scholarly journals Addition of admission lactate levels to Baux score improves mortality prediction in severe burns

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ingrid Steinvall ◽  
Moustafa Elmasry ◽  
Islam Abdelrahman ◽  
Ahmed El-Serafi ◽  
Folke Sjöberg
Keyword(s):  
Prediction Models ◽  
Total Body Surface Area ◽  
Simplified Acute Physiology Score ◽  
Mortality Prediction ◽  
Saps 3 ◽  
Severe Burns ◽  
Crude Mortality ◽  
Mortality Prediction Models ◽  
Total Body ◽  
Lactate Levels

AbstractRisk adjustment and mortality prediction models are central in optimising care and for benchmarking purposes. In the burn setting, the Baux score and its derivatives have been the mainstay for predictions of mortality from burns. Other well-known measures to predict mortality stem from the ICU setting, where, for example, the Simplified Acute Physiology Score (SAPS 3) models have been found to be instrumental. Other attempts to further improve the prediction of outcome have been based on the following variables at admission: Sequential Organ Failure Assessment (aSOFA) score, determinations of aLactate or Neutrophil to Lymphocyte Ratio (aNLR). The aim of the present study was to examine if estimated mortality rate (EMR, SAPS 3), aSOFA, aLactate, and aNLR can, either alone or in conjunction with the others, improve the mortality prediction beyond that of the effects of age and percentage total body surface area (TBSA%) burned among patients with severe burns who need critical care. This is a retrospective, explorative, single centre, registry study based on prospectively gathered data. The study included 222 patients with median (25th–75th centiles) age of 55.0 (38.0 to 69.0) years, TBSA% burned was 24.5 (13.0 to 37.2) and crude mortality was 17%. As anticipated highest predicting power was obtained with age and TBSA% with an AUC at 0.906 (95% CI 0.857 to 0.955) as compared with EMR, aSOFA, aLactate and aNLR. The largest effect was seen thereafter by adding aLactate to the model, increasing AUC to 0.938 (0.898 to 0.979) (p < 0.001). Whereafter, adding EMR, aSOFA, and aNLR, separately or in combinations, only marginally improved the prediction power. This study shows that the prediction model with age and TBSA% may be improved by adding aLactate, despite the fact that aLactate levels were only moderately increased. Thereafter, adding EMR, aSOFA or aNLR only marginally affected the mortality prediction.

scholarly journals High mortality rate of obstetric critically ill women in Rwanda and its predictability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amyl Lucille Cassidy ◽  
Théogène Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Mortality Prediction ◽  
Routine Practice ◽  
Icu Mortality ◽  
Resource Limited ◽  
Obstetric Haemorrhage ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission to intensive care units (ICUs) for obstetric patients vary from one setting to another. Outcomes from ICU and prediction models are not well explored in Rwanda owing to lack of appropriate scores. This study aimed to assess reasons for admission and accuracy of prediction models for mortality of obstetric patients admitted to ICUs of two public tertiary hospitals in Rwanda. Methods We prospectively collected data from all obstetric patients admitted to the ICUs of the two public tertiary hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admission, demographic and clinical characteristics, outcome including death and its predictability by both the Modified Early Obstetric Warning Score (MEOWS) and quick Sequential Organ Failure Assessment (qSOFA). We analysed the accuracy of mortality prediction models by MEOWS or qSOFA by using logistic regression adjusting for factors associated with mortality. Area under the Receiver Operating characteristic (AUROC) curves is used to show the predicting capacity for each individual tool. Results Obstetric patients (n = 94) represented 12.8 % of all 747 ICU admissions which is 1.8 % of all 4.999 admitted women for pregnancy or labor. Sepsis (n = 30; 31.9 %) and obstetric haemorrhage (n = 24; 25.5 %) were the two commonest reasons for ICU admission. Overall ICU mortality for obstetric patients was 54.3 % (n = 51) with average length of stay of 6.6 ± 7.525 days. MEOWS score was an independent predictor of mortality (adjusted (a)OR 1.25; 95 % CI 1.07–1.46) and so was qSOFA score (aOR 2.81; 95 % CI 1.25–6.30) with an adjusted AUROC of 0.773 (95 % CI 0.67–0.88) and 0.764 (95 % CI 0.65–0.87), indicating fair accuracy for ICU mortality prediction in these settings of both MEOWS and qSOFA scores. Conclusions Sepsis and obstetric haemorrhage were the commonest reasons for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA scores could accurately predict ICU mortality of obstetric patients in resource-limited settings, but larger studies are needed before a recommendation for their use in routine practice in similar settings.

scholarly journals Equitably Allocating Resources During Crises: Racial Differences in Mortality Prediction Models

2021 ◽  
Author(s):  
Deepshikha Charan Ashana ◽  
George L Anesi ◽  
Vincent X Liu ◽  
Gabriel J Escobar ◽  
Christopher Chesley ◽  
...  
Keyword(s):  
Racial Differences ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Mortality Prediction Models

scholarly journals High mortality rate of obstetric critically ill patients in Rwanda and its predictability

2020 ◽  
Author(s):  
Alcade Rudakemwa ◽  
Amy Lucille Cassidy ◽  
Theogene Twagirumugabe
Keyword(s):  
Prediction Models ◽  
Average Length ◽  
Roc Curves ◽  
Mortality Prediction ◽  
Icu Mortality ◽  
Average Length Of Stay ◽  
Referral Hospitals ◽  
Tertiary Hospitals ◽  
Mortality Prediction Models ◽  
Qsofa Score

Abstract Background Reasons for admission at the intensive care units (ICU) for obstetric patients vary from a setting to another. Outcomes from ICU and its prediction models are not well explored in Rwanda because of lack of appropriate scores. This study intended to assess profile and accuracy of predictive models for obstetric patients admitted in ICU in the two public tertiary hospitals in Rwanda.Methods We prospectively collected data from all obstetric patients admitted in the ICU of public referral hospitals in Rwanda from March 2017 to February 2018 to identify reasons for admissions and factors for prognosis. We analysed the accuracy of mortality prediction models including the quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Obstetric Warning Score (MEOWS) by using the Logistic Regression and adjusted Receiver Operating characteristic (ROC) curves. Results Obstetric patients represented 12.8% of all ICU admissions and 1.8% of all deliveries. Sepsis (31.9%) and haemorrhage (25.5%) were the two commonest reasons for ICU admission in our study participants. The overall ICU mortality for our obstetric patients was 54.3% while the average length of stay was 6.6 days. MEOWS score was an independent predictor to mortality (adjusted OR=1.25[1.07-1.46]; p=0.005) and so was the qSOFA score (adjusted OR=2.81[1.25-6.30]; p=0.012). The adjusted Area Under the ROC (AUROC) for MEOWS was 0.773[0.666-0.880] and that of the qSOFA was 0.764[0.654-0.873] signing fair accuracies for ICU mortality prediction in these settings for both models.Conclusion Sepsis is the commonest reason for admissions to ICU for obstetric patients in Rwanda. Simple models comprising MEOWS and qSOFA could accurately predict the mortality for those patients but further larger studies are needed before generalization.

scholarly journals Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China

2020 ◽  
Vol 71 (16) ◽  
pp. 2079-2088 ◽  
Author(s):  
Kun Wang ◽  
Peiyuan Zuo ◽  
Yuwei Liu ◽  
Meng Zhang ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Gradient Boosting ◽  
Laboratory Model ◽  
Training Cohort ◽  
Clinical Model ◽  
Extreme Gradient Boosting ◽  
Mortality Prediction Models

Abstract Background This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). Methods The training cohort included consecutive COVID-19 patients at the First People’s Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. Results A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80–.95); threshold, −2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92–.99); threshold, −2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68–.93) and 0.88 (.75–.96) for the clinical model and laboratory model, respectively. Conclusions We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

scholarly journals A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults

2020 ◽  
Author(s):  
Márlon Juliano Romero Aliberti ◽  
Kenneth E Covinsky ◽  
Flavia Barreto Garcez ◽  
Alexander K Smith ◽  
Pedro Kallas Curiati ◽  
...  
Keyword(s):  
Older Adults ◽  
Prediction Models ◽  
Mortality Prediction ◽  
Added Value ◽  
University Hospital ◽  
Early Warning Score ◽  
Risk Levels ◽  
Time To Death ◽  
Mortality Prediction Models ◽  
Key Aspects

Abstract Background Although coronavirus disease 2019 (COVID-19) disproportionally affects older adults, the use of conventional triage tools in acute care settings ignores the key aspects of vulnerability. Objective This study aimed to determine the usefulness of adding a rapid vulnerability screening to an illness acuity tool to predict mortality in hospitalised COVID-19 patients. Design Cohort study. Setting Large university hospital dedicated to providing COVID-19 care. Participants Participants included are 1,428 consecutive inpatients aged ≥50 years. Methods Vulnerability was assessed using the modified version of PRO-AGE score (0–7; higher = worse), a validated and easy-to-administer tool that rates physical impairment, recent hospitalisation, acute mental change, weight loss and fatigue. The baseline covariates included age, sex, Charlson comorbidity score and the National Early Warning Score (NEWS), a well-known illness acuity tool. Our outcome was time-to-death within 60 days of admission. Results The patients had a median age of 66 years, and 58% were male. The incidence of 60-day mortality ranged from 22% to 69% across the quartiles of modified PRO-AGE. In adjusted analysis, compared with modified PRO-AGE scores 0–1 (‘lowest quartile’), the hazard ratios (95% confidence interval) for 60-day mortality for modified PRO-AGE scores 2–3, 4 and 5–7 were 1.4 (1.1–1.9), 2.0 (1.5–2.7) and 2.8 (2.1–3.8), respectively. The modified PRO-AGE predicted different mortality risk levels within each stratum of NEWS and improved the discrimination of mortality prediction models. Conclusions Adding vulnerability to illness acuity improved accuracy of predicting mortality in hospitalised COVID-19 patients. Combining tools such as PRO-AGE and NEWS may help stratify the risk of mortality from COVID-19.

scholarly journals Predicting mortality in hemodialysis patients using machine learning analysis

2020 ◽  
Author(s):  
Victoria Garcia-Montemayor ◽  
Alejandro Martin-Malo ◽  
Carlo Barbieri ◽  
Francesco Bellocchio ◽  
Sagrario Soriano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Prediction Models ◽  
Initial Period ◽  
Area Under The Curve ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Prediction Of Mortality ◽  
Mortality Prediction Models

Abstract Background Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session. Results There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

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