Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation

Scales, Nathan; Herry, Christophe L.; Beinum, Amanda van; Hogue, Melanie L; Hornby, Laura; Shahin, Jason; Dhanani, Sonny; Seely, Andrew J. E.

doi:10.1097/cce.0000000000000675

Cited by 7 publications

(18 citation statements)

References 48 publications

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“…Mixed ICU patient populations were the focus of 15 studies [4,5,9,10,12,14,15,19,20,[25][26][27][29][30][31] with a total of 5131 patients. Neurointensive care patient populations were the focus of four studies [17,21,22,28] with a total of 1181 patients.…”

Section: Resultsmentioning

confidence: 99%

“…The proportion of patients who became asystolic within 60 min ranged from 44-76%. Eight studies 640 [4,9,13,14,17,19,21,26] evaluated the likelihood of asystole within 120 min, which occurred in 54-91% of patients. Full outcomes assessed are shown in Table S1.…”

Section: Resultsmentioning

confidence: 99%

“…A variety of predictive tools and models have been developed [4,5,[9][10][11][12][13][14][15][16][17][18][19] which can provide support for decision-making in this area. Whilst some prediction models initially appear to perform well, they are often not appropriately validated and when external validation has been undertaken, their performance typically does not generalise well [20][21][22]…”

Section: Introductionmentioning

confidence: 99%

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Predicting time to asystole following withdrawal of life‐sustaining treatment: a systematic review

Nicolson,

Burke,

Gardiner

et al. 2024

Anaesthesia

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SummaryThe planned withdrawal of life‐sustaining treatment is a common practice in the intensive care unit for patients where ongoing organ support is recognised to be futile. Predicting the time to asystole following withdrawal of life‐sustaining treatment is crucial for setting expectations, resource utilisation and identifying patients suitable for organ donation after circulatory death. This systematic review evaluates the literature for variables associated with, and predictive models for, time to asystole in patients managed on intensive care units. We conducted a comprehensive structured search of the MEDLINE and Embase databases. Studies evaluating patients managed on adult intensive care units undergoing withdrawal of life‐sustaining treatment with recorded time to asystole were included. Data extraction and PROBAST quality assessment were performed and a narrative summary of the literature was provided. Twenty‐three studies (7387 patients) met the inclusion criteria. Variables associated with imminent asystole (<60 min) included: deteriorating oxygenation; absence of corneal reflexes; absence of a cough reflex; blood pressure; use of vasopressors; and use of comfort medications. We identified a total of 20 unique predictive models using a wide range of variables and techniques. Many of these models also underwent secondary validation in further studies or were adapted to develop new models. This review identifies variables associated with time to asystole following withdrawal of life‐sustaining treatment and summarises existing predictive models. Although several predictive models have been developed, their generalisability and performance varied. Further research and validation are needed to improve the accuracy and widespread adoption of predictive models for patients managed in intensive care units who may be eligible to donate organs following their diagnosis of death by circulatory criteria.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting time to asystole following withdrawal of life‐sustaining treatment: a systematic review

Nicolson,

Burke,

Gardiner

et al. 2024

Anaesthesia

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“…The advent of machine learning and artificial intelligence may provide the means to develop such prediction tools. This technology, as well as the growing ability to manage and analyze "big data, " will hopefully allow for future development of prediction models, such as that proposed for adults by Scales et al (38), that incorporate real time physiologic waveforms with clinical variables to accurately predict time to death in children. It is essential that these tools be validated before they can be used in clinical practice; carefully considering the impacts if children are wrongly classified.…”

Section: At the Bedsidementioning

confidence: 99%

Pediatric Death After Withdrawal of Life-Sustaining Therapies: A Scoping Review

Francoeur,

Silva,

Hornby

et al. 2023

Pediatric Critical Care Medicine

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Objectives: Evaluate literature on the dying process in children after withdrawal of life sustaining measures (WLSM) in the PICU. We focused on the physiology of dying, prediction of time to death, impact of time to death, and uncertainty of the dying process on families, healthcare workers, and organ donation. Data Sources: MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, CINAHL, and Web of Science. Study Selection: We included studies that discussed the dying process after WLSM in the PICU, with no date or study type restrictions. We excluded studies focused exclusively on adult or neonatal populations, children outside the PICU, or on organ donation or adult/pediatric studies where pediatric data could not be isolated. Data Extraction: Inductive qualitative content analysis was performed. Data Synthesis: Six thousand two hundred twenty-five studies were screened and 24 included. Results were grouped into four categories: dying process, perspectives of healthcare professionals and family, WLSM and organ donation, and recommendations for future research. Few tools exist to predict time to death after WLSM in children. Most deaths after WLSM occur within 1 hour and during this process, healthcare providers must offer support to families regarding logistics, medications, and expectations. Providers describe the unpredictability of the dying process as emotionally challenging and stressful for family members and staff; however, no reports of families discussing the impact of time to death prediction were found. The unpredictability of death after WLSM makes families less likely to pursue donation. Future research priorities include developing death prediction tools of tools, provider and parental decision-making, and interventions to improve end-of-life care. Conclusions: The dying process in children is poorly understood and understudied. This knowledge gap leaves families in a vulnerable position and the clinical team without the necessary tools to support patients, families, or themselves. Improving time to death prediction after WLSM may improve care provision and enable identification of potential organ donors.

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“…AI algorithms may assist in making better decisions on this topic. For instance, Scales et al 3 employed ML models of vital sign variability data before WLST, along with the physician’s prediction and clinical features, to forecast the time to death. The combination of these factors resulted in a model with ROC AUC values of 0.78, 0.79, and 0.80 for predicting time to death within 30 minutes, 1 hour, or 2 hours, respectively.…”

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confidence: 99%

Artificial intelligence may help decision-making for withdrawal of life supporting treatment among the injured elderly

Karajizadeh,

Ghaedi,

Pakdaman

et al. 2023

J Trauma Acute Care Surg

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W e read with interest the article Diagnostic accuracy of computed tomography findings for hollow viscus injuries following thoracoabdominal gunshot wounds by Lian et al. 1 Computed tomography (CT) has been found to diagnose solid organ injuries with a good predictive value. 2 The concern for missing a hollow viscus injury (HVI); however, remains the biggest hurdle in selective nonoperative management (SNOM) of penetrating injuries.The authors have reported a reassuring sensitivity of more than 70% and specificity of almost 90% for several CT findings. Similar signs of HVI on CT have been previously described for blunt abdominal trauma as well. 3,4 These results are not surprising as the findings of a perforated viscus would remain similar irrespective of the mechanism of injury.An important distinction of penetrating injuries from blunt injury of the abdomen is the introduction of air into the peritoneal cavity through the wound track. Therefore, presence of free air, which is a definitive sign of HVI in blunt trauma, may not be so in penetrating trauma. The diagnostic dilemma is more when the amount of air in the peritoneal cavity is small. We conducted a randomized trial among patients with anterior abdominal stab wounds comparing SNOM and diagnostic laparoscopy (DL), wherein we faced a similar situation. 5 Of the 54 patients randomized to the SNOM arm, seven patients had a few specks of air and were operated. Surgery was nontherapeutic in two of these patients, in whom the presence of air was attributed to the stab wound. Other authors have reported this dilemma as well. 6 A comment on such CT findings and the ultimate management of such patients would have added value to the study as they represent the group that often causes a diagnostic dilemma.

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Predicting Time to Death After Withdrawal of Life-Sustaining Measures Using Vital Sign Variability: Derivation and Validation

Cited by 7 publications

References 48 publications

Predicting time to asystole following withdrawal of life‐sustaining treatment: a systematic review

Predicting time to asystole following withdrawal of life‐sustaining treatment: a systematic review

Pediatric Death After Withdrawal of Life-Sustaining Therapies: A Scoping Review

Artificial intelligence may help decision-making for withdrawal of life supporting treatment among the injured elderly

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