Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Abstract: Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside

Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation

Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers. This is the Author Accepted Manuscript, accepted after peer review by GigaByte

Pneumomediastinum and (bilateral) pneumothorax after high energy trauma: Indications for emergency bronchoscopy

High energy trauma may cause injury to tracheobronchial structures. This is sometimes difficult to diagnose immediately. Pneumomediastinum and (bilateral) pneumothorax seen on a CT-scan of the thorax may suggest possible damage to central airways. Emergency bronchoscopy should be performed to detect and locate a possible tracheobronchial injury

Amino Acid Loss during Continuous Venovenous Hemofiltration in Critically Ill Patients

BACKGROUND/OBJECTIVES: During continuous venovenous hemofiltration (CVVH), there is unwanted loss of amino acids (AA) in the ultrafiltrate (UF). Solutes may also be removed by adsorption to the filter membrane. The aim was to quantify the total loss of AA via the CVVH circuit using a high-flux polysulfone membrane and to differentiate between the loss by ultrafiltration and adsorption. METHODS: Prospective observational study in ten critically ill patients, receiving predilution CVVH with a new filter, blood flow 180 mL/min, and predilution flow 2,400 mL/h. Arterial blood, postfilter blood, and UF samples were taken at baseline, and 1, 8, and 24-h after CVVH initiation, to determine AA concentrations and hematocrit. Mass transfer calculations were used to determine AA loss in the filter and by UF, and the difference between these 2. RESULTS: The median AA loss in the filter was 10.4 g/day, the median AA loss by UF was 13.4 g/day, and the median difference was -2.9 g/day (IQR -5.9 to -1.4 g/day). For the individual AA, the difference ranged from -1 g/day to +0.4 g/day, suggesting that some AA were consumed or adsorbed and others were generated. AA losses did not significantly change over the 24-h study period. CONCLUSION: During CVVH with a modern polysulfone membrane, the estimated AA loss was 13.4 g/day, which corresponds to a loss of about 11.2 g of protein per day. Adsorption did not play a major role. However, individual AA behaved differently, suggesting complex interactions and processes at the filter membrane or peripheral AA production

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects