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Use of Statistical Process Control Methods for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacteria Outbreaks: A Single-Center Pilot Study.
Baker, Arthur W; Maged, Ahmed; Haridy, Salah; Stout, Jason E; Seidelman, Jessica L; Lewis, Sarah S; Anderson, Deverick J.
  • Baker AW; Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA.
  • Maged A; Duke Center for Antimicrobial Stewardship and Infection Prevention, Durham, North Carolina, USA.
  • Haridy S; Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
  • Stout JE; Department of Mechanical Engineering, Benha University, Benha, Egypt.
  • Seidelman JL; Department of Industrial Engineering and Engineering Management, College of Engineering, University of Sharjah, Sharjah, United Arab Emirates.
  • Lewis SS; Benha Faculty of Engineering, Benha University, Benha, Egypt.
  • Anderson DJ; Division of Infectious Diseases, Duke University School of Medicine, Durham, North Carolina, USA.
Clin Infect Dis ; 76(8): 1459-1467, 2023 04 17.
Article en En | MEDLINE | ID: mdl-36444485
ABSTRACT

BACKGROUND:

Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility-associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks.

METHODS:

We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery-associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts.

RESULTS:

The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%.

CONCLUSIONS:

SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Mycobacterium abscessus / Infecciones por Mycobacterium no Tuberculosas Tipo de estudio: Diagnostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Mycobacterium abscessus / Infecciones por Mycobacterium no Tuberculosas Tipo de estudio: Diagnostic_studies / Risk_factors_studies / Screening_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article