Interrater reliability of surveillance for ventilator-associated events and pneumonia

OBJECTIVETo compare interrater reliabilities for ventilator-associated event (VAE) surveillance, traditional ventilator-associated pneumonia (VAP) surveillance, and clinical diagnosis of VAP by intensivists.DESIGNA retrospective study nested within a prospective multicenter quality improvement study.SETTINGIntensive care units (ICUs) within 5 hospitals of the Centers for Disease Control and Prevention Epicenters.PATIENTSPatients who underwent mechanical ventilation.METHODSWe selected 150 charts for review, including all VAEs and traditionally defined VAPs identified during the primary study and randomly selected charts of patients without VAEs or VAPs. Each chart was independently reviewed by 2 research assistants (RAs) for VAEs, 2 hospital infection preventionists (IPs) for traditionally defined VAP, and 2 intensivists for any episodes of pulmonary deterioration. We calculated interrater agreement using κ estimates.RESULTSThe 150 selected episodes spanned 2,500 ventilator days. In total, 93–96 VAEs were identified by RAs; 31–49 VAPs were identified by IPs, and 29–35 VAPs were diagnosed by intensivists. Interrater reliability between RAs for VAEs was high (κ, 0.71; 95% CI, 0.59–0.81). Agreement between IPs using traditional VAP criteria was slight (κ, 0.12; 95% CI, −0.05–0.29). Agreement between intensivists was slight regarding episodes of pulmonary deterioration (κ 0.22; 95% CI, 0.05–0.39) and was fair regarding whether episodes of deterioration were attributable to clinically defined VAP (κ, 0.34; 95% CI, 0.17–0.51). The clinical correlation between VAE surveillance and intensivists’ clinical assessments was poor.CONCLUSIONSProspective surveillance using VAE criteria is more reliable than traditional VAP surveillance and clinical VAP diagnosis; the correlation between VAEs and clinically recognized pulmonary deterioration is poor.Infect Control Hosp Epidemiol 2017;38:172–178</jats:sec

Is there a correlation between infection control performance and other hospital quality measures?

Quality measures are increasingly reported by hospitals to the Centers for Medicare and Medicaid Services (CMS), yet there may be tradeoffs in performance between infection control (IC) and other quality measures. Hospitals that performed best on IC measures did not perform well on most CMS non–IC quality measures

Impact of Change to Molecular Testing for Clostridium difficile Infection on Healthcare Facility–Associated Incidence Rates

Background. Change from nonmolecular to molecular testing techniques is thought to contribute to the increasing trend in incidence of Clostridium difficile infection (CDI); however the degree of effect attributed to this versus other time-related epidemiologic factors is unclear. Methods. We compared the relative change in incidence rate (IRR) of healthcare facility–associated (HCFA) CDI among hospitals in the Duke Infection Control Outreach Network before and after the date of switch from nonmolecular tests to polymerase chain reaction (PCR) using prospectively collected surveillance data from July 2009 to December 2011. Data from 10 hospitals that switched and 22 control hospitals were included. Individual hospital estimates were determined using Poisson regression. We used an interrupted time series approach to develop a Poisson mixed-effects model. Additional regression adjustments were made for clustering and proportion of intensive care unit patient-days. The variable for PCR was treated as a fixed effect; other modeled variables were random effects. Results. For those hospitals that switched to PCR, mean incidence rate of HCFA CDI before the switch was 6.0 CDIs per 10,000 patient-days compared with 9.6 CDIs per 10,000 patient-days after the switch. Estimates of hospital-specific IRR that compared after the switch with before the switch ranged from 0.89 (95% confidence interval [CI], 0.32–2.44) to 6.91 (95% CI, 1.12–42.54). After adjustment in the mixed-effects model, the overall IRR comparing CDI incidence after the switch to before the switch was 1.56 (95% CI, 1.28–1.90). Time-trend variables did not reach statistical significance. Conclusion. Hospitals that switched from nonmolecular to molecular tests experienced an approximate 56% increase in the rate of HCFA CDI after testing change

Prevalence, underlying causes, and preventability of sepsis-associated mortality in US acute care hospitals

Importance: Sepsis is present in many hospitalizations that culminate in death. The contribution of sepsis to these deaths, and the extent to which they are preventable, is unknown. Objective: To estimate the prevalence, underlying causes, and preventability of sepsis-associated mortality in acute care hospitals. Design, Setting, and Participants: Cohort study in which a retrospective medical record review was conducted of 568 randomly selected adults admitted to 6 US academic and community hospitals from January 1, 2014, to December 31, 2015, who died in the hospital or were discharged to hospice and not readmitted. Medical records were reviewed from January 1, 2017, to March 31, 2018. Main Outcomes and Measures: Clinicians reviewed cases for sepsis during hospitalization using Sepsis-3 criteria, hospice-qualifying criteria on admission, immediate and underlying causes of death, and suboptimal sepsis-related care such as inappropriate or delayed antibiotics, inadequate source control, or other medical errors. The preventability of each sepsis-associated death was rated on a 6-point Likert scale. Results: The study cohort included 568 patients (289 [50.9%] men; mean [SD] age, 70.5 [16.1] years) who died in the hospital or were discharged to hospice. Sepsis was present in 300 hospitalizations (52.8%; 95% CI, 48.6%-57.0%) and was the immediate cause of death in 198 cases (34.9%; 95% CI, 30.9%-38.9%). The next most common immediate causes of death were progressive cancer (92 [16.2%]) and heart failure (39 [6.9%]). The most common underlying causes of death in patients with sepsis were solid cancer (63 of 300 [21.0%]), chronic heart disease (46 of 300 [15.3%]), hematologic cancer (31 of 300 [10.3%]), dementia (29 of 300 [9.7%]), and chronic lung disease (27 of 300 [9.0%]). Hospice-qualifying conditions were present on admission in 121 of 300 sepsis-associated deaths (40.3%; 95% CI 34.7%-46.1%), most commonly end-stage cancer. Suboptimal care, most commonly delays in antibiotics, was identified in 68 of 300 sepsis-associated deaths (22.7%). However, only 11 sepsis-associated deaths (3.7%) were judged definitely or moderately likely preventable; another 25 sepsis-associated deaths (8.3%) were considered possibly preventable. Conclusions and Relevance: In this cohort from 6 US hospitals, sepsis was the most common immediate cause of death. However, most underlying causes of death were related to severe chronic comorbidities and most sepsis-associated deaths were unlikely to be preventable through better hospital-based care. Further innovations in the prevention and care of underlying conditions may be necessary before a major reduction in sepsis-associated deaths can be achieved

Statewide costs of health care-associated infections: Estimates for acute care hospitals in North Carolina

State-specific healthcare-associated infection (HAI) cost estimates have not been calculated to guide Department of Public Health efforts and investments

The effect of adding comorbidities to current centers for disease control and prevention central-line–associated bloodstream infection risk-adjustment methodology

BACKGROUNDRisk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.METHODSUsing a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.RESULTSOverall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.CONCLUSIONSOur risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.Infect Control Hosp Epidemiol 2017;38:1019–1024</jats:sec

Clinical and Financial Outcomes Due to Methicillin Resistant Staphylococcus aureus Surgical Site Infection: A Multi-Center Matched Outcomes Study

The clinical and financial outcomes of SSIs directly attributable to MRSA and methicillin-resistance are largely uncharacterized. Previously published data have provided conflicting conclusions.We conducted a multi-center matched outcomes study of 659 surgical patients. Patients with SSI due to MRSA were compared with two groups: matched uninfected control patients and patients with SSI due to MSSA. Four outcomes were analyzed for the 90-day period following diagnosis of the SSI: mortality, readmission, duration of hospitalization, and hospital charges. Attributable outcomes were determined by logistic and linear regression.In total, 150 patients with SSI due to MRSA were compared to 231 uninfected controls and 128 patients with SSI due to MSSA. SSI due to MRSA was independently predictive of readmission within 90 days (OR = 35.0, 95% CI 17.3-70.7), death within 90 days (OR = 7.27, 95% CI 2.83-18.7), and led to 23 days (95% CI 19.7-26.3) of additional hospitalization and $61,681 (95$24,113 (95% 4,521-43,704) of additional charges.The attributable impact of S. aureus and methicillin-resistance on outcomes of surgical patients is substantial. Preventing a single case of SSI due to MRSA can save hospitals as much as $60,000