Background:Inadvertent intensive care unit(ICU)readmission is associated with longer length of stay and increased mortality.Conversely,delayed ICU discharge may represent inefficient use of resources.To better inform ...Background:Inadvertent intensive care unit(ICU)readmission is associated with longer length of stay and increased mortality.Conversely,delayed ICU discharge may represent inefficient use of resources.To better inform discharge timing,several hospitals have implemented machine learning models to predict readmission risk following discharge.However,these models are typically created locally and may not generalize well to other hospitals or patient populations.A single multi-hospital-based model might provide more accurate predictions and insight into features that are applicable across diverse clinical settings.Methods:This study involved a retrospective multi-center cohort from one academic hospital(Amsterdam University Medical Center[AUMC])and two large teaching hospitals(Maasstad Ziekenhuis[MSZ]and OLVG).Data from the latter two hospitals were combined to create a pooled model,which was tested on the academic hospital dataset.Data relating to all adult ICU patients were included,starting from the implementation of the electronic health record system until the commencement of model development for each hospital.An XGBoost model was trained to predict a composite outcome of readmission or mortality within 7 days and an autoencoder was used as an out-of-distribution(OOD)detector to capture dataset heterogeneity.Results:In total,44,837 patients were available for analysis across the three hospitals.The average readmission rates were 7.1%,6.9%,and 5.9%for MSZ,OLVG,and AUMC,respectively.Performance evaluation of the local models on AUMC data demonstrated weighted area under the receiver operating characteristic curves of 69.7%±0.8%,70.5%±0.5%,and 76.5%±1.9%,respectively,whereas the pooled model achieved a weighted area under the receiver operating characteristic curves of 71.1%±0.7%.The difference between internal and external performance was reduced when cardiac surgery patients were excluded.The key features across models were albumin levels and the use of oxygen therapy.Discussion:A single,multi-hospital-based model performed comparably on external datasets,especially when cardiac surgery patients were excluded.However,when applied externally,model predictions risk being uncalibrated for specific patient subgroups and require careful calibration before implementation.While external models were more stable than local ones over OOD scores,their performance was comparable after excluding cardiac surgery patients.Although pooling data marginally improved performance on external datasets,the incorporation of data from diverse hospitals is likely to provide greater benefits.展开更多
Background Sepsis,defined as life-threatening organ dysfunction due to dysregulated host response to an infection,often requiring intensive care treatment.There is a strong rationale for the administration of corticos...Background Sepsis,defined as life-threatening organ dysfunction due to dysregulated host response to an infection,often requiring intensive care treatment.There is a strong rationale for the administration of corticosteroids for immunomodulation;however,clinical trials are inconclusive,which may be attributable to heterogeneity in therapeutic effects between individual patients.Leveraging deep learning within a causality framework,we aimed to identify for which intensive care patients with sepsis corticosteroids lead to improved survival.Methods We trained the treatment agnostic representation network(TARNet)to estimate the reduction in predicted probability of 28-day mortality following initiation of corticosteroid treatment of intensive care patients with sepsis.We used the freely available and public AmsterdamUMCdb ICU database for causal model development,considering 19 predictor variables from the first 24 h of admission,and validated the model with Medical Information Mart for Intensive Care(MIMIC-IV)version 2.2 data.A cut-off of 10%reduction in predicted probability of mortality was used to classify treatment responders.Results According to the Sepsis-3 criteria,a total of 2920 admissions in AmsterdamUMCdb were eligible.Of these,1378 were assigned to the intervention group and 1542 to the control group.Internal validation of predictions of the observed outcomes showed an area under the receiver operating characteristic curve(AUROC)of 0.79,while external validation yielded an AUROC of 0.71.Covariate balance of the TARNet model latent representation,as measured by the Wasserstein distance,was 3.6×10^(-7)for the internal data set and 4.2×10^(-7)for the external data set.Based on the estimated reduction of predicted mortality,a distinction was made between treatment responders(n=245),non-responders(n=2098),and those predicted to be harmed by corticosteroid treatment(n=577).Conclusions Corticosteroid treatment responders were those with severe metabolic acidosis and impaired circulation,whereas patients who were less ill based on these parameters were more likely to have increased mortality rates by corticosteroid treatment.There was also a notable discrepancy between the model’s suggestions and the physicians’treatment that was carried out,implying improvements in the clinical assessment of patients with sepsis are necessary.Given recent years have not yielded new treatments for sepsis,computational clinical decision-support systems are worth exploring.展开更多
文摘Background:Inadvertent intensive care unit(ICU)readmission is associated with longer length of stay and increased mortality.Conversely,delayed ICU discharge may represent inefficient use of resources.To better inform discharge timing,several hospitals have implemented machine learning models to predict readmission risk following discharge.However,these models are typically created locally and may not generalize well to other hospitals or patient populations.A single multi-hospital-based model might provide more accurate predictions and insight into features that are applicable across diverse clinical settings.Methods:This study involved a retrospective multi-center cohort from one academic hospital(Amsterdam University Medical Center[AUMC])and two large teaching hospitals(Maasstad Ziekenhuis[MSZ]and OLVG).Data from the latter two hospitals were combined to create a pooled model,which was tested on the academic hospital dataset.Data relating to all adult ICU patients were included,starting from the implementation of the electronic health record system until the commencement of model development for each hospital.An XGBoost model was trained to predict a composite outcome of readmission or mortality within 7 days and an autoencoder was used as an out-of-distribution(OOD)detector to capture dataset heterogeneity.Results:In total,44,837 patients were available for analysis across the three hospitals.The average readmission rates were 7.1%,6.9%,and 5.9%for MSZ,OLVG,and AUMC,respectively.Performance evaluation of the local models on AUMC data demonstrated weighted area under the receiver operating characteristic curves of 69.7%±0.8%,70.5%±0.5%,and 76.5%±1.9%,respectively,whereas the pooled model achieved a weighted area under the receiver operating characteristic curves of 71.1%±0.7%.The difference between internal and external performance was reduced when cardiac surgery patients were excluded.The key features across models were albumin levels and the use of oxygen therapy.Discussion:A single,multi-hospital-based model performed comparably on external datasets,especially when cardiac surgery patients were excluded.However,when applied externally,model predictions risk being uncalibrated for specific patient subgroups and require careful calibration before implementation.While external models were more stable than local ones over OOD scores,their performance was comparable after excluding cardiac surgery patients.Although pooling data marginally improved performance on external datasets,the incorporation of data from diverse hospitals is likely to provide greater benefits.
文摘Background Sepsis,defined as life-threatening organ dysfunction due to dysregulated host response to an infection,often requiring intensive care treatment.There is a strong rationale for the administration of corticosteroids for immunomodulation;however,clinical trials are inconclusive,which may be attributable to heterogeneity in therapeutic effects between individual patients.Leveraging deep learning within a causality framework,we aimed to identify for which intensive care patients with sepsis corticosteroids lead to improved survival.Methods We trained the treatment agnostic representation network(TARNet)to estimate the reduction in predicted probability of 28-day mortality following initiation of corticosteroid treatment of intensive care patients with sepsis.We used the freely available and public AmsterdamUMCdb ICU database for causal model development,considering 19 predictor variables from the first 24 h of admission,and validated the model with Medical Information Mart for Intensive Care(MIMIC-IV)version 2.2 data.A cut-off of 10%reduction in predicted probability of mortality was used to classify treatment responders.Results According to the Sepsis-3 criteria,a total of 2920 admissions in AmsterdamUMCdb were eligible.Of these,1378 were assigned to the intervention group and 1542 to the control group.Internal validation of predictions of the observed outcomes showed an area under the receiver operating characteristic curve(AUROC)of 0.79,while external validation yielded an AUROC of 0.71.Covariate balance of the TARNet model latent representation,as measured by the Wasserstein distance,was 3.6×10^(-7)for the internal data set and 4.2×10^(-7)for the external data set.Based on the estimated reduction of predicted mortality,a distinction was made between treatment responders(n=245),non-responders(n=2098),and those predicted to be harmed by corticosteroid treatment(n=577).Conclusions Corticosteroid treatment responders were those with severe metabolic acidosis and impaired circulation,whereas patients who were less ill based on these parameters were more likely to have increased mortality rates by corticosteroid treatment.There was also a notable discrepancy between the model’s suggestions and the physicians’treatment that was carried out,implying improvements in the clinical assessment of patients with sepsis are necessary.Given recent years have not yielded new treatments for sepsis,computational clinical decision-support systems are worth exploring.
