Prediction of nosocomial sepsis in neonates by means of a computer-weighted bedside scoring system (NOSEP score)

Mahieu, Ludo; Muynck, A.O. De; Dooy, Jozef J. De; LaRoche, Suzette M.; Acker, K J Van

doi:10.1097/00003246-200006000-00058

Cited by 88 publications

(63 citation statements)

References 15 publications

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“…The internal validity of this score is good but its practical application is problematic. The majority of predictors by Mahieu et al 4 were based on laboratory data, which means that, in most circumstances, clinicians need to do CBC and CRP then wait for the results except only one situation; fever with parenteral nutrition >14 days. In contrast, our prediction score yields probability estimation for LNS primarily based on history and signs/symptoms that aid further clinical judgment at the bedside.…”

Section: Discussionmentioning

confidence: 99%

“…2 Few clinical scoring methods for the prediction of LNS have been developed, and they were mainly from the data of the neonates who had been worked-up as suspected sepsis. [3][4][5] The objectives of this study were to develop a practical, clinical prediction-scoring model for the diagnosis of LNS, and to validate the diagnostic performance of the model.…”

Section: Introductionmentioning

confidence: 99%

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A Bedside Prediction-Scoring Model for Late-Onset Neonatal Sepsis

et al. 2005

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OBJECTIVE:Insufficient tools for bedside prediction of late-onset neonatal sepsis (LNS) initiated this study. The objective was to develop and validate a simple prediction-scoring model for LNS defined as culture-proven sepsis occurring 72 hours after birth. METHODS:The study was performed at a university hospital in Bangkok. The derivation phase included medical records of 1870 neonates, randomly selected from 9347 records of neonates who had been hospitalized for >72 hours during 1998 to 2000, of which 1824 records were available. In all, 100 neonates were clinically suspected of sepsis and 17 had proven LNS. The validation phase included 73 neonates suspected of having sepsis during July 2002to June 2003 and 25 who had LNS. Weighted coefficients from Cox's proportional hazards model and receiveroperating-characteristic (ROC) curve analysis were used. RESULTS:The incidence density of LNS was 17/11355 (1.5/1000) person-days. A scoring model was developed and consisted of the following: hypotension (score 4), abnormal body temperature (score 3), respiratory insufficiency (score 2), neutrophil band form fraction >1% (score 2), platelet count <150 Â 10 3 /ml (score 2), and umbilical venous catheterization (1 to 7 or >7 days; score 2 or 4). The area under the ROC curves for prediction of LNS in a neonate suspected of sepsis in each of the two phases was 0.85 and 0.80, respectively (p ¼ 0.436). The mean probabilities of LNS were approximately 0.10 (low risk) for scores from 0 to 3; 0.50 (intermediate risk) for scores from 4 to 6; and 0.70 (high risk) for scores Z7. CONCLUSION:A simple prediction-scoring model for LNS was developed. Validation of the scores suggested good diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bedside Prediction-Scoring Model for Late-Onset Neonatal Sepsis

et al. 2005

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“…Often clinicians want to apply more than one test [21]. Unfortunately, results from different tests are usually not independent of each other.…”

Section: Considering Multiple Variablesmentioning

confidence: 99%

“…Patients with elevated procalcitonin levels often also have elevated interleukin-8 levels. Neutropenic newborns with sepsis are more likely to present with thrombocytopenia [21]. As noted above, the lack of independence renders it inappropriate to perform simple sequential Bayesian calculations.…”

Section: Considering Multiple Variablesmentioning

confidence: 99%

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

Fischer

Bachmann²,

Jaeschke

2003

Intensive Care Medicine

763

568

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Background: One of the most challenging practical and daily problems in intensive care medicine is the interpretation of the results from diagnostic tests. In neonatology and pediatric intensive care the early diagnosis of potentially life-threatening infections is a particularly important issue. Focus: A plethora of tests have been suggested to improve diagnostic decision making in the clinical setting of infection which is a clinical example used in this article. Several criteria that are critical to evidence-based appraisal of published data are often not adhered to during the study or in reporting. To enhance the critical appraisal on articles on diagnostic tests we discuss various measures of test accuracy: sensitivity, specificity, receiver operating characteristic curves, positive and negative predictive values, likelihood ratios, pretest probability, posttest probability, and diagnostic odds ratio. Conclusions: We suggest the following minimal requirements for reporting on the diagnostic accuracy of tests: a plot of the raw data, multilevel likelihood ratios, the area under the receiver operating characteristic curve, and the cutoff yielding the highest discriminative ability. For critical appraisal it is mandatory to report confidence intervals for each of these measures. Moreover, to allow comparison to the readers' patient population authors should provide data on study population characteristics, in particular on the spectrum of diseases and illness severity.

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“…Figure 1 shows the results of the search strategy. The research period spanned from 1993 to 2007 with 3 western European, [27][28][29] 4 South Asian, [30][31][32][33] 1 Canadian, 34 and 1 Turkish study. 35 All researches were performed in level III settings of which one 33 was conducted in a low-resource level III hospital.…”

Section: Resultsmentioning

confidence: 99%

Prediction Models for Neonatal Health Care–Associated Sepsis: A Meta-analysis

et al. 2015

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BACKGROUND AND OBJECTIVES: Blood culture is the gold standard to diagnose bloodstream infection but is usually time-consuming. Prediction models aim to facilitate early preliminary diagnosis and treatment. We systematically reviewed prediction models for health care-associated bloodstream infection (HABSI) in neonates, identified superior models, and pooled clinical predictors. Data sources: LibHub, PubMed, and Web of Science. METHODS:The studies included designed prediction models for laboratory-confirmed HABSI or sepsis. The target population was a consecutive series of neonates with suspicion of sepsis hospitalized for $48 hours. Clinical predictors had to be recorded at time of or before culturing. Methodologic quality of the studies was assessed. Data extracted included population characteristics, total suspected and laboratory-confirmed episodes and definition, clinical parameter definitions and odds ratios, and diagnostic accuracy parameters. RESULTS:The systematic search revealed 9 articles with 12 prediction models representing 1295 suspected and 434 laboratory-confirmed sepsis episodes. Models exhibit moderate-good methodologic quality, large pretest probability range, and insufficient diagnostic accuracy. Random effects meta-analysis showed that lethargy, pallor/mottling, total parenteral nutrition, lipid infusion, and postnatal corticosteroids were predictive for HABSI. Post hoc analysis with low-gestational-age neonates demonstrated that apnea/bradycardia, lethargy, pallor/mottling, and poor peripheral perfusion were predictive for HABSI. Limitations include clinical and statistical heterogeneity.CONCLUSIONS: Prediction models should be considered as guidance rather than an absolute indicator because they all have limited diagnostic accuracy. Lethargy and pallor and/or mottling for all neonates as well as apnea and/or bradycardia and poor peripheral perfusion for very low birth weight neonates are the most powerful clinical signs. However, the clinical context of the neonate should always be considered.

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Prediction of nosocomial sepsis in neonates by means of a computer-weighted bedside scoring system (NOSEP score)

Cited by 88 publications

References 15 publications

A Bedside Prediction-Scoring Model for Late-Onset Neonatal Sepsis

A Bedside Prediction-Scoring Model for Late-Onset Neonatal Sepsis

A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis

Prediction Models for Neonatal Health Care–Associated Sepsis: A Meta-analysis

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