Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Expression level of myocardial enzymes in patients with schizophrenia: Predictive value in the occurrence of violence

BACKGROUND Schizophrenic patients are prone to violence,frequent recurrence,and difficult to predict.Emotional and behavioral abnormalities during the onset of the disease,resulting in active myocardial enzyme spectrum.AIM To explored the expression level of myocardial enzymes in patients with schizo-phrenia and its predictive value in the occurrence of violence.METHODS A total of 288 patients with schizophrenia in our hospital from February 2023 to January 2024 were selected as the research object,and 100 healthy people were selected as the control group.Participants’information,clinical data,and labo-ratory examination data were collected.According to Modified Overt Aggression Scale score,patients were further divided into the violent(123 cases)and non-violent group(165 cases).RESULTS The comparative analysis revealed significant differences in serum myocardial enzyme levels between patients with schizophrenia and healthy individuals.In the schizophrenia group,the violent and non-violent groups also exhibited different levels of serum myocardial enzymes.The levels of myocardial enzymes in the non-violent group were lower than those in the violent group,and the patients in the latter also displayed aggressive behavior in the past.CONCLUSION Previous aggressive behavior and the level of myocardial enzymes are of great significance for the diagnosis and prognosis analysis of violent behavior in patients with schizophrenia.By detecting changes in these indicators,we can gain a more comprehensive understanding of a patient’s condition and treatment.

Predictive Factors of Renal Damage during Sickle Cell Disease at the Hematology-Oncology Department of Donka University Hospital

Introduction: Sickle cell disease, which is the most common hereditary hemoglobinopathy in the world, attacks all body systems, particularly the kidneys. The view of this study was to investigate the predictive factors of kidney damage during sickle cell disease. Materials and methods: It was a retrospective, descriptive and analytical study on files of sickle cell patients hospitalized in the Hematology-Oncology Department of Donka University Hospital during a period from January 1, 2016 to December 31, 2019. Records of sickle cell patients with one or more renal abnormalities were retained. Sickle cell patients without kidney damage were also selected for a comparative study. Only patients without sickle cell disease were excluded. Results: Seventy-five (75) medical records were collected during the study period. From these cases, thirteen (13) records with kidney disease were observed, a frequency of 17%. The mean age of patients was 24.2 years for extremes of 10 and 65 years. The sex ratio was 1.6 in favor of men. The SSFA2 form was the most represented with 92%. 24-hour proteinuria was measured in 13 patients between whom 6 patients (46.2%) had a proteinuria level ≤ 1 g. Eight (8) patients (61.5%) were in stage 1 of chronic kidney disease. The most common type of renal involvement was tubulo-interstitial nephropathy with 8 patients (61.5%). Bivariate analysis showed that elevated serum creatinine (P 2 form of the sickness (P Conclusion: After the observation of an increased serum creatinine and urea, a predominance observation of the SSFA2 form, it should be possible to target patients for whom screening for kidney damage should henceforth be systematic.

Predictive model for postpartum hemorrhage requiring hysterectomy in a minority ethnic region

BACKGROUND Postpartum hemorrhage(PPH)is a leading cause of maternal mortality,and hysterectomy is an important intervention for managing intractable PPH.Accurately predicting the need for hysterectomy and taking proactive emergency measures is crucial for reducing mortality rates.AIM To develop a risk prediction model for PPH requiring hysterectomy in the ethnic minority regions of Qiandongnan,China,to help guide clinical decision-making.METHODS The study included 23490 patients,with 1050 having experienced PPH and 74 who underwent hysterectomies.The independent risk factors closely associated with the necessity for hysterectomy were analyzed to construct a risk prediction model,and its predictive efficacy was subsequently evaluated.RESULTS The proportion of hysterectomies among the included patients was 0.32%(74/23490),representing 7.05%(74/1050)of PPH cases.The number of deliveries,history of cesarean section,placenta previa,uterine atony,and placenta accreta were identified in this population as independent risk factors for requiring a hysterectomy.Receiver operating characteristic curve analysis of the prediction model showed an area under the curve of 0.953(95%confidence interval:0.928-0.978)with a sensitivity of 90.50%and a specificity of 90.70%.CONCLUSION The model demonstrates excellent predictive power and is effective in guiding clinical decisions regarding PPH in the ethnic minority regions of Qiandongnan,China.

Three predictive scores compared in a retrospective multicenter study of nonunion tibial shaft fracture

BACKGROUND Delayed union,malunion,and nonunion are serious complications in the healing of fractures.Predicting the risk of nonunion before or after surgery is challenging.AIM To compare the most prevalent predictive scores of nonunion used in clinical practice to determine the most accurate score for predicting nonunion.METHODS We collected data from patients with tibial shaft fractures undergoing surgery from January 2016 to December 2020 in three different trauma hospitals.In this retrospective multicenter study,we considered only fractures treated with intramedullary nailing.We calculated the tibia FRACTure prediction healING days(FRACTING)score,Nonunion Risk Determination score,and Leeds-Genoa Nonunion Index(LEG-NUI)score at the time of definitive fixation.RESULTS Of the 130 patients enrolled,89(68.4%)healed within 9 months and were classified as union.The remaining patients(n=41,31.5%)healed after more than 9 months or underwent other surgical procedures and were classified as nonunion.After calculation of the three scores,LEG-NUI and FRACTING were the most accurate at predicting healing.CONCLUSION LEG-NUI and FRACTING showed the best performances by accurately predicting union and nonunion.

Establishment of predictive models and determinants of preoperative gastric retention in endoscopic retrograde cholangiopancreatography

BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Assessing recent recurrence after hepatectomy for hepatitis Brelated hepatocellular carcinoma by a predictive model based on sarcopenia

BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.

Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma

Objective To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.Methods Data from elderly individuals(age≥65 years)histologically diagnosed with brain glioma were sourced from the Surveillance,Epidemiology,and End Results(SEER)database.The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio.Additionally,data obtained from Tangdu Hospital constituted an external validation cohort for the study.The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator(LASSO)and multivariate Cox regression analysis,enabling the construction of a nomogram.Model performance was evaluated using C-index,ROC curves,calibration plot and decision curve analysis(DCA).Results A cohort of 20483 elderly glioma patients was selected from the SEER database.Five prognostic factors(age,marital status,histological type,stage,and treatment)were found to significantly impact overall survival(OS)and cancer-specific survival(CSS),with tumor location emerging as a sixth variable independently linked to CSS.Subsequently,nomogram models were developed to predict the probabilities of survival at 6,12,and 24 months.The assessment findings from the validation queue indicate a that the model exhibited strong performance.Conclusion Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients.They can potentially assist in risk stratification and clinical decision-making.

Lecithin-cholesterol acyltransferase is a potential tumor suppressor and predictive marker for hepatocellular carcinoma metastasis

BACKGROUND Hepatocellular carcinoma(HCC)is a major cause of cancer mortality worldwide,and metastasis is the main cause of early recurrence and poor prognosis.However,the mechanism of metastasis remains poorly understood.AIM To determine the possible mechanism affecting HCC metastasis and provide a possible theoretical basis for HCC treatment.METHODS The candidate molecule lecithin-cholesterol acyltransferase(LCAT)was screened by gene microarray and bioinformatics analysis.The expression levels of LCAT in clinical cohort samples was detected by quantitative realtime polymerase chain reaction and western blotting.The proliferation,migration,invasion and tumor-forming ability were measured by Cell Counting Kit-8,Transwell cell migration,invasion,and clonal formation assays,respectively.Tumor formation was detected in nude mice after LCAT gene knockdown or overexpression.The immunohistochemistry for Ki67,E-cadherin,N-cadherin,matrix metalloproteinase 9 and vascular endothelial growth factor were performed in liver tissues to assess the effect of LCAT on HCC.Gene set enrichment analysis(GSEA)on various gene signatures were analyzed with GSEA version 3.0.Three machine-learning algorithms(random forest,support vector machine,and logistic regression)were applied to predict HCC metastasis in The Cancer Genome Atlas and GEO databases.RESULTS LCAT was identified as a novel gene relating to HCC metastasis by using gene microarray in HCC tissues.LCAT was significantly downregulated in HCC tissues,which is correlated with recurrence,metastasis and poor outcome of HCC patients.Functional analysis indicated that LCAT inhibited HCC cell proliferation,migration and invasion both in vitro and in vivo.Clinicopathological data showed that LCAT was negatively associated with HCC size and metastasis(HCC size≤3 cm vs 3-9 cm,P<0.001;3-9 cm vs>9 cm,P<0.01;metastatic-free HCC vs extrahepatic metastatic HCC,P<0.05).LCAT suppressed the growth,migration and invasion of HCC cell lines via PI3K/AKT/mTOR signaling.Our results indicated that the logistic regression model based on LCAT,TNM stage and the serum level of α-fetoprotein in HCC patients could effectively predict high metastatic risk HCC patients.CONCLUSION LCAT is downregulated at translational and protein levels in HCC and might inhibit tumor metastasis via attenuating PI3K/AKT/mTOR signaling.LCAT is a prognostic marker and potential therapeutic target for HCC.

Enhancing Safety in Autonomous Vehicle Navigation:An Optimized Path Planning Approach Leveraging Model Predictive Control

This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.

Development and validation of a predictive model for acute-onchronic liver failure after transjugular intrahepatic portosystemic shunt

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is a cause of acute-onchronic liver failure(ACLF).AIM To investigate the risk factors of ACLF within 1 year after TIPS in patients with cirrhosis and construct a prediction model.METHODS In total,379 patients with decompensated cirrhosis treated with TIPS at Nanjing Drum Tower Hospital from 2017 to 2020 were selected as the training cohort,and 123 patients from Nanfang Hospital were included in the external validation cohort.Univariate and multivariate logistic regression analyses were performed to identify independent predictors.The prediction model was established based on the Akaike information criterion.Internal and external validation were conducted to assess the performance of the model.RESULTS Age and total bilirubin(TBil)were independent risk factors for the incidence of ACLF within 1 year after TIPS.We developed a prediction model comprising age,TBil,and serum sodium,which demonstrated good discrimination and calibration in both the training cohort and the external validation cohort.CONCLUSION Age and TBil are independent risk factors for the incidence of ACLF within 1 year after TIPS in patients with decompensated cirrhosis.Our model showed satisfying predictive value.

Comparative Study of Probabilistic and Least-Squares Methods for Developing Predictive Models

This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.

Enhancing predictive accuracy in hypertriglyceridemia-induced acute pancreatitis:Role of red cell distribution width and prospective studies

This letter addresses the study titled“Red cell distribution width:A predictor of the severity of hypertriglyceridemia-induced acute pancreatitis”by Lv et al published in the World Journal of Experimental Medicine.The study offers a valuable analysis of red cell distribution width(RDW)as a predictive marker for persistent organ failure in patients with hypertriglyceridemia-induced acute pancreatitis.The study results suggest that RDW,combined with the Bedside Index for Severity in Acute Pancreatitis score,could enhance the predictive accuracy for severe outcomes.Further investigation into the role of RDW in different severities of acute pancreatitis is recommended.Additionally,the need for large-scale and multicenter prospective studies to validate these findings is emphasized.

Predictive value of diaphragm ultrasound for mechanical ventilation outcome in patients with acute exacerbation of chronic obstructive pulmonary disease

BACKGROUND Acute exacerbation of chronic obstructive pulmonary disease(AECOPD)is often combined with respiratory failure,which increases the patient's morbidity and mortality.Diaphragm ultrasound(DUS)has developed rapidly in the field of critical care in recent years.Studies with DUS monitoring diaphragm-related rapid shallow breathing index have demonstrated important results in guiding intensive care unit patients out of the ventilator.Early prediction of the indications for withdrawal of non-invasive ventilator and early evaluation of patients to avoid or reduce disease progression are very important.AIM To explore the predictive value of DUS indexes for non-invasive ventilation outcome in patients with AECOPD.METHODS Ninety-four patients with AECOPD who received mechanical ventilation in our hospital from January 2022 to December 2023 were retrospectively analyzed,and they were divided into a successful ventilation group(68 cases)and a failed ventilation group(26 cases)according to the outcome of ventilation.The clinical data of patients with successful and failed noninvasive ventilation were compared,and the independent predictors of noninvasive ventilation outcomes in AECOPD patients were identified by multivariate logistic regression analysis.RESULTS There were no significant differences in gender,age,body mass index,complications,systolic pressure,heart rate,mean arterial pressure,respiratory rate,oxygen saturation,partial pressure of oxygen,oxygenation index,or time of inspiration between patients with successful and failed mechanical ventilation(P>0.05).The patients with successful noninvasive ventilation had shorter hospital stays and lower partial pressure of carbon dioxide(PaCO_(2))than those with failed treatment,while potential of hydrogen(pH),diaphragm thickening fraction(DTF),diaphragm activity,and diaphragm movement time were significantly higher than those with failed treatment(P<0.05).pH[odds ratio(OR)=0.005,P<0.05],PaCO_(2)(OR=0.430,P<0.05),and DTF(OR=0.570,P<0.05)were identified to be independent factors influencing the outcome of mechanical ventilation in AECOPD patients.CONCLUSION The DUS index DTF can better predict the outcome of non-invasive ventilation in AECOPD patients.

The Effect of Predictive Nursing Intervention on Cardiac Function and Clinical Outcomes in CCU Patients with Acute Myocardial Infarction After Interventional Therapy

Objective:To evaluate the impact of predictive nursing on the care of acute myocardial infarction(AMI)patients in the Coronary Care Unit(CCU)after interventional therapy.Methods:From September 2021 to September 2023,84 AMI patients admitted to the CCU were randomly divided into two groups:the experimental group(42 patients)received predictive nursing,and the reference group(42 patients)received conventional nursing.Cardiac function and clinical outcomes were compared between the groups.Results:Before nursing,there was no difference in cardiac function between the two groups(P>0.05).After nursing,the cardiac function of the experimental group was better than that of the reference group(P<0.05).The clinical outcomes of the experimental group were better than those of the reference group(P<0.05).Before nursing,there was no difference in psychological scores between the two groups(P>0.05).After nursing,the psychological scores of the experimental group were lower than those of the reference group(P<0.05).Conclusion:Predictive nursing can improve the cardiac function and clinical outcomes of AMI patients after interventional therapy and can also regulate patients’negative psychological states.

The Application Effect of Predictive Nursing on Cardiopulmonary Rehabilitation of Patients Undergoing Heart Valve Surgery with Extracorporeal Circulation

Objective:To evaluate the application effect of predictive nursing on patients undergoing heart valve surgery with extracorporeal circulation(ECC).Methods:92 ECC patients admitted to the hospital between July 2021 and July 2023 were selected and grouped by random number table method;the observation group practiced predictive nursing,while the reference group practiced conventional nursing.The cardiopulmonary rehabilitation and other indexes were compared between the groups.Results:The postoperative rehabilitation time of the observation group was shorter than that of the reference group,the treatment compliance was higher than that of the reference group,the cardiopulmonary function indexes were all better than that of the reference group,and the complication rate was lower than that of the reference group(P<0.05).Conclusion:The implementation of predictive nursing for ECC patients can promote postoperative rehabilitation,improve patients’treatment compliance,and enhance the cardiopulmonary rehabilitation effect,and nursing safety is high.

Model Predictive Control for Cascaded H-Bridge PV Inverter with Capacitor Voltage Balance

We designed an improved direct-current capacitor voltage balancing control model predictive control(MPC)for single-phase cascaded H-bridge multilevel photovoltaic(PV)inverters.Compared with conventional voltage balanc-ing control methods,the method proposed could make the PV strings of each submodule operate at their maximum power point by independent capacitor voltage control.Besides,the predicted and reference value of the grid-connected current was obtained according to the maximum power output of the maximum power point tracking.A cost function was con-structed to achieve the high-precision grid-connected control of the CHB inverter.Finally,the effectiveness of the proposed control method was verified through a semi-physical simulation platform with three submodules.