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Zusammenfassung Hintergrund Laut S3-Leitlinie „Allergieprävention“ (Update 2021) soll bei nicht (voll) gestillten Säuglingen mit erhöhtem Allergierisiko geprüft werden, ob eine Säuglingsnahrung mit nachgewiesener allergiepräventiver Wirksamkeit verfügbar ist. Einige Hersteller bewerben ihre Produkte entsprechend. Ziel der Arbeit Der Artikel gibt einen Überblick über die wissenschaftliche Evidenz und internationale Empfehlungen zum Einsatz von hydrolysierter Säuglingsnahrung (sog. hypoallergene [HA-]Nahrung) zur Prävention von atopischen Erkrankungen. Materialien und Methoden In medizinischen Datenbanken wurde eine Literaturrecherche (Zeitraum: 2016–2021) zum Nutzen von hydrolysierten Säuglingsnahrungen für die Allergieprävention bei Risikokindern und zu entsprechenden internationalen Leitlinien durchgeführt. Auf dieser Basis wurden die aktuelle Empfehlung der deutschen S3-Leitlinie sowie die Produkte auf dem deutschen Markt eingeordnet. Ergebnisse Es gibt keine hinreichenden Belege für einen generellen allergiepräventiven Nutzen von Säuglingsnahrungen mit Proteinhydrolysat bei Säuglingen mit erhöhtem Allergierisiko. Die GINI-Interventionsstudie deutet darauf hin, dass eine der eingesetzten Hydrolysatnahrungen zur Prävention atopischer Dermatitis bei Risikokindern wirksam gewesen sein könnte. Die Ergebnisse können aber, auch angesichts methodischer Schwächen, nicht als alleinige Basis für eine allgemeine Empfehlung zur Verwendung von HA-Nahrung herangezogen werden. Internationale Fachgesellschaften sprechen sich seit einigen Jahren eher gegen eine solche Empfehlung aus. Diskussion Für die derzeit in Deutschland vermarkteten Säuglingsnahrungen auf Basis von partiell hydrolysiertem Protein fehlen Wirksamkeitsbelege. Sie können daher nicht zur Allergieprävention empfohlen werden.

Acute kidney injury (AKI) is a common complication in critically ill patients and is associated with long-term complications and an increased mortality. This work presents preliminary findings from the first freely available European intensive care database released by Amsterdam UMC. A machine learning (ML) model was developed to predict AKI in the intensive care unit 12 hours before the actual event. Main features of the model included medications and hemodynamic parameters. Our models perform with an accuracy of 81.8% on moderate to severe AKI and 79.8% on all AKI patients. Those results can compete with models reported in the literature and introduce an ML model for AKI based on European patient data.

Introduction: Kidney transplantation was conducted in Mashhad University of Medical Sciences (MUMS) for more than 20 years. In recent years, about 160 kidney transplantation has been conducted here annually, that consisted of both cadaveric and living donor programs. Objectives: Since transplant outcomes have not been systematically monitored, we try by registration of this group of patients, donors and recipients, to analyze and share kidney transplantation outcomes. Patients and Methods: The kidney transplant registry program in Mashhad, Khorasan Razavi, started in 2016. This registry is a prospective, single center, longitudinal program, designed as a dynamic observational cohort that enrolls all transplant recipients and donors. Demographic data, etiology of chronic kidney disease and patient and graft survival were analyzed. Results: Baseline characteristics, treatment data, patient and donor condition before and after surgery, potential clinical events including hospitalization, co-morbidities and graft failure are all collected. Follow-up visits in recipients have been conducted in 1st, 3rd months and every year after operation. Data quality is ensured by automatic software validation and a manual data review process. Conclusion: By this system, it is easy to adhere to input. Likewise data is adequate for rapid statistical processing.

Introduction Early diagnosis of atrial fibrillation (AF) is essential to reduce complications such as stroke, and improve patient quality of life. Novel screening techniques using smartphone camera photoplethysmography (PPG) can be used for AF detection, but their clinical applicability remains unclear. Our aim was to assess the diagnostic accuracy of smartphone PPG compared to conventional ECG for AF detection. Methods We performed a systematic review of MEDLINE, EMBASE, Cochrane library, and other databases (1980-October 2019), including any study or abstract where smartphone finger-tip PPG was compared with a reference ECG (1, 3 or 12-lead). Outcomes were sensitivity (SE), specificity (SP), positive and negative predictive value (PPV; NPV) and overall accuracy. Bivariate hierarchical random effects meta-analysis was performed for studies with confidence intervals for SE and SP, and funnel plots were used to identify publication bias. Study quality was assessed using the established QUADAS-2 tool by two independent graders. Results 1350 publications were screened, of which 17 studies were included in the systematic review (7 full text publications and 10 abstracts), providing 21 comparisons of accuracy for AF detection. Most studies were based in secondary care and small (range n=33 to 1095), with a total of 5469 participants including 1384 with AF. Only 4 studies were multi-centre. Smartphone applications used were Cardiio Rhythm, Fibricheck, Preventicus and Heartbeats, with 7 studies not specifying the tool used. Overall SE and SP for AF detection were high, ranging from 76 to 100%, and 85 to 100% respectively. PPV ranged from 54 to 100% and NPV from 77 to 100%, with overall accuracy between 61 and 99%. The meta-analysis included 12 comparisons from 10 studies (n=2714; 936 with AF). The pooled SE was 93% (95% CI 90-96%) and SP 97% (95-99%); Figure A. QUADAS-2 assessment demonstrated poor quality of studies overall, with a high or unclear risk of bias in at least one domain for all studies. There was clear evidence of publication bias; Figure B. Conclusions PPG offers the potential for large scale, non-invasive, patient-led screening of AF. However, current evidence is limited to biased, low quality studies often with unrealistic results for AF detection. These are insufficient to advise clinicians on the true value of current smartphone PPG technology. Conflict of Interest EU grant -BigData@Heart EU/EFPIA IMI 11607

ObjectivesTimely diagnosis of atrial fibrillation (AF) is essential to reduce complications from this increasingly common condition. We sought to assess the diagnostic accuracy of smartphone camera photoplethysmography (PPG) compared with conventional electrocardiogram (ECG) for AF detection.MethodsThis is a systematic review of MEDLINE, EMBASE and Cochrane (1980–December 2020), including any study or abstract, where smartphone PPG was compared with a reference ECG (1, 3 or 12-lead). Random effects meta-analysis was performed to pool sensitivity/specificity and identify publication bias, with study quality assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) risk of bias tool.Results28 studies were included (10 full-text publications and 18 abstracts), providing 31 comparisons of smartphone PPG versus ECG for AF detection. 11 404 participants were included (2950 in AF), with most studies being small and based in secondary care. Sensitivity and specificity for AF detection were high, ranging from 81% to 100%, and from 85% to 100%, respectively. 20 comparisons from 17 studies were meta-analysed, including 6891 participants (2299 with AF); the pooled sensitivity was 94% (95% CI 92% to 95%) and specificity 97% (96%–98%), with substantial heterogeneity (pConclusionPPG provides a non-invasive, patient-led screening tool for AF. However, current evidence is limited to small, biased, low-quality studies with unrealistically high sensitivity and specificity. Further studies are needed, preferably independent from manufacturers, in order to advise clinicians on the true value of PPG technology for AF detection.