Development of the Combined Assessment of Risk Encountered in Surgery (CARES) surgical risk calculator for prediction of postsurgical mortality and need for intensive care unit admission risk: a single-center retrospective study

Postoperative intensive care allocation and mortality in high-risk surgical patients: evidence from a low- and middle-income country cohort

BACKGROUND

The escalation of surgeries for high-risk patients in Low- and Middle-Income Countries (LMICs) lacks evidence on the positive impact of Intensive Care Unit (ICU) admission and lacks universal criteria for allocation. This study explores the link between postoperative ICU allocation and mortality in high-risk patients within a LMIC. Additionally, it assesses the Ex-Care risk model's utility in guiding postoperative allocation decisions.

METHODS

A secondary analysis was conducted in a cohort of high-risk surgical patients from a 800-bed university-affiliated teaching hospital in Southern Brazil (July 2017 to January 2020). Inclusion criteria encompassed 1431 inpatients with Ex-Care Model-assessed all-cause postoperative 30-day mortality risk exceeding 5%. The study compared 30-day mortality outcomes between those allocated to the ICU and the Postanesthetic Care Unit (PACU). Outcomes were also assessed based on Ex-Care risk model classes.

RESULTS

Among 1431 high-risk patients, 250 (17.47%) were directed to the ICU, resulting in 28% in-hospital 30-day mortality, compared to 8.9% in the PACU. However, ICU allocation showed no independent effect on mortality (RR = 0.91; 95% CI 0.68‒1.20). Patients in the highest Ex-Care risk class (Class IV) exhibited a substantial association with mortality (RR = 2.11; 95% CI 1.54-2.90) and were more frequently admitted to the ICU (23.3% vs. 13.1%).

CONCLUSION

Patients in the highest Ex-Care risk class and those with complications faced elevated mortality risk, irrespective of allocation. Addressing the unmet need for adaptable postoperative care for high-risk patients outside the ICU is crucial in LMICs. Further research is essential to refine criteria and elucidate the utility of risk assessment tools like the Ex-Care model in assisting allocation decisions.

Combination of Creatinine with Inflammatory Biomarkers (PCT, CRP, hsCRP) for Predicting Postoperative ICU Admissions for Elderly Patients

INTRODUCTION

The number of elderly patients who require surgery as their primary treatment has increased rapidly in recent years. Among 300 million people globally who underwent surgery every year, patients aged 65 years and over accounted for more than 30% of cases. Despite medical advances, older patients remain at higher risk of postoperative complications. Early diagnosis and effective prediction are essential requirements for preventing serious postoperative complications. In this study, we aim to provide new biomarker combinations to predict the incidence of postoperative intensive care unit (ICU) admissions > 24 h in elderly patients.

METHODS

This investigation was conducted as a nested case-control study, incorporating 413 participants aged ≥ 65 years who underwent non-cardiac, non-urological elective surgeries. These individuals underwent a 30-day postoperative follow-up. Before surgery, peripheral venous blood was collected for analyzing serum creatinine (Scr), procalcitonin (PCT), C-reactive protein (CRP), and high-sensitivity CRP (hsCRP). The efficacy of these biomarkers in predicting postoperative complications was evaluated using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) values.

RESULTS

Postoperatively, 10 patients (2.42%) required ICU admission. Regarding ICU admissions, the AUCs with 95% confidence intervals (CIs) for the biomarker combinations of Scr × PCT and Scr × CRP were 0.750 (0.655-0.845, P = 0.007) and 0.724 (0.567-0.882, P = 0.015), respectively. Furthermore, cardiovascular events were observed in 14 patients (3.39%). The AUC with a 95% CI for the combination of Scr × CRP in predicting cardiovascular events was 0.688 (0.560-0.817, P = 0.017).

CONCLUSION

The innovative combinations of biomarkers (Scr × PCT and Scr × CRP) demonstrated efficacy as predictors for postoperative ICU admissions in elderly patients. Additionally, the Scr × CRP also had a moderate predictive value for postoperative cardiovascular events.

TRIAL REGISTRATION

China Clinical Trial Registry, ChiCTR1900026223.

Assessing the impact of frailty in elderly patients undergoing emergency laparotomies in Singapore

Introduction

The global rise in ageing populations poses challenges for healthcare systems. By 2030, Singapore anticipates a quarter of its population to be aged 65 or older. This study addresses the dearth of research on frailty's impact on emergency laparotomy (EL) outcomes in this demographic, emphasising the growing significance of this surgical intervention.

Method

Conducted at 2 tertiary centres in Singapore from January to December 2019, a retrospective cohort study examined EL outcomes in patients aged 65 or older. Frailty assessment, using the Clinical Frailty Scale (CFS), was integrated into demographic, diagnostic and procedural analyses. Patient data from Tan Tock Seng Hospital and Khoo Teck Puat Hospital provided a comprehensive view of frailty's role in EL.

Results

Among 233 participants, 26% were frail, revealing a higher vulnerability in the geriatric population. Frail individuals exhibited elevated preoperative risk, prolonged ICU stays, and significantly higher 90-day mortality (21.3% versus 6.4%). The study illuminated a nuanced connection between frailty and adverse outcomes, underlining the critical need for robust predictive tools in this context.

Conclusion

Frailty emerged as a pivotal factor influencing the postoperative trajectory of older adults undergoing EL in Singapore. The integration of frailty assessment, particularly when combined with established metrics like P-POSSUM, showcased enhanced predictive accuracy. This finding offers valuable insights for shared decision-making and acute surgical unit practices, emphasising the imperative of considering frailty in the management of older patients undergoing emergency laparotomy.

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study

BACKGROUND

Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients.

METHODS

The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model.

RESULTS

The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369-0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733-0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331-0.524) and an AUROC of 0.845 (95%CI: 0.796-0.888) in the internal validation.

CONCLUSIONS

This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis.

TRIAL REGISTRATION

The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019-473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019).

The impact of preoperative serum lactate dehydrogenase on mortality and morbidity after noncardiac surgery

Preoperative serum lactate dehydrogenase (LDH) has been reported to be associated with adverse outcomes following thoracic surgery. However, its association with outcomes in noncardiac surgery as a whole has not been investigated. We conducted a retrospective cohort study at West China Hospital, Sichuan University, from 2018 to 2020, including patients undergoing noncardiac surgery. Multivariable logistic regression and propensity score weighting were employed to assess the link between LDH levels and postoperative outcomes. Preoperative LDH was incorporated into four commonly used clinical models, and its discriminative ability, reclassification, and calibration were evaluated in comparison to models without LDH. Among 130,879 patients, higher preoperative LDH levels (cut-off: 220 U/L) were linked to increased in-hospital mortality (4.382% vs. 0.702%; OR 1.856, 95% CI 1.620-2.127, P < 0.001), myocardial injury after noncardiac surgery (MINS) (3.012% vs. 0.537%; OR 1.911, 95% CI 1.643-2.223, P < 0.001), and ICU admission (15.010% vs. 6.414%; OR 1.765, 95% CI 1.642-1.896, P < 0.001). The inverse probability of treatment-weighted estimation supported these results. Additionally, LDH contributed significantly to four surgical prognostic models, enhancing their predictive capability. Our study revealed a significant association between preoperative LDH and in-hospital mortality, MINS, and ICU admission following noncardiac surgery. Moreover, LDH provided supplementary predictive information, extending the utility of commonly used surgical prognostic scores.

Preoperative anemia in older individuals undergoing major abdominal surgery is associated with early postoperative morbidity: a prospective observational study

PURPOSE

Preoperative anemia is associated with poor postoperative outcomes. Older patients have limited physiologic reserves, which renders them vulnerable to the stress of major abdominal surgery. We aimed to determine if the severity of preoperative anemia is associated with early postoperative morbidity among older patients undergoing major abdominal surgery.

METHODS

Ethics approval was obtained from SingHealth Centralized Institutional Review Board. This is a prospective observational study conducted in the preoperative anesthesia clinic of a tertiary Singapore hospital from 2017 to 2021. Patient demographic data, comorbidities, and intraoperative details were collected. Outcome measures included blood transfusions, complications according to the Postoperative Morbidity Survey, days alive and out of hospital (DaOH), length of hospital stay, and mortality.

RESULTS

A total of 469 patients were analyzed, 37.5% of whom had preoperative anemia (serum hemoglobin of < 13 g·dL-1 in males and < 12 g·dL-1 in females). Anemia was significantly associated with older age, a higher age-adjusted Comprehensive Complication Index score, a higher incidence of diabetes mellitus, and a higher proportion of patients with an American Society of Anesthesiologists Physical Status of III or IV. The severity of anemia was associated with the presence of early postoperative morbidity at day 5, increased blood transfusions, longer length of hospital stay, and fewer DaOH at 30 days and six months.

CONCLUSION

Anemia is significantly associated with poorer postoperative outcomes in the older population. The impact of anemia on postoperative outcomes could be further evaluated with quality of life indicators, patient-reported outcome measures, and health economic tools.

Development and validation of a prediction model for postoperative intensive care unit admission in patients with non-cardiac surgery

BACKGROUND

Accurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited.

OBJECTIVE

To develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery.

METHODS

This study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis.

RESULTS

This study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907-0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/).

CONCLUSION

We developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery.

Preoperative shock index in major abdominal emergency surgery

Introduction

Major abdominal emergency surgery (MAES) patients have a high risk of mortality and complications. The time-sensitive nature of MAES necessitates an easily calculable risk-scoring tool. Shock index (SI) is obtained by dividing heart rate (HR) by systolic blood pressure (SBP) and provides insight into a patient's haemodynamic status. We aimed to evaluate SI's usefulness in predicting postoperative mortality, acute kidney injury (AKI), requirements for intensive care unit (ICU) and high-dependency monitoring, and the ICU length of stay (LOS).

Method

We retrospectively reviewed 212,089 MAES patients from January 2013 to December 2020. The cohort was propensity matched, and 3960 patients were included. The first HR and SBP recorded in the anaesthesia chart were used to calculate SI. Regression models were used to investigate the association between SI and outcomes. The relationship between SI and survival was explored with Kaplan-Meier curves.

Results

There were significant associations between SI and mortality at 1 month (odds ratio [OR] 2.40 [1.67-3.39], P<0.001), 3 months (OR 2.13 [1.56-2.88], P<0.001), and at 2 years (OR 1.77 [1.38-2.25], P<0.001). Multivariate analysis revealed significant relationships between SI and mortality at 1 month (OR 3.51 [1.20-10.3], P=0.021) and at 3 months (OR 3.05 [1.07-8.54], P=0.034). Univariate and multivariate analysis also revealed significant relationships between SI and AKI (P<0.001), postoperative ICU admission (P<0.005) and ICU LOS (P<0.001). SI does not significantly affect 2-year mortality.

Conclusion

SI is useful in predicting postopera-tive mortality at 1 month, 3 months, AKI, postoperative ICU admission and ICU LOS.

Transforming the perioperative medicine care model: The Singapore experience

More than 300 million surgeries are performed worldwide annually. Established perioperative centres in the UK, USA and Australia have demonstrated the impact of improving perioperative care in reducing costs, increasing patient satisfaction and improving population health. Likewise, the surgical burden of care in Asia is increasing, but with sociocultural, economic and epigenetic differences compared to the west. As Singapore's largest hospital, the Singapore General Hospital pre-admission perioperative clinic sees about 20,000 patients annually. We aim to illustrate Singapore General Hospital's perioperative model of care to contribute to the paucity of literature describing perioperative programme implementation within Asia, and to encourage the cross-sharing of perioperative practices internationally. Our perioperative framework navigates risk assessment, risk counselling, and mitigation of health, medical and functional risks to better patients' perioperative outcomes and population health. We have implemented evidence-based pathways for common conditions such as anaemia and malnutrition, including a multidisciplinary programme for the elderly to tackle frailty and reduce length of stay. We describe how we have enhanced local risk profiling with the Combined Assessment of Risk Encountered in Surgery surgical risk calculator derived locally using a gradient boosting machine learning model. Finally, we report clinical outcomes of these interventions and discuss further challenges and new initiatives at each tier of our perioperative model. Our perioperative care model provides a framework that other centres can adopt to promote value-driven care, while catering for differences in the Asian population, thereby promoting evidence-based improvements in the area of perioperative medicine.

A risk scoring system integrating postoperative factors for predicting early mortality after major non-cardiac surgery

We aimed to develop a risk scoring system for 1-week and 1-month mortality after major non-cardiac surgery, and assess the impact of postoperative factors on 1-week and 1-month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non-cardiac surgery and collected pre-, intra-, and postoperative features. We derived two patient cohorts to predict 1-week and 1-month mortality and randomly split each of them into training and test cohorts at a ratio of 8:2. All the modeling steps were carried out solely based on the training cohorts, whereas the test cohorts were reserved for the evaluation of predictive performance. Incorporation of postoperative information demonstrated no significant benefit in predicting 1-week mortality but led to substantial improvement in predicting 1-month mortality. Risk scores predicting 1-week and 1-month mortality were associated with area under receiver operating characteristic curves of 84.58% and 90.66%, respectively. Brain surgery, amount of intraoperative red blood cell transfusion, preoperative platelet count, preoperative serum albumin, and American Society of Anesthesiologists physical status were included in the risk score predicting 1-week mortality. Postoperative day (POD) 5 (neutrophil count × mean platelet volume) to (lymphocyte count × platelet count) ratio, preoperative and POD 5 serum albumin, and occurrence of acute kidney injury were included in the risk score predicting 1-month mortality. Our scoring system advocates the importance of postoperative complete blood count differential and serum albumin to better predict mortality beyond the first week post-surgery.

POTTER-ICU: An artificial intelligence smartphone-accessible tool to predict the need for intensive care after emergency surgery

BACKGROUND

Delays in admitting high-risk emergency surgery patients to the intensive care unit result in worse outcomes and increased health care costs. We aimed to use interpretable artificial intelligence technology to create a preoperative predictor for postoperative intensive care unit need in emergency surgery patients.

METHODS

A novel, interpretable artificial intelligence technology called optimal classification trees was leveraged in an 80:20 train:test split of adult emergency surgery patients in the 2007-2017 American College of Surgeons National Surgical Quality Improvement Program database. Demographics, comorbidities, and laboratory values were used to develop, train, and then validate optimal classification tree algorithms to predict the need for postoperative intensive care unit admission. The latter was defined as postoperative death or the development of 1 or more postoperative complications warranting critical care (eg, unplanned intubation, ventilator requirement ≥48 hours, cardiac arrest requiring cardiopulmonary resuscitation, and septic shock). An interactive and user-friendly application was created. C statistics were used to measure performance.

RESULTS

A total of 464,861 patients were included. The mean age was 55 years, 48% were male, and 11% developed severe postoperative complications warranting critical care. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application was created as the user-friendly interface of the complex optimal classification tree algorithms. The number of questions (ie, tree depths) needed to predict intensive care unit admission ranged from 2 to 11. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application had excellent discrimination for predicting the need for intensive care unit admission (C statistics: 0.89 train, 0.88 test).

CONCLUSION

We recommend the Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application as an accurate, artificial intelligence-based tool for predicting severe complications warranting intensive care unit admission after emergency surgery. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application can prove useful to triage patients to the intensive care unit and to potentially decrease failure to rescue in emergency surgery patients.

Discordant American Society of Anesthesiologists Physical Status Classification between anesthesiologists and surgeons and its correlation with adverse patient outcomes

The American Society of Anesthesiologists Physical Status Classification (ASA) is used for communication of patient health status, risk scoring, benchmarking and financial claims. Prior studies using hypothetical scenarios have shown poor concordance of ASA classification among healthcare providers. There is a paucity of studies using clinical data, and of clinical factors or patient outcomes associated with discordant classification. The study aims to assess ASA classification concordance between surgeons and anesthesiologists, factors surrounding discordance and its impact on patient outcomes. This retrospective cohort study was conducted in a tertiary medical center on 46,284 consecutive patients undergoing elective surgery between January 2017 and December 2019. The ASA class showed moderate concordance (weighted Cohen’s κ 0.53) between surgeons and anesthesiologists. We found significant associations between discordant classification and patient comorbidities, age and race. Patients with discordant classification had a higher risk of 30-day mortality (odds ratio (OR) 2.00, 95% confidence interval (CI) = 1.52–2.62, p < 0.0001), 1-year mortality (OR 1.53, 95% CI = 1.38–1.69, p < 0.0001), and Intensive Care Unit admission > 24 h (OR 1.69, 95% CI = 1.47–1.94, p < 0.0001). Hence, there is a need for improved standardization of ASA scoring and cross-specialty review in ASA-discordant cases.

Development and Validation of a Multivariable Prediction Model for Postoperative Intensive Care Unit Stay in a Broad Surgical Population

IMPORTANCE

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.

OBJECTIVE

To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population.

DESIGN, SETTING, AND PARTICIPANTS

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.

EXPOSURE

Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery.

MAIN OUTCOMES AND MEASURES

Use of ICU care up to 30 days after surgery.

RESULTS

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).

CONCLUSIONS AND RELEVANCE

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.

Can Red cell distribution width screen for metabolic abnormality in women with Polycystic Ovarian Syndrome?

Polycystic ovarian syndrome (PCOS) is a prevalent endocrinopathy in reproductive-age females, accredited to a chronic low-grade inflammatory reaction. Red distribution width (RDW), a parameter of complete blood count, was tested as an inflammatory marker ; higher RDW was linked to metabolic syndrome. We aimed to examine RDW in distinguishing PCOS-related metabolic and hormonal abnormalities. Methods : A case-control study recruited 128 women, divided into PCOS cases (64 / 128) and controls (64 / 128) according to Rotterdam criteria. Body mass index (BMI), estimated complete blood count parameters, hormonal markers (serum follicle-stimulating hormone (FSH), luteinizing hormone, and serum testosterone), and metabolic markers (HOMA-IR, serum high and low-density lipoprotein) were measured. Results showed that RDW was significantly higher in PCOS. HOMA-IR, LDL, testosterone, and LH / FSH were higher in PCOS and strongly correlated with RDW with positive correlations. HDL was elevated and correlated negatively with RDW in PCOS. ROC calculated (13.55) as RDW cut-off value for insulin-resistant with an AUC of 0.95, P < 0.001. In conclusion, a strong and remarkable correlation of RDW with metabolic abnormalities in PCOS cases with 100% sensitivity and specificity, in addition to being quick and inexpensive, makes it a reliable marker for screening for insulin resistance. J. Med. Invest. 69 : 191-195, August, 2022.

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Developing a Simpler Prognosticating Tool: Comparing the Combined Assessment of Risk Encountered in Surgery Score with Deyo-Charlson Comorbidity Index and The American Society of Anesthesiologists Physical Status Score in Predicting 2 years Mortality after Hip Fracture Surgery

Background: The use of risk stratification tools in identifying high-risk hip fracture patients plays an important role during treatment. The aim of this study was to compare our locally derived Combined Assessment of Risk Encountered in Surgery (CARES) score with the the American Society of Anesthesiologists physical status (ASA-PS) score and the Deyo–Charlson Comorbidity Index (D-CCI) in predicting 2-year mortality after hip fracture surgery. Methods and Material: A retrospective study was conducted on surgically treated hip fracture patients in a large tertiary hospital from Jan 2013 through Dec 2015. Age, gender, time to surgery, ASA-PS score, D-CCI, and CARES score were obtained. Univariate and multivariable logistic regression analyses were used to assess statistical significance of scores and risk factors, and area under the receiver operating characteristic (ROC) curve (AUC) was used to compare ASA-PS, D-CCI, and CARES as predictors of mortality at 2 years. Results: 763 surgically treated hip fracture patients were included in this study. The 2-year mortality rate was 13.1% (n = 100), and the mean ± SD CARES score of surviving and demised patients was 21.2 ± 5.98 and 25.9 ± 5.59, respectively. Using AUC, CARES was shown to be a better predictor of 2-year mortality than ASA-PS, but we found no statistical difference between CARES and D-CCI. A CARES score of 23, attributable primarily to pre-surgical morbidities and poor health of the patient, was identified as the statistical threshold for “high” risk of 2-year mortality. Conclusion: The CARES score is a viable risk predictor for 2-year mortality following hip fracture surgery and is comparable to the D-CCI in predictive capability. Our results support the use of a simpler yet clinically relevant CARES in prognosticating mortality following hip fracture surgery, particularly when information on the pre-existing comorbidities of the patient is not immediately available.

Estimated glomerular filtration rate and postoperative mortality in patients undergoing non-cardiac and non-neuron surgery: a single-center retrospective study

Background

There is limited evidence to clarify the specific relationship between preoperative estimated glomerular filtration rate (preop-eGFR) and postoperative 30-day mortality in Asian patients undergoing non-cardiac and non-neuron surgery. We aimed to investigate details of this relationship.

Methods

We reanalyzed a retrospective analysis of the clinical records of 90,785 surgical patients at the Singapore General Hospital from January 1, 2012 to October 31, 2016. The main outcome was postoperative 30-day mortality.

Results

The average age of these recruited patients was 53.96 ± 16.88 years, of which approximately 51.64% were female. The mean of preop-eGFR distribution was 84.45 ± 38.56 mL/min/1.73 m². Multivariate logistic regression analysis indicated that preop-eGFR was independently associated with 30-day mortality (adjusted odds ratio: 0.992; 95% confidence interval [CI] 0.990–0.995; P < 0.001). A U-shaped relationship was detected between preop-eGFR and 30-day mortality with an inflection point of 98.688 (P for log likelihood ratio test < 0.001). The effect sizes and confidence intervals on the right and left sides of the inflection point were 1.013 (1.007 to 1.019) [P < 0.0001] and 0.984 (0.981 to 0.987) [P < 0.0001], respectively. Preoperative comorbidities such as congestive heart failure (CHF), type 1 diabetes, ischemic heart disease (IHD), and anemia were associated with the odds ratio of preop-eGFR to 30-day mortality (interaction P < 0.05).

Discussion

The relationship between preop-eGFR and 30-day mortality is U-shaped. The recommended preop-eGFR at which the rate of the 30-day mortality was lowest was 98.688 mL/min/1.73 m².

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.

Real-world results of the implementation of preoperative anaemia clinic with intravenous iron therapy for treating iron-deficiency anaemia: a propensity-matched case-control study

Background

Preoperative anaemia is associated with blood transfusion and longer hospital length of stay. Preoperative iron deficiency anaemia (IDA) can be treated with oral or intravenous (IV) iron. IV iron can raise haemoglobin faster compared with oral iron. However, its ability to reduce blood transfusion and length of stay in clinical trials is inconclusive. This study aims to compare blood transfusion and hospital length of stay between anemic patients who received preoperative IV iron versus standard care, after implementation of a protocol in 2017 to screen patients for preoperative IDA, and its treatment with IV iron.

Methods

Retrospective before-after cohort study comparing 89 patients who received IV iron preoperatively in 2017, with historic patients who received oral iron therapy (selected by propensity score matching (PSM) from historic cohort of 7,542 patients who underwent surgery in 2016). Propensity score was calculated using ASA status, age, gender, surgical discipline, surgical risk and preoperative haemoglobin concentration. Both 1:1 and 1:2 matching were performed as sensitivity analysis.

Results

After PSM, there was no statistically significant difference in distribution of preoperative clinical variables. There was no significant difference in proportion of cases requiring transfusion nor a difference in average units transfused per patient. IV iron cohort stayed in hospital on average 8.0 days compared to non-IV iron cohort 14.1–15.1 days (P=0.006, P=0.013 respectively). Average time from IV iron therapy to surgery was 10.5 days.

Conclusions

Preoperative IV iron therapy for patients with IDA undergoing elective surgery may not reduce perioperative blood transfusion, but this could be due to the short time between therapy and surgery. Implementation of IV iron therapy may reduce hospital length of stay compared to standard care for anemic patients, although this may be enhanced by concomitant improvement in perioperative care.

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission

Objective:

To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the American Society of Anaesthesiologists-Physical Status (ASA-PS) in the prediction of 30-day postsurgical mortality and need for intensive care unit (ICU) stay >24 hours.

Background:

Prediction of surgical risk preoperatively is important for clinical shared decision-making and planning of health resources such as ICU beds. The current growth of electronic medical records coupled with machine learning presents an opportunity to improve the performance of established risk models.

Methods:

All patients aged 18 years and above who underwent noncardiac and nonneurological surgery at Singapore General Hospital (SGH) between 1 January 2012 and 31 October 2016 were included. Patient demographics, comorbidities, preoperative laboratory results, and surgery details were obtained from their electronic medical records. Seventy percent of the observations were randomly selected for training, leaving 30% for testing. Baseline models were CARES and ASA-PS. Candidate models were trained using random forest, adaptive boosting, gradient boosting, and support vector machine. Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

Results:

A total of 90,785 patients were included, of whom 539 (0.6%) died within 30 days and 1264 (1.4%) required ICU admission >24 hours postoperatively. Baseline models achieved high AUROCs despite poor sensitivities by predicting all negative in a predominantly negative dataset. Gradient boosting was the best performing model with AUPRCs of 0.23 and 0.38 for mortality and ICU admission outcomes respectively.

Conclusions:

Machine learning can be used to improve surgical risk prediction compared to traditional risk calculators. AUPRC should be used to evaluate model predictive performance instead of AUROC when the dataset is imbalanced.

Effects of night surgery on postoperative mortality and morbidity: a multicentre cohort study

Background

Surgery at night (incision time 17:00 to 07:00 hours) may lead to increased postoperative mortality and morbidity. Mechanisms explaining this association remain unclear.

Methods

We conducted a multicentre retrospective cohort study of adult patients undergoing non-cardiac surgery with general anaesthesia at two major, competing tertiary care hospital networks. In primary analysis, we imputed missing data and determined whether exposure to night surgery affects 30-day mortality using a mixed-effects model with individual anaesthesia and surgical providers as random effects. Secondary outcomes were 30-day morbidity and the mediating effect of blood transfusion rates and provider handovers on the effect of night surgery on outcomes. We further tested for effect modification by surgical setting.

Results

Among 350 235 participants in the primary imputed cohort, the mortality rate was 0.9% (n=2804/322 327) after day and 3.4% (n=940/27 908) after night surgery. Night surgery was associated with an increased risk of mortality (ORadj 1.26, 95% CI 1.15 to 1.38, p<0.001). In secondary analyses, night surgery was associated with increased morbidity (ORadj 1.41, 95% CI 1.33 to 1.48, p<0.001). The proportion of patients receiving intraoperative blood transfusion and anaesthesia handovers were higher during night-time, mediating 9.4% (95% CI 4.7% to 14.2%, p<0.001) of the effect of night surgery on 30-day mortality and 8.4% (95% CI 6.7% to 10.1%, p<0.001) of its effect on morbidity. The primary association was modified by the surgical setting (p-for-interaction<0.001), towards a greater effect in patients undergoing ambulatory/same-day surgery (ORadj 1.81, 95% CI 1.39 to 2.35) compared with inpatients (ORadj 1.17, 95% CI 1.02 to 1.34).

Conclusions

Night surgery was associated with an increased risk of postoperative mortality and morbidity. The effect was independent of case acuity and was mediated by potentially preventable factors: higher blood transfusion rates and more frequent provider handovers.

Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study

BACKGROUND

Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality.

METHODS AND FINDINGS

We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment.

CONCLUSIONS

In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.

Documentation of individualized preoperative risk assessment: a multi-center study

Background

Individual surgical risk assessment (ISRA) enhances patient care experience and outcomes by informing shared decision-making, strengthening the consent process, and supporting clinical management. Neither the use of individual pre-surgical risk assessment tools nor the rate of individual risk assessment documentation is known. The primary endpoint of this study was to determine the rate of physician documented ISRAs, with or without a named ISRA tool, within the records of patients with poor outcomes. Secondary endpoints of this work included the effects of age, sex, race, ASA class, and time and type of surgery on the rate of documented presurgical risk.

Methods

The records of non-obstetric surgical patients within 22 community-based private hospitals in Arizona, Colorado, Nebraska, Nevada, and Wyoming, between January 1 and December 31, 2017, were evaluated. A two-sample proportion test was used to identify the difference between surgical documentation and anesthesiology documentation of risk. Logistic regression was used to analyze both individual and group effects associated with secondary endpoints.

Results

Seven hundred fifty-six of 140,756 inpatient charts met inclusion criteria (0.54%, 95% CI 0.50 to 0.58%). ISRAs were documented by 16.08% of surgeons and 4.76% of anesthesiologists (p < 0.0001, 95% CI −0.002 to 0.228). Cardiac surgeons documented ISRAs more frequently than non-cardiac surgeons (25.87% vs 16.15%) [p = 0.0086, R-squared = 0.970%]. Elective surgical patients were more likely than emergency surgical patients (19.57 vs 12.03%) to have risk documented (p = 0.023, R-squared = 0.730%). Patients over the age of 65 were more likely than patients under the age of 65 to have ISRA documentation (20.31 vs 14.61%) [p = 0.043, R-squared = 0.580%]. Only 10 of 756 (1.3%) records included documentation of a named ISRA tool.

Conclusions

The observed rate of documented ISRA in our sample was extremely low. Surgeons were more likely than anesthesiologists to document ISRA. As these individualized risk assessment discussions form the bedrock of perioperative informed consent, the rate and quality of risk documentation must be improved.

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non-cardiac surgery

BACKGROUND

Risk prediction tools can be used in the perioperative setting to identify high-risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision-making, and to benchmark risk-adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context.

METHODS

A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients.

RESULTS

Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion.

CONCLUSION

Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

The concept of peri-operative medicine to prevent major adverse events and improve outcome in surgical patients: A narrative review

: Peri-operative Medicine is the patient-centred and value-based multidisciplinary peri-operative care of surgical patients. Peri-operative stress, that is the collective response to stimuli occurring before, during and after surgery, is, together with pre-existing comorbidities, the pathophysiological basis of major adverse events. The ultimate goal of Peri-operative Medicine is to promote high quality recovery after surgery. Clinical scores and/or biomarkers should be used to identify patients at high risk of developing major adverse events throughout the peri-operative period. Allocation of high-risk patients to specific care pathways with peri-operative organ protection, close surveillance and specific early interventions is likely to improve patient-relevant outcomes, such as disability, health-related quality of life and mortality.

Anemia and perioperative mortality in non-cardiac surgery patients: a secondary analysis based on a single-center retrospective study

Background

Evidence regarding the relationship between anemia and perioperative prognosis is controversial. The study was conducted to highlight the specific relationship between anemia and perioperative mortality in non-cardiac surgery patients over 18 years of age.

Methods

This study was a retrospective analysis of the electronic medical records of 90,784 patients at the Singapore General Hospital from January 1, 2012 to October 31, 2016. Multivariate regression, propensity score analysis, doubly robust estimation, and an inverse probability-weighting model was used to ensure the robustness of our findings.

Results

We identified 85,989 patients, of whom75, 163 had none or mild anemia (Hemoglobin>90g/L) and 10,826 had moderate or severe anemia (Hemoglobin≤90g/L). 8,857 patients in each study exposure group had similar propensity scores and were included in the analyses. In the doubly robust model, postoperative 30-day mortality rate was increased by 0.51% (n = 219) in moderate or severe anemia group (Odds Ratio, 1.510; 95% Confidence Interval (CI), 1.049 to 2.174) compared with none or mild anemia group (2.47% vs.1.22%, P<0.001). Moderate or severe anemia was also associated with increased postoperative blood transfusion rates (OR, 5.608; 95% CI, 4.026 to 7.811, P < 0.001). There was no statistical difference in Intensive Care Unit (ICU) admission rate among different anemia groups within 30 days after surgery (P=0.104).

Discussion

In patients undergoing non-cardiac surgery over 18 years old, moderate or severe preoperative anemia would increase the occurrence of postoperative blood transfusion and the risk of death, rather than ICU admission within 30 days after surgery.

The relationship between anesthetic technique and thirty-day mortality in patients undergoing noncardiac- and nonneurosurgery: A retrospective, propensity score-matched cohort study

BACKGROUND

Currently, 310 million patients undergo surgery every year worldwide, and there is still controversy over which anesthetic technique to choose for a considerable of surgeries.This study evaluates the association of the anesthetic technique with thirty-day mortality after noncardiac- and nonneurosurgery.

METHODS

Electronic medical records of 90,785 patients who underwent non-cardiac- and nonneurosurgery at the *** General Hospital from January 1, 2012 to October 31, 2016, were subject to secondary retrospective analysis. The principal exposure was regional versus general anesthesia. Outcome measures were death, intensive care unit (ICU) admission and blood transfusion requirement within 30 days after surgery. Propensity-score matching was used to assemble a cohort of patients with similar baseline characteristics.

RESULTS

We identified 90,785 patients, of whom 76,442 received regional anesthesia and 14,343 received general anesthesia. A total of 11,351 patients in the general anesthesia group had propensity scores similar to those of patients who received regional anesthesia and were included in the analyses. In the propensity-score matched cohort, the postoperative 30-day mortality rate was 0.75% (n = 85) in the regional anesthesia group (Odds Ratio, 0.567; 95% CI, 0.434 to 0.741; P = 0.00003) compared with 1.31% (n = 149) in the general anesthesia group. Regional anesthesia was also associated with a reduced rate of ICU admission compared with that of patients who received general anesthesia (0.44% vs. 2.68%; OR, 0.161; 95% CI, 0.119 to 0.217, P < 0.00001). There was a nonsignificant relationship between the anesthetic technique and postoperative blood transfusion (P = 0.082).

CONCLUSIONS

The results of this observational, propensity score-matched cohort study suggest a significant association between regional anesthesia and low thirty-day mortality and a worse postoperative prognosis in patients who underwent noncardiac- and nonneurosurgery, which provides information for anesthetic technique decision making in the clinical setting.

Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP)

A timely determination of the risk of post-traumatic stress disorder (PTSD) is a prerequisite for efficient service delivery and prevention. We provide a risk estimate tool allowing a calculation of individuals' PTSD likelihood from early predictors. Members of the International Consortium to Predict PTSD (ICPP) shared individual participants' item-level data from ten longitudinal studies of civilian trauma survivors admitted to acute care centers in six countries. Eligible participants (N=2,473) completed an initial clinical assessment within 60 days of trauma exposure, and at least one follow-up assessment 4-15 months later. The Clinician-Administered PTSD Scale for DSM-IV (CAPS) evaluated PTSD symptom severity and diagnostic status at each assessment. Participants' education, prior lifetime trauma exposure, marital status and socio-economic status were assessed and harmonized across studies. The study's main outcome was the likelihood of a follow-up PTSD given early predictors. The prevalence of follow-up PTSD was 11.8% (9.2% for male participants and 16.4% for females). A logistic model using early PTSD symptom severity (initial CAPS total score) as a predictor produced remarkably accurate estimates of follow-up PTSD (predicted vs. raw probabilities: r=0.976). Adding respondents' female gender, lower education, and exposure to prior interpersonal trauma to the model yielded higher PTSD likelihood estimates, with similar model accuracy (predicted vs. raw probabilities: r=0.941). The current model could be adjusted for other traumatic circumstances and accommodate risk factors not captured by the ICPP (e.g., biological, social). In line with their use in general medicine, risk estimate models can inform clinical choices in psychiatry. It is hoped that quantifying individuals' PTSD risk will be a first step towards systematic prevention of the disorder.

Preoperative ANemiA among the elderly undergoing major abdominal surgery (PANAMA) study: Protocol for a single-center observational cohort study of preoperative anemia management and the impact on healthcare outcomes

INTRODUCTION

Preoperative anemia and old age are independent risk factors for perioperative morbidity and mortality. However, despite the high prevalence of anemia in elderly surgical patients, there is limited understanding of the impact of anemia on postoperative complications and postdischarge quality of life in the elderly. This study aims to investigate how anemia impacts elderly patients undergoing major abdominal surgery in terms of perioperative morbidity, mortality and quality of life for 6 months postoperatively.

METHODS AND ANALYSIS

We will conduct a prospective observational study over 12 months of 382 consecutive patients above 65 years old, who are undergoing elective major abdominal surgery in Singapore General Hospital (SGH), a tertiary public hospital. Baseline clinical assessment including full blood count and iron studies will be done within 1 month before surgery. Our primary outcome is presence of morbidity at fifth postoperative day (POD) as defined by the postoperative morbidity survey (POMS). Secondary outcomes will include 30-day trend of POMS complications, morbidity defined by Clavien Dindo Classification system (CDC) and Comprehensive Complication Index (CCI), 6-month mortality, blood transfusion requirements, days alive out of hospital (DaOH), length of index hospital stay, 6-month readmission rates and Health Related Quality of Life (HRQoL). HRQoL will be assessed using EuroQol five-dimensional instrument (EQ-5D) scores at preoperative consult and at 1, 3, and 6 months.

ETHICS AND DISSEMINATION

The SingHealth Centralised Institutional Review Board (CIRB Ref: 2017/2640) approved this study and consent will be obtained from all participants. This study is funded by the National Medical Research Council, Singapore (HNIG16Dec003) and the findings will be published in peer-reviewed journals and presented at academic conferences. Deidentified data will be made available from Dryad Repository upon publication of the results.

Preoperative Red Cell Distribution Width and 30-day mortality in older patients undergoing non-cardiac surgery: a retrospective cohort observational study

Increased red cell distribution width (RDW) is associated with poorer outcomes in various patient populations. We investigated the association between preoperative RDW and anaemia on 30-day postoperative mortality among elderly patients undergoing non-cardiac surgery. Medical records of 24,579 patients aged 65 and older who underwent surgery under anaesthesia between 1 January 2012 and 31 October 2016 were retrospectively analysed. Patients who died within 30 days had higher median RDW (15.0%) than those who were alive (13.4%). Based on multivariate logistic regression, in our cohort of elderly patients undergoing non-cardiac surgery, moderate/severe preoperative anaemia (aOR 1.61, p = 0.04) and high preoperative RDW levels in the 3rd quartile (>13.4% and ≤14.3%) and 4th quartile (>14.3%) were significantly associated with increased odds of 30-day mortality - (aOR 2.12, p = 0.02) and (aOR 2.85, p = 0.001) respectively, after adjusting for the effects of transfusion, surgical severity, priority of surgery, and comorbidities. Patients with high RDW, defined as >15.7% (90th centile), and preoperative anaemia have higher odds of 30-day mortality compared to patients with anaemia and normal RDW. Thus, preoperative RDW independently increases risk of 30-day postoperative mortality, and future risk stratification strategies should include RDW as a factor.