penalties.
Background: Central lines (CL) are widely used in the inpatient setting and central-line–associated bloodstream infection (CLABSI) is a serious complication of CL use. Because CL insertion site inflammation (ISI) may precede the onset of CLABSI, we aimed to define ISI, to determine whether ISI was associated with CLABSI, and to develop an automated surveillance system for ISI. Methods: We extracted electronic medical records (EMRs) of adult patients hospitalized at the University of Iowa Hospitals & Clinics during January 2015–December 2018. Nurses routinely document CL insertion-site characteristics in specifically designed flow sheets in the EMR. An ISI was counted every time ≥1 of the following signs were documented during CL assessments: edema, erythema, induration, tenderness, or drainage. A 1:2 case-control investigation was performed by matching nonmucosal barrier injury (non-MBI) CLABSI patients (cases) to patients without a CLABSI diagnosis (controls). We matched for age (±10 years), sex, date (±30 days), inpatient unit, central-line days, and central-line type (temporary vs permanent). The main exposure of interest was having an ISI on or before CLABSI onset. CLABSIs were determined using CDC NHSN definitions. We then created a metric: ISI days (defined as the number of days with ≥1 ISI documented) and plotted ISI incidence (ISI days per central-line days) to quantify the burden of ISIs and to determine whether ISI and non-MBI CLABSI incidences were collinear. An automated surveillance system for ISI was created using structured query language queries to the EMR data repository and Tableau software. Results: During 2015–2018, we detected 194 CLABSI cases that were matched to 338 controls. CLABSI patients had greater odds of having an ISI (OR, 2.3; 95% CI, 1.3–4.0). Over the study period, ISI incidence decreased from ~80 to ~50 ISI days per 1,000 CL days. Non-MBI CLABSI rates also decreased from ~1.5 to ~1.0 CLABSIs per 1,000 CL days. Conclusions: ISI incidence is associated with non-MBI CLABSI incidence. Because ISI incidence is higher than CLABSI incidence, surveillance for ISI could be a more sensitive indicator for monitoring the impact of CLABSI prevention practices. Automated surveillance for novel process metrics is a promising infection prevention tool.
Background: Candidemia is a leading cause of bloodstream infections (BSIs), and community-onset candidemia is being recognized as a public health problem. In the era of electronic health records (EHRs), we can use machine learning to detect patterns in patient data that may predict infections. Objective: We aimed to predict community-onset candidemia in patients admitted to the University of Iowa Hospital & Clinics (UIHC) using machine-learning algorithms. Methods: We retrospectively reviewed data for patients admitted to UIHC during 2015–2018. All adult inpatients who had a requested blood culture were included. Candidemia was defined as a blood culture positive for Candida within 48 hours after admission. Variables of interest were extracted from the EHR: age, sex, body mass index, and month of admission. We also included comorbidities upon admission defined by the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM): cardiovascular diseases, neurological disorders, chronic pulmonary disease, dementia, rheumatoid disease, peptic ulcer disease, liver disease, diabetes mellitus, hypothyroidism, renal failure, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, anemia, alcohol abuse, drug abuse, psychiatric diseases, malignancy, and HIV/AIDS. We calculated Charlson and Elixhauser scores based on ICD-10-CM codes. We also included prehospitalization conditions (90 days before admission): Candida-positive cultures from sites other than blood, antibiotics/antifungals, hemodialysis, central lines, corticosteroids, surgeries, and intensive care unit (ICU) admissions. Mode and median imputation were used for missing information. Random forests with resampled training sets were used for prediction, and results were evaluated using 10-fold cross validation. Results: In total, 30,528 adult admissions were extracted; 73 admissions had an episode of candidemia (<1%). Median admission age was 61 years, and nearly half of admissions were female patients (44.7%). Mean BMI was 27.67. The most admissions occurred during the months of March, August, and November. The 3 most common ICD-10-CM codes were diabetes mellitus, hypertension, and cancer. Median Charlson and Elixhauser scores were 1 and 2, respectively. The model used 103 variables. The 3 most predictive variables were Elixhauser score on admission, and characteristics in the 90 days prior to admission were Candida from sites other than blood, use of a central line, and recent use of antibiotics/antifungals. The model’s area under the receiver operating characteristic curve was 0.72. Conclusions: Preadmission patient characteristics predicted community-onset candidemia. Machine-learning models may help detect patients eligible for screening for candidemia and prompt empiric antifungal therapy in high-risk patients in the first 48 hours of their admission.
We performed a retrospective analysis of the impact of using the International Classification of Diseases, Tenth Revision procedure coding system (ICD-10) or current procedural terminology (CPT) codes to calculate surgical site infection (SSI) rates. Denominators and SSI rates vary depending on the coding method used. The coding method used may influence interhospital performance comparisons.
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