Addition of biomarker panel improves prediction performance of American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator for cardiac risk assessment of elderly patients preparing for major non-cardiac surgery: a pilot study

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

What is the Accuracy of the ACS-NSQIP Surgical Risk Calculator in Emergency Abdominal Surgery? A Meta-Analysis

BACKGROUND

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator provides an estimation of 30-d post-operative complications including mortality. This tool has the potential to both aid in decision-making for patients and their families and also in optimizing the clinical management of high-risk patients. However, it's utility in patients requiring emergency abdominal surgery has shown to be inconsistent outside of NSQIP participating institutions. This study undertook a meta-analysis to assess the calculator's accuracy in predicting mortality in these patients.

METHODS

A literature search of PubMed, Medline and Cochrane databases was conducted between October 2019 to April 2020. The PubMed, Medline and Cochrane Databases were searched for relevant studies. The search strategy included studies from January 2013 to April 2020. Studies including elective surgery were excluded. A random effects model was used and fitted using restricted maximum likelihood estimation. The O:E ratio was used to validate the calculator's accuracy in predicting mortality.

RESULTS

Six studies were included in the meta-analysis, with a total of 1835 patients undergoing emergency intra-abdominal surgery. The summary estimate of the O:E ratio of the ACS-NSQIP surgical risk calculator in predicting 30-d post-operative mortality was 1.06 (95% CI 0.74-1.51). There was significant heterogeneity between studies with a Cochrane Q of 11.96 (P = 0.04) and I² = 57.5%.

CONCLUSIONS

The ACS-NSQIP surgical risk calculator is a reliable predictor of mortality in this external cohort and has potential to be utilised in the multi-disciplinary care of patients undergoing emergency abdominal surgery.

Perioperative Vascular Biomarker Profiling in Elective Surgery Patients Developing Postoperative Delirium: A Prospective Cohort Study

Background: Postoperative delirium (POD) ranks among the most common complications in surgical patients. Blood-based biomarkers might help identify the patient at risk. This study aimed to assess how serum biomarkers with specificity for vascular and endothelial function and for inflammation are altered, prior to or following surgery in patients who subsequently develop POD. Methods: This was a study on a subcohort of consecutively recruited elective non-cardiac as well as cardiac surgery patients (age > 60 years) of the single-center PROPDESC trial at a German tertiary care hospital. Serum was sampled prior to and following surgery, and the samples were subjected to bead-based multiplex analysis of 17 serum proteins (IL-3, IL-8, IL-10, Cripto, CCL2, RAGE, Resistin, ANGPT2, TIE2, Thrombomodulin, Syndecan-1, E-Selectin, VCAM-1, ICAM-1, CXCL5, NSE, and uPAR). Development of POD was assessed during the first five days after surgery, using the Confusion Assessment Method for ICU (CAM-ICU), the CAM, the 4-‘A’s test (4AT), and the Delirium Observation Scale (DOS). Patients were considered positive if POD was detected at least once during the visitation period by any of the applied methods. Non-parametric testing, as well as propensity score matching were used for statistical analysis. Results: A total of 118 patients were included in the final analysis; 69% underwent non-cardiac surgery, median overall patient age was 71 years, and 59% of patients were male. In the whole cohort, incidence of POD was 28%. The male gender was significantly associated with the development of POD (p = 0.0004), as well as a higher ASA status III (p = 0.04). Incidence of POD was furthermore significantly increased in cardiac surgery patients (p = 0.002). Surgery induced highly significant changes in serum levels of almost all biomarkers except uPAR. In preoperative serum samples, none of the analyzed parameters was significantly altered in subsequent POD patients. In postoperative samples, CCL2 was significantly increased by a factor of 1.75 in POD patients (p = 0.03), as compared to the no-POD cohort. Following propensity score matching, CCL2 remained the only biomarker that showed significant differences in postoperative values (p = 0.01). In cardiac surgery patients, postoperative CCL2 serum levels were more than 3.5 times higher than those following non-cardiac surgery (p < 0.0001). Moreover, after cardiac surgery, Syndecan-1 serum levels were significantly increased in POD patients, as compared to no-POD cardiac surgery patients (p = 0.04). Conclusions: In a mixed cohort of elective non-cardiac as well as cardiac surgery patients, preoperative serum biomarker profiling with specificity for vascular dysfunction and for systemic inflammation was not indicative of subsequent POD development. Surgery-induced systemic inflammation—as evidenced by the significant increase in CCL2 release—was associated with POD, particularly following cardiac surgery. In those patients, postoperative glycocalyx injury might furthermore contribute to POD development.

Perioperative Management of Elderly patients (PriME): recommendations from an Italian intersociety consensus

BACKGROUND

Surgical outcomes in geriatric patients may be complicated by factors such as multiple comorbidities, low functional performance, frailty, reduced homeostatic capacity, and cognitive impairment. An integrated multidisciplinary approach to management is, therefore, essential in this population, but at present, the use of such an approach is uncommon. The Perioperative Management of Elderly patients (PriME) project has been established to address this issue.

AIMS

To develop evidence-based recommendations for the integrated care of geriatric surgical patients.

METHODS

A 14-member Expert Task Force of surgeons, anesthetists, and geriatricians was established to develop evidence-based recommendations for the pre-, intra-, and postoperative care of hospitalized older patients (≥ 65 years) undergoing elective surgery. A modified Delphi approach was used to achieve consensus, and the strength of recommendations and quality of evidence was rated using the U.S. Preventative Services Task Force criteria.

RESULTS

A total of 81 recommendations were proposed, covering preoperative evaluation and care (30 items), intraoperative management (19 items), and postoperative care and discharge (32 items).

CONCLUSIONS

These recommendations should facilitate the multidisciplinary management of older surgical patients, integrating the expertise of the surgeon, the anesthetist, the geriatrician, and other specialists and health care professionals (where available) as needed. These roles may vary according to the phase and setting of care and the patient's conditions.

Pre and postoperative risk management: the role of scores and biomarkers

PURPOSE OF REVIEW

The goal of risk prediction is to identify high-risk patients who will benefit from further preoperative evaluation. Clinical scores and biomarkers are very well established tools for risk prediction but their accuracy remains a controversial issue.

RECENT FINDINGS

Current guidelines recommend one of the risk tools for preoperative cardiac risk assessment: American College of Surgeons National Surgical Quality Improvement Program (NSQIP) calculator or Revised Cardiac Risk Index. Although not as easy to use as risk scores, risk models are more accurate and can predict individual patient risk more precisely. A step forward in risk estimation was performed by introducing new risk models developed from the American College of Surgeons NSQIP database - NSQIP surgical risk calculator and Myocardial Infarction or Cardiac Arrest index. Although biomarkers, especially in cardiac risk assessment, are already present in current European and American guidelines, this use is still controversial. Novel biomarkers: microRNAs, heart-type fatty acid-binding protein and mid-regional proadrenomedullin, can be used as new potential biomarkers in clinical practice. Also some of the experimental biomarkers have not yet been introduced into clinical practice, preliminary results are encouraging.

SUMMARY

Different risk indices and biomarkers might lead to varying risk estimates. However, the importance of clinical judgment in risk assessment should not be underestimated.

Thyroidectomy in a Surgical Volunteerism Mission: Analysis of 464 Consecutive Cases

Although surgical volunteer missions (SVMs) have become a popular approach for reducing the burden of surgical disease worldwide, the outcomes of specific procedures in the context of a mission are underreported. The aim of this study was to evaluate outcomes and efficiency of thyroid surgery within a surgical mission. This was a retrospective analysis of medical records of all patients who underwent thyroid surgery within a SVM from 2006 to 2019. Postoperative complication rate was the safety endpoint, whereas length of hospital stay (LOS) was the efficiency endpoint. Serious complications were defined as Clavien-Dindo class 3-5 complications. Expected safety and efficiency outcomes were calculated using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) surgical risk calculator and compared to their observed counterparts. A total of 464 thyroidectomies were performed during the study period. Mean age of the patients was 40.3 ± 10.8 years, and male-to-female ratio was 72 : 392. Expected overall (p=0.127) and serious complication rates (p=0.738) were not significantly different from their observed counterparts. Expected LOS was found to be significantly shorter as compared to its observed counterpart (0.6 ± 0.2 vs. 2.5 ± 1.0 days; p < 0.001). This study found thyroid surgery performed within a surgical mission to be safe. NSQIP surgical risk calculator underestimates the LOS following thyroidectomy in surgical missions.

Cardiovascular Risk Assessment for Noncardiac Surgery: Are We Ready for Biomarkers?

Biomarkers aided perioperative cardiac assessment is a relatively new concept. Cardiac biomarkers with historical significance (aspartate transaminase, dehydrogenase, creatinine kinase and myoglobin) have paved the way for traditional biomarkers (cardiac troponin, C-reactive protein, lipoprotein). Contemporary biomarkers like natriuretic peptides (BNP and ProBNP) are validated risk markers in both acute and chronic cardiac diseases and are showing remarkable promise in predicting serious cardiovascular complications after non-cardiac surgery. This review is intended to provide a critical overview of traditional and contemporary biomarkers for perioperative cardiovascular assessment and management. This review also discusses the potential utility of newer biomarkers like galectin-3, sST-2, GDF-15, TNF-alpha, MiRNAs and many others that can predict inflammation, cardiac remodeling, injury and endogenous stress and need further investigations to establish their clinical utility. Though promising, biomarker led perioperative care is still in infancy and it has not been determined that it can improve clinical outcomes.

Cardiac biomarkers improve prediction performance of the combination of American Society of Anesthesiologists physical status classification and Americal College of Surgeons National Surgical Quality Improvement Program calculator for postoperative mortality in elderly patients: a pilot study

BACKGROUND

Our previous research has shown American Society of Anaesthesiologists physical status classification (ASA) score and Americal College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator to have the most accuracy in the prediction of postoperative mortality.

AIMS

The aim of our research was to define the most reliable combination of cardiac biomarkers with ASA and ACS NSQIP.

METHODS

We have included a total of 78 patients. ASA score has been determined in standard fashion, while we used the available interactive calculator for the ACS NSQIP score. Biomarkers BIRC5, H-FABP, and hsCRP have been measured in specialized laboratories.

RESULTS

All of the deceased patients had survivin (BIRC5) > 4.00 pg/ml, higher values of H-FABP and hsCRP and higher estimated levels of ASA and ACS NSQIP (P = 0.0001). ASA and ACS NSQIP alone had AUC of, respectively, 0.669 and 0.813. The combination of ASA and ACS NSQIP had AUC = 0.841. Combination of hsCRP with the two risk scores had AUC = 0.926 (95% CI 0.853-1.000, P < 0.0001). If we add three cardiac biomarkers to this model, we get AUC as high as 0.941 (95% CI 0.876-1.000, P < 0.0001). The correction of statistical models with comorbidities (CIRS-G score) did not change the accuracy of prediction models that we have provided.

DISCUSSION

Addition of ACS NSQIP and biomarkers adds to the accuracy of ASA score, which has already been proved by other authors.

CONCLUSION

Cardiac biomarker hsCRP can be used as the most reliable cardiac biomarker; however, the "multimarker approach" adds the most to the accuracy of the combination of clinical risk scores.

High tibial osteotomy with modern PEEK implants is safe and leads to lower hardware removal rates when compared to conventional metal fixation: a multi-center comparison study

PURPOSE

Various implant materials have been used in medial, opening-wedge high tibial osteotomy (HTO) including traditional metal and modern polyetheretherketone (PEEK) implants. The purpose of this study was to compare metal and PEEK implants and determine safety, varus deformity correction, as well as short- to mid-term hardware removal and arthroplasty rates.

METHODS

HTO performed with metal and PEEK implants were reviewed between 2000 and 2015 at two institutions with a minimum of 2 years follow-up. Postoperative complications, radiographic measures, and osteotomy union were compared between groups using Kruskal-Wallis and Fisher's exact testing. Survival free of hardware removal and arthroplasty was compared between groups using Kaplan-Meier testing. Risk factors for HTO conversion to arthroplasty were examined using Cox proportional hazards regression.

RESULTS

Ninety-five HTOs were performed in 90 patients (59 M, 31 F) using 50 metal and 45 PEEK implants. Mean follow-up was 4.2 years (range 2.0-16.5). Two metal and two PEEK HTO patients experienced nonunions, resulting in revision HTO at a mean of 1.0 years postoperatively (range 0.4-1.4 years). Both implant groups demonstrated similar, significant improvements in coronal deformity, with mean angulation improving from 6.0° and 5.4° varus preoperatively to 1.1° and 1.0° valgus postoperatively for the metal (p < 0.01) and PEEK groups (p < 0.01), respectively. 2- and 5-year hardware removal-free survival was 94% and 94% for PEEK, which was significantly superior to 80% and 73% observed for metal (p = 0.02). 2- and 5-year arthroplasty-free survival was similar for the metal (98% and 94%) and PEEK groups (100% and 78%) (n.s.). HTO performed for focal cartilage defects was observed to demonstrate decreased arthroplasty risk (HR 0.36, p = 0.03) when compared to HTO performed for osteoarthritis.

CONCLUSIONS

Both metal and PEEK implants were found to be effective in obtaining and maintaining coronal varus deformity correction, with 88% overall arthroplasty-free survival at 5 years. Metal fixation demonstrated a higher rate of hardware removal while HTO performed for medial compartment osteoarthritis predicted conversation to arthroplasty.

LEVEL OF EVIDENCE

III.

The prognostic significance of heart-type fatty acid binding protein in patients with stable coronary heart disease

To investigate the prognostic value of heart-type fatty acid binding protein (H-FABP) in patients with stable coronary heart disease (SCHD). A total of 1,071 patients with SCHD were prospectively enrolled in this Taiwan multicenter registry study, followed for 24 months. The cut-off value of H-FABP, 4.143 ng/mL, was determined using receiver operating characteristic curves. The primary cardiovascular (CV) outcome was composite CV events, defined as cardiovascular or cerebrovascular death, myocardial infarction (MI), stroke, angina related-hospitalization, PAOD-related hospitalization and heart failure. Secondary outcomes included CV or cerebrovascular death, nonfatal MI, nonfatal stroke, and acute heart failure-related hospitalization. We found that the high H-FABP group had more than a two-fold higher rate of primary CV outcomes than the low H-FABP group (32.36% vs. 15.78%, p < 0.001). Eleven patients (4.82%) of the high H-FABP group died during the 24 months of follow-up, compared to only one patient (0.12%) in the low H-FABP group. The acute heart failure-related hospitalization rate was also significantly higher in the high H-FABP group (3.5% vs. 0.95%, p < 0.005). The results remained significant after adjusting for baseline covariates. In conclusion, H-FABP was an independent predictor for CV outcomes in the patients with SCHD, mainly in CV death and acute heart failure-related hospitalization.