Background: The association of lung ultrasound score (LUS) with mortality risk in severe pneumonia patients remains unclear now. This study aimed to identify the predictive role of LUS for the risk of mortality among patients with severe pneumonia. Methods: PubMed, Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases were searched up to November 13, 2025. Odds ratios (ORs) with 95% confidence intervals (CIs) were combined to assess the association between LUS and mortality risk of severe pneumonia patients, which was performed by STATA 17.0 software. Sensitivity, specificity, diagnostic ORs (DORs), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and false positive rate (FPR) were estimated to clarify the diagnostic performance of LUS using the Meta-DiSc 2.0 tool. Results: Thirteen studies with 962 cases were included, with the mortality rate of 35.02% (337/962). Pooled results demonstrated that elevated LUS was significantly related to increased risk of mortality among severe pneumonia patients (OR =1.79, 95% CI: 1.23-2.61, P=0.003), which was further identified by subgroup analyses based on the country, study design and pathogen clarification. After combining the 11 diagnostic studies, the pooled sensitivity and specificity were 84% (95% CI: 0.77-0.89) and 78% (95% CI: 0.72-0.83), with the LR+, LR-and FPR of 3.78 (95% CI: 3.04-4.72), 0.21 (95% CI: 0.15-0.29) and 0.22 (95% CI: 0.17-0.28). Besides, the DOR was 18.28 (95% CI: 12.24-27.3). Then subgroup analyses based on the study design, country and age further identified the diagnostic performance of LUS for mortality risk in severe pneumonia. Conclusions: Based on our pooled analysis, LUS was demonstrated to play a role in predicting the mortality risk of severe pneumonia patients.

OBJECTIVES:To evaluate the convergent validity and prognostic validity of the 1-minute sit-to-stand test (1-min STST) as a practical functional assessment tool in patients hospitalized with acute decompensated heart failure (ADHF) during the early postdischarge period. DESIGN:Prospective cohort study SETTING: West China Hospital of Sichuan University. PARTICIPANTS:A total of 153 patients hospitalized with ADHF. INTERVENTIONS:At hospital discharge, participants underwent assessment with the 1-min STST, Short Physical Performance Battery (SPPB), and 6-minute walk test (6MWT). All patients were followed for 180 days after discharge. MAIN OUTCOME MEASURES:The primary outcome was a 180-day composite endpoint of all-cause mortality or HF-related readmission. Pearson correlation was used to examine associations between 1-min STST repetitions, SPPB score, and 6-minute walk distance. Receiver operating characteristic analysis assessed their ability to predict 180-day composite events (rehospitalization or death), with optimal cutoffs from the Youden Index and area under the curve values (AUCs) compared by DeLong's test. Receiver operating characteristic thresholds were used in Kaplan-Meier analyses (log-rank test), and Cox models estimated adjusted hazard ratios. Statistical significance was set at P≤.05. RESULTS:A total of 153 patients completed the 1-min STST, SPPB, and 6MWT, with mean values of 19.9 repetitions, 10.3 points, and 356.4 m, respectively. The 1-min STST demonstrated strong convergent validity with 6-minute walk distance (r=.727; P<.001). The 1-min STST showed good predictive performance for the 180-day composite endpoint (AUC=0.786), comparable to 6MWT (AUC=0.708). Patients achieving ≥16 repetitions had significantly higher cumulative survival and better functional capacity. CONCLUSIONS:The 1-min STST is a field exercise test that strongly correlates with the 6MWT and provides prognostic insight in patients with ADHF. A threshold of ≥16 repetitions identifies patients with better survival/less hospitalizations, supporting its utility in clinical risk stratification.

Background: Gastrointestinal (GI) dysfunction is a common postoperative complication in patients after acute type A aortic dissection (ATAAD) surgery. Recent evidence suggests that, in addition to early nutrition and feeding strategies, physiotherapy can help to reduce the incidence of postoperative GI dysfunction. This study aimed to investigate whether GI function after ATAAD open surgery can be recovered through surface gastrointestinal electrical stimulation (SGES). Methods: This was a prospective, parallel-group, assessor-blind, randomized controlled trial (RCT). A total of 74 participants were included and randomly divided into a control group (CG) and an SGES intervention group (IG) in a 1:1 ratio. The CG received a standardized perioperative management program developed by a multidisciplinary team, based on the principles of enhanced recovery after surgery (ERAS). The IG implemented SGES at ST36, ST25, and two additional GI pacemakers, as well as ERAS. The primary outcome was GI-2 recovery (tolerance of oral diet and passage of stool). Secondary outcomes included the Gastrointestinal Symptom Rating Scale (GSRS), acute gastrointestinal injury ultrasonography (AGIUS), the Gastrointestinal Quality of Life Index (GIQLI), the incidence of constipation and diarrhea, length of stay in the intensive care unit (ICU), and duration of hospitalization. Results: Of the 74 patients in this study, 24.32% were female, with a mean age of 49.61 years. The time to achieve GI-2 in the IG was significantly shorter, 1.9 days, than in the CG (log-rank test, p = 0.01). The GSRS scores in the IG were significantly lower than those in the CG (total scores: 1.2 vs. 1.6; p = 0.001). Moreover, the GIQLI values at all three follow-up visits were significantly higher in the IG group than in the CG group. Conclusions: To our knowledge, this is the first RCT to investigate the clinical effects of SGES on GI recovery after open-heart surgery for ATAAD. The results provide preliminary evidence supporting the feasibility and therapeutic potential of SGES in a high-risk population. SGES can promote the recovery of GI function, reduce GI-related symptoms, and improve the GI-related quality of life after open heart surgery in patients with ATAAD. Clinical Trial Registration: This trial was based on the Consolidated Standards of Reporting Trials (CONSORT) guidelines. This trial was registered in the Chinese Clinical Trial Registry (identifier ChiCTR2300075265, https://www.chictr.org.cn/showproj.html?proj=205523).

BACKGROUND:The shared goal of clinical physicians and cardiopulmonary physiotherapists is to tailor optimal comprehensive rehabilitation strategies for each patient undergoing cardiac surgery to improve outcomes. The sustained and large-scale acquisition of patient course and rehabilitation treatment-related data faces numerous challenges. This necessitates research and analysis based on large sample size data from cardiac surgery patients. OBJECTIVE:The Cardiopulmonary Physiotherapists Database System (CPPTherapists-DBS) was developed to enhance the management and analysis of data for researching risk factors associated with postoperative pulmonary complications (PPCs) in cardiac surgery patients. This system aims to establish comprehensive system design standards, frameworks, and validation procedures to support research in both cardiac surgery and cardiopulmonary physiotherapy. METHODS:The development of the CPPTherapists-DBS involved: (1) establishing system design standards and frameworks through a detailed software engineering requirements analysis, where clinical researchers defined data collection standards, business scope, and identification rules based on international guidelines and previous research; (2) designing and developing the system to integrate advanced functionalities for data management and analysis within the established frameworks; (3) validating the system by constructing a retrospective cohort for PPCs and developing and evaluating a predictive model based on the collected data. RESULTS:The CPPTherapists-DBS successfully established design standards and frameworks for system development. It has collected clinical data from 27,027 cardiac surgery patients across multiple medical centers from 2010 to 2021. Since January 2022, it has also included physical rehabilitation treatment records for 5,335 patients. The system's CCVPRA tool provides advanced visualization capabilities, enabling rapid data modeling for 6,608 patients and development of a predictive model for PPCs. The model demonstrated strong performance with an AUC of 0.78 in the training set and 0.76 in the testing set. CONCLUSIONS:The CPPTherapists-DBS effectively automates the collection and management of clinical and rehabilitation data, adhering to established system design standards and frameworks. It offers powerful tools for data visualization and modeling, representing a significant advancement in cohort database systems and providing a replicable model for supporting research on cardiac surgery patients and cardiopulmonary physiotherapy.

Background:The effect of inspiratory muscle training (IMT) in chronic heart failure (CHF) patients remains unclear now. This study aimed to comprehensively identify the therapeutic effects of IMT among CHF patients based on current evidence of randomized controlled trials (RCTs). Methods:Several databases were searched up to January 2, 2024 for RCTs investigating the clinical application of IMT in CHF patients. Primary outcomes were maximal inspiratory pressure (MIP) and pulmonary function. Secondary outcomes were exercise performance, including the six-minute walk test (6MWT) and Borg dyspnea index, quality of life evaluated by the Minnesota Living with Heart Failure Questionnaire (MLWHF) and N terminal-pro brain natriuretic peptide (NT-proBNP). Statistical analyses were conducted by the RevMan 5.3 software. Results:Fifteen RCTs with 494 cases were included in this meta-analysis. Pooled results demonstrated that IMT significantly increased the MIP [mean difference (MD) =16.36 cmH2O, 95% confidence interval (CI): 12.26 to 20.46, P<0.001] and VO2peak (MD =1.66 mL/kg/min, 95% CI: 0.27 to 3.05, P=0.02). Besides, patients receiving the IMT showed increased 6MWT (MD =37.40 m, 95% CI: 16.46 to 58.35, P<0.001) and decreased Borg dyspnea index (MD =-0.63, 95% CI: -0.83 to -0.44, P<0.001), MLWHF (MD =-8.51, 95% CI: -13.60 to -3.42, P=0.001) and NT-proBNP (MD =-81.67 pg/mL, 95% CI: -124.88 to -38.45, P<0.001). Conclusions:IMT plays a role in improving the clinical outcomes including the inspiratory muscle function, exercise performance, quality of life and NT-proBNP among CHF patients.

There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable machine learning (ML) models to assess the prognostic value of SDOH and PF at multiple time points. We analyzed data from adult patients admitted to the intensive care unit (ICU) for the first time with a diagnosis of HF. Key variables extracted from electronic health records included SDOH (e.g., primary language, insurance type), PF indicators (Braden mobility, nutrition, activity, and fall risk scores), vital signs, laboratory tests, and lung sounds (LS) from both ICU admission and discharge. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to build models for short- and long-term mortality prediction, and used SHapley Additive exPlanations (SHAP) to interpret model outputs and quantify the importance of each feature. The observed mortality rates were 14.8% in-hospital (n = 12,856), 7.0% at 90 days (n = 10,990), and 13.5% at 1 year (n = 10,221). The prediction models achieved area under the receiver operating characteristic curve (AUROC) scores of 0.836 (95% CI: 0.831-0.844) for in-hospital, 0.790 (95% CI: 0.780-0.800) for 90-day, and 0.789 (95% CI: 0.780-0.799) for 1-year mortality. These models outperformed baseline ML algorithms and conventional clinical risk scores. Key predictors of HF outcomes included age, fall risk, primary language, blood urea nitrogen, comorbidities, urine output, insurance type, and LS findings. Incorporating PF at ICU admission and discharge, along with SDOH such as language proficiency and insurance status, could enhance the identification of high-risk HF patients and may inform targeted interventions.

Abstract Background Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs. Methods A prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System’s electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs. Results In this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs. Conclusion The integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.

Background Clinical notes contain contextualized information beyond structured data related to patients’ past and current health status. Objective This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. Methods Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. Results The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. Conclusions The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

ObjectivesProlonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI.MethodsWe conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques.ResultsThe incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111–1.157], lactate level (OR, 1.131; 95% CI, 1.067–1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115–1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061–1.132), central venous pressure (OR, 1.052; 95% CI, 1.033–1.073), anion gap (OR, 1.075; 95% CI, 1.043–1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047–1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23–1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893–0.964), pH value (OR, 0.757; 95% CI, 0.629–0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003–1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840–0.865) in the training cohort, 0.867 (95% CI, 0.853–0.882) in the testing cohort, and 0.704 (95% CI, 0.679–0.727) in the external validation cohort.ConclusionsThrough multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.

Background: While prehabilitation (pre surgical exercise) effectively prevents postoperative pulmonary complications (PPCs), its cost-effectiveness in valve heart disease (VHD) remains unexplored. This study aims to evaluate the cost-effectiveness of a three-day prehabilitation program for reducing PPCs and improving quality adjusted life years (QALYs) in Chinese VHD patients.Methods: A cost-effectiveness analysis was conducted alongside a randomized controlled trial featuring concealed allocation, blinded evaluators, and an intention-to-treat analysis. In total, 165 patients scheduled for elective heart valve surgery at West China Hospital were randomized into intervention and control groups. The intervention group participated in a three-day prehabilitation exercise program supervised by a physiotherapist while the control group received only standard preoperative education. Postoperative hospital costs were audited through the Hospital Information System, and the EuroQol five-dimensional questionnaire was used to provide a 12-month estimation of QALY. Cost and effect differences were calculated through the bootstrapping method, with results presented in cost-effectiveness planes, alongside the associated cost-effectiveness acceptability curve (CEAC). All costs were denominated in Chinese Yuan (CNY) at an average exchange rate of 6.73 CNY per US dollar in 2022.Results: There were no statistically significant differences in postoperative hospital costs (8484 versus 9615 CNY, 95% CI -2403 to 140) or in the estimated QALYs (0.909 versus 0.898, 95% CI -0.013 to 0.034) between the intervention and control groups. However, costs for antibiotics (339 versus 667 CNY, 95% CI -605 to -51), nursing (1021 versus 1200 CNY, 95% CI -330 to -28), and electrocardiograph monitoring (685 versus 929 CNY, 95% CI -421 to -67) were significantly lower in the intervention group than in the control group. The CEAC indicated that the prehabilitation program has a 92.6% and 93% probability of being cost-effective in preventing PPCs and improving QALYs without incurring additional costs.Conclusions: While the three-day prehabilitation program did not significantly improve health-related quality of life, it led to a reduction in postoperative hospital resource utilization. Furthermore, it showed a high probability of being cost-effective in both preventing PPCs and improving QALYs in Chinese patients undergoing valve surgery.Clinical Registration Number: This trial is registered in the Chinese Clinical Trial Registry (URL: https://www.chictr.org.cn/) with the registration identifier ChiCTR2000039671.

QUESTION:What is the effect of 3 days of preoperative inspiratory muscle training (IMT) on lung function prior to heart valve surgery and on postoperative lung function and pulmonary complications compared with sham and no IMT? DESIGN:A three-arm, multicentre, randomised controlled trial with concealed allocation, intention-to-treat analysis and blinded assessment of some outcomes. PARTICIPANTS:This study included 228 adults scheduled for heart valve surgery. INTERVENTIONS:The IMT group received 3 days of IMT at 30% maximal inspiratory pressure, the sham IMT group received the same but at 10% maximal inspiratory pressure and the control group received no IMT. OUTCOME MEASURES:Spirometric measures, maximal inspiratory pressure and maximum voluntary ventilation were measured at hospital admission, the day before surgery and at discharge. The incidence of postoperative pulmonary complications (primary outcome) and adverse events were recorded. RESULTS:A total of 215 participants completed surgery as planned and all participants were followed up until discharge. Spirometric measures, maximal inspiratory pressure and maximum voluntary ventilation improved in all groups between admission and the day before surgery, but more so in the IMT group. At discharge, these measures had deteriorated in all groups, but less so in the IMT group. Preoperative IMT reduced the total number of participants experiencing a pulmonary complication in the IMT group compared with the sham IMT group (ARR -0.18, 95% CI -0.33 to -0.03) and compared with the control group (ARR -0.21, 95% CI -0.35 to -0.05). Very few adverse events occurred in all three groups. CONCLUSIONS:Preoperative IMT improved lung function prior to surgery and at hospital discharge and reduced postoperative pulmonary complications in adults undergoing elective heart valve surgery. REGISTRATION:ChiCTR2100054869.

Autonomic nervous system (ANS) dysfunction is a significant characteristic of patients with congestive heart failure (CHF). Respiratory sinus arrhythmia (RSA) serves as an index of parasympathetic nervous system (PNS) function usually quantified by the high-frequency power of heart rate variability (HRV). However, the high breathing rate of CHF patients results in deviations when estimating RSA by HRV. Multimodal coupling analysis (MMCA) is a novel method of quantifying RSA which decomposes the R-R signal adaptively into intrinsic mode functions (IMFs) followed by identification of the RSA-related IMF. MMCA also calculates the phase synchronization between RSA-related IMF and respiratory signals to exclude the influences that are unrelated to RSA. In this study, we introduced HRV and MMCA-derived parameters to quantify ANS function for CHF patients, along with their comparisons in the clinical efficacy evaluation. Thirty-seven CHF patients were recruited, including 17 with sinus rhythm (SRHF) and 20 with severe arrhythmia (ARHF). Our results showed that all parameters for SRHF patients increased after treatment except for LF/HF. Only LF/HF, alpha 2, and MMCA-derived RSA showed significant differences after treatment for ARHF patients, wherein the MMCA-derived RSA significantly decreased regardless of the left ventricular ejection fraction. The PNS function and ANS balance were recovered in all the CHF patients after treatment. Metrics including MeanRR, SDRR, LF, HF, TPower, SD1, SD2, and MMCAderived RSA showed more significant improvements in SRHF patients whose New York Heart Association functional class improved after treatment. These metrics can be used to guide prognosis and therapeutic efficacy monitoring.

Background and aims: Loneliness is a risk factor for cardiovascular disease (CVD), and the levels at which individuals experience it can transition over time. However, the impact of increased loneliness or decreased loneliness on later CVD risk remains unexplored. We aimed to identify the agespecific association between loneliness status transitions and subsequent CVD incidences in middle-aged and older adults. Methods and results: Data was extracted from the China Health and Retirement Longitudinal Study (CHARLS) on 8463 adults to evaluate how loneliness status transitions across two data collection points were associated with the subsequent CVD incidence at a five-year follow-up. Loneliness status transitions were divided into four categories: stable low loneliness, decreased loneliness, increased loneliness, and stable high loneliness. Data were analyzed using a Cox-proportional hazards model with age subgroups, accounting for covariates at baseline. During follow-up, the incidence rate of CVD per 1000 person -years was lower for the stable low loneliness group and decreased loneliness group compared to the increased loneliness and stable high loneliness group. Increased loneliness is associated with the highest risk of overall CVD and heart disease (HR 2.44, P < 0.001; HR 2.34, P < 0.001), while stable high loneliness is associated with the highest risk of stroke among the four loneliness categories (HR 4.29, P < 0.05). The age -specific analyses revealed no statistically significant interaction in terms of loneliness status transitions and age group. Conclusion: Increased loneliness and stable high loneliness are associated with higher CVD risk. In clinical practice, it is important to monitor patients ' loneliness status transitions to reduce CVD incidences. (c) 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

PURPOSE:To identify the clinical effect of inspiratory muscle training (IMT) among esophageal cancer patients undergoing esophagectomy based on randomized controlled trials (RCTs). METHODS:Several databases were searched for relevant RCTs up to August 23, 2023. Primary outcomes were respiratory muscle function, including the maximum inspiratory pressure (MIP) and maximum expiratory pressure (MEP), and pulmonary function, including the forced expiratory volume in one second % (FEV1%), forced vital capacity% (FVC%), maximal ventilator volume (MVV), FEV1/FVC% and FVC. The secondary outcomes were exercise performance, including the six-minute walk distance test (6MWT) and Borg index; mental function and quality of life, as evaluated by the Hospital Anxiety Depression Scale (HADS) and Nottingham Health Profile (NHP) score; and postoperative complications. All the statistical analyses were performed with REVMAN 5.3 software. RESULTS:Eight RCTs were included in this meta-analysis, with 368 patients receiving IMT and 371 control subjects. The pooled results demonstrated that IMT could significantly enhance respiratory muscle function (MIP: MD = 7.14 cmH2O, P = 0.006; MEP: MD = 8.15 cmH2O, P<0.001) and pulmonary function (FEV1%: MD = 6.15%, P<0.001; FVC%: MD = 4.65%, P<0.001; MVV: MD = 8.66 L, P<0.001; FEV1/FVC%: MD = 5.27%, P = 0.03; FVC: MD = 0.50 L, P<0.001). Furthermore, IMT improved exercise performance (6MWT: MD = 66.99 m, P = 0.02; Borg index: MD = -1.09, P<0.001), mental function and quality of life (HADS anxiety score: MD = -2.26, P<0.001; HADS depression score: MD = -1.34, P<0.001; NHP total score: MD = -48.76, P<0.001). However, IMT did not significantly decrease the incidence of postoperative complications. CONCLUSION:IMT improves clinical outcomes, such as respiratory muscle function and pulmonary function, in esophageal cancer patients receiving esophagectomy and has potential for broad applications in the clinic.

主动脉夹层作为一类危重症心血管疾病，具有高致死率的特点，并对幸存者存在长期显著的功能影响。手术治疗是优先选择的有效治疗方式，术后康复是此类患者综合管理中重要的一环。然而，目前对该类患者的术后康复研究多集中在出院后的康复，鲜有对住院超早期康复的报道。本文对近年来主动脉夹层术后患者相关的超早期康复研究进行综述，以期为物理治疗师对主动脉夹层术后患者实施超早期康复提供参考。

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

In recent years, wearable devices have seen a booming development, and the integration of wearable devices with clinical settings is an important direction in the development of wearable devices. The purpose of this study is to establish a prediction model for postoperative pulmonary complications (PPCs) by continuously monitoring respiratory physiological parameters of cardiac valve surgery patients during the preoperative 6-Minute Walk Test (6MWT) with a wearable device. By enrolling 53 patients with cardiac valve diseases in the Department of Cardiovascular Surgery, West China Hospital, Sichuan University, the grouping was based on the presence or absence of PPCs in the postoperative period. The 6MWT continuous respiratory physiological parameters collected by the SensEcho wearable device were analyzed, and the group differences in respiratory parameters and oxygen saturation parameters were calculated, and a prediction model was constructed. The results showed that continuous monitoring of respiratory physiological parameters in 6MWT using a wearable device had a better predictive trend for PPCs in cardiac valve surgery patients, providing a novel reference model for integrating wearable devices with the clinic.

目的 探讨留置股血管导管的危重症患者早期活动的安全性与可行性,为指导临床康复训练方案提供参考.方法 检索 PubMed、EMbase、OVID、Springer-link、Wiley Online Library、Web of Science 数据库中研究留置股血管导管的危重症患者早期活动安全性与可行性的文献,检索时间截至2021年6月,提取相关数据进行分析.结果 初步筛选文献72篇,最终纳入符合标准的文献12篇.总患者数为1 056例,其中男560例,留置股血管导管患者489例.患者共进行6495次早期活动,62例患者出现不良事件,其中留置股血管导管的危重症患者早期活动出现导管相关不良事件共14例(2.86％,14/489).结论 尽管留置股血管导管的危重症患者的早期活动可能导致导管相关不良事件的发生,但发生率较低,而且不需要临床额外的急救处理.临床专家应权衡相关风险和益处,不建议将留置股血管导管作为危重症患者早期活动的禁忌证.