[The very elderly surgical population in a critically ill scenario: clinical characteristics and outcomes]

Geriatric anesthesia: Demographics, epidemiology, state of the art at international level, educational needs, and future perspectives

Population aging has induced in the last year a corresponding aging inside the surgical population, currently accounting for 45%.50% of the total surgical population in the majority of the advanced countries. This has induced a number of new challenges in the daily anesthesia practice, ranging from the need of implementing specific education in the field of geriatric medicine, organizational adjustments aimed to adapt our preoperative evaluation methodology to the needs older patients present, and a careful redesign of the whole perioperative course for these patients. Today's anesthesiologists are called to became familiar with the aging processes and their impact on the patho.physiological perioperative course, with the concept of functional impairment and frailty and with the dimensions of polypathology, polymedication, and reduced functional reserves. The introduction of new trends such as the team-based approach and interdisciplinary culture are essential to overcome these emerging criticalities.

Nutritional profile and outcomes of noncritical hospitalized patients with COVID-19 in a large tertiary hospital in southern Brazil

INTRODUCTION:

Patients with chronic diseases, such as diabetes and cardiovascular diseases, and old age, which are associated with a high risk of malnutrition and worse outcomes, are at a higher risk for developing the severe presentation of COVID-19.

METHODS:

This is an observational and cross-sectional study with a sample defined by convenience. Data were collected in adult inpatient units through information obtained via telephone contact with the patient/companion, records collected by the nursing staff, and medical records, tabulating demographics, body composition, previous illnesses, nutritional diagnoses, diet acceptance, and hospitalization outcomes. The following symptoms were observed: inappetence, smell, dysgeusia, odynophagia, nausea, vomiting, and diarrhea.

RESULTS:

Most deaths occurred after transfer to the intensive care unit (79.6%). Patients with the worst outcome had lower food intake with a cutoff point of 60% for diet acceptance, which seems to be an adequate discriminator between those who survived and those who did not. Gastrointestinal symptoms were significantly associated with food consumption below 60% of the planned goal. The symptoms most associated with lower energy intake were inappetence, dysgeusia, and nausea/vomiting.

CONCLUSIONS:

Reduced caloric intake and the presence of nutritional risk or its appearance during hospitalization seemed to be associated with mortality in patients with COVID-19 admitted outside the intensive care unit.

LOSS et al.Nutrition as a risk for mortality and functionality in critically ill older adultsNutrition as a risk for mortality and functionality in critically ill older adults

BACKGROUND & AIMS

There is no specific recommendation for nutrition therapy for critically ill older adults. However, targeting caloric and protein balance and avoiding fasting could improve outcomes in this high-risk nutritional population. This study aimed to evaluate the associations between nutrition and mortality/functionality in critically ill older patients.

METHODS

Single-center retrospective observational study of critically ill patients aged 65 years or older. We extracted data from the dietician evaluations about calories, proteins, and the type of diet (fasting, oral, enteral, parenteral) prescribed in the first week of intensive care unit admission. Primary outcomes were intrahospital mortality and independence and functional capacity evaluated after hospital discharge.

RESULTS

Out of the 2,043 patients screened, 533 were included in the study. Most patients were men (52.1%), with a median age of 73 (68-78) years. Overall, the intrahospital mortality rate was 53.8%. SAPS-3, albumin, C-reactive protein, and surgical patients were independently associated with fasting in a multivariate analysis. The multivariate regression analyses showed that SAPS-3, albumin, and fasting were independently associated with mortality. Each fasting day increases the risk of mortality by 16.7%. Also, independence and functional capacity were not related to nutritional prescription.

CONCLUSION

Older adults (65 years or older) constitute a fragile population in which nutritional breaks were associated with increased hospital mortality. Furthermore, a prospective clinical trial is necessary to establish the best strategy to feed this population. This article is protected by copyright. All rights reserved.

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.