A hybrid mortality prediction model of patients with cavitary pulmonary carcinomas

Mapping Intimacies ◽

10.1101/341156 ◽

2018 ◽

Author(s):

Maliazurina Saad

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Mortality Prediction ◽

Added Value ◽

Medical Community ◽

Clinical Predictors ◽

Clinical Covariates ◽

Mortality Prediction Model ◽

Roc Area ◽

High Degree

AbstractTo date, developing a reliable mortality prediction model remains challenging. Although clinical predictors like age, gender and laboratory results are of considerable predictive value, the accuracy often ranges only between 60-80%. In this study, we proposed prediction models built on the basis of clinical covariates with adjustment for additional variables that was radiographically induced. The proposed method exhibited a high degree of prediction accuracy of between 83-92%, as well as overall improvement of between 6-20% in all other metrics, such as ROC Area, False Positive Rates, Recall and Root Mean Square Error. We provide a proof of concept that there is an added value for incorporating the additional variables while predicting 24-month mortality in pulmonary carcinomas patients with cavitary lesions. It is hoped that the findings will be clinically useful to the medical community.

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Abstract 238: Validation of In-hospital Mortality Prediction Models for Patients with Heart Failure

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.8.suppl_2.238 ◽

2015 ◽

Vol 8 (suppl_2) ◽

Author(s):

Tara Lagu ◽

Mihaela Stefan ◽

Quinn Pack ◽

Auras Atreya ◽

Mohammad A Kashef ◽

...

Keyword(s):

Heart Failure ◽

Hospital Mortality ◽

Prediction Model ◽

Clinical Data ◽

Prediction Models ◽

Mortality Prediction ◽

Mortality Prediction Model ◽

Mortality Prediction Models ◽

Clinical Models ◽

Administrative Model

Background: Mortality prediction models, developed with the goal of improving risk stratification in hospitalized heart failure (HF) patients, show good performance characteristics in the datasets in which they were developed but have not been validated in external populations. Methods: We used a novel multi-hospital dataset [HealthFacts (Cerner Corp)] derived from the electronic health record (years 2010-2012). We examined the performance of four published HF inpatient mortality prediction models developed using data from: the Acute Decompensated Heart Failure National Registry (ADHERE), the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study, and the Get With the Guidelines-Heart Failure (GWTG-HF) registry. We compared to an administrative HF mortality prediction model (Premier model) that includes selected patient demographics, comorbidities, prior heart failure admissions, and therapies administered (e.g., inotropes, mechanical ventilation) in the first 2 hospital days. We also compared to a model that uses clinical data but is not heart failure-specific: the Laboratory-Based Acute Physiology Score (LAPS2). We included patients aged ≥18 years admitted with HF to one of 62 hospitals in the database. We applied all 6 models to the data and calculated the c-statistics. Results: We identified 13,163 patients ≥18 years old with a diagnosis of heart failure. Median age was 74 years; approximately half were women; 65% of patients were white and 27% were black. In-hospital mortality was 4.3%. Bland-Altman plots revealed that, at higher predicted mortality, the Premier model outperformed the clinical models. Discrimination of the models varied: ADHERE model (0.68); EFFECT (0.70); GWTG-HF, Peterson (0.69); GWTG-HF, Eapen (0.70); LAPS2 (0.74); Premier (0.81) (Figure). Conclusions: Clinically-derived inpatient heart failure mortality models exhibited similar performance with c statistics hovering around 0.70. A generic clinical mortality prediction model (LAPS2) had slightly better performance, as did a detailed administrative model. Any of these models may be useful for severity adjustment in comparative effectiveness studies of heart failure patients. When clinical data are not available, the administrative model performs similarly to clinical models.

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Machine learning-based mortality prediction model for heat-related illness

Scientific Reports ◽

10.1038/s41598-021-88581-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yohei Hirano ◽

Yutaka Kondo ◽

Toru Hifumi ◽

Shoji Yokobori ◽

Jun Kanda ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Prediction Models ◽

Characteristic Curve ◽

Vital Signs ◽

Mortality Prediction ◽

Apache Ii ◽

Apache Ii Score ◽

Support Vector ◽

Mortality Prediction Model

AbstractIn this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set for development (n = 1516, data from 2014, 2017–2019) and the test set (n = 877, data from 2020) for validation. Twenty-four variables including characteristics of patients, vital signs, and laboratory test data at hospital arrival were trained as predictor features for machine learning. The outcome was death during hospital stay. In validation, the developed machine learning models (logistic regression, support vector machine, random forest, XGBoost) demonstrated favorable performance for outcome prediction with significantly increased values of the area under the precision-recall curve (AUPR) of 0.415 [95% confidence interval (CI) 0.336–0.494], 0.395 [CI 0.318–0.472], 0.426 [CI 0.346–0.506], and 0.528 [CI 0.442–0.614], respectively, compared to that of the conventional acute physiology and chronic health evaluation (APACHE)-II score of 0.287 [CI 0.222–0.351] as a reference standard. The area under the receiver operating characteristic curve (AUROC) values were also high over 0.92 in all models, although there were no statistical differences compared to APACHE-II. This is the first demonstration of the potential of machine learning-based mortality prediction models for heat-related illnesses.

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To TQIP or Not to TQIP? that is the Question

The American Surgeon ◽

10.1177/000313481408000422 ◽

2014 ◽

Vol 80 (4) ◽

pp. 386-390

Author(s):

Jiselle Bock Heaney ◽

Chrissy Guidry ◽

Eric Simms ◽

Jennifer Turney ◽

Peter Meade ◽

...

Keyword(s):

Quality Improvement ◽

Prediction Model ◽

Trauma Center ◽

Prediction Models ◽

Mortality Prediction ◽

Improvement Program ◽

Injury Score ◽

Mortality Prediction Model ◽

Mortality Prediction Models ◽

Abbreviated Injury Score

The Trauma Quality Improvement Program (TQIP) reports a feasible mortality prediction model. We hypothesize that our institutional characteristics differ from TQIP aggregate data, questioning its applicability. We conducted a 2-year (2008 to 2009) retrospective analysis of all trauma activations at a Level 1 trauma center. Data were analyzed using TQIP methodology (three groups: blunt single system, blunt multisystem, and penetrating) to develop a mortality prediction model using multiple logistic regression. These data were compared with TQIP data. Four hundred fifty-seven patients met TQIP inclusion criteria. Penetrating and blunt trauma differed significantly at our institution versus TQIP aggregates (61.9 vs 7.8%; 38.0 vs 92.2%, P < 0.01). There were more firearm mechanisms of injury and less falls compared with TQIP aggregates (28.9 vs 4.2%; 8.5 vs 34.8%, P < 0.01). All other mechanisms were not significantly different. Variables significant in the TQIP model but not found to be predictors of mortality included Glasgow Coma Score motor 2 to 5, systolic blood pressure greater than 90 mmHg, age, initial pulse rate in the emergency department, mechanism of injury, head Abbreviated Injury Score, and abdominal Abbreviated Injury Score. External benchmarking of trauma center performance using mortality prediction models is important in quality improvement for trauma patient care. From our results, TQIP methodology from the pilot study may not be applicable to all institutions.

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Development of a Machine Learning Model for the Prediction of the Real Time Mortality in Patients in the Intensive Care Unit

10.21203/rs.3.rs-1066192/v1 ◽

2021 ◽

Author(s):

Jaeyoung Yang ◽

Hong-Gook Lim ◽

Wonhyeong Park ◽

Dongseok Kim ◽

Jin Sun Yoon ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Learning Model ◽

Mortality Prediction ◽

Machine Learning Model ◽

Mortality Prediction Model

Abstract BackgroundPrediction of mortality in intensive care units is very important. Thus, various mortality prediction models have been developed for this purpose. However, they do not accurately reflect the changing condition of the patient in real time. The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using four easy-to-collect vital signs.MethodsTwo independent retrospective observational cohorts were included in this study. The primary training cohort included the data of 1968 patients admitted to the intensive care unit at the Veterans Health Service Medical Center, Seoul, South Korea, from January 2018 to March 2019. The external validation cohort comprised the records of 409 patients admitted to the medical intensive care unit at Seoul National University Hospital, Seoul, South Korea, from January 2019 to December 2019. Datasets of four vital signs (heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation [SpO2]) measured every hour for 10 h were used for the development of the machine learning model. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset.ResultsThe machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. Thus, to investigate the importance of variables that influence the performance of the machine learning model, machine learning models were generated for each observation time or vital sign using the RF algorithm. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89). ConclusionsThe mortality prediction model developed in this study using data from only four types of commonly recorded vital signs is simpler than any existing mortality prediction model. This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.

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