Postoperative troponin surveillance to detect myocardial infarction: an observational cohort modelling study

BACKGROUND

Clinical presentation of postoperative myocardial infarction (POMI) is often silent. Several international guidelines recommend routine troponin surveillance in patients at risk. We compared how these different guidelines select patients for surveillance after noncardiac surgery with our established risk stratification model.

METHODS

We used outcome data from two prospective studies: Measurement of Exercise Tolerance before Surgery (METS) and Troponin Elevation After Major non-cardiac Surgery (TEAMS). We compared the major American, Canadian, and European guideline recommendations for troponin surveillance with our established risk stratification model. For each guideline and model, we quantified the number of patients requiring monitoring, % POMI detected, sensitivity, specificity, diagnostic odds ratio, and number needed to screen (NNS).

RESULTS

METS and TEAMS contributed 2350 patients, of whom 319 (14%) had myocardial injury, 61 (2.5%) developed POMI, and 14 (0.6%) died. Our risk stratification model selected fewer patients for troponin monitoring (20%), compared with the Canadian (78%) and European (79%) guidelines. The sensitivity to detect POMI was highest with the Canadian and European guidelines (0.85; 95% confidence interval [CI] 0.74-0.92). Specificity was highest using the American guidelines (0.91; 95% CI 0.90-0.92). Our risk stratification model had the best diagnostic odds ratio (2.5; 95% CI 1.4-4.2) and a lower NNS (21 vs 35) compared with the guidelines.

CONCLUSIONS

Most postoperative myocardial infarctions were detected by the Canadian and European guidelines but at the cost of low specificity and a higher number of patients undergoing screening. Patient selection based on our risk stratification model was optimal.

Postoperative myocardial injury phenotypes and self-reported disability in patients undergoing noncardiac surgery: a multicentre observational study

BACKGROUND

Postoperative myocardial injury (PMI) comprises a spectrum of mechanisms resulting in troponin release. The impact of different PMI phenotypes on postoperative disability remains unknown.

METHODS

This was a multicentre prospective cohort study including patients aged ≥50 yr undergoing elective major noncardiac surgery. Patients were stratified in five groups based on the occurrence of PMI and clinical information on postoperative adverse events: PMI classified as myocardial infarction (MI; according to fourth definition), PMI plus adverse event other than MI, clinically silent PMI (PMI without adverse events), adverse events without PMI, and neither PMI nor an adverse event (reference). The primary endpoint was 6-month self-reported disability (assessed by WHO Disability Assessment Schedule 2.0 [WHODAS]). Disability-free survival was defined as WHODAS ≤16%.

RESULTS

We included 888 patients of mean age 69 (range 53-91) yr, of which 356 (40%) were women; 151 (17%) patients experienced PMI, and 625 (71%) experienced 6-month disability-free survival. Patients with PMI, regardless of its phenotype, had higher preoperative disability scores than patients without PMI (difference in WHODAS; β: 3.3, 95% confidence interval [CI]: 0.5-6.2), but scores remained stable after surgery (β: 1.2, 95% CI: -3.2-5.6). Before surgery, patients with MI (n=36, 4%) were more disabled compared with patients without PMI and no adverse events (β: 5.5, 95% CI: 0.3-10.8). At 6 months, patients with MI and patients without PMI but with adverse events worsened in disability score (β: 11.2, 95% CI: 2.3-20.2; β: 8.1, 95% CI: 3.0-13.2, respectively). Patients with clinically silent PMI did not change in disability score at 6 months (β: 1.39, 95% CI: -4.50-7.29, P=0.642).

CONCLUSIONS

Although patients with postoperative myocardial injury had higher preoperative self-reported disability, disability scores did not change at 6 months after surgery. However, patients experiencing myocardial infarction worsened in disability score after surgery.

[Dutch guideline to prevent perioperative pulmonal complications: how is the adherence?]

INTRODUCTION

Patients with a history of chronic obstructive pulmonary disease (COPD) or asthma undergoing surgery are at risk for developing postoperative pulmonary complications, associated with morbidity and mortality. A national guideline was composed on preventive strategies in non-thoracic surgery, in which perioperative administration of corticosteroids are a cornerstone. We investigated the implementation in our hospital.

METHOD

Patients with a history of asthma and/or COPD who underwent non-thoracic surgery between January 2017 and July 2018 in our hospital, were included. Primary outcome was perioperative administration of intravenous prednisolone or oral prednisone as preventative treatment. The reasons for (non)adherence were investigated as secondary outcome.

RESULTS

1,623 patients qualified for a preventative treatment. Overall, 653 patients (40%) received treatment according to protocol. The other 970 patients received no treatment (79%), another corticosteroid (12%) or received the other strategy (9%). Based on interviews and surveys, several barriers were found: fear of side effects of corticosteroids, indications for other types of corticosteroids, and lack of confidence in the literature.

CONCLUSION

The current guideline for prevention of postoperative pulmonary complications has not been implemented thoroughly at our hospital. The lack of adherence appears to be caused by lack in clarity in clinical practice, lack of confidence in the evidence on which the protocol is based, and fear of side effects of corticosteroids. There are also notable differences between the vision of pulmonologists and the anesthesiologists. Further research is needed to solidify the guideline, anticipating aforementioned compliance barriers to achieve better implementation.

Associations between intraoperative hypotension, duration of surgery and postoperative myocardial injury after noncardiac surgery: a retrospective single-centre cohort study

BACKGROUND

Studies of intraoperative hypotension typically specify a blood pressure threshold associated with adverse outcomes. Such thresholds are likely to be study-biased, investigator-biased, or both. We hypothesised that a newly developed modelling method without a threshold, which is biologically more plausible than a threshold-based approach, would reveal a continuous association between exposure to intraoperative hypotension and adverse outcomes.

METHODS

Single-centre, retrospective cohort study of subjects ≥60 yr old undergoing noncardiac surgery. We modelled intraoperative hypotension using three different approaches: (1) unweighted, (2) weighted for degree of hypotension (depth), and (3) weighted for duration of hypotension. The primary outcome was myocardial injury, defined as elevated troponin I (>60 ng L^-1) measured during the first 3 days after surgery. The associations between the three models, postoperative myocardial injury, and mortality (secondary outcome) were reported as penalised adjusted odds ratios (ORs) scaled between the 75th and 25th percentiles.

RESULTS

Myocardial injury occurred in 1812/15 452 (12%) procedures, with 554/15 452 (3.6%) procedures resulting in death before discharge from hospital. The unweighted lower blood pressure measure (OR: 0.26, 95% confidence interval [CI]: 0.12-0.53) and the depth-weighted measure (OR: 4.4, 95% CI: 2.6-7.4) were associated with myocardial injury. The duration-weighted measure was not associated with myocardial injury (OR: 0.89, 95% CI: 0.61-1.3). The unweighted measure (OR 0.08, 95% CI: 0.01-0.40) and the depth-weighted measure (OR: 12, 95% CI, 3.8-35) were associated with in-hospital mortality, but not the duration-weighted measure (OR: 1.3, 95% CI: 0.53-3.0).

CONCLUSIONS

Intraoperative hypotension appears to have a graded association with postoperative myocardial injury and mortality, with depth appearing to contribute more than duration.

Patient Selection for Routine Troponin Monitoring After Noncardiac Surgery

Background

Myocardial infarction is an important complication after noncardiac surgery. Therefore, perioperative troponin surveillance is recommended for patients at risk. The aim of this study was to identify patients at high risk of perioperative myocardial infarction (POMI), in order to aid appropriate selection and to omit redundant laboratory measurements in patients at low risk.

Methods and Results

This observational cohort study included patients ≥60 years of age who underwent intermediate to high risk noncardiac surgery. Routine postoperative troponin I monitoring was performed. The primary outcome was POMI. Classification and regression tree analysis was used to identify patient groups with varying risks of POMI. In each subgroup, the number needed to screen to identify 1 patient with POMI was calculated. POMI occurred in 216 (4%) patients and other myocardial injury in 842 (15%) of the 5590 included patients. Classification and regression tree analysis divided patients into 14 subgroups in which the risk of POMI ranged from 1.7% to 42%. Using a risk of POMI ≥2% to select patients for routine troponin I monitoring, this monitoring would be advocated in patients ≥60 years of age undergoing emergency surgery, or those undergoing elective surgery with a Revised Cardiac Risk Index class >2 (ie >1 risk factor). The number needed to screen to detect a patient with POMI would be 14 (95% CI 14–14) and 26% of patients with POMI would be missed.

Conclusions

To improve selection of high‐risk patients ≥60 years of age, routine postoperative troponin I monitoring could be considered in patients undergoing emergency surgery, or in patients undergoing elective surgery classified as having a revised cardiac risk index class >2.

Added value of subjective assessed functional capacity before non-cardiac surgery in predicting postoperative myocardial injury

BACKGROUND

Functional capacity is used as an indicator for cardiac testing before non-cardiac surgery and is often performed subjectively. However, the value of subjectively estimated functional capacity in predicting cardiac complications is under debate. We determined the predictive value of subjectively assessed functional capacity on postoperative cardiac complications and mortality.

DESIGN

An observational cohort study in patients aged 60 years and over undergoing elective inpatient non-cardiac surgery in a tertiary referral hospital.

METHODS

Subjective functional capacity was determined by anaesthesiologists. The primary outcome was postoperative myocardial injury. Secondary outcomes were postoperative inhospital myocardial infarction and one year mortality. Logistic regression analysis and area under the receiver operating curves were used to determine the added value of functional capacity.

RESULTS

A total of 4879 patients was included; 824 (17%) patients had a poor subjective functional capacity. Postoperative myocardial injury occurred in 718 patients (15%). Poor functional capacity was associated with myocardial injury (relative risk (RR) 1.7, 95% confidence interval (CI) 1.5-2.0; P < 0.001), postoperative myocardial infarction (RR 2.9, 95% CI 1.9-4.2; P < 0.001) and one year mortality (RR 1.7, 95% CI 1.4-2.0; P < 0.001). After adjustment for other predictors, functional capacity was still a significant predictor for myocardial injury (odds ratio (OR) 1.3, 95% CI 1.0-1.7; P = 0.023), postoperative myocardial infarction (OR 2.0, 95% CI 1.3-3.0; P = 0.002) and one year mortality (OR 1.4, 95% CI 1.1-1.8; P = 0.003), but had no added value on top of other predictors.

CONCLUSIONS

Subjectively assessed functional capacity is a predictor of postoperative myocardial injury and death, but had no added value on top of other preoperative predictors.

Reticulated Platelets as Predictor of Myocardial Injury and 30 Day Mortality After Non-cardiac Surgery

OBJECTIVE

A pre-operative marker for identification of patients at risk of peri-operative adverse events and 30 day mortality might be the percentage of young, reticulated platelets (pRP). This study aimed to determine the predictive value of pre-operative pRP on post-operative myocardial injury (PMI) and 30 day mortality, in patients aged ≥ 60 years undergoing moderate to high risk non-cardiac surgery.

METHODS

The incidence of PMI (troponin I > 0.06 μg/L) and 30 day mortality was compared for patients with normal and high pRP (≥2.82%) obtained from The Utrecht Patient Orientated Database. The predictive pRP value was assessed using logistic regression. A prediction model for PMI or 30 day mortality with known risk factors was compared with a model including increased pRP using the area under the receiving operator characteristics curve (AUROC).

RESULTS

In total, 26.5% (607/2289) patients showed pre-operative increased pRP. Increased pRP was associated with more PMI and 30 day mortality compared with normal pRP (36.1% vs. 28.3%, p < .001 and 8.6% vs. 3.6%, p < .001). The median pRP was higher in patients suffering PMI and 30 day mortality compared with not (2.21 [IQR: 1.57-3.11] vs. 2.07 [IQR: 1.52-1.78], p = .002, and 2.63 [IQR: 1.76-4.15] vs. 2.09 [IQR: 1.52-3.98], p < .001). pRP was independently related to PMI (OR: 1.28 [95% CI: 1.04-1.59], p = .02) and 30 day mortality (OR: 2.35 [95% CI: 1.56-3.55], p < .001). Adding increased pRP to the predictive model of PMI or 30 day mortality did not increase the AUROC 0.71 vs. 0.72, and 0.80 vs. 0.81.

CONCLUSION

In patients undergoing major non-cardiac surgery, increased pre-operative pRP is related to 30 day mortality and PMI.

One-Year Mortality, Causes of Death, and Cardiac Interventions in Patients with Postoperative Myocardial Injury

BACKGROUND

To evaluate the role of routine troponin surveillance in patients undergoing major noncardiac surgery, unblinded screening with cardiac consultation per protocol was implemented at a tertiary care center. In this study, we evaluated 1-year mortality, causes of death, and consequences of cardiac consultation of this protocol.

METHODS

This observational cohort included 3224 patients ≥60 years old undergoing major noncardiac surgery. Troponin I was measured routinely on the first 3 postoperative days. Myocardial injury was defined as troponin I >0.06 μg/L. Regression analysis was used to determine the association between myocardial injury and 1-year mortality. The causes of death, the diagnoses of the cardiologists, and interventions were determined for different levels of troponin elevation.

RESULTS

Postoperative myocardial injury was detected in 715 patients (22%) and was associated with 1-year all-cause mortality (relative risk [RR] 1.4, P = 0.004; RR 1.6, P < 0.001; and RR 2.2, P < 0.001 for minor, moderate, and major troponin elevation, respectively). Cardiac death within 1 year occurred in 3%, 5%, and 11% of patients, respectively, in comparison with 3% of the patients without myocardial injury (P = 0.059). A cardiac consultation was obtained in 290 of the 715 patients (41%). In 119 (41%) of these patients, the myocardial injury was considered to be attributable to a predisposing cardiac condition, and in 111 patients (38%), an intervention was initiated.

CONCLUSIONS

Postoperative myocardial injury was associated with an increased risk of 1-year all-cause but not cardiac mortality. A cardiac consultation with intervention was performed in less than half of these patients. The small number of interventions may be explained by a low suspicion of a cardiac etiology in most patients and lack of consensus for standardized treatment in these patients.

Kinetics of troponin I in patients with myocardial injury after noncardiac surgery

BACKGROUND

Myocardial injury after noncardiac surgery, as measured by troponin elevation, is strongly associated with mortality. However, it is unknown in which patients prognosis can be improved. The presence of kinetic changes of troponin may be associated with a worse prognosis and warrant more aggressive management. Therefore, we aimed to study the kinetics of troponin in patients with postoperative myocardial injury, and to determine the added predictive value of kinetic changes of troponin on mortality.

METHODS

This cohort study included patients with myocardial injury after noncardiac surgery. Troponin I (TnI) was measured on the first three postoperative days. The primary outcome was all-cause 1-year mortality. We studied both absolute and relative TnI changes, and determined the delta TnI that was associated with mortality to distinguish a rise-and-fall TnI pattern from a stable TnI pattern. Next, we determined the added predictive value of a rise-and-fall TnI pattern for mortality.

RESULTS

In total, 634 patients were included. The risk ratio (RR) for mortality increased significantly with an absolute delta TnI of ≥200 ng/L (RR 1.5, 99.4% CI 1.0-2.2, p=0.003). Using this delta TnI to define a rise-and-fall pattern, 459 patients (72%) had a stable TnI pattern and 175 patients (28%) had a rise-and-fall pattern. When added to a model including the highest TnI value and variables from the revised cardiac risk index (RCRI), the TnI pattern did not increase the predictive value for mortality.

CONCLUSIONS

A postoperative TnI rise-and-fall pattern was associated with 1-year mortality, but had no added value in addition to the highest TnI level to predict 1-year mortality. Therefore, postoperative TnI kinetics are not useful for further mortality risk stratification in patients with myocardial injury after noncardiac surgery.

[Asymptomatic myocardial infarction after non-cardiac surgery; importance of routine testing of troponin concentration]

Perioperative myocardial infarction (PMI) is associated with a high mortality rate. Detection of PMI, however, is difficult due to a lack of typical anginal symptoms associated with the use of strong analgesics. Also, characteristic changes on the electrocardiogram may be minimal and/or transient. Recent studies have shown that postoperative troponin elevation is a strong, independent predictor of short- and intermediate-term mortality. In this article, we illustrate the importance of routine troponin measurements after major non-cardiac surgery with two patient cases. Moreover, we provide a brief overview of current evidence on perioperative myocardial infarction, and highlight current and future perspectives of troponin measurements.

Artifacts in research data obtained from an anesthesia information and management system

PURPOSE

Artifacts in anesthesia information management system (AIMS) databases may influence research results. Filtering during data capturing can prevent artifacts from being stored. In this prospective study, we assessed the reliability of AIMS data by determining the incidence of artifactual values stored in the AIMS.

METHODS

Vital parameter values regarding 86 surgical patients were collected in the AIMS both manually and automatically after filtering using the median value per minute. The percentage of artifactual values with a 95% confidence interval (CI) was calculated for each parameter. Secondary outcomes included the number of values that deviated from a predefined baseline, the percentage of these deviations that were caused by artifacts, the number of episodes across which these artifacts were distributed, and the most common causes of artifacts.

RESULTS

Altogether, 9,534 min of anesthesia time were recorded. The overall percentages of artifacts were: 0.0 for heart rate (95% CI: 0.0 to 0.1), 0.3 for oxygen saturation (95% CI: 0.2 to 0.4), 4.7 for ST-segment (95% CI: 4.3 to 5.2), 2.3 for noninvasive blood pressure values (95% CI: 1.8 to 2.9), and 14 for invasive blood pressure values (95% CI: 12 to 15). Artifacts as a percentage of deviations from baseline were: 1.6 for heart rate (95% CI: 0.4 to 5.7), 24 for saturation (95% CI: 18 to 32), 83 for ST-segment (95% CI: 76 to 87), 3.3 for noninvasive blood pressure values (95% CI: 2.5 to 87), and 27 for invasive blood pressure values (95% CI: 24 to 31).

CONCLUSIONS

Storing a median value per minute to filter capturing of vital parameter values in an AIMS database provides reliable data for heart rate and oxygen saturation and acceptable reliability for noninvasive blood pressure data. Knowledge about the method of artifact filtering is essential in studies using AIMS data.