Hierarchical regression of ASA prediction model in predicting mortality prior to performing emergency laparotomy a systematic review

Risk factors and predictive models for postoperative surgical site infection in patients with massive hemorrhage

Background

This study aimed to identify risk factors associated with postoperative surgical site infection (SSI) in patients experiencing massive hemorrhage and develop a predictive model.

Methods

A retrospective analysis of 121 orthopedic surgery patients and experienced massive hemorrhage was conducted. According to postoperative SSI occurrence, the patients were divided into two groups: the infection group (n = 12) and the non-infection group (n = 109). Clinical data were collected, and a predictive model was developed using logistic regression analysis in patients with massive hemorrhage.

Results

Independent risk factors for postoperative SSI included ASA grade, urine volume, and type 2 diabetes. An area under the curve for the prediction of postoperative SSI based on the Receiver Operating Characteristic (ROC) curve for the risk score was 0.916.

Conclusions

Patients with a urine volume of ≥3.49 ml/kg/h, higher ASA grade, and type 2 diabetes are at an increased risk of developing postoperative SSI after experiencing massive hemorrhage.

Level of evidence

Level III.

The influence between frailty, sarcopenia and physical status on mortality in patients undergoing emergency laparotomy

BACKGROUND

Frailty and sarcopenia have been independently shown to predict mortality in emergency laparotomy (EmLap), and both can be indicative of poor physical status. We aim to assess the prevalence of frailty, sarcopenia, and physical status in EmLap and explore the relationship between these factors and 30-day, 90-day and 1-year mortality.

METHODS

Retrospective analysis was performed on prospectively maintained Emergency Laparotomy and Laparoscopic Scottish Audit (ELLSA) database (2017-2019) which included patients ≥ 18 years who underwent EmLap. Clinical frailty scale (CFS) was used to classify frailty (score ≥ 4 as frail). Sarcopenia was assessed using total psoas index (TPI). Poor physical status (PPS) was defined by American Society of Anaesthesiologists physical status classification (ASA) ≥ 4. Binary logistic regression and fisher's exact tests were used for statistical analysis.

RESULTS

215 patients were included in the study, with 57.2% female and median age of 64 years. Frailty was present in 17.2%, sarcopenia in 25.1% and 14.4% had PPS; 3.3% had all three factors. Frail patients had significantly higher risk for 30-day (p = 0.003), 90-day (p = 0.006) and 1-year mortality (p = 0.032). Patients with poor physical status also showed significantly higher mortality at 30-day (p < 0.001), 90-day (p < 0.001) and 1-year (p = 0.001). Sarcopenic patients did not show significant differences in mortality risks up to 1 year. Patients with all three factors had significantly higher 30-day (p = 0.003), 90-day (p = 0.046) and 1-year mortality (p = 0.108) compared to patients who had none of the factors.

CONCLUSIONS

Frailty, sarcopenia, and PPS are prevalent in EmLap. Frailty and PPS were independently associated with short and long-term mortality, but not sarcopenia. While overlap exists between three factors, more research is required to understand the complex interplay.

Synergistic effect of sarcopenia and ASA status in predicting mortality after emergency laparotomy: a systematic review and meta-analysis with meta-regression

The aim of this study was to investigate the relationship between sarcopenia and American Society of Anesthesiologists (ASA) status in predicting post-operative mortality after emergency laparotomy. A PRISMA-compliant systematic review and meta-analysis (using random effects modelling) was performed searching for studies reporting 30-day mortality risk in patients with sarcopenia undergoing emergency laparotomy. The ASA status of sarcopenic and non-sarcopenic patients was determined, and the effect of difference in ASA status on 30-day mortality in sarcopenic and non-sarcopenic patients was determined via a meta-regression model. The risk of bias and certainty was assessed using the QUIPS tool and the GRADE system, respectively. Seven studies comprising 2663 patients were included. Thirty-day mortality risk was 22.9% (95% CI 11.6-40.0%) in sarcopenic patients and 6.2% (95% CI 2.9-13.0%) in non-sarcopenic patients; the risk was significantly higher in sarcopenic patients (OR: 4.452, p = 0.016). In sarcopenic patients, ASA status IV-V increased the risk of mortality (Coefficient: 0.07612, p < 0.0001), while ASA status I-II (Coefficient: - 0.09039, p < 0.0001) or ASA status III (Coefficient: 0.01300, p = 0.344) did not. In non-sarcopenic patients, ASA status III (Coefficient: 0.06830, p < 0.0001) and ASA status IV-V (Coefficient: 0.17809, p < 0.0001) increased the risk of mortality, while ASA status I-II (Coefficient: - 0.05841, p < 0.0001) did not. The GRADE certainty was moderate. Sarcopenia and ASA status are two independent predictors of mortality after emergency laparotomy with no significant collinearity. Sarcopenia and ASA status synergistically increase the risk of mortality after emergency laparotomy. ASA status IV and ASA status III are critical thresholds for increased risk of mortality in sarcopenic and non-sarcopenic patients, respectively.

Combining sarcopenia and ASA status to inform emergency laparotomy outcomes: could it be that simple?

BACKGROUND

Risk assessment for emergency laparotomy (EL) is important for guiding decision-making and anticipating the level of perioperative care in acute clinical settings. While established tools such as the American College of Surgeons National Surgical Quality Improvement Program calculator (ACS-NSQIP), the National Emergency Laparotomy Audit Risk Prediction Calculator (NELA) and the Portsmouth Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity calculation (P-POSSUM) are accurate predictors for mortality, there has been increasing recognition of the benefits from including measurements for frailty in a simple and quantifiable manner. Psoas muscle to 3rd lumbar vertebra area ratio (PM:L3) measured on CT scans was proven to have a significant inverse association with 30-, 90- and 365-day mortality in EL patients.

METHODS

A retrospective analysis was conducted of 500 patients admitted to four Australian hospitals who underwent EL during 2016-2017, and had contemporaneous abdomino-pelvic CT scans. Radiological sarcopenia was measured as PM:L3 ratios. ASC-NSQIP, NELA and P-POSSUM were retrospectively calculated. Univariate and multivariate logistic regression modelling was used to assess these ratios and scores, as well as American Society of Anaesthesiologists (ASA) classification separated into ASA I-III and IV/V (simplified ASA), as potential predictors of 30-, 90- and 365-day mortality.

RESULTS

PM:L3, simplified ASA, ACS-NSQIP, NELA and P-POSSUM were each statistically significant predictors of 30-day, 90-day and 365-day mortality (P < 0.001). Logistic regression models of 30-, 90- and 365-day mortality combining PM:L3 (P = 0.001) and simplified ASA (P < 0.001) exhibited AUCs of 0.838 (0.780, 0.896), 0.805 (0.751, 0.860) and 0.775 (0.729, 0.822), respectively, which were comparable to that of ACS-NSQIP and NELA.

CONCLUSION

Combining the semi-physiological parameter ASA classification with PM:L3 provides a quick and simple alternative to the more complex established risk assessment scores and is superior to PM:L3 alone.

Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification

OBJECTIVE

The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record.

DESIGN

Retrospective multicenter hospital registry study.

SETTING

University-affiliated hospital networks.

PATIENTS

Patients who received anesthesia at Beth Israel Deaconess Medical Center (Boston, MA, training [n = 361,602] and internal validation cohorts [n = 90,400]) and Montefiore Medical Center (Bronx, NY, external validation cohort [n = 254,412]).

MEASUREMENTS

The ML-PS was created using a supervised random forest model with 35 preoperatively available variables. Its predictive ability for 30-day mortality, postoperative ICU admission, and adverse discharge were determined by logistic regression.

MAIN RESULTS

The anesthesiologist ASA-PS and ML-PS were in agreement in 57.2% of the cases (moderate inter-rater agreement). Compared with anesthesiologist rating, ML-PS assigned more patients into extreme ASA-PS (I and IV), (p < 0.01), and less patients in ASA II and III (p < 0.01). ML-PS and anesthesiologist ASA-PS had excellent predictive values for 30-day mortality, and good predictive values for postoperative ICU admission and adverse discharge. Among the 3594 patients who died within 30 days after surgery, net reclassification improvement analysis revealed that using the ML-PS, 1281 (35.6%) patients were reclassified into the higher clinical risk category compared with anesthesiologist rating. However, in a subgroup of multiple co-morbidity patients, anesthesiologist ASA-PS had a better predictive accuracy than ML-PS.

CONCLUSIONS

We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery.

Mortality following emergency laparotomy: a Swedish cohort study

Background

Emergency laparotomy (EL) is a central, high-risk procedure in emergency surgery. Patients in need of an EL present an acute pathology in the abdomen that must be operated on in order to save their lives. Usually, the underlying condition produces an affected physiology. The perioperative management of this critically ill patient group in need of high-risk surgery and anaesthesia is challenging and related to high mortality worldwide. However, outcomes in Sweden have yet to be studied. This retrospective cohort study explores the perioperative management and outcome after 710 ELs by investigating mortality, overall length of stay (LOS) in hospital, need for care at the intensive care unit (ICU), surgical complications and a general review of perioperative management.

Methods

Medical records after laparotomy was retrospectively analysed for a period of 38 months (2014–2017), the emergency cases were included. Children (< 18 years), aortic surgery, second look and other expected reoperations were excluded. Demographic, management and outcome data were collected after an extensive analysis of the cohort.

Results

A total of 710 consecutive operations, representing 663 patients, were included in the cohort (mean age 65.6 years). Mortality (30 days/1 year) after all operations was 14.2% and 26.6% respectively. The mean LOS in hospital was 12 days, while LOS in the ICU was five days. Of all operations, 23.8% patients were admitted at any time to the ICU postoperatively and the 30-day mortality seen among ICU patients was 37.9%. Mortality was strongly correlated to existing comorbidity, high ASA classification, ICU care and faecal peritonitis. The mean/median time from notification to operate until the first incision was 3:46/3:02 h and 87% of patients had their first incision within 6 h of notification.

Conclusions

In this present Swedish study, high mortality and morbidity were observed after emergency laparotomy, which is in agreement with other recent studies.

Trial registration: The study has been registered with ClinicalTrials.gov (NCT03549624, registered 8 June 2018).