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"ABSTRAKPendahuluan: Infeksi paru merupakan penyebab morbiditas dan mortalitas terbesar di Intensive Care Unit ICU. Pasien ICU umumnya dalam kondisi critically ill dan riwayat penggunaan antibiotic sebelumnya sehingga memiliki risiko resistensi terhadap antibiotik yang berpengaruh terhadap luaran pasien.Sistem skoring digunakan di ICU untuk menilai derajat keparahan penyakit dan luaran pasien. Penelitian ini menilai eta kuman pasien infeksi paru dan hubungannya dengan derajat keparahan penyakit yang dinilai dengan skor APACHE II. Metode: Penelitian ini menggunakan metode potong lintang yang dilaksanakan pada bulan Agustus-September 2017 di ICU RSUP Persahabatan. Total subjek terdiri atas 59 subjek dengan cara pengambilan sampel consecutive sampling. Semua pasien didiagnosis infeksi paru oleh dokter spesialis dan dikonfirmasi melalui foto toraks kemudian dinilai derajat keparahan penyakit dengan skor APACHE II dan dilakukan biakan dan resistensi mikroorganisme. Hasil: Kuman yang banyak ditemukan merupakan gram negative (37,2%) dengan risiko mortalitas tertinggi 75% jenis Acinetobacter pada skor (APACHE II 30-34). Rerata skor APACHE II 15,78+ 6,04 dengancut off point skor APACHE II 16,5 dan skor APACHE II >16 memiliki mortalitas terbesar (64%) (p=0,032). Diagnosis infeksi paru dengan mortalitas terbesar didapatkan pada CAP (56%). Kesimpulan: Acinetobacter baumanii merupakan kuman terbanyak yang menyebabkan kematian pada pasien infeksi paru dan skor APACHE II merupakan prediktor yang baik dalam menilai derajat keparahan penyakit dan luaran pasien.

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ABSTRACT<>br>Introduction: Lung infection are the most common cause of high mortality and morbidity in Intensive Care Unit (ICU). Patients in ICU mostly critically ill with history of antibiotic use and risk of drug resistant that will influence the outcome of the patients. Scoring system used in ICU to measure severity of the disease and the outcome of the patients. This study asseses the microbiological pattern of patients with lung infection and severity of the disease using APACHE II Score. Methods: This study used cross sectional methods that heldbetween August 2017-September 2017in Persahabatan Hospital Intensive Care Unit. Total subjects consisted of 59 patients with lung infection base on consecutive sampling. All of the patients diagnosed with lung infection from specialist and confirmed with radiological findings, measured the APACHE II Score and performed sputum culture and resistance. Results: The most common isolation found in lung infection patients was gram negative (37,2%) with mortality risk of Acinetobacter baumanii75% (APACHE II Score 30-34). Mean APACHE II Score was 15,78+ 6,04 with cut off point APACHE II Score 16,5 and APACHE II Score > 16 has the highest mortality (64%) (p=0,032). Diagnose of lung infection with the highest mortality found in patients with CAP (56%). Conclusions: Acinetobacter baumanii are the most common cause of mortality in lung infection patients. The APACHE II Score has good predictor in measure severity of the diseases and the outcome of the patients."

"ABSTRACTPurposesVascular surgery (VS) has a higher perioperative mortality than other types of surgery. We compared different scores for predicting mortality in patients admitted to the intensive care unit (ICU) after open VS.MethodsPatients admitted to the ICU after open VS from 2006 to 2013 were included. We calculated the Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Preoperative Score to Predict Postoperative Mortality (POSPOM). We performed multivariate logistic regression to assess independent factors with the calculation of odds ratios (ORs) and 95% confidence intervals (CIs). We tested the predictive ability of the scores using the area under the receiver operating characteristics curve (AUROC).ResultsA total of 833 consecutive patients were included. Hospital mortality was 5,1% (1,3% after intermediate-risk and 8,4% after high-risk surgery). In the multivariate analysis, the age (OR 1,04, 95% CI 1,01-1,08, p = 0,013), smoking status (OR 2,46, 95% CI 1,16-5,21, p = 0,019), surgery risk (OR 2,92, 95% CI 1,058,08, p = 0,040), serum sodium level (OR 1,17, 95% CI 1,10-1,26, p < 0,001), urea (OR 1,01, 95% CI 1,01-1,02, p = 0,001) and leukocyte count (OR 1,05, 95% CI 1,01-1,10, p = 0,009) at admission were considered independent predictors. Hematocrit (0,86, 95% CI 0,80-0,93, p < 0.001) was considered an independent protective factor. The AUROC of our model was 0,860, compared to SAPS (0,752), APACHE (0,774), POSPOM (0,798) and POSSUM (0,829).ConclusionThe observed mortality was within the predicted range (1-5% after intermediate-risk and > 5% after high-risk surgery). POSSUM and POSPOM had slightly better predictive capacity than SAPS or APACHE."

"The dynamic development of digital technologies, especially those dedicated to devices generating large data streams, such as all kinds of measurement equipment temperature and humidity sensors, cameras, radio telescopes and satellites Internet of Things enables more in depth analysis of the surrounding reality, including better understanding of various natural phenomenon, starting from atomic level reactions, through macroscopic processes e.g. meteorology to observation of the Earth and the outer space. On the other hand such a large quantitative improvement requires a great number of processing and storage resources, resulting in the recent rapid development of Big Data technologies. Since 2015, the European Space Agency ESA has been providing a great amount of data gathered by exploratory equipment a collection of Sentinel satellites which perform Earth observation using various measurement techniques. For example Sentinel 2 provides a stream of digital photos, including images of the Baltic Sea and the whole territory of Poland. This data is used in an experimental installation of a Big Data processing system based on the open source software at the Academic Computer Center in Gdansk. The center has one of the most powerful supercomputers in Poland the Tryton computing cluster, consisting of 1600 nodes interconnected by a fast Infiniband network 56 Gbps and over 6 PB of storage. Some of these nodes are used as a computational cloud supervised by an OpenStack platform, where the Sentinel 2 data is processed. A subsystem of the automatic, perpetual data download to object storage based on Swift is deployed, the required software libraries for the image processing are configured and the Apache Spark cluster has been set up. The above system enables gathering and analysis of the recorded satellite images and the associated metadata, benefiting from the parallel computation mechanisms. This paper describes the above solution including its technical aspects."