Development and validation of prediction models for neurocognitive disorders in adult patients admitted to the ICU with sleep disturbance

BACKGROUND The impact of the COVID-19 pandemic on health care utilisation and associated costs has been significant, with one in ten patients becoming severely ill and being admitted to hospital with serious complications during the first wave of the pandemic. Risk prediction models can help health care providers identify high-risk patients in their populations and intervene to improve health outcomes and reduce associated costs. OBJECTIVE To develop and validate a hospitalisation risk prediction model for adult patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). METHODS The model was developed using pre-linked and standardised data of adult patients with laboratory confirmed SARS-CoV-2 from Cerner’s population health management platform (HealtheIntent®) in the London Borough of Lewisham. A total of 14,203 patients who tested positive for SARS-CoV-2 between 1st March 2020 and 28th February 2021 were included in the development and internal validation cohorts. A second temporal validation cohort covered the period between 1st March 2021 to 30th April 2021. The outcome variable was hospital admission in adult patients with laboratory confirmed SARS-CoV-2. A generalised linear model was used to train the model. The predictive performance of the model was assessed using the area under the receiver operator characteristic curve (ROC-AUC). RESULTS Overall, 14,203 patients were included. Of those, 9,755 (68.7%) were assigned to the development cohort, 2,438 (17.2%) to the internal validation cohort, and 2,010 (14.1%) to the temporal validation cohort. A total of 917 (9.4%) patients were admitted to hospital in the development cohort, 210 (8.6%) in the internal validation cohort, and a further 204 (10.1%) in the temporal validation cohort. The model had a ROC-AUC of 0.85 in both the development and validation cohorts. The most predictive factors were older age, male sex, Asian or Other ethnic minority background, obesity, chronic kidney disease, hypertension and diabetes. CONCLUSIONS The COVID-19 hospitalisation risk prediction model demonstrated very good performance and can be used to stratify risk in the Lewisham population to help providers reduce unnecessary hospital admissions and associated costs, improve patient outcomes, and target those at greatest risk to ensure full vaccination against SARS-CoV-2. Further research may examine the external validity of the model in other populations.

Download Full-text

Development and validation of prediction models for blood concentrations of dioxins and PCBs using dietary intakes

Environment International ◽

10.1016/j.envint.2012.09.003 ◽

2012 ◽

Vol 50 ◽

pp. 15-21 ◽

Cited By ~ 19

Author(s):

Helen Engelstad Kvalem ◽

Anne Lise Brantsæter ◽

Helle Margrete Meltzer ◽

Hein Stigum ◽

Cathrine Thomsen ◽

...

Keyword(s):

Prediction Models ◽

Dietary Intakes ◽

Blood Concentrations ◽

Development And Validation

Download Full-text

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

JMIR Medical Informatics ◽

10.2196/30022 ◽

2021 ◽

Vol 9 (9) ◽

pp. e30022

Author(s):

Ann Corneille Monahan ◽

Sue S Feldman

Keyword(s):

Emergency Department ◽

Hospital Admission ◽

Real Time ◽

Prediction Models ◽

Assessment Tool ◽

Predictive Performance ◽

Patient Data ◽

Adult Patients ◽

Emergency Department Crowding ◽

Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

Download Full-text

Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study

BMJ ◽

10.1136/bmj.k4079 ◽

2018 ◽

pp. k4079 ◽

Cited By ~ 1

Keyword(s):

Cohort Study ◽

Subarachnoid Haemorrhage ◽

Outcome Prediction ◽

Prediction Models ◽

Aneurysmal Subarachnoid Haemorrhage ◽

Development And Validation

Download Full-text

Development and validation of prediction models for the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes

Metabolism ◽

10.1016/j.metabol.2018.02.003 ◽

2018 ◽

Vol 85 ◽

pp. 38-47 ◽

Cited By ~ 4

Author(s):

Tsai-Chung Li ◽

Chia-Ing Li ◽

Chiu-Shong Liu ◽

Wen-Yuan Lin ◽

Chih-Hsueh Lin ◽

...

Keyword(s):

Type 2 Diabetes ◽

Hospital Mortality ◽

Prediction Models ◽

Development And Validation

Download Full-text

Prediction models for the risk of total knee replacement: development and validation using data from multicentre cohort studies

The Lancet Rheumatology ◽

10.1016/s2665-9913(21)00324-6 ◽

2022 ◽

Author(s):

Qiang Liu ◽

Hongling Chu ◽

Michael P LaValley ◽

David J Hunter ◽

Hua Zhang ◽

...

Keyword(s):

Total Knee Replacement ◽

Cohort Studies ◽

Knee Replacement ◽

Prediction Models ◽

Total Knee ◽

Using Data ◽

Development And Validation ◽

Multicentre Cohort

Download Full-text

Evaluating Modeling and Validation Strategies for Tooth Loss

Journal of Dental Research ◽

10.1177/0022034519864889 ◽

2019 ◽

Vol 98 (10) ◽

pp. 1088-1095 ◽

Cited By ~ 2

Author(s):

J. Krois ◽

C. Graetz ◽

B. Holtfreter ◽

P. Brinkmann ◽

T. Kocher ◽

...

Keyword(s):

Tooth Loss ◽

Predictive Power ◽

Prediction Models ◽

Recursive Partitioning ◽

External Validation ◽

Model Development ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Complex Models ◽

Development And Validation

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients’ age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Download Full-text