Linking preoperative and early intensive care unit data for prolonged intubation prediction.

Objectives: Prolonged 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. Methods: We 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. Results: The 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. Conclusions: Through 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.

[A wearable six-minute walk-based system to predict postoperative pulmonary complications after cardiac valve surgery: an exploratory study].

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.

Comparison of Tools for Postoperative Pulmonary Complications After Cardiac Surgery.

OBJECTIVES: To review the efficacy of 2 score tools for identifying pulmonary complications after cardiac surgery. DESIGN: A retrospective observational study. SETTING: At the West China Hospital of Sichuan University General Hospital. PARTICIPANTS: Patients who underwent elective cardiac surgery (N = 508). INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: A total of 508 patients who underwent elective cardiac surgery between March 2021 and December 2021 were included in this observational study. Three independent physiotherapists used 2 different sets of score tools, as described by Kroenke et al. (Kroenke Score) and Reeve et al. (Melbourne Group Scale), to evaluate clinically defined pulmonary complications according to the European Perioperative Clinical Outcome definitions (including atelectasis, pneumonia, and respiratory failure) daily after surgery at midday. The incidence of postoperative pulmonary complications (PPCs) was 51.6% (262/508) with the Kroenke Score and 21.9% (111/508) with the Melbourne Group Scale. The clinically observed incidence of atelectasis was 51.4%, pneumonia was 20.9%, and respiratory failure at 6.5%. The receiver operator characteristics curve showed that the overall validity of the Kroenke Score was better than that of the Melbourne Group Scale in atelectasis (area under the curve [AUC], 91.5% v 71.3%). The Melbourne Group Scale performed better in pneumonia (AUC, 99.4% v 80.0%) and respiratory failure (AUC, 88.5% v 75.9%) than the Kroenke Score. CONCLUSION: The incidence of PPCs after cardiac surgery was highly prevalent. Both the Kroenke Score and the Melbourne Group Scale are effective in identifying patients with PPCs. Kroenke Score can identify patients with mild pulmonary adverse events, whereas the Melbourne Group Scale is more dominant in identifying moderate-to-severe pulmonary complications.

Perioperative respiratory muscle training improves respiratory muscle strength and physical activity of patients receiving lung surgery: A meta-analysis.

BACKGROUND: The clinical role of perioperative respiratory muscle training (RMT), including inspiratory muscle training (IMT) and expiratory muscle training (EMT) in patients undergoing pulmonary surgery remains unclear up to now. AIM: To evaluate whether perioperative RMT is effective in improving postoperative outcomes such as the respiratory muscle strength and physical activity level of patients receiving lung surgery. METHODS: The PubMed, EMBASE (via OVID), Web of Science, Cochrane Library and Physiotherapy Evidence Database (PEDro) were systematically searched to obtain eligible randomized controlled trials (RCTs). Primary outcome was postoperative respiratory muscle strength expressed as the maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP). Secondary outcomes were physical activity, exercise capacity, including the 6-min walking distance and peak oxygen consumption during the cardio-pulmonary exercise test, pulmonary function and the quality of life. RESULTS: Seven studies involving 240 participants were included in this systematic review and meta-analysis. Among them, four studies focused on IMT and the other three studies focused on RMT, one of which included IMT, EMT and also combined RMT (IMT-EMT-RMT). Three studies applied the intervention postoperative, one study preoperative and the other three studies included both pre- and postoperative training. For primary outcomes, the pooled results indicated that perioperative RMT improved the postoperative MIP (mean = 8.13 cmH2O, 95%CI: 1.31 to 14.95, P = 0.02) and tended to increase MEP (mean = 13.51 cmH2O, 95%CI: -4.47 to 31.48, P = 0.14). For secondary outcomes, perioperative RMT enhanced postoperative physical activity significantly (P = 0.006) and a trend of improved postoperative pulmonary function was observed. CONCLUSION: Perioperative RMT enhanced postoperative respiratory muscle strength and physical activity level of patients receiving lung surgery. However, RCTs with large samples are needed to evaluate effects of perioperative RMT on postoperative outcomes in patients undergoing lung surgery.