Associations of On-arrival Vital Signs with 24-hour In-hospital Mortality in Adult Trauma Patients Admitted to Four Public University Hospitals in Urban India: A Prospective Multi-Centre Cohort Study

Sarang, Bhakti; Bhandarkar, Prashant; Raykar, Nakul; O’Reilly, Gerard; Soni, Kapil Dev; Wärnberg, Martin Gerdin; Khajanchi, Monty; Dharap, Satish; Cameron, Peter; Howard, Teresa; Gadgil, Anita; Jarwani, Bhavesh; Mohan, Monali; Bhoi, Sanjeev; Roy, Nobhojit

doi:10.1016/j.injury.2021.02.075

Cited by 6 publications

(4 citation statements)

References 20 publications

(31 reference statements)

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“…RR is a readily available clinical indicator for early identification of high-risk patients after trauma, and shortness of breath correlates strongly with 24-h mortality [ 30 , 31 ]. This study confirmed that RR is a risk factor for death in patients with cervical spine fractures.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

Xing,

Cai,

et al. 2024

Eur J Med Res

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Background The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis. Methods This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer–Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit. Results The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853–0.961) and 0.856 (95% CI = 0.746–0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147–0.940) and 0.784 (95% CI = 0.282–1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004–0.123; P = 0.037) and 0.103 (95% CI = 0.002–0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system. Conclusion We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient’s status and implement interventions to improve prognosis accordingly.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

Xing,

Cai,

et al. 2024

Eur J Med Res

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“…The other factor, overcrowding of the ED with limitation of resources, may also lead to inadequate trauma care. 25 …”

Section: Discussionmentioning

confidence: 99%

“…Although the reasons for this undertriage are multifactorial, in this case, the lack of appropriate training or standard, uniform protocol for patient management in the ED to quickly identify these patients among those that have normal physiological parameters is most evident. The other factor, overcrowding of the ED with limitation of resources, may also lead to inadequate trauma care 25…”

Section: Discussionmentioning

confidence: 99%

Profile and triage validity of trauma patients triaged green: a prospective cohort study from a secondary care hospital in India

Anthony¹,

Dutta

Sarang

et al. 2023

BMJ Open

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ObjectivesTo evaluate the profile of non-urgent patients triaged ‘green’, as part of a triage trial in the emergency department (ED) of a secondary care hospital in India. The secondary aim was to validate the triage trial with the South African Triage Score (SATS).DesignProspective cohort study.SettingA secondary care hospital in Mumbai, India.ParticipantsPatients aged 18 years and above with a history of trauma defined as having any of the external causes of morbidity and mortality listed in block V01–Y36, chapter XX of the International Classification of Disease version 10 codebook, triaged green between July 2016 and November 2019.Primary and secondary outcome measuresOutcome measures were mortality within 24 hours, 30 days and mistriage.ResultsWe included 4135 trauma patients triaged green. The mean age of patients was 32.8 (±13.1) years, and 77% were males. The median (IQR) length of stay of admitted patients was 3 (13) days. Half the patients had a mild Injury Severity Score (3–8), with the majority of injuries being blunt (98%). Of the patients triaged green by clinicians, three-quarters (74%) were undertriaged on validating with SATS. On telephonic follow-up, two patients were reported dead whereas one died while admitted in hospital.ConclusionsOur study highlights the need for implementation and evaluation of training in trauma triage systems that use physiological parameters, including pulse, systolic blood pressure and Glasgow Coma Scale, for the in-hospital first responders in the EDs.

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“…The results of the statistical data's predictive power derived from the basic vital signs and simple demographic data such as age save the most resources and are the most useful. Vital signs were chosen as features mainly because most vital signs can be easily measured using non-invasive equipment, and vital signs are the most basic health indicators that are easily understood by all healthcare professionals [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

Chiu

Chien

et al. 2023

IJERPH

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An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient’s age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

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Associations of On-arrival Vital Signs with 24-hour In-hospital Mortality in Adult Trauma Patients Admitted to Four Public University Hospitals in Urban India: A Prospective Multi-Centre Cohort Study

Cited by 6 publications

References 20 publications

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

Profile and triage validity of trauma patients triaged green: a prospective cohort study from a secondary care hospital in India

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

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