Parkland Trauma Index of Mortality: Real-Time Predictive Model for Trauma Patients

Starr, Adam J.; Julka, Manjula; Nethi, Arun; Watkins, John D.; Fairchild, Ryan W.; Rinehart, Dustin; Park, Caroline; Dumas, Ryan P.; Box, Hayden N.; Cripps, Michael W.

doi:10.1097/bot.0000000000002290

Cited by 7 publications

(7 citation statements)

References 41 publications

(52 reference statements)

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“…The tool developed in Dallas is named the Parkland Trauma Index of Mortality (PTIM) [ 21 ] and was installed around the same time and also uses a hospital information system (EPIC). The PTIM is a machine learning algorithm using emergency room data to predict mortality within 48 h in trauma patients during the first 3 days of their hospitalization.…”

Section: Discussionmentioning

confidence: 99%

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The role of big data management, data registries, and machine learning algorithms for optimizing safe definitive surgery in trauma: a review

Pape,

Starr,

Gueorguiev

et al. 2024

Patient Saf Surg

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Digital data processing has revolutionized medical documentation and enabled the aggregation of patient data across hospitals. Initiatives such as those from the AO Foundation about fracture treatment (AO Sammelstudie, 1986), the Major Trauma Outcome Study (MTOS) about survival, and the Trauma Audit and Research Network (TARN) pioneered multi-hospital data collection. Large trauma registries, like the German Trauma Registry (TR-DGU) helped improve evidence levels but were still constrained by predefined data sets and limited physiological parameters. The improvement in the understanding of pathophysiological reactions substantiated that decision making about fracture care led to development of patient’s tailored dynamic approaches like the Safe Definitive Surgery algorithm. In the future, artificial intelligence (AI) may provide further steps by potentially transforming fracture recognition and/or outcome prediction. The evolution towards flexible decision making and AI-driven innovations may be of further help. The current manuscript summarizes the development of big data from local databases and subsequent trauma registries to AI-based algorithms, such as Parkland Trauma Mortality Index and the IBM Watson Pathway Explorer.

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Section: Discussionmentioning

confidence: 99%

“…AS is an expert in development of a hospital index to monitor the risk of mortality (Parkland mortality index) [ 21 ]. Moreover, he is the developer in a frame to facilitation reduction for pelvic fractures ( [ 22 ] Starr frame).…”

Section: Methodsmentioning

confidence: 99%

The role of big data management, data registries, and machine learning algorithms for optimizing safe definitive surgery in trauma: a review

Pape,

Starr,

Gueorguiev

et al. 2024

Patient Saf Surg

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“…The Parkland Trauma Index of Mortality (PTIM) was developed and validated to predict the mortality risk of trauma patients over the next 48 hours from calculating a score based on objective variables (Figures 1 and 2). 4 This tool is novel in that it is integrated directly into the EHR, extracts the data automatically, and calculates a PTIM score, requiring no input from the clinician (Figure 3). The clinician can then utilize this mortality prediction for planning, including operative intervention timing, deciding between damage control or definitive management, and goals of care.…”

Section: Methodsmentioning

confidence: 99%

A Real-Time Automated Machine Learning Algorithm for Predicting Mortality in Trauma Patients: Survey Says it’s Ready for Prime-Time

Park,

Loza-Avalos,

Harvey

et al. 2023

The American Surgeon™

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Background Though artificial intelligence (“AI”) has been increasingly applied to patient care, many of these predictive models are retrospective and not readily available for real-time decision-making. This survey-based study aims to evaluate implementation of a new, validated mortality risk calculator (Parkland Trauma Index of Mortality, “PTIM”) embedded in our electronic healthrecord (“EHR”) that calculates hourly predictions of mortality with high sensitivity and specificity. Methods This is a prospective, survey-based study performed at a level 1 trauma center. An anonymous survey was sent to surgical providers and regarding PTIM implementation. The PTIM score evaluates 23 variables including Glasgow Coma Score (GCS), vital signs, and laboratory data. Results Of the 40 completed surveys, 35 reported using PTIM in decision-making. Prior to reviewing PTIM, providers identified perceived top 3 predictors of mortality, including GCS (22/38, 58%), age (18/35, 47%), and maximum heart rate (17/35, 45%). Most providers reported the PTIM assisted their treatment decisions (27/35, 77%) and timing of operative intervention (23/35, 66%). Many providers agreed that PTIM integrated into rounds and patient assessment (22/36, 61%) and that it improved efficiency in assessing patients’ potential mortality (21/36, 58%). Conclusions Artificial intelligence algorithms are mostly retrospective and lag in real-time prediction of mortality. To our knowledge, this is the first real-time, automated algorithm predicting mortality in trauma patients. In this small survey-based study, we found PTIM assists in decision-making, timing of intervention, and improves accuracy in assessing mortality. Next steps include evaluating the short- and long-term impact on patient outcomes.

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“…Our group has previously reported about the conception of the PTIM. 9 The component set comprises vital signs (systolic blood pressure, heart rate, oxygenation, and body temperature), laboratory parameters (base deficit, lactate, hemoglobin concentration, platelet count, white blood cell count, international normalized ratio, potassium, aspartate aminotransferase, total bilirubin, albumin, and creatine), the Glasgow Coma Scale, age, and time since arrival. The PTIM model algorithm is a balanced bagging ensemble of decision tree classifiers.…”

Section: Scoring Systems Parkland Trauma Index Of Mortalitymentioning

confidence: 99%

“…The Parkland Trauma Index of Mortality (PTIM) is a machine-learning algorithm that predicts mortality starting 12 hours after trauma admission and then scores hourly for 72 hours. 9 The score is dynamic, updating every hour to reflect changes in physiological parameters and success of resuscitation. One of the main advantages is its automatic calculation and electronic medical record (EMR) integration.…”

Section: Introductionmentioning

confidence: 99%

Parkland Trauma Index of Mortality in Orthopaedic Trauma Patients: An Initial Report

Tiziani,

Hinkle,

Mesarick

et al. 2023

Journal of Orthopaedic Trauma

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Objectives: The extent and timing of surgery in severely injured patients remains an unsolved problem in orthopaedic trauma. Different laboratory values or scores have been used to try to predict mortality and estimate physiological reserve. The Parkland Trauma Index of Mortality (PTIM) has been validated as an electronic medical record–integrated algorithm to help with operative timing in trauma patients. The aim of this study was to report our initial experience with PTIM and how it relates to other scores. Methods: A retrospective chart review of level 1 and level 2 trauma patients admitted to our institution between December 2020 and November 2022 was conducted. Patients scored with PTIM with orthopaedic injuries were included in this study. Exclusion criteria were patients younger than 18 years. Results: Seven hundred seventy-four patients (246 female patients) with a median age of 40.5 (18–101) were included. Mortality was 3.1%. Patients in the PTIM high-risk category (≥0.5) had a 20% mortality rate. The median PTIM was 0.075 (0–0.89) and the median Injury Severity Score (ISS) was 9.0 (1–59). PTIM (P < 0.001) and ISS (P < 0.001) were significantly lower in surviving patients. PTIM was mentioned in 7.6% of cases, and in 1.7% of cases, providers indicated an action in response to the PTIM. PTIM and ISS were significantly higher in patients with documented PTIM. Conclusion: PTIM is better at predicting mortality compared with ISS. Our low rate of PTIM documentation in provider notes highlights the challenges of implementing a new algorithm. Level of Evidence: Level III, retrospective cohort study.

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Parkland Trauma Index of Mortality: Real-Time Predictive Model for Trauma Patients

Abstract: Supplemental Digital Content is Available in the Text.

Cited by 7 publications

References 41 publications

The role of big data management, data registries, and machine learning algorithms for optimizing safe definitive surgery in trauma: a review

The role of big data management, data registries, and machine learning algorithms for optimizing safe definitive surgery in trauma: a review

A Real-Time Automated Machine Learning Algorithm for Predicting Mortality in Trauma Patients: Survey Says it’s Ready for Prime-Time

Parkland Trauma Index of Mortality in Orthopaedic Trauma Patients: An Initial Report

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