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 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 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):  
Operating Characteristic ◽  
Prediction Models ◽  
Average Length ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Average Length Of Stay ◽  
Failure Assessment ◽  
Referral Hospitals ◽  
Tertiary Hospitals ◽  
Larger Sample

Abstract BackgroundReasons for obstetric admission in intensive care unit (ICU) 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.MethodsWe 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 by the quick Sequential Organ Failure Assessment (qSOFA) and Modified Early Obstetric Warning Score (MEOWS). ResultsObstetric 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 of admission. Mortality was 54.3% while the average length of stay was 6.6 days. MEOWS was an independent predictor to mortality (adjusted OR=1.25[1.07-1.46]; p=0.005). Similarly, one point of increase of the qSOFA increased odds of ICU mortality by 181% (adjusted OR=2.81[1.25-6.30]; p=0.012). The Area Under the Receiver Operating Characteristic Curve (AUROC) for MEOWS was 0.773[0.666-0.880], p=0.0001 and that of the qSOFA was 0.764[0.654-0.873]; p=0.0001.ConclusionSepsis is the most common reason for obstetric admissions to ICU in Rwanda. MEOWS and qSOFA could accurately predict the mortality for those patients but further explorations on a larger sample size are guaranteed.

Predictive Models of Intensive Care Unit Mortality - Severity of Illness Scores or Artificial Intelligence instruments? - Literature Review and Metanalysis (Preprint)

2021 ◽  
Author(s):  
Cristina Barboi ◽  
Andreas Tzavelis ◽  
Lutfiyya NaQiyba Muhammad
Keyword(s):  
Artificial Intelligence ◽  
Prediction Models ◽  
Meta Analysis ◽  
Severity Of Illness ◽  
Mortality Prediction ◽  
Risk Of Bias ◽  
Icu Mortality ◽  
Mortality Prediction Models ◽  
High Degree ◽  
Degree Of Heterogeneity

BACKGROUND The Severity of Illness Scores (SIS)- Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Sequential Organ Failure Assessment (SOFA) - are current risk stratification and mortality prediction tools used in Intensive Care Units (ICU) across the globe, and rely on scores that assess disease severity on admission. Developers of Artificial Intelligence (AI) or Machine Learning (ML) models predictive of ICU mortality use the SIS performance as a reference point when reporting the performance of these computational constructs. OBJECTIVE Using systematic review and meta-analysis, we evaluated studies that compare ML-based mortality prediction models to SIS-based models. The review should inform clinicians regarding the prognostic value of ML-based ICU mortality prediction models compared with SIS models and their validity in supporting clinical decision-making. METHODS We performed a systematic search using PubMed, Scopus, Embase, and IEEE databases. Studies that report the performance of newly developed ML models predictive of ICU mortality and compare it with the performance of SIS models on the same datasets were eligible for inclusion. ML and the SIS models with a reported Area Under the Receiver Operating Characteristic (AUROC) curve were included in the meta-analysis to identify the group with superior performance. Data were extracted with guidance from the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist[1] and was appraised for risk of bias and applicability using PROBAST (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies ) [2]. RESULTS After screening the literature, we identified and included 20 papers containing 47 ML models based on seven types of algorithms that were compared with three types of SIS models. The AUROC for predicting ICU mortality ranged between 0.828-0.875 for ML-based models and between 0.707-0.760 for SI-based models. We noted substantial heterogeneity among the models reported, and considerable variation among the AUROC estimates for both ML and SIS model types. Due to the high degree of heterogeneity, we performed a limited random-effect meta-analysis of externally validated subgroups of ML models and the subgroups of SIS used for comparison. CONCLUSIONS ML-based models can accurately predict ICU mortality as an alternative to traditional scoring models. The high degree of heterogeneity observed within and between studies limit the assessment of pooled results. The differences in development strategies, validation, statistical, and computational methods that these models rely on impede a head-to-head comparison, and we cannot declare the superiority of one model over the other. Consequently, we make no recommendation regarding the ML-based ICU mortality prediction models’ performance in clinical practice. To bridge the knowledge gap from design to practice, ML model developers must provide explainer models and make those knowledge objects reproducible, interoperable, and transparent[3]. CLINICALTRIAL the review was registered and approved by the international prospective register of systematic reviews, PROSPERO (reference number CRD42021203871).

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 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 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 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.

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