Derivation and diagnostic accuracy of the surgical lung injury prediction model. Anesthesiology. 2011;115:117-128. [PMID: 21694510 DOI: 10.1097/aln.0b013e31821b5839]

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery

BACKGROUND

Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery.

METHODS

Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds.

RESULTS

The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively.

CONCLUSIONS

Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.

Secondary Analysis of Fluids and Catheters Treatment Trial (FACTT) data reveal poor clinical outcomes in acute respiratory distress syndrome patients with diabetes

OBJECTIVES

Conflicting reports exist about the link between diabetes mellitus (DM) and acute respiratory distress syndrome (ARDS). Our study examines the impact of pre-existing DM on ARDS patients within the Fluid and Catheter Treatment Trial (FACTT).

DESIGN

Conducting a secondary analysis of FACTT data, we incorporated 967 participants with identified DM status (173 with DM, 794 without DM) and examined outcomes like 90-day mortality, hospital and ICU stays, and ventilator days until unassisted breathing. The primary outcome of hospital mortality at day 90 was evaluated through logistic regression using IBM SPSS software. Additionally, we assessed plasma cytokines and chemokines utilizing a human magnetic bead-based multiplex assay.

RESULTS

Patients with pre-existing DM exhibited a lower survival rate compared to non-DM patients (61.3 vs. 72.3 %, p = 0.006). Subjects with DM experienced significantly longer hospital lengths of stay (24.5 vs. 19.7 days; p = 0.008) and prolonged ICU stays (14.8 vs. 12.4 days; p = 0.029). No significant difference was found in ventilator days until unassisted breathing between the two groups (11.7 vs. 10; p = 0.1). Cytokine/chemokine analyses indicated a non-significant trend toward heightened levels of cytokines (TNF-α, IL-10, and IL-6) and chemokines (CRP, MCP-1) in DM patients compared to non-DM on both days 0 and 1. Notably, lipopolysaccharide-binding protein (LBP) exhibited significantly higher levels in DM compared to non-DM individuals.

CONCLUSIONS

ARDS patients with DM suffered worse clinical outcomes compared to non-DM patients, indicating that DM may negatively affect the respiratory functions in these subjects. Further comprehensive clinical and pre-clinical studies will strengthen this relationship.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population

BACKGROUND

Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.

RESEARCH QUESTION

Can a predictive model be developed to accurately identify patients at high risk of PRF?

STUDY DESIGN AND METHODS

In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores.

RESULTS

Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203).

INTERPRETATION

This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Critical evaluation of established risk prediction models for acute respiratory distress syndrome in adult patients: A systematic review and meta-analysis

AIM

To assess the performance of validated prediction models for acute respiratory distress syndrome (ARDS) by systematic review and meta-analysis.

METHODS

Eight databases (Medline, CINAHL, Embase, The Cochrane Library, CNKI, WanFang Data, Sinomed, and VIP) were searched up to March 26, 2023. Studies developed and validated a prediction model for ARDS in adult patients were included. Items on study design, incidence, derivation methods, predictors, discrimination, and calibration were collected. The risk of bias was assessed by the Prediction model Risk of Bias Assessment Tool. Models with a reported area under the curve of the receiver operating characteristic (AUC) metric were analyzed.

RESULTS

A total of 25 studies were retrieved, including 48 unique prediction models. Discrimination was reported in all studies, with AUC ranging from 0.701 to 0.95. Emerged AUC value of the logistic regression model was 0.837 (95% CI: 0.814 to 0.859). Besides, the value in the ICU group was 0.856 (95% CI: 0.812 to 0.899), the acute pancreatitis group was 0.863 (95% CI: 0.844 to 0.882), and the postoperation group was 0.835 (95% CI: 0.808 to 0.861). In total, 24 of the included studies had a high risk of bias, which was mostly due to the improper methods in predictor screening (13/24), model calibration assessment (9/24), and dichotomization of continuous predictors (6/24).

CONCLUSIONS

This study shows that most prediction models for ARDS are at high risk of bias, and the discrimination ability of the model is excellent. Adherence to standardized guidelines for model development is necessary to derive a prediction model of value to clinicians.

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.

Persistent Hyperglycemia Worsens the Oleic Acid Induced Acute Lung Injury in Rat Model of Type II Diabetes Mellitus

Aim

This research aimed to study the impacts of persistent hyperglycemia on oleic acid (OA)-induced acute lung injury (ALI) in a rat model of type II diabetes mellitus.

Materials and Methods

Healthy adult male albino rats that weigh 150 to 180 g were divided into four groups (n = 6). Group I-saline (75 μL i.v.) was injected and served as a control; group II-OA (75 μL i.v.) was injected to induce ALI. Group III-pretreated with a high-fat diet and streptozotocin (35 mg/kg), was injected with saline, and served as a control for group IV. Group IV was pretreated with a high-fat diet, and streptozotocin (35 mg/kg) was injected with OA (75 μL i.v). Urethane was used to anesthetize the animal. The jugular venous cannulation was done for drug/saline administration, carotid artery cannulation was done to record blood pressure, and the tracheal cannulation was done to maintain the respiratory tract's patent. Heart rate, mean arterial pressure, and respiratory frequency were recorded on a computerized chart recorder; an arterial blood sample was collected to measure PaO2/FiO2. Additionally, the pulmonary water content and lung histology were examined.

Result

Hyperglycemic rats showed no significant change in the cardio-respiratory parameter. Histology of the lungs shows fibroblastic proliferation; however, rats survived throughout the observation period. There was an early deterioration of all the cardio-respiratory parameters in hyperglycemic rats when induced ALI (OA- induced), and survival time was significantly less compared to nonhyperglycemic rats.

Conclusion

Persistent hyperglycemia may cause morphological changes in the lungs, which worsens the outcome of acute lung injury.

Postoperative acute respiratory distress syndrome in randomized controlled trials

BACKGROUND

Acute respiratory distress syndrome is a potentially fatal postoperative complication. We aimed to estimate temporal trends of the representation of patients with postoperative acute respiratory distress syndrome in clinical trials, determine their distinct clinical features, and identify predictors of mortality.

METHODS

This is a secondary analysis of 7 randomized controlled clinical trials conducted by the Acute Respiratory Distress Syndrome Network and the Clinical Trials Network for the Prevention and Early Treatment of Acute Lung Injury. Patients with acute respiratory distress syndrome were classified into a postoperative acute respiratory distress syndrome group (ie, patients who had undergone elective surgery in the immediate period before trial enrollment) and a non-postoperative acute respiratory distress syndrome group.

RESULTS

Out of 5,316 patients with acute respiratory distress syndrome, 256 (4.8%) had postoperative acute respiratory distress syndrome. Representation of postoperative acute respiratory distress syndrome in trials gradually declined from 2000 to 2011, but it remained stable afterward at 2.7%. Postoperative acute respiratory distress syndrome was associated with lower 90-day mortality (24.6% vs 30.9%, P = .032) than non-postoperative acute respiratory distress syndrome, even after adjusting for age, acute respiratory distress syndrome severity, usage of vasopressors at baseline, and the study publication year (hazard ratio 0.63, 95% confidence interval 0.49-0.82). Age (odds ratio 1.07, 95% confidence interval 1.04-1.09), immunosuppression (odds ratio 4.12, 95% confidence interval 1.43-11.86), and positive fluid balance (odds ratio 1.09, 95% confidence interval 1.04-1.14) were associated with 90-day mortality among patients with postoperative acute respiratory distress syndrome.

CONCLUSION

Representation of postoperative acute respiratory distress syndrome in trials of the Acute Respiratory Distress Syndrome Network and the Clinical Trials Network for the Prevention and Early Treatment of Acute Lung Injury gradually declined from 2000 to 2011 but remained stable afterward. Postoperative acute respiratory distress syndrome was associated with lower mortality than non-postoperative acute respiratory distress syndrome. These findings may put both temporal trends and the prognosis of postoperative acute respiratory distress syndrome in perspective. Also, positive fluid balance was associated with the mortality of patients with postoperative acute respiratory distress syndrome.

Early plasma proteomic biomarkers and prediction model of acute respiratory distress syndrome after cardiopulmonary bypass: a prospective nested cohort study

BACKGROUND

Early recognition of the risk of acute respiratory distress syndrome (ARDS) after cardiopulmonary bypass (CPB) may improve clinical outcomes. The main objective of this study was to identify proteomic biomarkers and develop an early prediction model for CPB-ARDS.

METHODS

The authors conducted three prospective nested cohort studies of all consecutive patients undergoing cardiac surgery with CPB at Union Hospital of Tongji Medical College Hospital. Plasma proteomic profiling was performed in ARDS patients and matched controls (Cohort 1, April 2021-July 2021) at multiple timepoints: before CPB (T1), at the end of CPB (T2), and 24 h after CPB (T3). Then, for Cohort 2 (August 2021-July 2022), biomarker expression was measured and verified in the plasma. Furthermore, lung ischemia/reperfusion injury (LIRI) models and sham-operation were established in 50 rats to explore the tissue-level expression of biomarkers identified in the aforementioned clinical cohort. Subsequently, a machine learning-based prediction model incorporating protein and clinical predictors from Cohort 2 for CPB-ARDS was developed and internally validated. Model performance was externally validated on Cohort 3 (January 2023-March 2023).

RESULTS

A total of 709 proteins were identified, with 9, 29, and 35 altered proteins between ARDS cases and controls at T1, T2, and T3, respectively, in Cohort 1. Following quantitative verification of several predictive proteins in Cohort 2, higher levels of thioredoxin domain containing 5 (TXNDC5), cathepsin L (CTSL), and NPC intracellular cholesterol transporter 2 (NPC2) at T2 were observed in CPB-ARDS patients. A dynamic online predictive nomogram was developed based on three proteins (TXNDC5, CTSL, and NPC2) and two clinical risk factors (CPB time and massive blood transfusion), with excellent performance (precision: 83.33%, sensitivity: 93.33%, specificity: 61.16%, and F1 score: 85.05%). The mean area under the receiver operating characteristics curve (AUC) of the model after 10-fold cross-validation was 0.839 (95% CI: 0.824-0.855). Model discrimination and calibration were maintained during external validation dataset testing, with an AUC of 0.820 (95% CI: 0.685-0.955) and a Brier Score of 0.177 (95% CI: 0.147-0.206). Moreover, the considerably overexpressed TXNDC5 and CTSL proteins identified in the plasma of patients with CPB-ARDS, exhibited a significant upregulation in the lung tissue of LIRI rats.

CONCLUSIONS

This study identified several novel predictive biomarkers, developed and validated a practical prediction tool using biomarker and clinical factor combinations for individual prediction of CPB-ARDS risk. Assessing the plasma TXNDC5, CTSL, and NPC2 levels might identify patients who warrant closer follow-up and intensified therapy for ARDS prevention following major surgery.

Performance Comparison of Pulmonary Risk Scoring Systems in Lung Resection

OBJECTIVE

To validate and compare the performance of different pulmonary risk scoring systems to predict postoperative pulmonary complications (PPCs) in lung resection surgery.

DESIGN

Retrospective cohort study SETTING: A historic single-center cohort of lung resection surgeries PARTICIPANTS: Adult patients undergoing lung resection surgery under 1-lung ventilation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The accuracy of the following pulmonary risk scoring systems were used to predict pulmonary complications: the ARISCAT (Assess respiratory RIsk in Surgical patients in CATalonia), the LAS VEGAS (Local Assessment of VEntilatory management during General Anesthesia for Surgery), the SPORC (Score for Prediction of Postoperative Respiratory Complications), and a recent thoracic-specific risk score, named CARDOT. Discrimination and calibration were assessed using the concordance (c) index and the intercept of LOESS (locally estimated scatterplot)-smoothed curves, respectively. Additional models were constructed that incorporated predicted postoperative forced expiratory volume (ppoFEV1) into each scoring system. Of the 2,104 patients undergoing lung surgery, 123 developed postoperative pulmonary complications (PPCs; 5.9%). All scoring systems had poor discriminatory power to predict PPCs (ARISCAT c-index 0.60, 95% confidence interval [CI] 0.55-0.65; LAS VEGAS c-index 0.68, 95% CI 0.63-0.73; SPORC c-index 0.63, 95% CI 0.59-0.68; CARDOT c-index 0.64, 95% CI 0.58-0.70), but the inclusion of ppoFEV1 slightly improved the performance of LAS VEGAS (c-index 0.70, 95% CI 0.66-0.75) and CARDOT (c-index 0.68, 95% CI 0.62-0.73). Analysis of calibration showed a slight overestimation when using ARISCAT (intercept -0.28) and LAS VEGAS (intercept -0.27).

CONCLUSIONS

None of the scoring systems appeared to have adequate discriminatory power to predict PPCs among patients undergoing lung resection. An alternative risk score is necessary to better predict patients at risk of PPCs after thoracic surgery.

Model for predicting early and late-onset postoperative pulmonary complications in perioperative patients receiving neuromuscular blockade: a secondary analysis

Pulmonary complications continue to be the most common adverse event after surgery. The main objective was to carry out two independent predictive models, both for early pulmonary complications in the Post-Anesthesia Care Unit and late-onset pulmonary complications after 30 postoperative days. The secondary objective was to determine whether presenting early complications subsequently causes patients to have other late-onset events. This is a secondary analysis of a cohort study. 714 patients were divided into four groups depending on the neuromuscular blocking agent, and spontaneous or pharmacological reversal. Incidence of late-onset complications if we have not previously had any early complications was 4.96%. If the patient has previously had early complications the incidence of late-onset complications was 22.02%. If airway obstruction occurs, the risk of atelectasis increased from 6.88 to 22.58% (p = 0.002). If hypoxemia occurs, the incidence increased from 5.82 to 21.79% (p < 0.001). Based on our predictive models, we conclude that diabetes mellitus and preoperative anemia are two risk factors for early and late-onset postoperative pulmonary complications, respectively. Hypoxemia and airway obstruction in Post-Anesthesia Care Unit increased four times the risk of the development of pneumonia and atelectasis at 30 postoperative days.

Predicting Persistent Acute Respiratory Failure in Acute Pancreatitis: The Accuracy of Two Lung Injury Indices

BACKGROUND/AIMS

Early and accurate identification of patients with acute pancreatitis (AP) at high risk of persistent acute respiratory failure (PARF) is crucial. We sought to determine the accuracy of simplified Lung Injury Prediction Score (sLIPS) and simplified Early Acute Lung Injury (sEALI) for predicting PARF in ward AP patients.

METHODS

Consecutive AP patients in a training cohort from West China Hospital of Sichuan University (n = 912) and a validation cohort from The First Affiliated Hospital of Nanchang University (n = 1033) were analyzed. PARF was defined as oxygen in arterial blood/fraction of inspired oxygen < 300 mmHg that lasts for > 48 h. The sLIPS was composed by shock (predisposing condition), alcohol abuse, obesity, high respiratory rate, low oxygen saturation, high oxygen requirement, hypoalbuminemia, and acidosis (risk modifiers). The sEALI was calculated from oxygen 2 to 6 L/min, oxygen > 6 L/min, and high respiratory rate. Both indices were calculated on admission.

RESULTS

PARF developed in 16% (145/912) and 22% (228/1033) (22%) of the training and validation cohorts, respectively. In these patients, sLIPS and sEALI were significantly increased. sLIPS ≥ 2 predicted PARF in the training (AUROC 0.87, 95% CI 0.84-0.89) and validation (AUROC 0.81, 95% CI 0.78-0.83) cohorts. sLIPS was significantly more accurate than sEALI and current clinical scoring systems in both cohorts (all P < 0.05).

CONCLUSIONS

Using routinely available clinical data, the sLIPS can accurately predict PARF in ward AP patients and outperforms the sEALI and current existing clinical scoring systems.

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.

Novel models for early prediction and prevention of acute respiratory distress syndrome in patients following hepatectomy: A clinical translational study based on 1,032 patients

Background

Acute respiratory distress syndrome (ARDS) is a serious organ failure and postoperative complication. However, the incidence rate, early prediction and prevention of postoperative ARDS in patients undergoing hepatectomy remain unidentified.

Methods

A total of 1,032 patients undergoing hepatectomy between 2019 and 2020, at the Eastern Hepatobiliary Surgery Hospital were included. Patients in 2019 and 2020 were used as the development and validation cohorts, respectively. The incidence rate of ARDS was assessed. A logistic regression model and a least absolute shrinkage and selection operator (LASSO) regression model were used for constructing ARDS prediction models.

Results

The incidence of ARDS was 8.8% (43/490) in the development cohort and 5.7% (31/542) in the validation cohort. Operation time, postoperative aspartate aminotransferase (AST), and postoperative hemoglobin (Hb) were all critical predictors identified by the logistic regression model, with an area under the curve (AUC) of 0.804 in the development cohort and 0.752 in the validation cohort. Additionally, nine predictors were identified by the LASSO regression model, with an AUC of 0.848 in the development cohort and 0.786 in the validation cohort.

Conclusion

We reported the incidence of ARDS in patients undergoing hepatectomy and developed two simple and practical prediction models for early predicting postoperative ARDS in patients undergoing hepatectomy. These tools may improve clinicians' ability to early estimate the risk of postoperative ARDS and timely prevent its emergence.

Effect of Uncomplicated Diabetes Mellitus on Acute Respiratory Distress Syndrome Among COVID-19 Patients in Aseer Region, Saudi Arabia

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; an ssRNA virus), which mainly affects the respiratory system but can also cause damage to other body systems. Acute respiratory distress syndrome (ARDS) is a serious complication of COVID-19 that requires early recognition and comprehensive management. ARDS is a diffuse inflammatory process that causes diffuse alveolar damage in the lung.  Aim: The study aimed to assess the effect of uncomplicated diabetes mellitus on ARDS among COVID-19 patients in the Aseer region.

METHODOLOGY

 A retrospective cohort study was conducted in Aseer Central Hospital between July 10, 2021 to Jan 15, 2022 where confirmed inpatient COVID-19 cases in the Aseer region were classified into two groups. The first group was diabetic patients without any diabetes-related complications and confirmed for COVID-19 infection (diabetes group). The second group was confirmed COVID-19 patients free from any chronic disease. Extracted data included patients' diabetes status, medical history, socio-demographic data, COVID-19 infection data and vaccination, experienced signs and symptoms, tachypnea, use of accessory muscles of respiration, nasal flaring, grunting, cyanosis, need for hospitalization, need for mechanical ventilation and ICU admission.  Results: The study included 144 patients with uncomplicated diabetes and 323 healthy patients with COVID-19 infection. The mean age of the diabetic group was 65.4 ± 12.9 years old compared to 40.2 ± 11.9 years old for the healthy group. Only one case of the diabetic group was vaccinated against COVID-19 at the study period versus two cases of the healthy group (P=.925). Also, 14 (9.7%) of the diabetic group were contacted with confirmed COVID-19 cases in comparison to 44 (13.6%) healthy cases (P=.238). A total of five (3.5%) diabetic cases needed mechanical ventilation during hospitalization compared to 23 (7.1%) healthy cases with no statistical significance (P=.125). Also, 12 (8.3%) diabetic cases admitted to ICU versus 42 (13%) of healthy cases (P=.145).  Conclusions: In conclusion, there is a great controversy regarding the effect of diabetes on the progression of COVID-19 infection to ARDS. The current study showed that there was no significant difference between diabetic and healthy COVID-19 infected cases regarding ARDS related clinical factors mainly need of ICU admission and mechanical ventilation.

Comparison of Clinical Characteristics and Predictors of Mortality between Direct and Indirect ARDS

Background and Objectives: Acute Respiratory Distress Syndrome (ARDS) is a heterogeneous syndrome that encompasses lung injury from a direct pulmonary or indirect systemic insult. Studies have shown that direct and indirect ARDS differ in their pathophysiologic process. In this study, we aimed to compare the different clinical characteristics and predictors of 28-day mortality between direct and indirect ARDS. Materials and Methods: The data of 1291 ARDS patients from September 2012 to December 2021 at the Second Affiliated Hospital of Chongqing Medical University were reviewed. We enrolled 451 ARDS patients in our study through inclusion and exclusion criteria. According to the risk factors, each patient was divided into direct (n = 239) or indirect (n = 212) ARDS groups. The primary outcome was 28-day mortality. Results: The patients with direct ARDS were more likely to be older (p < 0.001) and male (p = 0.009) and have more comorbidity (p < 0.05) and higher 28-day mortality (p < 0.001) than those with indirect ARDS. Age and multiple organ dysfunction syndrome (MODS) were predictors of 28-day mortality in the direct ARDS group, while age, MODS, creatinine, prothrombin time (PT), and oxygenation index (OI) were independent predictors of 28-day mortality in the indirect ARDS group. Creatinine, PT, and OI have interactions with ARDS types (all p < 0.01). Conclusions: The patients with direct ARDS were more likely to be older and male and have worse conditions and prognoses than those with indirect ARDS. Creatinine, PT, and OI were predictors of 28-day mortality only in the indirect ARDS group. The differences between direct and indirect ARDS suggest the need for different management strategies of ARDS.

Effect of Total Intravenous Anesthesia on Postoperative Pulmonary Complications in Patients Undergoing Microvascular Reconstruction for Head and Neck Cancer: A Randomized Clinical Trial

Importance

Free flap surgery is a lengthy procedure with massive tissue destruction and reconstruction, which makes postoperative pulmonary complications (PPCs) a noticeable issue among patients with head and neck cancer. Propofol-based total intravenous anesthesia (TIVA) has better survival outcomes than inhalational anesthesia (INH) in several types of cancer surgery. A previous retrospective study found that patients in the TIVA group had a lower PPC rate, which may be correlated with a lower intraoperative fluid requirement. We hypothesize that the protective effect remains among patients undergoing free flap surgery for head and neck cancer in a prospective and goal-directed fluid therapy setting.

Objective

To assess the effect of TIVA vs INH on PPCs in patients undergoing microvascular reconstruction for head and neck cancer.

Design, Setting, and Participants

This prospective, 2-arm, randomized clinical trial was conducted at a tertiary hospital in Taiwan; a total of 78 patients 18 years and older with American Society of Anesthesiologists physical status classification 1 to 3 who were scheduled for elective free flap surgery under general anesthesia were included. The trial started in October 2017, completed in October 2019, and finished analysis in January 2022.

Interventions

Patients were enrolled and randomized to the TIVA or INH group. All patients received goal-directed fluid therapy and hemodynamic management if they had a mean arterial pressure (MAP) below 75 mm Hg or a reduction of 10% from baseline MAP.

Main Outcomes and Measures

The primary outcome was a composite of PPCs. The secondary outcomes were the differences in intraoperative hemodynamic values (mean arterial pressure, MAP; cardiac index, CI; systemic vascular resistance index, SVRI; and stroke volume variation, SVV).

Results

A total of 70 patients (65 men [93%]; 5 women [7%]) completed the trial; median (IQR) age was 52.0 (48-59) years in the TIVA group and 57.0 (46-64) years in the INH group. The demographic characteristics were similar between the 2 groups, except that patients in the TIVA group had a slightly lower body mass index. Patients in the TIVA group had a lower risk of developing PPCs (unadjusted odds ratio, 0.25; 95% CI, 0.08-0.80). The TIVA group had significantly higher MAP, lower CI, and higher SVRI than the INH group after the third hour of monitoring. The TIVA group showed a relatively stable hourly MAP, CI, SVRI, and SVV across time points, while the INH group showed a more varying pattern. The generalized estimating equation showed no clinical differences in the trend of hemodynamic parameters across time between groups.

Conclusions and Relevance

In this randomized clinical trial, using propofol-based TIVA reduced the incidence of PPCs in free flap surgery. This finding may be related to more stable hemodynamic manifestations and a lower total balance of fluid throughout the surgery.

Trial Registration

ClinicalTrials.gov Identifier: NCT03263078.

Characterization of Platelet Biologic Markers in the Early Pathogenesis of Postoperative Acute Respiratory Distress Syndrome

Animal models and limited human studies have suggested a plausible role for platelets in the pathogenesis and resolution of acute respiratory distress syndrome (ARDS). However, there are little data regarding the role of platelets in ARDS development.

OBJECTIVES

The objective of this study was to characterize the role of platelets in a postoperative ARDS model through an analysis of two platelet-specific biologic markers: thromboxane A₂ (TxA₂) and soluble CD-40-ligand (sCD40L).

DESIGN SETTING AND PARTICIPANTS

This was a nested case-control study of ARDS cases matched to non-ARDS controls. Blood samples were collected from a cohort of 500 patients undergoing thoracic, aortic vascular, or cardiac surgery that placed them at high-risk of developing postoperative ARDS.

MAIN OUTCOMES AND MEASURES

TxA₂ and sCD40L were analyzed at baseline (prior to surgical incision) as well as 2 hours and 6 hours after the key intraoperative events believed to be associated with increased risk of postoperative ARDS.

RESULTS

Of 500 patients enrolled, 20 ARDS cases were matched 1:2 to non-ARDS controls based on age, sex, surgical procedure, and surgical lung injury prediction score. Those who developed ARDS had longer surgeries, greater fluid administration, and higher peak inspiratory pressures. There were no significant differences in levels of TxA₂ or sCD40L at baseline, at 2 hours, or at 6 hours. There was also no difference in the change in biomarker concentration between baseline and 2 hours or baseline and 6 hours.

CONCLUSIONS

Two novel platelet-associated biologic markers (TxA₂ and sCD40L) were not elevated in patients who developed ARDS in a postoperative ARDS model. Although limited by the relatively small study size, these results do not support a clear role for platelets in the early pathogenesis of postoperative ARDS.

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.

Outcomes and risk factors for delayed-onset postoperative respiratory failure: a multi-center case-control study by the University of California Critical Care Research Collaborative (UC3RC)

BACKGROUND

Few interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF.

METHODS

This was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF.

RESULTS

Among 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81-10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04-1.44), and maximum intraoperative peak inspiratory pressure per cm H₂0 (OR 1.14, 95% CI 1.06-1.22).

CONCLUSIONS

We identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation.

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.

Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery

Objective

This study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery.

Methods

Patients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data.

Results

Three hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance.

Conclusions

According to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications.

Effects of aerobic exercise on lipopolysaccharide-induced experimental acute lung injury in the animal model of type 1 diabetes mellitus

NEW FINDINGS

What is the central question of this study? We evaluated the effects of diabetes and exercise on lipopolysaccharide-induced acute lung injury. By providing a comprehensive analysis of redox status, blood gases and histological parameters, we aimed to contribute to the ongoing debate in the literature. What are the main findings and its importance? We demonstrated the preventive effect of exercise, but diabetes did not alter the severity of acute lung injury.

ABSTRACT

Acute lung injury (ALI) is a life-threatening respiratory condition. Diabetes (DM) is a metabolic disease characterized by hyperglycaemia. There is an ongoing debate concerning whether there is a protective effect of diabetes in ALI. Exercise is a special type of physical activity that has numerous beneficial effects. The aim of our study was to investigate the effects of diabetes and exercise on the prognosis of ALI. Male Wistar albino rats were divided into two groups (sedentary and exercise). Both groups were divided into four subgroups: Control, ALI, DM, DM+ALI (n = 6 each). Diabetes was induced by injection of streptozotocin (50 mg/kg i.p.). The maximal exercise capacity was determined with the incremental load test. Animals were exercised on a treadmill for 45 min at 70% of maximal exercise capacity, 5 days a week for 12 weeks. Acute lung injury was induced by intratracheal injection of lipopolysaccharide (100 μg/100 g body weight) 24 h before the end of the experiment. We performed arterial blood gas analysis. Redox status was measured in both plasma and lung tissue. Malondialdehyde and 8-hydroxy-2'-deoxyguanosine levels were measured in lung tissue. Lung tissue was evaluated histologically. Acute lung injury caused significant damage in the lung tissue, which was verified histologically, with an increase in oxidative stress parameters. Exercise prevented the lung damage induced by ALI and reduced oxidative stress in the lung tissue. Diabetes did not alter the magnitude of damage done by ALI. Exercise showed a protective effect against DM and ALI in rats. The effect of DM was insignificant for the prognosis of ALI.

Characteristics and Outcome of Periengraftment Respiratory Distress Syndrome after Autologous Hematopoietic Cell Transplant

Rationale: The periengraftment respiratory distress syndrome (PERDS) is an early important cause of morbidity following autologous hematopoietic cell transplantation (HCT). There are few contemporary data describing PERDS. Objectives: To determine prevalence, risk factors, and outcomes of PERDS after autologous HCT. Methods: This was a historical cohort study of adults undergoing autologous HCT at Mayo Clinic, Rochester, Minnesota, between 2005 and 2016. PERDS was defined as 1) respiratory failure requiring supplemental oxygen within 5 days on either side of the neutrophil engraftment date, 2) new pulmonary opacities on chest imaging, and 3) exclusion of an infectious or cardiac etiology to explain the clinical presentation. Results: Of 3,473 patients undergoing autologous HCT, 167 (4.8%) developed PERDS. Radiographic changes preceded engraftment in 77% of cases. In a multivariable regression model, risk factors for PERDS included female sex (odds ratio [OR], 1.73; P = 0.001), the number of preengraftment platelet transfusions (OR, 1.22; P = 0.002), and more rapid engraftment (OR, 0.72 per day longer; P < 0.001). PERDS cases were more likely to be admitted to the intensive care unit (47.3% vs. 9.5%, P < 0.001) and require intubation (20.4% vs. 1.6%, P < 0.001). In an adjusted 100-day death analysis, those diagnosed with PERDS were more likely to die (hazard ratio, 3.1; 95% confidence interval, 1.5-6.2; P = 0.002). Conclusions: PERDS is a common complication of autologous HCT and is associated with increased mortality and healthcare use. Radiographic evidence of pulmonary involvement precedes hematopoietic recovery. A larger number of platelet transfusions and more rapid engraftment appear to increase risk for PERDS.

High Flow Oxygen Therapy at Two Initial Flow Settings versus Conventional Oxygen Therapy in Cardiac Surgery Patients with Postextubation Hypoxemia: A Single-Center, Unblinded, Randomized, Controlled Trial

In cardiac surgery patients with pre-extubation PaO₂/inspired oxygen fraction (FiO₂) < 200 mmHg, the possible benefits and optimal level of high-flow nasal cannula (HFNC) support are still unclear; therefore, we compared HFNC support with an initial gas flow of 60 or 40 L/min and conventional oxygen therapy. Ninety nine patients were randomly allocated (respective ratio: 1:1:1) to I = intervention group 1 (HFNC initial flow = 60 L/min, FiO₂ = 0.6), intervention group 2 (HFNC initial flow = 40 L/min, FiO₂ = 0.6), or control group (Venturi mask, FiO₂ = 0.6). The primary outcome was occurrence of treatment failure. The baseline characteristics were similar. The hazard for treatment failure was lower in intervention group 1 vs. control (hazard ratio (HR): 0.11, 95% CI: 0.03–0.34) and intervention group 2 vs. control (HR: 0.30, 95% CI: 0.12–0.77). During follow-up, the probability of peripheral oxygen saturation (SpO₂) > 92% and respiratory rate within 12–20 breaths/min was 2.4–3.9 times higher in intervention group 1 vs. the other 2 groups. There was no difference in PaO₂/FiO₂, patient comfort, intensive care unit or hospital stay, or clinical course complications or adverse events. In hypoxemic cardiac surgery patients, postextubation HFNC with an initial gas flow of 60 or 40 L/min resulted in less frequent treatment failure vs. conventional therapy. The results in terms of SpO₂/respiratory rate targets favored an initial HFNC flow of 60 L/min.

Risk Factors Associated With Early Postoperative Respiratory Failure: A Matched Case-Control Study

BACKGROUND

Postoperative respiratory failure is the most common serious postoperative pulmonary complication, yet little is known about factors that can reduce its incidence. We sought to elucidate modifiable factors associated with respiratory failure that developed within the first 5 d after an elective operation.

MATERIALS AND METHODS

Matched case-control study of adults who had an operation at five academic medical centers between October 1, 2012 and September 30, 2015. Cases were identified using administrative data and confirmed via chart review by critical care clinicians. Controls were matched 1:1 to cases based on hospital, age, and surgical procedure.

RESULTS

Our total sample (n = 638) was 56.4% female, 71.3% white, and had a median age of 62 y (interquartile range 51, 70). Factors associated with early postoperative respiratory failure included male gender (odds ratio [OR] 1.72, 95% confidence interval [CI] 1.12-2.63), American Society of Anesthesiologists class III or greater (OR 2.85, 95% CI 1.74-4.66), greater number of preexisting comorbidities (OR 1.14, 95% CI 1.004-1.30), increased operative duration (OR 1.14, 95% CI 1.06-1.22), increased intraoperative positive end-expiratory pressure (OR 1.23, 95% CI 1.13-1.35) and tidal volume (OR 1.13, 95% CI 1.004-1.27), and greater net fluid balance at 24 h (OR 1.17, 95% CI 1.07-1.28).

CONCLUSIONS

We found greater intraoperative ventilator volume and pressure and 24-h fluid balance to be potentially modifiable factors associated with developing early postoperative respiratory failure. Further studies are warranted to independently verify these risk factors, explore their role in development of early postoperative respiratory failure, and potentially evaluate targeted interventions.

Predictive Role of Lung Injury Prediction Score in the Development of Acute Respiratory Distress Syndrome in Korea

PURPOSE

Early recognition and therapeutic intervention are important in patients at high risk of acute respiratory distress syndrome (ARDS). The lung injury prediction score (LIPS) has been used to predict ARDS development; however, it was developed based on the previous definition of ARDS. We investigated the predictive role of LIPS in ARDS development according to its Berlin definition in the Korean population.

MATERIALS AND METHODS

This was a retrospective study that enrolled adult patients admitted to the intensive care unit (ICU) at a single university-affiliated hospital in Korea from September 1, 2018, to August 31, 2019. LIPS at the time of ICU admission and the development of ARDS were evaluated.

RESULTS

Of the 548 enrolled patients, 33 (6.0%) fulfilled the Berlin ARDS definition. The LIPS for non-ARDS and ARDS groups were 4.96±3.05 and 8.53±2.45, respectively (p<0.001); it was significantly associated with ARDS development (odds ratio 1.48, 95% confidence interval, 1.29-1.69; p<0.001). LIPS >6 predicted the development of ARDS with a sensitivity of 84.8% and a specificity of 67.2% [area under the curve (AUC)=0.82]. A modified LIPS model adjusted for age and severity at ICU admission predicted ICU mortality in patients with ARDS (AUC=0.80), but not in those without ARDS (AUC=0.54).

CONCLUSION

LIPS predicted the development of ARDS as diagnosed by the Berlin definition in the Korean population. LIPS provides useful information for managing patients with ARDS.

Attributable mortality of acute respiratory distress syndrome: a systematic review, meta-analysis and survival analysis using targeted minimum loss-based estimation

BACKGROUND

Although acute respiratory distress syndrome (ARDS) is associated with high mortality, its direct causal link with death is unclear. Clarifying this link is important to justify costly research on prevention of ARDS.

OBJECTIVE

To estimate the attributable mortality, if any, of ARDS.

DESIGN

First, we performed a systematic review and meta-analysis of observational studies reporting mortality of critically ill patients with and without ARDS matched for underlying risk factor. Next, we conducted a survival analysis of prospectively collected patient-level data from subjects enrolled in three intensive care unit (ICU) cohorts to estimate the attributable mortality of critically ill septic patients with and without ARDS using a novel causal inference method.

RESULTS

In the meta-analysis, 44 studies (47 cohorts) involving 56 081 critically ill patients were included. Mortality was higher in patients with versus without ARDS (risk ratio 2.48, 95% CI 1.86 to 3.30; p<0.001) with a numerically stronger association between ARDS and mortality in trauma than sepsis. In the survival analysis of three ICU cohorts enrolling 1203 critically ill patients, 658 septic patients were included. After controlling for confounders, ARDS was found to increase the mortality rate by 15% (95% CI 3% to 26%; p=0.015). Significant increases in mortality were seen for severe (23%, 95% CI 3% to 44%; p=0.028) and moderate (16%, 95% CI 2% to 31%; p=0.031), but not for mild ARDS.

CONCLUSIONS

ARDS has a direct causal link with mortality. Our findings provide information about the extent to which continued funding of ARDS prevention trials has potential to impart survival benefit.

PROSPERO REGISTRATION NUMBER

CRD42017078313.

An improved predictive model for postoperative pulmonary complications after free flap reconstructions in the head and neck

BACKGROUND

Commonly used predictive models for postoperative pulmonary complications (PPCs) do not perform when applied to head and neck cases. A head and neck-specific risk prediction tool is needed.

METHODS

Data on 794 free flap head and neck surgery cases at a single center were abstracted from the electronic medical record. Each case was reviewed for the development of PPCs. A predictive model was developed and was then compared to existing predictive models for PPCs.

RESULTS

The least absolute shrinkage and selection operator procedure identified age, alcohol use, history of congestive heart failure, preoperative packed cell volume, preoperative oxygen saturation, and preoperative metabolic equivalents as predictors of PPCs in the head and neck population. The model demonstrated an area under the receiving operating characteristic curve of 0.75 (0.69-0.80) with moderately good calibration. Comparisons to the performance of existing models demonstrate superior performance.

CONCLUSIONS

The model for the development of PPCs developed in this article displays superior performance to existing models.

Lung Ultrasound Findings in the Postanesthesia Care Unit Are Associated With Outcome After Major Surgery: A Prospective Observational Study in a High-Risk Cohort

BACKGROUND

Postoperative pulmonary complications are associated with increased morbidity. Identifying patients at higher risk for such complications may allow preemptive treatment.

METHODS

Patients with an American Society of Anesthesiologists (ASA) score >1 and who were scheduled for major surgery of >2 hours were enrolled in a single-center prospective study. After extubation, lung ultrasound was performed after a median time of 60 minutes by 2 certified anesthesiologists in the postanesthesia care unit after a standardized tracheal extubation. Postoperative pulmonary complications occurring within 8 postoperative days were recorded. The association between lung ultrasound findings and postoperative pulmonary complications was analyzed using logistic regression models.

RESULTS

Among the 327 patients included, 69 (19%) developed postoperative pulmonary complications. The lung ultrasound score was higher in the patients who developed postoperative pulmonary complications (12 [7-18] vs 8 [4-12]; P < .001). The odds ratio for pulmonary complications in patients who had a pleural effusion detected by lung ultrasound was 3.7 (95% confidence interval, 1.2-11.7). The hospital death rate was also higher in patients with pleural effusions (22% vs 1.3%; P < .001). Patients with pulmonary consolidations on lung ultrasound had a higher risk of postoperative mechanical ventilation (17% vs 5.1%; P = .001). In all patients, the area under the curve for predicting postoperative pulmonary complications was 0.64 (95% confidence interval, 0.57-0.71).

CONCLUSIONS

When lung ultrasound is performed precociously <2 hours after extubation, detection of immediate postoperative alveolar consolidation and pleural effusion by lung ultrasound is associated with postoperative pulmonary complications and morbi-mortality. Further study is needed to determine the effect of ultrasound-guided intervention for patients at high risk of postoperative pulmonary complications.

Acute Respiratory Distress Syndrome Prediction Score: Derivation and Validation

BACKGROUND

Despite advances in treatment strategies, acute respiratory distress syndrome (ARDS) after cardiac surgery remains associated with high morbidity and mortality. A method of screening patients for risk of ARDS after cardiac surgery is needed.

OBJECTIVES

To develop and validate an ARDS prediction score designed to identify patients at high risk of ARDS after cardiac or aortic surgery.

METHODS

An ARDS prediction score was derived from a retrospective derivation cohort and validated in a prospective cohort. Discrimination and calibration of the score were assessed with area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test, respectively. A sensitivity analysis was conducted to assess model performance at different cutoff points.

RESULTS

The retrospective derivation cohort consisted of 201 patients with and 602 patients without ARDS who had undergone cardiac or aortic surgery. Nine routinely available clinical variables were included in the ARDS prediction score. In the derivation cohort, the score distinguished patients with versus without ARDS with area under the curve of 0.84 (95% CI, 0.81-0.88; Hosmer-Lemeshow P = .55). In the validation cohort, 46 of 1834 patients (2.5%) had ARDS develop within 7 days after cardiac or aortic surgery. Area under the curve was 0.78 (95% CI, 0.71-0.85), and the score was well calibrated (Hosmer-Lemeshow P = .53).

CONCLUSIONS

The ARDS prediction score can be used to identify high-risk patients from the first day after cardiac or aortic surgery. Patients with a score of 3 or greater should be closely monitored. The score requires external validation before clinical use.

Postoperative respiratory failure: An update on the validity of the Agency for Healthcare Research and Quality Patient Safety Indicator 11 in an era of clinical documentation improvement programs

BACKGROUND

Administrative data can be used to identify cases of postoperative respiratory failure (PRF). We aimed to determine if recent changes to the Agency for Healthcare Research and Quality Patient Safety Indicator 11 (PSI 11) and adoption of clinical documentation improvement programs have improved the validity of PSI 11. We also analyzed reasons why PSI 11 was falsely triggered.

STUDY DESIGN

Cross-sectional study of all eligible discharges using health record data from five academic medical centers between October 1, 2012 and September 30, 2015.

RESULTS

Of 437 flagged records, 434 (99.3%) were accurately coded and 414 (94.7%) represented true clinical PRF. None of the false positive records involved respiratory failure present on admission. Most (78.3%) false positive records required airway protection but did not have respiratory failure.

CONCLUSION

The validity of PSI 11 has improved with recent changes to the code criterion and adoption of clinical documentation improvement programs.

Perioperative strategies for the reduction of postoperative pulmonary complications

Postoperative pulmonary complications (PPCs), estimated between 2.0% and 5.6% in the general surgical population and 20-70% for upper abdominal and thoracic surgeries, are a significant factor leading to poor patient outcomes. Efforts to decrease the incidence of PPCs such as bronchospasm, atelectasis, exacerbations of underlying chronic lung conditions, infections (bronchitis and pneumonia), prolonged mechanical ventilation, and respiratory failure, begins with a detailed preoperative risk evaluation. There are several available preoperative tests to estimate the risk of PPCs. However, the value of some of these studies to estimate PPCs remains controversial and is still debated. In this review, the preoperative risk assessment of PPCs is examined along with preoperative pulmonary tests to estimate risk, intraoperative, and procedure-associated risk factors for PPCs, and perioperative strategies to decrease PPCs. The importance of minimizing these events is reflected in the fact that nearly 25% of postoperative deaths occurring in the first week after surgery are associated with PPCs. This review provides important information to help clinical anesthesiologists to recognize potential risks for pulmonary complications and allows strategies to create an appropriate perioperative plan for patients.

MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery

OBJECTIVE

To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data.

BACKGROUND

Postoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited.

METHODS

In a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance.

RESULTS

MySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85).

CONCLUSIONS

We constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.

Association of Perioperative Variables and the Acute Respiratory Distress Syndrome in Liver Transplant Recipients

Background

The assessment of perioperative risk factors for the development of acute respiratory distress syndrome (ARDS) has been described in various surgical populations. However, there are only limited data among patients undergoing liver transplantation (LT), particularly regarding the influence of intraoperative ventilation parameters. We sought to identify the perioperative risk factors associated with the development of ARDS in LT recipients.

Methods

This is a single-center, retrospective cohort study of adult patients who underwent LT at a tertiary academic medical center between January 1, 2006, and January 31, 2016. Postoperative ARDS was identified using the Berlin definition. Multivariable logistic regression analysis was used to identify perioperative risk factors for ARDS.

Results

Of 817 eligible patients who underwent an LT during the study period, 20 (2.45%) developed postoperative ARDS. In the preoperative model, ongoing dialysis (odds ratio, 6.41; P < 0.01) was identified as an independent risk factor of ARDS post-LT. A higher mean peak inspiratory pressure per increase of 1 cm H₂O (odds ratio, 1.31; P < 0.01) was the only independent risk factor in the intraoperative model. Patients who developed ARDS postoperatively had significantly greater intensive care unit and hospital stay compared to non-ARDS patients (P < 0.001). There were no significant differences in the 30-day (P = 0.16) and 1-year (P = 0.51) mortality between the groups.

Conclusions

Dialysis at the time of transplant and elevated intraoperative mean peak inspiratory pressure were associated with the development of ARDS. ARDS post LT was associated with increased intensive care unit and hospital length of stay, but not increased mortality.

Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery

Background

Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently.

Objective

This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these.

Methods

Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015.

Results

Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14–0.24, p < 0.001), age (standardized OR 1.10, 95%CI 1.03–1.17, p = 0.007), low body mass index (standardized OR 0.89, 95%CI 0.82–0.98, p = 0.012), operation year (standardized OR 0.91, 95%CI 0.85–0.97, p = 0.003), waist circumference (standardized OR 1.09, 95%CI 1.00–1.19, p = 0.059), and dyspepsia/GERD (standardized OR 1.08, 95%CI 1.02–1.15, p = 0.007) were all associated with risk for severe postoperative complication and were included in the risk prediction model. Despite high specificity, the sensitivity of the model was low.

Conclusion

Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

Diabetic lung disease: fact or fiction?

Diabetes mellitus is a chronic, progressive, incompletely understood metabolic disorder whose prevalence has been increasing steadily worldwide. Even though little attention has been paid to lung disorders in the context of diabetes, its prevalence has recently been challenged by newer studies of disease development. In this review, we summarize and discuss the role of diabetes mellitus involved in the progression of pulmonary diseases, with the main focus on pulmonary fibrosis, which represents a chronic and progressive disease with high mortality and limited therapeutic options.

Perioperative anaesthetic management of patients with or at risk of acute distress respiratory syndrome undergoing emergency surgery

Patients undergoing emergency surgery may present with the acute respiratory distress syndrome (ARDS) or develop this syndrome postoperatively. The incidence of ARDS in the postoperative period is relatively low, but the impact of ARDS on patient outcomes and healthcare costs is relevant Aakre et.al (Mayo Clin Proc 89:181-9, 2014).The development of ARDS as a postoperative pulmonary complication (PPC) is associated with prolonged hospitalisation, longer duration of mechanical ventilation, increased intensive care unit length of stay and high morbidity and mortality Ball et.al (Curr Opin Crit Care 22:379-85, 2016). In order to mitigate the risk of ARDS after surgery, the anaesthetic management and protective mechanical ventilation strategies play an important role. In particular, a careful integration of general anaesthesia with neuraxial or locoregional techniques might promote faster recovery and reduce opioid consumption. In addition, the use of low tidal volume, minimising plateau pressure and titrating a low-moderate PEEP level based on the patient's need can improve outcome and reduce intraoperative adverse events. Moreover, perioperative management of ARDS patients includes specific anaesthesia and ventilator settings, hemodynamic monitoring, moderately restrictive fluid administration and pain control.The aim of this review is to provide an overview and evidence- and opinion-based recommendations concerning the management of patients at risk of and with ARDS who undergo emergency surgical procedures.

Expanding the Presence of Primary Services at Rapid Response Team Activations: A Quality Improvement Project

Rapid response teams (RRTs) were implemented to provide critical care services for deteriorating patients outside of intensive care units. To date, research on RRT has been conflicting, with some studies showing significant mortality benefit and reduction in cardiac arrest events and others showing no benefit. However, studies have consistently showed improved outcomes when RRTs work closely with primary services. Baseline data analysis at our institution found that primary services were present only on 50% of RRT activations. This quality improvement project aimed to improve the presence of primary services during RRT activations by 25%. With a survey, the main barrier that prevented primary services to be present was identified as the primary services' failure to recognize them as a crucial part of the RRT. Education tools and in-person sessions were implemented reinforcing the importance of primary services presence during RRT activations. The intervention leads to increasing presence of primary services at RRT activations, transfers to higher level of care, and changes in code status. However, there was no difference in hospital or intensive care unit length of stay or in survival.

Diagnostic and prognostic values of serum activin-a levels in patients with acute respiratory distress syndrome

Background

We aimed to evaluate whether serum activin-A levels are elevated and have any value in predicting severity and prognosis in acute respiratory distress syndrome (ARDS).

Methods

Retrospective cohort study was performed with patients who were admitted to MICU with diagnosis of ARDS and have serum samples stored within 48 h of Intensive care unit (ICU) admission between March 2013 and December 2016 at a single tertiary referral hospital. Serum activin-A levels were measured with ELISA kit, and were compared with those of normal healthy control and non-ARDS sepsis patients.

Results

Total 97 ARDS patients were included for the study. Levels of Activin-A were elevated in ARDS patients compared to those of healthy controls (Log-transformed activin-A levels 2.89 ± 0.36 vs. 2.34 ± 0.11, p < 0.001, absolute activin-A levels 1525.6 ± 1060.98 vs. 225.9 ± 30.1, p = 0.016) and non-ARDS sepsis patients (Log-transformed activin-A levels 2.89 ± 0.36 vs. 2.73 ± 0.34, p = 0.002, Absolute activin-A levels 1525.6 ± 1060.98 vs. 754.8 ± 123.5 pg/mL, p = 0.036). When excluding five outliers with extremely high activin-A levels, activin-A showed statistically significant correlation with in-hospital mortalities (In-hospital survivors 676.2 ± 407 vs. non-survivors 897.9 ± 561.9 pg/mL, p = 0.047). In predicting in-hospital mortality, serum activin-A concentrations showed superior area under curve compared to that of Acute physiologic and chronic health evaluation II scores (0.653; 95% CI [0541, 0.765] vs. 0.591, 95% CI [0.471, 0.710]). With cut-off level of 708 pg/mL, those with high serum activin-A levels had more than twofold increased risk of in-hospital mortalities. However, those relations were missing when outliers were in.

Conclusions

Serum activin-A levels in ARDS patients are elevated. However, its levels are weakly associated with ARDS outcomes.

Electronic supplementary material

The online version of this article (10.1186/s12890-019-0879-6) contains supplementary material, which is available to authorized users.

The LAS VEGAS risk score for prediction of postoperative pulmonary complications: An observational study

BACKGROUND

Currently used pre-operative prediction scores for postoperative pulmonary complications (PPCs) use patient data and expected surgery characteristics exclusively. However, intra-operative events are also associated with the development of PPCs.

OBJECTIVE

We aimed to develop a new prediction score for PPCs that uses both pre-operative and intra-operative data.

DESIGN

This is a secondary analysis of the LAS VEGAS study, a large international, multicentre, prospective study.

SETTINGS

A total of 146 hospitals across 29 countries.

PATIENTS

Adult patients requiring intra-operative ventilation during general anaesthesia for surgery.

INTERVENTIONS

The cohort was randomly divided into a development subsample to construct a predictive model, and a subsample for validation.

MAIN OUTCOME MEASURES

Prediction performance of developed models for PPCs.

RESULTS

Of the 6063 patients analysed, 10.9% developed at least one PPC. Regression modelling identified 13 independent risk factors for PPCs: six patient characteristics [higher age, higher American Society of Anesthesiology (ASA) physical score, pre-operative anaemia, pre-operative lower SpO2 and a history of active cancer or obstructive sleep apnoea], two procedure-related features (urgent or emergency surgery and surgery lasting ≥ 1 h), and five intra-operative events [use of an airway other than a supraglottic device, the use of intravenous anaesthetic agents along with volatile agents (balanced anaesthesia), intra-operative desaturation, higher levels of positive end-expiratory pressures > 3 cmH2O and use of vasopressors]. The area under the receiver operating characteristic curve of the LAS VEGAS risk score for prediction of PPCs was 0.78 [95% confidence interval (95% CI), 0.76 to 0.80] for the development subsample and 0.72 (95% CI, 0.69 to 0.76) for the validation subsample.

CONCLUSION

The LAS VEGAS risk score including 13 peri-operative characteristics has a moderate discriminative ability for prediction of PPCs. External validation is needed before use in clinical practice.

TRIAL REGISTRATION

The study was registered at Clinicaltrials.gov, number NCT01601223.

Rationale and design of a prospective, multicentre, randomised, conventional treatment-controlled, parallel-group trial to evaluate the efficacy and safety of ulinastatin in preventing acute respiratory distress syndrome in high-risk patients

INTRODUCTION

Acute respiratory distress syndrome (ARDS) is challenging in the intensive care unit (ICU). Although pharmacotherapy for ARDS has gained increasing attention, most trials have yielded negative results. Patients with ARDS have usually been recruited as subjects; the inflammatory reaction has already expanded into a cascade at this point, and its severity is sufficient to damage the lung parenchyma. This raises the question of whether early treatment can prevent ARDS and the associated lung injury. We hypothesise that ARDS is preventable in high-risk patients by administration of ulinastatin as an anti-inflammatory drug before ARDS onset, and we are performing a study to test ulinastatin, a protease inhibitor, versus treatment-as-usual in a group of patients at increased risk for ARDS.

METHODS AND ANALYSIS

This report presents the protocol for a multicentre, randomised, conventional treatment-controlled, parallel group study to prevent the development of ARDS using ulinastatin in high-risk patients. The study population will comprise patients at risk of ARDS in the ICU (≥18 years of age and Lung Injury Prediction Score of >4); patients with confirmed ARDS and some other conditions (immunodeficiency, use of some drugs, etc.) will be excluded. The enrolled patients will be randomly allocated to an ulinastatin group (ulinastatin will be intravenously administered every 8 hours for a total of 600 000 U/day for five consecutive days) or control group. The efficacy of ulinastatin in preventing ARDS development will be evaluated by the incidence rate of ARDS as the primary outcome; the secondary outcomes include the severity of ARDS, clinical outcome, extrapulmonary organ function and adverse events incurred by ulinastatin. Based on the results of preliminary studies and presuming the incidence of ARDS will decrease by 9% in high-risk patients, 880 patients are needed to obtain statistical power of 80%.

ETHICS AND DISSEMINATION

This study has been approved by the Peking University Third Hospital Medical Science Research Ethics Committee. The findings will be published in peer-reviewed journals and presented at national and international conferences.

TRIAL REGISTRATION NUMBER

NCT03089957; Pre-results.

The effect of diabetes on the risk and mortality of acute lung injury/acute respiratory distress syndrome: A meta-analysis

BACKGROUND

The role of pre-existing diabetes in acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is still controversial. This systematic review and meta-analysis of observational studies aimed to evaluate the effect of diabetes on the risk and mortality of ALI/ARDS.

METHODS

A comprehensive literature search was performed in PubMed, Scopus, Cochrane Central Register of Controlled Trails and Web of Science for their inception to September 2018. Summary risk estimates were calculated with a DerSimonian and Laird random-effects model. Heterogeneity was evaluated using Cochran chi-square test and the I statistic.

RESULTS

Ultimately, 14 studies with a total of 6613 ALI/ARDS cases were included. The risk of ALI/ARDS was not significantly reduced in diabetes patients (OR 0.82, 95% CI 0.57-1.18, P = .283), with obvious heterogeneity across studies (I = 72.5%, P < .001). Further analyses in the meta-analysis also showed no statistically significant associations between pre-existing diabetes and in-hospital mortality (OR 0.79, 95% CI 0.51-1.21, P = .282) or 60-day mortality of ALI/ARDS (OR 0.91, 95% CI 0.75-1.11, P = .352).

CONCLUSION

This systematic review and meta-analysis of observational studies indicates that pre-existing diabetes have no effect on the risk and mortality of ALI/ARDS.