Quality of recovery after surgery for cancer treatment

Can the Tilburg Frailty Indicator predict post-operative quality of recovery in patients with gynecologic cancer? A prospective cohort study

OBJECTIVE

Frailty is a marker of physiologic decline within multiple organ systems. The Tilburg Frailty Indicator (TFI) is an instrument for assessing frailty. We evaluated the ability of the TFI to predict the quality of post-operative recovery in patients with gynecologic cancer and explored the associations between frailty, post-operative complications, and length of stay.

METHODS

We conducted a prospective cohort study of patients scheduled for radical gynecologic cancer surgery between May 2021 and January 2022, and defined a TFI score ≥5 as 'frailty'. Our primary outcome was the post-operative quality of recovery based on the Quality of Recovery-15 (QoR-15), and the secondary outcomes were post-operative complications and length of stay. Multiple logistic regression was used to examine the relationship between frailty and outcomes. We developed receiver operating characteristics (ROCs) and assessed areas under the ROC curves (AUCs) to explore the ability of frailty to predict the quality of post-operative recovery.

RESULTS

A total of 169 patients were included. The prevalence of frailty using the TFI was 47.9% in this cohort. In the multivariate regression analysis, frailty emerged as a significant predictor of the 3-day QoR-15 score (aOR 11.69, 95% CI 4.26 to 32.08; p˂0.001) and complications (aOR 10.05, 95% CI 1.66 to 60.72; p=0.012). Frailty was not associated with length of stay (aOR 2.12, 95% CI 0.87 to 5.16; p=0.099). The combination of the TFI, American Society of Anesthesiologists (ASA) classification, and types of cancer resulted in an increase in the AUC compared with the TFI alone (AUC 0.796, 95% CI 0.727 to 0.865; p˂0.05).

CONCLUSIONS

The use of the TFI may assist surgeons in estimating the risk with respect to post-operative quality of recovery and complications in patients with gynecologic cancer. Combining the TFI with ASA classification and cancer type is expected to improve the predictive ability of poor quality of recovery.

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.

Quality of Recovery Following Orthopedic Surgery in Patients at an Academic Hospital in South Africa

BACKGROUND

Measurement of perioperative quality of recovery (QoR) is an important tool in improving the patient's perioperative experience. By making use of the Quality of Recovery-15 (QoR-15) questionnaire, this study aimed to measure the QoR on day 1 in patients following elective and semiurgent orthopedic surgery at an academic hospital. A secondary aim was to determine factors that may influence the QoR.

METHODS

A cross-sectional research study was performed in 122 patients presenting for elective and semiurgent orthopedic surgery during core working hours between July and November 2019 at Helen Joseph Hospital, Johannesburg, South Africa. Patients completed a baseline QoR-15 assessment preoperatively and a day 1 QoR-15 assessment between 12 and 24 hours postoperatively. Additional information on various anesthetic, surgical, and patient factors was collected.

RESULTS

Nineteen patients (15.6%) had a poor QoR on day 1 and were found to have started with a significantly worse baseline QoR-15 score than those with no poor QoR (98.5 vs 128.5; P < .001). A significant worsening from the overall mean baseline QoR-15 to the overall mean day 1 QoR-15 score was found (123.8 vs 113.1; P = .001). This worsening score was more pronounced in patients with a poor day 1 QoR-15 score than no poor day 1 score (25.3 vs 8; P = .002). Surgical site was found to have a significant effect on day 1 score (P = .026). A significantly worse severe pain score was found in patients with foot/ankle and knee surgery compared to hand/arm surgery (P = .012 and P = .032, respectively) and is thought to be due to the decreased use of peripheral nerve blocks in lower limb surgery. Sex, American Society of Anesthesiologists (ASA) physical status, type of anesthesia, postoperative analgesia, and antiemetic given (only applicable in general anesthetic cases) were not found to have a significant effect on the day 1 QoR-15 scores.

CONCLUSIONS

Patients who had a poor QoR on day 1 started with a significantly worse baseline QoR-15 score and experienced a significantly greater negative change in the mean difference. Preoperative identification of patients who are at risk of a poor postoperative QoR may aid in preemptive targeting of limited resources to those who need them most. Postoperative pain, particularly in patients after foot/ankle and knee surgery, is a significant modifiable factor to improve postoperative QoR at our institution.