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 PERFORMANCE OF PROGNOSTIC SCORES IN AKI POPULATION IN ICU

2020 ◽  
pp. 1-4
Author(s):  
Jasmin das
Keyword(s):  
Hospital Mortality ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Apache Ii ◽  
Prognostic Scores ◽  
Apache Ii Score ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Good For

Acute kidney injury in hospitalized patients is associated with high mortality rates and increased length of hospital stay. Prognostication of patients with AKI is of immense value in making decisions regarding the optimal type and intensity of treatment, patient selection, and clinical discussions on prognosis and in assessment of the quality of an ICU. Prognostic scores are comprised of relevant clinical and laboratory variables of patients associated to the clinical endpoint. There are limited studies that have evaluated which prognostic score may be used in patients with AKI. Studies have shown that APACHE II underestimates hospital mortality whereas AKI specific Liano score has better statistical correlation with mortality. Materials and methods: All patients admitted to the ICU fulfilling the inclusion criteria during the study period were recruited and evaluated for AKI by both RIFLE and AKI criteria. Prognostic scores, APACHE II and Liano were used in predicting hospital mortality. Assessment of score performance was made through analysis of the discrimination and calibration using area under a receiver operating characteristic curve (AUROC) and Hosmer and Lemeshow goodness of fit test. Results: Mean APACHE II score was higher in AKI subjects compared to non AKI and was statistically significant and it increased with the severity of AKI. The AUROC for APACHE II score was 0.739 and 0.706 for AKIN and RIFLE respectively and signifies APACHE II score increases with AKI. An AUROC curve of prognostic scores for predicting mortality was 0.677 and 0.639 for Liano and APACHE II respectively and on comparison showed insignificant p value (0.6331). Assessment of calibration showed that the calibration was good for specific score. Conclusion:Assessment of performance of both the prognostic scores APACHE II and Liano had poor discrimination but calibration was good for Liano model

scholarly journals The role of triiodothyronine (T3) and T3/free thyroxine (fT4) on glucose metabolism during pregnancy: the Ma’anshan Birth Cohort Study

2021 ◽  
Author(s):  
Beibei Zhu ◽  
Yan Han ◽  
Fen Deng ◽  
Kun Huang ◽  
Shuangqin Yan ◽  
...  
Keyword(s):  
Cohort Study ◽  
Glucose Metabolism ◽  
Birth Cohort ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
First Trimester ◽  
Repeated Measurements ◽  
Free Thyroxine ◽  
Birth Cohort Study ◽  
Goodness Of Fit Test

Objectives: Compared with other thyroid markers, fewer studies explored the associations between triiodothyronine (T3) and T3/free thyroxine (fT4) and glucose abnormality during pregnancy. Thus, we aimed to: (1) examine the associations of T3 and T3/fT4 with glucose metabolism indicators; and (2) evaluate, in the first trimester, the performance of the two markers as predictors of gestational diabetes mellitus (GDM) risk. Methods: Longitudinal data from 2723 individuals, consisting of three repeated measurements of T3 and fT4, from the Man’anshan birth cohort study (MABC), China, were analyzed using a time-specific generalized estimating equation (GEE). The receiver operating characteristic curve (ROC) - area under the curve (AUC) and Hosmer-Lemeshow goodness of fit test were used to assess the discrimination and calibration of prediction models. Results: T3 and T3/fT4 presented stable associations with the level of fasting glucose, glucose at 1h/2h across pregnancy. T3 and T3/fT4 in both the first and second trimesters were positively associated with the risk of GDM, with the larger magnitude of association observed in the second trimester (Odds ratio (OR) = 2.50, 95%CI = 1.95, 3.21 for T3; OR = 1.09, 95%CI = 1.07, 1.12 for T3/fT4). T3 ((AUC) = 0.726, 95%CI = 0.698, 0.754) and T3/fT4 (AUC = 0.724, 95%CI = 0.696, 0.753) in the first trimester could improve the performance of the predicting model; however, the overall performance is not good. Conclusion: Significant and stable associations of T3, T3/fT4 and glucose metabolism indicators were documented. Both T3 and T3/fT4 improve the performance of the GDM predictive model.

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 Predictors of disease severity in children presenting from the community with febrile illnesses: a systematic review of prognostic studies

2021 ◽  
Vol 6 (1) ◽  
pp. e003451
Author(s):  
Arjun Chandna ◽  
Rainer Tan ◽  
Michael Carter ◽  
Ann Van Den Bruel ◽  
Jan Verbakel ◽  
...  
Keyword(s):  
At Risk ◽  
Prognostic Factors ◽  
Prediction Model ◽  
Disease Severity ◽  
Prediction Models ◽  
Severe Disease ◽  
Risk Of Bias ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Clinical Prediction Models

IntroductionEarly identification of children at risk of severe febrile illness can optimise referral, admission and treatment decisions, particularly in resource-limited settings. We aimed to identify prognostic clinical and laboratory factors that predict progression to severe disease in febrile children presenting from the community.MethodsWe systematically reviewed publications retrieved from MEDLINE, Web of Science and Embase between 31 May 1999 and 30 April 2020, supplemented by hand search of reference lists and consultation with an expert Technical Advisory Panel. Studies evaluating prognostic factors or clinical prediction models in children presenting from the community with febrile illnesses were eligible. The primary outcome was any objective measure of disease severity ascertained within 30 days of enrolment. We calculated unadjusted likelihood ratios (LRs) for comparison of prognostic factors, and compared clinical prediction models using the area under the receiver operating characteristic curves (AUROCs). Risk of bias and applicability of studies were assessed using the Prediction Model Risk of Bias Assessment Tool and the Quality In Prognosis Studies tool.ResultsOf 5949 articles identified, 18 studies evaluating 200 prognostic factors and 25 clinical prediction models in 24 530 children were included. Heterogeneity between studies precluded formal meta-analysis. Malnutrition (positive LR range 1.56–11.13), hypoxia (2.10–8.11), altered consciousness (1.24–14.02), and markers of acidosis (1.36–7.71) and poor peripheral perfusion (1.78–17.38) were the most common predictors of severe disease. Clinical prediction model performance varied widely (AUROC range 0.49–0.97). Concerns regarding applicability were identified and most studies were at high risk of bias.ConclusionsFew studies address this important public health question. We identified prognostic factors from a wide range of geographic contexts that can help clinicians assess febrile children at risk of progressing to severe disease. Multicentre studies that include outpatients are required to explore generalisability and develop data-driven tools to support patient prioritisation and triage at the community level.PROSPERO registration numberCRD42019140542.

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 Binary Logistic Model to Identify the Factors Associated with Households with Bank Accounts in Nepal

2020 ◽  
Vol 2 (2) ◽  
pp. 323-336
Author(s):  
Santosh Kumar Shah
Keyword(s):  
Logistic Regression ◽  
Financial Institutions ◽  
Goodness Of Fit ◽  
Receiver Operating Curve ◽  
P Value ◽  
Ratio Test ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Factors Associated ◽  
Bank Accounts

Introduction: Banks play an important role in ensuringthe economicand social stability, and the sustainablegrowth of the economy. The savings and other accounts in financial institutions, including banks, finances, microfinances and cooperatives, enable people to execute important financial functions. Thus, households that have accounts in any of financial institutions can have access to various banking services. Objective: The objective of the study is to identify the factors associated with households having bank accounts in Nepal. Methods: The analysis is based on household data extracted from the dataset of Nepal Demographic and Health Survey, 2016. The dependent variable is dichotomous, as the households with bank accounts and without bank accounts in any formal financial channels. In order to identify the factors associated with households receiving financial services in Nepal, multiple logistic regression models were developed by examining the model adequacy test. Results: The study finds that a total of 66.9% of the households had bank accounts. Several variables were found to be 1% of significance level. The predictive power of the model is found to be 31.2% and multicollinearity among the independent variables was absent. The Hosmer-Lemoshow goodness of fit test revealed that the data were poorly (p-value=0.056) fitted by the model. However, Osius-Rojek goodness of fit test (z=0.11; p-value=0.911), Stukel test (Z=0.683, p-value=0.494), likelihood ratio test (χ2=2770; p-value<0.0001) and area under receiver operating curve (79.8%) revealed that fitted model was good. Conclusion: Multiple logistic regression model revealed that in mountainous and hilly regions, women-headed households have less chances of not having bank accounts compared to the Terai region and men-headed households. The chances of having a bank account in province-2 is even worse than in Karnali and other provinces. The odds of not having bank accounts gradually decreased with the increase in size of agricultural land, wealth index, increase in family size and the number of family members who have completed secondary education.

scholarly journals Factors Associated with Cataract Surgical Outcomes in a Tertiary Health Facility in Oyo State, Nigeria: A Retrospective Study

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Abiola T. Owolabi ◽  
Susannah T. Adepoju ◽  
Olawale Oladejo ◽  
Kunle I. Oreagba
Keyword(s):  
Visual Acuity ◽  
Cataract Surgery ◽  
Goodness Of Fit ◽  
Intraoperative Complications ◽  
Visual Outcome ◽  
Sub Saharan Africa ◽  
P Value ◽  
Goodness Of Fit Test ◽  
Intraoperative Complication ◽  
Factors Associated

Background: Cataract surgery is the most common operation performed in ophthalmology. It is the commonest cause of reversible blindness globally, in Sub-Saharan Africa and Nigeria. The study examined some factors affecting the outcome of cataracts surgery measured by Visual acuity after 6 weeks. Methods: Data was collected from the records of ophthalmic patients who had cataract surgery at LAUTECH Teaching Hospital Ogbomoso, from the period of January 2013 to December 2018. Two hundred and twenty-seven patients’ records were retrieved for the study. Logistic Regression was used to investigate factors associated with the outcome of Cataracts Surgery. The goodness of fit test was used to determine the fit of the model to the data. Results: Two variables; intraoperative complication, and unaided visual acuity on the fir st postoperative day were statistically significant (p-value < 0.05). The outcome of surgery using unaided visual acuity after six weeks of surgery showed that 47.1% of the patients had a good visual outcome (6/18) or better and 52.9% had a poor outcome (worse than 6/60). Factors such as complications within six weeks, presence of ocular and systemic comorbidity, and presence of intraoperative complications were found to increase the likelihood of poor outcomes in cataract surgery. Conclusion: This study has shown that Intraoperative complications and unaided visual acuity on the first postoperative day are important to the outcome of cataract surgery. Therefore, the two factors should be given attention during cataract surgery

scholarly journals Validation and update of the thoracic surgery scoring system (Thoracoscore) risk model

2020 ◽  
Vol 58 (2) ◽  
pp. 350-356
Author(s):  
Julien Die Loucou ◽  
Pierre-Benoit Pagès ◽  
Pierre-Emmanuel Falcoz ◽  
Pascal-Alexandre Thomas ◽  
Caroline Rivera ◽  
...  
Keyword(s):  
Thoracic Surgery ◽  
Prediction Model ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Risk Model ◽  
Age Distribution ◽  
Original Data ◽  
Validation Group ◽  
Goodness Of Fit Test ◽  
Validation Data

Abstract OBJECTIVES The performance of prediction models tends to deteriorate over time. The purpose of this study was to update the Thoracoscore risk prediction model with recent data from the Epithor nationwide thoracic surgery database. METHODS From January 2016 to December 2017, a total of 56 279 patients were operated on for mediastinal, pleural, chest wall or lung disease. We used 3 recommended methods to update the Thoracoscore prediction model and then proceeded to develop a new risk model. Thirty-day hospital mortality included patients who died within the first 30 days of the operation and those who died later during the same hospital stay. RESULTS We compared the baseline patient characteristics in the original data used to develop the Thoracoscore prediction model and the validation data. The age distribution was different, with specifically more patients older than 65 years in the validation group. Video-assisted thoracoscopy accounted for 47% of surgeries in the validation group compared but only 18% in the original data. The calibration curve used to update the Thoracoscore confirmed the overfitting of the 3 methods. The Hosmer–Lemeshow goodness-of-fit test was significant for the 3 updated models. Some coefficients were overfitted (American Society of Anesthesiologists score, performance status and procedure class) in the validation data. The new risk model has a correct calibration as indicated by the Hosmer–Lemeshow goodness-of-fit test, which was non-significant. The C-index was strong for the new risk model (0.84), confirming the ability of the new risk model to differentiate patients with and without the outcome. Internal validation shows no overfitting for the new model CONCLUSIONS The new Thoracoscore risk model has improved performance and good calibration, making it appropriate for use in current clinical practice.

84 Development of a Risk Prediction Model and Comparison with Clinical Severity Scores in Burn Patients

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S54-S55
Author(s):  
Dohern Kym
Keyword(s):  
Prediction Model ◽  
Goodness Of Fit ◽  
Scoring Systems ◽  
Clinical Severity ◽  
Brier Score ◽  
P Value ◽  
Burn Patients ◽  
Goodness Of Fit Test ◽  
Prediction Of Mortality ◽  
Severity Scores

Abstract Introduction The purpose of this study was to develop a new prediction model to reflect the risk of mortality and severity of disease and to evaluate the ability of the developed model to predict mortality among adult burn patients. Methods This study included 2009 patients aged more than 18 years who were admitted to the intensive care unit (ICU) within 24 hours after a burn. We divided the patients into two groups; those admitted from January 2007 to December 2013 were included in the derivation group and those admitted from January 2014 to September 2017 were included in the validation group. Shrinkage methods with 10-folds cross-validation were performed to identify variables and limit overfitting of the model. The discrimination was analyzed using the area under the curve (AUC) of the receiver operating characteristic curve. The Brier score, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were also calculated. The calibration was analyzed using the Hosmer-Lemeshow goodness-of-fit test (HL test). The clinical usefulness was evaluated using a decision-curve analysis. Results The new prediction model showed good calibration with the HL test (χ2=8.785, p=0.361); the highest AUC and the lowest Brier score were 0.943 and 0.068, respectively. The NRI and IDI were 0.124 (p-value = 0.003) and 0.079 (p-value &lt; 0.001) when compared with FLAMES, respectively. Conclusions This model reflects the current risk factors of mortality among adult burn patients. Furthermore, it was a highly discriminatory and well-calibrated model for the prediction of mortality in this cohort. Applicability of Research to Practice There are many severity scoring systems widely used in the ICU to predict outcomes and characterize the severity of the disease. All of these scoring systems have been developed for the mixed population in the ICU. Their accuracy among subgroups, such as burn patients, is questionable and therefore, burn-specific scoring systems are required for accurate prediction.

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