scholarly journals 771. Prospective validation of the universal vital assessment (UVA) score to predict the in-hospital mortality of patients with acute illness admitted to a government district hospital in KwaZulu-Natal, South Africa

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S430-S430
Author(s):  
Amiya A Ahmed ◽  
Joseph B Ladines-Lim ◽  
Christopher Moore ◽  
Sipho Malinga ◽  
Anthony Moll ◽  
...  
Keyword(s):  
South Africa ◽  
High Risk ◽  
District Hospital ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Routine Care ◽  
Resource Limited ◽  
Tract Infections ◽  
Better Than

Abstract Background Critical illness is a frequent cause of mortality in resource-limited settings. Improved triage on admission could improve mortality, but existing tools depend on variables that often are not available. We prospectively evaluated the universal vital assessment (UVA) score to predict mortality among patients admitted to a district hospital in rural, highly HIV-prevalent South Africa. Figure 1. Receiver operator characteristic (ROC) curves for the UVA and qSOFA scoring tools. Methods In February-March 2020, adults admitted to the medical wards were enrolled, prior to interruption by covid19, and 30-day mortality assessed. Clinical parameters including temperature, heart and respiratory rates, systolic blood pressure, oxygen saturation, Glasgow Coma Scale score, and HIV status were collected within 24 hours of admission as part of routine care. Lower respiratory tract infections (LRTI) included pneumonia and suspected pulmonary tuberculosis. To evaluate the predictive ability of the UVA score, area under the receiver operating characteristic curve (aROC) and age-sex adjusted binary logistic regression models were generated, and compared to the sequential organ failure assessment (qSOFA). Results Sixty one patients were enrolled; outcomes were available for 56 patients. Patients had a mean age of 52 (SD+17), 51% were women, and 46% were HIV infected. The 30-day mortality was 16.1% (9/56) with infections and non-communicable diseases comprising 47% and 47% of admission diagnoses, respectively. The most common admitting diagnosis was LRTI (24.6%). The median (+IQR) UVA score was 2 (+3) accounting for 36% of participants. Medium-risk (2-4) and high-risk (>4) UVA groups were not associated with 30-day mortality, although the high-risk score trended towards significance (p=0.07). However, a UVA score > 3 was significantly associated with 30-day mortality, both alone and after adjusting for age and sex (aOR 6.2, 95% CI 1.2-33.1; p=0.03). The aROC (95% CI) for the UVA score was 0.74 (0.55 – 0.93), which performed better than qSOFA (aROC 0.59, 95% CI 0.37-0.81) and is shown in Figure 1. Conclusion In this resource-limited, HIV-prevalent setting, the UVA score predicted mortality better than the qSOFA score. A moderate-risk UVA score (>3) was predictive of 30-day mortality, though needs to be confirmed in larger studies. Disclosures All Authors: No reported disclosures

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

scholarly journals Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hsin-Yun Wu ◽  
Cihun-Siyong Alex Gong ◽  
Shih-Pin Lin ◽  
Kuang-Yi Chang ◽  
Mei-Yung Tsou ◽  
...  
Keyword(s):  
High Risk ◽  
Epidural Analgesia ◽  
Predictive Model ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Roc Curves ◽  
Supervised Machine Learning ◽  
Patient Specific ◽  
Support Vector ◽  
Orthopedic Patients

Abstract Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

Major Cardiac Events in Patients Admitted to Intensive Care After Vascular Noncardiac Surgery: A Retrospective Cohort

2019 ◽  
Vol 23 (3) ◽  
pp. 293-299 ◽  
Author(s):  
Pedro Videira Reis ◽  
Ana Isabel Lopes ◽  
Diana Leite ◽  
João Moreira ◽  
Leonor Mendes ◽  
...  
Keyword(s):  
High Risk ◽  
Characteristic Curve ◽  
Risk Index ◽  
Previous History ◽  
Predictive Ability ◽  
Cardiac Risk ◽  
Noncardiac Surgery ◽  
Intermediate Risk ◽  
Cardiac Events ◽  
Predictive Capacity

Introduction. Patients proposed to vascular noncardiac surgery (VS) have several comorbidities associated with major adverse cardiac events (MACE). We evaluated incidence, predictors, and outcomes, and compared different scores to predict MACE after VS. Methods. We included all patients admitted from 2006 to 2013. Perioperative MACE included cardiac arrhythmias, myocardial infarction (MI), cardiogenic pulmonary edema (CPE), acute heart failure (AHF), and cardiac arrest (CA). Lee Revised Cardiac Risk Index (RCRI), Vascular Quality Initiative (VQI-CRI), Vascular Study Group of New England (VSG-CRI), and South African Vascular Surgical (SAVS-CRI) Cardiac Risk Indexes were calculated and analyzed. We performed multivariate logistic regression to assess independent predictors with calculation of odds ratio (OR) and 95% confidence interval (CI). To reduce overfitting, we used leave-one-out cross-validation approach. The Predictive ability of scores was tested using area under receiver operating characteristic curve (AUROC). Results. A total of 928 patients were included. We observed 81 MACE (28 MI, 22 arrhythmias, 10 CPE, 9 AHF, 12 CA) in 60 patients (6.5%): 3.3% in intermediate-risk surgery and 9.8% in high-risk surgery. Previous history of coronary artery disease (OR = 3.2, CI = 1.8-5.7), atrial fibrillation (OR = 5.1, CI = 2.4-11.0), insulin-treated diabetes mellitus (OR = 3.26, CI = 1.51-7.06), mechanical ventilation (OR = 2.75, CI = 1.41-4.63), and heart rate (OR = 1.02, CI = 1.01-1.03) at admission were considered independent risk factors in multivariate analysis. The AUROC of our model was 0.79, compared with RCRI (0.66), VSG-CRI (0.69), VQI-CRI (0.71), and SAVS-CRI (0.73). Conclusions. Observed MACE were within predicted range (1% to 5% after intermediate-risk surgery and >5% after high-risk surgery). SAVS-CRI and VQI-CRI had slightly better predictive capacity than VSG-CRI or RCRI.

Time and Latitude in South Africa

1972 ◽  
Vol 1 ◽  
pp. 27-38
Author(s):  
J. Hers
Keyword(s):  
South Africa ◽  
Cape Town ◽  
Astronomical Observations ◽  
Royal Observatory ◽  
The Republic ◽  
Astronomical Determination ◽  
Limited Accuracy ◽  
Better Than

In South Africa the modern outlook towards time may be said to have started in 1948. Both the two major observatories, The Royal Observatory in Cape Town and the Union Observatory (now known as the Republic Observatory) in Johannesburg had, of course, been involved in the astronomical determination of time almost from their inception, and the Johannesburg Observatory has been responsible for the official time of South Africa since 1908. However the pendulum clocks then in use could not be relied on to provide an accuracy better than about 1/10 second, which was of the same order as that of the astronomical observations. It is doubtful if much use was made of even this limited accuracy outside the two observatories, and although there may – occasionally have been a demand for more accurate time, it was certainly not voiced.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals Procalcitonin Correlates With but Is Not Superior to Other Diagnostic Markers of Bacterial Pneumonia

2020 ◽  
Author(s):  
Nadia Ayala-Lopez ◽  
David R Peaper ◽  
Roa Harb
Keyword(s):  
Respiratory Tract ◽  
Lower Respiratory Tract ◽  
Bacterial Pneumonia ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Chest Imaging ◽  
Wbc Count ◽  
Tract Infections ◽  
Guided Therapy

Abstract Objectives Despite extensive research on procalcitonin (PCT)-guided therapy in lower respiratory tract infections, the association between PCT and bacterial pneumonia remains unclear. Methods We evaluated retrospectively the performance of PCT in patients presenting with lower respiratory tract infection symptoms and grouped by seven diagnoses. All patients had microbial testing, chest imaging, and CBC counts within 1 day of PCT testing. Results Median PCT level in patients diagnosed with bacterial pneumonia was significantly higher than in patients diagnosed with other sources of infections or those not diagnosed with infections. Median PCT levels were not different among patients grouped by type or quantity of pathogen detected. They were significantly higher in patients with higher pathogenicity scores for isolated bacteria, those with abnormal WBC count, and those with chest imaging consistent with bacterial pneumonia. A diagnostic workup that included imaging, WBC count, and Gram stain had an area under the receiver operating characteristic curve of 0.748, and the addition of PCT increased it to 0.778. Conclusions PCT was higher in patients diagnosed with bacterial pneumonia. Less clear is its diagnostic ability to detect bacterial pneumonia over and above imaging and laboratory data routinely available to clinicians.

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