The statistical importance of P-POSSUM scores for predicting mortality after emergency laparotomy in geriatric patients

Intraoperative hypotension associated with postoperative acute kidney injury in hypertension patients undergoing non-cardiac surgery: a retrospective cohort study

Background

Acute kidney injury (AKI) is a common surgical complication and is associated with intraoperative hypotension. However, the total duration and magnitude of intraoperative hypotension associated with AKI remains unknown. In this study, the causal relationship between the intraoperative arterial pressure and postoperative AKI was investigated among chronic hypertension patients undergoing non-cardiac surgery.

Methods

A retrospective cohort study of 6552 hypertension patients undergoing non-cardiac surgery (2011 to 2019) was conducted. The primary outcome was AKI as diagnosed with the Kidney Disease-Improving Global Outcomes criteria and the primary exposure was intraoperative hypotension. Patients' baseline demographics, pre- and post-operative data were harvested and then analyzed with multivariable logistic regression to assess the exposure-outcome relationship.

Results

Among 6552 hypertension patients, 579 (8.84%) had postoperative AKI after non-cardiac surgery. The proportions of patients admitted to ICU (3.97 vs. 1.24%, p < 0.001) and experiencing all-cause death (2.76 vs. 0.80%, p < 0.001) were higher in the patients with postoperative AKI. Moreover, the patients with postoperative AKI had longer hospital stays (13.50 vs. 12.00 days, p < 0.001). Intraoperative mean arterial pressure (MAP) < 60 mmHg for >20 min was an independent risk factor of postoperative AKI. Furthermore, MAP <60 mmHg for >10 min was also an independent risk factor of postoperative AKI in patients whose MAP was measured invasively in the subgroup analysis.

Conclusions

Our work suggested that MAP < 60 mmHg for >10 min measured invasively or 20 min measured non-invasively during non-cardiac surgery may be the threshold of postoperative AKI development in hypertension patients. This work may serve as a perioperative management guide for chronic hypertension patients.

Trial registration

clinical trial number: ChiCTR2100050209 (8/22/2021). http://www.chictr.org.cn/showproj.aspx?proj=132277.

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Background

The relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification.

Methods

The dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process.

Results

An initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output.

Conclusion

The interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.

Cardiac risk stratification and adverse outcomes in surgically managed patients with isolated traumatic spine injuries

INTRODUCTION

As the incidence of traumatic spine injuries has been steadily increasing, especially in the elderly, the ability to categorize patients based on their underlying risk for the adverse outcomes could be of great value in clinical decision making. This study aimed to investigate the association between the Revised Cardiac Risk Index (RCRI) and adverse outcomes in patients who have undergone surgery for traumatic spine injuries.

METHODS

All adult patients (18 years or older) in the 2013-2019 TQIP database with isolated spine injuries resulting from blunt force trauma, who underwent spinal surgery, were eligible for inclusion in the study. The association between the RCRI and in-hospital mortality, cardiopulmonary complications, and failure-to-rescue (FTR) was determined using Poisson regression models with robust standard errors to adjust for potential confounding.

RESULTS

A total of 39,391 patients were included for further analysis. In the regression model, an RCRI ≥ 3 was associated with a threefold risk of in-hospital mortality [adjusted IRR (95% CI): 3.19 (2.30-4.43), p < 0.001] and cardiopulmonary complications [adjusted IRR (95% CI): 3.27 (2.46-4.34), p < 0.001], as well as a fourfold risk of FTR [adjusted IRR (95% CI): 4.27 (2.59-7.02), p < 0.001], compared to RCRI 0. The risk of all adverse outcomes increased stepwise along with each RCRI score.

CONCLUSION

The RCRI may be a useful tool for identifying patients with traumatic spine injuries who are at an increased risk of in-hospital mortality, cardiopulmonary complications, and failure-to-rescue after surgery.

Comparison between P-POSSUM and NELA risk score for patients undergoing emergency laparotomy in Egyptian patients

BACKGROUND AND AIMS

The Portsmouth-Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity (P-POSSUM) is one of the scores that is used most frequently for determining the likelihood of mortality in patients undergoing emergency laparotomy. National Emergency Laparotomy Audit (NELA) presents a novel and validated score. Therefore, we aimed to compare the performance of the NELA and P-POSSUM mortality risk scores in predicting 30-day and 90-day mortality in patients undergoing emergency laparotomy.

METHODS

Between August 2020 and October 2022, this cohort study was undertaken at Menoufia University Hospital. We compared the P-POSSUM, preoperative NELA, and postoperative NELA scores in patients undergoing emergency laparotomy. All variables needed to calculate the used scores were collected. The outcomes included the death rates at 30 and 90 days. By calculating the area under the curve (AUC) for every mortality instrument, the discrimination of the various methods was evaluated and compared.

RESULTS

Data from 670 patients were included. The observed risk of 30-day and 90-day mortality was 10.3% (69/670) and 13.13% (88/670), respectively. Concerning 30-day mortality, the AUC was 0.774 for the preoperative NELA score, 0.763 for the preoperative P-POSSUM score, and 0.780 for the postoperative NELA score. Regarding 90-day mortality, the AUCs for the preoperative NELA score, preoperative P-POSSUM score, and postoperative NELA score were 0.649 (0.581-0.717), 0.782 (0.737-0.828), and 0.663 (0.608-0.718), respectively. There was noticeable difference in the three models' capacity for discrimination, according to pairwise comparisons.

CONCLUSIONS

The probability of 30-day and 90-day death across the entire population was underestimated by the NELA and P-POSSUM scores. There was discernible difference in predictive performance between the two scores.

Clinical prediction models and risk tools for early detection of patients at risk of surgical site infection and surgical wound dehiscence: a scoping review

OBJECTIVE

Despite advances in surgical techniques, intraoperative practice and a plethora of advanced wound therapies, surgical wound complications (SWCs), such as surgical site infection (SSI) and surgical wound dehiscence (SWD), continue to pose a considerable burden to the patient and healthcare setting. Predicting those patients at risk of a SWC may give patients and healthcare providers the opportunity to implement a tailored prevention plan or potentially ameliorate known risk factors to improve patient postoperative outcomes.

METHOD

A scoping review of the literature for studies which reported predictive power and internal/external validity of risk tools for clinical use in predicting patients at risk of SWCs after surgery was conducted. An electronic search of three databases and two registries was carried out with date restrictions. The search terms included 'prediction surgical site infection' and 'prediction surgical wound dehiscence'.

RESULTS

A total of 73 records were identified from the database search, of which six studies met the inclusion criteria. Of these, the majority of validated risk tools were predominantly within the cardiothoracic domain, and targeted morbidity and mortality outcomes. There were four risk tools specifically targeting SWCs following surgery.

CONCLUSION

The findings of this review have highlighted an absence of well-developed risk tools specifically for SSI and/or SWD in most surgical populations. This review suggests that further research is required for the development and clinical implementation of rigorously validated and fit-for-purpose risk tools for predicting patients at risk of SWCs following surgery. The ability to predict such patients enables the implementation of preventive strategies, such as the use of prophylactic antibiotics, delayed timing of surgery, or advanced wound therapies following a procedure.

Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations

Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.

Use of POSSUM (Physiologic and Operative Severity Score for the Study of Mortality and Morbidity) and Portsmouth-POSSUM for Surgical Assessment in Patients Undergoing Emergency Abdominal Surgeries

INTRODUCTION

The POSSUM (Physiologic and Operative Severity Score for the Study of Mortality and Morbidity) and Portsmouth-POSSUM (P-POSSUM) models have been popularly recommended as appropriate for predicting postoperative mortality and morbidity in surgical practice. This study aims to evaluate the efficacy and accuracy of both scoring systems for surgical risk assessment in predicting postoperative mortality and morbidity in patients undergoing emergency abdominal surgeries.

METHODOLOGY

The study was conducted as a part of a post-doctoral fellowship program. A total of 150 patients, undergoing emergency abdominal surgery in a tertiary care hospital in Bhubaneswar, were evaluated using POSSUM and P-POSSUM. Physiological scoring was done prior to surgery and operative scoring was performed intra-operatively. Patients were followed up for 30 days after the operative period. The observed mortality rate was then compared with POSSUM and P-POSSUM predicted mortality rates.

RESULTS

POSSUM predicted a morbidity rate of 116, whereas the actual morbidity rate was 92 (p < 0.05). P-POSSUM predicted a morbidity rate of 109, whereas the actual morbidity rate was 92 (p < 0.05). POSSUM predicted a mortality rate of 23, whereas the actual mortality rate was 21 (p < 0.05). P-POSSUM predicted a mortality rate of 25, whereas the actual mortality rate was 21 (p < 0.05).

CONCLUSIONS

With a reasonably good prediction of morbidity and mortality rate, POSSUM and P-POSSUM scores are both effective scoring systems in clinical practice for use in abdominal surgery.

The revised cardiac risk index is associated with morbidity and mortality independent of injury severity in elderly patients with rib fractures

BACKGROUND

Risk factors for mortality and in-hospital morbidity among geriatric patients with traumatic rib fractures remain unclear. Such patients are often frail and demonstrate a high comorbidity burden. Moreover, outcomes anticipated by current rubrics may reflect the influence of multisystem injury or surgery, and thus not apply to isolated injuries in geriatric patients. We hypothesized that the Revised Cardiac Risk Index (RCRI) may assist in risk-stratifying geriatric patients following rib fracture.

METHODS

All geriatric patients (age ≥65 years) with a conservatively managed rib fracture owing to an isolated thoracic injury (thorax AIS ≥1), in the 2013-2019 TQIP database were assessed including demographics and outcomes. The association between the RCRI and in-hospital morbidity as well as mortality was analyzed using Poisson regression models while adjusting for potential confounders.

RESULTS

96,750 geriatric patients sustained rib fractures. Compared to those with RCRI 0, patients with an RCRI score of 1 had a 16% increased risk of in-hospital mortality [adjusted incidence rate ratio (adj-IRR), 95% confidence interval (CI): 1.16 (1.02-1.32), p=0.020]. An RCRI score of 2 [adj-IRR (95% CI): 1.72 (1.44-2.06), p<0.001] or ≥3 [adj-IRR (95% CI): 3.07 (2.31-4.09), p<0.001] was associated with an even greater mortality risk. Those with an increased RCRI also exhibited a higher incidence of myocardial infarction, cardiac arrest, stroke, and acute respiratory distress syndrome.

CONCLUSIONS

Geriatric patients with rib fractures and an RCRI ≥1 represent a vulnerable and high-risk group. This index may inform the decision to admit for inpatient care and can also guide patient and family counseling as well as computer-based decision-support.

Applying Evidence-based Principles to Guide Emergency Surgery in Older Adults

Although outcomes for older adults undergoing elective surgery are generally comparable to younger patients, outcomes associated with emergency surgery are poor. These adverse outcomes are in part because of the physiologic changes associated with aging, increased odds of comorbidities in older adults, and a lower probability of presenting with classic "red flag" physical examination findings. Existing evidence-based perioperative best practice guidelines perform better for elective compared with emergency surgery; so, decision making for older adults undergoing emergency surgery can be challenging for surgeons and other clinicians and may rely on subjective experience. To aid surgical decision making, clinicians should assess premorbid functional status, evaluate for the presence of geriatric syndromes, and consider social determinants of health. Documentation of care preferences and a surrogate decision maker are critical. In discussing the risks and benefits of surgery, patient-centered narrative formats with inclusion of geriatric-specific outcomes are important. Use of risk calculators can be meaningful, although limitations exist. After surgery, daily evaluation for common postoperative complications should be considered, as well as early discharge planning and palliative care consultation, if appropriate. The role of the geriatrician in emergency surgery for older adults may vary based on the acuity of patient presentation, but perioperative consultation and comanagement are strongly recommended to optimize care delivery and patient outcomes.

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention

Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predicting the preprocedural risk of AKI, although the scoring system requires a number of clinical contents. We sought to examine whether machine learning (ML) techniques could predict AKI with fewer NCDR-AKI risk model variables within a comparable PCI database in Japan. We evaluated 19,222 consecutive patients undergoing PCI between 2008 and 2019 in a Japanese multicenter registry. AKI was defined as an absolute or a relative increase in serum creatinine of 0.3 mg/dL or 50%. The data were split into training (N = 16,644; 2008-2017) and testing datasets (N = 2578; 2017-2019). The area under the curve (AUC) was calculated using the light gradient boosting model (GBM) with selected variables by Lasso and SHapley Additive exPlanations (SHAP) methods among 12 traditional variables, excluding the use of an intra-aortic balloon pump, since its use was considered operator-dependent. The incidence of AKI was 9.4% in the cohort. Lasso and SHAP methods demonstrated that seven variables (age, eGFR, preprocedural hemoglobin, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction/unstable angina, heart failure symptoms, and cardiogenic shock) were pertinent. AUC calculated by the light GBM with seven variables had a performance similar to that of the conventional logistic regression prediction model that included 12 variables (light GBM, AUC [training/testing datasets]: 0.779/0.772; logistic regression, AUC [training/testing datasets]: 0.797/0.755). The AKI risk model after PCI using ML enabled adequate risk quantification with fewer variables. ML techniques may aid in enhancing the international use of validated risk models.

Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review

BACKGROUND

Machine learning (ML) has been introduced in various fields of healthcare. In colorectal surgery, the role of ML has yet to be reported. In this systematic review, an overview of machine learning models predicting surgical outcomes after colorectal surgery is provided.

METHODS

Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using machine learning models for patients undergoing colorectal surgery. To be eligible for inclusion, studies needed to apply machine learning models for patients undergoing colorectal surgery. Absence of machine learning or colorectal surgery or studies reporting on reviews, children, study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of machine learning models.

RESULTS

A total of 1821 studies were analysed, resulting in the inclusion of 31 articles. A vast proportion of ML algorithms have been used to predict the course of disease and response to neoadjuvant chemoradiotherapy. Radiomics have been applied most frequently, along with predictive accuracies up to 91%. However, most studies included a retrospective study design without external validation or calibration.

CONCLUSIONS

Machine learning models have shown promising potential in predicting surgical outcomes after colorectal surgery. However, large-scale data is warranted to bridge the gap between calibration and external validation. Clinical implementation is needed to demonstrate the contribution of ML within daily practice.

Association of preoperative risk factors and mortality in elderly patients with emergency abdominal surgery: A retrospective cohort study

Background

Older patients undergoing emergency laparotomy have high morbidity and mortality rates. Preoperative risk assessment with good predictors is an appropriate measure in this population. Frailty status is significantly associated with postoperative outcomes in older adults. This study aimed to investigate the effect of preoperative risk factors and frailty on short-term outcomes following emergency surgery for acute abdomen in older patients.

Methods

This study included older patients (≥65 years of age) who underwent emergency abdominal surgery. We retrospectively analyzed their demographic and clinical variables and used the modified Frailty Index-11 to evaluate their frailty status. The primary outcome was the 30-day mortality rate. We also analyzed risk factors of mortality in these patients.

Results

The study included 150 patients with a median age of 74 years. The mortality rate was 17.3% (n=26). We observed significantly higher mortality rates in patients who were obese and who had higher American Society of Anesthesiology (ASA grades) (p<0.05). Frailty status was worse in deceased group (p<0.001), when compared to individuals who survived. Septic shock was associated with the development of mortality (p<0.001). Multivariate regression analysis revealed that ASA grade was the only independent risk factor for mortality (odds ratio=19.642; 95% confidence interval, 3.886–99.274; p<0.001).

Conclusion

Older patients with obesity and frailty presenting with higher ASA grades and septic shock had the worst survival following emergency abdominal surgery. The ASA grade was an independent risk factor for mortality.

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review

PURPOSE

An indication for surgical therapy includes balancing benefits against risk, which remains a key task in all surgical disciplines. Decisions are oftentimes based on clinical experience while guidelines lack evidence-based background. Various medical fields capitalized the application of machine learning (ML), and preliminary research suggests promising implications in surgeons' workflow. Hence, we evaluated ML's contemporary and possible future role in clinical decision-making (CDM) focusing on abdominal surgery.

METHODS

Using the PICO framework, relevant keywords and research questions were identified. Following the PRISMA guidelines, a systemic search strategy in the PubMed database was conducted. Results were filtered by distinct criteria and selected articles were manually full text reviewed.

RESULTS

Literature review revealed 4,396 articles, of which 47 matched the search criteria. The mean number of patients included was 55,843. A total of eight distinct ML techniques were evaluated whereas AUROC was applied by most authors for comparing ML predictions vs. conventional CDM routines. Most authors (N = 30/47, 63.8%) stated ML's superiority in the prediction of benefits and risks of surgery. The identification of highly relevant parameters to be integrated into algorithms allowing a more precise prognosis was emphasized as the main advantage of ML in CDM.

CONCLUSIONS

A potential value of ML for surgical decision-making was demonstrated in several scientific articles. However, the low number of publications with only few collaborative studies between surgeons and computer scientists underpins the early phase of this highly promising field. Interdisciplinary research initiatives combining existing clinical datasets and emerging techniques of data processing may likely improve CDM in abdominal surgery in the future.

Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir

We aimed to create the prediction model of in-hospital mortality using machine learning methods for patients with coronavirus disease 2019 (COVID-19) treated with steroid and remdesivir. We reviewed 1571 hospitalized patients with laboratory confirmed COVID-19 from the Mount Sinai Health System treated with both steroids and remdesivir. The important variables associated with in-hospital mortality were identified using LASSO (least absolute shrinkage and selection operator) and SHAP (SHapley Additive exPlanations) through the light gradient boosting model (GBM). The data before February 17th, 2021 (N = 769) was randomly split into training and testing datasets; 80% versus 20%, respectively. Light GBM models were created with train data and area under the curves (AUCs) were calculated. Additionally, we calculated AUC with the data between February 17th, 2021 and March 30th, 2021 (N = 802). Of the 1571 patients admitted due to COVID-19, 331 (21.1%) died during hospitalization. Through LASSO and SHAP, we selected six important variables; age, hypertension, oxygen saturation, blood urea nitrogen, intensive care unit admission, and endotracheal intubation. AUCs using training and testing datasets derived from the data before February 17th, 2021 were 0.871/0.911. Additionally, the light GBM model has high predictability for the latest data (AUC: 0.881) (https://risk-model.herokuapp.com/covid). A high-value prediction model was created to estimate in-hospital mortality for COVID-19 patients treated with steroid and remdesivir.

Mining Pre-Surgical Patterns Able to Discriminate Post-Surgical Outcomes in the Oncological Domain

Understanding the individualized risks of undertaking surgical procedures is essential to personalize preparatory, intervention and post-care protocols for minimizing post-surgical complications. This knowledge is key in oncology given the nature of interventions, the fragile profile of patients with comorbidities and cytotoxic drug exposure, and the possible cancer recurrence. Despite its relevance, the discovery of discriminative patterns of post-surgical risk is hampered by major challenges: i) the unique physiological and demographic profile of individuals, as well as their differentiated post-surgical care; ii) the high-dimensionality and heterogeneous nature of available biomedical data, combining non-identically distributed risk factors, clinical and molecular variables; iii) the need to generalize tumors have significant histopathological differences and individuals undertake unique surgical procedures; iv) the need to focus on non-trivial patterns of post-surgical risk, while guaranteeing their statistical significance and discriminative power; and v) the lack of interpretability and actionability of current approaches. Biclustering, the discovery of groups of individuals correlated on subsets of variables, has unique properties of interest, being positioned to satisfy the aforementioned challenges. In this context, this work proposes a structured view on why, when and how to apply biclustering to mine discriminative patterns of post-surgical risk with guarantees of usability, a subject remaining unexplored up to date. These patterns offer a comprehensive view on how the patient profile, cancer histopathology and entailed surgical procedures determine: i) post-surgical complications, ii) survival, and iii) hospitalization needs. The gathered results confirm the role of biclustering in comprehensively finding interpretable, actionable and statistically significant patterns of post-surgical risk. The found patterns are already assisting healthcare professionals at IPO-Porto to establish specialized pre-habilitation protocols and bedside care.

Cardiac risk stratification in emergency resection for colonic tumours

Background

Despite advances in perioperative care, the postoperative mortality rate after emergency oncological colonic resection remains high. Risk stratification may allow targeted perioperative optimization and cardiac risk stratification. This study aimed to test the hypothesis that the Revised Cardiac Risk Index (RCRI), a user-friendly tool, could identify patients who would benefit most from perioperative cardiac risk mitigation.

Methods

Patients who underwent emergency resection for colonic cancer from 2007 to 2017 and registered in the Swedish Colorectal Cancer Registry (SCRCR) were analysed retrospectively. These patients were cross-referenced by social security number to the Swedish National Board of Health and Welfare data set, a government registry of mortality, and co-morbidity data. RCRI scores were calculated for each patient and correlated with 90-day postoperative mortality risk, using Poisson regression with robust error of variance.

Results

Some 5703 patients met the study inclusion criteria. A linear increase in crude 90-day postoperative mortality was detected with increasing RCRI score (37.3 versus 11.3 per cent for RCRI 4 or more versus RCRI 1; P < 0.001). The adjusted 90-day all-cause mortality risk was also significantly increased (RCRI 4 or more versus RCRI 1: adjusted incidence rate ratio 2.07, 95 per cent c.i. 1.49 to 2.89; P < 0.001).

Conclusion

This study documented an association between increasing cardiac risk and 90-day postoperative mortality. Those undergoing emergency colorectal surgery for cancer with a raised RCRI score should be considered high-risk patients who would most likely benefit from enhanced postoperative monitoring and critical care expertise.

Patients over 65 years with Acute Complicated Calculous Biliary Disease are Treated Differently-Results and Insights from the ESTES Snapshot Audit

BACKGROUND

Accrued comorbidities are perceived to increase operative risk. Surgeons may offer operative treatments less often to their older patients with acute complicated calculous biliary disease (ACCBD). We set out to capture ACCBD incidence in older patients across Europe and the currently used treatment algorithms.

METHODS

The European Society of Trauma and Emergency Surgery (ESTES) undertook a snapshot audit of patients undergoing emergency hospital admission for ACCBD between October 1 and 31 2018, comparing patients under and ≥ 65 years. Mortality, postoperative complications, time to operative intervention, post-acute disposition, and length of hospital stay (LOS) were compared between groups. Within the ≥ 65 cohort, comorbidity burden, mortality, LOS, and disposition outcomes were further compared between patients undergoing operative and non-operative management.

RESULTS

The median age of the 338 admitted patients was 67 years; 185 patients (54.7%) of these were the age of 65 or over. Significantly fewer patients ≥ 65 underwent surgical treatment (37.8% vs. 64.7%, p < 0.001). Surgical complications were more frequent in the ≥ 65 cohort than younger patients, and the mean postoperative LOS was significantly longer. Postoperative mortality was seen in 2.2% of patients ≥ 65 (vs. 0.7%, p = 0.253). However, operated elderly patients did not differ from non-operated in terms of comorbidity burden, mortality, LOS, or post-discharge rehabilitation need.

CONCLUSIONS

Few elderly patients receive surgical treatment for ACCBD. Expectedly, postoperative morbidity, LOS, and the requirement for post-discharge rehabilitation are higher in the elderly than younger patients but do not differ from elderly patients managed non-operatively. With multidisciplinary perioperative optimization, elderly patients may be safely offered optimal treatment.

TRIAL REGISTRATION

ClinicalTrials.gov (Trial # NCT03610308).

Prediction of ICU admission after orthopedic surgery in elderly patients

Objectives:

Prediction of ICU admission after surgery are important for rational decision-making for different patients in clinical practice. Little information is available about the risk factors of postoperative ICU admission in elderly patients undergoing orthopedic surgery. This study aimed to identify risk factors and develop a predictive model for postoperative ICU admission in elderly patients undergoing orthopedic surgery.

Methods:

A total of 2826 cases of elderly patients receiving orthopedic surgery from October 2010 to September 2016 were retrospectively collected and analyzed. Logistic regression was used to evaluate the impacts of covariates. Support vector machine (SVM) was employed to develop a predictive model based on all pre-operative covariates and the demographic information.

Results:

There were 256 patients transferred to ICU after surgery. ASA III or IV and emergency surgery were found to be independent risk factors while neuraxial anesthesia and joint surgery were protective factors. In addition, a SVM-based predictive model was developed, which had a sensitivity of 90.99%, a specificity of 99.10% and an area under ROC curve of 0.9678.

Conclusions:

Our study revealed that emergency surgery, anesthesia method, surgery type and ASA grade were risk factors to predict postoperative ICU admission in elderly orthopedic patients.