scholarly journals Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction

2020 ◽  
Vol 27 (9) ◽  
pp. 1393-1400
Author(s):  
Timothy Bergquist ◽  
Yao Yan ◽  
Thomas Schaffter ◽  
Thomas Yu ◽  
Vikas Pejaver ◽  
...  
Keyword(s):  
Predictive Models ◽  
Performance Metrics ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Patient Privacy ◽  
University Of Washington ◽  
Mortality Prediction Models ◽  
The University

Abstract Objective The development of predictive models for clinical application requires the availability of electronic health record (EHR) data, which is complicated by patient privacy concerns. We showcase the “Model to Data” (MTD) approach as a new mechanism to make private clinical data available for the development of predictive models. Under this framework, we eliminate researchers’ direct interaction with patient data by delivering containerized models to the EHR data. Materials and Methods We operationalize the MTD framework using the Synapse collaboration platform and an on-premises secure computing environment at the University of Washington hosting EHR data. Containerized mortality prediction models developed by a model developer, were delivered to the University of Washington via Synapse, where the models were trained and evaluated. Model performance metrics were returned to the model developer. Results The model developer was able to develop 3 mortality prediction models under the MTD framework using simple demographic features (area under the receiver-operating characteristic curve [AUROC], 0.693), demographics and 5 common chronic diseases (AUROC, 0.861), and the 1000 most common features from the EHR’s condition/procedure/drug domains (AUROC, 0.921). Discussion We demonstrate the feasibility of the MTD framework to facilitate the development of predictive models on private EHR data, enabled by common data models and containerization software. We identify challenges that both the model developer and the health system information technology group encountered and propose future efforts to improve implementation. Conclusions The MTD framework lowers the barrier of access to EHR data and can accelerate the development and evaluation of clinical prediction models.

scholarly journals Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine

2021 ◽  
Author(s):  
Timothy Bergquist ◽  
Thomas Schaffter ◽  
Yao Yan ◽  
Thomas Yu ◽  
Justin Prosser ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Improve Patient Care ◽  
Mortality Prediction ◽  
Direct Access ◽  
Healthcare Outcomes ◽  
Patient Privacy ◽  
Mortality Prediction Models ◽  
And Performance ◽  
Similar Accuracy

AbstractApplications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

scholarly journals Predicting mortality in intensive care patients with acute renal failure treated with dialysis.

1997 ◽  
Vol 8 (1) ◽  
pp. 111-117
Author(s):  
C E Douma ◽  
W K Redekop ◽  
J H van der Meulen ◽  
R W van Olden ◽  
J Haeck ◽  
...  
Keyword(s):  
Renal Failure ◽  
Acute Renal Failure ◽  
Intensive Care ◽  
Mortality Rate ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Dialysis Treatment ◽  
Apache Iii ◽  
Mortality Prediction Models

Existing prognostic methods were compared in their ability to predict mortality in intensive care unit (ICU) patients on dialysis for acute renal failure (ARF). The clinical goal of this study was to determine whether these models could identify a group of patients where dialysis would provide no benefit because of a near 100% certainty of death even with dialysis treatment. This retrospective cohort study included 238 adult patients who received a first dialysis treatment for ARF in the ICU. This study examined the performance of seven general ICU mortality prediction models and four mortality prediction models developed for patients with ARF. These models were assessed for their ability to discriminate mortality form survival and for their ability to calibrate the observed mortality rate with the expected mortality rate. The observed in hospital mortality was 76% for our patient group. Areas under the receiver operating characteristic curve ranged from 0.50 to 0.78. With the Acute Physiology and Chronic Health Evaluation (APACHE) III and the Liano models, the observed mortality in the highest quintiles of risk were 97% and 98%. In conclusion, although none of the models examined in this study showed excellent discrimination between those patients who died in hospital and those who did not, some models (APACHE III, Liano) were able to identify a group of patients with a near 100% chance of mortality. This indicates that these models may have some use in supporting the decision not to initiate dialysis in a subgroup of patients.

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 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.

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