Survival of mechanically ventilated ward patients and association with organisational factors: a multicentre prospective study

ObjectivesDetermine 90-day mortality of mechanically ventilated ward patients outside the intensive care unit (ICU) and its association with organisational factors.DesignMulticentre prospective observational study of mechanically ventilated ward patients. Modified Poisson regression was used to assess association between nurse to patient ratio (NPR) and 90-day mortality, adjusted for designated medical team, Society of Critical Care Medicine (SCCM) triage priority and centre effect. NPR was divided into low (1:9.6 to 1:10), medium (1:6 to 1:8) and high (1:2.6). Sensitivity analysis was conducted for pneumonia with or without acute respiratory distress syndrome (ARDS) to assess magnitude of association.Setting7 acute public hospitals in Hong Kong.ParticipantsAll 485 mechanically ventilated patients in wards from participating hospitals between 18 January 2016 and 17 April 2016 were recruited. Three hundred patients were included after excluding patients with limitation of therapy within 24 hours of intubation.Main outcomes90-day mortality, Mortality Prediction Model III Standardised mortality ratio (MPMIII0 SMR).Results201 patients died within 90 days after intubation (67.0%, 95% CI 61.5% to 72.1%), with MPMIII0 SMR 1.88, 95% CI 1.63 to 2.17. Compared with high NPR, medium and low NPRs were associated with higher risk of 90-day mortality (adjusted relative risk (RRadj) 1.84, 95% CI 1.70 to 1.99 and 1.64, 95% CI 1.47 to 1.83, respectively). For 114 patients with pneumonia with or without ARDS, low to medium NPR, too sick to benefit from ICU (SCCM priority 4b), no ICU consultation and designated medical team were associated with risk of 90-day mortality (RRadj 1.49, 95% CI 1.40 to 1.58; RRadj 1.60, 95% CI 1.49 to 1.72; RRadj 1.34, 95% CI 1.27 to 1.40; RRadj 0.85, 95% CI 0.78 to 0.93, respectively).ConclusionThe 90-day mortality rates of mechanically ventilated ward patients were high. NPR was an independent predictor of survival for mechanically ventilated ward patients.

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

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.

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying

Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.