Critical Care Database Comprising Patients With Infection

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https://physionet.org/content/icu-infection-zigong-fourth/1.0/).

Associations Between Elevated Systolic Blood Pressure and Outcomes in Critically Ill Patients: A Retrospective Cohort Study and Propensity Analysis

INTRODUCTION

Studies have shown nonlinear relationships between systolic blood pressure (SBP) and outcomes, with increased risk observed at both low and high blood pressure levels. However, the relationships between cumulative times at different SBP levels and outcomes in critically ill patients remain unclear. We hypothesized that an appropriate SBP level is associated with a decrease in adverse outcomes after intensive care unit (ICU) admission.

METHODS

This study was a retrospective analysis of data from the Medical Information Mart for Intensive Care (MIMIC) III database, which includes more than 1,000,000 SBP records from 12,820 patients. Associations of cumulative times at four SBP ranges (<100 mm Hg, 100-120 mm Hg, 120-140 mm Hg, and ≥140 mm Hg) with mortality (12-, 3-, 1-month mortality and in-hospital mortality) were evaluated. Restricted cubic splines and multivariable Cox regression models were employed to assess associations between mortality and cumulative times at SBP levels (4 levels: <2, 2-12, 12-36, and ≥36 h) over 72 h of ICU admission. Additionally, 120 mm Hg to 140 mm Hg was subdivided into <12 h (Group L) and ≥12 h (Group M) subsets and subjected to propensity-score matching and subgroup analyses.

RESULTS

At 120 mm Hg to 140 mm Hg, level-4 SBP was associated with lower adjusted risks of mortality at 12 months (OR, 0.71; CI, 0.61-0.81), 3 months (OR, 0.72; CI, 0.61-0.85), and 1 month (OR, 0.61; CI, 0.48-0.79) and in the hospital (OR, 0.71; CI, 0.58-0.88) than level-1 SBP. The cumulative times at the other 3 SBP ranges (<100 mm Hg, 100-120 mm Hg, and ≥140 mm Hg) were not independent risk predictors of prognosis. Furthermore, Group M had lower 12-month mortality than Group L, which remained in the propensity-score matched and subgroup analyses.

CONCLUSIONS

SBP at 120 mm Hg to 140 mm Hg was associated with decreased adverse outcomes. Randomized trials are required to determine whether the outcomes in critically ill patients improve with early maintenance of a SBP level at 120 mm Hg to 140 mm Hg.

Effectiveness of anisodamine for the treatment of critically ill patients with septic shock: a multicentre randomized controlled trial

BACKGROUND

Septic shock is characterized by an uncontrolled inflammatory response and microcirculatory dysfunction. There is currently no specific agent for treating septic shock. Anisodamine is an agent extracted from traditional Chinese medicine with potent anti-inflammatory effects. However, its clinical effectiveness remains largely unknown.

METHODS

In a multicentre, open-label trial, we randomly assigned adults with septic shock to receive either usual care or anisodamine (0.1-0.5 mg per kilogram of body weight per hour), with the anisodamine doses adjusted by clinicians in accordance with the patients' shock status. The primary end point was death on hospital discharge. The secondary end points were ventilator-free days at 28 days, vasopressor-free days at 28 days, serum lactate and sequential organ failure assessment (SOFA) score from days 0 to 6. The differences in the primary and secondary outcomes were compared between the treatment and usual care groups with the χ² test, Student's t test or rank-sum test, as appropriate. The false discovery rate was controlled for multiple testing.

RESULTS

Of the 469 patients screened, 355 were assigned to receive the trial drug and were included in the analyses-181 patients received anisodamine, and 174 were in the usual care group. We found no difference between the usual care and anisodamine groups in hospital mortality (36% vs. 30%; p = 0.348), or ventilator-free days (median [Q1, Q3], 24.4 [5.9, 28] vs. 26.0 [8.5, 28]; p = 0.411). The serum lactate levels were significantly lower in the treated group than in the usual care group after day 3. Patients in the treated group were less likely to receive vasopressors than those in the usual care group (OR [95% CI] 0.84 [0.50, 0.93] for day 5 and 0.66 [0.37, 0.95] for day 6).

CONCLUSIONS

There is no evidence that anisodamine can reduce hospital mortality among critically ill adults with septic shock treated in the intensive care unit. Trial registration ClinicalTrials.gov ( NCT02442440 ; Registered on 13 April 2015).

The attenuation of Th1 and Th17 responses via autophagy protects against methicillin-resistant Staphylococcus aureus-induced sepsis

Whether autophagy affects methicillin-resistant Staphylococcus aureus (MRSA)-induced sepsis and the associated mechanisms are largely unknown. This study investigated the role of autophagy in MRSA-induced sepsis. The levels of microtubule-associated protein light chain 3 (LC3)-II/I, Beclin-1 and p62 after USA300 infection were examined by Western blotting and immunohistochemical staining. Bacterial burden analysis, hematoxylin-eosin staining, and Kaplan-Meier analysis were performed to evaluate the effect of autophagy on MRSA-induced sepsis. IFN-γ and IL-17 were analyzed by ELISA, and CD4⁺ T cell differentiation was assessed by flow cytometry. Our results showed that LC3-II/I and Beclin-1 were increased, while p62 was decreased after infection. Survival rates were decreased in the LC3B^-/- and Beclin-1^+/- groups, accompanied by worsened organ injuries and increased IFN-γ and IL-17 levels, whereas rapamycin alleviated organ damage, decreased IFN-γ and IL-17 levels, and improved the survival rate. However, there was no significant difference in bacterial burden. Flow cytometric analysis showed that rapamycin treatment decreased the frequencies of Th1 and Th17 cells, whereas these cells were upregulated in the LC3B^-/- and Beclin-1^+/- groups. Therefore, autophagy plays a protective role in MRSA-induced sepsis, which may be partly associated with the alleviation of organ injuries via the downregulation of Th1 and Th17 responses. These results provide a nonantibiotic treatment strategy for sepsis.

Assessment of respiratory system compliance under pressure control ventilation without an inspiratory pause maneuver

Objective. The measurement of the static compliance of the respiratory system (C_stat) during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.Approach. We propose a method to measure the quasi-static respiratory compliance (C_qstat) under pressure control ventilation mode without the inspiratory pause maneuver. First, a screening strategy was applied to filter out breaths affected strongly by spontaneous breathing efforts or artifacts. Then, we performed a virtual extrapolation of the flow-time waveform when the end-inspiratory flow was not zero, to allow for the calculation ofC_qstatfor each kept cycle. Finally, the outputC_qstatwas obtained as the average of the smallest 40C_qstatmeasurements. The proposed method was validated against the gold standardC_statmeasured from real clinical settings and compared with two reported algorithms. The gold standardC_statwas obtained by applying an end-inspiratory pause maneuver in the volume-control ventilation mode.Main results. Sixty-nine measurements from 36 patients were analyzed. The Bland-Altman analysis showed that the bias of agreement forC_qstatversus the gold standard measurement was -0.267 ml/cmH₂O (95% limits of agreement was -4.279 to 4.844 ml/cmH₂O). The linear regression analysis indicated a strong correlation (R² = 0.90) between theC_qstatand gold standard.Significance. The results showed that theC_qstatcan be accurately estimated from continuous ventilator waveforms, including spontaneous breathing without an inspiratory pause maneuver. This method promises to provide continuous measurements compliant with mechanical ventilation.

Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

BACKGROUND

Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class.

METHODS

Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset.

RESULTS

A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion.

CONCLUSIONS

Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.

Development and Validation of a Practical Model to Identify Patients at Risk of Bleeding After TAVR

OBJECTIVES

No standardized algorithm exists to identify patients at risk of bleeding after transcatheter aortic valve replacement (TAVR). The aim of this study was to generate and validate a useful predictive model.

BACKGROUND

Bleeding events after TAVR influence prognosis and quality of life and may be preventable.

METHODS

Using machine learning and multivariate regression, more than 100 clinical variables from 5,185 consecutive patients undergoing TAVR in the prospective multicenter RISPEVA (Registro Italiano GISE sull'Impianto di Valvola Aortica Percutanea; NCT02713932) registry were analyzed in relation to Valve Academic Research Consortium-2 bleeding episodes at 1 month. The model's performance was externally validated in 5,043 TAVR patients from the prospective multicenter POL-TAVI (Polish Registry of Transcatheter Aortic Valve Implantation) database.

RESULTS

Derivation analyses generated a 6-item score (PREDICT-TAVR) comprising blood hemoglobin and serum iron concentrations, oral anticoagulation and dual antiplatelet therapy, common femoral artery diameter, and creatinine clearance. The 30-day area under the receiver-operating characteristic curve (AUC) was 0.80 (95% confidence interval [CI]: 0.75-0.83). Internal validation by optimism bootstrap-corrected AUC was 0.79 (95% CI: 0.75-0.83). Score quartiles were in graded relation to 30-day events (0.8%, 1.1%, 2.5%, and 8.5%; overall p <0.001). External validation produced a 30-day AUC of 0.78 (95% CI: 0.72-0.82). A simple nomogram and a web-based calculator were developed to predict individual patient probabilities. Landmark cumulative event analysis showed greatest bleeding risk differences for top versus lower score quartiles in the first 30 days, when most events occurred. Predictivity was maintained when omitting serum iron values.

CONCLUSIONS

PREDICT-TAVR is a practical, validated, 6-item tool to identify patients at risk of bleeding post-TAVR that can assist in decision making and event prevention.

An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation

BACKGROUND AND OBJECTIVE

Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic.

METHODS

We proposed an interpretable one-dimensional convolutional neural network (1DCNN) to detect four most manifestation types of PVA (double triggering, ineffective efforts during expiration, premature cycling and delayed cycling) under pressure control ventilation mode and pressure support ventilation mode. A global average pooling (GAP) layer was incorporated with the 1DCNN model to highlight the sections of the respiratory waveform the model focused on when making a classification. Dilation convolution and batch normalization were introduced to the 1DCNN model for compensating the reduction of performance caused by the GAP layer.

RESULTS

The proposed interpretable 1DCNN exhibited comparable performance with the state-of-the-art deep learning model in PVA detection. The F1 scores for the detection of four types of PVA under pressure control ventilation and pressure support ventilation modes were greater than 0.96. The critical sections of the waveform used to detect PVA were highlighted, and found to be well consistent with the understanding of the respective type of PVA by experts.

CONCLUSIONS

The findings suggest that the proposed 1DCNN can help detect PVA, and enhance the interpretability of the classification process to help clinicians better understand the results obtained from deep learning technology.

Pancreatoduodenectomy within 2 weeks after endoscopic retrograde cholangio-pancreatography increases the risk of organ/space surgical site infections: a 5-year retrospective cohort study in a high-volume centre

BACKGROUND

Organ/space surgical site infections (OSSI) after pancreaticoduodenectomy (PD) are not rare events. The role of diagnosis and treatment for pancreatic and biliary diseases with an endoscopic retrograde cholangio-pancreatography (ERCP) procedure is currently controversial. However, the ERCP procedure might play a role in surgical outcomes after PD.

METHODS

We conducted a retrospective cohort study for patients who underwent PD in the First Affiliated Hospital with the Nanjing Medical University from 1st September 2012 to 31st January 2018. The relationship between ERCP exposure and OSSI after PD was analyzed by univariate and forward stepwise multivariate logistic regression model.

RESULTS

Of the 1,365 patients who underwent PD, 136 developed OSSI (10.0%). We found that ERCP exposure before PD (EEBPD) was significantly associated with an increased incidence rate of post-operative pancreas fistula (POPF) [24.2% (23/95) vs. 14.9% (189/1,270), risk ratio (RR) =1.63, 95% confidence interval (CI), 1.11-2.38, P=0.015]. Hypertension, a higher level of preoperative low-density lipoprotein (LDL) and creatinine (Cr) were associated with elevated risks of post-operative OSSI [adjusted odds ratio (Adj-OR) (95% CI) were 1.59 (1.09-2.32), 1.70 (1.16-2.51), 1.99 (1.36-2.92)], whereas a preoperatively higher level of aspartate aminotransferase (AST) would decrease the risk [Adj-OR (95% CI), 0.62 (0.42-0.91)]. Remarkably, EEBPD would significantly increase and more than double the OSSI risk [Adj-OR (95% CI), 2.56 (1.46-4.47)] especially if it was within 14 days before surgery (Spearman =-0.698, P<0.001).

CONCLUSIONS

ERCP, as an independent risk factor, significantly increased the risk of post-operative OSSI after PD if it is performed within 14 days prior to surgery. Our findings would assist clinical decision-making, and improve OSSI control and prevention.

Individualized Mechanical power-based ventilation strategy for acute respiratory failure formalized by finite mixture modeling and dynamic treatment regimen

BACKGROUND

Mechanical ventilation (MV) is the key to the successful treatment of acute respiratory failure (ARF) in the intensive care unit (ICU). The study aims to formalize the concept of individualized MV strategy with finite mixture modeling (FMM) and dynamic treatment regime (DTR).

METHODS

ARF patients requiring MV for over 48 h from 2008 to 2019 were included. FMM was conducted to identify classes of ARF. Static and dynamic mechanical power (MP_static and MP_dynamic) and relevant clinical variables were calculated/collected from hours 0 to 48 at an interval of 8 h. Δ M P was calculated as the difference between actual and optimal MP.

FINDINGS

A total of 8768 patients were included for analysis with a mortality rate of 27%. FFM identified three classes of ARF, namely, the class 1 (baseline), class 2 (critical) and class 3 (refractory respiratory failure). The effect size of MP_static on mortality is the smallest in class 1 (HR for every 5 Joules/min increase: 1.29; 95% CI: 1.15 to 1.45; p < 0.001) and the largest in class 3 (HR for every 5 Joules/min increase: 1.83; 95% CI: 1.52 to 2.20; p < 0.001).

INTERPRETATION

MP has differing therapeutic effects for subtypes of ARF. Optimal MP estimated by DTR model may help to improve survival outcome.

FUNDING

The study was funded by Health Science and Technology Plan of Zhejiang Province (2021KY745), Key Research & Development project of Zhejiang Province (2021C03071) and Yilu "Gexin" - Fluid Therapy Research Fund Project (YLGX-ZZ-2,020,005).

Risk factors for mortality of coronavirus disease 2019 (COVID-19) patients during the early outbreak of COVID-19: a systematic review and meta-analysis

BACKGROUND

Identification of risk factors for poor prognosis of patients with coronavirus disease 2019 (COVID-19) is necessary to enable the risk stratification and modify the patient's management. Thus, we performed a systematic review and meta-analysis to evaluate the in-hospital mortality and risk factors of death in COVID-19 patients.

METHODS

All studies were searched via the PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP, and Wanfang databases. The in-hospital mortality of COVID-19 patients was pooled. Odds ratios (ORs) or mean difference (MD) with 95% confidence intervals (CIs) were calculated for evaluation of risk factors.

RESULTS

A total of 80 studies were included with a pooled in-hospital mortality of 14% (95% CI: 12.2-15.9%). Older age (MD =13.32, 95% CI: 10.87-15.77; P<0.00001), male (OR =1.66, 95% CI: 1.37-2.01; P<0.00001), hypertension (OR =2.67, 95% CI: 2.08-3.43; P<0.00001), diabetes (OR =2.14, 95% CI: 1.76-2.6; P<0.00001), chronic respiratory disease (OR =3.55, 95% CI: 2.65-4.76; P<0.00001), chronic heart disease/cardiovascular disease (OR =3.15, 95% CI: 2.43-4.09; P<0.00001), elevated levels of high-sensitive cardiac troponin I (MD =66.65, 95% CI: 16.94-116.36; P=0.009), D-dimer (MD =4.33, 95% CI: 2.97-5.68; P<0.00001), C-reactive protein (MD =48.03, 95% CI: 27.79-68.27; P<0.00001), and a decreased level of albumin at admission (MD =-3.98, 95% CI: -5.75 to -2.22; P<0.0001) are associated with higher risk of death. Patients who developed acute respiratory distress syndrome (OR =62.85, 95% CI: 29.45-134.15; P<0.00001), acute cardiac injury (OR =25.16, 95% CI: 6.56-96.44; P<0.00001), acute kidney injury (OR =22.86, 95% CI: 4.60-113.66; P=0.0001), and septic shock (OR =24.09, 95% CI: 4.26-136.35; P=0.0003) might have a higher in-hospital mortality.

CONCLUSIONS

Advanced age, male, comorbidities, increased levels of acute inflammation or organ damage indicators, and complications are associated with the risk of mortality in COVID-19 patients, and should be integrated into the risk stratification system.

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Dynamic programming for solving a simulated clinical scenario of sepsis resuscitation

BACKGROUND

A major challenge in clinical research is population heterogeneity and we need to consider both historical response and current condition of an individual in considering medical decision making. The idea of precise medicine cannot be fully accounted for in traditional randomized controlled trials. Reinforcement learning (RL) is developing rapidly and has found its way into various fields including clinical medicine in which RL is employed to find an optimal treatment strategy. The key idea of RL is to optimize the treatment policy depending on the current state and previous treatment history, which is consistent with the idea behind dynamic programming (DP). DP is a prototype of RL and can be implemented when the system dynamics can be fully quantified.

METHODS

The present article aims to illustrate how to perform DP algorithm in a clinical scenario of Sepsis resuscitation. The state transition dynamics are constructed in the framework of Markov Decision Process. The state space is defined by mean arterial pressure (MAP) and lactate; the action space is comprised of fluid administration and vasopressor. The implementation of policy evaluation, policy improvement and iteration are explained with R code.

RESULTS

the DP algorithm was able to find the optimal treatment policy depending on the current states and previous conditions. The iteration process converged at finite steps. We defined several functions such as nextStep(), policyEval() and policy_iteration() to implement the DP algorithm.

CONCLUSIONS

This article illustrates how DP can be used to solve a clinical problem. We show that DP is a potential useful tool to tailor treatment strategy to patients with different conditions/states. Potential audience of the paper are those who are interested in using DP for solving clinical problems with dynamic changing states.

Efficacy and safety of corticosteroids for septic shock in immunocompromised patients: A cohort study from MIMIC

BACKGROUND

Corticosteroids have been widely used as adjunct therapy for septic shock for many decades, but both the efficacy and safety remain unclear. The study was designed to investigate overall benefits and potential risks of corticosteroids in immunocompromised patients with septic shock.

METHODS

The Medical Information Mart for Intensive Care III (MIMIC-III) database was employed to conduct a cohort study. Immunocompromised patients with septic shock were enrolled and categorized by whether exposure to intravenous corticosteroids. Cox Proportional-Hazards models were used to control for confounders and assess the relationship between corticosteroids use and mortality.

RESULTS

A total of 866 patients were enrolled in this study, including 395 in the corticosteroids group and 471 in the non-corticosteroids group. Corticosteroids infusion was not associated with improved 30-day mortality in overall immunocompromised population [34.7% vs 32.1%; adjusted hazard ratio (HR) 1.11, 95% confidence interval (CI) 0.87-1.43, p = 0.37]. The mortality effects were similar in 90-day, 180-day, 1-year and hospital mortality. For the subgroup of patients with metastatic cancer, corticosteroids infusion was associated with a statistically significant increase in the 30-day mortality risk (HR 1.58, 95% CI 1.06-2.37; p = 0.02). Corticosteroids had adverse effects on hemodynamic stability, prolonged ICU and hospital duration, and increased risk of hyperglycemia.

CONCLUSIONS

Corticosteroids therapy for the maintenance of blood pressure was not associated with improved mortality or hemodynamic stability in overall immunocompromised population with septic shock. Future randomized clinical trials are required to validate the effects of corticosteroids for septic shock in the special immunocompromised population.

Five-Year Comparative Efficacy of Everolimus-Eluting vs. Resolute Zotarolimus-Eluting Stents in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Among drug-eluting stents (DESs), the durable polymer everolimus-eluting stent (EES) and resolute zotarolimus-eluting stent (R-ZES) are widely used in clinical practice and have contributed to improve the outcomes of patients undergoing percutaneous coronary intervention (PCI). Few studies addressed their long-term comparative performance in patients with acute coronary syndrome (ACS). We aimed to investigate the 5 year comparative efficacy of EES and R-ZES in ACS. We queried ACTION-ACS, a large-scale database of ACS patients undergoing PCI. The treatment groups were analyzed using propensity score matching. The primary endpoint was a composite of mortality, myocardial infarction (MI), stroke, repeat PCI, and definite or probable stent thrombosis, which was addressed at the five-year follow-up. A total of 3497 matched patients were analyzed. Compared with R-ZES, a significant reduction in the primary endpoint at 5 years was observed in patients treated with EES (hazard ratio (HR) [95%CI] = 0.62 [0.54-0.71], p < 0.001). By landmark analysis, differences between the two devices emerged after the first year and were maintained thereafter. The individual endpoints of mortality (HR [95%CI] = 0.70 [0.58-0.84], p < 0.01), MI (HR [95%CI] = 0.55 [0.42-0.74], p < 0.001), and repeat PCI (HR [95%CI] = 0.65 [0.53-0.73], p < 0.001) were all significantly lower in the EES-treated patients. Stroke risk did not differ between EES and R-ZES. In ACS, a greater long-term clinical efficacy with EES vs. R-ZES was observed. This difference became significant after the first year of the ACS episode and persisted thereafter.

Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

OBJECTIVES

Light sedation is recommended over deep sedation for invasive mechanical ventilation to improve clinical outcome but may increase the risk of agitation. This study aimed to develop and prospectively validate an ensemble machine learning model for the prediction of agitation on a daily basis.

DESIGN

Variables collected in the early morning were used to develop an ensemble model by aggregating four machine learning algorithms including support vector machines, C5.0, adaptive boosting with classification trees, and extreme gradient boosting with classification trees, to predict the occurrence of agitation in the subsequent 24 hours.

SETTING

The training dataset was prospectively collected in 95 ICUs from 80 Chinese hospitals on May 11, 2016, and the validation dataset was collected in 20 out of these 95 ICUs on December 16, 2019.

PATIENTS

Invasive mechanical ventilation patients who were maintained under light sedation for 24 hours prior to the study day and who were to be maintained at the same sedation level for the next 24 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 578 invasive mechanical ventilation patients from 95 ICUs in 80 Chinese hospitals, including 459 in the training dataset and 119 in the validation dataset, were enrolled. Agitation was observed in 36% (270/578) of the invasive mechanical ventilation patients. The stepwise regression model showed that higher body temperature (odds ratio for 1°C increase: 5.29; 95% CI, 3.70-7.84; p < 0.001), greater minute ventilation (odds ratio for 1 L/min increase: 1.15; 95% CI, 1.02-1.30; p = 0.019), higher Richmond Agitation-Sedation Scale (odds ratio for 1-point increase: 2.43; 95% CI, 1.92-3.16; p < 0.001), and days on invasive mechanical ventilation (odds ratio for 1-d increase: 0.95; 95% CI, 0.93-0.98; p = 0.001) were independently associated with agitation in the subsequent 24 hours. In the validation dataset, the ensemble model showed good discrimination (area under the receiver operating characteristic curve, 0.918; 95% CI, 0.866-0.969) and calibration (Hosmer-Lemeshow test p = 0.459) in predicting the occurrence of agitation within 24 hours.

CONCLUSIONS

This study developed an ensemble model for the prediction of agitation in invasive mechanical ventilation patients under light sedation. The model showed good calibration and discrimination in an independent dataset.

Electronic healthcare records and external outcome data for hospitalized patients with heart failure

Heart failure is one of the most important reasons for hospitalization among elderly individuals and is associated with significant mortality and morbidity. Epidemiological studies require the establishment of high-quality databases. Several datasets that primarily involve heart failure populations have been established in Western countries and have generated many high-quality studies. However, no such dataset is available from China. Due to differences in genetic background and healthcare systems between China and Western countries, the establishment of a heart failure database for the Chinese population is urgently needed. We performed a retrospective single-center observational study to collect data regarding the characteristics of heart failure patients in China by integrating electronic healthcare records and follow-up outcome data. The study collected information for a total of 2,008 patients with heart failure, containing 166 attributes.

Tools Are Needed to Promote Sedation Practices for Mechanically Ventilated Patients

Suboptimal sedation practices continue to be frequent, although the updated guidelines for management of pain, agitation, and delirium in mechanically ventilated (MV) patients have been published for several years. Causes of low adherence to the recommended minimal sedation protocol are multifactorial. However, the barriers to translation of these protocols into standard care for MV patients have yet to be analyzed. In our view, it is necessary to develop fresh insights into the interaction between the patients' responses to nociceptive stimuli and individualized regulation of patients' tolerance when using analgesics and sedatives. By better understanding this interaction, development of novel tools to assess patient pain tolerance and to define and predict oversedation or delirium may promote better sedation practices in the future.

Application of artificial intelligence in pancreaticobiliary diseases

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

Latent transition analysis of cardiac arrest patients treated in the intensive care unit

BACKGROUND AND OBJECTIVE

Post-cardiac arrest (CA) syndrome is heterogenous in their clinical presentations and outcomes. This study aimed to explore the transition and stability of subphenotypes (profiles) of CA treated in the intensive care unit (ICU).

PATIENTS AND METHODS

Clinical features of CA patients on day 1 and 3 after ICU admission were modeled by latent transition analysis (LTA) to explore the transition between subphenotypes over time. The association between different transition patterns and mortality outcome was explored using multivariable logistic regression.

RESULTS

We identified 848 eligible patients from the database. The LPA identified three distinct subphenotypes: Profile 1 accounted for the largest proportion (73%) and was considered as the baseline subphenotype. Profile 2 (13%) was characterized by brain injury and profile 3 (14%) was characterized by multiple organ dysfunctions. The same three subphenotypes were identified on day 3. The LTA showed consistent subphenotypes. A majority of patients in profile 2 (72%) and 3 (82%) on day 1 switched to profile 1 on day 3. In the logistic regression model, patients in profile 1 on day 1 transitioned to profile 3 had worse survival outcome than those continue to remain in profile 1 (OR: 20.64; 95% CI: 6.01 to 70.94; p < 0.001) and transitioned to profile 2 (OR: 8.42; 95% CI: 2.22 to 31.97; p = 0.002) on day 3.

CONCLUSION

The study identified three subphenotypes of CA, which was consistent on day 1 and 3 after ICU admission. Patients who transitioned to profile 3 on day 3 had significantly worse survival outcome than those remained in profile 1 or 2.

Risk Potential for Organ Dysfunction Associated With Sodium Bicarbonate Therapy in Critically Ill Patients With Hemodynamic Worsening

Background: The role of sodium bicarbonate therapy (SBT) remains controversial. This study aimed to investigate whether hemodynamic status before SBT contributed to the heterogeneous outcomes associated with SBT in acute critically ill patients. Methods: We obtained data from patients with metabolic acidosis from the Medical Information Mart for Intensive Care (MIMIC)-III database. Propensity score matching (PSM) was applied to match the SBT group with the control group. Logistic regression and Cox regression were used to analyze a composite of newly "developed or exacerbated organ dysfunction" (d/eOD) within 7 days of ICU admission and 28-day mortality associated with SBT for metabolic acidosis. Results: A total of 1,765 patients with metabolic acidosis were enrolled, and 332 pairs obtained by PSM were applied to the final analyses in the study. An increased incidence of newly d/eOD was observed in the SB group compared with the control group (54.8 vs. 44.6%, p < 0.01). Multivariable logistic regression indicated that the adjusted OR of SBT for this composite outcome was no longer significant [OR (95% CI): 1.39 (0.9, 1.85); p = 0.164]. This effect of SBT did not change with the quintiles stratified by pH. Interestingly, SBT was associated with an increased risk of the composite of newly d/eOD in the subgroup of patients with worsening hemodynamics before SBT [adjusted OR (95% CI): 3.6 (1.84, 7.22), p < 0.001]. Moreover, the risk potential for this composite of outcomes was significantly increased in patients characterized by both worsening [adjusted OR (95% CI): 2.91 (1.54, 5.47), p < 0.001] and unchanged hemodynamics [adjusted OR (95% CI): 1.94 (1.01, 3.72), p = 0.046] compared to patients with improved hemodynamics before SBT. Our study failed to demonstrate an association between SBT and 28-day mortality in acute critically ill patients with metabolic acidosis. Conclusions: Our findings did not demonstrate an association between SBT and outcomes in critically ill patients with metabolic acidosis. However, patients with either worsening or unchanged hemodynamic status in initial resuscitation had a significantly higher risk potential of newly d/eOD subsequent to SBT.

Corticosteroid therapy in critically ill patients with COVID-19: a multicenter, retrospective study

BACKGROUND

Corticoid therapy has been recommended in the treatment of critically ill patients with COVID-19, yet its efficacy is currently still under evaluation. We investigated the effect of corticosteroid treatment on 90-day mortality and SARS-CoV-2 RNA clearance in severe patients with COVID-19.

METHODS

294 critically ill patients with COVID-19 were recruited between December 30, 2019 and February 19, 2020. Logistic regression, Cox proportional-hazards model and marginal structural modeling (MSM) were applied to evaluate the associations between corticosteroid use and corresponding outcome variables.

RESULTS

Out of the 294 critically ill patients affected by COVID-19, 183 (62.2%) received corticosteroids, with methylprednisolone as the most frequently administered corticosteroid (175 accounting for 96%). Of those treated with corticosteroids, 69.4% received corticosteroid prior to ICU admission. When adjustments and subgroup analysis were not performed, no significant associations between corticosteroids use and 90-day mortality or SARS-CoV-2 RNA clearance were found. However, when stratified analysis based on corticosteroid initiation time was performed, there was a significant correlation between corticosteroid use (≤ 3 day after ICU admission) and 90-day mortality (logistic regression adjusted for baseline: OR 4.49, 95% CI 1.17-17.25, p = 0.025; Cox adjusted for baseline and time varying variables: HR 3.89, 95% CI 1.94-7.82, p < 0.001; MSM adjusted for baseline and time-dependent variants: OR 2.32, 95% CI 1.16-4.65, p = 0.017). No association was found between corticosteroid use and SARS-CoV-2 RNA clearance even after stratification by initiation time of corticosteroids and adjustments for confounding factors (corticosteroids use ≤ 3 days initiation vs no corticosteroids use) using MSM were performed.

CONCLUSIONS

Early initiation of corticosteroid use (≤ 3 days after ICU admission) was associated with an increased 90-day mortality. Early use of methylprednisolone in the ICU is therefore not recommended in patients with severe COVID-19.

Five novel clinical phenotypes for critically ill patients with mechanical ventilation in intensive care units: a retrospective and multi database study

BACKGROUND

Although protective mechanical ventilation (MV) has been used in a variety of applications, lung injury may occur in both patients with and without acute respiratory distress syndrome (ARDS). The purpose of this study is to use machine learning to identify clinical phenotypes for critically ill patients with MV in intensive care units (ICUs).

METHODS

A retrospective cohort study was conducted with 5013 patients who had undergone MV and treatment in the Department of Critical Care Medicine, Peking Union Medical College Hospital. Statistical and machine learning methods were used. All the data used in this study, including demographics, vital signs, circulation parameters and mechanical ventilator parameters, etc., were automatically extracted from the electronic health record (EHR) system. An external database, Medical Information Mart for Intensive Care III (MIMIC III), was used for validation.

RESULTS

Phenotypes were derived from a total of 4009 patients who underwent MV using a latent profile analysis of 22 variables. The associations between the phenotypes and disease severity and clinical outcomes were assessed. Another 1004 patients in the database were enrolled for validation. Of the five derived phenotypes, phenotype I was the most common subgroup (n = 2174; 54.2%) and was mostly composed of the postoperative population. Phenotype II (n = 480; 12.0%) led to the most severe conditions. Phenotype III (n = 241; 6.01%) was associated with high positive end-expiratory pressure (PEEP) and low mean airway pressure. Phenotype IV (n = 368; 9.18%) was associated with high driving pressure, and younger patients comprised a large proportion of the phenotype V group (n = 746; 18.6%). In addition, we found that the mortality rate of Phenotype IV was significantly higher than that of the other phenotypes. In this subgroup, the number of patients in the sequential organ failure assessment (SOFA) score segment (9,22] was 198, the number of deaths was 88, and the mortality rate was higher than 44%. However, the cumulative 28-day mortality of Phenotypes IV and II, which were 101 of 368 (27.4%) and 87 of 480 (18.1%) unique patients, respectively, was significantly higher than those of the other phenotypes. There were consistent phenotype distributions and differences in biomarker patterns by phenotype in the validation cohort, and external verification with MIMIC III further generated supportive results.

CONCLUSIONS

Five clinical phenotypes were correlated with different disease severities and clinical outcomes, which suggested that these phenotypes may help in understanding heterogeneity in MV treatment effects.

Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

BACKGROUND

Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets.

METHODS

The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. Autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance.

FINDINGS

A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364]; p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707; 95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610; 95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681; 95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82]; p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09]; p = 0.211).

INTERPRETATION

Our study identified two classes of sepsis that showed different mortality rates and responses to hydrocortisone therapy. Class 1 was characterized by immunosuppression with higher mortality rate than class 2. We further developed a 5-gene class model to predict class membership.

FUNDING

The study was funded by the National Natural Science Foundation of China (Grant No. 81,901,929).

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Individualized fluid administration for critically ill patients with sepsis with an interpretable dynamic treatment regimen model

Fluid strategy is the key to the successful management of patients with sepsis. However, previous studies failed to consider individualized treatment strategy, and clinical trials typically included patients with sepsis as a homogeneous study population. We aimed to develop sequential decision rules for managing fluid intake in patients with sepsis by using the dynamic treatment regimen (DTR) model. A retrospective analysis of the eICU Collaborative Research Database comprising highly granular data collected from 335 units at 208 hospitals was performed. The DTR model used a backward induction algorithm to estimate the sequence of optimal rules. 22,868 patients who had sepsis according to the Acute Physiology and Chronic Health Evaluation (APACHE) IV diagnosis group were included. Optimal fluid management (liberal [> 40 ml/kg/d] versus restricted [< 40 ml/kg/d]) strategy were developed on the Day 1, 3 and 5 after ICU admission according to current states and treatment history. Important determinants of optimal fluid strategy included mean blood pressure, heart rate, previous urine output, previous fluid strategy, ICU type and mechanical ventilation. Different functional forms such as quadratic function and interaction terms were used at different stages. The proportion of subjects being inappropriately treated with liberal fluid strategy (i.e. those actually received liberal fluid strategy, but could have longer survival time if they received restricted fluid strategy) increased from day 1 to 5 (19.3% to 29.5%). The survival time could be significantly prolonged had all patients been treated with optimal fluid strategy (5.7 [2.0, 5.9] vs. 4.1 [2.0, 5.0] days; p < 0.001). With a large volume of sepsis data, we successfully computed out a sequence of dynamic fluid management strategy for sepsis patients over the first 5 days after ICU admission. The decision rules generated by the DTR model predicted a longer survival time compared to the true observed strategy, which sheds light for improving patient outcome with the aim from computer-assisted algorithm.

Gene correlation network analysis to identify regulatory factors in sepsis

BACKGROUND AND OBJECTIVES

Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments.

METHODS

The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA.

RESULTS

A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018).

CONCLUSIONS

The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

Identification of risk factors for mortality associated with COVID-19

OBJECTIVES

Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).

METHODS

This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.

RESULTS

A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.

CONCLUSIONS

Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

Clinical characteristics and treatment of critically ill patients with COVID-19 in Hebei

BACKGROUND

In December, 2019, a novel coronavirus disease 2019 (COVID-19) emerged in Wuhan, China. We aimed to clarify the epidemiology, laboratory examinations, imaging findings, and treatment of critically ill patients with COVID-19 in Hebei province, China.

METHODS

In this retrospective study, the demographic, laboratory and imaging, and treatment data of patients with severe COVID-19 treated in 13 designated hospitals in Hebei were collected and analyzed.

RESULTS

A total of 319 severe COVID-19 patients were treated at the 13 designated hospitals between 22 January, 2020 and 25 March, 2020. Eventually, 51 critically ill (31 severe cases and 20 critically severe cases) patients were included in the analysis. The patients had an average age of 58.9±13.7 years, and 27 (52.9%) were men. Twenty-one (41.2%) were familial cluster, and 33 (64.7%) had chronic illnesses. The patients in critically severe group had longer duration from symptom to confirmation, more severe infections, more severe lung injury, and a lower percentage of lymphocytes. All 51 patients received antiviral drugs, 47 (92.2%) received antibacterial agents, 49 (96.1%) received traditional Chinese drugs, and 46 (90.2%) received methylprednisolone. The critically severe patients received more fluid and more diuretic treatment; 14 (70.0%) required invasive mechanical ventilation, and 13 (65.0%) developed extrapulmonary complications.

CONCLUSIONS

COVID-19 patients who had underlying diseases and longer confirmation times were more likely to progress to critically severe COVID-19. These patients also presented with a higher risk of respiratory depression, circulatory collapse, extrapulmonary complications, and infection.

Thromboelastography as a tool for monitoring blood coagulation dysfunction after adequate fluid resuscitation can predict poor outcomes in patients with septic shock

BACKGROUND

Coagulation abnormalities are universal in patients with septic shock and likely play a key role in multiple organ dysfunction syndrome. Early diagnosis and management of sepsis-induced coagulopathy can influence the outcome. Thromboelastography (TEG) can effectively distinguish hypercoagulability and hypocoagulability in patients with septic shock. TEG may be a useful tool to objectively evaluate the degree and risk of sepsis.

METHODS

A total of 76 adult patients with septic shock were enrolled and divided into four groups: patients with hypotension requiring vasopressor and serum lactate level >2 mmol/L (group A), patients with hypotension requiring vasopressor and serum lactate level ≤2 mmol/L (group B), patients with mean arterial pressure ≥65 mmHg and serum lactate level >2 mmol/L (group C), and patients with mean arterial pressure ≥65 mmHg and serum lactate level ≤2 mmol/L (group D) after adequate fluid resuscitation. TEG values were obtained at the emergency room and after 6 hours of adequate fluid resuscitation. Data on fibrinogen (FIB) levels, international normalized ratio (INR), activated partial thromboplastin time (aPTT), blood gas, platelet count, and D-dimers were also collected.

RESULTS

The length of stay in the intensive care unit was 9.11 ± 5.36 days. Mortality rate was 6.58%. The values of reaction time, kinetics time, maximum amplitude, alpha angle, aPTT, INR, serum creatinine, FIB, and sepsis-related organ failure assessment (SOFA) score showed a significant differences. The results of the routine coagulation tests, blood gas volume, platelet count, procalcitonin level, D-dimer level, white blood cell count, creatinine level, disseminated intravascular coagulation score, SOFA score, and TEG values after adequate fluid resuscitation were significantly different between groups A and B, groups A and C, groups A and D, groups B and D, and groups C and D.

CONCLUSION

TEG is helpful in predicting the severity of sepsis and outcome of patients.

Dexmedetomidine For The Treatment Of Acute Lung Injury: A Fact Or Fiction?

Dexmedetomidine has been widely used in the intensive care unit (ICU), with the primary aim to keep patients on an appropriate level of sedation. Both observational and randomized controlled trials have observed that the use of dexmedetomidine is associated with improved outcomes for mechanically ventilated patients [1]. In ICU patients receiving prolonged mechanical ventilation, dexmedetomidine was not inferior to other sedatives in maintaining sedation level, but was associated with shortened MV duration and improved ability to communicate pain [2]. MV is an important factor for delirium and dexmedetomidine was found to be associated with lower risk of delirium [3, 4]. Prophylactic low-dose dexmedetomidine is able to reduce the occurrence of delirium during the first 7 days after surgery for patients aged over 65 years who are admitted to the ICU after surgery [4]. Thus, the beneficial effect of might be explained by the reduction of delirium in the treated group. In fact, delirium can be considered as a type of acute organ dysfunction mediated via inflammatory response. There has been evidence that inflammatory biomarkers such as C-reactive protein was positively correlated with the occurrence of delirium [5].

Prevalence and impact of acute renal impairment on COVID-19: a systematic review and meta-analysis

BACKGROUND

The aim of this study is to assess the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 patients and to determine the association of acute kidney injury (AKI) with the severity and prognosis of COVID-19 patients.

METHODS

The electronic database of Embase and PubMed were searched for relevant studies. A meta-analysis of eligible studies that reported the prevalence of abnormal urine analysis and kidney dysfunction in COVID-19 was performed. The incidences of AKI were compared between severe versus non-severe patients and survivors versus non-survivors.

RESULTS

A total of 24 studies involving 4963 confirmed COVID-19 patients were included. The proportions of patients with elevation of sCr and BUN levels were 9.6% (95% CI 5.7-13.5%) and 13.7% (95% CI 5.5-21.9%), respectively. Of all patients, 57.2% (95% CI 40.6-73.8%) had proteinuria, 38.8% (95% CI 26.3-51.3%) had proteinuria +, and 10.6% (95% CI 7.9-13.3%) had proteinuria ++ or +++. The overall incidence of AKI in all COVID-19 patients was 4.5% (95% CI 3.0-6.0%), while the incidence of AKI was 1.3% (95% CI 0.2-2.4%), 2.8% (95% CI 1.4-4.2%), and 36.4% (95% CI 14.6-58.3%) in mild or moderate cases, severe cases, and critical cases, respectively. Meanwhile, the incidence of AKI was 52.9%(95% CI 34.5-71.4%), 0.7% (95% CI - 0.3-1.8%) in non-survivors and survivors, respectively. Continuous renal replacement therapy (CRRT) was required in 5.6% (95% CI 2.6-8.6%) severe patients, 0.1% (95% CI - 0.1-0.2%) non-severe patients and 15.6% (95% CI 10.8-20.5%) non-survivors and 0.4% (95% CI - 0.2-1.0%) survivors, respectively.

CONCLUSION

The incidence of abnormal urine analysis and kidney dysfunction in COVID-19 was high and AKI is closely associated with the severity and prognosis of COVID-19 patients. Therefore, it is important to increase awareness of kidney dysfunction in COVID-19 patients.

Performance of the MEDS score in predicting mortality among emergency department patients with a suspected infection: a meta-analysis

OBJECTIVES

To carry out a meta-analysis to examine the prognostic performance of the Mortality in Emergency Department Sepsis (MEDS) score in predicting mortality among emergency department patients with a suspected infection.

METHODS

Electronic databases-PubMed, Embase, Scopus, EBSCO and the Cochrane Library-were searched for eligible articles from their respective inception through February 2019. Sensitivity, specificity, likelihood ratios and receiver operator characteristic area under the curve were calculated. Subgroup analyses were performed to explore the prognostic performance of MEDS in selected populations.

RESULTS

We identified 24 studies involving 21 246 participants. The pooled sensitivity of MEDS to predict mortality was 79% (95% CI 72% to 84%); specificity was 74% (95% CI 68% to 80%); positive likelihood ratio 3.07 (95% CI 2.47 to 3.82); negative likelihood ratio 0.29 (95% CI 0.22 to 0.37) and area under the curve 0.83 (95% CI 0.80 to 0.86). Significant heterogeneity was seen among included studies. Meta-regression analyses showed that the time at which the MEDS score was measured and the cut-off value used were important sources of heterogeneity.

CONCLUSION

The MEDS score has moderate accuracy in predicting mortality among emergency department patients with a suspected infection. A study comparison MEDS and qSOFA in the same population is needed.

Defining persistent critical illness based on growth trajectories in patients with sepsis

BACKGROUND

Persistent critical illness is common in critically ill patients and is associated with vast medical resource use and poor clinical outcomes. This study aimed to define when patients with sepsis would be stabilized and transitioned to persistent critical illness, and whether such transition time varies between latent classes of patients.

METHODS

This was a retrospective cohort study involving sepsis patients in the eICU Collaborative Research Database. Persistent critical illness was defined at the time when acute physiological characteristics were no longer more predictive of in-hospital mortality (i.e., vital status at hospital discharge) than antecedent characteristics. Latent growth mixture modeling was used to identify distinct trajectory classes by using Sequential Organ Failure Assessment score measured during intensive care unit stay as the outcome, and persistent critical illness transition time was explored in each latent class.

RESULTS

The mortality was 16.7% (3828/22,868) in the study cohort. Acute physiological model was no longer more predictive of in-hospital mortality than antecedent characteristics at 15 days after intensive care unit admission in the overall population. Only a minority of the study subjects (n = 643, 2.8%) developed persistent critical illness, but they accounted for 19% (15,834/83,125) and 10% (19,975/198,833) of the total intensive care unit and hospital bed-days, respectively. Five latent classes were identified. Classes 1 and 2 showed increasing Sequential Organ Failure Assessment score over time and transition to persistent critical illness occurred at 16 and 27 days, respectively. The remaining classes showed a steady decline in Sequential Organ Failure Assessment scores and the transition to persistent critical illness occurred between 6 and 8 days. Elevated urea-to-creatinine ratio was a good biochemical signature of persistent critical illness.

CONCLUSIONS

While persistent critical illness occurred in a minority of patients with sepsis, it consumed vast medical resources. The transition time differs substantially across latent classes, indicating that the allocation of medical resources should be tailored to different classes of patients.

Risk Factors for Patient-Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm

Background and objectives: Patient-ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact on clinical outcomes using big data analytics. Methods: The study was conducted in a tertiary care hospital; all patients with mechanical ventilation from June to December 2019 were included for analysis. Negative binomial regression and distributed lag non-linear models (DLNM) were used to explore risk factors for PVAs. PVAs were included as a time-varying covariate into Cox regression models to investigate its influence on the hazard of mortality and ventilator-associated events (VAEs). Results: A total of 146 patients involving 50,124 h and 51,451,138 respiratory cycles were analyzed. The overall mortality rate was 15.6%. Double triggering was less likely to occur during day hours (RR: 0.88; 95% CI: 0.85-0.90; p < 0.001) and occurred most frequently in pressure control ventilation (PCV) mode (median: 3; IQR: 1-9 per hour). Ineffective effort was more likely to occur during day time (RR: 1.09; 95% CI: 1.05-1.13; p < 0.001), and occurred most frequently in PSV mode (median: 8; IQR: 2-29 per hour). The effect of sedatives and analgesics showed temporal patterns in DLNM. PVAs were not associated mortality and VAE in Cox regression models with time-varying covariates. Conclusions: Our study showed that counts of PVAs were significantly influenced by time of the day, ventilation mode, ventilation settings (e.g., tidal volume and plateau pressure), and sedatives and analgesics. However, PVAs were not associated with the hazard of VAE or mortality after adjusting for protective ventilation strategies such as tidal volume, plateau pressure, and positive end expiratory pressure (PEEP).

Cumulative oxygen deficit is a novel predictor for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress

BACKGROUND AND OBJECTIVES

The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification.

METHODS

The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models.

RESULTS

A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO₂ (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57-16.93]; p = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28-85.37]; p = 0.029).

CONCLUSIONS

The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.

Analysis of daily goal sheets on physician-nurse collaboration attitude

BACKGROUND

Optimal communication and collaboration between inter-disciplinary health care providers is critical to ensuring high quality patient care. We aimed to quantify the impact on physician-nurse collaboration (PNC) of implementing daily goal sheets (DGSs) in emergency settings.

METHODS

The usage of a DGS was administered in morning rounds in an emergency intensive care unit (ICU) for four consecutive months. A Jefferson Scale of Attitudes Toward Physician-Nurse Collaboration (JSAPNC) form was used before (n=113) and after (n=107) the intervention to evaluate the attitudes of PNCs from the perspective of both physicians and nurses.

RESULTS

There is a significant positive relation between the attitude to PNC and the participant age, educational background, and professional rank and title before DGS application (P<0.01 for each), whereas there was no significant difference observed after the initiation of the DGS.

CONCLUSIONS

The use of a DGS improves physician-nurse collaborations in emergency care settings.

Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit

Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. Methods: In this retrospective cohort study, we employed the Medical Information Mart for Intensive Care III (MIMIC III) database for model development and the eICU database for external validation. We identified septic patients by sepsis-3 criteria on day 1 of ICU entry. The Least Absolute Shrinkage and Selection Operator (LASSO) technique was performed to select predictive variables. We also developed a sepsis mortality prediction model and associated risk stratification score. We then compared model discrimination and calibration with other traditional severity scores. Results: For model development, we enrolled a total of 5,443 patients fulfilling the sepsis-3 criteria. The 30-day mortality was 16.7%. With 5,658 septic patients in the validation set, there were 1,135 deaths (mortality 20.1%). The score had good discrimination in development and validation sets (area under curve: 0.789 and 0.765). In the validation set, the calibration slope was 0.862, and the Brier value was 0.140. In the development dataset, the score divided patients according to mortality risk of low (3.2%), moderate (12.4%), high (30.7%), and very high (68.1%). The corresponding mortality in the validation dataset was 2.8, 10.5, 21.1, and 51.2%. As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH₂O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Association Between the Use of Sodium Bicarbonate and Mortality in Acute Kidney Injury Using Marginal Structural Cox Model

OBJECTIVE

Acute kidney injury with metabolic acidosis is common in critically ill patients. This study assessed the associations between the use of IV sodium bicarbonate and mortality of patients with acute kidney injury and acidosis.

DESIGN

The study was conducted by using data from Beth Israel Deaconess Medical Center, which included several ICUs such as coronary care unit, cardiac surgery recovery unit, medical ICU, surgical ICU, and trauma-neuro ICU. Marginal structural Cox model was used to assess the relationship between receipt of sodium bicarbonate and hospital mortality, allowing pH, PaCO2, creatinine, and bicarbonate concentration as time-varying predictors of sodium bicarbonate exposure while adjusting for baseline characteristics of age, gender, Sequential Organ Failure Assessment score, acute kidney injury stage, Elixhauser score, quick Sequential Organ Failure Assessment, and Simplified Acute Physiology Score II.

SETTING

A large U.S.-based critical care database named Medical Information Mart for Intensive Care.

PATIENTS

Patients with Kidney Disease: Improving Global Outcomes acute kidney injury stage greater than or equal to 1 (> 1.5 (Equation is included in full-text article.)baseline creatinine) and one measurement of acidosis (pH ≤ 7.2). Baseline creatinine was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 3,406 eligible patients, 836 (24.5%) had received sodium bicarbonate treatment. Patients who received sodium bicarbonate treatment had a higher Sequential Organ Failure Assessment (9 vs 7; p < 0.001), lower pH (7.16 vs 7.18; p < 0.001), and bicarbonate concentration (16.51 ± 7.04 vs 20.57 ± 6.29 mmol/L; p < 0.001) compared with those who did not receive sodium bicarbonate. In the marginal structural Cox model by weighing observations with inverse probability of receiving sodium bicarbonate, sodium bicarbonate treatment was not associated with mortality in the overall population (hazard ratio, 1.16; 95% CI, 0.98-1.42; p = 0.132), but it appeared to be beneficial in subgroups of pancreatitis (hazard ratio, 0.53; 95% CI, 0.28-0.98; p = 0.044) and severe acidosis (pH < 7.15; hazard ratio, 0.75; 95% CI, 0.58-0.96; p = 0.024). Furthermore, sodium bicarbonate appeared to be beneficial in patients with severe bicarbonate deficit (< -50 kg·mmol/L).

CONCLUSIONS

In the analysis by adjusting for potential confounders, there is no evidence that IV sodium bicarbonate is beneficial for patients with acute kidney injury and acidosis. Although the study suggested potential beneficial effects in some highly selected subgroups, the results need to be validated in experimental trials.

Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database

Cardiac arrest (CA) may occur due to a variety of causes with heterogeneity in their clinical presentation and outcomes. This study aimed to identify clinical patterns or subphenotypes of CA patients admitted to the intensive care unit (ICU). The clinical and laboratory data of CA patients in a large electronic healthcare database were analyzed by latent profile analysis (LPA) to identify whether subphenotypes existed. Multivariable Logistic regression was used to assess whether mortality outcome was different between subphenotypes. A total of 1,352 CA patients fulfilled the eligibility criteria were included. The LPA identified three distinct subphenotypes: Profile 1 (13%) was characterized by evidence of significant neurological injury (low GCS). Profile 2 (15%) was characterized by multiple organ dysfunction with evidence of coagulopathy (prolonged aPTT and INR, decreased platelet count), hepatic injury (high bilirubin), circulatory shock (low mean blood pressure and elevated serum lactate); Profile 3 was the largest proportion (72%) of all CA patients without substantial derangement in major organ function. Profile 2 was associated with a significantly higher risk of death (OR: 2.09; 95% CI: 1.30 to 3.38) whilst the mortality rates of Profiles 3 was not significantly different from Profile 1 in multivariable model. LPA using routinely collected clinical data could identify three distinct subphenotypes of CA; those with multiple organ failure were associated with a significantly higher risk of mortality than other subphenotypes. LPA profiling may help researchers to identify the most appropriate subphenotypes of CA patients for testing effectiveness of a new intervention in a clinical trial.

Protocol for a prospective observational study on the association of variables obtained by contrast-enhanced ultrasonography and sepsis-associated acute kidney injury

INTRODUCTION

Sepsis commonly results in acute kidney injury (AKI), whereas about 50% of AKI cases are due to sepsis. Sepsis-associated acute kidney injury (SA-AKI) increases morbidity and mortality especially among critically ill patients. This study aims to monitor renal microcirculation perfusion during sepsis using contrast-enhanced ultrasonography (CEUS), and to explore whether CEUS is useful for predicting the development of SA-AKI.

METHODS AND ANALYSIS

This prospective observational study will enrol patients who were diagnosed with sepsis-3 definition. The total of septic or septic shock patients were stratified into AKI (including stages 1, 2 and 3) and non-AKI groups according to Kidney Disease Improving Global Outcomes criteria on days 0, 1, 2 and 7 after admission to the emergency intensive care unit, meanwhile, the CEUS technique will be performed to monitor renal microcirculation perfusion. A multivariable model including all CEUS variables were expected to create for predicting the development of AKI during sepsis. Ultrasonography results, demographic information, therapeutic interventions, survival outcomes, laboratory and other clinical datas will also be collected for further analysis.

ETHICS AND DISSEMINATION

The study protocol was approved on 2 August 2017 by the Ethics Committee of Sir Run Run Shaw Hospital (Zhejiang University Medical College) (approval number: 2016C91401). The results will be published in a peer-reviewed journal and shared with the worldwide medical community within 2 years after the start of the recruitment.

TRIAL REGISTRATION NUMBER

ISRCTN14728986.