Dataset Information

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study.

ABSTRACT: Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation-validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51-81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841-0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.

SUBMITTER: Martin-Rodriguez F

PROVIDER: S-EPMC10116443 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study.

Martín-Rodríguez Francisco F Vaquerizo-Villar Fernando F López-Izquierdo Raúl R Castro-Villamor Miguel A MA Sanz-García Ancor A Del Pozo-Vegas Carlos C Hornero Roberto R

Internal and emergency medicine 20230420 6

Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation-validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-base ...[more]

PMID: 37079244

Similar Datasets

Project description:BackgroundRheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a biomarker-based model for predicting CVD risk in RA patients.MethodsMedicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ≥ 40 years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3 years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models.Results30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46-3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%. Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10-0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001-0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models.ConclusionA prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.

Project description:ImportanceA scoring tool to identify which adults with traumatic injury will require early laparotomy could help improve prehospital triage and system readiness.ObjectiveTo develop and validate a prediction model using prehospital information for early laparotomy following trauma.Design, setting, and participantsThis retrospective cohort study analyzed data from the 2017 version of the American College of Surgeons Trauma Quality Improvement Program database. All adult patients with traumatic injury aged 18 years or older who were admitted to a US trauma center in 2017 were included. Patients were randomly assigned to a derivation or validation cohort. Data were collected and analyzed between July 2020 and September 2020.Main outcomes and measuresThe primary outcome was laparotomy within 2 hours of hospital arrival. A scoring system was developed to predict early laparotomy using a logistic regression model in the derivation cohort. This was validated in the validation cohort using the area under the receiver operating characteristic curve.ResultsA total of 379 890 US adults with traumatic injury were included; 190 264 patients were in the derivation cohort and 189 626 patients were in the validation cohort. The cohorts had the same proportion of laparotomy within 2 hours of hospital arrival (1.1%). The median (IQR) age was 32 (25-46) years in the early laparotomy group and 54 (33-72) years in the group with no early laparotomy. The early laparotomy group contained 113 776 of 188 211 (60.5%) male patients, while the group with no early laparotomy contained 1702 of 2053 (82.9%) male patients. The variable most strongly associated with early laparotomy was penetrating injury to the head, neck, torso, or extremities proximal to the elbow or knee (odds ratio, 13.47; 95% CI, 12.22-14.86) with a point value of 10 (maximum overall score 20). Other variables included in the scoring system were the male sex, a systolic blood pressure less than 90 mm Hg, a Glasgow Coma Scale of less than or equal to 13, having chest wall instability or deformity, pelvic fracture, and high-risk blunt mechanism. In the validation cohort, the C statistic of the scoring system was 0.78 (95% CI, 0.77-0.79).Conclusions and relevanceIn this study, a novel scoring tool using prehospital information was derived and validated to identify which adults with traumatic injury will require laparotomy within 2 hours of hospital arrival. This tool may help trauma professionals allocate operative team resources before patient arrival.

Project description:IntroductionPeople 65 years and older represent the fastest growing segment of the surgical population. Older age is associated with doubling of risk when undergoing emergency general surgery (EGS) procedures and often coexists with medical complexity and considerations of end-of-life care, creating prognostic and decisional uncertainty. Combined with the time-sensitive nature of EGS, it is challenging to gauge perioperative risk and ensure that clinical decisions are aligned with the patient values. Current preoperative risk prediction models for older EGS patients have major limitations regarding derivation and validation, and do not address the specific risk profile of older patients. Accurate and externally validated models specific to older patients are needed to inform care and decision making.Methods and analysisWe will derive, internally and externally validate a multivariable model to predict 30-day mortality in EGS patients >65 years old. Our derivation sample will be individuals enrolled in the National Surgical Quality Improvement Program (NSQIP) database between 2012 and 2016 having 1 of 7 core EGS procedures. Postulated predictor variables have been identified based on previous research, clinical and epidemiological knowledge. Our model will be derived using logistic regression penalised with elastic net regularisation and ensembled using bootstrap aggregation. The resulting model will be internally validated using k-fold cross-validation and bootstrap validation techniques and externally validated using population-based health administrative data. Discrimination and calibration will be reported at each step.Ethics and disseminationEthics for NSQIP data use was obtained from the Ottawa Hospital Research Ethics Board; external validation will use routinely collected anonymised data legally exempt from research ethics review. The final risk score will be published in a peer-reviewed journal. We plan to further disseminate the model as an online calculator or application for clinical use. Future research will be required to test the clinical application of the final model.

Project description:BackgroundRespiratory viruses are important etiologies of community-acquired pneumonia. However, current knowledge on the prognosis of respiratory virus-related pneumonia (RV-p) is limited. Thus, here we aimed to establish a clinical predictive model for mortality of patients with RV-p.MethodsA total of 1431 laboratory-confirmed patients with RV-p, including 1169 and 262 patients from respective derivation and validation cohorts from five teaching hospitals in China were assessed between January 2010 and December 2019. A prediction rule was established on the basis of risk factors for 30-day mortality of patients with RV-p from the derivation cohort using a multivariate logistic regression model.ResultsThe 30-day mortality of patients with RV-p was 16.8% (241/1431). The RV-p score was composed of nine predictors (including respective points of mortality risk): (a) age ⩾65 years (1 point); (b) chronic obstructive pulmonary disease (1 point); (c) mental confusion (1 point); (d) blood urea nitrogen (1 point); (e) cardiovascular disease (2 points); (f) smoking history (2 points); (g) arterial pressure of oxygen/fraction of inspiration oxygen (PaO2/FiO2) < 250 mmHg (2 points); (h) lymphocyte counts <0.8 × 109/L (2 points); (i) arterial PH < 7.35 (3 points). A total of six points was used as the cut-off value for mortality risk stratification. Our model showed a sensitivity of 0.831 and a specificity of 0.783. The area under the receiver operating characteristic curve was more prominent for RV-p scoring [0.867, 95% confidence interval (CI)0.846-0.886] when compared with both pneumonia severity index risk (0.595, 95% CI 0.566-0.624, p < 0.001) and CURB-65 scoring (0.739, 95% CI 0.713-0.765, p < 0.001).ConclusionRV-p scoring was able to provide a good predictive accuracy for 30-day mortality, which accounted for a more effective stratification of patients with RV-p into relevant risk categories and, consequently, help physicians to make more rational clinical decisions.The reviews of this paper are available via the supplemental material section.

Dataset Information

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study.

Publications

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets