Performance Assessment of the Mortality in Emergency Department Sepsis Score, Modified Early Warning Score, Rapid Emergency Medicine Score, and Rapid Acute Physiology Score in Predicting Survival Outcomes of Adult Renal Abscess Patients in the Emergency Department

Chang, Su Han; Hsieh, Chiao Hsuan; Weng, Yi Ming; Hsieh, Ming-Shun; Goh, Zhong Ning Leonard; Chen, Hsien Yi; Chang, Te‐Sheng; Ng, Chau Yee; Seak, Joanna Chen Yeen; Seak, Chen Ken; Seak, Chen-June

doi:10.1155/2018/6983568

Cited by 31 publications

(40 citation statements)

References 30 publications

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“…Thus far, both rapid scoring systems have been widely used in the ED 15,16 . To our knowledge, no studies have compared the effectiveness of different scoring systems for critically ill patients with COVID‐19.…”

mentioning

confidence: 99%

Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

Qiu

2020

Academic Emergency Medicine

155

128

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Objectives: Rapid and early severity-of-illness assessment appears to be important for critically ill patients with novel coronavirus disease . This study aimed to evaluate the performance of the rapid scoring system on admission of these patients.Methods: A total of 138 medical records of critically ill patients with COVID-19 were included in the study.Demographic and clinical characteristics on admission used for calculating Modified Early Warning Score (MEWS) and Rapid Emergency Medicine Score (REMS) and outcomes (survival or death) were collected for each case and extracted for analysis. All patients were divided into two age subgroups (<65 years and ≥65 years). The receiver operating characteristic (ROC) curve analyses were performed for overall patients and both subgroups.Results: The median [25th quartile, 75th quartile] of MEWS of survivors versus nonsurvivors were 1 [1, 2] and 2 [1, 3] and those of REMS were 5 [2, 6] and 7 [6, 10], respectively. In overall analysis, the area under the ROC curve for the REMS in predicting mortality was 0.833 (95% confidence interval [CI] = 0.737 to 0.928), higher than that of MEWS (0.677, 95% CI = 0.541 to 0.813). An optimal cutoff of REMS (≥6) had a sensitivity of 89.5%, a specificity of 69.8%, a positive predictive value of 39.5%, and a negative predictive value of 96.8%. In the analysis of subgroup of patients aged <65 years, the area under the ROC curve for the REMS in predicting mortality was 0.863 (95% CI = 0.743 to 0.941), higher than that of MEWS (0.603, 95% CI = 0.462 to 0.732). Conclusion:To our knowledge, this study was the first exploration on rapid scoring systems for critically ill patients with COVID-19. The REMS could provide emergency clinicians with an effective adjunct risk stratification tool for critically ill patients with COVID-19, especially for the patients aged <65 years. The effectiveness of REMS for screening these patients is attributed to its high negative predictive value.From the

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confidence: 99%

Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

Qiu

2020

Academic Emergency Medicine

155

128

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“…A machine learning prediction of higher than 0.50 was considered as a positive prediction. Next, we calculated the mREMS, abbMEDS and SOFA clinical risk scores as described previously (S1 supporting information) [8,36].…”

Section: Comparison Of Machine Learning With Internal Medicine Physicmentioning

confidence: 99%

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Doorn

Borggreve

Schalkwijk

et al. 2020

Preprint

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IntroductionPatients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.MethodsA single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n=1,244) and validation dataset (n=100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.ResultsA number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82).ConclusionMachine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

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“…Sumber Viskositas (21) Medis (10 ) Kelarutan Dalam Air (18) Bau (23 ) Titik Leleh (24) Titik Didih (19) Masa Molar (29) Densitas (24) Sifat Fisika (28) Sejarah Pengeboran Minyak (39) Pengolahan Air (26) Kapasitas Kalor (13) Penurunan Titik Beku (14) Kelimpahan CaCl 2 (6) Pengering (22) Sterilisasi Hewan (16) Makanan (10) Sumber Ion Kalsium (11) Kontrol Debu Jalan (17) Proses Indutri (5) Grup Ruang (12) Konstanta Kisi (25) Grup Titik (10) Geometri Koordinasi (15) Struktur Kristal (40) Entalpi Pembentukan Standar (20) Entropi Molar Standar (8; 9) Energi Bebas Gibbs (7) Termokimia (27) CaCl 2…”

Section: Struktur Dan Termokimiaunclassified

Calcium Chloride (CaCl2) : Characteristics and Molecular Interaction in Solution

Alfionita¹,

Zainul²

2019

Preprint

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Kalsium klorida merupakan senyawa anorganik dengan rumus kimia CaCl2 berupa padatan kristal yang tak berwarna pada suhu kamar dan sangat larut dalam air. Dalam analisis struktur molekul Kalsium Klorida (CaCl2), riset ini bertujuan mengetahui karakteristik molekul, struktur, sifat, permukaan, jari-jari molekul, energi stabiliti dan MM2 molekul tersebut. Metoda yang digunakan yaitu penelusuran literatur dengan Endnote X7, metode pemodelan dengan menggunakan software ChemOffice 15.0, dan juga perhitungan matematis. Pada dasarnya untuk menemukan literasi jurnal yang bagus digunakanlah aplikasi Endnote X7 yang dijelaskan melalui fishbone state of art. Senyawa ini juga mempunyai total energi dan juga jari-jari molekul CaCl2 yang hasilnya bisa diketahui dengan menggunakan chemoffice 15.0 yaitu sebesar 526,865 kcal/mol dan 2.730 Å Kemudian untuk mengetahui kecepatan hanyut, konduktiviti, gerakan ion, viskositas, kelarutan dan bilangan transpor digunakan perhitungan matematis dari review jurnal mengenai kalsium klorida. Dimana kalsium klorida mempunyai kapasitas kalor (C), entropi molar standar (So), entalpi pembentukan standar (∆fH) dan energi bebas gibbs (∆fG) yang masing-masingnya mempunyai nilai C 72,89 J/molK, So 108,4 J/molK, ∆fH 795,42 kJ/mol, dan ∆fG -748,81nkJ/mol. Serta nilai kecepatan hanyut didapatkan sebesar 4,5125 V/cm setelah dilakukan perhitungan dengan menggunakan rumus. Nilai viskositas pada suhu 10oC sebesar 1.20, suhu 20oC sebesar 1.02, dan pada suhu 30oC sebesar 0,79. Nilai konduktiviti yang beragam tergantung dari suhu dan spesinya. Dimana nilai konduktiviti pada suhu 25oC sebesar 0.0617 jika nilai molarnya 0.3857 mole/L, 0.1289 jika nilai molarnya 0.905 mole/L.Hasil dari viskositas dan konduktiviti ini dituangkan dalam bentuk grafik.

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Performance Assessment of the Mortality in Emergency Department Sepsis Score, Modified Early Warning Score, Rapid Emergency Medicine Score, and Rapid Acute Physiology Score in Predicting Survival Outcomes of Adult Renal Abscess Patients in the Emergency Department

Cited by 31 publications

References 30 publications

Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

Comparing Rapid Scoring Systems in Mortality Prediction of Critically Ill Patients With Novel Coronavirus Disease

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Calcium Chloride (CaCl2) : Characteristics and Molecular Interaction in Solution

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