Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Roimi, Michael; Gutman, Rom; Somer, Jonathan; Arie, Asaf Ben; Calman, Ido; Bar-Lavie, Yaron; Gelbshtein, Udi; Liverant-Taub, Sigal; Ziv, Arnona; Eytan, Danny; Gorfine, Malka; Shalit, Uri

doi:10.1093/jamia/ocab005

Cited by 35 publications

(29 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The next generation of bed capacity predictions tools should use patient bed pathways with local information on length of stay. To the best of our knowledge, there are only three other published models available which used patient bed pathways [33][34][35]. The first one used the pathways "Ward", "Ward, ICU (not ventilated)", "Ward, ICU (ventilated)", "Ward, ICU (not ventilated), Ward", "Ward, ICU (ventilated), Ward" [33].…”

Section: Results In Contextmentioning

confidence: 99%

“…On the other hand, the advantage of our analysis is that our code and length of stay data are both publicly available (https://github.com/ qleclerc/COVID_bed_occupancy). As for the second model, it focused on machine-learning methods to estimate transition probabilities between the clinical states moderate/severe and critical, and is hence not comparable [34]. The third model is closely aligned to our own work here, as it uses multi-state methods to estimate length of stay using a local hospital and a national dataset from the UK [35].…”

Section: Results In Contextmentioning

confidence: 99%

See 1 more Smart Citation

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Leclerc

Fuller

Keogh

et al. 2021

BMC Health Serv Res

View full text Add to dashboard Cite

Background Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient’s “bed pathway” - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy. Methods We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020. Results In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: “Ward, CC, Ward”, “Ward, CC”, “CC” and “CC, Ward”. Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities. Conclusions We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19. Trial registration The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

show abstract

Section: Results In Contextmentioning

confidence: 99%

Section: Results In Contextmentioning

confidence: 99%

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Leclerc

Fuller

Keogh

et al. 2021

BMC Health Serv Res

View full text Add to dashboard Cite

show abstract

“…The best results were found using XGBoost and achieved an AUC of 0.953 for a small dataset ( 15 ). In addition, the illness trajectory (moderate, severe, critical – states as defined by Israeli Ministry of Health) of COVID-19 patients was predicted by Roimi et al with an AUC of 0.88 using only patients' age, sex and day-by-day clinical state using a multistate Cox regression-based model ( 16 ).…”

Section: Related Workmentioning

confidence: 99%

Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada

Snider

McBean

Yawney

et al. 2021

Front. Public Health

View full text Add to dashboard Cite

The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and 90+. Findings resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions.

show abstract

“…Studies have shown that risk factors, such as obesity, sex, and age, are highly correlated with adverse outcomes in COVID-19 patients. 2–7 Furthermore, recent studies suggest such risk factors also may affect certain aspects of COVID-19 progression, specifically disease onset, 8 hospital utilization, 9 and time-to-death. 10 However, the effects of individual patient characteristics on the entire course of COVID-19 progression during a patient’s hospitalization is still not well characterized.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors

Soper

Cadena

Nguyen

et al. 2022

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective The study sought to investigate the disease state–dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of SARS-CoV-2 coronavirus infections. Materials and Methods A covariate-dependent, continuous-time hidden Markov model with four states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1,362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs and laboratory test results were retrospectively evaluated to infer a patient’s clinical progression. Results The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state. Discussion Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients. Conclusion Dynamic risk stratification models have the potential not only to improve clinical outcomes in COVID-19, but also a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.

show abstract

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Cited by 35 publications

References 21 publications

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada

Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors

Contact Info

Product

Resources

About