BACKGROUND:Postoperative myocardial injury occurs frequently after noncardiac surgery and is strongly associated with mortality. Intraoperative hypotension (IOH) is hypothesized to be a possible ...cause. The aim of this study was to determine the association between IOH and postoperative myocardial injury.
METHODS:This cohort study included 890 consecutive patients aged 60 yr or older undergoing vascular surgery from two university centers. The occurrence of myocardial injury was assessed by troponin measurements as part of a postoperative care protocol. IOH was defined by four different thresholds using either relative or absolute values of the mean arterial blood pressure based on previous studies. Either invasive or noninvasive blood pressure measurements were used. Poisson regression analysis was used to determine the association between IOH and postoperative myocardial injury, adjusted for potential clinical confounders and multiple comparisons.
RESULTS:Depending on the definition used, IOH occurred in 12 to 81% of the patients. Postoperative myocardial injury occurred in 131 (29%) patients with IOH as defined by a mean arterial pressure less than 60 mmHg, compared with 87 (20%) patients without IOH (P = 0.001). After adjustment for potential confounding factors including mean heart rates, a 40% decrease from the preinduction mean arterial blood pressure with a cumulative duration of more than 30 min was associated with postoperative myocardial injury (relative risk, 1.8; 99% CI, 1.2 to 2.6, P < 0.001). Shorter cumulative durations (less than 30 min) were not associated with myocardial injury. Postoperative myocardial infarction and death within 30 days occurred in 26 (6%) and 17 (4%) patients with IOH as defined by a mean arterial pressure less than 60 mmHg, compared with 12 (3%; P = 0.08) and 15 (3%; P = 0.77) patients without IOH, respectively.
CONCLUSIONS:In elderly vascular surgery patients, IOH defined as a 40% decrease from the preinduction mean arterial blood pressure with a cumulative duration of more than 30 min was associated with postoperative myocardial injury.
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.
Intraoperative hypotension (IOH) is frequently associated with adverse outcome such as 1-yr mortality. However, there is no consensus on the correct definition of IOH. The authors studied a number of ...different definitions of IOH, based on blood pressure thresholds and minimal episode durations, and their association with 1-yr mortality after noncardiac surgery.
This cohort study included 1,705 consecutive adult patients who underwent general and vascular surgery. Data on IOH and potentially confounding variables were obtained from electronic record-keeping systems. Mortality data were collected up to 1 yr after surgery. The authors used two different techniques to reduce the influence of confounding variables, multivariable Cox proportional hazard regression modeling and classification and regression tree analysis.
The mortality within 1 yr after surgery was 5.2% (88 patients). After adjustment for confounding, the Cox regression analysis did not show an association between IOH and the risk of dying within 1 yr after surgery (hazard ratio around 1.00 with high P values for different definitions of IOH). Additional classification and regression tree analysis identified IOH as a predictor for 1-yr mortality in elderly patients. When the blood pressure threshold for IOH was decreased, the duration of IOH at which this association was found was decreased as well.
This observational study showed no causal relation between IOH and 1-yr mortality after noncardiac surgery for any of the definitions of IOH. Nevertheless, additional analysis suggested that for elderly patients, the mortality risk increases when the duration of IOH becomes long enough. The length of this duration depends on the designated blood pressure threshold, suggesting that lower blood pressures are tolerated for shorter durations. The effect of IOH on 1-yr mortality remains debatable, and no firm conclusions on the lowest acceptable intraoperative blood pressures can be drawn from this study.
A 59-year-old woman with a pituitary mass and acromegaly had hypotension and ECG changes after the administration of anesthetics before transsphenoidal hypophysectomy. Management decisions were made.
Graphical Abstract
Graphical Abstract
The World Health Organization’s criteria to assess the evidence on benefits, risks, and consequences of introducing a population-screening program, applied to ...routine post-operative troponin surveillance.
BACKGROUND:Although noninvasive blood pressure (NIBP) monitoring during anesthesia is a standard of care, reference ranges for blood pressure in anesthetized children are not available. We developed ...sex- and age-specific reference ranges for NIBP in children during anesthesia and surgery.
METHODS:In this retrospective observational cohort study, we included NIBP data of children with no or mild comorbidity younger than 18 yr old from the Multicenter Perioperative Outcomes Group data set. Sex-specific percentiles of the NIBP values for age were developed and extrapolated into diagrams and reference tables representing the 50th percentile (0 SD), +1 SD, −1 SD, and the upper (+2 SD) and lower reference ranges (−2 SD).
RESULTS:In total, 116,362 cases from 10 centers were available for the construction of NIBP age- and sex-specific reference curves. The 0 SD of the mean NIBP during anesthesia varied from 33 mmHg at birth to 67 mmHg at 18 yr. The low cutoff NIBP (2 SD below the 50th percentile) varied from 17 mmHg at birth to 47 mmHg at 18 yr old.
CONCLUSIONS:This is the first study to present reference ranges for blood pressure in children during anesthesia. These reference ranges based on the variation of values obtained in daily care in children during anesthesia could be used for rapid screening of changes in blood pressure during anesthesia and may provide a consistent reference for future blood pressure–related pediatric anesthesia research.
Postoperative stroke is a rare but major complication after surgery. The most often proposed mechanism is an embolus originating from the heart or great vessels. The role of intraoperative ...hypotension in the occurrence and evolution of postoperative stroke is largely unknown.
A case-control study was conducted among 48,241 patients who underwent noncardiac and nonneurosurgical procedures in the period from January 2002 to June 2009. A total of 42 stroke cases (0.09%) were matched on age and type of surgery to 252 control patients. Conditional logistic regression analysis was used to estimate the effect of the duration of intraoperative hypotension (defined according to a range of blood pressure thresholds) on the occurrence of an ischemic stroke within 10 days after surgery, adjusted for potential confounding factors.
After correction for potential confounders and multiple testing, the duration that the mean blood pressure was decreased more than 30% from baseline remained statistically significantly associated with the occurrence of a postoperative stroke.
Intraoperative hypotension might play a role in the development of postoperative ischemic stroke. Especially for mean blood pressure values decreasing more than 30% from baseline blood pressure, an association with postoperative ischemic stroke risks was observed.
To identify patients at risk for postoperative myocardial injury and death, measuring cardiac troponin routinely after noncardiac surgery has been suggested. Such monitoring was implemented in our ...hospital. The aim of this study was to determine the predictive value of postoperative myocardial injury, as measured by troponin elevation, on 30-day mortality after noncardiac surgery.
This observational, single-center cohort study included 2232 consecutive intermediate- to high-risk noncardiac surgery patients aged ≥60 years who underwent surgery in 2011. Troponin was measured on the first 3 postoperative days. Log binomial regression analysis was used to estimate the association between postoperative myocardial injury (troponin I level >0.06 μg/L) and all-cause 30-day mortality. Myocardial injury was found in 315 of 1627 patients in whom troponin I was measured (19%). All-cause death occurred in 56 patients (3%). The relative risk of a minor increase in troponin (0.07-0.59 μg/L) was 2.4 (95% confidence interval, 1.3-4.2; P<0.01), and the relative risk of a 10- to 100-fold increase in troponin (≥0.60 μg/L) was 4.2 (95% confidence interval, 2.1-8.6; P<0.01). A myocardial infarction according to the universal definition was diagnosed in 10 patients (0.6%), of whom 1 (0.06%) had ST-segment elevation myocardial infarction.
Postoperative myocardial injury is an independent predictor of 30-day mortality after noncardiac surgery. Implementation of postoperative troponin monitoring as standard of care is feasible and may be helpful in improving the prognosis of patients undergoing noncardiac surgery.
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