Prediction of postoperative pulmonary complications

Driving pressure during general anesthesia for minimally invasive abdominal surgery (GENERATOR)-study protocol of a randomized clinical trial

BACKGROUND

Intraoperative driving pressure (ΔP) has an independent association with the development of postoperative pulmonary complications (PPCs) in patients receiving ventilation during general anesthesia for major surgery. Ventilation with high intraoperative positive end-expiratory pressure (PEEP) with recruitment maneuvers (RMs) that result in a low ΔP has the potential to prevent PPCs. This trial tests the hypothesis that compared to standard low PEEP without RMs, an individualized high PEEP strategy, titrated to the lowest ΔP, with RMs prevents PPCs in patients receiving intraoperative protective ventilation during anesthesia for minimally invasive abdominal surgery.

METHODS

"DrivinG prEssure duriNg gEneRal AnesThesia fOr minimally invasive abdominal suRgery (GENERATOR)" is an international, multicenter, two-group, patient and outcome-assessor blinded randomized clinical trial. In total, 1806 adult patients scheduled for minimally invasive abdominal surgery and with an increased risk of PPCs based on (i) the ARISCAT risk score for PPCs (≥ 26 points) and/or (ii) a combination of age > 40 years and scheduled surgery lasting > 2 h and planned to receive an intra-arterial catheter for blood pressure monitoring during the surgery will be included. Patients are assigned to either an intraoperative ventilation strategy with individualized high PEEP, titrated to the lowest ΔP, with RMs or one with a standard low PEEP of 5 cm H2O without RMs. The primary outcome is a collapsed composite endpoint of PPCs until postoperative day 5.

DISCUSSION

GENERATOR will be the first adequately powered randomized clinical trial to compare the effects of individualized high PEEP with RMs versus standard low PEEP without RMs on the occurrence of PPCs after minimally invasive abdominal surgery. The results of the GENERATOR trial will support anesthesiologists in their decisions regarding PEEP settings during minimally invasive abdominal surgery.

TRIAL REGISTRATION

GENERATOR is registered at ClinicalTrials.gov (study identifier: NCT06101511) on 26 October 2023.

Sex dependence of postoperative pulmonary complications - A post hoc unmatched and matched analysis of LAS VEGAS

STUDY OBJECTIVE

Male sex has inconsistently been associated with the development of postoperative pulmonary complications (PPCs). These studies were different in size, design, population and preoperative risk. We reanalysed the database of 'Local ASsessment of Ventilatory management during General Anaesthesia for Surgery study' (LAS VEGAS) to evaluate differences between females and males with respect to PPCs.

DESIGN, SETTING AND PATIENTS

Post hoc unmatched and matched analysis of LAS VEGAS, an international observational study in patients undergoing intraoperative ventilation under general anaesthesia for surgery in 146 hospitals across 29 countries. The primary endpoint was a composite of PPCs in the first 5 postoperative days. Individual PPCs, hospital length of stay and mortality were secondary endpoints. Propensity score matching was used to create a similar cohort regarding type of surgery and epidemiological factors with a known association with development of PPCs.

MAIN RESULTS

The unmatched cohort consisted of 9697 patients; 5342 (55.1%) females and 4355 (44.9%) males. The matched cohort consisted of 6154 patients; 3077 (50.0%) females and 3077 (50.0%) males. The incidence in PPCs was neither significant between females and males in the unmatched cohort (10.0 vs 10.7%; odds ratio (OR) 0.93 [0.81-1.06]; P = 0.255), nor in the matched cohort (10.5 vs 10.0%; OR 1.05 [0.89-1.25]; P = 0.556). New invasive ventilation occurred less often in females in the unmatched cohort. Hospital length of stay and mortality were similar between females and males in both cohorts.

CONCLUSIONS

In this conveniently-sized worldwide cohort of patients receiving intraoperative ventilation under general anaesthesia for surgery, the PPC incidence was not significantly different between sexes.

REGISTRATION

LAS VEGAS was registered at clinicaltrial.gov (study identifier NCT01601223).

Risk factors for postoperative pulmonary complications in elderly patients undergoing video-assisted thoracoscopic surgery lobectomy under general anesthesia: a retrospective study

BACKGROUND

The objective of this study is to identify and evaluate the risk factors associated with the development of postoperative pulmonary complications (PPCs) in elderly patients undergoing video-assisted thoracoscopic surgery lobectomy under general anesthesia.

METHODS

The retrospective study consecutively included elderly patients (≥ 70 years old) who underwent thoracoscopic lobectomy at Xuanwu Hospital of Capital Medical University from January 1, 2018 to August 31, 2023. The demographic characteristics, the preoperative, intraoperative and postoperative parameters were collected and analyzed using multivariate logistic regression to identify the prediction of risk factors for PPCs.

RESULTS

322 patients were included for analysis, and 115 patients (35.7%) developed PPCs. Multifactorial regression analysis showed that ASA ≥ III (P = 0.006, 95% CI: 1.230 ∼ 3.532), duration of one-lung ventilation (P = 0.033, 95% CI: 1.069 ∼ 4.867), smoking (P = 0.027, 95% CI: 1.072 ∼ 3.194) and COPD (P = 0.015, 95% CI: 1.332 ∼ 13.716) are independent risk factors for PPCs after thoracoscopic lobectomy in elderly patients.

CONCLUSION

Risk factors for PPCs are ASA ≥ III, duration of one-lung ventilation, smoking and COPD in elderly patients over 70 years old undergoing thoracoscopic lobectomy. It is necessary to pay special attention to these patients to help optimize the allocation of resources and enhance preventive efforts.

[Preoperative evaluation of adult patients before elective, non-cardiothoracic surgery : A joint recommendation of the German Society for Anesthesiology and Intensive Care Medicine, the German Society for Surgery and the German Society for Internal Medicine]

The 70 recommendations summarize the current status of preoperative risk evaluation of adult patients prior to elective non-cardiothoracic surgery. Based on the joint publications of the German scientific societies for anesthesiology and intensive care medicine (DGAI), surgery (DGCH), and internal medicine (DGIM), which were first published in 2010 and updated in 2017, as well as the European guideline on preoperative cardiac risk evaluation published in 2022, a comprehensive re-evaluation of the recommendation takes place, taking into account new findings, the current literature, and current guidelines of international professional societies. The revised multidisciplinary recommendation is intended to facilitate a structured and common approach to the preoperative evaluation of patients. The aim is to ensure individualized preparation for the patient prior to surgery and thus to increase patient safety. Taking into account intervention- and patient-specific factors, which are indispensable in the preoperative risk evaluation, the perioperative risk for the patient should be minimized and safety increased. The recommendations for action are summarized under "General Principles (A)," "Advanced Diagnostics (B)," and the "Preoperative Management of Continuous Medication (C)." For the first time, a rating of the individual measures with regard to their clinical relevance has been given in the present recommendation. A joint and transparent agreement is intended to ensure a high level of patient orientation while avoiding unnecessary preliminary examinations, to shorten preoperative examination procedures, and ultimately to save costs. The joint recommendation of DGAI, DGCH and DGIM reflects the current state of knowledge as well as the opinion of experts. The recommendation does not replace the individualized decision between patient and physician about the best preoperative strategy and treatment.

Systematic development and validation of a predictive model for major postoperative complications in the Peri-operative Quality Improvement Project (PQIP) dataset

Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien-Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.

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 predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm

We constructed an early prediction model for postoperative pulmonary complications after thoracoscopic surgery using machine learning and deep learning algorithms. The artificial intelligence prediction models were built in Python, primarily using artificial intelligencealgorithms including both machine learning and deep learning algorithms. Correlation analysis showed that postoperative pulmonary complications were positively correlated with age and surgery duration, and negatively correlated with serum albumin. Using the light gradient boosting machine(LGBM) algorithm, weighted feature engineering revealed that single lung ventilation duration, history of smoking, surgery duration, ASA score, and blood glucose were the main factors associated with postoperative pulmonary complications. Results of artificial intelligence algorithms for predicting pulmonary complications after thoracoscopy in the test group: In terms of accuracy, the two best algorithms were Logistic Regression (0.831) and light gradient boosting machine(0.827); in terms of precision, the two best algorithms were Gradient Boosting (0.75) and light gradient boosting machine (0.742); in terms of recall, the three best algorithms were gaussian naive bayes (0.581), Logistic Regression (0.532), and pruning Bayesian neural network (0.516); in terms of F1 score, the two best algorithms were LogisticRegression (0.589) and pruning Bayesian neural network (0.566); and in terms of Area Under Curve(AUC), the two best algorithms were light gradient boosting machine(0.873) and pruning Bayesian neural network (0.869). The results of this study suggest that pruning Bayesian neural network (PBNN) can be used to assess the possibility of pulmonary complications after thoracoscopy, and to identify high-risk groups prior to surgery.

Risk prediction models for postoperative delirium in elderly patients with fragility hip fracture: A systematic review and critical appraisal

BACKGROUND

Elderly patients with fragility hip fracture continue to experience a high rate of postoperative delirium (POD), which has a significant negative impact on prognosis and imposes a huge economic burden. A number of risk prediction models have been developed to detect POD early. However, the risk of bias and clinical applicability of the models remain unclear. The aim of this study was to systematically evaluate risk prediction models for POD.

METHODS

CNKI, WanFang DATA, Vip Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched for studies published by July 2023. The literature was screened independently by two investigators. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST) were respectively used for data extraction, risk of bias, and applicability assessment.

RESULTS

A total of 16 studies on the construction of POD risk prediction models were included. The area under the ROC curve of the models ranges from 0.670 to 0.957. The most common predictors of POD included age, history of dementia, history of delirium, ASA classification, preoperative waiting time, and preoperative albumin level. All models had a high risk of bias, mainly due to inadequate sample size, inappropriate handling of missing data, a lack of model performance evaluation, and overfitting of the models.

CONCLUSIONS

Overall, risk prediction models for POD in fragility hip fracture patients are still in the development stage. The majority of POD prediction models have substantial bias risks, attributable primarily to poor reporting of analysis and evaluation of model performance. In future research, it is recommended to conduct validation of the models or develop localized prediction models with demonstrated high performance, with the aim of benefiting POD screening.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023449153.

Concordance between ARISCAT risk score and cardiopulmonary exercise test values in risk prediction of postoperative pulmonary complications of major abdominal surgeries in a tertiary cancer hospital: A cross-sectional study

Background and Aims

Pulmonary complications are common after surgery. They include vascular thrombosis, pneumonia, respiratory failure (RF), and pain-related atelectasis. There are a number of models to predict the risk of postoperative respiratory events other than thrombosis. The aim of this study was to explore the correlation of assess respiratory risk in surgical patients in Catalonia (ARISCAT) scoring and cardiopulmonary exercise test (CPET) values in prediction of postoperative pulmonary complications (PPCs).

Methods

Cancer patients referred to a tertiary hospital for elective major abdominal surgeries were studied. Patients were evaluated by ARISCAT score and then CPET was performed to determine the risk of surgery based on maximal oxygen consumption (VO2) value. Patients were followed for RF occurrence up to 72 h after surgery. Finally, the concordance of ARISCAT score and CPET values was evaluated in risk prediction of PPCs.

Results

The results showed that parameters VO2, ARISCAT score, and anaerobic threshold could predict postoperative RF. Of these parameters, ARISCAT showed the highest sensitivity (100%) and the highest specificity (90.5%) compared with other parameters (Youden's J statistic = 0.905). However, VO2 value showed the highest validity. The percentage of agreement between different subgroups (low, medium, and high) of both criteria (VO2 and ARISCAT) was equal to 81.45% (p < 0.001) and the ϰ coefficient of the given weight was equal to 0.54 (p < 0.001), indicating a good agreement between these two criteria.

Conclusion

ARISCAT scoring showed high sensitivity and specificity to PPCs in cancer patients and good correlation with CPET value for prediction of PPCs. Therefore, it is a reliable and robust risk prediction tool in major abdominal surgeries on cancer patients.

Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study

STUDY OBJECTIVE

Postoperative respiratory failure is a major surgical complication and key quality metric. Existing prediction tools underperform, are limited to specific populations, and necessitate manual calculation. This limits their implementation. We aimed to create an improved, machine learning powered prediction tool with ideal characteristics for automated calculation.

DESIGN, SETTING, AND PATIENTS

We retrospectively reviewed 101,455 anesthetic procedures from 1/2018 to 6/2021. The primary outcome was the Standardized Endpoints in Perioperative Medicine consensus definition for postoperative respiratory failure. Secondary outcomes were respiratory quality metrics from the National Surgery Quality Improvement Sample, Society of Thoracic Surgeons, and CMS. We abstracted from the electronic health record 26 procedural and physiologic variables previously identified as respiratory failure risk factors. We randomly split the cohort and used the Random Forest method to predict the composite outcome in the training cohort. We coined this the RESPIRE model and measured its accuracy in the validation cohort using area under the receiver operating curve (AUROC) analysis, among other measures, and compared this with ARISCAT and SPORC-1, two leading prediction tools. We compared performance in a validation cohort using score cut-offs determined in a separate test cohort.

MAIN RESULTS

The RESPIRE model exhibited superior accuracy with an AUROC of 0.93 (95% CI, 0.92-0.95) compared to 0.82 for both ARISCAT and SPORC-1 (P-for-difference < 0.0001 for both). At comparable 80-90% sensitivities, RESPIRE had higher positive predictive value (11%, 95% CI: 10-12%) and lower false positive rate (12%, 95% CI: 12-13%) compared to 4% and 37% for both ARISCAT and SPORC-1. The RESPIRE model also better predicted the established quality metrics for postoperative respiratory failure.

CONCLUSIONS

We developed a general-purpose, machine learning powered prediction tool with superior performance for research and quality-based definitions of postoperative respiratory failure.

Prediction of postoperative complications after oesophagectomy using machine-learning methods

BACKGROUND

Oesophagectomy is an operation with a high risk of postoperative complications. The aim of this single-centre retrospective study was to apply machine-learning methods to predict complications (Clavien-Dindo grade IIIa or higher) and specific adverse events.

METHODS

Patients with resectable adenocarcinoma or squamous cell carcinoma of the oesophagus and gastro-oesophageal junction who underwent Ivor Lewis oesophagectomy between 2016 and 2021 were included. The tested algorithms were logistic regression after recursive feature elimination, random forest, k-nearest neighbour, support vector machine, and neural network. The algorithms were also compared with a current risk score (the Cologne risk score).

RESULTS

457 patients had Clavien-Dindo grade IIIa or higher complications (52.9 per cent) versus 407 patients with Clavien-Dindo grade 0, I, or II complications (47.1 per cent). After 3-fold imputation and 3-fold cross-validation, the overall accuracies were: logistic regression after recursive feature elimination, 0.528; random forest, 0.535; k-nearest neighbour, 0.491; support vector machine, 0.511; neural network, 0.688; and Cologne risk score, 0.510. For medical complications, the results were: logistic regression after recursive feature elimination, 0.688; random forest, 0.664; k-nearest neighbour, 0.673; support vector machine, 0.681; neural network, 0.692; and Cologne risk score, 0.650. For surgical complications, the results were: logistic regression after recursive feature elimination, 0.621; random forest, 0.617; k-nearest neighbour, 0.620; support vector machine, 0.634; neural network, 0.667; and Cologne risk score, 0.624. The calculated area under the curve of the neural network was 0.672 for Clavien-Dindo grade IIIa or higher, 0.695 for medical complications, and 0.653 for surgical complications.

CONCLUSION

The neural network scored the highest accuracies compared with all of the other models for the prediction of postoperative complications after oesophagectomy.

Stair-Climbing Tests or Self-Reported Functional Capacity for Preoperative Pulmonary Risk Assessment in Patients with Known or Suspected COPD-A Prospective Observational Study

BACKGROUND

This prospective study aims to determine whether preoperative stair-climbing tests (SCT) predict postoperative pulmonary complications (PPC) better than self-reported poor functional capacity (SR_PFC) in patients with known or suspected COPD.

METHODS

A total of 320 patients undergoing scheduled for major non-cardiac surgery, 240 with verified COPD and 80 with GOLD key indicators but disproved COPD, underwent preoperative SR_PFC and SCT and were analyzed. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection. Two multivariable regression models were fitted, the SR_PFC model (baseline variables such as sociodemographic, surgical and procedural characteristics, medical preconditions, and GOLD key indicators plus SR_PFC) and the SCT model (baseline variables plus SCT_PFC).

RESULTS

Within all stair-climbing variables, LASSO exclusively selected self-reported poor functional capacity. The cross-validated area under the receiver operating characteristic curve with bias-corrected bootstrapping 95% confidence interval (95% CI) did not differ between the SR_PFC and SCT models (0.71; 0.65-0.77 for both models). SR_PFC was an independent risk factor (adjusted odds ratio (OR) 5.45; 95% CI 1.04-28.60; p = 0.045 in the SR_PFC model) but SCT_PFC was not (adjusted OR 3.78; 95% CI 0.87-16.34; p = 0.075 in the SCT model).

CONCLUSIONS

Our findings indicate that preoperative SR_PFC adequately predicts PPC while additional preoperative SCTs are dispensable in patients with known or suspected COPD.

Comparison of 6-min walk test distance vs. estimated maximum oxygen consumption for predicting postoperative pulmonary complications in patients undergoing upper abdominal surgery: a prospective cohort study

OBJECTIVE

The present study aims to evaluate the predictive ability of estimated maximum oxygen consumption (e[Formula: see text]O_2max) and 6-min walk distance (6MWD) for postoperative pulmonary complications (PPCs) in adult surgical patients undergoing major upper abdominal surgery.

METHOD

This study was conducted by collecting data prospectively from a single center. The two predictive variables in the study were defined as 6MWD and e[Formula: see text]O_2max. Patients scheduled for elective major upper abdominal surgery from March 2019 to May 2021 were included. The 6MWD was measured for all patients before surgery. e[Formula: see text]O_2max was calculated using the regression model of Burr, which uses 6MWD, age, gender, weight, and resting heart rate (HR) to predict aerobic fitness. The patients were categorized into PPC and non-PPC group. The sensitivity, specificity, and optimum cutoff values for 6MWD and e[Formula: see text]O_2max were calculated to predict PPCs. The area under the receiver operating characteristic curve (AUC) of 6MWD or e[Formula: see text]O_2max was constructed and compared using the Z test. The primary outcome measure was the AUC of 6MWD and e[Formula: see text]O_2max in predicting PPCs. In addition, the net reclassification index (NRI) was calculated to assess ability of e[Formula: see text]O_2max compared with 6MWT in predicting PPCs.

RESULTS

A total of 308 patients were included 71/308 developed PPCs. Patients unable to complete the 6-min walk test (6MWT) due to contraindications or restrictions, or those taking beta-blockers, were excluded. The optimum cutoff point for 6MWD in predicting PPCs was 372.5 m with a sensitivity of 63.4% and specificity of 79.3%. The optimum cutoff point for e[Formula: see text]O_2max was 30.8 ml/kg/min with a sensitivity of 91.6% and specificity of 79.3%. The AUC for 6MWD in predicting PPCs was 0.758 (95% confidence interval (CI): 0.694-0.822), and the AUC for e[Formula: see text]O_2max was 0.912 (95%CI: 0.875-0.949). A significantly increased AUC was observed in e[Formula: see text]O_2max compared to 6MWD in predicting PPCs (P < 0.001, Z = 4.713). And compared with 6MWT, the NRI of e[Formula: see text]O_2max was 0.272 (95%CI: 0.130, 0.406).

CONCLUSION

The results suggested that e[Formula: see text]O_2max calculated from the 6MWT is a better predictor of PPCs than 6MWD in patients undergoing upper abdominal surgery and can be used as a tool to screen patients at risk of PPCs.

A novel nephrectomy-specific respiratory failure index using the ACS-NSQIP dataset

PURPOSE

Post-operative pulmonary failure is a major complication of nephrectomy that may lead to severe morbidity and mortality. Hence, we aimed to derive a nephrectomy-specific post-operative respiratory failure index.

METHODS

Our cohort was derived from The American College of Surgeons-National Surgical Quality Improvement Program database between 2005 and 2019. The outcome of interest was post-operative respiratory failure (PRF) defined as any incidence of unplanned intubation post-operatively or requiring mechanical ventilation post-operatively for a period > 48 h. A multivariable logistic regression model was constructed, and model calibration and performance were assessed using a ROC analysis and the Hosmer-Lemeshow test. Finally, we derived the nephrectomy-specific respiratory failure (NSRF) index and compared it to Gupta's index.

RESULTS

Seventy-nine thousand five hundred and twenty-three patients underwent nephrectomy between the years 2005 and 2019 of which nine hundred and sixty-two patients developed PRF. The final NSRF model encompassed ten variables: age, smoking status, American society of anesthesiology class, abnormal creatinine (≥ 1.5 mg/dL), anemia (< 36%), functional health status, chronic obstructive pulmonary disease, surgical approach, emergency case, and obesity (≥ 40 kg/m²). The NSRF ROC analysis provided C-statistic = 0.78, calibration R² = 0.99, and proper goodness of fit. In comparison, the C-statistics of Gupta's index was found to be 0.71 (p value < 0.001).

CONCLUSION

The NSRF is a procedure tailored index for predicting post-operative respiratory failure. It is a valuable tool in the pre-operative evaluation setting that can help identify high-risk patients who will require additional respiratory evaluation and preparation for their surgery.

Protocol for the derivation and external validation of a 30-day postoperative pulmonary complications (PPCs) risk prediction model for elderly patients undergoing thoracic surgery: a cohort study in southern China

INTRODUCTION

Postoperative pulmonary complications (PPCs) occur after up to 60% of non-cardiac thoracic surgery (NCTS), especially for multimorbid elderly patients. Nevertheless, current risk prediction models for PPCs have major limitations regarding derivation and validation, and do not account for the specific risks of NCTS patients. Well-founded and externally validated models specific to elderly NCTS patients are warranted to inform consent and treatment decisions.

METHODS AND ANALYSIS

We will develop, internally and externally validate a multivariable risk model to predict 30-day PPCs in elderly NCTS patients. Our cohort will be generated in three study sites in southern China with a target population of approximately 1400 between October 2021 and December 2023. Candidate predictors have been selected based on published data, clinical expertise and epidemiological knowledge. Our model will be derived using the combination of multivariable logistic regression and bootstrapping technique to lessen predictors. The final model will be internally validated using bootstrapping validation technique and externally validated using data from different study sites. A parsimonious risk score will then be developed on the basis of beta estimates derived from the logistic model. Model performance will be evaluated using area under the receiver operating characteristic curve, max-rescaled Brier score and calibration slope. In exploratory analysis, we will also assess the net benefit of Probability of PPCs Associated with THoracic surgery in elderly patients score in the complete cohort using decision curve analysis.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Institutional Review Board of the Affiliated Cancer Hospital and Institute of Guangzhou Medical University, the Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine and the University of Hongkong-Shenzhen Hospital, respectively. The final risk prediction model will be published in an appropriate journal and further disseminated as an online calculator or nomogram for clinical application. Approved and anonymised data will be shared.

TRIAL REGISTRATION NUMBER

ChiCTR2100051170.

Risk stratification for post-operative pulmonary complications following major cardiothoracic or abdominal surgery: Validation of the PPC Risk Prediction Score for physiotherapist's clinical decision-making

INTRODUCTION

Patients undergoing major cardiothoracic or abdominal surgery are at increased risk of developing post-operative pulmonary complications (PPC), but respiratory physiotherapy can prevent PPC. We have previously developed the PPC Risk Prediction Score to allocate physiotherapists' resources and stratify patients into three risk groups. In this study, we performed a temporal external validation of the PPC Risk Prediction Score. Such validation is rare and adds to the originality of this study.

METHODS

A cohort of 360 patients, admitted to undergo elective cardiothoracic or abdominal surgery, were included. Performance and clinical usefulness of the PPC Risk Prediction Score were estimated through discrimination, calibration and clinical usefulness, and the score was updated.

RESULTS

The score showed c-statistics of 0.62. Related to clinical usefulness, a cut point at 8 gave a sensitivity of 0.49 and a specificity of 0.70, whereas a cut point at 12 gave a sensitivity of 0.13 and a specificity of 0.95. Two predictors included in the development sample score, thoraco-abdominal incision odds ratio (OR) 2.74 (1.12;6.71) and sternotomy OR 2.09 (1.18;3.72), were statistically significantly associated to PPC in the validation sample.

CONCLUSIONS

The score was not able to discriminate between patients with and without PPC; neither was the updated score, but the study identified clinically relevant risk factors for developing PPC.

Identification of risk factors for postoperative pulmonary complications in general surgery patients in a low-middle income country

BACKGROUND

Postoperative pulmonary complications (PPCs) are an important cause of perioperative morbidity and mortality. Although risk factors for PPCs have been identified in high-income countries, less is known about PPCs and their risk factors in low- and middle-income countries, such as South Africa. This study examined the incidence of PPCs and their associated risk factors among general surgery patients in a public hospital in the province of KwaZulu-Natal, South Africa to inform future quality improvement initiatives to decrease PPCs in this clinical population.

METHODS

A retrospective secondary analysis of adult patients with general surgery admissions from January 1, 2013 to December 31, 2017 was conducted using data from the health system's Hybrid Electronic Medical Registry. The sample was comprised of 5352 general surgery hospitalizations. PPCs included pneumonia, atelectasis, acute respiratory distress syndrome, pulmonary edema, pulmonary embolism, prolonged ventilation, hemothorax, pneumothorax, and other respiratory morbidity which encompassed empyema, aspiration, pleural effusion, bronchopleural fistula, and lower respiratory tract infection. Risk factors examined were age, tobacco use, number and type of pre-existing comorbidities, emergency surgery, and number and type of surgeries. Bivariate and multivariable logistic regression models were conducted to identify risk factors for developing a PPC.

RESULTS

The PPC rate was 7.8%. Of the 418 hospitalizations in which a patient developed a PPC, the most common type of PPC was pneumonia (52.4%) and the mortality rate related to the PPC was 11.7%. Significant risk factors for a PPC were increasing age, greater number of comorbidities, emergency surgery, greater number of general surgeries, and abdominal surgery.

CONCLUSIONS

PPCs are common in general surgery patients in low- and middle-income countries, with similar rates observed in high-income countries. These complications worsen patient outcomes and increase mortality. Quality improvement initiatives that employ resource-conscious methods are needed to reduce PPCs in low- and middle-income countries.

A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model

Aim

Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.

Methods

We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.

Results

The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889).

Conclusions

This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.

Preoperative risk assessment and spirometry is a cost-effective strategy to reduce post-operative complications and mortality in Mexico

Aim

Combining preoperative spirometry with the Assess Respiratory Risk in Surgical Patients in Catalunia (ARISCAT) risk scale can reduce post-operative complications and improve patient survival. This study aimed to assess the cost-effectiveness of performing spirometry or not in conjunction with the ARISCAT scale, to reduce post-operative complications and improve survival among adult patients undergoing elective surgery in Mexico.

Methods

A cost-effectiveness analysis (CEA) was performed to compare the specific cost and health outcomes associated with the combined use of the ARISCAT scale and preoperative spirometry (Group 1), and the use of the ARISCAT scale without preoperative spirometry (Group 2). The health outcomes evaluated were post-operative complications and survival. The perspective was from the health care provider (Hospital General de México) and direct medical costs were reported in 2019 US dollars. A decision tree with a time horizon of eight months was used for each health outcome and ARISCAT risk level.

Results

The combined use of the ARISCAT scale and spirometry is more cost-effective for reducing post-operative complications in the low and moderate-risk levels and is cost-saving in the high-risk level, than use of the ARISCAT scale without spirometry. To improve patient survival, ARISCAT and spirometry are also more cost-effective at the moderate risk level, and cost-saving for high-risk patients, than using the ARISCAT scale alone.

Conclusions

The use of preoperative spirometry among patients with a high ARISCAT risk level was cost-saving, reduced post-operative complications, and improved survival. Our findings indicate an urgent need to implement spirometry as part of preoperative care in Mexico, which is already the standard of care in other countries.

Individualized risk estimation for postoperative pulmonary complications after hepatectomy based on perioperative variables

BACKGROUND

At present, there is no perfect system to evaluate pulmonary complications of liver surgery using perioperative variables.

AIM

To design and verify a risk assessment system for predicting postoperative pulmonary complications (PPCs) after hepatectomy based on perioperative variables.

METHODS

A retrospective analysis was performed on 1633 patients who underwent liver surgery. The variables were screened using univariate and multivariate analyses, and graded scores were assigned to the selected variables. Logistic regression was used to develop the liver operation pulmonary complication scoring system (LOPCSS) for the prediction of PPCs. The LOPCSS was verified using the receiver operating characteristic curve.

RESULTS

According to the multivariate correlation analysis, the independent factors which influenced PPCs of liver surgery were age [≥ 65 years old/< 65 years old, odds ratio (OR) = 1.926, P = 0.011], medical diseases requiring drug treatment (yes/no, OR = 3.523, P < 0.001), number of liver segments to be removed (≥ 3/≤ 2, OR = 1.683, P = 0.002), operation duration (≥ 180 min/< 180 min, OR = 1.896, P = 0.004), and blood transfusion (yes/no, OR = 1.836, P = 0.003). The area under the curve (AUC) of the LOPCSS was 0.742. The cut-off value of the expected score for complications was 5. The incidence of complications in the group with ≤ 4 points was significantly lower than that in the group with ≥ 6 points (2.95% vs 33.40%, P < 0.001). Furthermore, in the validation dataset, the corresponding AUC of LOPCSS was 0.767.

CONCLUSION

As a novel and simplified assessment system, the LOPCSS can effectively predict PPCs of liver surgery through perioperative variables.

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

BACKGROUND

Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications.

METHODS

We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC).

FINDINGS

In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683-0·717]).

INTERPRETATION

In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required.

FUNDING

British Journal of Surgery Society.

Machine learning-based modeling of acute respiratory failure following emergency general surgery operations

Background

Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.

Objective

The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.

Methods

Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016–18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R²). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models.

Results

Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899–0.901 vs 0.894, 95% CI 0.862–0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R²: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8–11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0–3.2) and $11,900 (95% CI 11,600–12,300) in costs.

Conclusions

Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.

Clinical guide to perioperative management for videothoracoscopy lung resection (Section of Cardiac, Vascular and Thoracic Anesthesia, SEDAR; Spanish Society of Thoracic Surgery, SECT; Spanish Society of Physiotherapy)

The introduction of video-assisted thoracoscopic (VATS) techniques has led to a new approach in thoracic surgery. VATS is performed by inserting a thoracoscope through a small incisions in the chest wall, thus maximizing the preservation of muscle and tissue. Because of its low rate of morbidity and mortality, VATS is currently the technique of choice in most thoracic procedures. Lung resection by VATS reduces prolonged air leaks, arrhythmia, pneumonia, postoperative pain and inflammatory markers. This reduction in postoperative complications shortens hospital length of stay, and is particularly beneficial in high-risk patients with low tolerance to thoracotomy. Compared with conventional thoracotomy, the oncological results of VATS surgery are similar or even superior to those of open surgery. This aim of this multidisciplinary position statement produced by the thoracic surgery working group of the Spanish Society of Anesthesiology and Reanimation (SEDAR), the Spanish Society of Thoracic Surgery (SECT), and the Spanish Association of Physiotherapy (AEF) is to standardize and disseminate a series of perioperative anaesthesia management guidelines for patients undergoing VATS lung resection surgery. Each recommendation is based on an in-depth review of the available literature by the authors. In this document, the care of patients undergoing VATS surgery is organized in sections, starting with the surgical approach, and followed by the three pillars of anaesthesia management: preoperative, intraoperative, and postoperative anaesthesia.

Choice of neuromuscular block reversal agent to reduce postoperative pulmonary complications

The definition of postoperative pulmonary complications (PPCs) is inconsistent in literature; however, PPCs include pulmonary abnormalities that adversely affect patient outcomes, such as respiratory failure, atelectasis, pneumonia, pleural effusion, and exacerbation of underlying lung conditions. Furthermore, although the incidence of PPCs varies according to its definition, surgery type, and patient population, they can lead to increased morbidity, mortality, duration of hospitalization, and medical costs; thus, efforts to identify and reduce the risk factors are important to improve patient outcomes. Among the risk factors for PPCs, residual neuromuscular block is a representative and preventable anesthesia-related risk factor that is affected by the choice of the reversal agent. However, it is not clear whether the chosen reversal agent, i.e., sugammadex, reduces PPCs better when compared to anticholinesterases. Additionally, the effects of the reversal agents on PPCs in high-risk patients, such as elderly patients, pediatric patients, those with end-stage renal disease, obesity, obstructive sleep apnea, or those undergoing specific surgeries, are diverse. To reduce the PPCs associated with the use of neuromuscular blocking agents, it is important to confirm complete reversal of the neuromuscular block under neuromuscular monitoring. Additionally, efforts to reduce the incidence of PPCs through interdisciplinary communication are required.

Risk Stratification for Postoperative Pulmonary Complications following Major Cardiothoracic and Abdominal Surgery - development of the PPC Risk Prediction Score for Physiotherapists Clinical Decision-making

INTRODUCTION

Major cardiothoracic or abdominal surgery can lead to the development of postoperative pulmonary complications (PPC), associated with increased morbidity and prolonged length of hospital stay. Preventive chest physiotherapy is routinely provided, but optimization of treatment strategies is needed to improve patient outcome and resource utilization.

OBJECTIVE

To develop a preoperative risk prediction scorelr to assist clinical decision-making regarding physiotherapy interventions.

METHODS

A prospective observational single-center study included 339 of 577 eligible patients admitted for major elective cardiothoracic or abdominal surgery. Primary outcome measure was PPC amendable to chest physiotherapy.

RESULTS

A total of 113 patients (33.3%) developed a PPC. Logistic regression modeling identified four independent predictors of PPC presented with odds ratio (OR) and 95% confidence interval. Reduced lung function (FEV₁ > 50% to <75% OR 2.4 (1.4; 4.3) and FEV₁ ≤ 50% OR 4.7 (1.4;16.0)), Recent unintended weight loss OR 4.5 (1.1; 18.7), Sternotomy OR 3.5 (2.0; 6.0) and Thoraco-abdominal incision OR 4.5 (2.1; 10.1). Based on assigned point values, a score dividing patients into three risk groups was developed. The score had moderate discrimination (c-statistic 0.70).

CONCLUSION

By following recommended guidelines (TRIPOD) a preoperative risk prediction score including four predictors of PPC was developed. External validation of the score is currently being investigated.

Geo-economic variations in epidemiology, ventilation management and outcome of patients receiving intraoperative ventilation during general anesthesia- posthoc analysis of an observational study in 29 countries

BACKGROUND

The aim of this analysis is to determine geo-economic variations in epidemiology, ventilator settings and outcome in patients receiving general anesthesia for surgery.

METHODS

Posthoc analysis of a worldwide study in 29 countries. Lower and upper middle-income countries (LMIC and UMIC), and high-income countries (HIC) were compared. The coprimary endpoint was the risk for and incidence of postoperative pulmonary complications (PPC); secondary endpoints were intraoperative ventilator settings, intraoperative complications, hospital stay and mortality.

RESULTS

Of 9864 patients, 4% originated from LMIC, 11% from UMIC and 85% from HIC. The ARISCAT score was 17.5 [15.0-26.0] in LMIC, 16.0 [3.0-27.0] in UMIC and 15.0 [3.0-26.0] in HIC (P = .003). The incidence of PPC was 9.0% in LMIC, 3.2% in UMIC and 2.5% in HIC (P < .001). Median tidal volume in ml kg^- 1 predicted bodyweight (PBW) was 8.6 [7.7-9.7] in LMIC, 8.4 [7.6-9.5] in UMIC and 8.1 [7.2-9.1] in HIC (P < .001). Median positive end-expiratory pressure in cmH₂O was 3.3 [2.0-5.0]) in LMIC, 4.0 [3.0-5.0] in UMIC and 5.0 [3.0-5.0] in HIC (P < .001). Median driving pressure in cmH₂O was 14.0 [11.5-18.0] in LMIC, 13.5 [11.0-16.0] in UMIC and 12.0 [10.0-15.0] in HIC (P < .001). Median fraction of inspired oxygen in % was 75 [50-80] in LMIC, 50 [50-63] in UMIC and 53 [45-70] in HIC (P < .001). Intraoperative complications occurred in 25.9% in LMIC, in 18.7% in UMIC and in 37.1% in HIC (P < .001). Hospital mortality was 0.0% in LMIC, 1.3% in UMIC and 0.6% in HIC (P = .009).

CONCLUSION

The risk for and incidence of PPC is higher in LMIC than in UMIC and HIC. Ventilation management could be improved in LMIC and UMIC.

TRIAL REGISTRATION

Clinicaltrials.gov , identifier: NCT01601223.

Acute Respiratory Failure

Acute respiratory failure occurs when the lungs fail to oxygenate arterial blood adequately and it is one of the commonest postoperative complications. The preoperative identification of risk factors for postoperative acute respiratory failure allows identification of those patients who may benefit from preoperative optimization and increased postoperative vigilance. Multiple postoperative pulmonary complications are associated with acute hypoxemic respiratory failure and this chapter discusses atelectasis, pulmonary embolism, aspiration, and acute respiratory distress syndrome in detail, as well as providing a unified clinical approach to the acutely hypoxemic perioperative patient.

[A Nomogram for Prediction of Complications Based on TM&M System of VATS Major Lung Surgery for Lung Cancer]

BACKGROUND

Postoperative complications are an important cause of death after lung resection. At present, the adoption of video assisted thoracoscopic surgery (VATS) for lung cancer in China is increasing every year, but the prediction model of postoperative complications of VATS for lung cancer is still lack of evidence based on large sample database. In this study, Thoracic Mortality and Morbidity (TM&M) classification system was used to comprehensively describe the postoperative complications of VATS major lung resection in our center, and the prediction model of complications was established and verified. The model can provide basis for the prevention and intervention of postoperative complications in such patients, and accelerate the recovery of patients.

METHODS

The clinical data of patients underwent VATS major lung resection in our center from January 2007 to December 2018 were collected retrospectively. Only patients with stage I-III lung cancer were included. The postoperative complications were registered strictly by TM&M classification system. The patients were divided into two groups according to the operation period: the early phase group (From 2007 to 2012) and the late phase group (From 2013 to 2018). The baseline data of the two groups were matched by propensity score matching. After matching, binary logistic regression analysis was used to establish the prediction model of complications, and bootstrap internal sampling was used for internal verification.

RESULTS

A total of 2,881 patients with lung cancer were included in the study, with an average age of (61.0±10.1) years, including 180 major complications (6.2%). Binary Logistic regression analysis of 1,268 matched patients showed: age (OR=1.04, 95%CI: 1.02-1.06, P<0.001), other period (OR=0.62, 95%CI: 0.49-0.79, P<0.001), pathological type (OR=1.73, 95%CI: 1.24-2.41, P=0.001), blood loss (OR=1.001, 95%CI: 1.000-1.003, P=0.03), dissected lymph nodes (OR=1.022, 95%CI: 1.00-1.04, P=0.005) were independent risk factors for postoperative complications. The ROC curve indicates that the model has good discrimination (C-index=0.699), and the C-index is 0.680 verified by bootstrap internal sampling for 1,000 times. The calibration curve shows a good calibration of the prediction model.

CONCLUSIONS

TM&M system can comprehensively and accurately report the postoperative complications of thoracoscopic lung cancer surgery. Age, operative period, pathological type, intraoperative bleeding and dissected lymph nodes were independent risk factors for postoperative complications of VATS major lung resection for lung cancer. The established complication prediction model has good discrimination and calibration.

Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? An observational study at a single university hospital

Background

The incidence of postoperative pulmonary complications (PPCs) is increasing in line with the rise in the number of surgical procedures performed on geriatric patients. In this study, we determined the incidence and risk factors of PPCs in elderly Thai patients who underwent upper abdominal procedures, and we investigated whether the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score helps to predict PPCs in Thais.

Methods

A retrospective study was conducted on upper abdominal surgical patients aged over 65 years who had been admitted to the surgical ward of Siriraj Hospital, Mahidol University, Thailand, between January 2016 and December 2019. Data were collected on significant PPCs using the European Perioperative Clinical Outcome definitions. To identify risk factors, evaluations were made of the relationships between the PPCs and various preoperative, intraoperative, and postoperative factors, including ARISCAT scores.

Results

In all, 1100 elderly postoperative patients were analyzed. Their mean age was 73.6 years, and 48.5% were male. Nearly half of their operations were laparoscopic cholecystectomies. The incidence of PPCs was 7.7%, with the most common being pleural effusion, atelectasis, and pneumonia. The factors associated with PPCs were preoperative oxygen saturation less than 96% (OR = 2.6, 1.2–5.5), albumin level below 3.5 g/dL (OR = 1.7, 1.0–2.8), duration of surgery exceeding 3 h (OR = 2.0, 1.0–4.2), and emergency surgery (OR = 2.8, 1.4–5.8). There was a relationship between ARISCAT score and PPC incidence, with a correlation coefficient of 0.226 (P < 0.001). The area under the curve was 0.72 (95% CI, 0.665–0.774; P < 0.001).

Conclusions

PPCs are common in elderly patients. They are associated with increased levels of postoperative morbidities and extended ICU and hospital stays. Using the ARISCAT score as an assessment tool facilitates the classification of Thai patients into PPC risk groups. The ARISCAT scoring system might be able to be similarly applied in other Southeast Asian countries.

How can we minimize the risks by optimizing patient's condition shortly before thoracic surgery?

The “moderate-to-high-risk” surgical patient is typically older, frail, malnourished, suffering from multiple comorbidities and presenting with unhealthy life style such as smoking, hazardous drinking and sedentarity. Poor aerobic fitness, sarcopenia and “toxic” behaviors are modifiable risk factors for major postoperative complications. The physiological challenge of lung cancer surgery has been likened to running a marathon. Therefore, preoperative patient optimization or “ prehabilitation “ should become a key component of improved recovery pathways to enhance general health and physiological reserve prior to surgery. During the short preoperative period, the patients are more receptive and motivated to adhere to behavioral interventions (e.g., smoking cessation, weaning from alcohol, balanced food intake and active mobilization) and to follow a structured exercise training program. Sufficient protein intake should be ensured (1.5–2 g/kg/day) and nutritional defects should be corrected to restore muscle mass and strength. Currently, there is strong evidence supporting the effectiveness of various modalities of physical training (endurance training and/or respiratory muscle training) to enhance aerobic fitness and to mitigate the risk of pulmonary complications while reducing the hospital length of stay. Multimodal interventions should be individualized to the patient's condition. These bundle of care are more effective than single or sequential intervention owing to synergistic benefits of education, nutritional support and physical training. An effective prehabilitation program is necessarily patient-centred and coordinated among health care professionals (nurses, primary care physician, physiotherapists, nutritionists) to help the patient regain some control over the disease process and improve the physiological reserve to sustain surgical stress.

Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery

Objective: Investigate whether machine learning can predict pulmonary complications (PPCs) after emergency gastrointestinal surgery in patients with acute diffuse peritonitis.

Methods: This is a secondary data analysis study. We use five machine learning algorithms (Logistic regression, DecisionTree, GradientBoosting, Xgbc, and gbm) to predict postoperative pulmonary complications.

Results: Nine hundred and twenty-six cases were included in this study; 187 cases (20.19%) had PPCs. The five most important variables for the postoperative weight were preoperative albumin, cholesterol on the 3rd day after surgery, albumin on the day of surgery, platelet count on the 1st day after surgery and cholesterol count on the 1st day after surgery for pulmonary complications. In the test group: the logistic regression model shows AUC = 0.808, accuracy = 0.824 and precision = 0.621; Decision tree shows AUC = 0.702, accuracy = 0.795 and precision = 0.486; The GradientBoosting model shows AUC = 0.788, accuracy = 0.827 and precision = 1.000; The Xgbc model shows AUC = 0.784, accuracy = 0.806 and precision = 0.583. The Gbm model shows AUC = 0.814, accuracy = 0.806 and precision = 0.750.

Conclusion: Machine learning algorithms can predict patients' PPCs with acute diffuse peritonitis. Moreover, the results of the importance matrix for the Gbdt algorithm model show that albumin, cholesterol, age, and platelets are the main variables that account for the highest pulmonary complication weights.

Clinical guide to perioperative management for videothoracoscopy lung resection (Section of Cardiac, Vascular and Thoracic Anesthesia, SEDAR; Spanish Society of Thoracic Surgery, SECT; Spanish Society of Physiotherapy)

The introduction of video-assisted thoracoscopic (VATS) techniques has led to a new approach in thoracic surgery. VATS is performed by inserting a thoracoscope through a small incisions in the chest wall, thus maximizing the preservation of muscle and tissue. Because of its low rate of morbidity and mortality, VATS is currently the technique of choice in most thoracic procedures. Lung resection by VATS reduces prolonged air leaks, arrhythmia, pneumonia, postoperative pain and inflammatory markers. This reduction in postoperative complications shortens hospital length of stay, and is particularly beneficial in high-risk patients with low tolerance to thoracotomy. Compared with conventional thoracotomy, the oncological results of VATS surgery are similar or even superior to those of open surgery. This aim of this multidisciplinary position statement produced by the thoracic surgery working group of the Spanish Society of Anesthesiology and Reanimation (SEDAR), the Spanish Society of Thoracic Surgery (SECT), and the Spanish Association of Physiotherapy (AEF) is to standardize and disseminate a series of perioperative anaesthesia management guidelines for patients undergoing VATS lung resection surgery. Each recommendation is based on an in-depth review of the available literature by the authors. In this document, the care of patients undergoing VATS surgery is organized in sections, starting with the surgical approach, and followed by the three pillars of anaesthesia management: preoperative, intraoperative, and postoperative anaesthesia.

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Background

Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods.

Methods

Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model.

Results

591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality (P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na⁺, TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time.

Conclusion

Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01690-3.

Effects of lung-protective ventilation strategy on lung aeration loss and postoperative pulmonary complications in moderate-risk patients undergoing abdominal surgery

BACKGROUND

There is a controversy about whether the use of a lung-protective ventilation strategy(LPVS) can reduce the incidence of postoperative pulmonary complications (PPCs) and improve the clinical outcomes in moderate-risk patients were assessed by the Assess Respiratory Risk in Surgical Patients in Catalonia(ARISCAT).

METHODS

One hundred moderate-risk patients predicted by the ARISCAT, scheduled to undergo abdominal surgery were randomized into two groups: conventional ventilation strategy group (G<inf>0</inf>) and lung-protective ventilation strategy group (G<inf>1</inf>). Lung ultrasonography (LUS) and the LUS score were performed before induction of anesthesia (T<inf>0</inf>), 30min after extubation (T<inf>1</inf>), and 24h (T<inf>2</inf>), 72h (T<inf>3</inf>) after surgery. The incidence and severity of PPCs within the postoperative 7 days, the duration of postoperative oxygen supplementation, and postoperative hospital stay (PHS) were recorded.

RESULTS

The LUS score of both groups at T<inf>1-3</inf> was higher than those at T<inf>0</inf> (P<0.05), moreover, the LUS score of G<inf>1</inf> was lower than that of G<inf>0</inf> at T<inf>1-3</inf>. The incidence of PPCs of G<inf>1</inf> (10.9%) was lower than that of G<inf>0</inf> (29.8%) (relative risk, 0.37; 95% confidence interval [CI], 0.14 to 0.93; P=0.02) and the severity of PPCs of G<inf>1</inf> were lower than those of G<inf>0</inf> (P<0.05). The PHS of G<inf>1</inf> was less than that of G<inf>0</inf> (8[7-10] vs. 9[8-11], P<0.05).

CONCLUSIONS

The LPVS can decrease lung aeration loss assessed by LUS and reduce the incidence of PPCs in moderate-risk patients.

Identification of the Risk Factors of Postoperative Pulmonary Complications in Elderly Patients Undergoing Elective Colorectal Surgery

OBJECTIVE

This study was to identify the perioperative related risk factors of postoperative pulmonary complications (PPCs) in elderly patients undergoing elective colorectal surgery, which will provide new insight for better prevention and intervention of PPCs in elderly patients.

METHODS

A retrospective study involving 445 patients (age ≥65), who registered in Shengjing Hospital affiliated to China Medical University for elective colorectal surgery from October 2014 to March 2017, was conducted. Clinical data, including demographic information, medical history, preoperative examination, and surgery-related factors, were analyzed and compared between the patient group with PPCs and the group without PPCs. t-test or χ² test was performed for statistical analysis between the 2 groups. Binary logistic regression analysis was further employed to identify the potential independent risk factors of PPCs.

RESULTS

Among the 445 patients enrolled in the study, 49 (11%) had PPCs, while 396 (89%) did not. The main risk factors of PPC occurrence in the elderly patients undergoing elective colorectal surgery included older age (age ≥75 years), ASA >II, hypertension, myocardial ischemia, basic pulmonary diseases, laparotomy, blood transfusion, preoperative hemoglobin <100 g/L, and albumin <35 g/L. Laparotomy (compared with laparoscope) and ASA >II were independent risk factors for the increased incidence of PPCs.

CONCLUSION

More attention should be paid to patients with older age and ASA >II in elective colorectal surgery. Choice of laparoscopic operation, proper treatment of hypertension, myocardial ischemia, basic pulmonary diseases, and correction of anemia and nutritional status can effectively reduce the incidence of PPCs. An adequate and comprehensive evaluation of the potential risk factors related to PPCs is required before surgery.

Predictors of postoperative acute exacerbation of interstitial lung disease: a case-control study

Introduction

Patients with interstitial lung disease (ILD) are known to develop an acute exacerbation (AE) after surgery. Previous studies have evaluated the predictors of postoperative AE. However, it remains unclear whether the results of those studies can be generalised to patients with different types of ILD and/or extrapolated to those who undergo non-pulmonary surgery. This study aimed to elucidate the predictors of the development of AE after surgery with general anaesthesia in patients with ILD.

Methods

We conducted a nested matched case–control study of 700 patients from an initial cohort of 50 840 patients. We excluded those who underwent solid organ or bone marrow transplantation. The cases were patients with ILD who developed AE within 30 days postoperatively, whereas the controls did not develop AE. Each case (n=28) was matched with four controls (n=112) for sex, year of surgery and multiple operations within 30 days. Furthermore, a multivariable conditional logistic regression analysis was used to identify significant predictors, as indicated by a p value of <0.05.

Results

After adjusting for potential confounders, the multivariable conditional logistic regression analysis identified honeycombing on CT (OR 3.09; 95% CI 1.07 to 8.92), a per cent predicted FVC <80% (OR 4.21; 95% CI 1.46 to 12.2) and an ARISCAT score ≥45 (OR 6.14; 95% CI 2.10 to 18.0) significantly associated with the development of postoperative AE.

Conclusions

We found that the three factors were independent predictors for the development of postoperative AE in patients with ILD. These predictors are advantageous because they can be readily evaluated before surgery by surgeons and anaesthesiologists even without consulting experienced pulmonologists.

Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study

Background

Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection.

Objective

This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU).

Methods

In this retrospective cohort study, we included 36,023 consecutive patients who underwent general surgery between October and December 2017 at a tertiary referral hospital in South Korea. The primary outcome was the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for detecting bacteremia by the deep learning model, and the secondary outcome was the feature explainability of the model by occlusion analysis.

Results

Out of the 36,023 patients in the data set, 720 cases of bacteremia were included. Our deep learning–based model showed an AUROC of 0.97 (95% CI 0.974-0.981) and an AUPRC of 0.17 (95% CI 0.147-0.203) for detecting bacteremia in surgical in-patients. For predicting bacteremia within the previous 24-hour period, the AUROC and AUPRC values were 0.93 and 0.15, respectively. Occlusion analysis showed that vital signs and laboratory measurements (eg, kidney function test and white blood cell group) were the most important variables for detecting bacteremia.

Conclusions

A deep learning model based on time series electronic health records data had a high detective ability for bacteremia for surgical in-patients in the general ward and the ICU. The model may be able to assist clinicians in evaluating infection among in-patients, ordering blood cultures, and prescribing antibiotics with real-time monitoring.

The concept of peri-operative medicine to prevent major adverse events and improve outcome in surgical patients: A narrative review

: Peri-operative Medicine is the patient-centred and value-based multidisciplinary peri-operative care of surgical patients. Peri-operative stress, that is the collective response to stimuli occurring before, during and after surgery, is, together with pre-existing comorbidities, the pathophysiological basis of major adverse events. The ultimate goal of Peri-operative Medicine is to promote high quality recovery after surgery. Clinical scores and/or biomarkers should be used to identify patients at high risk of developing major adverse events throughout the peri-operative period. Allocation of high-risk patients to specific care pathways with peri-operative organ protection, close surveillance and specific early interventions is likely to improve patient-relevant outcomes, such as disability, health-related quality of life and mortality.

Dexmedetomidine for prevention of postoperative pulmonary complications in patients after oral and maxillofacial surgery with fibular free flap reconstruction:a prospective, double-blind, randomized, placebo-controlled trial

BACKGROUND

Postoperative pulmonary complications (PPCs) are common and significant problems for oral and maxillofacial surgery patients. Dexmedetomidine (DEX), an α₂-adrenoreceptor agonist, has been proven having lung protection effects. However, since now, there has not been final conclusion about whether DEX can reduce the incidence of PPCs. We hypothesize that, in oral and maxillofacial surgery with fibular free flap reconstruction patients, DEX may decrease the incidence of PPCs.

METHODS

This was a prospective, double-blind, randomized, placebo-controlled, single-centered trial with two parallel arms. A total of 160 patients at intermediate-to-high risk of PPCs undergoing oral and maxillofacial surgery with fibular free flap reconstruction and tracheotomy were enrolled and randomized to receive continuous infusion of either DEX or placebo (normal saline). 0.4 μg/kg of DEX was given over 10mins as an initial dose followed by a maintaining dose of 0.4 μg/kg/h till the second day morning after surgery. At the same time, the normal saline was administered a similar quantity. The primary outcome was the incidence of PPCs according to Clavien-Dindo score within 7 days after surgery.

RESULTS

The two groups had similar characteristics at baseline. 18(22.5%) of 80 patients administered DEX, and 32(40.0%) of 80 patient administered placebo experienced PPCs within the first 7 days after surgery (relative risk [RR] 0.563,95% confidence interval [CI] 0.346-0.916; P = 0.017). In the first 7 days after surgery, the DEX group had a lower incidence of PPCs and a better postoperative survival probability (Log-rank test, P = 0.019), and was less prone to occur PPCs (Cox regression, P = 0.025, HR = 0.516). When the total dose of DEX was more than 328 μg, the patients were unlikely to have PPCs (ROC curve, AUC = 0.614, P = 0.009).

CONCLUSIONS

For patients undergoing oral and maxillofacial surgery with fibular free flap reconstruction and tracheotomy who were at intermediate or high risk of developing PPCs, continuous infusion of DEX could decrease the occurrence of PPCs during the first 7 days after surgery and shorten the length of hospital stay after surgery, but did not increase the prevalence of bradycardia or hypotension.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, www.chictr.org.cn, number: ChiCTR1800016153; Registered on May 15, 2018.

Driving Pressure During General Anesthesia for Open Abdominal Surgery (DESIGNATION): study protocol of a randomized clinical trial

BACKGROUND

Intraoperative driving pressure (ΔP) is associated with development of postoperative pulmonary complications (PPC). When tidal volume (V_T) is kept constant, ΔP may change according to positive end-expiratory pressure (PEEP)-induced changes in lung aeration. ΔP may decrease if PEEP leads to a recruitment of collapsed lung tissue but will increase if PEEP mainly causes pulmonary overdistension. This study tests the hypothesis that individualized high PEEP, when compared to fixed low PEEP, protects against PPC in patients undergoing open abdominal surgery.

METHODS

The "Driving prESsure durIng GeNeral AnesThesIa for Open abdomiNal surgery trial" (DESIGNATION) is an international, multicenter, two-group, double-blind randomized clinical superiority trial. A total of 1468 patients will be randomly assigned to one of the two intraoperative ventilation strategies. Investigators screen patients aged ≥ 18 years and with a body mass index ≤ 40 kg/m², scheduled for open abdominal surgery and at risk for PPC. Patients either receive an intraoperative ventilation strategy with individualized high PEEP with recruitment maneuvers (RM) ("individualized high PEEP") or one in which PEEP of 5 cm H₂O without RM is used ("low PEEP"). In the "individualized high PEEP" group, PEEP is set at the level at which ΔP is lowest. In both groups of the trial, V_T is kept at 8 mL/kg predicted body weight. The primary endpoint is the occurrence of PPC, recorded as a collapsed composite of adverse pulmonary events.

DISCUSSION

DESIGNATION will be the first randomized clinical trial that is adequately powered to compare the effects of individualized high PEEP with RM versus fixed low PEEP without RM on the occurrence of PPC after open abdominal surgery. The results of DESIGNATION will support anesthesiologists in their decisions regarding PEEP settings during open abdominal surgery.

TRIAL REGISTRATION

Clinicaltrials.gov, NCT03884543. Registered on 21 March 2019.