The surgical apgar score predicts postoperative ICU admission

Implementation of the Surgical Apgar Score in Laboratory Animal Science: A Showcase Pilot Study in a Porcine Model and a Review of the Literature

INTRODUCTION

In an attempt to further improve surgical outcomes, a variety of outcome prediction and risk-assessment tools have been developed for the clinical setting. Risk scores such as the surgical Apgar score (SAS) hold promise to facilitate the objective assessment of perioperative risk related to comorbidities of the patients or the individual characteristics of the surgical procedure itself. Despite the large number of scoring models in clinical surgery, only very few of these models have ever been utilized in the setting of laboratory animal science. The SAS has been validated in various clinical surgical procedures and shown to be strongly associated with postoperative morbidity. In the present study, we aimed to review the clinical evidence supporting the use of the SAS system and performed a showcase pilot trial in a large animal model as the first implementation of a porcine-adapted SAS (pSAS) in an in vivo laboratory animal science setting.

METHODS

A literature review was performed in the PubMed and Embase databases. Study characteristics and results using the SAS were reported. For the in vivo study, 21 female German landrace pigs have been used either to study bleeding analogy (n = 9) or to apply pSAS after abdominal surgery in a kidney transplant model (n = 12). The SAS was calculated using 3 criteria: (1) estimated blood loss during surgery; (2) lowest mean arterial blood pressure; and (3) lowest heart rate.

RESULTS

The SAS has been verified to be an effective tool in numerous clinical studies of abdominal surgery, regardless of specialization confirming independence on the type of surgical field or the choice of surgery. Thresholds for blood loss assessment were species specifically adjusted to >700 mL = score 0; 700-400 mL = score 1; 400-55 mL score 2; and <55 mL = score 3 resulting in a species-specific pSAS for a more precise classification.

CONCLUSION

Our literature review demonstrates the feasibility and excellent performance of the SAS in various clinical settings. Within this pilot study, we could demonstrate the usefulness of the modified SAS (pSAS) in a porcine kidney transplantation model. The SAS has a potential to facilitate early veterinary intervention and drive the perioperative care in large animal models exemplified in a case study using pigs. Further larger studies are warranted to validate our findings.

The Surgical Apgar Score: A Systematic Review of Its Discriminatory Performance

To review the current literature evaluating the performance of the Surgical Apgar Score (SAS).

Background

The SAS is a simple metric calculated at the end of surgery that provides clinicians with information about a patient's postoperative risk of morbidity and mortality. The SAS differs from other prognostic models in that it is calculated from intraoperative rather than preoperative parameters. The SAS was originally derived and validated in a general and vascular surgery population. Since its inception, it has been evaluated in many other surgical disciplines, large heterogeneous surgical populations, and various countries.

Methods

A database and gray literature search was performed on March 3, 2020. Identified articles were reviewed for applicability and study quality with prespecified inclusion criteria, exclusion criteria, and quality requirements. Thirty-six observational studies are included for review. Data were systematically extracted and tabulated independently and in duplicate by two investigators with differences resolved by consensus.

Results

All 36 included studies reported metrics of discrimination. When using the SAS to correctly identify postoperative morbidity, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.59 in a general orthopedic surgery population to 0.872 in an orthopedic spine surgery population. When using the SAS to identify mortality, the area under the receiver operating characteristic curve or concordance-statistic ranged from 0.63 in a combined surgical population to 0.92 in a general and vascular surgery population.

Conclusions

The SAS provides a moderate and consistent degree of discrimination for postoperative morbidity and mortality across multiple surgical disciplines.

The utility of surgical Apgar score in predicting postoperative morbidity and mortality in general surgery

Objectives

Many surgical scoring systems are used to predict operative risk but most are complicated. The aim of the study was to determine the utility of the Surgical Apgar Score (SAS) in predicting post operative mortality and morbidity in general surgical cases.

Material and Methods

This was a prospective observational study. All adult patients for emergency and elective general surgical procedures were included. Intraoperative data was collected, and post operative outcomes were followed up till 30 days. SAS was calculated from intraoperative lowest heart rate, lowest MAP and blood loss.

Results

A total of 220 patients were included in the study. All consecutive general surgical procedures were included. Sixty of the 220 cases were emergency and the rest were elective. Forty-five (20.5%) of the patients developed complication. Mortality rate was 3.2% (7 out of 220). The cases were divided into high risk (0-4), moderate risk (5-8) and low risk (9-10) based on SAS. Complication and mortality rates were 50% and 8.3% in the high risk group, 23% and 3.7% in the moderate risk and 4.2% and 0 in the low risk group, respectively.

Conclusion

The surgical Apgar score is a simple and valid predictor of postoperative morbidity and 30-day mortality among patients undergoing general surgeries. It is applicable to all types of surgeries for emergency and elective cases and irrespective of the patient general condition and type of anesthesia and surgery planned.

Predicting Unplanned Intensive Care Unit Admission for Trauma Patients: The CRASH Score

INTRODUCTION

Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission.

METHODS

The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve.

RESULTS

A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78.

CONCLUSIONS

The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation.

Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review

OBJECTIVE

Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions.

SUMMARY BACKGROUND DATA

Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (ie, resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms.

METHODS

Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines.

RESULTS

Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriche these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to intensive care units (ICUs) has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions.

CONCLUSION

To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

Validation of the Emergency Surgery Score (ESS) in a Greek patient population: a prospective bi-institutional cohort study

Purpose

The Emergency Surgery Score (ESS) is a reliable point-based score that predicts mortality and morbidity in emergency surgery patients. However, it has been validated only in the U.S. patients. We aimed to prospectively validate ESS in a Greek patient population.

Methods

All patients who underwent an emergent laparotomy were prospectively included over a 15-month period. A systematic chart review was performed to collect relevant preoperative, intraoperative, and postoperative variables based on which the ESS was calculated for each patient. The relationship between ESS and 30-day mortality, morbidity (i.e., the occurrence of at least one complication), and the need for intensive care unit (ICU) admission was evaluated and compared between the Greek and U.S. patients using the c-statistics methodology. The study was registered on "Research Registry" with the unique identifying number 5901.

Results

A total of 214 patients (102 Greek) were included. The mean age was 64 years, 44% were female, and the median ESS was 7. The most common indication for surgery was hollow viscus perforation (25%). The ESS reliably and incrementally predicted mortality (c-statistics = 0.79 [95% CI 0.67–0.90] and 0.83 [95% CI 0.74–0.92]), morbidity (c-statistics = 0.83 [95% CI 0.76–0.91] and 0.79 [95% CI 0.69–0.88]), and ICU admission (c-statistics = 0.88 [95% CI 0.81–0.96] and 0.84 [95% CI 0.77–0.91]) in both Greek and U.S. patients.

Conclusion

The correlation between the ESS and the surgical outcomes was statistically significant in both Greek and U.S. patients undergoing emergency laparotomy. ESS could prove globally useful for preoperative patient counseling and quality-of-care benchmarking.

Surgical Apgar Score predictive value for early postoperative organ dysfunction in cancer patients

BACKGROUND

Surgical APGAR Score (SAS) is based only on intraoperative data and has the advantage of being easy to calculate. Low SAS was associated with an increased risk for postoperative complications, but its utility for specific outcomes prediction, such as postoperative cardiovascular, renal, or metabolic dysfunction is less investigated. Our study aimed to investigate SAS predictive value for early postoperative organ dysfunction in a surgical oncological population.

METHODS

This is a prospective observational study that enrolled all consecutive patients submitted to oncologic surgery over 20-days. Registered parameters included demographics, comorbidities, diagnosis and surgery data, SAS score, postoperative complications, organ dysfunction and in-hospital mortality. SAS predictive value for postoperative organ dysfunction was assessed using logistic regression and ROC curves.

RESULTS

The study included 205 oncological patients with a mean age (standard deviation) of 60 (12.8) years. SAS was between 8 and 10 in 60% of patients and between 0 and 7 in 40% of patients. Postoperative complications developed in 33 patients (16.1%) and organ dysfunction in 26 patients (12.7%). The rates of postoperative complications, organ dysfunction and mortality, were significantly higher in patients with a low SAS (0-7) than high SAS (8-10). SAS had a low discrimination capacity to distinguish between patients who will develop postoperative complications and those who will not (AUROC 0.65) but was more accurate in identifying surgical oncological patients at risk for cardiovascular and metabolic dysfunction (AUROC 0.83 and 0.85 respectively).

CONCLUSION

SAS may be a useful tool to identify cancer surgery patients at risk for postoperative cardiovascular and metabolic dysfunction.

Surgical Apgar score is strongly associated with postoperative ICU admission

Immediate postoperative intensive care unit (ICU) admission can increase the survival rate in patients undergoing high-risk surgeries. Nevertheless, less than 15% of such patients are immediately admitted to the ICU due to no reliable criteria for admission. The surgical Apgar score (SAS) (0–10) can be used to predict postoperative complications, mortality rates, and ICU admission after high-risk intra-abdominal surgery. Our study was performed to determine the relationship between the SAS and postoperative ICU transfer after all surgeries. All patients undergoing operative anesthesia were retrospectively enrolled. Among 13,139 patients, 68.4% and < 9% of whom had a SASs of 7–10 and 0–4. Patients transferred to the ICU immediately after surgery was 7.8%. Age, sex, American Society of Anesthesiologists (ASA) class, emergency surgery, and the SAS were associated with ICU admission. The odds ratios for ICU admission in patients with SASs of 0–2, 3–4, and 5–6 were 5.2, 2.26, and 1.73, respectively (P < 0.001). In general, a higher ASA classification and a lower SAS were associated with higher rates of postoperative ICU admission after all surgeries. Although the SAS is calculated intraoperatively, it is a powerful tool for clinical decision-making regarding the immediate postoperative ICU transfer.

Can the emergency surgery score (ESS) be used as a triage tool predicting the postoperative need for an ICU admission?

BACKGROUND

The emergency surgery score (ESS) is a preoperative risk calculator recently validated as a mortality predictor in emergency surgery (ES) patients. We sought to evaluate the utility of ESS as an ICU admission triage tool.

METHODS

A four-step methodology was designed. First, the 2007-2015 ACS-NSQIP database was examined to identify all ES patients using the "emergent" variable and CPT codes for "digestive system". Second, we created a composite variable called ICUneed, defined as death or the development of one or more postoperative complication warranting critical care (e.g. unplanned intubation, ventilator dependent ≥48 h, cardiac arrest, septic shock and coma ≥24 h). Third, for each patient, ESS was calculated. Fourth, the correlation between ESS and ICUneed was assessed by calculating the model c-statistics (AUROC).

RESULTS

Out of a total of 4,456,809 patients, 65,989 patients were included. The mean population age was 56 years; 51% were female, and 71% were white. The overall 30-day postoperative mortality and morbidity were 8.2% and 31.7%, respectively. ESS gradually and accurately predicted ICUneed, with 1%, 40% and 98% of patients with ESS of 2, 9 and 16 requiring critical care, respectively. Only 6.2% of patients with ESS ≤7 had an ICUneed, while 97.2% of patients with ESS ≥15 had an ICUneed. The c-statistic of the predictive model was 0.90.

CONCLUSIONS

ESS accurately predicts the need for postoperative critical care and ICU admission. In resource-limited settings, ESS may prove useful as an ICU triage tool ensuring a prompt rescue of the clinically deteriorating patient without unnecessary and burdensome ICU admissions.

Validation of the Surgical Apgar Score After Laparotomy at a Tertiary Referral Hospital in Rwanda

BACKGROUND

The surgical Apgar score (SAS) has demonstrated utility in predicting postoperative outcomes in a variety of surgical disciplines. However, there has not been a study validating the utility of the SAS in surgical patients in low-income countries. We conducted a prospective, observational study of patients undergoing laparotomy at a tertiary referral hospital in Rwanda and determined the ability of SAS to predict inpatient major complications and mortality.

METHODS

All adult patients undergoing laparotomy in a tertiary referral hospital in Rwanda from October 2014 to January 2015 were included. Data were collected on patient and operative characteristics. SAS was calculated and patients were divided into four SAS categories. Primary outcomes were in-hospital mortality and major complications. Rates and odds of in-hospital mortality and major complications were examined across the four SAS categories. Logistic regression modeling and calculation of c-statistics was used to determine the discriminative ability of SAS.

RESULTS

218 patients underwent laparotomy during the study period. One hundred and forty-three (65.6%) were male, and the median age was 34 years (IQR 27-51 years). The most common diagnosis was intestinal obstruction (97 [44.5%]). A high proportion of patients (170 [78%]) underwent emergency surgery. Thirty-nine (18.3%) patients died, and 61 (28.6%) patients had a major complication. In-hospital mortality occurred in 25 (50%) patients in the high-risk group, 12 (16%) in the moderate-risk group, 2 (3%) in the mild-risk group and there were no deaths in the low-risk group. Major complications occurred in 32 (64%) patients in the high-risk group, 22 (29%) in the moderate-risk group, 7 (11%) in the mild-risk group and there were no complications in the low-risk group. SAS was a good predictor of postoperative mortality (c-statistic 0.79) and major complications (c-statistic 0.75).

CONCLUSIONS

SAS can be used to predict in-hospital mortality and major complications after laparotomy in a Rwandan tertiary referral hospital.