Comparative study between P- POSSUM and Apache II scores in predicting outcomes of perforation peritonitis: Prospective observational cohort study

Microbiological profiles and clinical outcomes of critically ill surgical patients with lower gastrointestinal perforation in Japan: a single-center retrospective observational study

BACKGROUND

Lower gastrointestinal perforation (LGP) is a surgical emergency disease that can result in secondary bacterial peritonitis. Microbiological studies on LGP are rare. The present study aimed to ascertain the microbiological profile of LGP in patients admitted to the intensive care unit (ICU) at our institute after surgery. In addition, we investigated whether initial empirical therapy with vancomycin was associated with in-hospital mortality, duration of ICU stay, and duration of ventilator support.

METHODS

This single-center, retrospective, observational study was conducted at Kobe City Medical Center General Hospital, Japan. The study population included all patients diagnosed with LGP who were admitted to the ICU after emergency surgery between 2017 and 2023. The primary outcome assessed was the microbiological profile of microorganisms isolated from ascites fluid and blood of the participants. The secondary end-points were in-hospital mortality, duration of ICU stay, and duration of ventilator support. We performed univariate and multivariate regression analyses to evaluate the end-points.

RESULTS

During the study period, 89 patients were included in the analysis. The most commonly identified pathogen from the ascites cultures was Escherichia coli (65.2%), followed by Enterococcus spp. (51.7%). E. faecium was identified in 16 (18.0%) ascites samples. The microbiological profile of critically ill patients with LGP admitted to the ICU after surgery was similar to that of previous studies on intra-abdominal infection (IAI). Therefore, the initial empirical therapy in the IAI guidelines is more appropriate for LGP. Multivariate regression analysis suggested that the combination of initial empirical therapy with vancomycin was not associated with in-hospital mortality (odds ratio [OR] = 0.96, 95% confidence interval [CI] 0.23-3.00, p = 0.955), duration of ICU stay (coefficient=-0.92, 95% CI -3.04-1.21, p = 0.393), or duration of ventilator-support (coefficient=-9.03, 95%CI -49.69-31.63, p = 0.659).

CONCLUSION

The microbiological profile of critically ill patients with LGP admitted to the ICU after surgery was similar to that of previous studies on IAI. However, the frequency of E. faecium in the present study was higher than that in previous studies. Initial empirical therapy with drugs such as meropenem in combination with vancomycin for E. faecium was not associated with in-hospital mortality, duration of ICU stay, and duration of ventilator support, after adjusting for confounding factors.

Predicting surgical outcomes of acute diffuse peritonitis: Updated risk models based on real-world clinical data

Aim

The existing predictive risk models for the surgical outcome of acute diffused peritonitis (ADP) need renovation by adding relevant variables such as ADP's definition or causative etiology to pursue outstanding data collection reflecting the real world. We aimed to revise the risk models predicting mortality and morbidities of ADP using the latest Japanese Nationwide Clinical Database (NCD) variable set.

Methods

Clinical dataset of ADP patients who underwent surgery, and registered in the NCD between 2016 and 2019, were used to develop a risk model for surgical outcomes. The primary outcome was perioperative mortality.

Results

After data cleanup, 45 379 surgical cases for ADP were derived for analysis. The perioperative and 30-day mortality were 10.6% and 7.2%, respectively. The prediction models have been created for the mortality and 10 morbidities associated with the mortality. The top five relevant predictors for perioperative mortality were age >80, advanced cancer with multiple metastases, platelet count of <50 000/mL, serum albumin of <2.0 g/dL, and unknown ADP site. The C-indices of perioperative and 30-day mortality were 0.859 and 0.857, respectively. The predicted value calculated with the risk models for mortality was highly fitted with the actual probability from the lower to the higher risk groups.

Conclusions

Risk models for postoperative mortality and morbidities with good predictive performance and reliability were revised and validated using the recent real-world clinical dataset. These models help to predict ADP surgical outcomes accurately and are available for clinical settings.

Comparison of the PIPAS severity score tool and the QSOFA criteria for predicting in-hospital mortality of peritonitis in a tertiary hospital in Uganda: a prospective cohort study

BACKGROUND

The majority of the prognostic scoring tools for peritonitis are impractical in low resource settings because they are complex while others are quite costly. The quick Sepsis-related Organ Failure Assessment (qSOFA) score and the Physiologic Indicators for Prognosis in Abdominal Sepsis (PIPAS) severity score are two strictly bedside prognostic tools but their predictive ability for mortality of peritonitis is yet to be compared. We compared the predictive ability of the qSOFA criteria and the PIPAS severity score for in-hospital mortality of peritonitis.

METHOD

This was a prospective cohort study on consecutive peritonitis cases managed surgically in a tertiary hospital in Uganda between October 2020 to June 2021. PIPAS severity score and qSOFA score were assessed preoperatively for each case and all cases were then followed up intra- and postoperatively until discharge from the hospital, or up to 30 days if the in-hospital stay was prolonged; the outcome of interest was in-hospital mortality. We used Receiver Operating Characteristic curve analysis to assess and compare the predictive abilities of these two tools for peritonitis in-hospital mortality. All tests were 2 sided (p < 0.05) with 95% confidence intervals.

RESULTS

We evaluated 136 peritonitis cases. Their mean age was 34.4 years (standard deviation = 14.5). The male to female ratio was 3:1. The overall in-hospital mortality rate for peritonitis was 12.5%. The PIPAS severity score had a significantly better discriminative ability (AUC = 0.893, 95% CI 0.801-0.986) than the qSOFA score (AUC = 0.770, 95% CI 0.620-0.920) for peritonitis mortality (p = 0.0443). The best PIPAS severity cut-off score (a score of > = 2) had sensitivity and specificity of 76.5%, and 93.3% respectively, while the corresponding values for the qSOFA criteria (score > = 2), were 58.8% and 98.3% respectively.

CONCLUSIONS

The in-hospital mortality in this cohort of peritonitis cases was high. The PIPAS severity score tool has a superior predictive ability and higher sensitivity for peritonitis in-hospital mortality than the qSOFA score tool although the latter tool is more specific. We recommend the use of the PIPAS severity score as the initial prognostic tool for peritonitis cases in the emergency department.

Improvement of APACHE II score system for disease severity based on XGBoost algorithm

BACKGROUND

Prognostication is an essential tool for risk adjustment and decision making in the intensive care units (ICUs). In order to improve patient outcomes, we have been trying to develop a more effective model than Acute Physiology and Chronic Health Evaluation (APACHE) II to measure the severity of the patients in ICUs. The aim of the present study was to provide a mortality prediction model for ICUs patients, and to assess its performance relative to prediction based on the APACHE II scoring system.

METHODS

We used the Medical Information Mart for Intensive Care version III (MIMIC-III) database to build our model. After comparing the APACHE II with 6 typical machine learning (ML) methods, the best performing model was screened for external validation on anther independent dataset. Performance measures were calculated using cross-validation to avoid making biased assessments. The primary outcome was hospital mortality. Finally, we used TreeSHAP algorithm to explain the variable relationships in the extreme gradient boosting algorithm (XGBoost) model.

RESULTS

We picked out 14 variables with 24,777 cases to form our basic data set. When the variables were the same as those contained in the APACHE II, the accuracy of XGBoost (accuracy: 0.858) was higher than that of APACHE II (accuracy: 0.742) and other algorithms. In addition, it exhibited better calibration properties than other methods, the result in the area under the ROC curve (AUC: 0.76). we then expand the variable set by adding five new variables to improve the performance of our model. The accuracy, precision, recall, F1, and AUC of the XGBoost model increased, and were still higher than other models (0.866, 0.853, 0.870, 0.845, and 0.81, respectively). On the external validation dataset, the AUC was 0.79 and calibration properties were good.

CONCLUSIONS

As compared to conventional severity scores APACHE II, our XGBoost proposal offers improved performance for predicting hospital mortality in ICUs patients. Furthermore, the TreeSHAP can help to enhance the understanding of our model by providing detailed insights into the impact of different features on the disease risk. In sum, our model could help clinicians determine prognosis and improve patient 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.