Background and Purpose— It is unknown whether admission systolic blood pressure (SBP) differs among causes of intracerebral hemorrhage (ICH). We sought to elucidate an association between admission BP and ICH cause. Methods— We compared admission SBP across ICH causes among patients in the Cornell Acute Stroke Academic Registry, which includes all adults with ICH at our center from 2011 through 2017. Trained analysts prospectively collected demographics, comorbidities, and admission SBP, defined as the first recorded value in the emergency department or on transfer from another hospital. ICH cause was adjudicated by a panel of neurologists using the SMASH-U criteria. We used ANOVA to compare mean admission SBP among ICH causes. We used multiple linear regression to adjust for age, sex, race, Glasgow Coma Scale score, and hematoma size. In secondary analyses, we compared hourly SBP measurements during the first 72 hours after admission, using mixed-effects linear models adjusted for the covariates above plus antihypertensive agents. Results— Among 484 patients with ICH, admission SBP varied significantly across ICH causes, ranging from 138 (±24) mm Hg in those with structural vascular lesions to 167 (±35) mm Hg in those with hypertensive ICH ( P <0.001). The mean admission SBP in hypertensive ICH was 17 (95% CI, 11–24) mm Hg higher than in ICH of all other causes combined. These differences remained significant after adjustment for age, sex, race, Glasgow Coma Scale score, and hematoma size ( P <0.001), and this persisted throughout the first 72 hours of hospitalization ( P <0.001). Conclusions— In a single-center ICH registry, SBP varied significantly among ICH causes, both on admission and during hospitalization. Our results suggest that BP in the acute post-ICH setting is at least partly associated with ICH cause rather than simply representing a physiological reaction to the ICH itself.
Little is known regarding the prevalence and predictors of prolonged cognitive and psychological symptoms of COVID-19 among community-dwellers. We aimed to quantitatively measure self-reported metrics of fatigue, cognitive dysfunction, anxiety, depression, and sleep and identify factors associated with these metrics among United States residents with or without COVID-19. We solicited 1000 adult United States residents for an online survey conducted February 3-5, 2021 utilizing a commercial crowdsourcing community research platform. The platform curates eligible participants to approximate United States demographics by age, sex, and race proportions. COVID-19 was diagnosed by laboratory testing and/or by exposure to a known positive contact with subsequent typical symptoms. Prolonged COVID-19 was self-reported and coded for those with symptoms ≥ 1 month following initial diagnosis. The primary outcomes were NIH PROMIS/Neuro-QoL short-form T-scores for fatigue, cognitive dysfunction, anxiety, depression, and sleep compared among those with prolonged COVID-19 symptoms, COVID-19 without prolonged symptoms and COVID-19 negative subjects. Multivariable backwards step-wise logistic regression models were constructed to predict abnormal Neuro-QoL metrics. Among 999 respondents, the average age was 45 years (range 18-84), 49% were male, 76 (7.6%) had a history of COVID-19 and 19/76 (25%) COVID-19 positive participants reported prolonged symptoms lasting a median of 4 months (range 1-13). Prolonged COVID-19 participants were more often younger, female, Hispanic, and had a history of depression/mood/thought disorder (all P < 0.05). They experienced significantly higher rates of unemployment and financial insecurity, and their symptoms created greater interference with work and household activities compared to other COVID-19 status groups (all P < 0.05). After adjusting for demographics, past medical history and stressor covariates in multivariable logistic regression analysis, COVID-19 status was independently predictive of worse Neuro-QoL cognitive dysfunction scores (adjusted OR 11.52, 95% CI 1.01-2.28, P = 0.047), but there were no significant differences in quantitative measures of anxiety, depression, fatigue, or sleep. Prolonged symptoms occurred in 25% of COVID-19 positive participants, and NeuroQoL cognitive dysfunction scores were significantly worse among COVID-19 positive subjects, even after accounting for demographic and stressor covariates. Fatigue, anxiety, depression, and sleep scores did not differ between COVID-19 positive and negative respondents.
Objective: To examine the practical difficulties in managing hyperglycaemia in critical illness and to present recently developed model-based glycaemic management protocols to provide tight control. Background: Hyperglycaemia is prevalent in critical care. Current published protocols require significant added clinical effort and have highly variable results. No currently published methods successfully address the practical clinical difficulties and patient variation, while also providing safe, tight control. Methods: We developed a unique model-based approach that manages both nutritional inputs and exogenous insulin infusions. Computerised glycaemic control methods and proof-of-concept clinical trial results are presented. The protocol has been simplified to a set of tables and adopted as a clinical practice change. Eight pilot test cases are presented to demonstrate the overall approach. Results: Computerised control methods lowered blood glucose (BG) levels to the range 4.0–6.1mmol/L within 10 hours. Over 90% of pre-set hourly blood glucose targets were achieved within measurement error. Eight pilot tests of the simplified, table-based SPRINT protocol, covering 1651 patient-hours produced an average BG level of 5.7mmol/L (SD, 0.9mmol/L). BG levels were in the 4.0–6.1mmol/L band for 60% of the controlled time. Just under 90% of measurements were in the range 4.0–7.0mmol/L, with 96% in the range 4.0–7.75 mmol/L. There were no hypoglycaemic episodes, with a minimum glucose level of 3.2 mmol/L, and no additional clinical intervention was required. Summary: The overall approach of modulating nutrition as well as insulin challenges the current practice of relying on insulin alone to reduce glycaemic levels, which often results in large variability and poor control. The protocol was developed from model-based analysis and proof-of-concept clinical trials, and then generalised to a simple, clinical practice improvement. The results show extremely tight control within safe glycaemic bands.
Hyperglycaemia is prevalent in critical care and tight control can reduce mortality from 9-43% depending on the level of control and the cohort. This research presents a table-based method that varies both insulin dose and nutritional input to achieve tight control. The system mimics a previously validated model-based system, but can be used for long term, large patient number clinical evaluation. This paper evaluates this method in simulation using retrospective data and then compares clinical measurements over 15,000 patient hours to validate the models and development approach. This validation thus also validates the in silico comparison to the landmark clinical tight glycaemic control protocols. Overall, an average clinical glucose level is 5.9plusmn1.0 mmol/L, matching simulation, however the overall clinical glucose distribution is slightly tighter than that obtained in simulation, indicating that the retrospective virtual trial design approach is slightly conservative. Finally, the model based approach is shown to have tighter control than existing, more ad-hoc clinical approaches based on the simulation results that qualitatively match reported clinical results, but also show significant variation around the average levels obtained in both the hypo-and hyperglycaemic ranges
Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome.The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoctitration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95(th) percentile (+/-2sigma) standard error (SE(95%)) to enable better comparison across cohorts.Model-based control trials lower blood glucose into a 72-110 mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE(95%) shows potentially high correlation (r=0.84) between ICU mortality and tightness of control.Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patient-specific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to the current one size fits all sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or one method fits all control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient.
Stress-induced hyperglycemia is prevalent in critical care, even in patients with no history of diabetes. Increased counter-regulatory hormone response increases gluconeogenesis and effective insulin resistance, which can be exacerbated by drug therapy. Control of blood glucose levels to the 4.0-6.1 mmol/L range has been shown to reduce mortality and improve clinical outcomes. The Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol is a simple alternative intensive care unit protocol for modulating insulin and nutritional input to gain tight blood glucose control in the 4.0-6.1 mmol/L target band. The look-up tables, implemented in a wheel-based format, are used by nurses to determine glycemic control actions based on hourly or 2-hourly blood glucose measurements and nutrition and insulin administration rates.An 11 patient pilot study was conducted comprising 2,152 hours of blood glucose level control using the SPRINT protocol. The patient cohort average Acute Physiology and Chronic Health Evaluation II score was 22, which was higher than previous intensive insulin clinical studies.Overall, 64% of measurements were in the 4.0-6.1 mmol/L band, 89% in the 4.0-7.0 mmol/L band, and 96% of all measurements in the 4.0-7.75 mmol/L band. The average value was 5.8 +/- 0.9 mmol/L. Only 1.4% of all measurements were below 4 mmol/L, with a minimum of 3.2 mmol/L. The maximum value recorded was 11.8 mmol/L.Control of blood glucose level was achieved using a protocol implemented by the nursing staff without the need for physician intervention or interpretation, where control is defined as maximizing time within a desired band. The results led to a high level of support for the SPRINT protocol among clinical staff and acceptance of the frequent measurement requirement for effective control. The ease-of-use of the protocol resulted in minimal noncompliance by clinical staff.
Background: Numerous medical society guidelines recommend discontinuation of antibiotics at a maximum of 24 hours after noninstrumented spinal surgery, even when a drain is left in place. As a result of these recommendations, our institution’s Neurosurgery Quality Improvement Committee decided to stop administering prolonged prophylactic systemic antibiotics (PPSAs) to patients with drains after noninstrumented spinal surgery. Methods: We retrospectively reviewed data for patients who had noninstrumented spinal surgery performed by a neurosurgeon at our institution between December 2012 and July 2014 (PPSA period) and December 2014 and July 2016 (non-PPSA period) and had a drain left in place postoperatively. In the PPSA period, patients received antibiotics until drain removal. In the non-PPSA period, patients received antibiotics for a maximum of 24 hours. Results: We identified 58 patients in the PPSA period and 55 in the non-PPSA period. Discontinuation of PPSAs resulted in a nonsignificant increase in the frequency of surgical site infections (SSIs; 0% in the PPSA period vs 4% in the non-PPSA period; P = .24). Conclusion: After discontinuing PPSAs for patients with noninstrumented spinal procedures, as is recommended for quality improvement, we saw a nonsignificant increase in our rate of SSIs. Further monitoring of this population is warranted.
Objectives: The goals of this study were to develop (1) a safe and effective protocol for the clinical control of type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements and multiple daily injections with insulin analogues, and (2) an in silico simulation tool of type 1 diabetes to predict long-term glycemic control outcomes of clinical interventions. Methods: The virtual patient method was used to develop a simulation tool for type 1 diabetes using data from a type 1 diabetes patient cohort ( n = 40). The tool was used to test the adaptive protocol (AC) and a conventional intensive insulin therapy (CC) against results from a representative control cohort. Optimal and suboptimal basal insulin replacements were evaluated as a function of SMBG frequency in conjunction with the (AC and CC) prandial control protocols. Results: In long-term glycemic control, the AC protocol significantly decreased hemoglobin A1c in conditions of suboptimal basal insulin replacement for SMBG frequencies ≥6/day, and reduced the occurrence of mild and severe hypoglycemia by 86–100% over controls, over all SMBG frequencies in conditions of optimal basal insulin. Conclusions: A simulation tool to predict long-term glycemic control outcomes from clinical interventions has been developed to test a novel, adaptive control protocol for type 1 diabetes. The protocol is effective and safe compared to conventional intensive insulin therapy and controls. As fear of hypoglycemia is a large psychological barrier to glycemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept.
The goal of this study was to develop a unified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role. The model must model the plasma insulin appearance of a wide range of current insulins, especially monomer insulin and insulin glargine, utilizing common chemical states and transport rates, where appropriate.A compartmental model was developed with 13 patient-specific model parameters covering six diverse insulin types [rapid-acting, regular, neutral protamine Hagedorn (NPH), lente, ultralente, and glargine insulin]. Model parameters were identified using 37 sets of mean plasma insulin time-course data from an extensive literature review via nonlinear optimization methods.All fitted parameters have a coefficient of variation <100% (median 51.3%, 95th percentile 3.6-60.6%) and can be considered a posteriori identifiable.A model is presented to describe SC injected insulin appearance in plasma in a diabetes decision support role. Clinically current insulin types (monomeric insulin, regular insulin, NPH, insulin, and glargine) and older insulin types (lente and ultralente) are included in a unified framework that accounts for nonlinear concentration and dose dependency. Future work requires clinical validation using published pharmacokinetic studies.
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