Multi-organ spatiotemporal information aware model for sepsis mortality prediction.

BACKGROUND: Sepsis is a syndrome involving multi-organ dysfunction, and the mortality in sepsis patients correlates with the number of lesioned organs. Precise prognosis models play a pivotal role in enabling healthcare practitioners to administer timely and accurate interventions for sepsis, thereby augmenting patient outcomes. Nevertheless, the majority of available models consider the overall physiological attributes of patients, overlooking the asynchronous spatiotemporal interactions among multiple organ systems. These constraints hinder a full application of such models, particularly when dealing with limited clinical data. To surmount these challenges, a comprehensive model, denoted as recurrent Graph Attention Network-multi Gated Recurrent Unit (rGAT-mGRU), was proposed. Taking into account the intricate spatiotemporal interactions among multiple organ systems, the model predicted in-hospital mortality of sepsis using data collected within the 48-hour period post-diagnosis. MATERIAL AND METHODS: Multiple parallel GRU sub-models were formulated to investigate the temporal physiological variations of single organ systems. Meanwhile, a GAT structure featuring a memory unit was constructed to capture spatiotemporal connections among multi-organ systems. Additionally, an attention-injection mechanism was employed to govern the data flowing within the network pertaining to multi-organ systems. The proposed model underwent training and testing using a dataset of 10,181 sepsis cases extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. To evaluate the model's superiority, it was compared with the existing common baseline models. Furthermore, ablation experiments were designed to elucidate the rationale and robustness of the proposed model. RESULTS: Compared with the baseline models for predicting mortality of sepsis, the rGAT-mGRU model demonstrated the largest area under the receiver operating characteristic curve (AUROC) of 0.8777 ± 0.0039 and the maximum area under the precision-recall curve (AUPRC) of 0.5818 ± 0.0071, with sensitivity of 0.8358 ± 0.0302 and specificity of 0.7727 ± 0.0229, respectively. The proposed model was capable of delineating the varying contribution of the involved organ systems at distinct moments, as specifically illustrated by the attention weights. Furthermore, it exhibited consistent performance even in the face of limited clinical data. CONCLUSION: The rGAT-mGRU model has the potential to indicate sepsis prognosis by extracting the dynamic spatiotemporal interplay information inherent in multi-organ systems during critical diseases, thereby providing clinicians with auxiliary decision-making support.

Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy.

Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes.

Identification of novel biomarkers for sepsis diagnosis via serum proteomic analysis using iTRAQ-2D-LC-MS/MS.

BACKGROUND: Sepsis is a common cause of morbidity and mortality in the ICU patients. Early diagnosis and appropriate patient management is the key to improve the patient survival and to limit disabilities in sepsis patients. This study was aimed to find new diagnostic biomarkers of sepsis. METHODS: In this study, serum proteomic profiles in sepsis patients by iTRAQ2D-LC-MS/MS. Thirty seven differentially expressed proteins were identified in patients with sepsis, and six proteins including ApoC3, SERPINA1, VCAM1, B2M, GPX3, and ApoE were selected for further verification by ELISA and immunoturbidimetry in 53 patients of non-sepsis, 37 patients of sepsis, and 35 patients of septic shock. Descriptive statistics, functional enrichment analysis, and ROC curve analysis were conducted. RESULTS: The level of ApoC3 was gradually decreased among non-sepsis, sepsis, and septic shock groups (p = 0.049). The levels of VCAM1 (p = 0.010), B2M (p = 0.004), and ApoE (p = 0.039) were showing an increased tread in three groups, with the peak values of B2M and ApoE in the sepsis group. ROC curve analysis for septic diagnosis showed that the areas under ROC curve (AUC) of ApoC3, VCAM1, B2M, and ApoE were 0.625, 0.679, 0.581, and 0.619, respectively, which were lower than that of PCT (AUC 0.717) and CRP (AUC 0.706), but there were no significant differences between each index and PCT or CRP. The combination including four validated indexes and two classical infection indexes for septic diagnosis had the highest AUC-ROC of 0.772. CONCLUSION: Proteins of ApoC3, VCAM1, B2M, and ApoE provide a supplement to classical biomarkers for septic diagnosis.

Development of Novel Fractal Method for Characterizing the Distribution of Blood Flow in Multi-Scale Vascular Tree.

Blood perfusion is an important index for the function of the cardiovascular system and it can be indicated by the blood flow distribution in the vascular tree. As the blood flow in a vascular tree varies in a large range of scales and fractal analysis owns the ability to describe multi-scale properties, it is reasonable to apply fractal analysis to depict the blood flow distribution. The objective of this study is to establish fractal methods for analyzing the blood flow distribution which can be applied to real vascular trees. For this purpose, the modified methods in fractal geometry were applied and a special strategy was raised to make sure that these methods are applicable to an arbitrary vascular tree. The validation of the proposed methods on real arterial trees verified the ability of the produced parameters (fractal dimension and multifractal spectrum) in distinguishing the blood flow distribution under different physiological states. Furthermore, the physiological significance of the fractal parameters was investigated in two situations. For the first situation, the vascular tree was set as a perfect binary tree and the blood flow distribution was adjusted by the split ratio. As the split ratio of the vascular tree decreases, the fractal dimension decreases and the multifractal spectrum expands. The results indicate that both fractal parameters can quantify the degree of blood flow heterogeneity. While for the second situation, artificial vascular trees with different structures were constructed and the hemodynamics in these vascular trees was simulated. The results suggest that both the vascular structure and the blood flow distribution affect the fractal parameters for blood flow. The fractal dimension declares the integrated information about the heterogeneity of vascular structure and blood flow distribution. In contrast, the multifractal spectrum identifies the heterogeneity features in blood flow distribution or vascular structure by its width and height. The results verified that the proposed methods are capable of depicting the multi-scale features of the blood flow distribution in the vascular tree and further are potential for investigating vascular physiology.

Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model.

BACKGROUND: Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model. MATERIAL AND METHODS: In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM). RESULTS: The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models. CONCLUSIONS: The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.

Macrophages enhance lipopolysaccharide induced apoptosis via Ang1 and NF-κB pathways in human umbilical vein endothelial cells.

Lipopolysaccharide (LPS) could induce apoptosis and dysfunction of endothelial cells. We aimed to reveal the effects of macrophages on cell proliferation and apoptosis in LPS induced human umbilical vein endothelial cells (HUVECs). THP-1 derived macrophages and HUVECs were co-cultured in the presence of LPS. Cell viability was measured by Cell Counting Kit-8 and apoptosis was analyzed by flow cytometry. Expression of Ang1, the NF-κB component p65 was evaluated by western blot and quantitative PCR. Small interfering RNAs (siRNAs) were used to knockdown the expression of proinflammatory cytokines and p65 in HUVECs. Plasmid transfection-mediated overexpression of Ang1 was employed to see its effects on cell proliferation and apoptosis in HUVECs. Macrophages enhanced LPS-induced proliferation impairments and apoptosis in HUVECs, which could be attenuated by siRNA-mediated knockdown of cytokines TNF-α, IL-1ß, IL-6 and IL-12p70 in macrophages. The dysfunction of HUVECs was tightly associated with reduced Ang1 expression and increased phosphorylated p65 (p-65). Overexpression of Ang1 in HUVECs significantly decreased p-p65, suggesting negatively regulation of p-p65 by Ang1. Overexpression of Ang1, adding recombinant Ang1 or silencing of p65 substantially attenuated the dysfunction of HUVECs in terms of cell proliferation and apoptosis. In conclusions, THP-1-derived macrophages enhance LPS induced dysfunction of HUVECs via Ang1 and NF-κB pathways, suggesting new therapeutic targets for sepsis.

An adaptive fractal model for sublingual microcirculation.

The hemodynamic conditions and partial pressure of oxygen in microcirculation generally indicate the status of tissue perfusion, which provides essential information for the assessment and treatment of critical diseases such as sepsis. The human tongue is known to have abundant microcirculation and is an ideal window to observe the microcirculation. At present, the monitoring of sublingual microcirculation is mostly achieved using handheld vital microscopy (HVM). Microcirculation is organized and works as a network. However, HVM can obtain only limited view of few vessels and is not able to acquire information regarding the entire network. In this work, we proposed a method to construct a mathematical network model of sublingual microcirculation to solve the problems. The proposed method is based on fractal analysis to model and simulate the hemodynamic and functional activities of sublingual microcirculation. Specifically, the HVM technology is used to obtain the partial morphological and hemodynamic data of sublingual microcirculation, and fractal analysis is applied thereafter to establish the hemodynamic model of the network based on the data from few vessels. Further, the adaptive regulation mechanism of microcirculation is introduced to enhance the performance of the model. The model was validated by the experimental data and the results are consistent with the characteristics of microcirculation. The work demonstrates the potential of the proposed method in sublingual microcirculation research and for the further assessment of tissue perfusion.

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large prediction error and instability, the long short-term memory transferring algorithm (transLSTM) was proposed. Based on the idea of transfer learning, the algorithm leverages the correlation between diseases to transfer information of different disease prognostic models, constructs the effictive model of target disease of small samples with the aid of large data of related diseases, hence improves the prediction performance and reduces the requirement for target training sample quantity. The transLSTM algorithm firstly uses the related disease samples to pretrain partial model parameters, and then further adjusts the whole network with the target training samples. The testing results on MIMIC-Ⅲ database showed that compared with traditional LSTM classification algorithm, the transLSTM algorithm had 0.02-0.07 higher AUROC and 0.05-0.14 larger AUPRC, while its number of training iterations was only 39%-64% of the traditional algorithm. The results of application on sepsis revealed that the transLSTM model of only 100 training samples had comparable mortality prediction performance to the traditional model of 250 training samples. In small sample situations, the transLSTM algorithm has significant advantages with higher prediciton accuracy and faster training speed. It realizes the application of transfer learning in the prognostic model of critical disease with small samples.

A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit.

In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy. Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction. However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data. The eLSTM algorithm was evaluated by the acknowledged database of ICU admissions Medical Information Mart for Intensive Care III (MIMIC-III). The investigation in total of 18415 cases shows that compared with clinical scoring systems SAPS II, SOFA, and APACHE II, random forests classification algorithm, and the single LSTM classifier, the eLSTM model achieved the superior performance with the largest value of area under the receiver operating characteristic curve (AUROC) of 0.8451 and the largest area under the precision-recall curve (AUPRC) of 0.4862. Furthermore, it offered an early prognosis of ICU patients. The results demonstrate that the eLSTM is capable of dynamically predicting the mortality of patients in complex clinical situations.

Diagnostic efficacy of serum procalcitonin, C-reactive protein concentration and clinical pulmonary infection score in Ventilator-Associated Pneumonia.

OBJECTIVE: The aim of this study was to evaluate the diagnostic efficacy of serum procalcitonin (PCT), c-reactive protein (CRP) concentration and clinical pulmonary infection score(CPIS) in ventilator-associated pneumonia(VAP). METHODS: Forty-nine patients who were admitted to the intensive care unit (ICU) of Zhejiang Hospital with suspected VAP were recruited in this study. The serum level of PCT and CRP of all patients were measured and CPIS was calculated at the time of VAP suspected diagnosis. Of the included 49 patients, 24 were finally confirmed of VAP by microbiology assay. And the other 25 patients were considered as clinical suspected VAP without microbiology confirmation. The diagnostic sensitivity, specificity and area under the receiver operating characteristic (ROC) curve (AUC) were calculated using the serum PCT, CRP concentration and CPIS. The correlation among serum PCT, CRP concentration and CPIS were also evaluated by Spearson correlation test. RESULTS: A total of 100 bronchoscopic aspiration sputum specimen were examined in bacterial culture. 30 samples were found with suspected pathogenic bacteria. Six samples were found with 2 types of suspected pathogenic bacteria. PCT serum concentration and CPIS score were significantly different (P<0.05) between the patient group [1.4 (0.68 ∼ 2.24), 6.0 (4.25 ∼ 8.00)] and the control group [0.4 (0.17 ∼ 1.39), 3.0 (1.00 ∼ 5.00)] ; However, the serum CRP [102.8(66.75 ∼ 130.90) vs 86.1(66.95 ∼ 110.10)] was not statistically different between the two groups (P>0.05). A significant correlation was found between serum PCT and CRP concentrations (r=0.55, P<0.01), but not between PCT vs CPIS and CRP vs CPIS (p>0.05). The diagnostic sensitivity, specificity and AUC were 72.0%, 75.0%, 0.81 (0.69 ∼ 0.93) for CPIS; 60.0%, 87.5%, 0.76 (0.62 ∼ 0.90) for PCT and 68.0%, 58.3%, 0.59 (0.43 ∼ 0.76) for CRP. CONCLUSION: PCT serum level and CPIS score are elevated in VAP patients and could therefore represent potential biomarkers for VAP early diagnosis.

Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm.

With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods.

[The effects of early goal-directed therapy on mortality rate in patients with severe sepsis and septic shock: a systematic literature review and Meta-analysis].

OBJECTIVE: To investigate whether early goal-directed therapy (EGDT) could lower the mortality rate in patients with severe sepsis and septic shock. METHODS: Articles with items sepsis, severe sepsis, septic shock, EGDT were retrieved from MEDLINE, EMBASE, Cochrane, Wanfang Data and CNKI. Inclusion criteria included randomized controlled trial, subjects concerning patients with severe sepsis or septic shock, endpoints with short-term mortality [ in-hospital, intensive care unit (ICU) or 28-day] and long-term mortality (60-day or 90-day). Related risk (RR) and 95% confidence interval (95%CI) were used as indices to judge the difference in mortality rate between EGDT group and standard treatment group. RevMan 5.2 software was used for Meta analysis. RESULTS: There were 8 studies meeting inclusive criteria with a total of 4,853 patients. For patients with severe sepsis and septic shock, compared with the group with routine treatment, EGDT showed a decrease in the short-term mortality (RR=0.74, 95%CI=0.66-0.82, P<0.00001), but did not decrease the long-term mortality (RR=0.99, 95%CI=0.92-1.06, P=0.81). CONCLUSIONS: EGDT strategy may decrease the short-term mortality in patients with severe sepsis and septic shock, but it showed no influence on the long-term mortality.

[The prediction value of noninvasive bioreactance-based passive leg raising test in assessing fluid responsiveness in elderly patients with sepsis].

OBJECTIVE: To investigate the value of bioreactance-based passive leg raising (PLR) test predicting fluid responsiveness of elderly patients with sepsis. METHODS: This prospective and self-controlled clinical study included 31 elderly patients with sepsis in the Department of Intensive Care Medicine of Zhejiang Hospital. Hemodynamic parameters including cardiac output (CO), stroke volume variation (SVV) were continuously recorded by bioreactance-based device (noninvasive cardiac output monitoring, NICOM) before and after PLR and volume expansion (VE) test. Patients were defined as responders if CO increased ≥ 10% after VE. RESULTS: A total of 100 PLR and VE tests in these 31 patients were evaluated.In 28 responders, CO[(5.11 ± 2.10) L/min vs (5.91 ± 2.45) L/min, P < 0.05; (5.06 ± 2.06) L/min vs (5.77 ± 2.47) L/min, P < 0.05] and SV [(59.61 ± 18.22) ml vs (69.29 ± 21.32) ml, P < 0.05; (60.10 ± 15.95) ml vs (70.06 ± 17.96) ml, P < 0.05] were obviously increased both after PLR and VE. The ΔCO after PLR (ΔCOPLR) and ΔCOVE was highly correlated (r = 0.819, P = 0.001) while the SVV before VE and Δ COVE was uncorrelated (r = -0.218, P = 0.059). The areas under the ROC curve of ΔCOPLR, SVV predicting fluid responsiveness were 0.859 and 0.459 respectively. The ΔCOPLR ≥ 10% was found to predict fluid responsiveness with a sensitivity and specificity of 85% and 83% respectively. CONCLUSION: Compared with SVV, PLR test is a simple, effective method for accurately predicting fluid responsiveness of elderly patients with sepsis.

[Efficacies of fluid resuscitation as guided by lactate clearance rate and central venous oxygen saturation in patients with septic shock].

OBJECTIVE: To estimate the efficacies of fluid resuscitations as guided by lactate clearance rate (LC) and central venous oxygen saturation (ScvO2) in patients with septic shock. METHODS: 100 patients diagnosed with septic shock from June 2012 to June 2014 in department of critical care medicine of sixteen hospitals were enrolled. They were randomly divided into two groups of study and control (each n = 50). After a diagnosis of sepsis shock, they were treated symptomatically timely and fluid resuscitation was started as early as possible according to the 2008 Guideline for Managing Sepsis & Septic Shock. Central venous pressure (CVP) ≥ 8 mmHg (1 mmHg = 0.133 kPa), mean arterial pressure (MAP) ≥ 65 mmHg, urine output ≥ 0.5 ml × kg⁻¹ × h⁻¹, ScvO2≥ 70% and LC ≥ 10% (or lactate ≤ 2.0 mmol) served as target values for fluid resuscitation therapy in study group versus CVP ≥ 8 mmHg, MAP ≥ 65 mmHg, urine output ≥ 0.5 ml × kg⁻¹ × h⁻¹ and ScvO2≥ 70% in control group. The general conditions and clinical characteristics, changes in CVP, MAP, urine output, ScvO2, lactate level and/or LC before (0 hour) and every hour (1-6 h) after the start of fluid resuscitation and other related outcome indicators were recorded. RESULTS: No significant difference existed in general data. The 28-day mortality was 40% for study group versus 56% for control group. There was no significant inter-group difference (P > 0.05). The time of mechanical ventilation and length of intensive care unit (ICU) stay were lower in study group than those in control group [mechanical ventilation time (11.200 ± 17.069) vs (15.760 ± 14.215), P = 0.150; length of ICU stay (13.240 ± 17.127) vs (23.980 ± 18.298), P = 0.003]. The 28-day mortality was independently associated with LC and ScvO2reaching target values for fluid resuscitation in study group (χ² = 10.930, P = 0.001) while the 28-day mortality was independently associated with ScvO2reaching target value for fluid resuscitation in control group (χ² = 6.395, P = 0.011). Among all patients, the 28-day mortality was independently associated with ScvO2reaching target value for fluid resuscitation (χ² = 14.530, P = 0.000), but not LC (χ² = 1.175, P = 0.278). CONCLUSION: A combination of LC and ScvO2may serve an index in confirming the endpoint of fluid resuscitation for patients with septic shock. Fluid resuscitation therapy under the guidance of LC and ScvO2is more accurate and reliable than the guidance of ScvO2alone.

Value of plasma brain natriuretic peptide levels for predicting postoperative atrial fibrillation: a systemic review and meta-analysis.

BACKGROUND: Blood natriuretic peptide (NP) levels have been reported to be useful for predicting postoperative atrial fibrillation (AF). We aimed to quantitatively synthesize the current evidence of the accuracy of using NP levels in predicting postoperative AF. METHODS AND RESULTS: Medline, Embase, and reference lists were searched. Studies were included if either brain natriuretic peptide (BNP) or N-terminal pro-b type natriuretic peptide (NT-proBNP) had been evaluated perioperatively to predict postoperative AF. Data were analyzed to obtain summary accuracy estimates. Data from 1,844 patients in 10 studies were analyzed. Summary estimates for the sensitivity and specificity of using NP levels for predicting postoperative AF were 75 % [95 % confidence interval (CI) 67-79 %] and 80 % (95 % CI 62-91 %), respectively. The overall diagnostic odds ratio was 3.28 (95 % CI 2.23-4.84). Subgroup analysis showed that elevated NP levels in the perioperative period were a strong independent predictor of postoperative AF. NT-proBNP appeared to have better predictive value than BNP, as did postoperative assessment over preoperative assessment. BNP had a better correlation with postoperative AF in patients undergoing thoracic surgery than in patients undergoing cardiac surgery. CONCLUSIONS: Perioperative assessment of the natriuretic peptide level in patients undergoing major cardiothoracic surgery could be a valuable diagnostic aid for identifying patients at high risk of developing postoperative AF, and for providing critical clinical information to guide prophylactic antiarrhythmic therapy in the perioperative period.

[Microcirculation characters in the elderly septic patients].

OBJECTIVE: To explore the changes of sublingual microcirculation in elderly patients with severe sepsis/septic shock. METHODS: Twenty-three patients with sepsis, 10 patients without sepsis and 10 healthy elderly patients were enrolled. Sublingual microcirculation was evaluated by sidestream darkfield (SDF) imaging. And the 28-day mortality rates of all septic patients were recorded. RESULTS: Compared with the healthy group, all elderly patients had significant sublingual microcirculation dysfunctions. Compared with the severe septic and nonseptic patients, perfused vessel density (PDV) , proportion of perfused vessels (PPV) and microvascular flow index (MFI) of septic shocks were significantly lower. Compared with the severe septic patients, PDV, PPV and MFI instead of lactate and MAP of septic shocks were significantly lower from Day 1 to Day 3. The values of PDV, PPV, MFI and lactate but not MAP of the surviving septic patients were significantly higher than those of the deceased ones. CONCLUSIONS: The elderly patients with septic shock have severe sublingual microcirculatory alterations. And these abnormalities are more marked in septic shock patients. Nonsurvivors showed more severe alterations than survivors. Microcirculatory alterations may be measured to guide the therapy.

[An evaluation of stroke volume variation as a predictor of fluid responsiveness in mechanically ventilated elderly patients with severe sepsis].

OBJECTIVE: To evaluate stroke volume variation (SVV) as a predictor of fluid responsiveness in mechanically ventilated (MV) elderly patients with severe sepsis. METHODS: A prospective observation of 31 fluid challenges during fluid resuscitation for treatment of hemodynamic instability in 17 elderly MV patients with severe sepsis was conducted. SVV was measured by pulse indicator continuous cardiac output (PiCCO) system. Fluid responsiveness was defined as the changes in cardiac index (CI) increase after fluid loading (DeltaCI) > or =10%. The changes in hemodynamic parameters and lung water index were observed at the onset of and after fluid therapy. The correlation between DeltaCI and SVV or central venous pressure (CVP) were analyzed. RESULTS: SVV was decreased significantly after fluid loading [(6.6+/-2.1)% vs.(12.1+/-3.7)%, P<0.01], whereas CVP increased significantly [(12.5+/-3.6) mm Hg vs. (8.9+/-4.1) mm Hg, 1 mm Hg=0.133 kPa, P<0.01]. DeltaCI in response to fluid loading were positively correlated to initial values of SVV (r=0.447, P=0.012), but there was no relationship between CVP and DeltaCI (r=-0.082, P=0.674). The areas under the receiver operating characteristic curve (ROC curve) for SVV was 0.672 [95% confidence interval (95%CI) 0.463-0.885] and CVP was 0.336 (95%CI 0.133-0.539), respectively. A SVV value of 11.5% had the sensitivity of 71% and specificity of 67% for prediction of fluid responsiveness. CONCLUSION: Functional hemodynamic parameter SVV can predict fluid responsiveness in elderly MV patients with severe sepsis during fluid resuscitation, it may serve as a useful index for guiding fluid therapy in elderly patients with severe sepsis.

[A novel missense mutation, K124N, in the troponin T gene of Chinese populations with hypertrophic cardiomyopathy].

OBJECTIVE: To study the cardiac troponin T (TNNT2) gene mutation in Chinese patients with hypertrophic cardiomyopathy (HCM) and to analyze the correlation between the genotype and phenotype. METHODS: Specimens of peripheral blood were collected from 71 unrelated Chinese probands with HCM, aged 40 +/- 18. The genome DNA was extracted. Single-strand conformation polymorphism gel analysis of the polymerase chain reaction-amplified products was conducted to search for mutations in the exons 8, 9, 10, 11, and 16 of the TNNT2 gene. Relevant clinical data were collected. One hundred normal persons, aged 44 +/- 14, were used as controls. RESULTS: A missense mutation, K124N, in the exon 9 of the TNNT2 gene was identified in a 41-year-old female patient with HCM and failed to be detected in the 100 normal controls, which suggested the disease-causing mutation. The patient began to have the symptoms of chest distress and palpitation since the age of 38, presented moderate hypertrophy of the intraventricular septum, and did not have a family history of sudden cardiac death. CONCLUSION: A novel missense mutation of troponin T gene has been identified. Mutation in tail part of cardiac troponin T, essential for it's binding function, causes the disease of HCM. Correlative analysis confirms the genetic heterogeneity of the disease.