Estimating Neurologic Prognosis in Children: High Stakes, Poor Data.
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ABSTRACT:
SUBMITTER: Lemmon ME
PROVIDER: S-EPMC7472892 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Estimating Neurologic Prognosis in Children: High Stakes, Poor Data.
Lemmon Monica E ME Ubel Peter A PA Janvier Annie A
JAMA neurology 20190801 8
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Project description:Prognosis can no longer be relegated behind diagnosis and therapy in high-quality neurologic care. High-stakes decisions that patients (or their surrogates) make often rest upon perceptions and beliefs about prognosis, many of which are poorly informed. The new science of prognostication--the estimating and communication "what to expect"--is in its infancy and the evidence base to support "best practices" is lacking. We propose a framework for formulating a prediction and communicating "what to expect" with patients, families, and surrogates in the context of common neurologic illnesses. Because neurologic disease affects function as much as survival, we specifically address 2 important prognostic questions: "How long?" and "How well?" We provide a summary of prognostic information and highlight key points when tailoring a prognosis for common neurologic diseases. We discuss the challenges of managing prognostic uncertainty, balancing hope and realism, and ways to effectively engage surrogate decision-makers. We also describe what is known about the nocebo effects and the self-fulfilling prophecy when communicating prognoses. There is an urgent need to establish research and educational priorities to build a credible evidence base to support best practices, improve communication skills, and optimize decision-making. Confronting the challenges of prognosis is necessary to fulfill the promise of delivering high-quality, patient-centered care.
| S-EPMC3589298 | biostudies-literature
Project description:Background and purposeAlthough intracerebral hemorrhage (ICH) volume and location are important predictors of outcome in adults, few data exist in children.MethodsA consecutive cohort of children, including full-term newborns to those younger than 18 years of age with nontraumatic, acute ICH and head CT available for analysis were studied. Clinical information was abstracted via chart review. Hemorrhage volume was expressed as percentage of total brain volume (TBV) with large hemorrhage defined as >or=4% of TBV. Hemorrhages were manually traced on each head CT slice and volumes were calculated by multiplying by slice thickness. Location was classified as supratentorial or infratentorial. Logistic regression was used to identify predictors of poor neurological outcome, defined as a Glasgow outcome scale <or=2 (death or persistent vegetative state).ResultsThirty children were included, median age 6 years. Median ICH volume was 20.4 cm(3) and median ICH size as a percentage of TBV was 1.9%. Only 4 of 22 children with ICH <4% of TBV had poor outcomes, vs 5 of 8 children with ICH >or=4% of TBV (P=0.03). In multivariate analysis, hemorrhage >or=4% of TBV (OR, 22.5; 95% CI, 1.4-354; P=0.03) independently predicted poor outcome 30 days after ICH. In this small sample, infratentorial hemorrhage location and the presence of intraventricular hemorrhage did not predict poor outcome.ConclusionsICH volume predicts neurological outcome at 30 days in children, with worst outcome when hemorrhage is >or=4% of TBV. Location and ICH etiology may also be important. These findings identify children with ICH who are candidates for aggressive management and may influence counseling regarding prognosis.
| S-EPMC2830068 | biostudies-literature
Project description:To measure the frequency of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis among decedents in hospitals of different sizes and teaching statuses.DesignWe performed a multicenter, retrospective cohort study.SettingFour large teaching hospitals, four affiliated small teaching hospitals, and nine affiliated nonteaching hospitals in the United States.PatientsWe included a sample of all adult inpatient decedents between August 2017 and August 2019.Measurements and main resultsWe reviewed inpatient notes and categorized the immediately preceding circumstances as withdrawal of life-sustaining therapy for perceived poor neurologic prognosis, withdrawal of life-sustaining therapy for nonneurologic reasons, limitations or withholding of life support or resuscitation, cardiac death despite full treatment, or brain death. Of 2,100 patients, median age was 71 years (interquartile range, 60-81 yr), median hospital length of stay was 5 days (interquartile range, 2-11 d), and 1,326 (63%) were treated at four large teaching hospitals. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis occurred in 516 patients (25%) and was the sole contributing factor to death in 331 (15%). Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis was common in all hospitals: 30% of deaths at large teaching hospitals, 19% of deaths in small teaching hospitals, and 15% of deaths at nonteaching hospitals. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis happened frequently across all hospital units. Withdrawal of life-sustaining therapy for perceived poor neurologic prognosis contributed to one in 12 deaths in patients without a primary neurologic diagnosis. After accounting for patient and hospital characteristics, significant between-hospital variability in the odds of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis persisted.ConclusionsA quarter of inpatient deaths in this cohort occurred after withdrawal of life-sustaining therapy for perceived poor neurologic prognosis. The rate of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis occurred commonly in all type of hospital settings. We observed significant unexplained variation in the odds of withdrawal of life-sustaining therapy for perceived poor neurologic prognosis across participating hospitals.
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Project description:Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA’s ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA’s ToxCast high-throughput screening program, together with in vitro to in vivo extrapolation and 2) thresholds of toxicological concern (TTCs) determined using a structure-based decision-tree using the Toxtree open source software. To ground-truth these computational approaches, we compared the MoEs based on predicted noncancer TTC and OED values to those derived using the traditional method of deriving points of departure from no-observed adverse effect levels (NOAELs) from in vivo oral exposures in rodents. TTC-based MoEs were lower than NOAEL-based MoEs for 520 out of 522 (99.6%) compounds in this smaller overlapping dataset, but were relatively well correlated with the same (r2 = 0.59). TTC-based MoEs were also lower than OED-based MoEs for 590 (83.2%) of the 709 evaluated chemicals, indicating that TTCs may serve as a conservative surrogate in the absence of chemical-specific experimental data. The TTC-based MoE prioritization process was then applied to over 45,000 curated environmental chemical structures as a proof-of-concept for high-throughput prioritization using TTC-based MoEs. This study demonstrates the utility of exploiting existing computational methods at the pre-assessment phase of a tiered risk-based approach to quickly, and conservatively, prioritize thousands of untested chemicals for further study.
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Project description:Adults report more willingness to help siblings over close friends when the stakes are extremely high, such as when deciding whether to donate a kidney or risk injury to rescue someone in peril. When dividing plentiful, low-value resources, in contrast, children expect people to share equally with friends and siblings. Even when distributing limited resources-one instead of many-and distributing to their own social partners rather than fictional characters, children share more with kin and friends than with strangers but do not favor kin over friends until 5.5 years of age. However, no study has tested whether children would preferentially benefit kin if the rewards require that children incur a higher personal cost of their own time and effort. In the present experiment, therefore, we asked if children would work harder for kin over non-kin when playing a challenging geometry game that allowed them to earn rewards for others. We found that 4.5-year-old children calibrated their time and effort in the game differently according to who received the rewards-they played for more trials and answered more trials correctly for kin over non-kin, but 5.5-year-old children did not. The older children may have found the task easier and less costly or may have different social experiences affecting their efforts to benefit others. Nonetheless, 4.5-year-old children's social decisions favored kin as recipients of their generosity.
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Project description:Using an incentivized experiment with statistical power, this paper explores the role of stakes in charitable giving of lottery prizes, where subjects commit to donate a fraction of the prize before they learn the outcome of the lottery. We study three stake levels: 5€ (n = 177), 100€ (n = 168), and 1,000€ (n = 171). Although the donations increase in absolute terms as the stakes increase, subjects decrease the donated fraction of the pie. However, people still share roughly 20% of 1,000€, an amount as high as the average monthly salary of people at the age of our subjects. The number of people sharing 50% of the pie is remarkably stable across stakes, but donating the the whole pie-the modal behavior in charity-donation experiments-disappears with stakes. Such hyper-altruistic behavior thus seems to be an artifact of the stakes typically employed in economic and psychological experiments. Our findings point out that sharing with others is a prevalent human feature, but stakes are an important determinant of sharing. Policies promoted via prosocial frames (e.g., stressing the effects of mask-wearing or social distancing on others during the Covid-19 pandemic or environmentally-friendly behaviors on future generations) may thus be miscalibrated if they disregard the stakes at play.
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2023-12-11 | GSE249935 | GEO
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