Preoperative outpatient blood pressure variability predicts postoperative mortality, readmission and morbidity after surgery.
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ABSTRACT: Background
Outpatient blood pressure variability (BPV) predicts hospitalization and death in non-surgical patients independently of hypertension. We hypothesized that preoperative BPV predicts postoperative outcomes.Methods
We assessed 22,233 veterans undergoing CABG, colectomy, hip replacement, pancreatectomy, carotid endarterectomy or AV-fistula with ≥10 outpatient BP's over three preoperative years. Calculating BPV as SD of systolic or diastolic BP, we used logistic regression considering demographics, comorbidities, and pre-admission cardiovascular medications to estimate odds ratios for 90-day mortality or readmission, MI, CVA, renal failure, and wound infection, choosing the lowest 5%ile of systolic/diastolic BPV for reference.Results
Covariate-adjusted ORs for adverse outcomes increased as BPV increased. For instance, the highest 5%ile of systolic BPV had covariate-adjusted ORs of 2.96 and 1.78 for 90-day mortality and readmission. Systolic and diastolic BPV trended together but affected outcomes independently.Conclusions
Preoperative BPV predicts postoperative outcomes. BPV should be considered in individualized risk assessment and subgroup risk stratification.
SUBMITTER: Basson MD
PROVIDER: S-EPMC7483253 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
Preoperative outpatient blood pressure variability predicts postoperative mortality, readmission and morbidity after surgery.
Basson Marc D MD Klug Marilyn G MG Newman William E WE Dyke Cornelius C
American journal of surgery 20200227 4
<h4>Background</h4>Outpatient blood pressure variability (BPV) predicts hospitalization and death in non-surgical patients independently of hypertension. We hypothesized that preoperative BPV predicts postoperative outcomes.<h4>Methods</h4>We assessed 22,233 veterans undergoing CABG, colectomy, hip replacement, pancreatectomy, carotid endarterectomy or AV-fistula with ≥10 outpatient BP's over three preoperative years. Calculating BPV as SD of systolic or diastolic BP, we used logistic regression ...[more]
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Project description:Individualised risk prediction is crucial if targeted pre-operative risk reduction strategies are to be deployed effectively. Radiologically determined sarcopenia has been shown to predict outcomes across a range of intra-abdominal pathologies. Access to pre-operative cross-sectional imaging has resulted in a number of studies investigating the predictive value of radiologically assessed sarcopenia over recent years. This systematic review and meta-analysis aimed to determine whether radiologically determined sarcopenia predicts post-operative morbidity and mortality following abdominal surgery.CENTRAL, EMBASE and MEDLINE databases were searched using terms to capture the concept of radiologically assessed sarcopenia used to predict post-operative complications in abdominal surgery. Outcomes included 30 day post-operative morbidity and mortality, 1-, 3- and 5-year overall and disease-free survival and length of stay. Data were extracted and meta-analysed using either random or fixed effects model (Revman ® 5.3).A total of 24 studies involving 5267 patients were included in the review. The presence of sarcopenia was associated with a significant increase in major post-operative complications (RR 1.61 95% CI 1.24-4.15 p = <0.00001) and 30-day mortality (RR 2.06 95% CI 1.02-4.17 p = 0.04). In addition, sarcopenia predicted 1-, 3- and 5-year survival (RR 1.61 95% CI 1.36-1.91 p = <0.0001, RR 1.45 95% CI 1.33-1.58 p = <0.0001, RR 1.25 95% CI 1.11-1.42 p = 0.0003, respectively) and 1- and 3-year disease-free survival (RR 1.30 95% CI 1.12-1.52 p = 0.0008).Peri-operative cross-sectional imaging may be utilised in order to predict those at risk of complications following abdominal surgery. These findings should be interpreted in the context of retrospectively collected data and no universal sarcopenic threshold. Targeted prehabilitation strategies aiming to reverse sarcopenia may benefit patients undergoing abdominal surgery.
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Project description:BACKGROUND:Malnutrition with hypoalbuminemia (albumin < 35 g/L) is an important factor in predicting risks associated with colorectal cancer surgery. However, there is limited data about the effects of mild hypoalbuminemia with small decreases in albumin on postoperative complications. METHODS:This is a retrospective study using the multi-institutional, nationally validated database of the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) to investigate mild hypoalbuminemia and its association with postoperative mortality and morbidity by using a propensity score matching method. RESULTS:In a group of 30,676 colorectal cancer patients who received surgery, 5230 had mild hypoalbuminemia (< 35 and > =30 g/L) and 21,310 had normal albumin levels (> = 35 g/L). Significant differences were noted in 21 clinical characteristics between the two groups. After 1:2 propensity score matching postoperative mortality was significantly associated with mild hypoalbuminemia (OR = 1.74; p < 0.001). There were significant associations between mild hypoalbuminemia and 11 postoperative morbidities including deep vein thrombosis, pulmonary embolism, superficial and deep surgical site infection, pneumonia, septic shock, ventilator> 48 h, blood transfusion, return to operating room, stroke and re-intubation. Mild hypoalbuminemia was also associated with overall complication (B = 0.064, p < 0.001) and length of total hospital stay (B = 2.236, p < 0.001). CONCLUSIONS:In colorectal cancer, this is the first propensity score matching study of malnutrition with mild hypoalbuminemia which demonstrates that a mild decrease in serum albumin contributes significantly to poor postoperative outcome.
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Project description:ImportanceA variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied.ObjectiveTo examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions.Design, setting, and participantsIn this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021.ExposureCardiac surgery.Main outcomes and measuresPostoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values.ResultsData from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk.Conclusions and relevanceThis study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations.
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