Electronically self-assessed functional capacity and exercise testing: A comparison of the Duke Activity Status Index and Patient-Reported Outcomes Measurement Information System tools.

PURPOSE: Electronic screening tools, such as Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short-Form 12a (PF-SF12a), may aid in the assessment of functional capacity. However, PROMIS PF-SF12a has not been validated against exercise capacity, or compared with established questionnaires, including the Duke Activity Status Index (DASI). We compared the DASI and PROMIS PF-SF12a to the maximum metabolic equivalents (METs) achieved during exercise stress testing. METHODS: DASI and PROMIS PF-SF12a were electronically administered to 100 adult patients (median age 56years, 61% male) immediately before exercise stress testing. DASI-predicted METs and PROMIS T score were calculated. Correlations with exercise METs with and without age adjustment were examined. Linear regression lines were derived and adjusted r2 statistic was calculated. We compared models with the Davidson-Mackinnon J test. RESULTS: The median (interquartile range) DASI-predicted METs, PROMIS Tscore, and exercise METs were 8.97 (7.61-9.89), 47.90 (43.33-52.40), and 10.10 (10.10-12.80), respectively. In unadjusted correlation analyses, PROMIS accounted for 26% of the variance in exercise METs compared with 38% with DASI. With age adjustment, the r2values increased to 0.36 (PROMIS) and 0.46 (DASI). In both unadjusted and age-adjusted analyses, inclusion of DASI improved prediction of exercise METs beyond PROMIS T score (P<.0001). In contrast, PROMIS T score did not improve exercise MET prediction compared with DASI alone (P>.10). CONCLUSION: Among patients undergoing clinically indicated exercise stress testing, DASI outperformed PROMIS PF-SF12a as a predictor of exercise METs.

Nondepolarizing Neuromuscular Blocking Agents, Reversal, and Risk of Postoperative Pneumonia.

BACKGROUND: Residual postoperative paralysis from nondepolarizing neuromuscular blocking agents (NMBAs) is a known problem. This paralysis has been associated with impaired respiratory function, but the clinical significance remains unclear. The aims of this analysis were two-fold: (1) to investigate if intermediate-acting NMBA use during surgery is associated with postoperative pneumonia and (2) to investigate if nonreversal of NMBAs is associated with postoperative pneumonia. METHODS: Surgical cases (n = 13,100) from the Vanderbilt University Medical Center National Surgical Quality Improvement Program database who received general anesthesia were included. The authors compared 1,455 surgical cases who received an intermediate-acting nondepolarizing NMBA to 1,455 propensity score-matched cases who did not and 1,320 surgical cases who received an NMBA and reversal with neostigmine to 1,320 propensity score-matched cases who did not receive reversal. Postoperative pneumonia incidence rate ratios (IRRs) and bootstrapped 95% CIs were calculated. RESULTS: Patients receiving an NMBA had a higher absolute incidence rate of postoperative pneumonia (9.00 vs. 5.22 per 10,000 person-days at risk), and the IRR was statistically significant (1.79; 95% bootstrapped CI, 1.08 to 3.07). Among surgical cases who received an NMBA, cases who were not reversed were 2.26 times as likely to develop pneumonia after surgery compared to cases who received reversal with neostigmine (IRR, 2.26; 95% bootstrapped CI, 1.65 to 3.03). CONCLUSIONS: Intraoperative use of intermediate nondepolarizing NMBAs is associated with developing pneumonia after surgery. Among patients who receive these agents, nonreversal is associated with an increased risk of postoperative pneumonia.

Intraoperative Care Transitions Are Not Associated with Postoperative Adverse Outcomes.

BACKGROUND: Whether anesthesia care transitions and provision of short breaks affect patient outcomes remains unclear. METHODS: The authors determined the number of anesthesia handovers and breaks during each case for adults admitted between 2005 and 2014, along with age, sex, race, American Society of Anesthesiologists physical status, start time and duration of surgery, and diagnosis and procedure codes. The authors defined a collapsed composite of in-hospital mortality and major morbidities based on primary and secondary diagnoses. The relationship between the total number of anesthesia handovers during a case and the collapsed composite outcome was assessed with a multivariable logistic regression. The relationship between the total number of anesthesia handovers during a case and the components of the composite outcome was assessed using multivariate generalized estimating equation methods. Additionally, the authors analyzed major complications and/or death within 30 days of surgery based on the American College of Surgeons National Surgical Quality Improvement Program-defined events. RESULTS: A total of 140,754 anesthetics were identified for the primary analysis. The number of anesthesia handovers was not found to be associated (P = 0.19) with increased odds of postoperative mortality and serious complications, as measured by the collapsed composite, with odds ratio for a one unit increase in handovers of 0.957; 95% CI, 0.895 to 1.022, when controlled for potential confounding variables. A total of 8,404 anesthetics were identified for the NSQIP analysis (collapsed composite odds ratio, 0.868; 95% CI, 0.718 to 1.049 for handovers). CONCLUSIONS: In the analysis of intraoperative handovers, anesthesia care transitions were not associated with an increased risk of postoperative adverse outcomes.

Preoperative Surgical Risk Predictions Are Not Meaningfully Improved by Including the Surgical Apgar Score: An Analysis of the Risk Quantification Index and Present-On-Admission Risk Models.

BACKGROUND: Estimating surgical risk is critical for perioperative decision making and risk stratification. Current risk-adjustment measures do not integrate dynamic clinical parameters along with baseline patient characteristics, which may allow a more accurate prediction of surgical risk. The goal of this study was to determine whether the preoperative Risk Quantification Index (RQI) and Present-On-Admission Risk (POARisk) models would be improved by including the intraoperative Surgical Apgar Score (SAS). METHODS: The authors identified adult patients admitted after noncardiac surgery. The RQI and POARisk were calculated using published methodologies, and model performance was compared with and without the SAS. Relative quality was measured using Akaike and Bayesian information criteria. Calibration was compared by the Brier score. Discrimination was compared by the area under the receiver operating curves (AUROCs) using a bootstrapping procedure for bias correction. RESULTS: SAS alone was a statistically significant predictor of both 30-day mortality and in-hospital mortality (P < 0.0001). The RQI had excellent discrimination with an AUROC of 0.8433, which increased to 0.8529 with the addition of the SAS. The POARisk had excellent discrimination with an AUROC of 0.8608, which increased to 0.8645 by including the SAS. Similarly, overall performance and relative quality increased. CONCLUSIONS: While AUROC values increased, the RQI and POARisk preoperative risk models were not meaningfully improved by adding intraoperative risk using the SAS. In addition to the estimated blood loss, lowest heart rate, and lowest mean arterial pressure, other dynamic clinical parameters from the patient's intraoperative course may need to be combined with procedural risk estimate models to improve risk stratification.