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Yousefi MR, Karajizadeh M, Ghasemian M, Paydar S. Comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set: a diagnostic study. BMC Emerg Med 2024; 24:163. [PMID: 39251893 PMCID: PMC11382384 DOI: 10.1186/s12873-024-01084-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 09/02/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND In the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients' mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set. METHODS This cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022-2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set. RESULTS A total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3-84.4] and 93.99, CI 95% [93.2-94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001). CONCLUSION This novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set.
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Affiliation(s)
| | - Mehrdad Karajizadeh
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Mehdi Ghasemian
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Paydar
- Trauma Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
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Xing Z, Cai L, Wu Y, Shen P, Fu X, Xu Y, Wang J. Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury. Eur J Med Res 2024; 29:80. [PMID: 38287435 PMCID: PMC10823604 DOI: 10.1186/s40001-024-01655-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024] Open
Abstract
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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Affiliation(s)
- Zhibin Xing
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Lingli Cai
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuxuan Wu
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Pengfei Shen
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xiaochen Fu
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yiwen Xu
- The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jing Wang
- The First Affiliated Hospital of Jinan University, Guangzhou, China.
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Anthony AA, Dutta R, Sarang B, David S, O'Reilly G, Raykar NP, Khajanchi M, Attergrim J, Soni KD, Sharma N, Mohan M, Gadgil A, Roy N, Gerdin Wärnberg M. Profile and triage validity of trauma patients triaged green: a prospective cohort study from a secondary care hospital in India. BMJ Open 2023; 13:e065036. [PMID: 37156594 PMCID: PMC10173999 DOI: 10.1136/bmjopen-2022-065036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
OBJECTIVES To 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). DESIGN Prospective cohort study. SETTING A secondary care hospital in Mumbai, India. PARTICIPANTS Patients 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 MEASURES Outcome measures were mortality within 24 hours, 30 days and mistriage. RESULTS We 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. CONCLUSIONS Our 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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Affiliation(s)
| | - Rohini Dutta
- World Health Organization Collaborating Center for Research in Surgical Care Delivery in Low-and-Middle Income Countries, Mumbai, India
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Bhakti Sarang
- World Health Organization Collaborating Center for Research in Surgical Care Delivery in Low-and-Middle Income Countries, Mumbai, India
- Department of Surgery, Terna Medical College & Hospital, New Mumbai, India
| | - Siddarth David
- Department of Global Public Health, Karolinska Institutet, Solna, Sweden
| | - Gerard O'Reilly
- Department of Emergency Medicine, Monash University, Clayton, Victoria, Australia
| | - Nakul P Raykar
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Department of Emergency Surgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Monty Khajanchi
- Department of Surgery, Seth Gowardhandas Sunderdas Medical College and King Edward Memorial Hospital, Mumbai, India
| | - Jonatan Attergrim
- Department of Global Public Health, Karolinska Institutet, Solna, Sweden
- Function Perioperative Medicine and Intensive Care, Karolinska University Hospital, Solna, Sweden
| | - Kapil Dev Soni
- Critical Care, All India Institute of Medical Sciences, New Delhi, India
| | - Naveen Sharma
- Department of Surgery, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | - Monali Mohan
- World Health Organization Collaborating Center for Research in Surgical Care Delivery in Low-and-Middle Income Countries, Mumbai, India
| | - Anita Gadgil
- World Health Organization Collaborating Center for Research in Surgical Care Delivery in Low-and-Middle Income Countries, Mumbai, India
| | - Nobhojit Roy
- World Health Organization Collaborating Center for Research in Surgical Care Delivery in Low-and-Middle Income Countries, Mumbai, India
| | - Martin Gerdin Wärnberg
- Department of Global Public Health, Karolinska Institutet, Solna, Sweden
- Function Perioperative Medicine and Intensive Care, Karolinska University Hospital, Solna, Sweden
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Chiu CC, Wu CM, Chien TN, Kao LJ, Li C, Chu CM. Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4340. [PMID: 36901354 PMCID: PMC10001457 DOI: 10.3390/ijerph20054340] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
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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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chengcheng Li
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
| | - Chuan-Mei Chu
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
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An Epidemiological and Clinical Study of Traumatic Brain Injury in Papua New Guinea Managed by General Surgeons in Two Provincial Hospitals. Indian J Surg 2022. [DOI: 10.1007/s12262-022-03612-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Wu CA, Dutta R, Virk S, Roy N, Ranganathan K. The need for craniofacial trauma and oncologic reconstruction in global surgery. J Oral Biol Craniofac Res 2021; 11:563-567. [PMID: 34430193 DOI: 10.1016/j.jobcr.2021.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 07/22/2021] [Indexed: 10/20/2022] Open
Abstract
The global burden of surgical disease is concentrated in low- and middle-income countries and primarily consists of injuries and malignancies. While global reconstructive surgery has a long and well-established history, efforts thus far have been focused on addressing congenital anomalies. Craniofacial trauma and oncologic reconstruction are comparatively neglected despite their higher prevalence. This review explores the burden, management, and treatment gaps of craniofacial trauma and head and neck cancer reconstruction in low-resource settings. We also highlight successful alternative treatments used in low-resource settings and pearls that can be learned from these areas.
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Affiliation(s)
| | - Rohini Dutta
- WHO Collaborating Centre for Research in Surgical Care Delivery in LMICs, BARC Hospital (Government of India), Mumbai, India.,Christian Medical College and Hospital, Ludhiana, Punjab, India
| | - Sargun Virk
- Sri Guru Ram Das Institute of Medical Sciences and Research, Amritsar, Punjab, India
| | - Nobhojit Roy
- WHO Collaborating Centre for Research in Surgical Care Delivery in LMICs, BARC Hospital (Government of India), Mumbai, India
| | - Kavitha Ranganathan
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
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