Validation of POSSUM, P-POSSUM and the surgical risk scale in major general surgical operations in Harare: A prospective observational study

Predictive value of POSSUM scoring system for postoperative complications and mortality in elderly patients with colorectal cancer

BACKGROUND

The POSSUM scoring system, widely employed in assessing surgical risks, offers a simplified and objective approach for the prediction of complications and mortality in patient. Despite its effectiveness in various surgical fields, including orthopedics and cardiovascular surgery, yet its utilization in elderly patients undergoing colorectal cancer surgery is infrequent.

OBJECTIVE

To analyze the predictive value of POSSUM scoring system for postoperative complications and mortality in elderly with colorectal cancer.

METHODS

306 elderly colorectal cancer patients were grouped according to the complications and death within 30 days after surgery. Among them, 108 cases in complication group, 198 cases in non-complication group, 16 cases in death group and 290 cases in survival group. POSSUM scores of all subjects were obtained and its predictive value for postoperative complications and mortality of elderly was conducted by ROC curve.

RESULTS

No apparent difference were observed in complications and mortality among patients with different disease types, operation types and operation timing (P> 0.05). The R2 in complication group was higher than non-complication group (P< 0.05). The R1 in death group were higher than survival group (P< 0.05). The AUC of R2 for predicting postoperative complications was 0.955 with a sensitivity of 88.89% and a specificity of 94.44% and the AUC of R1 for evaluating postoperative mortality of elderly with colorectal cancer was 0.783 with a sensitivity of 56.25% and a specificity of 82.93%.

CONCLUSION

POSSUM score system has a certain predictive value for postoperative complications and mortality in elderly with colorectal cancer. However, the predicted mortality rate is higher than actual mortality rate.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

Surgical Apgar Score can accurately predict the severity of post-operative complications following emergency laparotomy

BACKGROUND

The Surgical Apgar Score (SAS) describes a feasible and objective tool for predicting surgical outcomes. However, the accuracy of the score and its correlation with the complication severity has not been well established in many grounds of low resource settings.

OBJECTIVE

To determine the accuracy of Surgical Apgar Score in predicting the severity of post-operative complications among patients undergoing emergency laparotomy at Muhimbili National Hospital.

METHODS

A prospective cohort study was conducted for a period of 12 months; patients were followed for 30 days, the risk of complication was classified using the Surgical Apgar Score (SAS), severity of complication was estimated using the Clavien Dindo Classification (CDC) grading scheme and Comprehensive Complication Index (CCI). Spearman correlation and simple linear regression statistic models were applied to establish the relationship between Surgical Apgar Score (SAS) and Comprehensive Complication Index (CCI). The Accuracy of SAS was evaluated by determining its discriminatory capacity on Receiver Operating Characteristics (ROC) curve, data normality was tested by Shapiro-Wilk statistic 0.929 (p < 0.001).Analysis was done using International Business Machine Statistical Product and Service Solution (IBM SPSS) version 27.

RESULTS

Out of the 111 patients who underwent emergency laparotomy, 71 (64%) were Male and the median age (IQR) was 49 (36, 59).The mean SAS was 4.86 (± 1.29) and the median CCI (IQR) was 36.20 (26.2, 42.40). Patients in the high-risk SAS group (0-4) were more likely to experience severe and life-threatening complications, with a mean CCI of 53.3 (95% CI: 47.2-63.4), compared to the low-risk SAS group (7-10) with a mean CCI of 21.0 (95% CI: 5.3-36.2). A negative correlation was observed between SAS and CCI, with a Spearman r of -0.575 (p < 0.001) and a regression coefficient b of -11.5 (p < 0.001). The SAS demonstrated good accuracy in predicting post-operative complications, with an area under the curve of 0.712 (95% CI: 0.523-0.902, p < 0.001) on the ROC.

CONCLUSION

This study has demonstrated that SAS can accurately predict the occurrence of complications following emergency laparotomy at Muhimbili National Hospital.

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.

Preoperative functional capacity and postoperative outcomes following abdominal and pelvic cancer surgery: a systematic review and meta-analysis

BACKGROUND

There is clinical uncertainty regarding an association between preoperative functional capacity of cancer patients, and postoperative outcomes. The aim of this systematic review and meta-analysis is to investigate whether poor performance on preoperative six-minute walk test (6MWT) or five-times sit to stand test (5STS) is associated with worse postoperative complication rates and prolonged length of hospital stay (LOS) in cancer patients.

METHODS

An electronic search was performed from earliest available record to 26th February 2021 in MEDLINE, Embase and AMED. Studies investigating the association between preoperative physical function (measured using either 6MWT or 5STS) and postoperative outcomes (complications and LOS) in patients with gastrointestinal, abdominal and pelvic cancers were included. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. Where possible, summary odds ratios (OR) or mean differences (MD), and 95% confidence intervals (CI) were calculated using random-effect models.

RESULTS

Five studies (379 patients) were included, of which none utilized the 5STS. Overall, studies were rated as having low to moderate risk of bias. Higher preoperative performance on the 6MWT (≥400 m) was associated with low grade postoperative complications (OR = 0.38; 95% CI = 0.15-0.95) but was not associated with a shorter LOS (MD = 3.29; 95%CI = -1.07-7.66).

CONCLUSION

The available evidence suggests that in cancer patients, a higher preoperative functional capacity may be associated with reduced postoperative complications. Conversely, there is no significant association between preoperative function and LOS. Further high-quality studies are needed in this area, including studies involving 5STS.

Morbimortality assessment in abdominal surgery: are we predicting or overreacting?

BACKGROUND

High-risk surgical procedures represent a fundamental part of general surgery practice due to its significant rates of morbidity and mortality. Different predictive tools have been created in order to quantify perioperative morbidity and mortality risk. POSSUM (Physiological and Operative Severity Score for the enumeration of Mortality and morbidity) is one of the most widely validated predictive scores considering physiological and operative variables to precisely define morbimortality risk. Nevertheless, seeking greater accuracy in predictions P-POSSUM was proposed. We aimed to compare POSSUM and P-POSSUM for patients undergoing abdominal surgery.

METHODS

A retrospective observational study with a prospective database was conducted. Patients over 18 years old who complied with inclusion criteria between 2015 and 2016 were included. Variables included in the POSSUM and P-POSSUM Scores were analyzed. Descriptive statistics of all study parameters were provided. The analysis included socio-demographic data, laboratory values ​​, and imaging. Bivariate analysis was performed.

RESULTS

350 Patients were included in the analysis, 55.1% were female. The mean age was 55.9 ± 20.4 years old. POSSUM revealed a moderated index score in 61.7% of the patients, mean score of 12.85 points ± 5.61. 89.1% of patients had no neoplastic diagnosis associated. Overall morbidity and mortality rate was 14.2% and 7.1%. P-POSSUM could predict more precisely mortality (p < 0.00).

CONCLUSIONS

The POSSUM score is likely to overestimate the risk of morbidity and mortality in patients with high/moderate risk, while the P-POSSUM score seems to be a more accurate predictor of mortality risk. Further studies are needed to confirm our results.

Perioperative Risk Stratification: A Need for an Improved Assessment in Surgery and Anesthesia-A Pilot Study

Background and Objectives: Numerous scoring systems have been introduced into modern medicine. None of the scoring systems assessed both anesthetic and surgical risk of the patient, predict the morbidity, mortality, or the need for postoperative intensive care unit admission. The aim of this study was to compare the anesthetic and surgical scores currently used, for a better evaluation of perioperative risks, morbidity, and mortality. Material and Methods: This is a pilot, prospective, observational study. We enrolled 50 patients scheduled for elective surgery. Anesthetic and surgery risk was assessed using American Society of Anesthesiologists (ASA) scale, Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (P-POSSUM), Acute Physiology and Chronic Health Evaluation (APACHE II), and Surgical APGAR Score (SAS) scores. The real and the estimated length of stay (LOS) were registered. Results: We obtained several statistically significant positive correlations: ASA score–P-POSSUM (p < 0.01, r = 0.465); ASA score–SAS, (p < 0.01, r = −0.446); ASA score–APACHE II, (p < 0.01 r = 0.519); predicted LOS and ASA score (p < 0.01, r = 0.676); predicted LOS and p-POSSUM (p < 0.01, r = 0.433); and predicted LOS and APACHE II (p < 0.01, r = 0.454). A significant negative correlation between predicted LOS, real LOS, ASA class, and SAS (p < 0.05) was observed. We found a statistically significant difference between the predicted and actual LOS (p < 001). Conclusions: Anesthetic, surgical, and severity scores, used together, provide clearer information about mortality, morbidity, and LOS. ASA scale, associated with surgical scores and severity scores, presents a better image of the patient’s progress in the perioperative period. In our study, APACHE II is the best predictor of mortality, followed by P-POSSUM and SAS. P-POSSUM score and ASA scale may be complementary in terms of preoperative physiological factors, providing valuable information for postoperative outcomes.

Prediction of Mortality in Patients After Oncologic Gastrointestinal Surgery: Comparison of the ASA, APACHE II, and POSSUM Scoring Systems

Scoring systems have been developed to predict the expected mortality and morbidity in surgical procedures. In this study, our aim was to compare the ASA (American Society of Anesthesiologists), APACHE (Acute Physiology and Chronic Health Evaluation) II, POSSUM (Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity) scoring systems as predictors of mortality in patients who underwent gastrointestinal oncologic surgery, followed, and were admitted to the intensive care unit during the postoperative period. We examined the files of 82 patients who underwent oncologic gastrointestinal surgery and followed up in the intensive care units (ICUs). The patients’ APACHE II scores and predicted mortality rates (PMR) according to the APACHE II, POSSUM, and ASA scores were ​​calculated. The receiver operator characteristic (ROC) curve analysis was used when evaluating the performances of the ASA, APACHE, and POSSUM scoring systems in terms of accurate assessment of mortality. Accordingly, the area under the curve (AUC) = 0.5 no distinction, 0.5 <AUC <0.7 discriminative power of the test is statistically not significant, 0.7 <AUC <0.8 acceptable, 0.8 <AUC <0.9 very good and 0.9 <AUC <1 perfect. The evaluations showed that APACHE II had the best performance with 0.81, followed by POSSUM, which had an acceptable level at 0.78. On the other hand, the ASA score was 0.63 and its discriminative power was identified as statistically insignificant. Our results show that the POSSUM and APACHE II scoring systems were better at predicting mortality than the ASA scoring system for the prediction of mortality in the postoperative period. Both the POSSUM and APACHE II scoring systems can be confidently used for the prediction of mortality in patients undergoing operations due to oncologic gastrointestinal diseases.

How to Develop a Risk Prediction Smartphone App

Purpose. Powered by big data, predictive models provide individualized risk stratification to inform clinical decision-making and mitigate long-term morbidity. We describe how to transform a large institutional dataset into a real-time, interactive clinical decision support mobile user interface for risk prediction. Methods. A clinical decision point ideal for risk stratification and modification was identified. Demographics, medical comorbidities, and operative characteristics were abstracted from the electronic medical record (EMR) using ICD-9 codes. Surgery-specific predictive models were generated using regression modeling and corroborated with internal validation. A clinical support interface was designed in partnership with an app developer, followed by subsequent beta testing and clinical implementation of the final tool. Results. Individual, specialty-specific, and preoperatively actionable models incorporating clustered procedural codes were created. Using longitudinal inpatient, outpatient, and office-based data from a large multicenter health system, all patient and operative variables were weighted according to ß-coefficients. The individual risk model parameters were incorporated into specialty-specific modules and implemented into an accessible iOS/Android compatible mobile application. Conclusions. As proof of concept, we provide a framework for developing a clinical decision support mobile user interface, through the use of clinical and administrative longitudinal data. Point-of-care applications, particularly ones designed with implementation and actionability in mind, have the potential to aid clinicians in identifying and optimizing risk factors that impact the outcome of interest's occurrence, thereby enabling clinicians to take targeted risk-reduction actions. In addition, such applications may help facilitate counseling, informed consent, and shared decision-making, leading to improved patient-centered care.

Predicting morbidity and mortality after radical cystectomy using risk calculators: A comprehensive review of the literature

INTRODUCTION

Radical cystectomy (RC) with urinary diversion is associated with significant perioperative morbidity and mortality, varying between 30% and 70% and between 0.3% and 10.6%, respectively. Risk calculators have been extensively studied in the general surgery literature to predict 30- and 90-day postoperative morbidity and mortality but have not been widely accepted in the RC literature.

MATERIALS AND METHODS

We performed a search of MEDLINE and Embase databases during May 2020 to identify all relevant studies using the following keywords: radical cystectomy, surgical complication predictive model, surgical complication predictive equation, surgical complication predictive nomogram, surgical risk calculator, morbidity, and mortality. We determined the existing surgical predictive nomograms, calculators, and indices and their accuracy in predicting morbidity, mortality, and major complications after RC.

RESULTS

National Surgical Quality Improvement Program had poor accuracy at predicting 30-day morbidity at mortality (AUC 0.5-0.6). LACE index showed good discrimination at predicting 90-day mortality (AUC 0.7). The various frailty and sarcopenia indices have shown poor to fair accuracy at predicting (AUC 0.5-0.7). The Isbarn and Aziz nomograms have equivalent accuracy at predicting 90-day mortality (AUC 0.7) but are limited by inclusion of tumor histology and presence of metastatic disease as variables. POSSUM and P-POSSUM have poor ability at predicting morbidity and mortality (AUC 0.5) and are cumbersome calculators. The surgical Apgar score has been able to predict 30-day morbidity and mortality but can only be used in the postoperative setting.

DISCUSSION

The currently available surgical risk calculators have either poor accuracy at predicting post-RC morbidity and mortality or are limited by types of variables included. An ideal risk calculator would be comprised of preoperative factors only and have a high accuracy to serve as a tool for preoperative patient counseling prior to surgery.

CONCLUSION

There exists a strong need to develop a comprehensive and accurate preoperative risk calculator that predicts morbidity and mortality after RC.

Application of POSSUM and P-POSSUM in Surgical Risk Assessment of Elderly Patients Undergoing Hepatobiliary and Pancreatic Surgery

Purpose

To investigate the efficacy and accuracy of the Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scoring systems in the risk assessment of postoperative complications and death in elderly patients undergoing hepatobiliary and pancreatic surgery.

Patients and Methods

Using POSSUM and P-POSSUM, 274 elderly patients undergoing hepatobiliary and pancreatic surgery were evaluated, and the complications and deaths predicted by the systems were compared with the actual situation. The accuracy and predictive ability of POSSUM and P-POSSUM were evaluated using chi-squared and t-tests, consistency of predicted and actual complication rates (observed/expected, OE ratio), and receiver operating characteristic (ROC) curve.

Results

The complication rate predicted by POSSUM (R1) was 22.57%, while the actual postoperative complication rate was 17.88% (P>0.05). The mortality rate predicted by POSSUM (R2) was 4.61%, while the actual rate was 1.09% (P<0.05). The mortality rate predicted by P-POSSUM (R) was 1.42%, while the actual rate was 1.09% (P>0.05). Patients with complications had higher physiology scores (PS), operative severity scores (OS), and POSSUM scores than those without complications (P<0.05). Furthermore, PS, OS, and POSSUM scores were higher in the mortality group than in the survival group. However, the number of individuals in the mortality group was too small to accurately reflect the overall situation. Stratified analysis showed that consistency of the OE ratio in different subgroups was close to 1. The ROC curve showed that the area under the curve for the complication rate predicted by POSSUM was 0.76.

Conclusion

Although the postoperative mortality rate was higher than the actual value, POSSUM could accurately predict the postoperative complication rate in elderly patients undergoing hepatobiliary and pancreatic surgery. The P-POSSUM accurately predicted the postoperative mortality rate in this population. Patients with complications had higher POSSUM scores.

Prehabilitation program for African sub-Saharan surgical patients is an unmet need

Approximately 4.2 million people worldwide die within 30 days of surgery each year. Half of these deaths occur in low- and middle-income countries. Postoperative deaths account for 7.7% of all deaths globally, making it the third-highest contributor to deaths, after heart disease and stroke. In sub-Saharan Africa, there is a higher rate of mortality following postoperative complications compared to high-income countries. The WHO has tools to help countries provide safer surgery. However, implementation remains poor in most African countries. Interventions focused on intraoperative or postoperative measures to improve perioperative prognosis may be too late for high-risk patients. Poor preoperative cardiorespiratory functional capacity, poor management of pre-existing comorbidities and risk factors and no assessment of the patient´s surgical risk is associated with adverse postoperative outcomes, including mortality, complications, slower recovery, longer intensive care stay, extended hospital length of stay and reduced postoperative quality of life. To significantly decrease morbidity and mortality following surgery in Africa, we propose the implementation of a comprehensive preoperative intervention, that must include: i) risk assessment of surgical patients to identify those at greater risk of postoperative complications for elective surgery; ii) increase the preoperative functional reserve of these high-risk patients, to enhance their tolerance to surgical stress and improve postoperative recovery; iii) anticipate postoperative care needs and organize tools, resources and establish simple workflows to manage postoperative complications. We believe this approach is simple, feasible and will significantly reduce postoperative burden for both patients, hospitals and society.

Prediction of postoperative mortality and morbidity in octogenarians with gastric cancer - Comparison of P-POSSUM, O-POSSUM, and E-POSSUM: A retrospective single-center cohort study

BACKGROUND

The purpose of this study was to evaluate various POSSUM scoring systems in predicting postoperative morbidity and mortality in elderly patients with gastric cancer.

METHODS

A total of 1262 patients with gastric cancer who underwent curative gastrectomy between January 2006 and December 2013 were retrospectively reviewed. The subjects were stratified by age into <80 years old and ≥80 years old. To assess the predictability and efficacy of various POSSUM scores (POSSUM, P-POSSUM, O-POSSUM, and E-POSSUM), the observed-to-expected (O:E) ratio and area under the receiver operating characteristic curve (AUC) were calculated and compared with actual postoperative morbidity and mortality.

RESULTS

Among the 1262 patients, 75 were elderly (≥80 years old). The observed mortality rates were 0.5% (n = 6) in the whole cohort, and 4.0% (n = 3) in elderly patients. The predicted mortalities of POSSUM, P-POSSUM, E-POSSUM, and O-POSSUM for elderly patients were 13.2%, 5.3%, 5.7%, and 21.8%, respectively (O:E ratio = 0.3, 0.75, 0.7, and 0.18, respectively). P-POSSUM and E-POSSUM showed superior discriminatory power compared to POSSUM and O-POSSUM. In terms of morbidity, E-POSSUM showed better predictive capabilities than POSSUM in elderly patients (O:E ratio = 0.56 and 0.74, respectively).

CONCLUSIONS

All POSSUM scoring systems tend to overestimate postoperative mortality and morbidity in gastric cancer patients. E-POSSUM and P-POSSUM provided a better prediction of mortality and morbidity after curative gastrectomy in elderly patients compared to other POSSUM scores.