Usefulness of pre- and intraoperative risk scores in nonagenarian surgical patients

Predictors and preventers of postoperative bedridden status in the elderly ages over 75 after emergency general surgery: a retrospective cohort study

Aim

We investigated the proportion of bedridden patients after emergency surgery among the elderly ages over 75; defined as the latter-stage elderly in Japan, the associated factors, and interventions used to prevent it.

Methods

Eighty-two latter-stage elderly patients who underwent emergency surgery for non-traumatic illness between January 2020 and June 2021 in our hospital were included in the study. Backgrounds and various perioperative factors were compared retrospectively between the groups including patients who became bedridden from Performance Status Scale 0 to 3 before admission (Bedridden group) and those who did not (Keep group).

Results

Three cases of death and seven patients who were bedridden before admission were excluded. The 72 remaining patients were divided into the Bedridden group (n = 10, 13.9%) and the Keep group (n = 62, 86.1%). There were significant differences in the prevalence of dementia, pre- and postoperative circulatory dynamics, renal dysfunction, coagulation abnormality, length of stay in the high care unit/intensive care unit, and number of hospital days, with a relative risk of 13 (1.74-96.71), a sensitivity of 1.00, and a specificity of 0.67 for a preoperative shock index of 0.7 or higher being associated with the Bedridden group. Among patients with a preoperative shock index of 0.7 or higher, there was a significant difference in SI at 24 h postoperatively between the two groups.

Conclusion

Preoperative shock index may be the most sensitive predictor. Early circulatory stabilization seems to be protective against patients becoming bedridden.

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.

P-POSSUM as mortality predictor in COVID-19-infected patients submitted to emergency digestive surgery. A retrospective cohort study

BACKGROUND

COVID-19 infection is associated with a higher mortality rate in surgical patients, but surgical risk scores have not been validated in the emergency setting. We aimed to study the capacity for postoperative mortality prediction of the P-POSSUM score in COVID-19-positive patients submitted to emergency general and digestive surgery.

MATERIAL AND METHODS

Consecutive patients undergoing emergency general and digestive surgery from March to June 2020, and from March to June 2019 in 25 Spanish hospitals were included in a retrospective cohort study.

MAIN OUTCOME

30-day mortality. P-POSSUM discrimination was quantified by the area under the curve (AUC) of ROC curves; calibration was assessed by linear regression slope (β estimator); and sensitivity and specificity were expressed as percentage and 95% confidence interval (CI).

RESULTS

4988 patients were included: 177 COVID-19-positive; 2011 intra-pandemic COVID-19-negative; and 2800 pre-pandemic. COVID-19-positive patients were older, with higher surgical risk, more advanced pathologies, and higher P-POSSUM values (1.79% vs. 1.09%, p < 0.001, in both the COVID-19-negative and control cohort). 30-day mortality in the COVID-19-positive, intra-pandemic COVID-19-negative and pre-pandemic cohorts were: 12.9%, 4.6%, and 3.2%. The P-POSSUM predictive values in the three cohorts were, respectively: AUC 0.88 (95% CI 0.81-0.95), 0.89 (95% CI 0.87-0.92), and 0.91 (95% CI 0.88-0.93); β value 0.97 (95% CI 0.74-1.2), 0.99 (95% CI 0.82-1.16), and 0.78 (95% CI 0.74-0.82); sensitivity 83% (95% CI 61-95), 91% (95% CI 84-96), and 89% (95% CI 80-94); and specificity 81% (95% CI 74-87), 76% (95% CI 74-78), and 80% (95% CI 79-82).

CONCLUSION

The P-POSSUM score showed a good predictive capacity for postoperative mortality in COVID-19-positive patients submitted to emergency general and digestive surgery.

Post-Operative All-Cause Mortality in Elderly Patients Undergoing Abdominal Emergency Surgery: Role of Charlson Comorbidity Index

(1) Background: The Charlson comorbidity index (CCI) score has been shown to predict 10-year all-cause mortality, but its validity is a matter of debate in surgical patients. We wanted to evaluate CCI on predicting all-cause mortality in elderly patients undergoing emergency abdominal surgery (EAS); (2) Methods: This retrospective single center study included all patients aged 65 years or older consecutively admitted from January 2017 to December 2019, who underwent EAS and were discharged alive. CCI was calculated by using of the International Classification of Diseases, 9th Revision, Clinical Modification codes. Our outcome was all-cause death recorded during the 20.8 ± 8.8 month follow-up; (3) Results: We evaluated 197 patients aged 78.4 ± 7.2 years of whom 47 (23.8%) died. Mortality was higher in patients who underwent open abdominal surgery than in those treated with laparoscopic procedure (74% vs. 26%, p < 0.001), and in those who needed colon, small bowel, and gastric surgery. Mean CCI was 4.98 ± 2.2, and in subjects with CCI ≥ 4 survival was lower. Cox regression analysis showed that CCI (HR 1.132, 95% CI 1.009-1.270, p = 0.035), and open surgery (HR 10.298, 95%CI 1.409-75.285, p = 0.022) were associated with all-cause death independently from age and sex; (4) Conclusions: Calculation of CCI, could help surgeons in the preoperative stratification of risk of death after discharge in subjects aged ≥65 years who need EAS. CCI ≥ 4, increases the risk of all-causes mortality independently from age.