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A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.
Baker, Meghan A; Septimus, Edward; Kleinman, Ken; Moody, Julia; Sands, Kenneth E; Varma, Neha; Isaacs, Amanda; McLean, Laura E; Coady, Micaela H; Blanchard, Eunice J; Poland, Russell E; Yokoe, Deborah S; Stelling, John; Haffenreffer, Katherine; Clark, Adam; Avery, Taliser R; Sljivo, Selsebil; Weinstein, Robert A; Smith, Kimberly N; Carver, Brandon; Meador, Brittany; Lin, Michael Y; Lewis, Sarah S; Washington, Chamaine; Bhattarai, Megha; Shimelman, Lauren; Kulldorff, Martin; Reddy, Sujan C; Jernigan, John A; Perlin, Jonathan B; Platt, Richard; Huang, Susan S.
Afiliación
  • Baker MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
  • Septimus E; Department of Medicine, Brigham and Women's Hospital, Boston.
  • Kleinman K; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
  • Moody J; Texas A&M College of Medicine and Memorial Hermann Health System, Houston.
  • Sands KE; Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst.
  • Varma N; HCA Healthcare, Nashville.
  • Isaacs A; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
  • McLean LE; HCA Healthcare, Nashville.
  • Coady MH; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Blanchard EJ; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Poland RE; HCA Healthcare, Nashville.
  • Yokoe DS; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Stelling J; HCA Healthcare, Nashville.
  • Haffenreffer K; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
  • Clark A; HCA Healthcare, Nashville.
  • Avery TR; Department of Medicine, University of California, San Francisco School of Medicine, San Francisco.
  • Sljivo S; Department of Medicine, Brigham and Women's Hospital, Boston.
  • Weinstein RA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Smith KN; Department of Medicine, Brigham and Women's Hospital, Boston.
  • Carver B; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston.
  • Meador B; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Lin MY; Rush University Medical Center, Chicago.
  • Lewis SS; John Stroger Hospital of Cook County, Chicago.
  • Washington C; HCA Healthcare, Nashville.
  • Bhattarai M; HCA Healthcare, Nashville.
  • Shimelman L; HCA Healthcare, Nashville.
  • Kulldorff M; Rush University Medical Center, Chicago.
  • Reddy SC; Duke University Hospital, Durham, NC.
  • Jernigan JA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Perlin JB; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Platt R; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston.
  • Huang SS; Department of Medicine, Brigham and Women's Hospital, Boston.
NEJM Evid ; 3(5): EVIDoa2300342, 2024 May.
Article en En | MEDLINE | ID: mdl-38815164
ABSTRACT

BACKGROUND:

Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.

METHODS:

We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.

RESULTS:

Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).

CONCLUSIONS:

Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Brotes de Enfermedades / COVID-19 Límite: Humans Idioma: En Revista: NEJM Evid Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Brotes de Enfermedades / COVID-19 Límite: Humans Idioma: En Revista: NEJM Evid Año: 2024 Tipo del documento: Article