The role of the American Society of anesthesiologists physical status classification in predicting trauma mortality and outcomes

Standard spontaneous breathing trial parameters may not predict unplanned reintubation for trauma patients

BACKGROUND

The applicability of spontaneous breathing trial (SBT) factors such as negative inspiratory force (NIF) and rapid shallow breathing index (RSBI) as predictors of reintubation in trauma patients (TPs) is unclear. This study aimed to identify predictors of unplanned reintubation (UR) in TPs.

METHODS

A single center, retrospective (1/2017-12/2023) study of TPs ≥18 years-old extubated from endotracheal mechanical ventilation was performed. Patients with UR during admission were compared to patients without UR. A multivariable logistic regression was performed to identify risk factors associated with UR.

RESULTS

39 of 424 ​TPs (9.2 ​%) had UR. UR patients were older (median: 55 vs 39 years-old, p ​= ​0.012) and more often had congestive heart failure (10.3 ​% vs 1.6 ​%, p ​< ​0.001), cirrhosis (7.7 ​% vs 1.9 ​%, p ​= ​0.025), end stage renal disease (7.7 ​% vs 1.6 ​%, p ​= ​0.044), and a higher injury severity scores (ISS) (median: 27 vs 18, p ​< ​0.001). UR patients had increased ventilator days (median: 6 vs 2, p ​< ​0.001) prior to extubation, whereas RSBI and NIF were similar (median: 36 vs 32, p ​= ​0.508) and (median: -24.0 vs -27.0 ​cm ​H2O, p ​= ​0.190). On multivariable analysis, RSBI <50 or <105 and NIF ​< ​-20 were not associated with UR. Age (OR 1.03, CI 1.01-1.05, p ​= ​0.006) and ISS (OR 1.04, CI 1.01-1.08, p ​= ​0.022) were independently associated with increased risk of UR.

CONCLUSIONS

SBT parameters (RSBI and NIF) were not associated with UR. Age and ISS were independently associated with UR. This suggests additional patient-specific factors should help guide extubation decisions for TPs.

ASA score is an independent predictor of 1-year outcome after moderate-to-severe traumatic brain injury

PURPOSE

This study aimed to investigate whether incorporating pre-injury health status, measured by the American Society of Anesthesiologists (ASA) score, improves outcome prediction models for moderate-to-severe traumatic brain injury (msTBI) patients.

METHODS

We conducted a retrospective single-center study of msTBI patients (2005-2021). The primary outcome was 1-year Glasgow Outcome Scale (GOS, dichotomized as GOS1-3 (unfavorable) vs. 4-5 (favorable)), and secondary outcome was 90-day mortality. Logistic regression evaluated the contribution of ASA score to the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) core + CT outcome prediction model incorporating age, admission GCS, pupillary reactivity, Marshall CT classification, hypoxia, hypotension, epidural hematoma, and subarachnoid hemorrhage.

RESULTS

Among the 720 adult patients that were included 51% had an unfavorable GOS at 1 year. The 90-day mortality was 19%. ASA score and TRISS were independently associated with both outcomes (p < 0.001). Incorporating the ASA score to our IMPACT model significantly enhanced its explanatory value of dichotomized GOS (35% vs. 32% variance explained, p < 0.001) and improved the model's prognostic accuracy.

CONCLUSION

In this retrospective single-center cohort study, we found that ASA score improves existing prognostic models for msTBI. Incorporating this simple comorbidity measure could enhance outcome prediction and support more personalized acute management. Future prospective studies are needed to validate these results.

Lack of Concordance Between Abbreviated Injury Scale and American Association for the Surgery of Trauma Organ Injury Scale in Patients with High-Grade Solid Organ Injury

BACKGROUND

The Abbreviated Injury Scale (AIS) is widely used for body region-specific injury severity. The American Association for the Surgery of Trauma Organ Injury Scale (AAST-OIS) provides organ-specific injury severity but is not included in trauma databases. Previous researchers have used AIS as a surrogate for OIS. This study aims to assess AIS-abdomen concordance with AAST-OIS grade for liver and spleen injuries, hypothesizing concordance in terms of severity (grade of OIS and AIS) and patient outcomes.

STUDY DESIGN

This retrospective study (July 2020 to June 2022) was performed at 3 trauma centers. Adult trauma patients with AAST-OIS grade III to V liver and spleen injury were included. AAST-OIS grade for each organ was compared with AIS-abdomen by evaluating the percentage of AAST-OIS grade correlating with each AIS score as well as rates of operative intervention for these injuries. Analysis was performed with chi-square tests and univariate analysis.

RESULTS

Of 472 patients, 274 had liver injuries and 205 had spleen injuries grades III to V. AAST-OIS grade III to V liver injuries had concordances rates of 85.5%, 71%, and 90.9% with corresponding AIS 3 to 5 scores. AAST-OIS grade III to V spleen injuries had concordances rates of 89.7%, 87.8%, and 87.3%, respectively. There was a statistical lack of concordance for both liver and spleen injuries (both p < 0.001). Additionally, there were higher rates of operative intervention for AAST-OIS grade IV and V liver injuries and grade III and V spleen injuries vs corresponding AIS scores (p < 0.05).

CONCLUSIONS

AIS should not be used interchangeably with OIS due to lack of concordance. AAST-OIS should be included in trauma databases to facilitate improved organ injury research and quality improvement projects.

Immediate Operative Trauma Assessment Score: A Simple and Reliable Predictor of Mortality in Trauma Patients Undergoing Urgent/Emergent Surgery

Objective: Many current trauma mortality prediction tools are either too intricate or rely on data not readily available during a trauma patient's initial evaluation. Moreover, none are tailored to those necessitating urgent or emergent surgery. Our objective was to design a practical, user-friendly scoring tool using immediately available variables, and then compare its efficacy to the widely-known Revised Trauma Score (RTS). Methods: The adult 2017-2021 Trauma Quality Improvement Program (TQIP) database was queried to identify patients ≥18 years old undergoing any urgent/emergent operation (direct from Emergency Department to operating room). Patients were divided into derivation and validation groups. A three-step methodology was used. First, multiple logistic regression models were created to determine risk of death using only variables available upon arrival. Second, the weighted average and relative impact of each independent predictor was used to derive an easily calculated Immediate Operative Trauma Assessment Score (IOTAS). We then validated IOTAS using AUROC and compared it to RTS. Results: From 249 208 patients in the derivation-set, 14 635 (5.9%) died. Age ≥65, Glasgow Coma Scale score <9, hypotension (SBP <90 mmHg), and tachycardia (>120/min) on arrival were identified as independent predictors for mortality. Using these, the IOTAS was structured, offering scores between 0-8. The AUROC for this was .88. A clear escalation in mortality was observed across scores: from 4.4% at score 1 to 60.5% at score 8. For the validation set (250 182 patients; mortality rate 5.8%), the AUROC remained consistent at .87, surpassing RTS's AUROC of .83. Conclusion: IOTAS is a novel, accurate, and now validated tool that is intuitive and efficient in predicting mortality for trauma patients requiring urgent or emergent surgeries. It outperforms RTS, and thereby may help guide clinicians when determining the best course of action in patient management as well as counseling patients and their families.

ASA-score is associated with 90-day mortality after complicated mild traumatic brain injury - a retrospective cohort study

PURPOSE

This study explores the association of the American Society of Anesthesiologists (ASA) score with 90-day mortality in complicated mild traumatic brain injury (mTBI) patients, and in trauma patients without a TBI.

METHODS

This retrospective study was conducted using a cohort of trauma patients treated at a level III trauma center in Stockholm, Sweden from January to December 2019. The primary endpoint was 90-day mortality. The population was identified using the Swedish Trauma registry. The Trauma and Injury Severity Score (TRISS) was used to estimate the likelihood of survival. Trauma patients without TBI (NTBI) were used for comparison. Data analysis was conducted using R software, and statistical analysis included univariate and multivariate logistic regression.

RESULTS

A total of 244 TBI patients and 579 NTBI patients were included, with a 90-day mortality of 8.2% (n = 20) and 5.4% (n = 21), respectively. Deceased patients in both cohorts were generally older, with greater comorbidities and higher injury severity. Complicated mTBI constituted 97.5% of the TBI group. Age and an ASA score of 3 or higher were independently associated with increased mortality risk in the TBI group, with odds ratios of 1.04 (95% 1.00-1.09) and 3.44 (95% CI 1.10-13.41), respectively. Among NTBI patients, only age remained a significant mortality predictor. TRISS demonstrated limited predictive utility across both cohorts, yet a significant discrepancy was observed between the outcome groups within the NTBI cohort.

CONCLUSION

This retrospective cohort study highlights a significant association between ASA score and 90-day mortality in elderly patients with complicated mTBI, something that could not be observed in comparative NTBI cohort. These findings suggest the benefit of incorporating ASA score into prognostic models to enhance the accuracy of outcome prediction models in these populations, though further research is warranted.

A Prospective Multicenter Comparison of Trauma and Injury Severity Score, American Society of Anesthesiologists Physical Status, and National Surgical Quality Improvement Program Calculator's Ability to Predict Operative Trauma Outcomes

BACKGROUND

Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours.

METHODS

A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications.

RESULTS

From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS ( P = .082) and TRISS+NSQIP-SRC ( P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS.

CONCLUSIONS

TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.

Perioperative mortality among trauma patients in Northwest Ethiopia: a prospective cohort study

Trauma is the leading cause of mortality in persons under 45 and a significant public health issue. Trauma is the most frequent cause of perioperative mortality among all surgical patients. Little is known about perioperative outcomes among trauma patients in low-income countries. This study aimed to assess the incidence and identify predictors of perioperative mortality among adult trauma victims at Tibebe Ghion Specialised Hospital. From June 1, 2019, to June 30, 2021, a prospective cohort study was conducted at Tibebe Ghion Specialized Hospital. Demographic, pre-hospital and perioperative clinical data were collected using an electronic data collection tool, Research Electronic Data Capture (REDCap). Cox proportional hazard model regression was used to assess the association between predictors and perioperative mortality among trauma victims. Crude and adjusted hazard ratio (HR) with a 95% confidence interval (CI) was computed; a p-value < 0.05 was a cutoff value to declare statistical significance. One thousand sixty-nine trauma patients were enrolled in this study. The overall incidence of perioperative mortality among trauma patients was 5.89%, with an incidence rate of 2.23 (95% CI 1.74 to 2.86) deaths per 1000 person-day observation. Age ≥ 65 years (AHR = 2.51, 95% CI: 1.04, 6.08), patients sustained blunt trauma (AHR = 3.28, 95% CI: 1.30, 8.29) and MVA (AHR = 2.96, 95% CI: 1.18, 7.43), trauma occurred at night time (AHR = 2.29, 95% CI: 1.15, 4.56), ASA physical status ≥ III (AHR = 3.84, 95% CI: 1.88, 7.82), and blood transfusion (AHR = 2.01, 95% CI: 1.08, 3.74) were identified as a significant predictor for perioperative mortality among trauma patients. In this trauma cohort, it was demonstrated that perioperative mortality is a healthcare burden. Risk factors for perioperative mortality among trauma patients were old age, patients sustaining blunt trauma and motor vehicle accidents, injuries at night, higher ASA physical status, and blood transfusion. Trauma care services need improvement in pre-hospital and perioperative care.

Which curve is better? A comparative analysis of trauma scoring systems in a South Asian country

Objectives

A diverse set of trauma scoring systems are used globally to predict outcomes and benchmark trauma systems. There is a significant potential benefit of using these scores in low and middle-income countries (LMICs); however, its standardized use based on type of injury is still limited. Our objective is to compare trauma scoring systems between neurotrauma and polytrauma patients to identify the better predictor of mortality in low-resource settings.

Methods

Data were extracted from a digital, multicenter trauma registry implemented in South Asia for a secondary analysis. Adult patients (≥18 years) presenting with a traumatic injury from December 2021 to December 2022 were included in this study. Injury Severity Score (ISS), Trauma and Injury Severity Score (TRISS), Revised Trauma Score (RTS), Mechanism/GCS/Age/Pressure score and GCS/Age/Pressure score were calculated for each patient to predict in-hospital mortality. We used receiver operating characteristic curves to derive sensitivity, specificity and area under the curve (AUC) for each score, including Glasgow Coma Scale (GCS).

Results

The mean age of 2007 patients included in this study was 41.2±17.8 years, with 49.1% patients presenting with neurotrauma. The overall in-hospital mortality rate was 17.2%. GCS and RTS proved to be the best predictors of in-hospital mortality for neurotrauma (AUC: 0.885 and 0.874, respectively), while TRISS and ISS were better predictors for polytrauma patients (AUC: 0.729 and 0.722, respectively).

Conclusion

Trauma scoring systems show differing predictability for in-hospital mortality depending on the type of trauma. Therefore, it is vital to take into account the region of body injury for provision of quality trauma care. Furthermore, context-specific and injury-specific use of these scores in LMICs can enable strengthening of their trauma systems.

Level of evidence

Level III.

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator and Trauma and Injury Severity Score Risk Assessment Tools in Predicting Outcomes in High-Risk Operative Trauma Patients

BACKGROUND

The Trauma and Injury Severity Score (TRISS) uses anatomic/physiologic variables to predict outcomes. The National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) includes functional status and comorbidities. It is unclear which of these tools is superior for high-risk trauma patients (American Society of Anesthesiologists Physical Status (ASA-PS) class IV or V). This study compares risk prediction of TRISS and NSQIP-SRC for mortality, length of stay (LOS), and complications for high-risk operative trauma patients.

METHODS

This is a prospective study of high-risk (ASA-PS IV or V) trauma patients (≥18 years-old) undergoing surgery at 4 trauma centers. We compared TRISS vs NSQIP-SRC vs NSQIP-SRC + TRISS for ability to predict mortality, LOS, and complications using linear, logistic, and negative binomial regression.

RESULTS

Of 284 patients, 48 (16.9%) died. The median LOS was 16 days and number of complications was 1. TRISS + NSQIP-SRC best predicted mortality (AUROC: .877 vs .723 vs .843, P = .0018) and number of complications (pseudo-R2/median error (ME) 5.26%/1.15 vs 3.39%/1.33 vs 2.07%/1.41, P < .001) compared to NSQIP-SRC or TRISS, but there was no difference between TRISS + NSQIP-SRC and NSQIP-SRC with LOS prediction (P = .43).

DISCUSSION

For high-risk operative trauma patients, TRISS + NSQIP-SRC performed better at predicting mortality and number of complications compared to NSQIP-SRC or TRISS alone but similar to NSQIP-SRC alone for LOS. Thus, future risk prediction and comparisons across trauma centers for high-risk operative trauma patients should include a combination of anatomic/physiologic data, comorbidities, and functional status.

What Factors Are Associated With Delayed Wound Closure in Open Reduction and Internal Fixation of Adult Both-bone Forearm Fractures?

BACKGROUND

Delayed wound closure is often used after open reduction and internal fixation (ORIF) of both-bone forearm fractures to reduce the risk of skin necrosis and subsequent infection caused by excessive swelling. However, no studies we are aware of have evaluated factors associated with the use of delayed wound closure after ORIF.

QUESTIONS/PURPOSES

(1) What proportion of patients undergo delayed wound closure after ORIF of adult both-bone forearm fractures? (2) What factors are associated with delayed wound closure?

METHODS

The medical records of all patients who underwent ORIF with plate fixation for both-bone fractures by the adult orthopaedic trauma service at our institution were considered potentially eligible for analysis. Between January 2010 and April 2022, we treated 74 patients with ORIF for both-bone forearm fractures. Patients were excluded if they had fractures that were fixed more than 2 weeks from injury (six patients), if their fracture was treated with an intramedullary nail (one patient), or if the patient experienced compartment syndrome preoperatively (one patient). No patients with Gustilo-Anderson Type IIIB and C open fractures were included. Based on these criteria, 89% (66 of 74) of the patients were eligible. No further patients were excluded for loss of follow-up because the primary endpoint was the use of delayed wound closure, which was performed at the time of ORIF. However, one further patient was excluded for having bilateral forearm fractures to ensure that each patient had a single fracture for statistical analysis. Thus, 88% (65 of 74) of patients were included in the analysis. These patients were captured by an electronic medical record search of CPT code 25575. The mean ± SD age was 34 ± 15 years and mean BMI was 28 ± 7 kg/m 2 . The mean follow-up duration was 4 ± 5 months. The primary endpoint was the use of delayed wound closure, which was determined at the time of definitive fixation if tension-free closure could not be achieved. All surgeons used a volar Henry or modified Henry approach and a dorsal subcutaneous approach to the ulna for ORIF. Univariate logistic regression was used to identify which factors might be associated with delayed wound closure. A multivariable logistic regression analysis was then performed for male gender, open fractures, age, and BMI.

RESULTS

Twenty percent (13 of 65) of patients underwent delayed wound closure, 18% (12 of 65) of which occurred in patients who had high-energy injuries and 14% (nine of 65) in patients who had open fractures. Being a man (adjusted odds ratio 9.9 [95% confidence interval 1 to 87]; p = 0.04) was independently associated with delayed wound closure, after adjusting for open fractures, age, and BMI.

CONCLUSION

One of five patients had delayed wound closure after ORIF of both-bone forearm fractures. Being a man was independently associated with greater odds of delayed wound closure. Surgeons should counsel all patients with these fractures about the possibility of delayed wound closure, with particular attention to men with high-energy and open fractures. Future larger-scale studies are necessary to confirm which factors are associated with the use of delayed wound closure in ORIF of both-bone fractures and its effects on fracture healing.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Long-term health-related quality of life after trauma with and without traumatic brain injury: a prospective cohort study

To purpose was to assess and compare the health-related quality of life (HRQoL) and risk of depression two years after trauma, between patients with and without traumatic brain injury (TBI) in a mixed Swedish trauma cohort. In this prospective cohort study, TBI and non-TBI trauma patients included in the Swedish Trauma registry 2019 at a level II trauma center in Stockholm, Sweden, were contacted two years after admission. HRQoL was assessed with RAND-36 and EQ-5D-3L, and depression with Montgomery Åsberg depression Rating Scale self-report (MADRS-S). Abbreviated Injury Score (AIS) head was used to grade TBI severity, and American Society of Anesthesiologists (ASA) score was used to assess comorbidities. Data were compared using Chi-squared test, Mann Whitney U test and ordered logistic regression, and Bonferroni correction was applied. A total of 170 of 737 eligible patients were included. TBI was associated with higher scores in 5/8 domains of RAND-36 and 3/5 domains of EQ-5D (p < 0.05). No significant difference in MADRS-S. An AIS (head) of three or higher was associated with lower scores in five domains of RAND-36 and two domains of EQ-5D but not for MADRS-S. An ASA-score of three was associated with lower scores in all domains of both RAND-36 (p < 0.05, except mental health) and EQ-5D (p < 0.001, except anxiety/depression), but not for MADRS-S. In conclusion, patients without TBI reported a lower HRQoL than TBI patients two years after trauma. TBI severity assessed according to AIS (head) was associated with HRQoL, and ASA-score was found to be a predictor of HRQoL, emphasizing the importance of considering pre-injury health status when assessing outcomes in TBI patients.

Predicting mortality in elderly trauma patients: a review of the current literature

PURPOSE OF REVIEW

Advances in medical care allow patients to live longer, translating into a larger geriatric patient population. Adverse outcomes increase with older age, regardless of injury severity. Age, comorbidities, and physiologic deterioration have been associated with the increased mortality seen in geriatric trauma patients. As such, outcome prediction models are critical to guide clinical decision making and goals of care discussions for this population. The purpose of this review was to evaluate the various outcome prediction models for geriatric trauma patients.

RECENT FINDINGS

There are several prediction models used for predicting mortality in elderly trauma patients. The Geriatric Trauma Outcome Score (GTOS) is a validated and accurate predictor of mortality in geriatric trauma patients and performs equally if not better to traditional scores such as the Trauma and Injury Severity Score. However, studies recommend medical comorbidities be included in outcome prediction models for geriatric patients to further improve performance.

SUMMARY

The ideal outcome prediction model for geriatric trauma patients has not been identified. The GTOS demonstrates accurate predictive ability in elderly trauma patients. The addition of medical comorbidities as a variable in outcome prediction tools may result in superior performance; however, additional research is warranted.

Blunt Trauma Mortality: Does Trauma Center Level Matter?

INTRODUCTION

Trauma centers have improved outcomes compared to nontrauma centers when caring for injured patients. A multicenter report found blunt trauma patients treated at American College of Surgeons' Level I trauma centers have improved survival compared to Level II centers. In a subsequent multicenter study, Level II centers had improved survival in all trauma patients. We sought to provide a more granular analysis by stratifying blunt mechanisms-to determine if there was a difference in mortality between Level I and Level II centers.

METHODS

The Trauma Quality Improvement Program (2010-2016) was queried for patients presenting to an American College of Surgeons' Level I or II trauma center after blunt trauma. A multivariable logistic regression analysis was performed controlling for comorbidities and Trauma and Injury Severity Score.

RESULTS

From 734,473 patients with blunt trauma, 507,715 (69.1%) were treated at a Level I center and 226,758 (30.9%) at a Level II center. The Level I cohort was younger (median age, 53 versus 58, P = 0.01), with a higher median injury severity score (13 versus 10, P < 0.001) and with more patients presenting after a motor vehicle accident (MVA) (27.9% versus 22.4%, P < 0.001) and lower rates of falls (46.6% versus 54.5%, P < 0.001). After adjusting for covariates, there was no difference in mortality between Level I and Level II centers (P > 0.05). When stratifying by mechanisms, Level I centers had a decreased associated mortality for MVA (odds ratio = 0.94, CI: 0.88-0.99, P = 0.04) and bicycle accidents (odds ratio = 0.77, CI: 0.74-0.03, P = 0.01) but no difference in falls or pedestrians struck (P > 0.05).

CONCLUSIONS

Overall, blunt trauma patients presenting to a Level I center have no difference in mortality compared to a Level II center. However, when stratified by mechanism, those involved in MVA or bicycle accidents have a decreased associated risk of mortality. Future prospective studies examining variations in practice to account for these differences are warranted.

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator, Trauma and Injury Severity Score, and American Society of Anesthesiologists Physical Status to predict operative trauma mortality in elderly patients

BACKGROUND

The Trauma and Injury Severity Score (TRISS) uses anatomical and physiologic variables to predict mortality. Elderly (65 years or older) trauma patients have increased mortality and morbidity for a given TRISS, in part because of functional status and comorbidities. These factors are incorporated into the American Society of Anesthesiologists Physical Status (ASA-PS) and National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC). We hypothesized scoring tools using comorbidities and functional status to be superior at predicting mortality, hospital length of stay (LOS), and complications in elderly trauma patients undergoing operation.

METHODS

Four level I trauma centers prospectively collected data on elderly trauma patients undergoing surgery within 24 hours of admission. Using logistic regression, five scoring models were compared: ASA-PS, NSQIP-SRC, TRISS, TRISS-ASA-PS, and TRISS-NSQIP-SRC.Brier scores and area under the receiver operator characteristics curve were calculated to compare mortality prediction. Adjusted R2 and root mean squared error were used to compare LOS and predictive ability for number of complications.

RESULTS

From 122 subjects, 9 (7.4%) died, and the average LOS was 12.9 days (range, 1-110 days). National Surgical Quality Improvement Program Surgical Risk Calculator was superior to ASA-PS and TRISS at predicting mortality (area under the receiver operator characteristics curve, 0.978 vs. 0.768 vs. 0.903; p = 0.007). Furthermore, NSQIP-SRC was more accurate predicting LOS (R2, 25.9% vs. 13.3% vs. 20.5%) and complications (R2, 34.0% vs. 22.6% vs. 29.4%) compared with TRISS and ASA-PS. Adding TRISS to NSQIP-SRC improved predictive ability compared with NSQIP-SRC alone for complications (R2, 35.5% vs. 34.0%; p = 0.046). However, adding ASA-PS or TRISS to NSQIP-SRC did not improve the predictive ability for mortality or LOS.

CONCLUSION

The NSQIP-SRC, which includes comorbidities and functional status, had superior ability to predict mortality, LOS, and complications compared with TRISS alone in elderly trauma patients undergoing surgery.

LEVEL OF EVIDENCE

Prognostic and Epidemiologic; Level III.

The Evolution, Current Value, and Future of the American Society of Anesthesiologists Physical Status Classification System

The American Society of Anesthesiologists (ASA) Physical Status classification system celebrates its 80th anniversary in 2021. Its simplicity represents its greatest strength as well as a limitation in a world of comprehensive multisystem tools. It was developed for statistical purposes and not as a surgical risk predictor. However, since it correlates well with multiple outcomes, it is widely used-appropriately or not-for risk prediction and many other purposes. It is timely to review the history and development of the system. The authors describe the controversies surrounding the ASA Physical Status classification, including the problems of interrater reliability and its limitations as a risk predictor. Last, the authors reflect on the current status and potential future of the ASA Physical Status system.

Is Cleft Lip or Palate a Risk Factor for Perioperative Complications in Orthognathic Surgery?

PURPOSE

Perioperative outcomes following cleft orthognathic surgery are not well established. The purpose of this study was to compare the incidence of orthognathic specific complications (OSCs) in patients with and without cleft lip and/or palate.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program adult and pediatric databases were used to enroll patients undergoing orthognathic surgery. The primary predictor variable was a prior diagnosis of cleft lip and/or palate: cleft versus noncleft. The primary outcome variable was OSCs (yes/no) within 30 days of the index operation. Descriptive, bivariate, and multiple logistic regression statistics were computed to measure the association between cleft status and OSCs.

RESULTS

The study sample was composed of 1,149 subjects: 98 in the cleft group and 1,051 in the noncleft group. The incidence of OSCs was 6.1 and 4.7% for the cleft and noncleft groups, respectively (P = .461). After adjusting for age, cleft status, bone grafting, segmentation of the maxilla, and history of bleeding disorder, classification as American Society of Anesthesiologists (ASA) III (P = .002, odds ratio [OR] = 3.92, 95% confidence interval [CI] 1.63-9.40), ASA IV (P = .039, OR = 9.47, 95% CI 1.12-80.4), and isolated mandibular osteotomies (P = .006, OR = 3.23, 95% CI 1.40-7.48) were independent predictors of OSCs. Length of stay was 1.66 ± 1.14 days compared to 1.37 ± 3.74 days for the cleft and noncleft groups, respectively (P = .443).

CONCLUSIONS

There was no significant difference in the incidence of perioperative OSCs and length of hospital stay between cleft and noncleft patients. Cleft status was not an independent predictor of OSCs; instead, greater ASA classification and isolated mandibular osteotomies were the only predisposing factors. Patients with clefts undergoing orthognathic surgery do not have an increased risk of short-term OSCs within the limitations of this study.

Prehospital Variables Alone Can Predict Mortality After Blunt Trauma: A Novel Scoring Tool

BACKGROUND

We sought to develop a novel Prehospital Injury Mortality Score (PIMS) to predict blunt trauma mortality using only prehospital variables.

STUDY DESIGN

The 2017 Trauma Quality Improvement Program database was queried and divided into two equal sized sets at random (derivation and validation sets). Multiple logistic regression models were created to determine the risk of mortality using age, sex, mechanism, and trauma activation criterion. The PIMS was derived using the weighted average of each independent predictor. The discriminative power of the scoring tool was assessed by calculating the area under the receiver operating characteristics (AUROC) curve. The PIMS ability to predict mortality was then assessed by using the validation cohort. The score was compared to the Revised Trauma Score (RTS) using the AUROC curve, including a subgroup of patients with normal vital signs.

RESULTS

The derivation and validation groups each consisted of 163 694 patients. Seven independent predictors of mortality were identified, and the PIMS was derived with scores ranging from 0 to 20. The mortality rate increased from 1.4% to 43.9% and then 100% at scores of 1, 10, and 19, respectively. The model had very good discrimination with an AUROC of .79 in both the derivation and validation groups. When compared to the RTS, the AUROC were similar (.79 vs. .78). On subgroup analysis of patients with normal prehospital vital signs, the PIMS was superior to the RTS (.73 vs. .56).

CONCLUSION

The PIMS is a novel scoring tool to predict mortality in blunt trauma patients using prehospital variables. It had improved discriminatory power in blunt trauma patients with normal vital signs compared to the RTS.

Development and validation of a nomogram to predict anastomotic leakage after esophagectomy for esophageal carcinoma

Background

This study aimed to identify variables associated with anastomotic leakage after esophagectomy and established a tool for anastomotic leakage prediction.

Methods

Twenty-six preoperative and postoperative variables were retrospectively collected from esophageal cancer patients who were treated with radical esophagectomy from January 2018 to June 2020 in the Affiliated Hospital of Qingdao University. SPSS Version 23.0 and Empower Stats software were used for establishing a nomogram after screening relevant variables by univariate and multivariate Logistic regression analyses. The established nomogram was identified by depicting the receiver operating characteristic (ROC) curves and calibration curve, which was verified by 1,000 bootstrap resamples method.

Results

A total of 604 eligible esophageal cancer patients were included, of which 51 (8.4%) patients had anastomotic leakage. Multivariate Logistic regression analysis showed that smoking, anastomotic location, anastomotic technique, prognostic nutritional index (PNI) and ASA score were independent risks of anastomotic leakage. The area under curve (AUC) of ROC in the established nomogram was 0.764 (95% CI, 0.69–0.83). The internal validation confirmed that the nomogram had a great discrimination ability (AUC =0.766). Depicted calibration curve demonstrated a well-fitted prediction and observation probability. In addition, the decision curve analysis concluded that the newly established nomogram is significant for clinical decision-making.

Conclusions

This nomogram provided the individual prediction of anastomotic leakage for esophageal cancer patients after surgery, which might benefit treatment results for patients and clinicians, as well as pre-and postoperative intervention strategy-making.

Comparison of comorbidity indices for prediction of morbidity and mortality after major surgical procedures

BACKGROUND

Assessing perioperative risk is essential for surgical decision-making. Our study compares the accuracy of comorbidity indices to predict morbidity and mortality.

METHODS

Analyzing the National Surgical Quality Improvement Program, 16 major procedures were identified and American Society of Anesthesiologists (ASA), Charlson Comorbidity Index and modified Frailty Index were calculated. We fit models with each comorbidity index for prediction of morbidity, mortality, and prolonged length of stay (pLOS). Decision Curve Analysis determined the effectiveness of each model.

RESULTS

Of 650,437 patients, 11.7%, 6.0%, 17.0% and 0.75% experienced any, major complication, pLOS, and mortality, respectively. Each index was an independent predictor of morbidity, mortality, and pLOS (p < 0.05). While the indices performed similarly for morbidity and pLOS, ASA demonstrated greater net benefit for threshold probabilities of 1-5% for mortality.

CONCLUSIONS

Models including readily available factors (age, sex) already provide a robust estimation of perioperative morbidity and mortality, even without considering comorbidity indices. All comorbidity indices show similar accuracy for prediction of morbidity and pLOS, while ASA, the score easiest to calculate, performs best in prediction of mortality.

ASA Physical Status Classification Improves Predictive Ability of a Validated Trauma Risk Score

Introduction:

The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is a validated mortality risk score that evaluates 4 major physiologic criteria: age, comorbidities, vital signs, and anatomic injuries. The aim of this study was to investigate whether the addition of ASA physical status classification system to the STTGMA tool would improve risk stratification of a middle-aged and elderly trauma population.

Methods:

A total of 1332 patients aged 55 years and older who sustained a hip fracture through a low-energy mechanism between October 2014 and February 2020 were included. The STTGMA and STTGMAASA mortality risk scores were calculated. The ability of the models to predict inpatient mortality was compared using area under the receiver operating characteristic curves (AUROCs) by DeLong’s test. Patients were stratified into minimal, low, moderate, and high risk cohorts based on their risk scores. Comparative analyses between risk score stratification distribution of mortality, complications, length of stay, ICU admission, and readmission were performed using Fisher’s exact test. Total cost of admission was fitted by univariate linear regression with STTGMA and STTGMAASA.

Results:

There were 27 inpatient mortalities (2.0%). When STTGMA was used, the AUROC was 0.742. When STTGMAASA was used, the AUROC was 0.823. DeLong’s test resulted in significant difference in predictive capacity for inpatient mortality between STTGMA and STTGMAASA (p = 0.04). Risk score stratification yielded significantly different distribution of all outcomes between risk cohorts (p < 0.01). STTGMAASA stratification produced a larger percentage of all negative outcomes with increasing risk cohort. Total hospital cost was statistically correlated with both STTGMAASA (p < 0.01) and STTGMA (p = 0.02).

Conclusion:

Including ASA physical status as a variable in STTGMA improves the model’s ability to predict inpatient mortality and risk stratify middle-aged and geriatric hip fracture patients.