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

Yelamanchi, Raghav; Gupta, Nikhil; Durga, C. K.; Korpal, Manit

doi:10.1016/j.ijsu.2020.09.006

Cited by 7 publications

(10 citation statements)

References 15 publications

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“…Some of the studies recommended that the POSSUM scoring system was better to predict mortality than the APACHE II scoring system [10,13,19]. We found in our study that APACHE II is more suitable than the POSSUM score in predicting mortality, similar to some studies' results [16]. On the other hand, the ASA classification system was insufficient with 0.63, similar to Cuijpers' 108-patient series [20].…”

Section: Discussionsupporting

confidence: 87%

See 1 more Smart Citation

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

Kisa¹,

Kisa²,

Çevik³

2021

Cureus

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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

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Section: Discussionsupporting

confidence: 87%

“…The POSSUM score was calculated in various papers and an AUC value between 0.68-0.95 was figured out [10,13,[15][16][17][18]. In our study, we found a value of 0.78 for the POSSUM scoring system, and this result was as remarkable as the APACHE II scores result.…”

Section: Discussionsupporting

confidence: 63%

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

Kisa¹,

Kisa²,

Çevik³

2021

Cureus

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“…We found that higher bilirubin and GCS had a more significant impact on our model compared to urine. And most of the indicators have a positive effect on the risk of death, this result was broadly consistent with the medical literature [ 44 – 47 ]. Also, we notice that within a certain range, patients with higher levels of potassium (K) and hematocrit (Ht) had a lower risk than those with lower levels.…”

Section: Discussionsupporting

confidence: 90%

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

Luo

Wang

2021

BMC Med Inform Decis Mak

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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.

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“…Several prognostic scoring systems both specific and nonspecific have been validated for use in peritonitis such as the Mannheim Peritonitis Index (MPI) [ 11 ], The World Society of Emergency Surgery Sepsis Severity Score (WSESSSS) [ 12 ], the Predisposition, Infection Response Organ dysfunction score for Intra-Abdominal sepsis (PIRO-IAS) [ 13 ] and the APACHE II score [ 14 – 16 ]. These tools, however, are not strictly preoperative, and/or bedside.…”

Section: Introductionmentioning

confidence: 99%

“…The World Society of Emergency Surgery Sepsis Severity Score (WSESSSS) [12], the Predisposition, Infection Response Organ dysfunction score for Intra-Abdominal sepsis (PIRO-IAS) [13] and the APACHE II score [14][15][16]. These tools, however, are not strictly preoperative, and/or bedside.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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

Cited by 7 publications

References 15 publications

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

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

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

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

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