Development and validation of risk prediction model for adverse outcomes in trauma patients.
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ABSTRACT: Background
The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis.Objective
To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China.Methods
This was a retrospective prognostic study using data from 8 public tertiary hospitals in China from 2018. The data were randomly divided into a development set and a validation set. Simple, improved and extended models predicting adverse outcomes were developed, with adverse outcomes defined as in-hospital death or ICU transfer, and patient clinical characteristics, vital signs, diagnoses, and laboratory test values as predictors. The results of the models were presented in the form of nomograms, and performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), precision-recall (PR) curves (PR-AUC), Hosmer-Lemeshow goodness-of-fit test, calibration curve, and decision curve analysis (DCA).Results
Our final dataset consisted of 18,629 patients (40.2% female, mean age of 52.3), 1,089 (5.85%) of whom resulted in adverse outcomes. In the external validation set, three models achieved ROC-AUC of 0.872, 0.881, and 0.903, and a PR-AUC of 0.339, 0.337, and 0.403, respectively. In terms of the calibration curves and DCA, the models also performed well.Conclusions
This prognostic study found that three prediction models and nomograms including the patient clinical characteristics, vital signs, diagnoses, and laboratory test values can support clinicians in more accurately identifying patients who are at risk of adverse outcomes in different settings based on data availability.
SUBMITTER: Zhuang Q
PROVIDER: S-EPMC11334749 | biostudies-literature | 2024 Dec
REPOSITORIES: biostudies-literature
Publications
Development and validation of risk prediction model for adverse outcomes in trauma patients.
Zhuang Qian Q Liu Jianchao J Liu Wei W Ye Xiaofei X Chai Xuan X Sun Songmei S Feng Cong C Li Lin L
Annals of medicine 20240819 1
<h4>Background</h4>The prognosis of trauma patients is highly dependent on early medical diagnosis. By constructing a nomogram model, the risk of adverse outcomes can be displayed intuitively and individually, which has important clinical implications for medical diagnosis.<h4>Objective</h4>To develop and evaluate models for predicting patients with adverse outcomes of trauma that can be used in different data availability settings in China.<h4>Methods</h4>This was a retrospective prognostic stu ...[more]
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Project description:Importance:Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective:To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants:In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures:A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region. Results:Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance:The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals.
| S-EPMC6537785 | biostudies-literature
Project description:BackgroundThe ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.MethodsA prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis.FindingsThe final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome.InterpretationThe PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria.FundingThis work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.
| S-EPMC9596305 | biostudies-literature
Project description:Risk calculators can facilitate shared medical decision-making 1 . Demographics, comorbidities, medication use, geographic region, and other factors may increase the risk for COVID-19-related complications among patients with IBD 2,3 . Develop an individualized prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with IBD. This study developed and validated prognostic penalized logistic regression models 4 using reports to Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) from March-October 2020. Model development was done using a training data set (85% of cases reported March 13 - September 15, 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported September 16-October 20, 2020). COVID-19 related:Hospitalization+: composite outcome of hospitalization, ICU admission, mechanical ventilation, or deathICU+: composite outcome of ICU admission, mechanical ventilation, or deathDeathWe assessed the resulting models' discrimination using the area under the curve (AUC) of the receiver-operator characteristic (ROC) curves and reported the corresponding 95% confidence intervals (CIs). We included 2709 cases from 59 countries (mean age 41.2 years [s.d. 18], 50.2% male). A total of 633 (24%) were hospitalized, 137 (5%) were admitted to the ICU or intubated, and 69 (3%) died. 2009 patients comprised the training set and 700 the test set.The models demonstrated excellent discrimination, with a test set AUC (95% CI) of 0.79 (0.75, 0.83) for Hospitalization+, 0.88 (0.82, 0.95) for ICU+, and 0.94 (0.89, 0.99) for Death. Age, comorbidities, corticosteroid use, and male gender were associated with higher risk of death, while use of biologic therapies was associated with a lower risk. Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of IBD patients. A free online risk calculator ( https://covidibd.org/covid-19-risk-calculator/ ) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with IBD patients. The tool numerically and visually summarizes the patient's probabilities of adverse outcomes and associated CIs. Helping physicians identify their highest-risk patients will be important in the coming months as cases rise in the US and worldwide. This tool can also serve as a model for risk stratification in other chronic diseases. Question: How well can a multivariable risk model predict the risk of hospitalization, intensive care unit (ICU) stay, or death due to COVID-19 in patients with inflammatory bowel disease (IBD)?Findings: Multivariable prediction models developed using data from an international voluntary registry of IBD patients and available for use online ( https://covidibd.org/ ) have very good discrimination for predicting hospitalization (Test set AUC 0.79) and excellent discrimination for ICU admission (Test set AUC 0.88) and death (Test set AUC 0.94). The models were developed with a training sample of 2009 cases and validated in an independent test sample of 700 cases comprised of a random sub-sample of cases and all cases entered in the registry during a one-month period after model development. Meaning: This risk prediction model may serve as an effective tool for healthcare providers to facilitate conversations about COVID-19-related risks with IBD patients.
| S-EPMC7836127 | biostudies-literature
Project description:ObjectivesDevelop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD).Design and settingThis study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March-15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September-20 October 2020).ParticipantsWe included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included.Primary and secondary outcome measuresCOVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models' discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs.ResultsOf the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk.ConclusionsPrognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD.
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Project description:BackgroundThis study was conducted to evaluate how the preeclampsia integrated estimate of risk (fullPIERS) model performs in the prediction of adverse maternal outcomes when the predictor variables are all obtained within 24-h of admission for preeclampsia.MethodsA prospective cohort study on 323 women who fulfilled definite inclusion and exclusion criteria was conducted. Subjects were monitored for clinical symptoms of preeclampsia, biochemical parameters, and adverse maternal and neonatal outcomes. A risk prediction score was calculated using the fullPIERS calculator. Statistical analysis of rates and ratios was carried out by assessing χ (2) test and odds ratio.Results18.3 % (n = 60) had adverse maternal outcome and 42.8 % (n = 138) had adverse fetal outcome, and 43 (13.35 %) had combined adverse maternal and perinatal outcome. Dyspnea, visual disturbances, epigastric pain, and [Formula: see text] appeared to be highly significant risk factors. In the biochemical variables studied, serum creatinine and serum uric acid were found to have a significant association. The association between adverse perinatal outcome and vaginal delivery was highly significant (OR 0.35, 95 % CI 0.19, 0.63), and the P value was 0.0005. The likelihood ratio associated with the highest risk group (predicted probability of the outcome ≥30 %) showed excellent performance (i.e., 17.5) of fullPIERS model as a rule in test.ConclusionThe fullPIERS model performed well in the prediction of adverse maternal outcomes in women with preeclampsia. It is easy to use. The model is based on the use of few important clinical and biochemical parameters and does not require extensive laboratory testing. Although it might be of limited use in a well-equipped tertiary care facility, this model can be utilized in the setting of district or sub-district level hospitals to identify patients who are at risk of complications due to preeclampsia. Timely referral to a higher center will help in reducing the morbidity and mortality associated with this condition.
| S-EPMC5016414 | biostudies-literature
Project description:ObjectivesEarly warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients.MethodsBased on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC).ResultsResults show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708-0.820), 0.775 (95% CI: 0.728-0.823), and 0.756 (95% CI: 0.715-0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department.ConclusionsThis study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.
| S-EPMC9729037 | biostudies-literature
Project description:BackgroundThe incidence of refeeding syndrome (RFS) in critically ill patients is high, which is detrimental to their prognoses. However, the current status and risk factors for the occurrence of RFS in neurocritical patients remain unclear. Elucidating these aspects may provide a theoretical basis for screening populations at high risk of RFS.MethodsA total of 357 patients from January 2021 to May 2022 in a neurosurgery ICU of a tertiary hospital in China were included using convenience sampling. Patients were divided into RFS and non-RFS groups, based on the occurrence of refeeding-associated hypophosphatemia. Risk factors for RFS were determined using univariate and logistic regression analyses, and a risk prediction model for RFS in neurocritical patients was developed. The Hosmer-Lemeshow test was used to determine the goodness of fit of the model, and the receiver operator characteristic curve was used to examine its discriminant validity.ResultsThe incidence of RFS in neurocritical patients receiving enteral nutrition was 28.57%. Logistic regression analyses showed that history of alcoholism, fasting hours, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Sequential Organ Failure Assessment (SOFA) scores, low serum albumin, and low baseline serum potassium were risk factors of RFS in neurocritical patients (p < 0.05). The Hosmer-Lemeshow test showed p = 0.616, and the area under the ROC curve was 0.791 (95% confidence interval: 0.745-0.832). The optimal critical value was 0.299, the sensitivity was 74.4%, the specificity was 77.7%, and the Youden index was 0.492.ConclusionThe incidence of RFS in neurocritical patients was high, and the risk factors were diverse. The risk prediction model in this study had good predictive effects and clinical utility, which may provide a reference for assessing and screening for RFS risk in neurocritical patients.
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Project description:Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.
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Project description:ImportanceRecent evidence from an animal model suggests that peripheral loss of eosinophils in Clostridium difficile infection (CDI) is associated with severe disease. The ability to identify high-risk patients with CDI as early as the time of admission could improve outcomes by guiding management decisions.ObjectiveTo construct a model using clinical indices readily available at the time of hospital admission, including peripheral eosinophil counts, to predict inpatient mortality in patients with CDI.Design, setting, and participantsIn a cohort study, a total of 2065 patients admitted for CDI through the emergency department of 2 tertiary referral centers from January 1, 2005, to December 31, 2015, formed a training and a validation cohort. The sample was stratified by admission eosinophil count (0.0 cells/μL or >0.0 cells/μL), and multivariable logistic regression was used to construct a predictive model for inpatient mortality as well as other disease-related outcomes.Main outcomes and measuresInpatient mortality was the primary outcome. Secondary outcomes included the need for a monitored care setting, need for vasopressors, and rates of inpatient colectomy.ResultsOf the 2065 patients in the study, 1092 (52.9%) were women and patients had a mean (SD) age of 63.4 (18.4) years. Those with an undetectable eosinophil count at admission had increased in-hospital mortality in both the training (odds ratio [OR], 2.01; 95% CI, 1.08-3.73; P = .03) and validation (OR, 2.26; 95% CI, 1.33-3.83; P = .002) cohorts in both univariable and multivariable analysis. Undetectable eosinophil counts were also associated with indicators of severe sepsis, such as admission to monitored care settings (OR, 1.40; 95% CI, 1.06-1.86), the need for vasopressors (OR, 2.08; 95% CI, 1.32-3.28), and emergency total colectomy (OR, 2.56; 95% CI, 1.12-5.87). Other significant predictors of mortality at admission included increasing comorbidity burden (for each 1-unit increase: OR, 1.13; 95% CI, 1.05-1.22) and lower systolic blood pressures (for each 1-mm Hg increase: OR, 0.99; 95% CI, 0.98-1.00). In a subgroup analysis of patients presenting without initial tachycardia or hypotension, only patients with undetectable admission eosinophil counts, but not those with an elevated white blood cell count, had significantly increased odds of inpatient mortality (OR, 5.76; 95% CI, 1.99-16.64).Conclusions and relevanceThis study describes a simple, widely available, inexpensive model predicting CDI severity and mortality to identify at-risk patients at the time of admission.
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Project description:Acute Kidney Injury (AKI) complicating major trauma is associated with increased mortality and morbidity. Traumatic AKI has specific risk factors and predictable time-course facilitating diagnostic modelling. In a single centre, retrospective observational study we developed risk prediction models for AKI after trauma based on data around intensive care admission. Models predicting AKI were developed using data from 830 patients, using data reduction followed by logistic regression, and were independently validated in a further 564 patients. AKI occurred in 163/830 (19.6%) with 42 (5.1%) receiving renal replacement therapy (RRT). First serum creatinine and phosphate, units of blood transfused in first 24 h, age and Charlson score discriminated need for RRT and AKI early after trauma. For RRT c-statistics were good to excellent: development: 0.92 (0.88-0.96), validation: 0.91 (0.86-0.97). Modelling AKI stage 2-3, c-statistics were also good, development: 0.81 (0.75-0.88) and validation: 0.83 (0.74-0.92). The model predicting AKI stage 1-3 performed moderately, development: c-statistic 0.77 (0.72-0.81), validation: 0.70 (0.64-0.77). Despite good discrimination of need for RRT, positive predictive values (PPV) at the optimal cut-off were only 23.0% (13.7-42.7) in development. However, PPV for the alternative endpoint of RRT and/or death improved to 41.2% (34.8-48.1) highlighting death as a clinically relevant endpoint to RRT.
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