About: OBJECTIVES: Conducting manual surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP) using ECDC (European Centre for Disease Prevention and Control) surveillance criteria is very resource intensive. We developed and validated a semi-automated surveillance system for nvHAP, and describe nvHAP incidence and aetiology at our hospital. METHODS: We applied an automated classification algorithm mirroring ECDC definition criteria to distinguish patients ‘not at risk’ from patients ‘at risk’ for suffering from nvHAP. ‘At risk’-patients were manually screened for nvHAP. For validation, we applied the reference standard of full manual evaluation to three validation samples comprising 2091 patients. RESULTS: Among the 39 519 University Hospital Zurich inpatient discharges in 2017, the algorithm identified 2454 ‘at-risk’ patients, reducing the number of medical records to be manually screened by 93.8%. From this subset, nvHAP was identified in 251 patients (0.64%, 95%CI: 0.57–0.73). Sensitivity, negative predictive value, and accuracy of semi-automated surveillance versus full manual surveillance were lowest in the validation sample consisting of patients with HAP according to the International Classification of Diseases (ICD-10) discharge diagnostic codes, with 97.5% (CI: 93.7–99.3%), 99.2% (CI: 97.9–99.8%), and 99.4% (CI: 98.4–99.8%), respectively. The overall incidence rate of nvHAP was 0.83/1000 patient days (95%CI: 0.73–0.94), with highest rates in haematology/oncology, cardiac and thoracic surgery, and internal medicine including subspecialties. CONCLUSIONS: The semi-automated surveillance demonstrated a very high sensitivity, negative predictive value, and accuracy. This approach significantly reduces manual surveillance workload, thus making continuous nvHAP surveillance feasible as a pivotal element for successful prevention efforts.   Goto Sponge  NotDistinct  Permalink

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  • OBJECTIVES: Conducting manual surveillance of non-ventilator-associated hospital-acquired pneumonia (nvHAP) using ECDC (European Centre for Disease Prevention and Control) surveillance criteria is very resource intensive. We developed and validated a semi-automated surveillance system for nvHAP, and describe nvHAP incidence and aetiology at our hospital. METHODS: We applied an automated classification algorithm mirroring ECDC definition criteria to distinguish patients ‘not at risk’ from patients ‘at risk’ for suffering from nvHAP. ‘At risk’-patients were manually screened for nvHAP. For validation, we applied the reference standard of full manual evaluation to three validation samples comprising 2091 patients. RESULTS: Among the 39 519 University Hospital Zurich inpatient discharges in 2017, the algorithm identified 2454 ‘at-risk’ patients, reducing the number of medical records to be manually screened by 93.8%. From this subset, nvHAP was identified in 251 patients (0.64%, 95%CI: 0.57–0.73). Sensitivity, negative predictive value, and accuracy of semi-automated surveillance versus full manual surveillance were lowest in the validation sample consisting of patients with HAP according to the International Classification of Diseases (ICD-10) discharge diagnostic codes, with 97.5% (CI: 93.7–99.3%), 99.2% (CI: 97.9–99.8%), and 99.4% (CI: 98.4–99.8%), respectively. The overall incidence rate of nvHAP was 0.83/1000 patient days (95%CI: 0.73–0.94), with highest rates in haematology/oncology, cardiac and thoracic surgery, and internal medicine including subspecialties. CONCLUSIONS: The semi-automated surveillance demonstrated a very high sensitivity, negative predictive value, and accuracy. This approach significantly reduces manual surveillance workload, thus making continuous nvHAP surveillance feasible as a pivotal element for successful prevention efforts.
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  • Mythology
  • Surgical specialties
  • Healthcare in Zürich
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