Delirium is a neuropsychiatric syndrome commonly encountered in critically ill patients, and systemic inflammation has been strongly implicated to underlie its pathophysiology. This study aimed to investigate the predictive value of the platelet-to-lymphocyte ratio (PLR) for delirium in the intensive care unit (ICU).In this retrospective observational study, we analyzed the clinical and laboratory data of 319 ICU patients from October 2016 to December 2017. Using the Locally Weighted Scatterplot Smoothing technique, a PLR knot was detected at a value of approximately 100. Logistic regression was used to investigate the association between the PLR and delirium.Of the 319 patients included in this study, 29 (9.1%) were diagnosed with delirium. In the delirium group, the duration of mechanical ventilation was significantly longer than that in the no-delirium group (40.2 ± 65.5 vs. 19.9 ± 26.5 hours, respectively; P < .001). A multiple logistic regression analysis showed that PLR > 100 (odds ratio [OR]: 1.003, 95% confidence interval [CI]: 1.001–1.005), age (OR: 2.76, 95% CI: 1.110–6.861), and the ratio of arterial oxygen partial pressure to the inspired oxygen fraction (OR: 0.996, 95% CI: 0.992–0.999) were independent predictors of delirium.In our study, a high PLR value on ICU admission was associated with a higher incidence of delirium. Owing to easy calculability, the PLR could be a useful delirium predictive index in ICUs, thereby enabling early interventions to be implemented.
Sepsis-associated thrombocytopenia (SAT) is a common complication in the intensive care unit (ICU), which significantly increases the mortality rate and leads to poor prognosis of diseases. Machine learning (ML) is widely used in disease prediction in critically ill patients. Here, we aimed to establish prediction models for platelet decrease and severe platelet decrease in ICU patients with sepsis based on four common ML algorithms and identify the best prediction model. The research subjects were 1,455 ICU sepsis patients admitted to Dongyang People's Hospital affiliated with Wenzhou Medical University from January 1, 2015, to October 31, 2019. Basic clinical demographic information, biochemical indicators, and clinical outcomes were recorded. The prediction models were based on four ML algorithms: random forest, neural network, gradient boosting machine, and Bayesian algorithms. Thrombocytopenia was found to occur in 732 patients (49.7%). The mechanical ventilation time and length of ICU stay were longer, and the mortality rate was higher for the thrombocytopenia group than for the non-thrombocytopenia group. The models were validated on an online international database (Medical Information Mart for Intensive Care III). The areas under the receiver operating characteristic curves (AUCs) of the four models for the prediction of thrombocytopenia were between 0.54 and 0.72. The AUCs of the models for the prediction of severe thrombocytopenia were between 0.70 and 0.77. The neural network and gradient boosting machine models effectively predicted the occurrence of SAT, and the Bayesian models had the best performance in predicting severe thrombocytopenia. Therefore, these models can be used to identify such high-risk patients at an early stage and guide individualized clinical treatment, to improve the prognosis of diseases.
BackgroundEarly enteral nutrition (EN) is associated with shorter hospital stay and lower infection and mortality rates in patients with intracerebral haemorrhage. However, high-energy support always causes clinical complications, such as diarrhoea and aspiration pneumonia, and the true benefit of high-energy support in these patients has not been investigated. The appropriate amount of energy support still needs further investigation. Therefore, we are performing a randomised controlled trial to investigate whether early low-energy EN can decrease mortality and feeding-related complications and improve neurological outcomes as compared with high-energy EN in traumatic intracerebral haemorrhage (TICH) patients.Methods/analysisThis is a randomised, single-blind clinical trial performed in one teaching hospital. 220 TICH patients will be randomly allocated to one of two groups in a 1:1 ratio: an intervention group, and a control group. The intervention group will receive early low-energy EN (10 kcal/kg/day) and the control group will receive high-energy EN (25 kcal/kg/day) for 7 days. All these patients will be followed up for 90 days. The primary outcome is all-cause 90-day mortality. Secondary outcomes include the modified Rankin score, Glasgow Outcome Scale (GOS) and the National Institutes of Health Stroke Scale (NIHSS). Outcomes will be assessed at admission, 7, 30 and 90 days after onset of this trial. The safety of EN strategies will be assessed every day during hospitalisation.Ethics and disseminationThe trial will be conducted in accordance with the Declaration of Helsinki and has been approved by the ethics committee of Dongyang People’s Hospital. The findings will be published in peer-reviewed medical journals.Trial registration numberChiCTR-INR-17011384; Pre-results.
Acute kidney injury (AKI) often occurs in patients in the intensive care unit (ICU). AKI duration is closely related to the prognosis of critically ill patients. Identifying the disease course length in AKI is critical for developing effective individualised treatment. To predict persistent AKI at an early stage based on a machine learning algorithm and integrated models. Overall, 955 patients admitted to the ICU after surgery complicated by AKI were retrospectively evaluated. The occurrence of persistent AKI was predicted using three machine learning methods: a support vector machine (SVM), decision tree, and extreme gradient boosting and with an integrated model. External validation was also performed. The incidence of persistent AKI was 39.4–45.1%. In the internal validation, SVM exhibited the highest area under the receiver operating characteristic curve (AUC) value, followed by the integrated model. In the external validation, the AUC values of the SVM and integrated models were 0.69 and 0.68, respectively, and the model calibration chart revealed that all models had good performance. Critically ill patients with AKI after surgery had high incidence of persistent AKI. Our machine learning model could effectively predict the occurrence of persistent AKI at an early stage.
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