Although deviations in intraoperative blood pressure are assumed to be associated with postoperative mortality, critical blood pressure thresholds remain undefined. Therefore, the authors estimated ...the intraoperative thresholds of systolic blood pressure (SBP), mean blood pressure (MAP), and diastolic blood pressure (DBP) associated with increased risk-adjusted 30-day mortality.
This retrospective cohort study combined intraoperative blood pressure data from six Veterans Affairs medical centers with 30-day outcomes to determine the risk-adjusted associations between intraoperative blood pressure and 30-day mortality. Deviations in blood pressure were assessed using three methods: (1) population thresholds (individual patient sum of area under threshold AUT or area over threshold 2 SDs from the mean of the population intraoperative blood pressure values), (2). absolute thresholds, and (3) percent change from baseline blood pressure.
Thirty-day mortality was associated with (1) population threshold: systolic AUT (odds ratio, 3.3; 95% CI, 2.2 to 4.8), mean AUT (2.8; 1.9 to 4.3), and diastolic AUT (2.4; 1.6 to 3.8). Approximate conversions of AUT into its separate components of pressure and time were SBP < 67 mmHg for more than 8.2 min, MAP < 49 mmHg for more than 3.9 min, DBP < 33 mmHg for more than 4.4 min. (2) Absolute threshold: SBP < 70 mmHg for more than or equal to 5 min (odds ratio, 2.9; 95% CI, 1.7 to 4.9), MAP < 49 mmHg for more than or equal to 5 min (2.4; 1.3 to 4.6), and DBP < 30 mmHg for more than or equal to 5 min (3.2; 1.8 to 5.5). (3) Percent change: MAP decreases to more than 50% from baseline for more than or equal to 5 min (2.7; 1.5 to 5.0). Intraoperative hypertension was not associated with 30-day mortality with any of these techniques.
Intraoperative hypotension, but not hypertension, is associated with increased 30-day operative mortality.
To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and ...outcomes.
Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation.
We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores.
Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration.
Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.
To use factor analysis to cluster the 18 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) perioperative complications into a reproducible, smaller number of ...clinically meaningful groups of postoperative complications, facilitating and streamlining future study and application in live clinical settings.
The ACS NSQIP collects and reports on eighteen 30-day postoperative complications (excluding mortality), which are variably grouped in published analyses using ACS NSQIP data. This hinders comparison between studies of this widely used quality improvement dataset.
Factor analysis was used to develop a series of complication clusters, which were then analyzed to identify a parsimonious, clinically meaningful grouping, using 2,275,240 surgical cases in the ACS NSQIP Participant Use File (PUF), 2005 to 2012. The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complications derived from factor solutions.
Factor analysis solutions for 5 to 9 latent factors were examined for their percent of total variance, parsimony, and clinical interpretability. Applying the first 2 of these criteria, we identified the 7-factor solution, which included clusters of pulmonary, infectious, wound disruption, cardiac/transfusion, venous thromboembolic, renal, and neurological complications, as the best solution for parsimony and clinical meaningfulness. Applying the last (clinical interpretability), we combined the wound disruption with the infectious clusters resulting in 6 clusters for future clinical applications.
Factor analysis of ACS NSQIP postoperative complication data provides 6 clinically meaningful complication clusters in lieu of 18 postoperative morbidities, which will facilitate comparisons and clinical implementation of studies of postoperative morbidities.
The novel Surgical Risk Preoperative Assessment System (SURPAS) requires entry of five predictor variables (the other three variables of the eight-variable model are automatically obtained from the ...electronic health record or a table look-up), provides patient risk estimates compared to national averages, is integrated into the electronic health record, and provides a graphical handout of risks for patients. The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC).
Predicted risk of postoperative mortality and morbidity was calculated using both SURPAS and ACS-SRC for 1,006 randomly selected 2007–2016 ACS National Surgical Quality Improvement Program (NSQIP) patients with known outcomes. C-indexes, Hosmer-Lemeshow graphs, and Brier scores were compared between SURPAS and ACS-SRC.
ACS-SRC risk estimates for overall morbidity underestimated risk compared to observed postoperative overall morbidity, particularly for the highest risk patients. SURPAS accurately estimates morbidity risk compared to observed morbidity.
SURPAS risk predictions were more accurate than ACS-SRC's for overall morbidity, particularly for high risk patients.
The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). SURPAS risk predictions were more accurate than those of the ACS-SRC for overall morbidity, particularly for high risk patients.
•SURPAS risk estimates for mortality were slightly higher than the ACS-SRC estimates.•ACS-SRC underestimates morbidity risk compared to observed outcomes.•This is particularly true for the highest risk patients.•SURPAS accurately estimates morbidity risk compared to observed outcomes.
Drivers of readmissions in vascular surgery patients Glebova, Natalia O., MD, PhD; Bronsert, Michael, PhD, MS; Hammermeister, Karl E., MD ...
Journal of vascular surgery,
07/2016, Volume:
64, Issue:
1
Journal Article
Peer reviewed
Open access
Objective Postoperative readmissions are frequent in vascular surgery patients, but it is not clear which factors are the main drivers of readmissions. Specifically, the relative contributions of ...patient comorbidities vs those of operative factors and postoperative complications are unknown. We sought to study the multiple potential drivers of readmission and to create a model for predicting the risk of readmission in vascular patients. Methods The 2012-2013 American College of Surgeons National Surgical Quality Improvement Program data set was queried for unplanned readmissions in 86,238 vascular patients. Multivariable forward selection logistic regression analysis was used to model the relative contributions of patient comorbidities, operative factors, and postoperative complications for readmission. Results The unplanned readmission rate was 9.3%. The preoperative model based on patient demographics and comorbidities predicted readmission risk with a low C index of .67; the top five predictors of readmission were American Society of Anesthesiologists class, preoperative open wound, inpatient operation, dialysis dependence, and diabetes mellitus. The postoperative model using operative factors and postoperative complications predicted readmission risk better (C index, .78); postoperative complications were the most significant predictor of readmission, overpowering patient comorbidities. Importantly, postoperative complications identified before discharge from the hospital were not a strong predictor of readmission as the model using predischarge postoperative complications had a similar C index to our preoperative model (.68). However, the inclusion of complications identified after discharge from the hospital appreciably improved the predictive power of the model (C index, .78). The top five predictors of readmission in the final model based on patient comorbidities and postoperative complications were postdischarge deep space infection, superficial surgical site infection, pneumonia, myocardial infection, and sepsis. Conclusions Readmissions in vascular surgery patients are mainly driven by postoperative complications identified after discharge. Thus, efforts to reduce vascular readmissions focusing on inpatient hospital data may prove ineffective. Our study suggests that interventions to reduce vascular readmissions should focus on prompt identification of modifiable postdischarge complications.
With inpatient length of stay decreasing, discharge destination after surgery can serve as an important metric for quality of care. Additionally, patients desire information on possible discharge ...destination. Adequate planning requires a multidisciplinary approach, can reduce healthcare costs and ensure patient needs are met. The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious risk assessment tool using 8 predictor variables developed from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) dataset. SURPAS is applicable to more than 3,000 operations in adults in 9 surgical specialties, predicts important adverse outcomes, and is incorporated into our electronic health record. We sought to determine whether SURPAS can accurately predict discharge destination.
A “full model” for risk of postoperative “discharge not to home” was developed from 28 nonlaboratory preoperative variables from ACS NSQIP 2012-2017 dataset using logistic regression. This was compared with the 8-variable SURPAS model using the C index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration.
Of 5,303,519 patients, 447,153 (8.67%) experienced a discharge not to home. The SURPAS model's C index, 0.914, was 99.24% of that of the full model's (0.921); the Hosmer-Lemeshow plots indicated good calibration and the Brier score was 0.0537 and 0.0514 for the SUPAS and full model, respectively.
The 8-variable SURPAS model preoperatively predicts risk of postoperative discharge to a destination other than home as accurately as the 28 nonlaboratory variable ACS NSQIP full model. Therefore, discharge destination can be integrated into the existing SURPAS tool, providing accurate outcomes to guide decision-making and help prepare patients for their postoperative recovery.
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The universal Surgical Risk Preoperative Assessment System (SURPAS) prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for ...surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations.
The SURPAS models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores.
The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the ACS NSQIP database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk.
SURPAS displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where SURPAS was not accurate, developing disease or operation-specific risk models might be appropriate.
Despite limited capacity and expensive cost, there are minimal objective data to guide postoperative allocation of intensive care unit (ICU) beds. The Surgical Risk Preoperative Assessment System ...(SURPAS) uses 8 preoperative variables to predict many common postoperative complications, but it has not yet been evaluated in predicting postoperative ICU admission.
To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population.
This decision analytical model was a retrospective, observational analysis of prospectively collected patient data from the 2012 to 2018 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, which were merged with individual patients' electronic health record data to capture postoperative ICU use. Multivariable logistic regression modeling was used to determine how the 8 preoperative variables of the SURPAS model predicted ICU use compared with a model inputting all 28 preoperatively available NSQIP variables. Data included in the analysis were collected for the ACS NSQIP at 5 hospitals (1 tertiary academic center, 4 academic affiliated hospitals) within the University of Colorado Health System between January 1, 2012, and December 31, 2018. Included patients were those undergoing surgery in 9 surgical specialties during the 2012 to 2018 period. Data were analyzed from May 29 to July 30, 2021.
Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery.
Use of ICU care up to 30 days after surgery.
A total of 34 568 patients were included in the analytical data set: 32 032 (92.7%) in the cohort without postoperative ICU use and 2545 (7.4%) in the cohort with postoperative ICU use (no ICU use: mean SD age, 54.9 16.6 years; 18 188 women 56.8%; ICU use: mean SD age, 60.3 15.3 years; 1333 men 52.4%). For the internal chronologic validation of the 7-variable SURPAS model, data from 2012 to 2016 were used as the training data set (n = 24 250, 70.2% of the total sample size of 34 568) and data from 2017 to 2018 were used as the test data set (n = 10 318, 29.8% of the total sample size of 34 568). The C statistic improved in the test data set compared with the training data set (0.933; 95% CI, 0.924-0.941 vs 0.922; 95% CI, 0.917-0.928), whereas the Brier score was slightly worse in the test data set compared with the training data set (0.045; 95% CI, 0.042-0.048 vs 0.045; 95% CI, 0.043-0.047). The SURPAS model compared favorably with the model inputting all 28 NSQIP variables, with both having good calibration between observed and expected outcomes in the Hosmer-Lemeshow graphs and similar Brier scores (model inputting all variables, 0.044; 95% CI, 0.043-0.048; SURPAS model, 0.045; 95% CI, 0.042-0.046) and C statistics (model inputting all variables, 0.929; 95% CI, 0.925-0.934; SURPAS model, 0.925; 95% CI, 0.921-0.930).
Results of this decision analytical model study revealed that the SURPAS prediction model accurately predicted postoperative ICU use across a diverse surgical population. These results suggest that the SURPAS prediction model can be used to help with preoperative planning and resource allocation of limited ICU beds.
Abstract Objective Although postoperative readmissions are frequent in vascular surgery patients, the reasons for these readmissions are not well characterized, and effective approaches to their ...reduction are unknown. Our aim was to analyze the reasons for vascular surgery readmissions and to report potential areas for focused efforts aimed at readmission reduction. Methods The 2012 to 2013 American College of Surgeons National Quality Improvement Program (ACS NSQIP) data set was queried for vascular surgery patients. Multivariable models were developed to analyze risk factors for postdischarge infections, the major drivers of unplanned 30-day readmissions. Results We identified 86,403 vascular surgery patients for analysis. Thirty-day readmission occurred in 8827 (10%), of which 8054 (91%) were unplanned. Of the unplanned readmissions, 61% (n = 4951) were related to the index vascular surgery procedure. Infectious complications were the most common reason for a surgery-related readmission (1940 39%), with surgical site infection being the most common type of infection related to unplanned readmission. Multivariable analysis showed the top five preoperative risk factors for postdischarge infections were the presence of a preoperative open wound, inpatient operation, obesity, work relative value unit, and insulin-dependent diabetes (but not diabetes managed with oral medications). Cigarette smoking was a weak predictor and came in tenth in the mode (overall C index, 0.657). When operative and postoperative factors were included in the model, total operative time was the strongest predictor of postdischarge infectious complications (odds ratio OR 1.2 for each 1-hour increase in operative time), followed by presence of a preoperative open wound (OR, 1.5), inpatient operation (OR, 2), obesity (OR, 1.8), and discharge to rehabilitation facility (OR, 1.7; P < .001 for all). Insulin-dependent diabetes, cigarette smoking, dialysis dependence, and female gender were also predictive, albeit with smaller effects (OR, 1.1-1.3 for all; P < .001). The overall fit of the multivariable model was fair (C statistic, 0.686). Conclusions Infectious complications dominate the reasons for unplanned 30-day readmissions in vascular surgery patients. We have identified preoperative, operative, and postoperative risk factors for these infections with the goal of reducing these complications and thus readmissions. Expected patient risk factors, such as diabetes, obesity, renal insufficiency, and cigarette smoking, were less important in predicting infectious complications compared with operative time, presence of a preoperative open wound, and inpatient operation. Our findings suggest that careful operative planning and expeditious operations may be the most effective approaches to reducing infections and thus readmissions in vascular surgery patients.
Nondepolarizing neuromuscular blocking drugs (NNMBDs) are commonly used as an adjunct to general anesthesia. Residual blockade is common, but its potential adverse effects are incompletely known. ...This study was designed to assess the association between NNMBD use with or without neostigmine reversal and postoperative morbidity and mortality.
This is a retrospective observational study of 11,355 adult patients undergoing general anesthesia for noncardiac surgery at 5 Veterans Health Administration (VA) hospitals. Of those, 8984 received NNMBDs, and 7047 received reversal with neostigmine. The primary outcome was a composite of respiratory complications (failure to wean from the ventilator, reintubation, or pneumonia), which was "yes" if a patient had any of the 3 component events and "no" if they had none. Secondary outcomes were nonrespiratory complications, 30-day and long-term all-cause mortality. We adjusted for differences in patient risk using propensity matched (PM) followed by assessment of the association of interest by logistic regression between the matched pairs as our primary analysis and multivariable logistic regression (MLR) as a sensitivity analysis.
Our primary aim was to assess the adverse outcomes in the patients who had received NNMBDs with and without neostigmine. Administration of an NNMBD without neostigmine reversal compared with NNMBD with neostigmine reversal was associated with increased odds of respiratory complications (PM odds ratio OR, 1.75 95% confidence interval CI, 1.23-2.50; MLR OR, 1.71 CI, 1.24-2.37) and a marginal increase in 30-day mortality (PM OR, 1.83 CI, 0.99-3.37; MLR OR, 1.78 CI, 1.02-3.13). However, there were no statistically significant associations with nonrespiratory complications or long-term mortality. Patients who were administered an NNMBD followed by neostigmine had no differences in outcomes compared with patients who had general anesthesia without an NNMBD.
The use of NNMBDs without neostigmine reversal was associated with increased odds of our composite respiratory outcome compared with patients reversed with neostigmine. Based on these data, we conclude that reversal of NNMBDs should become a standard practice if extubation is planned.
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