Preoperative Score to Predict Postoperative Mortality (POSPOM)

2016 ◽  
Vol 124 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Yannick Le Manach ◽  
Gary Collins ◽  
Reitze Rodseth ◽  
Christine Le Bihan-Benjamin ◽  
Bruce Biccard ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Postoperative Mortality ◽  
Clinical Decision ◽  
Derivation Cohort ◽  
International Statistical Classification ◽  
Hospital Deaths ◽  
Preoperative Score

Abstract Background An accurate risk score able to predict in-hospital mortality in patients undergoing surgery may improve both risk communication and clinical decision making. The aim of the study was to develop and validate a surgical risk score based solely on preoperative information, for predicting in-hospital mortality. Methods From January 1, 2010, to December 31, 2010, data related to all surgeries requiring anesthesia were collected from all centers (single hospital or hospitals group) in France performing more than 500 operations in the year on patients aged 18 yr or older (n = 5,507,834). International Statistical Classification of Diseases, 10th revision codes were used to summarize the medical history of patients. From these data, the authors developed a risk score by examining 29 preoperative factors (age, comorbidities, and surgery type) in 2,717,902 patients, and then validated the risk score in a separate cohort of 2,789,932 patients. Results In the derivation cohort, there were 12,786 in-hospital deaths (0.47%; 95% CI, 0.46 to 0.48%), whereas in the validation cohort there were 14,933 in-hospital deaths (0.54%; 95% CI, 0.53 to 0.55%). Seventeen predictors were identified and included in the PreOperative Score to predict PostOperative Mortality (POSPOM). POSPOM showed good calibration and excellent discrimination for in-hospital mortality, with a c-statistic of 0.944 (95% CI, 0.943 to 0.945) in the development cohort and 0.929 (95% CI, 0.928 to 0.931) in the validation cohort. Conclusion The authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.

scholarly journals A novel severity score to predict inpatient mortality in COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David J. Altschul ◽  
Santiago R. Unda ◽  
Joshua Benton ◽  
Rafael de la Garza Ramos ◽  
Phillip Cezayirli ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

scholarly journals Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248477
Author(s):  
Khushal Arjan ◽  
Lui G. Forni ◽  
Richard M. Venn ◽  
David Hunt ◽  
Luke Eliot Hodgson
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Risk Stratification ◽  
Hospital Mortality ◽  
Hospital Stay ◽  
Risk Score ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Early Warning Score

Objectives of the study Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. Methods Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. Results Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78–0.80), superior to NEWS2 0.65 (0.62–0.67) and CFS 0.76 (0.74–0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8–16.1) to stay >30 days. Conclusions Our simple validated model (Older Persons’ Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

Prognostic nomogram for patients with minor stroke and transient ischaemic attack

2020 ◽  
pp. postgradmedj-2020-137680
Author(s):  
Zhihao Lei ◽  
Shuanglin Li ◽  
Hongye Feng ◽  
Yupeng Lai ◽  
Yanxia Zhou ◽  
...  
Keyword(s):  
Transient Ischaemic Attack ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Decision ◽  
Favourable Outcome ◽  
Minor Stroke ◽  
Derivation Cohort ◽  
Actual Observation ◽  
Ischaemic Attack

BackgroundIschaemic stroke and transient ischaemic attack (TIA) share a common cause. We aim to develop and validate a concise prognostic nomogram for patients with minor stroke and TIA.MethodsA total of 994 patients with minor stroke and TIA were included. They were split into a derivation (n=746) and validation (n=248) cohort. The modified Rankin Scale (mRS) scores 3 months after onset were used to assess the prognosis as unfavourable outcome (mRS≥2) or favourable outcome (mRS<2).ResultThe final model included seven independent predictors: gender, age, baseline National Institute of Health Stroke Scale (NIHSS), hypertension, diabetes mellitus, white blood cell and serum uric acid. The Harrell’s concordance index (C-index) of the nomogram for predicting the outcome was 0.775 (95% CI 0.735 to 0.814), which was confirmed by the validation cohort (C-index=0.787 (95% CI 0.722 to 0.853)). The calibration curve showed that the nomogram-based predictions were consistent with actual observation in both derivation cohort and validation cohort.ConclusionThe proposed nomogram showed favourable predictive accuracy for minor stroke and TIA. This has the potential to contribute to clinical decision-making.

scholarly journals A Novel Severity Score to Predict Inpatient Mortality in COVID-19 patients

2020 ◽  
Author(s):  
David Altschul ◽  
Santiago R Unda ◽  
Joshua Benton ◽  
Rafael de La Garza Ramos ◽  
Mark Mehler ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract IntroductionCOVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality.Methods4,711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n=2,355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2,356 patients.ResultsMortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0-3), moderate (4-6) and high (7-10) COVID-19 severity score.ConclusionThis developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

scholarly journals Obesity, chronic disease, age, and in-hospital mortality in patients with covid-19: analysis of ISARIC clinical characterisation protocol UK cohort

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Thomas Yates ◽  
Francesco Zaccardi ◽  
Nazrul Islam ◽  
Cameron Razieh ◽  
Clare L. Gillies ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Hospital Mortality ◽  
Chronic Diseases ◽  
Clinical Decision Making ◽  
Age Groups ◽  
Disease Status ◽  
Clinical Decision ◽  
Emerging Infections ◽  
Hospital Deaths

Abstract Background Although age, obesity and pre-existing chronic diseases are established risk factors for COVID-19 outcomes, their interactions have not been well researched. Methods We used data from the Clinical Characterisation Protocol UK (CCP-UK) for Severe Emerging Infection developed by the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC). Patients admitted to hospital with COVID-19 from 6th February to 12th October 2020 were included where there was a coded outcome following hospital admission. Obesity was determined by an assessment from a clinician and chronic disease by medical records. Chronic diseases included: chronic cardiac disease, hypertension, chronic kidney disease, chronic pulmonary disease, diabetes and cancer. Mutually exclusive categories of obesity, with or without chronic disease, were created. Associations with in-hospital mortality were examined across sex and age categories. Results The analysis included 27,624 women with 6407 (23.2%) in-hospital deaths and 35,065 men with 10,001 (28.5%) in-hospital deaths. The prevalence of chronic disease in women and men was 66.3 and 68.5%, respectively, while that of obesity was 12.9 and 11.1%, respectively. Association of obesity and chronic disease status varied by age (p < 0.001). Under 50 years of age, obesity and chronic disease were associated with in-hospital mortality within 28 days of admission in a dose-response manner, such that patients with both obesity and chronic disease had the highest risk with a hazard ratio (HR) of in-hospital mortality of 2.99 (95% CI: 2.12, 4.21) in men and 2.16 (1.42, 3.26) in women compared to patients without obesity or chronic disease. Between the ages of 50–69 years, obesity and chronic disease remained associated with in-hospital COVID-19 mortality, but survival in those with obesity was similar to those with and without prevalent chronic disease. Beyond the age of 70 years in men and 80 years in women there was no meaningful difference between those with and without obesity and/or chronic disease. Conclusion Obesity and chronic disease are important risk factors for in-hospital mortality in younger age groups, with the combination of chronic disease and obesity being particularly important in those under 50 years of age. These findings have implications for targeted public health interventions, vaccination strategies and in-hospital clinical decision making.

scholarly journals Perioperative Mortality Risk Score using Pre- and Post-operative Risk Factors in Older Patients

2009 ◽  
Vol 37 (3) ◽  
pp. 392-398 ◽  
Author(s):  
D. A. Story ◽  
M. Fink ◽  
K. Leslie ◽  
P. S. Myles ◽  
S.-J. Yap ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Mortality Risk ◽  
Validation Cohort ◽  
Significant Risk ◽  
Postoperative Mortality ◽  
Risk Scores ◽  
Perioperative Mortality ◽  
Derivation Cohort ◽  
C Statistic

We developed a risk score for 30-day postoperative mortality: the Perioperative Mortality risk score. We used a derivation cohort from a previous study of surgical patients aged 70 years or more at three large metropolitan teaching hospitals, using the significant risk factors for 30-day mortality from multivariate analysis. We summed the risk score for each of six factors creating an overall Perioperative Mortality score. We included 1012 patients and the 30-day mortality was 6%. The three preoperative factors and risk scores were (“three A's”): 1) age, years: 70 to 79=1, 80 to 89=3, 90+=6; 2) ASA physical status: ASA I or II=0, ASA III=3, ASA IV=6, ASA V=15; and 3) preoperative albumin <30 g/l=2.5. The three postoperative factors and risk scores were (“three I's”) 1) unplanned intensive care unit admission =4.0; 2) systemic inflammation =3; and 3) acute renal impairment=2.5. Scores and mortality were: <5=1%, 5 to 9.5=7% and ≥10=26%. We also used a preliminary validation cohort of 256 patients from a regional hospital. The area under the receiver operating characteristic curve (C-statistic) for the derivation cohort was 0.80 (95% CI 0.74 to 0.86) similar to the validation C-statistic: 0.79 (95% CI 0.70 to 0.88), P=0.88. The Hosmer-Lemeshow test (P=0.35) indicated good calibration in the validation cohort. The Perioperative Mortality score is straightforward and may assist progressive risk assessment and management during the perioperative period. Risk associated with surgical complexity and urgency could be added to this baseline patient factor Perioperative Mortality score.

scholarly journals Validation of parsimonious prognostic models for patients infected with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Author(s):  
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Inclusion Criteria ◽  
Local Data ◽  
External Data ◽  
Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

Abstract 17048: Dederivation and Validation of a Simple Heart Risk Score in 608,544 Thai Adults: HCUR Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Parinya Chamnan ◽  
Weera Mahawanakul ◽  
Prasert Boongird ◽  
Wannee Nitiyanant ◽  
Wichai Aekplakorn ◽  
...  
Keyword(s):  
General Population ◽  
Risk Score ◽  
Retrospective Cohort ◽  
Validation Cohort ◽  
Information Criteria ◽  
Global Model ◽  
Derivation Cohort ◽  
Risk Equation ◽  
Risk Algorithm ◽  
Incident Cases

Introduction: Most heart risk prediction equations were developed in Western populations. These risk scores are likely to perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new risk algorithm for estimating 5-year risk of developing coronary heart disease (CHD) in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 608,544 men and women aged 20 years and above residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of CHD over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on CHD risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 304,272 individuals, who contributed 1,757,369 person-years of follow-up and 1,272 incident cases of CHD, while the validation cohort comprised of 304,272 individuals (1,757,312 person-years), with 1,290 incident cases of stroke. The risk equation was 0.0580 x Age (years) + 0.5739 x Sex (Male=1) + 0.3850 x Hypertension (present=1) + 0.7080 x Diabetes (present=1) + 0.0386 x Body mass index (kg/m 2 ) + 0.2117 x Central obesity (present=1) - 0.1389 (if exercise 1-2 days/week) or -0.3975 (if exercise 3-5 days/week) or - 0.5598 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort with the area under the receiver operating characteristic curve of 0.790 (95%CI 0.779-0.801). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 809.45 (p<0.001). Conclusions: This simple heart risk score is the first risk algorithm to estimate the 5-year risk of CHD in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and by the public.

A New Risk Score to Predict In-Hospital Mortality in Elderly Patients With Acute Heart Failure: On Behalf of the Journey HF-TR Study Investigators

Angiology ◽  
2020 ◽  
Vol 71 (10) ◽  
pp. 948-954
Author(s):  
Gülay Gök ◽  
Mehmet Karadağ ◽  
Ümit Yaşar Sinan ◽  
Mehdi Zoghi
Keyword(s):  
Heart Failure ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Risk Score ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Previous History ◽  
Turkish Population ◽  
Patient Journey

We aimed to predict in-hospital mortality of elderly patients with heart failure (HF) by using a risk score model which could be easily applied in routine clinical practice without using an electronic calculator. The study population (n = 1034) recruited from the Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) study was divided into a derivation and a validation cohort. The parameters related to in-hospital mortality were first analyzed by univariate analysis, then the variables found to be significant in that analysis were entered into a stepwise multivariate logistic regression (LR) analysis. Patients were classified as low, intermediate, and high risk. A risk score obtained by taking into account the regression coefficients of the significant variables as a result of the LR analysis was tested in the validation cohort using receiver operating characteristic curve analysis. In total, 6 independent variables (age, blood urea nitrogen, previous history of hemodialysis/hemofiltration, inotropic agent use, and length of intensive care stay) associated with in-hospital mortality were included in the analysis. The risk score had a good discrimination in both the derivation and validation cohorts. A new validated risk score to determine the risk of in-hospital mortality of elderly hospitalized patients with HF was developed by including 6 independent predictors.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

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