Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): An approach to care stratification

The side effects of the pandemic on all-cause postoperative mortality in a COVID reference Hospital in Brazil: a before and after cohort study with 15156 patients

BACKGROUND

Before the pandemic, healthcare systems in Low-Middle Income Countries (LMIC) experienced a limited capacity to treat postoperative complications. It is uncertain whether the interference of the Coronavirus (COVID-19) pandemic on surgical systems has increased postoperative mortality.

METHODS

This before and after cohort study aimed to assess the pandemic's impact on in-hospital postoperative mortality in a university COVID-19 reference hospital in southern Brazil. Data from patients who underwent surgery before (January 2018 to December 2019) the pandemic were compared to data from patients who underwent surgery during the pandemic (February to December 2020). The primary outcome was in-hospital mortality. We developed Poisson regression models to examine the mortality risk of being operated on during the COVID-19 pandemic.

RESULTS

We assessed 15156 surgical patients, 12207 of whom underwent surgery before the pandemic and 2949 during the first year of the pandemic. Mortality rates were 2.5% (309/12207) in the pre-pandemic versus 7.2% (212/2949) in the pandemic. Of these, 25.8% (32/124) of patients with COVID-19 and 6.4% (80/2816) of patients without COVID-19 died. The proportion of urgent surgeries and ASA-PS III was higher in the pandemic group. After adjusting for mortality-related variables, the Relative Risk (RR) associated with undergoing surgery during the pandemic was 1.51 (95% CI 1.27 to 1.79). We excluded COVID-19-positive to perform a sensitivity analysis that confirmed the increased risk of undergoing surgery during the pandemic RR = 1.50 (95% CI 1.27 to 1.78).

CONCLUSION

The substantial number of additional deaths, even amongst those without COVID-19 infection, suggests the pandemic disrupted the surgical service in an LMIC context. Fragile surgical systems may suffer more significant adverse impacts from external stressors such as a pandemic, and urging measures are needed to increase their performance and resilience.

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.

Does preoperative multidisciplinary team assessment of high-risk patients improve the safety and outcomes of patients undergoing surgery?

BACKGROUND

International guidelines recommend preoperative multidisciplinary team (MDT) assessment for high-risk surgical patients. Preoperative MDT meetings can help to improve surgical care, but there is little evidence on whether they improve patient outcomes.

METHODS

This paper aims to share our experience of MDT meetings for high-risk surgical patients to underline their added value to the current standard of care. An observational study of a retrospective cohort of preoperative high-risk MDT meetings of a tertiary referral hospital between January 2015 and December 2020. For 249 patients the outcomes preoperative data, MDT decisions, and patient outcomes were collected from electronic health records.

MAIN RESULTS

A total of 249 patients were discussed at high-risk MDT meetings. Most of the patients (97%) were assessed as having an American Society of Anesthesiology score ≥ 3, and 219 (88%) had a European Society of Cardiology and European Society of Anaesthesiology risk score of intermediate or high. After MDT assessment, 154 (62%) were directly approved for surgery, and 39 (16%) were considered ineligible for surgery. The remaining 56 (23%) patients underwent additional assessments before reconsideration at a high-risk MDT meeting. The main reason for patients being discussed at the high-risk MDT meeting was to assess the risk-benefit ratio of surgery. Ultimately, 184 (74%) patients underwent surgery. Of the operated patients, 122 (66%) did not have a major complication in the postoperative period, and 149 patients (81%) were alive after one year.

CONCLUSIONS

This cohort study shows the vulnerability and complexity of high-risk patients but also shows that the use of an MDT assessment contributes too improved peri- and postoperative treatment strategies in high-risk patients. Most patients underwent surgery after careful risk assessment and, if deemed necessary, preoperative and perioperative treatment optimization to reduce their risk.

Developing and validating a machine learning ensemble model to predict postoperative delirium in a cohort of high-risk surgical patients: A secondary cohort analysis

BACKGROUND

Postoperative delirium (POD) has a negative impact on prognosis, length of stay and the burden of care. Although its prediction and identification may improve postoperative care, this need is largely unmet in the Brazilian public health system.

OBJECTIVE

To develop and validate a machine-learning prediction model and estimate the incidence of delirium. We hypothesised that an ensemble machine-learning prediction model that incorporates predisposing and precipitating features could accurately predict POD.

DESIGN

A secondary analysis nested in a cohort of high-risk surgical patients.

SETTING

An 800-bed, quaternary university-affiliated teaching hospital in Southern Brazil. We included patients operated on from September 2015 to February 2020.

PATIENTS

We recruited 1453 inpatients with an all-cause postoperative 30-day mortality risk greater than 5% assessed preoperatively by the ExCare Model.

MAIN OUTCOME MEASURE

The incidence of POD classified by the Confusion Assessment Method, up to 7 days postoperatively. Predictive model performance with different feature scenarios were compared with the area under the receiver operating characteristic curve.

RESULTS

The cumulative incidence of delirium was 117, giving an absolute risk of 8.05/100 patients. We developed multiple machine-learning nested cross-validated ensemble models. We selected features through partial dependence plot analysis and theoretical framework. We treated the class imbalance with undersampling. Different feature scenarios included: 52 preoperative, 60 postoperative and only three features (age, preoperative length of stay and the number of postoperative complications). The mean areas (95% confidence interval) under the curve ranged from 0.61 (0.59 to 0.63) to 0.74 (0.73 to 0.75).

CONCLUSION

A predictive model composed of three indicative readily available features performed better than those with numerous perioperative features, pointing to its feasibility as a prognostic tool for POD. Further research is required to test the generalisability of this model.

TRIAL REGISTRATION

Institutional Review Board Registration number 04448018.8.0000.5327 (Brazilian CEP/CONEP System, available in https://plataformabrasil.saude.gov.br/ ).

Intertrochanteric Fracture Surgery Patients with Diabetes Mellitus are Prone to Suffer Perioperative Neurological and Endocrine/Metabolic Complications: A Propensity-Score Matched Analysis

Background

In older adults, the recovery after hip fracture surgery is not always to be well expected due to high risks of adverse outcomes including perioperative complications and mortality. We aimed to evaluate the intertrochanteric fracture (IF) patients with diabetes mellitus (DM) and receiving surgical fixation with intramedullary nail on the perioperative complications, total hospital costs (THC), length of hospital stay (LOS), and mortality.

Methods

In this retrospective cohort study, among 487 consecutive intertrochanteric fracture patients with age over 65 years and treated surgically by using intramedullary nail between Jan. 2015 and Mar. 2020, 353 patients were included, including 81 with DM and 272 without DM. After using propensity-score matched (PSM) analysis, 80 patients remained in each group. The perioperative complications, 30-day, 90-day, 1-year, and 2-year survival rates, THC, and LOS were observed and compared between two groups. Overall survival was compared by Kaplan–Meier method.

Results

No significant between-group differences were found in 30-day, 90-day, 1-year, and 2-year mortality rates, THC, LOS, and other perioperative complications after PSM and McNemar’s tests (all p>0.05), except for neurological complications (p<0.004) and endocrine/metabolic complications (p<0.001). At a mean follow-up time of 36.2 months, there were no statistically significant differences between the groups based on the Kaplan–Meier survival curve (p=0.171, log-rank).

Conclusion

IF surgery patients with DM are more prone to suffer perioperative neurological and endocrine/metabolic complications and they should be managed individually while being aware of these risks henceforth. Further high evidence clinical trials are needed to expand in DM patients with IF.

Measuring emotional preoperative stress by an app approach and its applicability to predict postoperative pain

BACKGROUND

The Brief Measure of Emotional Preoperative Stress (B-MEPS) was developed to evaluate the preoperative individual vulnerability to emotional stress. To obtain a refined version of B-MEPS suitable for an app approach, this study aimed: (i) to identify items with more discriminant properties; (ii) to classify the level of preoperative emotional stress based on cut-off points; (iii) to assess concurrent validity through correlation with the Central Sensitization Inventory (CSI) score; (iv) to confirm whether the refined version of B-MEPS is an adequate predictive measure for identification of patients prone to intense postoperative pain.

METHODS

We include 1016 patients who had undergone surgical procedures in a teaching hospital. The generalized partial credit model of item response theory and latent class model were employed, respectively, to reduce the number of items and to create cut-off points. We applied the CSI and assessed pain by Visual Analog Scale (0-10) and by the amount of postoperative morphine consumption.

RESULTS

The refined B-MEPS shows satisfactory reliability (Cronbach's alpha 0.79). Preoperative emotional stress, according to the cut-off points, is classified into categories: low, intermediate or high stress. The refined B-MEPS exhibited a linear association with the CSI scores (r2 = 0.53, p < 0.01). Patients with higher levels of emotional stress displayed a positive association with moderate to severe pain and greater morphine consumption.

CONCLUSION

The refined version of B-MEPS, along with an interface of easy applicability, assess emotional vulnerability at the bedside before surgery. This app may support studies focused on intervening with perioperative stress levels.

Enhanced peri-operative care to improve outcomes for high-risk surgical patients in Brazil: a single-centre before-and-after cohort study

Mortality and morbidity for high-risk surgical patients are often high, especially in low-resource settings. Enhanced peri-operative care has the potential to reduce preventable deaths but must be designed to meet local needs. This before-and-after cohort study aimed to assess the effectiveness of a postoperative 48-hour enhanced care pathway for high-risk surgical patients ('high-risk surgical bundle') who did not meet the criteria for elective admission to intensive care. The pathway comprised of six elements: risk identification and communication; adoption of a high-risk post-anaesthesia care unit discharge checklist; prompt nursing admission to ward; intensification of vital signs monitoring; troponin measurement; and prompt access to medical support if required. The primary outcome was in-hospital mortality. Data describing 1189 patients from two groups, before and after implementation of the pathway, were compared. The usual care group comprised a retrospective cohort of high-risk surgical patients between September 2015 and December 2016. The intervention group prospectively included high-risk surgical patients from February 2019 to March 2020. Unadjusted mortality rate was 10.5% (78/746) for the usual care and 6.3% (28/443) for the intervention group. After adjustment, the intervention effect remained significant (RR 0.46 (95%CI 0.30-0.72). The high-risk surgical bundle group received more rapid response team calls (24% vs. 12.6%; RR 0.63 [95%CI 0.49-0.80]) and surgical re-interventions (18.9 vs. 7.5%; RR 0.41 [95%CI 0.30-0.59]). These data suggest that a clinical pathway based on enhanced surveillance for high-risk surgical patients in a resource-constrained setting could reduce in-hospital mortality.

The impact of the incorporation of a feasible postoperative mortality model at the Post-Anaesthestic Care Unit (PACU) on postoperative clinical deterioration: A pragmatic trial with 5,353 patients

Background

Practical use of risk predictive tools and the assessment of their impact on outcome reduction is still a challenge. This pragmatic study of quality improvement (QI) describes the preoperative adoption of a customised postoperative death probability model (SAMPE model) and the evaluation of the impact of a Postoperative Anaesthetic Care Unit (PACU) pathway on the clinical deterioration of high-risk surgical patients.

Methods

A prospective cohort of 2,533 surgical patients compared with 2,820 historical controls after the adoption of a quality improvement (QI) intervention. We carried out quick postoperative high-risk pathways at PACU when the probability of postoperative death exceeded 5%. As outcome measures, we used the number of rapid response team (RRT) calls within 7 and 30 postoperative days, in-hospital mortality, and non-planned Intensive Care Unit (ICU) admission.

Results

Not only did the QI succeed in the implementation of a customised risk stratification model, but it also diminished the postoperative deterioration evaluated by RRT calls on very high-risk patients within 30 postoperative days (from 23% before to 14% after the intervention, p = 0.05). We achieved no survival benefits or reduction of non-planned ICU. The small group of high-risk patients (13% of the total) accounted for the highest proportion of RRT calls and postoperative death.

Conclusion

Employing a risk predictive tool to guide immediate postoperative care may influence postoperative deterioration. It encouraged the design of pragmatic trials focused on feasible, low-technology, and long-term interventions that can be adapted to diverse health systems, especially those that demand more accurate decision making and ask for full engagement in the control of postoperative morbi-mortality.

Derivation and validation of a national multicenter mortality risk stratification model - the ExCare model: a study protocol

Introduction

Surgical care is essential for proper management of various diseases. However, it can result in unfavorable outcomes. In order to identify patients at higher risk of complications, several risk stratification models have been developed. Ideally, these tools should be simple, reproducible, accurate, and externally validated. Unfortunately, none of the best-known risk stratification instruments have been validated in Brazil. In this sense, the Ex-Care model was developed by retrospective data analysis of surgical patients in a major Brazilian university hospital. It consists of four independent predictors easily collected in the preoperative evaluation, showing high accuracy in predicting death within 30 days after surgery.

Objectives

To update and validate a Brazilian national-based model of postoperative death probability within 30 days based on the Ex-Care model. Also, to develop an application for smartphones that allows preoperative risk stratification by Ex-Care model.

Methods

Ten participating centers will collect retrospective data from digital databases. Variables age, American Society of Anesthesiologists (ASA) physical status, surgical severity (major or non-major) and nature (elective or urgent) will be evaluated as predictors for in-hospital mortality within 30 postoperative days, considered the primary outcome.

Expected results

We believe that the Ex-Care model will present discriminative capacity similar to other classically used scores validated for surgical mortality prediction. Furthermore, the mobile application to be developed will provide a practical and easy-to-use tool to the professionals enrolled in perioperative care.

A Standardized Framework for Evaluating Surgical Enhanced Recovery Pathways: A Recommendations Statement from the TDABC in Health-care Consortium

Background: Innovative methodologies to redesign care delivery are being applied to increase value in health care, including the creation of enhanced recovery pathways (ERPs) for surgical patients. However, there is a lack of standardized methods to evaluate ERP implementation costs. Objectives: This Recommendations Statement aims to introduce a standardized framework to guide the economic evaluation of ERP care-design initiatives, using the Time-Driven Activity-Based Costing (TDABC) methodology. Methods: We provide recommendations on using the proposed framework to support the decision-making processes that incorporate ERPs. Since ERPs are usually composed of activities distributed throughout the patient care pathway, the framework can demonstrate how the TDABC may be a valuable method to evaluate the incremental costs of protocol implementation. Our recommendations are based on the review of available literature and expert opinions of the members of the TDABC in Healthcare Consortium. Results: The ERP framework, composed of 11 steps, was created describing how the techniques and methods can be applied to evaluate the economic impact of an ERP and guide health-care leaders to optimize the decision-making process of incorporating ERPs into health-care settings. Finally, six recommendations are introduced to demonstrate that using the suggested framework could increase value in ERP care-design initiatives by reducing variability in care delivery, educating multidisciplinary teams about value in health, and increasing transparency when managing surgical pathways. Conclusions: Our proposed standardized framework can guide decisions and support measuring improvements in value achieved by incorporating the perioperative redesign protocols.

Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data

Background

Personalized risk assessment provides opportunities for tailoring treatment, optimizing healthcare resources and improving outcome. The aim of this study was to develop a 90-day mortality-risk prediction model for identification of high- and low-risk patients undergoing surgery for colorectal cancer.

Methods

This was a nationwide cohort study using records from the Danish Colorectal Cancer Group database that included all patients undergoing surgery for colorectal cancer between 1 January 2004 and 31 December 2015. A least absolute shrinkage and selection operator logistic regression prediction model was developed using 121 pre- and intraoperative variables and internally validated in a hold-out test data set. The accuracy of the model was assessed in terms of discrimination and calibration.

Results

In total, 49 607 patients were registered in the database. After exclusion of 16 680 individuals, 32 927 patients were included in the analysis. Overall, 1754 (5.3 per cent) deaths were recorded. Targeting high-risk individuals, the model identified 5.5 per cent of all patients facing a risk of 90-day mortality exceeding 35 per cent, corresponding to a 6.7 times greater risk than the average population. Targeting low-risk individuals, the model identified 20.9 per cent of patients facing a risk less than 0.3 per cent, corresponding to a 17.7 times lower risk compared with the average population. The model exhibited discriminatory power with an area under the receiver operating characteristics curve of 85.3 per cent (95 per cent c.i. 83.6 to 87.0) and excellent calibration with a Brier score of 0.04 and 32 per cent average precision.

Conclusion

Pre- and intraoperative data, as captured in national health registries, can be used to predict 90-day mortality accurately after colorectal cancer surgery.

Discriminative Ability for Adverse Outcomes After Hip Fracture Surgery: A Comparison of Three Commonly Used Comorbidity-Based Indices

INTRODUCTION

Preoperative risk assessment can predict adverse outcomes following hip fracture surgery, helping with decision-making and management strategies. Several risk adjustment models based on coded comorbidities such as Charlson Comorbidity Index (CCI), modified Elixhauser's Comorbidity Measure (mECM), and modified frailty index (mFI-5) are currently prevalent for orthopedic patients, but there is no consensus regarding which is optimal. The primary purpose was to identify the risk factors of CCI, mECM, and mFI-5, as well as patient characteristics for predicting (1) 1-month, 3-month, 1-year, and 2-year mortality, (2) perioperative complications, and (3) extended length of stay (LOS) following hip fractured surgery. The secondary aim was to compare the best-performing comorbidity index combined with characteristics identified in terms of their discriminative ability for adverse outcomes.

METHODS

We retrospectively reviewed 3,379 consecutive patients presenting with intertrochanteric fractures at our Level I trauma center from 2013 to 2018. After eliminated by exclusion criteria, 2,241 patients undergoing hip fracture surgery by PFNA, with age ≥65 years, were included. Three main multivariate logistic regression models were constructed. Cox proportional hazards models were used to calculate hazard ratios for mortality. A base model included age, BMI, surgical delay, anesthesia type, hemoglobin record at admission, and American Society of Anesthesiologists grade (ASA) also was constructed and assessed.

RESULTS

Base model + mECM outperformed other models for the occurrence of major complications including severe complications, cardiac complications, and pulmonary complications [the area under the receiver operating characteristic curve (AUC), 0.647; 95% CI, 0.616-0.677; AUC, 0.637; 95% CI, 0.610-0.664; AUC, 0.679; 95% CI, 0.642-0.715, respectively], while base model + CCI provided better prediction of minor complications of neurological complications and hematological complications (AUC, 0.659; 95% CI, 0.609, 0.709; AUC, 0.658; 95% CI, 0.635, 0.680). In addition, BMI, surgical delay, anesthesia type, and ASA were found highly relevant to extended LOS. Age-group (with a 10-year interval) was indicated to be mostly associated with all-cause mortality with fully adjusted hazard ratio of 1.35 and 95% CI range 1.20-1.51.

CONCLUSIONS

In comparison with mFI-5 and CCI, mECM so far may be the best comorbidity index combined with the base model for predicting major complications following hip fracture. The base model already achieved good discrimination for all-cause mortality and extended LOS, further addition of risk adjustment indices led to only 1% increase in the amount of variation explained.

General anaesthesia related mortality in a limited resource settings region: a retrospective study in two teaching hospitals of Butembo

Background

General anaesthesia (GA) in developing countries is still a high-risk practice, especially in Africa, accompanied with high morbidity and mortality. No study has yet been conducted in Butembo in the Democratic Republic of the Congo to determine the mortality related to GA practice. The main objective of this study was to assess mortality related to GA in Butembo.

Methods

This was a retrospective descriptive and analytic study of patients who underwent surgery under GA in the 2 main teaching hospitals of Butembo from January 2011 to December 2015. Data were collected from patients files, anaesthesia registries and were analysed with SPSS 26.

Results

From a total of 921 patients, 539 (58.5%) were male and 382 (41.5%) female patients. A total of 83 (9.0%) patients died representing an overall perioperative mortality rate of 90 per 1000. Out of the 83 deaths, 38 occurred within 24 h representing GA related mortality of 41 per 1000. There was a global drop in mortality from 2011 to 2015. The risk factors of death were: being a neonate or a senior adult, emergency operation, ASA physical status > 2 and a single deranged vital sign preoperatively, presenting any complication during GA, anaesthesia duration > 120 minutes as well as visceral surgeries/laparotomies. Ketamine was the most employed anaesthetic.

Conclusion

GA related mortality is very high in Butembo. Improved GA services and outcomes can be obtained by training more anaesthesia providers, proper patients monitoring, improved infrastructure, better equipment and drugs procurement and considering regional anaesthesia whenever possible.

Prognostic profiling of children with serious post-operative complications: A novel probability model for failure to rescue

BACKGROUND

Failure to rescue (FTR), mortality after a major postoperative complication, is a superior surgical quality metric compared to surgical mortality or complications rates alone. Our objective was to develop and validate a novel pediatric profiling to identify high-risk subjects among the subset of children who develop serious post-operative complications.

METHODS

We performed a retrospective study of children who developed one or more serious postoperative complications following inpatient surgery across NSQIP-Pediatric hospitals (2012-2017). We evaluated the rate of FTR according to pre-operative comorbidity burden.

RESULTS

We identified 45,504 surgical cases with major post-operative complications (FTR rates: 2.4%). Surgical cases with greater than six pre-operative comorbidities (n = 12,148;28%) accounted for 80% of FTR events. The expected probability of FTR was 0.1%(95%CI:0.1%-0.2%) among low-risk cases, 3.3%(95%CI:3.0%-3.5%) among intermediate-risk cases, and 22.6%(95%CI:20.9%-24.3%) among high-risk cases. About half of surgical cases in the high-risk profile group died within 48 h of surgery. Comparatively, cases in the intermediate-risk group had a much longer time to mortality (10 days).

CONCLUSION

We propose a prognostic index to accurately identify children at risk for FTR. The use of such an index may provide surgeons with a window of opportunity to implement aggressive monitoring and therapeutic strategies to reduce mortality.

LEVEL OF EVIDENCE

IV.

Few and feasible preoperative variables can identify high-risk surgical patients: derivation and validation of the Ex-Care risk model

BACKGROUND

The development of feasible preoperative risk tools is desirable, especially for low-middle income countries with limited resources and complex surgical settings. This study aimed to derive and validate a preoperative risk model (Ex-Care model) for postoperative mortality and compare its performance with current risk tools.

METHODS

A multivariable logistic regression model predicting in-hospital mortality was developed using a large Brazilian surgical cohort. Patient and perioperative predictors were considered. Its performance was compared with the Charlson comorbidity index (CCI), Revised Cardiac Risk Index (RCRI), and the Surgical Outcome Risk Tool (SORT).

RESULTS

The derivation cohort included 16 618 patients. In-hospital death occurred in 465 patients (2.8%). Age, with adjusted splines, degree of procedure (major vs non-major), ASA physical status, and urgency were entered in a final model. It showed high discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.926 (95% confidence interval [CI], 0.91-0.93). It had superior accuracy to the RCRI (AUROC, 0.90 vs 0.76; P<0.01) and similar to the CCI (0.90 vs 0.82; P=0.06) and SORT models (0.90 vs 0.92; P=0.2) in the temporal validation cohort of 1173 patients. Calibration was adequate in both development (Hosmer-Lemeshow, 9.26; P=0.41) and temporal validation cohorts (Hosmer-Lemeshow 5.29; P=0.71).

CONCLUSIONS

The Ex-Care risk model proved very efficient at identifying high-risk surgical patients. Although multicentre studies are needed, it should have particular value in low resource settings to better inform perioperative health policy and clinical decision-making.

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.

Super elderly patients with intertrochanteric fractures do not predict worse outcomes and higher mortality than elderly patients: a propensity score matched analysis

We aimed to investigate whether super elderly patients aged over 90 years had significantly worse functional outcomes, perioperative complications, and survival rates. Among 3560 patients aged over 65 years presenting with intertrochanteric fractures and treated surgically between Jan 2014 and Jan 2019, 2242 patients were included, including 206 in super elderly group and 2036 in elderly group. After using propensity score matching to minimize the effects of possible confounding variables, 192 remained in each group. No significant difference was observed in functional outcomes, perioperative complications, or 30-day, 90-day, and 1-year mortality after propensity score matching and McNemar's tests (p>0.05). After an average follow-up of 37 months, the Kaplan-Meier survival curve showed no significant difference between the two groups in terms of cumulative survival rate (p=0.081, log-rank). Our data demonstrated progressive increases in mortality and poor outcomes with increasing Elixhauser comorbidity scores, which represented the severity index of patients preoperatively. Our study also found that there were weak correlations between five characteristics and the patient age. These results all suggested that it is not the advanced age itself but other concomitant factors, that appear to be responsible for the adverse functional outcomes, perioperative complications, and mortality in super elderly patients.

Association between intraoperative nociception and surgical invasiveness in patients undergoing non-cardiac surgery under general anesthesia: a retrospective cohort study

There are several indexes of intraoperative nociception during surgery under general anesthesia. Although a higher degree of surgical invasiveness increases intraoperative nociception, an association between the index of intraoperative nociception and severity of surgical invasiveness has not been reported. We hypothesized that there is associations between averaged values of nociceptive response (NR) throughout surgery (mean NR), as the index of intraoperative nociception, and surgical severity codes in the Surgical outcome risk tool (SORT) or procedure risk codes in the Surgical mortality probability model (S-MPM). The primary outcome was the association between mean NR and severity scores of surgical procedure. Hence, a single institutional retrospective cohort study was performed in consecutive patients undergoing non-cardiac surgery under general anesthesia from June 2018 to May 2019. There were significant increases in the three categories of procedure risk in the S-MPM corresponding to the increase in mean NR values in 5090 patients. In the SORT, the highest intensity in the four categories of surgical severity also significantly correlated with the increase in mean NR values. Increasing intensity of intraoperative nociception is likely associated with higher severity codes of surgical invasiveness in prediction models for postoperative morbidity and mortality.

Who is a high-risk surgical patient?

PURPOSE OF REVIEW

Timely identification of high-risk surgical candidates facilitate surgical decision-making and allows appropriate tailoring of perioperative management strategies. This review aims to summarize the recent advances in perioperative risk stratification.

RECENT FINDINGS

Use of indices which include various combinations of preoperative and postoperative variables remain the most commonly used risk-stratification strategy. Incorporation of biomarkers (troponin and natriuretic peptides), comprehensive objective assessment of functional capacity, and frailty into the current framework enhance perioperative risk estimation. Intraoperative hemodynamic parameters can provide further signals towards identifying patients at risk of adverse postoperative outcomes. Implementation of machine-learning algorithms is showing promising results in real-time forecasting of perioperative outcomes.

SUMMARY

Perioperative risk estimation is multidimensional including validated indices, biomarkers, functional capacity estimation, and intraoperative hemodynamics. Identification and implementation of targeted strategies which mitigate predicted risk remains a greater challenge.

Pediatric Risk Stratification Is Improved by Integrating Both Patient Comorbidities and Intrinsic Surgical Risk

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Background

Recently developed risk stratification models for perioperative mortality incorporate patient comorbidities as predictors but fail to consider the intrinsic risk of surgical procedures. In this study, the authors used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to demonstrate the relationship between the intrinsic surgical risk and 30-day mortality and develop and validate an accessible risk stratification model that includes the surgical procedures in addition to the patient comorbidities and physical status.

Methods

A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program Pediatric database was performed. The incidence of 30-day mortality was the primary outcome. Surgical Current Procedural Terminology codes with at least 25 occurrences were included. Multivariable logistic regression model was used to determine the predictors for mortality including patient comorbidities and intrinsic surgical risk. An internal validation using bootstrap resampling, and an external validation of the model were performed.

Results

The authors analyzed 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes with an incidence of overall 30-day mortality of 0.34%. Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles instead of broad categorization was significantly associated with 30-day mortality (P &lt; 0.001). Predicted risk of 30-day mortality ranges from 0% with no comorbidities to 4.7% when all comorbidities are present among low-risk surgical procedures and from 0.07 to 46.7% among high-risk surgical procedures. Using an external validation cohort of 110,474 observations, the multivariable predictive risk model displayed good calibration and excellent discrimination with area under curve (c-index) equals 0.95 (95% CI, 0.94 to 0.96; P &lt; 0.001).

Conclusions

Understanding and accurately estimating perioperative risk by accounting for the intrinsic risk of surgical procedures and patient comorbidities will lead to a more comprehensive discussion between patients, families, and providers and could potentially be used to conduct cost analysis and allocate resources.

Transfer of the care of patients with low risk of mortality in postoperative: experience report

OBJECTIVE

Describe the implementation of care transfer flow chart in postoperative, based on a risk classification model.

METHOD

Experience report on the implementation of a pilot project between the post-anesthetic recovery room and the surgical hospitalization unit, developed between December/2016 and March/2017, aimed at transferring s the care of patients with low risk of postoperative mortality, in a university hospital in the South of Brazil.

RESULTS

The project made it possible to expedite the discharge of the patient from the Post-Anesthetic Recovery Room to the surgical hospitalization unit, to qualify the records regarding nursing care and to optimize the time of nurses in both units for care activities.

CONCLUSION

The implementation of a care transfer flow chart from the risk classification for postoperative patients contributed to a more effective communication, culminating in improvements in patient safety.

Predictors of in-hospital mortality in patients undergoing elective surgery in a university hospital: a prospective cohort

Introduction

Morbidity and mortality associated with urgent or emergency surgeries are high compared to elective procedures. Perioperative risk scores identify the non-elective character as an independent factor of complications and death. The present study aims to characterize the population undergoing non-elective surgeries at the Hospital de Clínicas de Porto Alegre and identify the clinical and surgical factors associated with death within 30 days postoperatively.

Methodology

A prospective cohort study of 187 patients undergoing elective surgeries between April and May 2014 at the Hospital de Clínicas, Porto Alegre. Patient-related data, pre-operative risk situations, and surgical information were evaluated. Death in 30 days was the primary outcome measured.

Results

The mean age of the sample was 48.5 years, and 84.4% of the subjects had comorbidities. The primary endpoint was observed in 14.4% of the cases, with exploratory laparotomy being the procedure with the highest mortality (47.7%). After multivariate logistic regression, age (odds ratio [OR] 1.0360, p < 0.05), anemia (OR 3.961, p < 0.05), acute or chronic renal insufficiency (OR 6.075, p < 0.05), sepsis (OR 7.027, p < 0.05), and patient-related risk factors for mortality, in addition to the large surgery category (OR 7.502, p < 0.05) were identified.

Conclusion

The high mortality rate found may reflect the high complexity of the institution's patients. Knowing the profile of the patients assisted helps in the definition of management priorities, suggesting the need to create specific care lines for groups identified as high risk in order to reduce perioperative complications and deaths.

[Predictors of in-hospital mortality in patients undergoing elective surgery in a university hospital: a prospective cohort]

INTRODUCTION

Morbidity and mortality associated with urgent or emergency surgeries are high compared to elective procedures. Perioperative risk scores identify the non-elective character as an independent factor of complications and death. The present study aims to characterize the population undergoing non-elective surgeries at the Hospital de Clínicas de Porto Alegre and identify the clinical and surgical factors associated with death within 30 days postoperatively.

METHODOLOGY

A prospective cohort study of 187 patients undergoing elective surgeries between April and May 2014 at the Hospital de Clínicas, Porto Alegre. Patient-related data, pre-operative risk situations, and surgical information were evaluated. Death in 30 days was the primary outcome measured.

RESULTS

The mean age of the sample was 48.5 years, and 84.4% of the subjects had comorbidities. The primary endpoint was observed in 14.4% of the cases, with exploratory laparotomy being the procedure with the highest mortality (47.7%). After multivariate logistic regression, age (odds ratio [OR] 1.0360, p <0.05), anemia (OR 3.961, p <0.05), acute or chronic renal insufficiency (OR 6.075, p <0.05), sepsis (OR 7.027, p <0.05), and patient-related risk factors for mortality, in addition to the large surgery category (OR 7.502, p <0.05) were identified.

CONCLUSION

The high mortality rate found may reflect the high complexity of the institution's patients. Knowing the profile of the patients assisted helps in the definition of management priorities, suggesting the need to create specific care lines for groups identified as high risk in order to reduce perioperative complications and deaths.

Usefulness of simple biomarkers at admission as independent indicators and predictors of in-hospital mortality in older hip fracture patients

INTRODUCTION

The data on predictive value of the routinely obtained preoperative biochemical parameters in hip fracture (HF) patients are limited. The aims of this study were to examine in older HF patients (1) the relationships between a broad set of routine laboratory parameters at admission and in-hospital mortality, and (2) evaluate the prognostic value the biomarkers and clinical characteristics (alone or in combination) provide to predict a fatal outcome.

PATIENTS AND METHODS

In 1820 consecutive patients with low-trauma osteoporotic HF aged >60 years (mean age 82.8 ± 8.1 years; 76.4% women; 65% community-dwelling) 35 laboratory variables along with 20 clinical and socio-demographic characteristics at admission were analysed. The validation cohort included data on 455 older (≥60 years of age) HF patients (mean age 82.1 ± 8.0 years, 72.1% women).

RESULTS

The mortality rate was 6% (n = 109). On univariate analysis 14 laboratory and 8 clinical parameters have been associated with in-hospital mortality. Multiple regression analyses determined 7 variables at admission as independent indicators of a fatal outcome: 4 biomarkers (albumin <33 g/L; alanine aminotransferase/gamma-glutamyl transferase ratio [GGT/ALT] >2.5; parathyroid hormone [PTH] >6.8 pmol/L; 25(OH)vitamin D < 25 nmol/L) and 3 pre-fracture clinical conditions (history of myocardial infarction, chronic kidney disease [GFR <60 ml/min/1.73 m²] and chronic obstructive pulmonary disease); the area under the receiver operating characteristic curve (AUC) was 0.75 (95%CI 0.70-0.80). The risk of in-hospital death was 1.6-2.6 times higher in subjects with any of these risk factors (RFs), and increased by 2.6-6.0-fold in patients with any two RFs (versus no RFs). The mortality rate increased stepwise as the number of RFs increased (from 0.43% -none RF to 16.8%- ≥4RF). The prognostic value of a single RF was low (AUC ≤0.635) but combination of 2 or more RFs improved the prediction significantly; AUC reached 0.84(95%CI 0.77-0.90) when ≥4 RFs (versus 0-1RF) were present. In the validated and main cohorts the number of predicted by 1, 2, 3 or ≥4 RFs and observed deaths were practically similar.

CONCLUSIONS

In HF patients, seven easily identifiable at admission characteristics, including 4 biomarkers, are strong and independent indicators of in-hospital mortality and can be used for risk stratification and individualised management.