scholarly journals Evaluation of a modified early warning system for acute medical admissions and comparison with C-reactive protein/albumin ratio as a predictor of patient outcome

2009 ◽  
Vol 9 (1) ◽  
pp. 30-33 ◽  
Author(s):  
Emily Fairclough ◽  
Eleanor Cairns ◽  
Jennifer Hamilton ◽  
Clive Kelly
Keyword(s):  
Patient Outcome ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein

scholarly journals Diagnostic utility of C-reactive protein to albumin ratio as an early warning sign in hospitalized severe COVID-19 patients

2021 ◽  
Vol 91 ◽  
pp. 107285
Author(s):  
Inanc Karakoyun ◽  
Ayfer Colak ◽  
Melda Turken ◽  
Zeynep Altin ◽  
Fatma Demet Arslan ◽  
...  
Keyword(s):  
Early Warning ◽  
Warning Sign ◽  
Diagnostic Utility ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Early Warning Sign

Increased pretreatment C-reactive protein-to-albumin ratio predicts severe coronavirus disease 2019

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Independent Risk Factors

Abstract Objective The aim of this study was to identify early warning signs for severe coronavirus disease 2019 (COVID-19). Methods We retrospectively analysed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center, comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values were determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factors for severe COVID-19. Results The patients with mild and severe COVID-19 had significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) ( P =0.000; P =0.008; P=0.000; P =0.000). The severity of COVID-19 was positively correlated with comorbid cancer, age, NLR, and CAR ( P <0.005). Multivariate logistic regression analysis showed that age, the NLR and the CAR were independent risk factors for severe COVID-19 (OR=1.086, P =0.008; OR=1.512, P =0.007; OR=17.652, P =0.001). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

An increased pretreatment C-reactive protein-to-albumin ratio predicts severe novel coronavirus-infected pneumonia

2020 ◽  
Author(s):  
Xiaoyue Wang ◽  
Yan Xu ◽  
Huang Huang ◽  
Desheng Jiang ◽  
Chunlei Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Early Warning ◽  
Multivariate Logistic Regression Analysis ◽  
Warning Signs ◽  
C Reactive Protein ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Novel Coronavirus

Abstract Objective The aim of this study was to identify early warning signs for severe novel coronavirus-infected pneumonia (COVID-19). Methods We retrospectively analyzed the clinical data of 90 patients with COVID-19 from Guanggu District of Hubei Women and Children Medical and Healthcare Center comprising 60 mild cases and 30 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory blood test results were compared between the two groups. The cutoff values was determined by receiver operating characteristic curve analysis. Logistic regression analysis was performed to identify the independent risk factor that predicted the severe COVID-19. Results The patients with mild and severe COVID-19 showed significant differences in terms of cancer incidence, age, pretreatment neutrophil-to-lymphocyte ratio (NLR), and pretreatment C-reactive protein-to-albumin ratio (CAR) (P < 0.05). The severity of COVID-19 was correlated positively with the comorbidity of cancer, age, NLR, and CAR (P < 0.05). Multivariate logistic regression analysis showed that age, NLR and CAR were independent risk factors for severe COVID-19 (OR = 1.086, P = 0.008; OR = 1.512, P = 0.007; OR = 17.652, P = 0.001, respectively). Conclusion An increased CAR can serve as an early warning sign of severe COVID-19 in conjunction with the NLR and age.

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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