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 < 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.

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 Desempenho de índices de gravidade para estimar risco de morte em Unidades de Terapia Intensiva

2012 ◽  
Vol 46 (4) ◽  
pp. 846-850
Author(s):  
Lilian Carvalho da Silva ◽  
Lilia de Souza Nogueira ◽  
Cristina Helena Costanti Settervall ◽  
Regina Marcia Cardoso de Sousa ◽  
Katia Grillo Padilha
Keyword(s):  
Organ Dysfunction ◽  
Simplified Acute Physiology Score ◽  
Sao Paulo ◽  
Acute Physiology Score ◽  
Receiver Operator Characteristic ◽  
São Paulo ◽  
Saps Ii ◽  
Logistic Organ Dysfunction ◽  
Terapia Intensiva

O Simplified Acute Physiology Score II (SAPS II) e o Logistic Organ Dysfunction System (LODS) são instrumentos utilizados para classificar pacientes internados em Unidades de Terapia Intensiva (UTI) conforme a gravidade e o risco de morte, sendo um dos parâmetros da qualidade da assistência de enfermagem. Este estudo teve por objetivo avaliar e comparar as performances do SAPS II e do LODS para predizer mortalidade de pacientes admitidos em UTI. Participaram do estudo 600 pacientes de quatro diferentes UTIs de São Paulo, Brasil. A curva Receiver Operator Characteristic (ROC) foi utilizada para comparar o desempenho discriminatório dos índices. Os resultados foram: as áreas sob a curva do LODS (0.69) e do SAPS II (0.71) apresentaram moderada capacidade discriminatória para predizer mortalidade. Não foi encontrada diferença estatisticamente significativa entre as áreas (p=0,26). Concluiu-se que houve equivalência entre SAPS II e LODS para estimar risco de morte de pacientes em UTI.

scholarly journals New PELOD-2 cut-off score for predicting death in children with sepsis

2021 ◽  
Vol 61 (1) ◽  
pp. 39-45
Author(s):  
Ni Made Rini Suari ◽  
Abdul Latief ◽  
Antonius H. Pudjiadi
Keyword(s):  
Organ Dysfunction ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Organ Damage ◽  
Goodness Of Fit Test ◽  
Logistic Organ Dysfunction ◽  
Life Threatening ◽  
Definition Of ◽  
Pediatric Sepsis

Background According to the most recent Sepsis-3 Consensus, the definition of sepsis is life-threatening organ dysfunction caused by dysregulated immune system against infection. Currently, one of the most commonly used prognostic scoring system is pediatric logistic organ damage-2 (PELOD-2) score. Objective To determine and validate the pediatric logistic organ dysfunction-2 (PELOD-2) cut-off score to predict mortality in pediatric sepsis patients. Methods A prospective cohort study was conducted in the intensive care units of Cipto Mangunkusumo Hospital, Jakarta. We assessed subjects with PELOD-2 and calculated the predicted death rate (PDR) using SFAR software. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate calibration and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) to estimate discrimination. Results Of 2,735 children admitted to the emergency department, 52 met the inclusion criteria. Patients had various types of organ dysfunction: 53.8% respiratory, 28.8% neurological, 15.4% cardiovascular, 1.9% hematological. The mortality rate in this study was 38.5%. Mean PELOD-2 score was higher in patients who died than in those who survived [13.9 (SD 4.564) vs. 7.59 (SD 3.025), respectively, P=0.000]. The discrimination of PELOD-2 score with the lactate component had an AUC of 85.5% (95%CI 74.5 to 96.5), while PELOD-2 without lactate had an AUC of 85.4% (95%CI 74.5 to 96.3%). We propose a new PELOD-2 cut-off score to predict organ dysfunction and death of 10, with 75% sensitivity, 72% specificity, 62.5% PPV, and 82% NPV. PELOD-2 score > 10 had a moderate, statistically significant correlation to mortality (r=0.599; P<0.001). Conclusion A PELOD-2 score > 10 is valid for predicting life-threatening organ dysfunction in pediatric patients with sepsis.

Performance of the simplified acute physiology score III in acute organophosphate poisoning: A retrospective observational study

2017 ◽  
Vol 37 (3) ◽  
pp. 221-228 ◽  
Author(s):  
DH Lee ◽  
BK Lee
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Fatality ◽  
Simplified Acute Physiology Score ◽  
Acute Physiology Score ◽  
Apache Ii ◽  
Organophosphate Poisoning ◽  
Hospital Case ◽  
Saps Ii ◽  
Standardized Mortality Ratios

The performances of acute physiology and chronic health evaluation (APACHE) II and simplified acute physiology score (SAPS) II have previously been evaluated in acute organophosphate poisoning. We aimed to compare the performance of the SAPS III with those of the APACHE II and SAPS II, as well as to identify the best tool for predicting case fatality using the standardized mortality ratios (SMRs) in acute organophosphate poisoning. A retrospective analysis of organophosphate poisoning was conducted. The APACHE II, SAPS II, and SAPS III were calculated within 24 h of admission. Discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The SMRs were calculated as 95% confidence intervals (CIs). In total, 100 cases of organophosphate poisoning were included. The in-hospital case fatality was 19%. The median scores of the APACHE II, SAPS II, and SAPS III were 20.0 (10.0–27.0), 41.0 (28.0–54.8), and 53.0 (36.3–68.8), respectively. The AUROCs were not significantly different among the APACHE II (0.815; 95% CI, 0.712–0.919), SAPS II (0.820; 95% CI, 0.719–0.912), and SAPS III (0.850; 95% CI, 0.763–0.936). Based on these scores and in-hospital case fatality, the SMRs for the APACHE II, SAPS II, and SAPS III were 1.01 (95% CI, 0.50–2.72), 1.01 (95% CI, 0.54 -2.78), and 0.98 (95% CI, 0.33–1.99), respectively. The SAPS III provided a good discrimination and satisfactory calibration in acute organophosphate poisoning. It was therefore a useful tool in predicting case fatality in acute organophosphate poisoning, similar to the APACHE II and SAPS II.

Normal Tissue Complication Probability (NTCP) model for erectile dysfunction (ED) following external beam radiotherapy (RT) for prostate cancer.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 135-135
Author(s):  
Julia Murray ◽  
Clare Griffin ◽  
Emma Hall ◽  
Jamie Dean ◽  
Isabel Syndikus ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clinical Data ◽  
Goodness Of Fit ◽  
Risk Group ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Performance ◽  
Goodness Of Fit Test ◽  
Dosimetric Data ◽  
Significant Difference

135 Background: ED remains a common toxicity of prostate RT despite technological advances. Penile bulb (PB) dose has been proposed as a predictor of ED post RT. The main objective of this study was to develop NTCP models for ED. Methods: 162 men treated within the CHHiP IGRT substudy (CRUK/06/16) had baseline clinical data, PB dosimetric data & evaluation of ED using EPIC-26 at least 3 years post RT. Planning CT and reference dose distributions were imported into analysis software (VODCA, MSS GmbH) and PB retrospectively contoured by one clinician. The defined endpoint (severe ED) was a standardised average value of 0-33 for EPIC-26 sexual domain. Predictive models of ED were generated using PB dose in EQD2 (α/β ratio = 3Gy) & clinical data (age, diabetes, hypertension, NCCN risk group, baseline PSA, hormone therapy, IGRT, margin size, PB volume). Multivariate logistic regression method using resampling methods was applied to select model order and parameters. Models were fitted using logistic regression of the form Probability = eA(x)/1+eA(x), where A(x) = constant + sum of (variables * associated regression coefficients). Model performance was evaluated through area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results: 101/162 (62%) men had severe ED with statistically significant difference in PB max and mean dose between those patients with or without severe ED (max: 61.8Gy vs 43Gy & mean: 27.4Gy vs 14Gy respectively; p = 0.001). In the univariate analyses, age, diabetes, risk group, PB mean and max doses were significantly associated with EPIC calculated severe ED. The optimal NTCP model (AUC 0.78; CI 0.71-0.86: p for HL = 0.75) for EPIC calculated severe ED included age, PB mean dose and diabetes where A(x) = -10.13+(0.14*age)+(0.03*PB mean dose)+(2.88 if diabetic). A comparable model using clinician completed outcomes will be reported. Conclusions: This study provides the first known clinical prediction model for ED including PB dose, with good model performance. The determined predictors for the NTCP model of severe ED in this cohort were PB mean dose, age & diabetes. External validation of this model is desirable. Clinical trial information: 97182923.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

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 Equine Simplified Acute Physiology Score (EqSAPS): personalized medicine for the equine emergency patient

2020 ◽  
Author(s):  
Aline Barros ◽  
Ana Flavia Rocha da Silva ◽  
Miriam Zibordi ◽  
Julio David Spagnolo ◽  
Rodrigo Romero Corrêa ◽  
...  
Keyword(s):  
Decision Tree ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Simplified Acute Physiology Score ◽  
Acute Physiology Score ◽  
Final Decision ◽  
Human Patient ◽  
Disease Evolution ◽  
Saps 3 ◽  
Probability Of Death

Scoring models are useful tools that guide the attending clinician in gauging the severity of disease evolution, and in evaluating the efficacy of treatment. There are few tools available with this purpose for the non-human patient, including horses. We aimed (i) to adapt the Simplified Acute Physiology Score 3 (SAPS-3) model for the equine species, reaching a margin of accuracy greater than 75% in the calculation of the probability of death, and (ii) to build a decision tree that helps the attending veterinarian in assessment of the clinical evolution of the equine patient. From an initial pool of 5 568 medical records from University-based Veterinary Hospitals, a final cohort of 1 000 was further mined manually for data extraction. A set of 19 variables were evaluated and tested by five data mining algorithms. The final scoring model, named EqSAPS for Equine Simplified Acute Physiology Score, reached 91.83% of correct estimates for probability of death within 24 hours upon hospitalization. The Area Under Receiver Operating Characteristic Curve (AUROC) for outcome “death” was 0.742, while for “survival” was 0.652. The final decision tree was able to refine prognosis of patients whose EqSAPS score suggested “death”. EqSAPS is an useful tool to gauge the severity of the clinical presentation of the equine patient.

Sign in / Sign up

Export Citation Format

Share Document