Perioperative Risk Stratification Research Articles

Anastomotic leakage in hepato-pancreato-biliary surgery

Severe complications following hepato-pancreato-biliary surgery are frequently due to leakage of anastomoses. Local intraoperative and systematic measures can reduce the incidence of leaks and leak-related sequelae. The early identification of leak-related sequelae, such as hemorrhage is pivotal to reduce the mortality risk. Therefore, perioperative risk stratification incorporating surgical and patient risk factors is crucial. The management of anastomotic leaks is complex and an interdisciplinary treatment is therefore recommended. The treatment depends on the institutional expertise, localization, characteristic features of the anastomosis and the onset of insufficiency. This article describes the different concepts of the diagnostics, prevention, consequences and management of anastomotic leakage in hepato-pancreato-biliary surgery in more detail.

Comparison of a Risk Calculator With Frailty Indices in Patients Undergoing Lung Cancer Resection.

While frailty has gained attention for its utility in risk stratification, no studies have directly compared them to existing risk calculators. The objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other than home. Patients undergoing anatomic lung resection for primary, nonsmall cell lung cancer were identified within the ACS National Quality Improvement Program (ACS NSQIP) database. Tools were compared for discrimination in the primary outcomes. 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (IQR 59-74) years. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% CI). This included perioperative mortality (AUC 0.74, 95% CI 0.71-0.78), compared to RAI-rev (AUC 0.66, 95% CI 0.62-0.69) and 5-mFI (AUC 0.61, 95% CI 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.

Impact of frailty on outcomes of patients undergoing elective endovascular thoraco-abdominal aortic aneurysm repair.

Frailty and it score assessment by the Clinical Frailty Scale (CFS) have been recently proposed in surgery to overcome chronological age and major comorbidities as predictor tools of the surgical risks. We aim to evaluate the impact of frailty on outcomes of patients undergoing TAAA endovascular repair and whether CFS may be used as screening tool in the preoperative work-up and peri-operative risk stratification. REtrospective analysis of 76 patients (61 male, 74.9±6.9 years) undergoing elective branched-EVAR. Patients were divided in Group A (CFS<5) and Group B (CFS≥5). Post-operative morbidity, access-site related-complications, ICU- and in-hospital length-of-stay, reintervention rate, surgery- and all-causes related mortality were evaluated. Fifty-four patients (71.1%) were classified as CFS<5, whereas twenty-two as CFS≥5. Demographics and comorbidities were homogeneous regardless of CFS class. No differences in term of MAE and of access-site related-complication but a greater perioperative and early mortality rate in the group of frail patients was noted (P=0.009, OR 11.8, 95% CI 1.35-3.58; P=0.019, respectively), as a longer hospitalization (P=0.007) and more frequent non-home discharge. Mid-term aneurysm- and all-causes related mortality was similar in both groups. Frailty seems to be associated with worse perioperative outcomes. CFS is a reliable tool to quantify the degree of disability due to frailty and to better assess the risks and benefits of endovascular TAAA repair. Frailty is not equated with inoperability but indicate the need for a tailored approach for the more vulnerable patients. Larger studies and a widespread use of frailty screening methods are needed to confirm its efficacy in the prediction of outcomes after endovascular interventions.

Beyond body mass index: Body composition profiling for perioperative risk stratification in intrahepatic cholangiocarcinoma patients.

Intrahepatic cholangiocarcinoma (iCC) is an aggressive tumor, usually detected at an advanced stage. Our aim was to investigate the potential of body composition analysis (BCA) derived from presurgical staging computed tomography (CT) in predicting perisurgical complications. In this retrospective cohort study, we enrolled 86 patients who underwent CT imaging prior to liver surgery. Cox and logistic regression were performed to assess risk factors for prolonged hospital and intensive care unit (ICU) stays, as well as the occurrence of various complications. BCA parameters served as covariates besides conventional risk factors. Postoperative complications after resection of iCC significantly prolonged the overall length of hospitalization (p < .001). Presence of sarcopenia was associated with longer ICU stays. Complications were common, with 62.5% classified as Clavien-Dindo grade IIIa or lower and 37.5% as more severe. Subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) were identified as risk factors for complications, including bile leakage (in 24 cases, p = .025), pleural effusions (in 26 cases, p = .025), and intra-abdominal abscess formation (in 24 cases, p = .043). SAT was associated with severe complications requiring interventional therapy, whereas VAT was correlated with abscess formation. Despite normal prevalence of obesity (22%), body mass index (BMI) did not have an impact on the development of perioperative complications. BCA is a useful tool for preoperative risk stratification in patients with iCC and is superior to BMI assessment. Increased SAT and VAT were associated with the risk of perisurgical complications, prolonging hospitalization. Therefore, BCA derived from routine staging CT should be considered in the preoperative assessment of patients with iCC.

Can the Pioneering Impact of Artificial Intelligence in Anaesthetic Practice Uphold Good Medical Practice?

The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.

Perioperative risk stratification in patients undergoing multi-resection surgeries. Is ASA classification sufficient?

Perioperative risk stratification in patients undergoing multi-resection surgeries. Is ASA classification sufficient?

Comparison of a risk calculator with frailty indices in patients undergoing lung cancer resection.

Perioperative risk stratification is an essential component of preoperative planning for cancer surgery. While frailty has gained attention for its utility in risk stratification, no studies have directly compared it to existing risk calculators. Therefore, the objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other-than-home. Patients undergoing anatomic lung resection for primary, non-small cell lung cancer were identified within the American College of Surgeons National Quality Improvement Program (ACS NSQIP) database. The ACS-SRC, RAI-rev, and 5-mFI tools were used to predict adverse postoperative events. Tools were compared for discrimination in the primary outcomes. 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (interquartile range = 59-74) years. Cardiothoracic surgeons performed 89% and general surgeons performed 11.0% of the operations. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% confidence interval [CI]). This included perioperative mortality (AUC = 0.74, 95% CI = 0.71-0.78), compared to RAI-rev (AUC = 0.66, 95% CI = 0.62-0.69) and 5-mFI (AUC = 0.61, 95% CI = 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.

A Randomized, Controlled Trial Evaluating Perioperative Risk-Stratification and Risk-Based, Protocol-Driven Management After Elective Major Cancer Surgery.

We evaluated the efficacy of risk-based, protocol-driven management versus (vs) usual management after elective major cancer surgery to reduce 30-day rates of postoperative death or serious complications (DSC) . Major cancer surgery is associated with significant perioperative risks which result in worse long-term outcomes. Adults scheduled for elective major cancer surgery were stratified/randomized to risk-based escalating levels of care, monitoring, and co-management vs usual management. The primary study outcome was 30-day rate of DSC. Additional outcomes included complications, adverse events, health care utilization, health-related quality of life (HRQOL), and disease-free and overall survival (DFS and OS). Between August 2014 and June 2020, 1529 patients were enrolled and randomly allocated to the study arms; 738 patients in the Intervention Arm and 732 patients in the Control Arm were eligible for analysis. 30-day rate of DSC with the intervention was 15.0% (95% CI, 12.5-17.6%) vs 14.1%, (95% CI, 11.6-16.6%) with usual management (P=0.65). There were no differences in 30-day rates of complications or adverse events (including return to the operating room); postoperative length of stay; rate of discharge to home; or 30, 60, or 90-day HRQOL or rates of hospital readmission or receipt of anti-neoplastic therapy between the study arms. At median follow-up of 48 months, OS (P=0.57) and DFS (P=0.91) were similar. Risk-based, protocol-driven management did not reduce 30-day rate of DSC after elective major cancer surgery compared to usual management, nor improve postoperative health care utilization, HRQOL, or cancer outcomes. Trials are needed to identify cost-effective, tailored perioperative strategies to optimize outcomes after major cancer surgery.

Predictors of operative ischemic cerebrovascular complications in skull base tumor resections: Experience in low-resource setting

Abstract Background Ischemic cerebrovascular complications following skull base tumor resections remain a significant factor impacting both short-term and long-term patient outcomes. This study aims to improve risk stratification, surgical decision-making, and postoperative care protocols. Methods A retrospective cohort study on predictors of ischemic cerebrovascular complications among patients who underwent skull base tumor resection was conducted at 2 high-volume neurosurgical centers in Ethiopia from 2018 to 2023. Binary logistic analysis was performed to see the association of each predictor variable. Results The study included 266 patients, with 65.5% being female. The median age and tumor size were 37 (± IQR = 17) years and 4.9 cm (± IQR 1.5), respectively. Ischemic cerebrovascular complications occurred in 19.9% of patients. Middle cranial fossa tumors and tumors spanning both anterior and middle cranial fossa (AOR = 6.75, 95% CI: 1.66–27.54, P &amp;lt; .008), grades 3–5 vascular encasement (AOR = 5.04, 95% CI: 1.79–14.12, P &amp;lt; .002), near-total resection and gross total resection (AOR = 2.89, 95% CI: 1.01–8.24, P &amp;lt; .048), and difficult hemostasis (AOR = 9.37, 95% CI: 3.19–27.52, P &amp;lt; .000) were significantly associated with iatrogenic vascular injury. Subarachnoid hemorrhage had a statistically significant association with vasospasm (AOR = 12.27, 95% CI: 1.99–75.37, P = .007). Conclusions Surgery-related ischemic cerebrovascular complications are common. Thorough perioperative risk stratification and proactive treatment planning are crucial to mitigate vascular insults associated with it. In low-resource settings, neurosurgical services are provided without advanced instruments, leading to more complications. Therefore, it is important to focus on improving neurosurgical setup to enhance patient outcomes.

The current role of pentafecta in the reporting of radical cystectomy outcomes: a scoping review.

Pentafecta has recently been validated for reporting radical cystectomy (RC) outcomes in open, laparoscopic and robotic series. We aim in this review to explore the current role of pentafecta in the reporting of RC outcomes. A comprehensive literature search was performed in the PubMed database to identify relevant articles. The pentafecta achievement (PA) was defined originally as negative soft tissue surgical margin (NSTSM), lymph node (LN) dissection (LND) with removal of ≥16 LNs, absence of 90-days grade ≥3 Clavien-Dindo (CD) complications, a time interval of less than 3 months between the last transurethral resection of bladder tumor (TURBT) with evidence of muscle invasive bladder cancer (MIBC) and RC, and absence of local pelvic recurrence within 1 year. The definition was later modified and the last two criteria were replaced by absence of urinary diversion (UD) related complications and any clinical recurrence at one year. Twelve studies with 4,946 patients were enrolled in the present review. All the studies were retrospective except one recently published randomized study comparing open and robotic-assisted RC. Pentafecta was totally achieved in 34% and main causes of missing pentafecta were the number of resected LNs and 90-days major complications. Type of UD, increasing age, advanced tumor stage, and decreasing surgical experience were the factors most commonly associated with a lower likelihood of PA. A positive correlation was seen between PA and long-term oncological outcome and quality of life. The main limitations in the present studies are their retrospective nature, relatively small sample size, and short median follow-up, most of which was less than 3 years. The new pentafecta definition provides a comprehensive tool for reporting RC outcomes by including measures of postoperative morbidity, functional outcomes and local cancer control. Pentafecta include standards that could be useful for improving surgical quality, surgical education and comparing different techniques. However, pentafecta is not yet suitable for perioperative risk stratification and patient counseling.

Risk factors for MACE and bleeding in atrial fibrillation patients undergoing surgery: Insights from the bridge trial

IntroductionPatients with atrial fibrillation (AF) undergoing elective procedures are at risk for Major Adverse Cardiovascular Events (MACE) and symptomatic bleeding. We aimed to identify risk factors to guide perioperative risk stratification. MethodsWe conducted a post-hoc analysis of the “Bridging Anticoagulation in Patients who Require Temporary Interruption of Warfarin Therapy for an Elective Invasive Procedure or Surgery” randomized trial. The primary outcomes were MACE and symptomatic bleeding. Our statistical approach encompassed standard univariate analysis, logistic stepwise regression, and Cox regression models. Additional interaction analyses evaluated the interplay between low-molecular-weight heparin bridge therapy and other identified risk factors. ResultsAmong a total of 1,813 participants (mean age 71.6 ± 8.8, 73.3 % male), MACE occurred in 25 (1.4 %) individuals, with pre-procedure clopidogrel use (adjusted hazard ratio [aHR] 7.73, 95 % CI 2.63-22.72, p < 0.001) and CHA2DS2-VASc score ≥ 5 (aHR 2.89, 95 % CI 1.26-6.63, p = 0.012) identified as risk factors. Symptomatic bleeding occurred in 57 (3.1 %) individuals, with bridge therapy (aHR 1.84, 95 % CI 1.07-3.19, p = 0.029), renal disease (aHR 2.50, 95 % CI 1.34-4.67, p = 0.004), post-procedure aspirin use (aHR 2.86, 95 % CI 1.66-4.91, p < 0.001), post-procedure nonsteroidal anti-inflammatory drug use excluding aspirin (aHR 3.40, 95 % CI 1.22-9.43, p = 0.019), and major surgery (aHR 3.94, 95 % CI 2.26-6.85, p < 0.001) identified as risk factors. The interactions between risk factors and bridging therapy on MACE and symptomatic bleeding outcomes were not significant (p > 0.05). ConclusionWe identified predictors for MACE and symptomatic bleeding in AF patients undergoing elective procedures. These insights may help guide perioperative decisions to reduce the risk of adverse outcomes.

Ultrasound Shear Wave Elastography Evaluation of the Liver and Implications for Perioperative Medicine.

Ultrasound shear wave elastography (SWE) is a non-invasive, low risk technology allowing the assessment of tissue stiffness. Used clinically for nearly two decades to diagnose and stage liver fibrosis and cirrhosis, it has recently been appreciated for its ability to differentiate between more subtle forms of liver dysfunction. In this review, we will discuss the principle of ultrasound shear wave elastography, its traditional utilization in grading liver cirrhosis, as well as its evolving role in identifying more subtle degrees of liver injury. Finally, we will show how this capacity to distinguish nuanced changes may provide an opportunity for its use in perioperative risk stratification.

Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication

General-domain large language models may be able to perform risk stratification and predict postoperative outcome measures using a description of the procedure and a patient's electronic health record notes. To examine predictive performance on 8 different tasks: prediction of American Society of Anesthesiologists Physical Status (ASA-PS), hospital admission, intensive care unit (ICU) admission, unplanned admission, hospital mortality, postanesthesia care unit (PACU) phase 1 duration, hospital duration, and ICU duration. This prognostic study included task-specific datasets constructed from 2 years of retrospective electronic health records data collected during routine clinical care. Case and note data were formatted into prompts and given to the large language model GPT-4 Turbo (OpenAI) to generate a prediction and explanation. The setting included a quaternary care center comprising 3 academic hospitals and affiliated clinics in a single metropolitan area. Patients who had a surgery or procedure with anesthesia and at least 1 clinician-written note filed in the electronic health record before surgery were included in the study. Data were analyzed from November to December 2023. Compared original notes, note summaries, few-shot prompting, and chain-of-thought prompting strategies. F1 score for binary and categorical outcomes. Mean absolute error for numerical duration outcomes. Study results were measured on task-specific datasets, each with 1000 cases with the exception of unplanned admission, which had 949 cases, and hospital mortality, which had 576 cases. The best results for each task included an F1 score of 0.50 (95% CI, 0.47-0.53) for ASA-PS, 0.64 (95% CI, 0.61-0.67) for hospital admission, 0.81 (95% CI, 0.78-0.83) for ICU admission, 0.61 (95% CI, 0.58-0.64) for unplanned admission, and 0.86 (95% CI, 0.83-0.89) for hospital mortality prediction. Performance on duration prediction tasks was universally poor across all prompt strategies for which the large language model achieved a mean absolute error of 49 minutes (95% CI, 46-51 minutes) for PACU phase 1 duration, 4.5 days (95% CI, 4.2-5.0 days) for hospital duration, and 1.1 days (95% CI, 0.9-1.3 days) for ICU duration prediction. Current general-domain large language models may assist clinicians in perioperative risk stratification on classification tasks but are inadequate for numerical duration predictions. Their ability to produce high-quality natural language explanations for the predictions may make them useful tools in clinical workflows and may be complementary to traditional risk prediction models.

Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial.

Anaesthesiology clinicians can implement risk mitigation strategies if they know which patients are at greatest risk for postoperative complications. Although machine learning models predicting complications exist, their impact on clinician risk assessment is unknown. This single-centre randomised clinical trial enrolled patients age ≥18 undergoing surgery with anaesthesiology services. Anaesthesiology clinicians providing remote intraoperative telemedicine support reviewed electronic health records with (assisted group) or without (unassisted group) also reviewing machine learning predictions. Clinicians predicted the likelihood of postoperative 30-day all-cause mortality and postoperative acute kidney injury within 7 days. Area under the receiver operating characteristic curve (AUROC) for the clinician predictions was determined. Among 5,071 patient cases reviewed by 89 clinicians, the observed incidence was 2% for postoperative death and 11% for acute kidney injury. Clinician predictions agreed with the models more strongly in the assisted versus unassisted group (weighted kappa 0.75 versus 0.62 for death [difference 0.13, 95%CI 0.10-0.17] and 0.79 versus 0.54 for kidney injury [difference 0.25, 95%CI 0.21-0.29]). Clinicians predicted death with AUROC of 0.793 in the assisted group and 0.780 in the unassisted group (difference 0.013, 95%CI -0.070 to 0.097). Clinicians predicted kidney injury with AUROC of 0.734 in the assisted group and 0.688 in the unassisted group (difference 0.046, 95%CI -0.003 to 0.091). Although there was evidence that the models influenced clinician predictions, clinician performance was not statistically significantly different with and without machine learning assistance. Further work is needed to clarify the role of machine learning in real-time perioperative risk stratification. ClinicalTrials.gov NCT05042804.

Anaesthetic and perioperative considerations for extrapleural pneumonectomy and extended pleurectomy/decortication: a scoping review protocol

IntroductionExtrapleural pneumonectomy (EPP) and extended pleurectomy/decortication (ePD) are surgical cytoreductive techniques aimed at achieving macroscopic resection in malignant pleural tumours such as pleural mesothelioma, non-mesothelioma pleural malignancies such as thymoma...

Perioperative echocardiographic strain analysis: what anesthesiologists should know.

Echocardiographic strain analysis by speckle tracking allows assessment of myocardial deformation during the cardiac cycle. Its clinical applications have significantly expanded over the last two decades as a sensitive marker of myocardial dysfunction with important diagnostic and prognostic values. Strain analysis has the potential to become a routine part of the perioperative echocardiographic examination for most anesthesiologist-echocardiographers but its exact role in the perioperative setting is still being defined. This clinical report reviews the principles underlying strain analysis and describes its main clinical uses pertinent to the field of anesthesiology and perioperative medicine. Strain for assessment of left and right ventricular function as well as atrial strain is described. We also discuss the potential role of strain to aid in perioperative risk stratification, surgical patient selection in cardiac surgery, and guidance of anesthetic monitor choice and clinical decision-making in the perioperative period. Echocardiographic strain analysis is a powerful tool that allows seeing what conventional 2D imaging sometimes fails to reveal. It often provides pathophysiologic insight into various cardiac diseases at an early stage. Strain analysis is readily feasible and reproducible thanks to the use of highly automated software platforms. This technique shows promising potential to become a valuable tool in the arsenal of the anesthesiologist-echocardiographer and aid in perioperative risk-stratification and clinical decision-making.

Predictors of prolonged hospital stay after segmentectomy

ObjectiveSegmentectomy is becoming the standard of care for small, peripheral non–small cell lung cancer. To improve perioperative management in this population, this study aims to identify factors influencing hospital length of stay after segmentectomy. MethodsPatients who underwent segmentectomy for any indication between January 2018 and May 2023 were identified using a prospectively maintained institutional database. Multivariable logistic regression models were used to estimate associations between clinical features and prolonged (≥3 days) hospital stay. A nomogram was designed to understand better and possibly calculate the individual risk of prolonged hospital stays. ResultsIn total, 533 cases were included; 337 (63%) were female. Median age was 66 years (interquartile range [IQR], 63-75). The median size of resected lesions was 1.6 cm (IQR, 1.3-2.1 cm). Median hospital stay was 3 days (IQR, 2-4 days). Major adverse events occurred in 31 (5.8%) cases. The 30-day readmission rate was 5.8% (n = 31). There was no 30-day mortality; 90-day mortality was <1%. Patients older than 75 years (odds ratio [OR], 2.01, 95% confidence interval [CI], 1.15-3.57, P = .02), those with forced expiratory volume in 1 second <88% predicted (OR, 1.99; 95% CI, 1.38-2.89, P < .001), or positive smoking history (OR, 1.72; 95% CI, 1.15-2.60, P = .01) were more likely to have prolonged hospital stays after segmentectomy. A nomogram accounting for age, sex, forced expiratory volume in 1 second, body mass index, smoking history, and comorbidities was created to predict the probability of prolonged hospital stay with an area under the receiver operating characteristic curve of 0.66. ConclusionsOlder patients, those with reduced pulmonary function, and current and past smokers have elevated risk for prolonged hospital stays after segmentectomy. Validation of our nomogram could improve perioperative risk stratification in patients who undergo segmentectomy.

701 A Modified Frailty Index to Predict Adverse Outcomes After Decompressive Hemicraniectomy for Acute Ischemic Stroke

INTRODUCTION: Surgical decision-making for decompressive hemicraniectomy (DHC) is challenging because of the heterogeneity of health status and the paucity of tools for predicting the operative risk of DHC for malignant edema following acute infarction. METHODS: The 2008–2018 National Surgical Quality Improvement Program (NSQIP) data set was accessed for patients undergoing DHC for Acute ischemic stroke (AIS) based on Current Procedural Terminology (CPT) codes. We used a modified frailty index (mFI) based on eleven preoperative clinical NSQIP variables. The outcomes assessed were 30-day occurrences of adverse events. RESULTS: A total of 394 patients were identified in the database, there were 55 (14%) patients with mFI 0, 118 patients (30%) with mFI 0.09, 115(29.2 %) with mFI 0.18 and 106 (27%) patients with mFI = 0.27. The rate of return to the operating room for a second procedure significantly increased from 4/55 (7.3%) in patients with mFI 0 to 23/106 (31.1%) in patients with mFI = 0.27. The discharge disposition was significantly different in patients with higher mFI (mFI &gt; 0.27), with only 6/106 (5.7%) discharged to home versus 8/55 (14.5%) with mFI 0. Multivariate logistic regression analysis showed that compared to age, gender, ASA class, and pre albumin levels, mFI was a better predictor of the development of pneumonia (OR 6.278, 95 % CI 1.8, 22.0, p = 0.004) and return to the operating room (OR 4.0, 95 % CI 1.2 ,13.4, p = 0.02). CONCLUSIONS: A higher mFI was associated with an increased risk of return to the operating room and worse discharge disposition. The mFI is based on easily identifiable patient characteristics and can be an additional tool to improve perioperative risk stratification in elderly patients considered for DHC after AIS.

A modified Hospital Frailty Risk Score for patients with cirrhosis undergoing abdominal operations.

Existing tools for perioperative risk stratification in patients with cirrhosis do not incorporate measures of comorbidity. The Hospital Frailty Risk Score (HFRS) is a widely used measure of comorbidity burden in administrative dataset analyses. However, it is not specific to patients with cirrhosis, and application of this index is limited by its complexity. Adult patients with cirrhosis who underwent nontransplant abdominal operations were identified from the National Inpatient Sample, 2016-2018. Adjusted associations between HFRS and in-hospital mortality and length of stay were computed with logistic and Poisson regression. Lasso regularization was used to identify the components of the HFRS most predictive of mortality and develop a simplified index, the cirrhosis-HFRS. Of 10,714 patients with cirrhosis, the majority were male, the median age was 62 years, and 32% of operations were performed electively. HFRS was associated with an increased risk of both in-hospital mortality (OR=6.42; 95% CI: 4.93, 8.36) and length of stay (incidence rate ratio [IRR]=1.79; 95% CI: 1.72, 1.88), with adjustment. Using lasso, we found that a subset of 12 of the 109 ICD-10 codes within the HFRS resulted in superior prediction of mortality in this patient population (AUC = 0.89 vs. 0.79, p < 0.001). While the 109-component HFRS was associated with adverse surgical outcomes, 12 components accounted for much of the association between the HFRS and mortality. We developed the cirrhosis-HFRS, a tool that demonstrates superior predictive accuracy for in-hospital mortality and more precisely reflects the specific comorbidity pattern of hospitalized patients with cirrhosis undergoing general surgery procedures.

Perioperative copeptin: predictive value and risk stratification in patients undergoing major noncardiac surgery—a prospective observational cohort study

PurposeBiomarkers can aid in perioperative risk stratification. While preoperative copeptin has been associated with adverse events, intraoperative information is lacking and this association may rather reflect a baseline risk. Knowledge about correlations between postoperative copeptin measurements and clinically relevant outcomes is scarce. We examined the association of perioperative copeptin concentrations with postoperative all-cause mortality and/or major adverse cardiac and cerebrovascular events (MACCE) at 12 months and 30 days as well as with perioperative myocardial injury (PMI).MethodsWe conducted a prospective observational cohort study of adults undergoing noncardiac surgery with intermediate to high surgical risk in Basel, Switzerland, and Düsseldorf, Germany from February 2016 to December 2020. We measured copeptin and cardiac troponin before surgery, immediately after surgery (0 hr) and once between the second and fourth postoperative day (POD 2–4).ResultsA primary outcome event of a composite of all-cause mortality and/or MACCE at 12 months occurred in 48/502 patients (9.6%). Elevated preoperative copeptin (> 14 pmol·L−1), immediate postoperative copeptin (> 90 pmol·L−1), and copeptin on POD 2–4 (> 14 pmol·L−1) were associated with lower one-year MACCE-free and/or mortality-free survival (hazard ratio [HR], 2.89; 95% confidence interval [CI], 1.62 to 5.2; HR, 2.07; 95% CI, 1.17 to 3.66; and HR, 2.47; 95% CI, 1.36 to 4.46, respectively). Multivariable analysis continued to show an association for preoperative and postoperative copeptin on POD 2–4. Furthermore, elevated copeptin on POD 2–4 showed an association with 30-day MACCE-free survival (HR, 2.15; 95% CI, 1.18 to 3.91). A total of 64 of 489 patients showed PMI (13.1%). Elevated preoperative copeptin was not associated with PMI, while immediate postoperative copeptin was modestly associated with PMI.ConclusionThe results of the present prospective observational cohort study suggest that perioperative copeptin concentrations can help identify patients at risk for all-cause mortality and/or MACCE. Other identified risk factors were revised cardiac risk index, body mass index, surgical risk, and preoperative hemoglobin.Trial registrationClinicalTrials.gov (NCT02687776); first submitted 9 February 2016.