Automatic Detection of Patients with Nosocomial Infection by a Computer-Based Surveillance System: A Validation Study in a General Hospital

Pokorny, Lois J.; Rovira, Aleix; Martı́n-Baranera, Montserrat; Gimeno, Concepción; Alonso-Tarrés, Carles; Vilarasau, J.

doi:10.1086/502685

Cited by 47 publications

(25 citation statements)

References 18 publications

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“…laboratory data, ICD-10 coding and inpatient prescriptions) can achieve better sensitivity and positive predictive values (PPVs). [19] RESEARCH However, lack of IT infrastructure and electronic health records precludes the use of automated HAI surveillance in most low-and middle-income countries (LMICs), including SA.…”

Section: Researchmentioning

confidence: 99%

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

2016

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Background. In 2012, the South African (SA) National Department of Health mandated surveillance of healthcare-associated infection (HAI), but made no recommendations of appropriate surveillance methods. Methods. Prospective clinical HAI surveillance (the reference method) was conducted at Tygerberg Children's Hospital, Cape Town, from 1 May to 31 October 2015. Performance of three surveillance methods (point prevalence surveys (PPSs), laboratory surveillance and tracking of antimicrobial prescriptions) was compared with the reference method using surveillance evaluation guidelines. Factors associated with failure to detect HAI were identified by logistic regression analysis. Results. The reference method detected 417 HAIs among 1 347 paediatric hospitalisations (HAI incidence of 31/1000 patient days; 95% confidence interval (CI) 28.2 -34.2). Surveillance methods had variable sensitivity (S) and positive predictive value (PPV): PPS S = 24.9% (95% CI 21 -29.3), PPV = 100%; laboratory surveillance S = 48.4% (95% CI 43.7 -53.2), PPV = 55.2% (95% CI 50.1 -60.2); and antimicrobial prescriptions S = 66.4% (95% CI 61.8 -70.8%), PPV = 88.5% (95% CI 84.5 -91.6). Combined laboratory-antimicrobial surveillance achieved superior HAI detection (S = 84.7% (95% CI 80.9 -87.8%), PPV = 97% (95% CI 94.6 -98.4%)). Factors associated with failure to detect HAI included patient transfer (odds ratio (OR) 2.0), single HAI event (OR 2.8), age category 1 -5 years (OR 2.1) and hospitalisation in a general ward (OR 2.3). Conclusions. Repeated PPSs, laboratory surveillance and/or antimicrobial prescription tracking are feasible HAI surveillance methods for low-resource settings. Combined laboratory-antimicrobial surveillance achieved the best sensitivity and PPV. SA paediatric healthcare facilities should individualise HAI surveillance, selecting a method suited to available resources and practice context.

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Section: Researchmentioning

confidence: 99%

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

2016

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“…Twenty-five studies involved academic institutions, three involved community hospitals and five studies were performed in a mixed community and academic setting. A thematic abundance of ESS systems was identified, covering a wide spectrum of clinical areas encompassing acute lung injury [13][14][15], patient-ventilator interaction [16][17][18], identification of seizures [19], rapid patient deterioration [20] nosocomial infection surveillance [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], heart failure [36], life-threatening electrocardiographic changes [37][38][39] and hemodynamic stability monitoring [40][41][42].…”

Section: Resultsmentioning

confidence: 99%

“…Included studies totaled 102,611 ESS decision points for systematic evaluation . Sensitivities of the ESS under examination ranged from 21% [32] to 100% [18,21,30,36,38]. Specificities ranged from 5% [37] to 100% [27,31,38].…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective:To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

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“…It appears, however, that most surveillance is still manual. 49 "Homegrown" automated systems developed within individual facilities have been reported, 55,[58][59][60] but few descriptions of their actual use for supporting clinical decisions have been published. Despite initial costs of system development, automated systems in the long run are projected to be cost saving.…”

Section: Automated Surveillance Systemsmentioning

confidence: 99%

Isolation Precautions for Methicillin-Resistant Staphylococcus aureus: Electronic Surveillance to Monitor Adherence

Larson¹,

Cohen²,

Ross³

et al. 2009

American Journal of Critical Care

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The Centers for Disease Control and Prevention recently updated guidelines for isolation precautions and added specific recommendations for the management of multidrug-resistant organisms. However, the extent to which these recommendations are followed is unknown. Although the recommendations are based on studies with high internal validity, the effectiveness of these interventions in clinical practice also is unknown. Evidence of the effectiveness of isolation precautions for preventing transmission of infections caused by multidrug-resistant organisms in acute care settings, with methicillin-resistant Staphylococcus aureus as an example, was reviewed. Despite a lack of experimental data, numerous descriptive and correlational studies and a sound theoretical rationale strongly suggest that barrier precautions play an important role in the prevention of transmission of infections due to multidrugresistant organisms. Two major problems, however, still exist. First, staff members' adherence to national recommendations on isolation precautions, although insufficiently described, appears to be inadequate. Second, efficient, reproducible methods for ongoing surveillance of practices such as isolation precautions are not readily available. Automated surveillance systems that provide support for making decisions are promising for this purpose, are likely to result in cost savings, and therefore warrant more widespread development, testing, and implementation.

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Automatic Detection of Patients with Nosocomial Infection by a Computer-Based Surveillance System: A Validation Study in a General Hospital

Abstract: The hospital information system was a useful tool for retrospectively detecting patients with an NI during the ICU stay. Given its high sensitivity, it may be useful as an alert for the NI team.

Cited by 47 publications

References 18 publications

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Isolation Precautions for Methicillin-Resistant Staphylococcus aureus: Electronic Surveillance to Monitor Adherence

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