Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty-A multicenter study.

OBJECTIVE: Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance. DESIGN: Multicenter retrospective cohort study. METHODS: Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance. RESULTS: Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection. CONCLUSIONS: This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.

An automated algorithm to preselect patients to be assessed individually in point prevalence surveys for hospital-acquired infections in surgery.

In this pilot study, we evaluate an algorithm that uses predictive clinical and laboratory parameters to differentiate between patients with hospital-acquired infection (HAI) and patients without HAI. Seventy-four percent of the studied population of surgical patients could be reliably (negative predictive value of 98%) excluded from detailed assessment by the infection control practitioner.

Evaluation of an algorithm for electronic surveillance of hospital-acquired infections yielding serial weekly point prevalence scores.

Surveillance of hospital-acquired infections can be approximated by repeated surveys that are performed in a standardized, cost-effective manner. We developed an integrated software system for serial electronic hospital-wide point prevalence surveys using algorithms that proved highly sensitive and specific over a 5-year period in a large university medical center.

Diagnostic performance of selected commercial HEV IgM and IgG ELISAs for immunocompromised and immunocompetent patients.

BACKGROUND: Hepatitis E virus (HEV) genotype 3 is recognised as an emerging pathogen in industrialised countries. The currently commercially available HEV-specific enzyme linked immunosorbent assays (ELISAs) are primarily designed for the detection of antibodies against genotypes 1 (Burma) and 2 (Mexico) and may not sensitively detect HEV genotypes 3 or 4. OBJECTIVES: This study aimed to evaluate the analytical and clinical performances of eight commercially available HEV serum antibody immunoglobulin M (IgM)- and immunoglobulin G (IgG)-specific ELISAs for genotype 1 and 3 HEV infections in a clinical setting and to study the antibody responses against HEV of immunocompromised versus immunocompetent patient groups. STUDY DESIGN: Analytical performance and diagnostic sensitivity and specificity were assessed using well-defined reference samples and samples from patients with polymerase chain reaction (PCR)-confirmed HEV infection (n=88) and a specificity panel (n=98). RESULTS: Limiting dilutions indicated that the highest analytical sensitivity in head-to-head comparison was measured for the Mikrogen_new IgG assay. Taking the serum working dilutions of each assay into account, the Wantai IgG assay was the most sensitive assay. Receiver operator curve (ROC) analysis showed area under the curve (AUC) values of 0.943, 0.964, 0.969, 0.971, 0.974 and 0.994 for the DSI, Mikrogen_old, MP Diagnostics, Mikrogen_new, Wantai and DiaPro anti-HEV IgM assays, respectively. The highest specificity of currently available assays was found for the IgM Wantai assay (>99%). If anti-HEV IgM and IgG results from each supplier were combined, DSI and Wantai assays were able to detect the highest number of (passed) HEV infections. CONCLUSIONS: Our study showed that current commercial HEV ELISAs could be used to diagnose HEV genotype 3 infection adequately in a clinical setting.