Jonathan M Weimer1, Amy S Nowacki, Jennifer A Frontera. 1. 1Cleveland Clinic Lerner College of Medicine, Cerebrovascular Center of the Neurological Institute, Cleveland Clinic, Cleveland, OH. 2Quantitative Health Sciences Department, Cleveland Clinic, Cleveland, OH.
Abstract
OBJECTIVES: Withdrawal of life-sustaining therapy may lead to premature limitations of life-saving treatments among patients with intracranial hemorrhage, representing a self-fulfilling prophecy. We aimed to determine whether our algorithm for the withdrawal of life-sustaining therapy decision would accurately identify patients with a high probability of poor outcome, despite aggressive treatment. DESIGN: Retrospective analysis of prospectively collected data. SETTING: Tertiary-care Neuro-ICU. PATIENTS: Intraparenchymal, subdural, and subarachnoid hemorrhage patients. INTERVENTIONS: Baseline demographics, clinical status, and hospital course were assessed to determine the predictors of in-hospital mortality and 12-month death/severe disability among patients receiving maximal therapy. Multivariable logistic regression models developed on maximal therapy patients were applied to patients who underwent withdrawal of life-sustaining therapy to predict their probable outcome had they continued maximal treatment. A validation cohort of propensity score-matched patients was identified from the maximal therapy cohort, and their predicted and actual outcomes compared. MEASUREMENTS AND MAIN RESULTS: Of 383 patients enrolled, there were 128 subarachnoid hemorrhage (33.4%), 134 subdural hematoma (35.0%), and 121 intraparenchymal hemorrhage (31.6%). Twenty-six patients (6.8%) underwent withdrawal of life-sustaining therapy and died, 41 (10.7%) continued maximal therapy and died in hospital, and 316 (82.5%) continued maximal therapy and survived to discharge. The median predicted probability of in-hospital death among withdrawal of life-sustaining therapy patients was 35% had they continued maximal therapy, whereas the median predicted probability of 12-month death/severe disability was 98%. In the propensity-matched validation cohort, 16 of 20 patients had greater than or equal to 80% predicted probability of death/severe disability at 12 months, matching the observed outcomes and supporting the strength and validity of our prediction models. CONCLUSIONS: The withdrawal of life-sustaining therapy decision may contribute to premature in-hospital death in some patients who may otherwise have been expected to survive to discharge. However, based on probability models, nearly all of the patients who underwent withdrawal of life-sustaining therapy would have died or remained severely disabled at 12 months had maximal therapy been continued. Withdrawal of life-sustaining therapy may not represent a self-fulfilling prophecy.
OBJECTIVES: Withdrawal of life-sustaining therapy may lead to premature limitations of life-saving treatments among patients with intracranial hemorrhage, representing a self-fulfilling prophecy. We aimed to determine whether our algorithm for the withdrawal of life-sustaining therapy decision would accurately identify patients with a high probability of poor outcome, despite aggressive treatment. DESIGN: Retrospective analysis of prospectively collected data. SETTING: Tertiary-care Neuro-ICU. PATIENTS: Intraparenchymal, subdural, and subarachnoid hemorrhagepatients. INTERVENTIONS: Baseline demographics, clinical status, and hospital course were assessed to determine the predictors of in-hospital mortality and 12-month death/severe disability among patients receiving maximal therapy. Multivariable logistic regression models developed on maximal therapy patients were applied to patients who underwent withdrawal of life-sustaining therapy to predict their probable outcome had they continued maximal treatment. A validation cohort of propensity score-matched patients was identified from the maximal therapy cohort, and their predicted and actual outcomes compared. MEASUREMENTS AND MAIN RESULTS: Of 383 patients enrolled, there were 128 subarachnoid hemorrhage (33.4%), 134 subdural hematoma (35.0%), and 121 intraparenchymal hemorrhage (31.6%). Twenty-six patients (6.8%) underwent withdrawal of life-sustaining therapy and died, 41 (10.7%) continued maximal therapy and died in hospital, and 316 (82.5%) continued maximal therapy and survived to discharge. The median predicted probability of in-hospital death among withdrawal of life-sustaining therapy patients was 35% had they continued maximal therapy, whereas the median predicted probability of 12-month death/severe disability was 98%. In the propensity-matched validation cohort, 16 of 20 patients had greater than or equal to 80% predicted probability of death/severe disability at 12 months, matching the observed outcomes and supporting the strength and validity of our prediction models. CONCLUSIONS: The withdrawal of life-sustaining therapy decision may contribute to premature in-hospital death in some patients who may otherwise have been expected to survive to discharge. However, based on probability models, nearly all of the patients who underwent withdrawal of life-sustaining therapy would have died or remained severely disabled at 12 months had maximal therapy been continued. Withdrawal of life-sustaining therapy may not represent a self-fulfilling prophecy.
Authors: David Y Hwang; Stacy Y Chu; Cameron A Dell; Mary J Sparks; Tiffany D Watson; Carl D Langefeld; Mary E Comeau; Jonathan Rosand; Thomas W K Battey; Sebastian Koch; Mario L Perez; Michael L James; Jessica McFarlin; Jennifer L Osborne; Daniel Woo; Steven J Kittner; Kevin N Sheth Journal: Neurocrit Care Date: 2017-12 Impact factor: 3.210
Authors: Bertrand Guidet; Hans Flaatten; Ariane Boumendil; Alessandro Morandi; Finn H Andersen; Antonio Artigas; Guido Bertolini; Maurizio Cecconi; Steffen Christensen; Loredana Faraldi; Jesper Fjølner; Christian Jung; Brian Marsh; Rui Moreno; Sandra Oeyen; Christina Agwald Öhman; Bernardo Bollen Pinto; Ivo W Soliman; Wojciech Szczeklik; Andreas Valentin; Ximena Watson; Tilemachos Zafeiridis; Dylan W De Lange Journal: Intensive Care Med Date: 2018-05-17 Impact factor: 17.440
Authors: Ayham Alkhachroum; Antonio J Bustillo; Negar Asdaghi; Erika Marulanda-Londono; Carolina M Gutierrez; Daniel Samano; Evie Sobczak; Dianne Foster; Mohan Kottapally; Amedeo Merenda; Sebastian Koch; Jose G Romano; Kristine O'Phelan; Jan Claassen; Ralph L Sacco; Tatjana Rundek Journal: Stroke Date: 2021-09-29 Impact factor: 7.914
Authors: Mercedes Ibarz; Ariane Boumendil; Lenneke E M Haas; Marian Irazabal; Hans Flaatten; Dylan W de Lange; Alessandro Morandi; Finn H Andersen; Guido Bertolini; Maurizio Cecconi; Steffen Christensen; Loredana Faraldi; Jesper Fjølner; Christian Jung; Brian Marsh; Rui Moreno; Sandra Oeyen; Christina Agwald Öhman; Bernardo Bollen Pinto; Ivo W Soliman; Wojciech Szczeklik; Andreas Valentin; Ximena Watson; Tilemachos Zaferidis; Bertrand Guidet; Antonio Artigas Journal: Ann Intensive Care Date: 2020-05-13 Impact factor: 6.925
Authors: Jens Witsch; Bob Siegerink; Christian H Nolte; Maximilian Sprügel; Thorsten Steiner; Matthias Endres; Hagen B Huttner Journal: Neurol Res Pract Date: 2021-05-03