Impact of systemic inflammatory response syndrome and surgical Apgar score on post-operative acute kidney injury

Ability to predict surgical outcomes by surgical Apgar score: a systematic review

BACKGROUND

The Surgical Apgar score (SAS) is a straightforward and unbiased measure to assess the probability of experiencing complications after surgery. It is calculated upon completion of the surgical procedure and provides valuable predictive information. The SAS evaluates three specific factors during surgery: the estimated amount of blood loss (EBL), the lowest recorded mean arterial pressure (MAP), and the lowest heart rate (LHR) observed. Considering these factors, the SAS offers insights into the probability of encountering postoperative complications.

METHODS

Three authors independently searched the Medline, PubMed, Web of Science, Scopus, and Embase databases until June 2022. This search was conducted without any language or timeframe restrictions, and it aimed to cover relevant literature on the subject. The inclusion criteria were the correlation between SAS and any modified/adjusted SAS (m SAS, (Modified SAS). eSAS, M eSAS, and SASA), and complications before, during, and after surgeries. Nevertheless, the study excluded letters to the editor, reviews, and case reports. Additionally, the researchers employed Begg and Egger's regression model to evaluate publication bias.

RESULTS

In this systematic study, a total of 78 studies \were examined. The findings exposed that SAS was effective in anticipating short-term complications and served as factor for a long-term prognostic following multiple surgeries. While the SAS has been validated across various surgical subspecialties, based on the available evidence, the algorithm's modifications may be necessary to enhance its predictive accuracy within each specific subspecialty.

CONCLUSIONS

The SAS enables surgeons and anesthesiologists to recognize patients at a higher risk for certain complications or adverse events. By either modifying the SAS (Modified SAS) or combining it with ASA criteria, healthcare professionals can enhance their ability to identify patients who require continuous observation and follow-up as they go through the postoperative period. This approach would improve the accuracy of identifying individuals at risk and ensure appropriate measures to provide necessary care and support.

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.

Serum Procalcitonin Level Predicts Acute Kidney Injury After Traumatic Brain Injury

BACKGROUND

A common non-neurologic complication after traumatic brain injury (TBI), acute kidney injury (AKI) is a risk factor of mortality. Some studies confirmed the predictive value of procalcitonin (PCT) on AKI in several clinical settings. We designed this study to explore the predictive value of PCT on AKI after TBI.

METHODS

We retrospectively enrolled patients with TBI admitted to our hospital from February 2015 to June 2019. Multivariate logistic regression analysis was performed to find the risk factors of AKI and construct a predictive model for AKI. Receiver operating characteristics curves were drawn to compare the predictive value of PCT and the constructed model.

RESULTS

A total of 214 patients were included in this study. The incidence of AKI after TBI was 25.70% in this study. Compared with the non-AKI group, the AKI group had higher age (P = 0.031), lower Glasgow Coma Scale (P < 0.001), and higher incidence of coagulopathy (P < 0.001) and shock (P < 0.001). Moreover, patients complicated with AKI had higher in-hospital mortality (P < 0.001) and worse 90-day outcome (P < 0.001). Multivariate logistic regression analysis indicated that age (P = 0.033), PCT (P = 0.002), serum chlorine (P = 0.011), and creatinine (P < 0.001) were independent risk factors of AKI. We constructed a predictive model using these 4 risk factors. The area under receiver operating characteristics curves of the predictive model was 0.928, which was significantly higher than that of a single PCT value (area under receiver operating characteristics curves = 0.833) (Z = 2.395, P < 0.05).

CONCLUSIONS

PCT is valuable in predicting AKI after TBI. To avoid AKI after TBI, physicians can adjust treatment strategies according to the level of PCT.