Mortality risk scoring in emergency general surgery: Are we using the best tool?

2020 ◽  
pp. 175045892092013
Author(s):  
Azeem Thahir ◽  
Rui Pinto-Lopes ◽  
Stavroula Madenlidou ◽  
Laura Daby ◽  
Chandima Halahakoon
Keyword(s):  
Mortality Rate ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Emergency Laparotomy ◽  
Receiver Operator Characteristic ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Risk Of Death ◽  
Possum Score ◽  
Operative Severity Score

Background It is imperative that an accurate assessment of risk of death is undertaken preoperatively on all patients undergoing an emergency laparotomy. Portsmouth-Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity (P-POSSUM) is one of the most widely used scores. National Emergency Laparotomy Audit (NELA) presents a novel, validated score, but no direct comparison with P-POSSUM exists. We aimed to determine which would be the best predictor of mortality. Methods We analysed all the entries on the online NELA database over a four-and-a-half-year period. The Hosmer–Lemeshow goodness of fit test was performed to assess model calibration. For the outcome of death and for each scoring system, a non-parametric receiver operator characteristic analysis was done. The sensitivity, specificity, area under receiver operator characteristic curve and their standard errors were calculated. Results Data pertaining to 650 patients were included. There were 59 deaths, giving an overall observed mortality rate of 9.1%. Predicted mortality rate for the P-POSSUM score and NELA score were 15.2% and 7.8%, respectively. The discriminative power for mortality was highest for the NELA score (C-index = 0.818, CI: 0.769–0.867, p < 0.001), when compared to P-POSSUM (C-index = 0.769, CI: 0.712–0.827, p < 0.001). Conclusions The NELA score showed good discrimination in predicting mortality in the entire cohort. The P-POSSUM over-predicted observed mortality and the NELA score under-predicted observed mortality.

scholarly journals Evaluation of the validity of POSSUM and P-POSSUM score in predicting the risk of morbidity and mortality respectively in patients undergoing emergency laparotomy

2020 ◽  
Vol 7 (10) ◽  
pp. 3224
Author(s):  
Vivian Anandith Paul ◽  
Agnigundala Anusha ◽  
Alluru Sarath Chandra
Keyword(s):  
Mortality Rate ◽  
Linear Method ◽  
Morbidity And Mortality ◽  
Emergency Laparotomy ◽  
Medical Sciences ◽  
Mortality And Morbidity ◽  
Possum Score ◽  
Operative Severity Score ◽  
Significant Difference ◽  
Predicted Values

Background: Aim of this study is to examine the efficacy of Physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and Portsmouth predictor modification (P-POSSUM) equations in predicting morbidity and mortality in patients undergoing emergency laparotomy, to study the morbidity and mortality patterns in patients undergoing emergency laparotomy at Malla Reddy Institute of Medical Sciences, Hyderabad. Methods: The study was conducted for a period of 2 years from February 2018 to February 2020. 100 Patients undergoing emergency laparotomy were studied in the Department of General surgery MRIMS, Hyderabad. POSSUM and P-POSSUM scores are used to predict mortality and morbidity. The ratio of observed to expected deaths (O:E ratio) was calculated for each analysis. Results: The study included total 100 patients, 83 men and 17 women. Observed mortality rate was compared to mortality rate with POSSUM, the O:E ratio was 0.62, and there was no significant difference between the observed and predicted values (χ²=10.79, 9 degree of freedom (df) p=0.148). Observed morbidity rates were compared to morbidity rates predicted by POSSUM, there was no significant difference between the observed and predicted values (χ²=9.89, 9 df, p=0.195) and the overall O:E ratio was 0.91. P-POSSUM predicted mortality equally well when the linear method of analysis was used, with an O:E ratio of 0.65 and no significant difference between the observed and predicted values (χ²= 5.33, 9 df, p= 0.617).Conclusion: POSSUM and P-POSSUM scoring is an accurate predictor of mortality and morbidity following emergency laparotomy and is a valid means of assessing adequacy of care provided to the patient. 

scholarly journals Pre-NELA vs NELA – has anything changed, or is it just an audit exercise?

2016 ◽  
Vol 98 (8) ◽  
pp. 554-559 ◽  
Author(s):  
M Mak ◽  
AR Hakeem ◽  
V Chitre
Keyword(s):  
Critical Care ◽  
High Risk ◽  
Uneventful Recovery ◽  
Emergency Laparotomy ◽  
Mortality And Morbidity ◽  
High Risk Patients ◽  
Possum Score ◽  
Operative Severity Score ◽  
Risk Patients ◽  
Set Up

BACKGROUND Following evidence suggestive of high mortality following emergency laparotomies, the National Emergency Laparotomy Audit (NELA) was set up, highlighting key standards in emergency service provision. Our aim was to compare our NHS trust’s adherence to these recommendations immediately prior to, and following, the launch of NELA, and to compare patient outcome. METHODS This was a retrospective study of patients who underwent an emergency laparotomy over the course of 6 months – 3 months either side of the initiation of NELA. RESULTS There were 44 patients before the initiation of NELA (pre-NELA, PN group) and 55 in the first 3 months of NELA (N group). We saw a significant increase in the proportion of patients whose decision to operate was made by the consultant: 75.0% in the PN group vs 100% in N group (subsequent data presented in this order) (P < 0.001). The presence of a consultant surgeon (75.0% vs 83.6%, P = 0.321) and anaesthetist (100.0% vs 90.9%, P = 0.064) in theatres were comparable in both groups. Risk stratification based on Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (P-POSSUM) score showed no difference in high-risk patients in both groups (47.7% vs 36.4%, P = 0.306). With the NELA initiative, however, significantly more patients were admitted directly from theatres to the critical care unit, when compared with the pre-NELA period (9.1% vs 27.3%, P = 0.038). This also reflected a significant reduction in unexpected escalation to a higher level of care during this period (10.0% vs 0%, P = 0.036). Significantly more patients had uneventful recovery in the NELA period (52.3 vs 76.4%, P = 0.018), although there was no difference in 30-day mortality between the groups (2.3% vs 7.3%, P = 0.378). CONCLUSIONS This study demonstrated a greater degree of consultant involvement in the decision to operate during NELA. More high-risk patients have been identified preoperatively with diligent risk assessment and, hence, have been proactively admitted to critical care units following laparotomy, which may account for the significant reduction in unexpected escalation to level 2 or level 3 care and thus in overall better patient outcomes.

Predictive Performance and Variability of the Cardiac Anesthesia Risk Evaluation Score

2004 ◽  
Vol 100 (6) ◽  
pp. 1405-1410 ◽  
Author(s):  
Alexandre Ouattara ◽  
Michaëla Niculescu ◽  
Sarra Ghazouani ◽  
Ario Babolian ◽  
Marc Landi ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Risk Evaluation ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Cardiac Anesthesia ◽  
Goodness Of Fit Test ◽  
Significant Difference ◽  
Receiver Operating

Background The Cardiac Anesthesia Risk Evaluation (CARE) score, a simple Canadian classification for predicting outcome after cardiac surgery, was evaluated in 556 consecutive patients in Paris, France. The authors compared its performance to those of two multifactorial risk indexes (European System for Cardiac Operative Risk Evaluation [EuroSCORE] and Tu score) and tested its variability between groups of physicians (anesthesiologists, surgeons, and cardiologists). Methods Each patient was simultaneously assessed using the three scores by an attending anesthesiologist in the immediate preoperative period. In a blinded study, the CARE score category was also determined by a cardiologist the day before surgery, by a surgeon in the operating room, and by a second anesthesiologist at arrival in intensive care unit. Calibration and discrimination for predicting outcomes were assessed by goodness-of-fit test and area under the receiver operating characteristic curve, respectively. The level of agreement of the CARE scoring between the three physicians was then assessed. Results The calibration analysis revealed no significant difference between expected and observed outcomes for the three classifications. The areas under the receiver operating characteristic curves for mortality were 0.77 with the CARE score, 0.78 with the EuroSCORE, and 0.73 with the Tu score (not significant). The agreement rate of the CARE scoring between two anesthesiologists, between anesthesiologists and surgeons, and between anesthesiologists and cardiologists were 90%, 83%, and 77%, respectively. Conclusions Despite its simplicity, the CARE score predicts mortality and major morbidity as well the EuroSCORE. In addition, it remains devoid of significant variability when used by groups of physicians of different specialties.

scholarly journals A transformation of oxygen saturation (the saturation virtual shunt) to improve clinical prediction model calibration and interpretation

2018 ◽  
Author(s):  
Guohai Zhou ◽  
Walter Karlen ◽  
Rollin Brant ◽  
Matthew Wiens ◽  
Niranjan Kissoon ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Disease Severity ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Approximation Formula ◽  
P Value ◽  
Clinical Prediction ◽  
Goodness Of Fit Test ◽  
Peripheral Oxygen Saturation

ABSTRACTBackgroundThe relationship between peripheral oxygen saturation (SpO2) and the inspired oxygen concentration is non-linear. SpO2 is frequently used as a dichotomized predictor, to manage this non-linearity. We propose the saturation virtual shunt (VS) as a transformation of SpO2 to a continuous linear variable to improve interpretation of disease severity within clinical prediction models.MethodWe calculate the saturation VS based on an empirically derived approximation formula between physiological VS and SpO2. We evaluated the utility of the saturation VS in a clinical study predicting the need for facility admission in children in a low resource health-care setting.ResultsThe transformation was saturation VS = 68.864*log10(103.711 − SpO2) −52.110. The ability to predict hospital admission based on a dichotomized SpO2 produced an area under the receiver operating characteristic curve of 0.57, compared to 0.71 based on the untransformed SpO2 and saturation VS. However, the untransformed SpO2 demonstrated a lack of fit compared to the saturation VS (goodness-of-fit test p-value <0.0001 versus 0.098). The observed admission rates varied non-linearly with the untransformed SpO2 but varied linearly with the saturation VS.ConclusionThe saturation VS estimates a continuous linearly interpretable disease severity based on SpO2 and improves clinical prediction.

scholarly journals Impact of Operator Expertise on Collection of the APACHE II Score and on the Derived Risk of Death and Standardized Mortality Ratio

2005 ◽  
Vol 33 (5) ◽  
pp. 585-590 ◽  
Author(s):  
D. Ledoux ◽  
S. Finfer ◽  
S. Mckinley
Keyword(s):  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Staff ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Standardized Mortality Ratio ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
The Impact

We assessed the impact of operator expertise on collection of the APACHE II score, the derived risk of death and standardized mortality ratio in 465 consecutive patients admitted to a multi-disciplinary tertiary hospital ICU. Research coordinators and junior clinical staff independently collected the APACHE II variables; experts (senior clinical staff) rescored 20 % of the records. Agreement was moderate between junior clinical staff and research coordinators or senior clinical staff for most variables of the acute physiology score (weighted κ<0.6); agreement between research coordinators and senior clinical staff data collectors was good (weighted κ >0.75). The APACHE II score and its derived risk of death (ROD) were significantly lower using the junior clinical staff dataset compared to research coordinators and senior clinical staff (APACHE II score: 13.4±9.2 vs 16.8±8.5 vs 17.1±7.7, P<0.001; ROD: 14.7%±22.4% vs 21.6%±22.6% vs 20.8%±22.4%, P<0.01 respectively). The discriminative capacity was not altered by the lack of agreement (area under Receiver Operator Characteristic curve >0.8) but calibration of ROD from the junior clinical staff dataset was poor (Goodness-of-fit: P=0.001). The standardized mortality ratio (SMR) was higher with the junior clinical staff dataset (SMR: 1.22, 95% CI: 0.96-1.52 vs 0.87, 95% CI: 0.70-1.06 vs 0.76, 95% CI: 0.40-1.3 calculated from junior clinical staff, research coordinators and senior clinical staff data-sets respectively). We conclude that the expertise of data collectors significantly influences the APACHE II score, the derived risk of death and the standardized mortality ratio. Given the importance of such scores, ICUs should be provided with sufficient resources to train and employ dedicated data collectors.

Validity of the Framingham Risk Model Applied to Japanese Men

2002 ◽  
Vol 41 (03) ◽  
pp. 213-215 ◽  
Author(s):  
H. Sugimori ◽  
K. Yoshida ◽  
M. Suka
Keyword(s):  
Goodness Of Fit ◽  
Risk Model ◽  
Area Under The Curve ◽  
Health Examination ◽  
Characteristic Analysis ◽  
Goodness Of Fit Test ◽  
Japanese Men ◽  
Discriminatory Accuracy ◽  
Framingham Risk ◽  
Chd Risk

Summary Objectives: To examine whether the Framingham Risk Model can appropriately predict coronary heart disease (CHD) events detected by electrocardiography (ECG) in Japanese men. Methods: Using the annual health examination database of a Japanese company 5611 male workers, between the ages of 30 to 59, who were free of cardiovascular disease, were followed up to observe the occurrence of CHD events detected by ECG over a period of five to seven years. The probability of CHD was calculated for each individual from the equations of the Framingham risk model (with total cholesterol). Results: The incidence of CHD increased with the estimated CHD risk. The Hosmer-Lemeshow goodness of fit test showed an adequate fit of the risk model to the data of the study subjects. In the receiver operating characteristic analysis, the area under the curve reached 0.67 which indicated an acceptable discriminatory accuracy of the risk model. Conclusions: The Framingham risk model provides useful information on future CHD events in Japanese men.

scholarly journals Pediatric Simplified Acute Physiology Score II: Establishment of a New, Repeatable Pediatric Mortality Risk Assessment Score

2021 ◽  
Vol 9 ◽  
Author(s):  
Stefan Irschik ◽  
Jelena Veljkovic ◽  
Johann Golej ◽  
Gerald Schlager ◽  
Jennifer B. Brandt ◽  
...  
Keyword(s):  
Mortality Risk ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Simplified Acute Physiology Score ◽  
Acute Physiology Score ◽  
Goodness Of Fit Test ◽  
Assessment Score ◽  
Saps Ii ◽  
Logistic Organ Dysfunction ◽  
Significant Difference

Objectives: In critical care it is crucial to appropriately assess the risk of mortality for each patient. This is especially relevant in pediatrics, with its need for accurate and repeatable scoring. Aim of this study was to evaluate an age-adapted version of the expanded Simplified Acute Physiology Score II; (p-SAPS II), a repeatable, newly-designed scoring system compared to established scores (Pediatric Sequential Organ Failure Assessment Score/pSOFA, Pediatric Logistic Organ Dysfunction Score-2/PELOD-2 and Pediatric Index of Mortality 3/PIM3).Design: This retrospective cohort pilot study included data collected from patients admitted to the Pediatric Intensive Care Unit (PICU) at the Medical University of Vienna between July 2017 through December 2018.Patients: 231 admissions were included, comprising neonates (gestational age of ≥ 37 weeks) and patients up to 18 years of age with a PICU stay longer than 48 h.Main Outcomes: Mortality risk prediction and discrimination between survivors and non-survivors were the main outcomes of this study. The primary statistical methods for evaluating the performance of each score were the area under the receiver operating characteristic curve (AUROC) and goodness-of-fit test.Results: Highest AUROC curve was calculated for p-SAPS II (AUC = 0.86; 95% CI: 0.77–0.96; p &lt; 0.001). This was significantly higher than the AUROCs of PELOD-2/pSOFA but not of PIM3. However, in a logistic regression model including p-SAPS II and PIM3 as covariates, p-SAPS II had a significant effect on the accuracy of prediction (p = 0.003). Nevertheless, according to the goodness-of-fit test for p-SAPS II and PIM3, p-SAPS II overestimated the number of deaths, whereas PIM3 showed acceptable estimations. Repeatability testing showed increasing AUROC values for p-SAPS II throughout the clinical stay (0.96 at day 28) but still no significant difference to PIM 3. The prediction accuracy, although improved over the days and even exceeded PIM 3.Conclusions: The newly-created p-SAPS II performed better than the established PIM3 in terms of discriminating between survivors and non-survivors. Furthermore, p-SAPS II can be assessed repeatably throughout a patient's PICU stay what improves mortality prediction. However, there is still a need to optimize calibration of the score to accurately predict mortality sooner throughout the clinical stay.

scholarly journals The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

2020 ◽  
Author(s):  
Yang Wang ◽  
Ziru Niu ◽  
Liyuan Tao ◽  
Xiaoying Zheng ◽  
Yifeng Yuan ◽  
...  
Keyword(s):  
Pregnancy Outcomes ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Model Performance ◽  
Goodness Of Fit Test ◽  
Discriminatory Ability ◽  
Female Age ◽  
Lemeshow Test ◽  
Development And Validation ◽  
Good Calibration

Abstract Background: To study which characteristics of a pre-oocyte-retrieval patient can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. Methods: Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Data was collected from the Reproductive Center, Peking University Third Hospital of China. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots.Results: The predictors in the model of ‘no embryo to transfer’ are female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH level (OR= 1.205, 95% CI=1.051-1.382, P=0.0084), basal E2 level (OR=1.006, 95% CI=1.001-1.010, P=0.012) and sperm from MESA (OR=7.741, 95% CI=2.905-20.632, P<0.001). Upon assessing predictive ability, the AUC for this model was 0.799 (95% CI: 0.722–0.875, p＜0.001). The Hosmer-Lemeshow test (p=0.721) and calibration curve showed good calibration. The predictors in the cumulative live birth were the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The AUC for this model was 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow test (p=0.562) and calibration curve showed good calibration for the prediction of cumulative live birth. Conclusion: The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, basal FSH and E2 level, the number of follicles with a diameter greater than 10 mm on the day of hCG administration, endometriosis and sperm from microdissection testicular sperm extraction (MESA).

Sign in / Sign up

Export Citation Format

Share Document