Optimierung von Impfsicherheitsbeobachtungen sowie der Meldung und Differentialdiagnose von unerwarteten Ereignissen nach Impfung in der klinischen Forschung und pädiatrischen Akutversorgung

Accurate and timely reporting of adverse events following immunization (AEFI) is key to vaccine safety surveillance. Before the causality of a presumed AEFI can be investigated, the AEFI need to be ‘ascertained’, i.e. mapped to pre- defined case definitions (CD). Common symptoms in pediatrics, such as fever and influenza-like-illness are often mistaken for AEFI. We conducted a systematic review (Medline, Embase, 1989–2011) of developing country randomized clinical vaccine trials (RCT) studying utilization of CD for the reporting of safety outcomes. We also conducted a 31-item online questionnaire among members of the Russian and German Professional Pediatric Associations, assessing exposure to vaccine safety training, awareness of reporting pathways and utilization of CD. At Charité, we tested the value of point-of-care diagnostics for influenza and RSV as a means of differentiating “natural infection” from AEFI. In 50 vaccine safety clinical trials, 70% used at least one CD. The most commonly defined AEFI was fever, but 16 different CD were used. Logistic regression showed a positive correlation between implementation of any fever CD with the likelihood of detecting fever as an AEFI (p=0.027). Analysis of 1.632 online questionnaires from German and Russian pediatricians revealed that at least one hour per workday was spent on vaccine consultations, even though the majority (57%) had never received any vaccine safety training. Accurate AEFI reporting pathways were known to 35%, CD to only one-third. Pediatricians who had been trained in vaccine safety, were significantly more likely to apply CD and to report AEFI accurately (p<0.05). Novel fluorescence-labeled point-of-care tests for influenza and RSV (SOFIA™) were compared to “traditional” rapid tests, (QuickVue™) using real-time PCR at the Robert Koch Institute as gold standard. Novel, fluorescence-based SOFIATM tests showed increased sensitivities/specificities of 78.6/93.9% (RSV), 80.6/99.3% (Influenza A) and 71.9/99.0% (Influenza B) compared to real-time PCR. Vaccine safety reporting relies on accurate AEFI ascertainment. International standards are available and should be streamlined to facilitate the pooled analysis of large numbers of vaccine safety data across sites, ensuring ‘meta-analyzability’ and the detection of rare AEFI. Second- generation point-of-care tests for influenza and RSV provide highly accurate results assisting in the timely vaccine safety communication in the acute care setting. Formal vaccine safety training is urgently needed to strengthen pediatric core competencies for AEFI reporting and the accurate conduct of vaccine clinical trials.Präzise und zeitgerechte Meldungen von unerwarteten Ereignissen nach Impfung (UENI) sind entscheidend für die Auswertung von Impfsicherheitssignalen. Bevor ein kausaler Zusammenhang zwischen der verabreichten Impfung und dem Ereignis untersucht werden kann, muss dieses durch z.B. definierte Falldefinitionen erhoben werden. Typische pädiatrische Symptome, wie Fieber und Influenza- ähnliche Erkrankungen werden in der Pädiatrie oft als UENI fehlinterpretiert. Wir erstellten eine systematische Übersicht (Medline, Embase, 1989-2011) welche die Verwendung von Falldefinitionen zur Erhebung von Impfsicherheitssignalen in randomisierten, klinischen Studien in Entwicklungsländern analysiert. Eine Onlineumfrage unter Mitgliedern der Deutschen und Russischen pädiatrischen Vereinigungen, untersuchte die Integrierung einer formalen Impfausbildung in der pädiatrischen Weiterbildung, die Kenntnis von Meldewegen sowie die Nutzung von Falldefinitionen zur Erhebung von UENI. An der Charité wurde der Nutzen von Influenza- und RSV- Schnelltests zur Differenzierung eines UENI von einer natürlichen Infektion untersucht. In 70% der 50 Impfsicherheitsstudien wurde ≥1 Falldefinition genutzt. Fieber wurde am häufigsten definiert; 16 verschiedene Fieberdefinitionen wurden verzeichnet. Die logistische Regressionsanalyse zeigte eine positive Korrelation zwischen der Verwendung einer beliebigen Fieberdefinition und der Wahrscheinlichkeit Fieber als ein UENI zu detektieren (p=0.027). Unter 1.632 analysierten Onlinefragebögen zeigte sich, dass Pädiater ≥1 Stunde/Arbeitstag für die Impfberatung aufwenden, obwohl die Mehrheit (57%) nie eine Impfausbildung erhielt. Korrekte Meldewege für UENI kannten 35%, Falldefinitionen nur ein Drittel der Befragten. Impfsicherheitsgeschulte Pädiater wendeten signifikant häufiger Falldefinitionen und korrekte Meldewege an (p<0.05). Fluoreszenz-basierte Influenza- und RSV-Schnelltests (SOFIA™) wurden mit Immunoassay-basierten Schnelltests (QuickVue™) verglichen; als Goldstandard diente die am Robert Koch Institut durchgeführte quantitative Echtzeit-PCR. Fluoreszenz-basierte Schnelltests zeigten höhere Sensitivitäten/Spezifitäten für RSV (78.6/93.9%) und Influenza A (80.6/99.3%) und Influenza B (71.9/99.0%). Die Analyse von Impfsicherheitssignalen ist stark von der exakten Erhebung von UENI abhängig. Durch die Optimierung vorhandener, internationaler Impfsicherheitsstandards können umfassende Metaanalysen zahlreicher Impfsicherheitsdaten vereinfacht und somit seltene UENI besser detektiert werden. Zuverlässige fluoreszenz- basierte Influenza- und RSV-Schnelltests stellen eine sinnvolle Ergänzung zur Impfsicherheitskommunikation in der pädiatrischen Grundversorgung dar. Eine pädiatrische Impfsicherheitsausbildung ist unbedingt erforderlich, um das Wissen um die Meldung von UENI, in Zulassungsstudien und auch Anwendungsbeobachtungen von Impfstoffen, zu stärken

Rates of agreement between QIFT and VL increase/decrease.

<p>Rates of agreement between QIFT and VL increase/decrease.</p

Comparison of viral clearance rates for CL_VL versus CL_QIFT and for treated versus untreated patients.

<p>Variance in viral clearance rates for quantitative is smaller in treated than in untreated patients, for both VL and QIFT.</p

Characteristics of eligible PCR- confirmed influenza cases.

a<p><37 gestation weeks.</p

Binning analysis: Estimation of CT, median values based on categorized QIFT readouts.

<p>The following QIFT categories were used: “Negative QIFT”: 0 to 1 (n = 333). “Low QIFT”: >1 to 100 (n = 254). “Moderate QIFT”: >100 to 199 (n = 56). “High QIFT”: >199 (n = 26).</p

Quantitative influenza follow-up testing (QIFT)--a novel biomarker for the monitoring of disease activity at the point-of-care.

BACKGROUND: Influenza infections induce considerable disease burden in young children. Biomarkers for the monitoring of disease activity at the point-of-care (POC) are currently lacking. Recent methodologies for fluorescence-based rapid testing have been developed to provide improved sensitivities with the initial diagnosis. The present study aims to explore the utility of second-generation rapid testing during longitudinal follow-up of influenza patients (Rapid Influenza Follow-up Testing = RIFT). Signal/control fluorescent readouts (Quantitative Influenza Follow-up Testing = QIFT) are evaluated as a potential biomarker for the monitoring of disease activity at the POC. METHODS AND FINDINGS: RIFT (SOFIA) and QIFT were performed at the POC and compared to blinded RT-PCR at the National Reference Centre for Influenza. From 10/2011-4/2013, a total of 2048 paediatric cases were studied prospectively; 273 cases were PCR-confirmed for influenza. During follow-up, RIFT results turned negative either prior to PCR (68%), or simultaneously (30%). The first negative RIFT occurred after a median of 8 days with a median virus load (VL) of 5.6×10∧3 copies/ml and cycle threshold of 37, with no evidence of viral rebound. Binning analysis revealed that QIFT differentiated accurately between patients with low, medium and high viral titres. QIFT increase/decrease showed 88% agreement (sensitivity = 52%, specificity = 95%) with VL increase/decrease, respectively. QIFT-based viral clearance estimates showed similar values compared to PCR-based estimates. Variations in viral clearance rates were lower in treated compared to untreated patients. The study was limited by use of non-invasive, semi-quantitative nasopharyngeal samples. VL measurements below the limit of detection could not be quantified reliably. CONCLUSIONS: During follow-up, RIFT provides a first surrogate measure for influenza disease activity. A "switch" from positive to negative values may indicate a drop in viral load below a critical threshold, where rebound is no longer expected. QIFT may provide a useful tool for the monitoring of disease burden and viral clearance at the POC