scholarly journals Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Juan Luis Fernández-Martínez ◽  
Zulima Fernández-Muñiz ◽  
Ana Cernea ◽  
Andrzej Kloczkowski
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Local Level ◽  
Urgent Care ◽  
Short Term ◽  
Number Of Patients ◽  
Historical Series ◽  
Verhulst Model

The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients’ admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use.

Short-Term Ventilation of Critically Ill Patients in Recovery

2003 ◽  
Vol 13 (5) ◽  
pp. 214-217 ◽  
Author(s):  
David Ryan ◽  
Gillian Tobin
Keyword(s):  
Critical Care ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Short Term ◽  
Recovery Unit ◽  
Number Of Patients ◽  
Care Facilities

There is often a shortfall of critical care facilities which can result in a number of patients who need management in intensive care units (ICUs) being treated in a recovery unit prior to being found an ICU bed. This article describes a study which examined this situation. The patients’ origins, durations of stay in recovery, outcomes and final destinations are discussed. The authors conclude that recovery provides a hidden resource to supplement the lack of intensive care beds and suggest ways that the problem might be addressed.

Pharmacological Management of Seizures and Status Epilepticus in Critically Ill Patients

2010 ◽  
Vol 23 (5) ◽  
pp. 441-454 ◽  
Author(s):  
Eljim P. Tesoro ◽  
Gretchen M. Brophy
Keyword(s):  
Status Epilepticus ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Neurological Disease ◽  
Medical Emergency ◽  
Significant Morbidity ◽  
First Line ◽  
Short Term ◽  
Pharmacological Management ◽  
First Line Treatment

Seizures are serious complications seen in critically ill patients and can lead to significant morbidity and mortality if the cause is not identified and treated quickly. Uncontrolled seizures can lead to status epilepticus (SE), which is considered a medical emergency. The first-line treatment of seizures is an intravenous (IV) benzodiazepine followed by anticonvulsant therapy. Refractory SE can evolve into a nonconvulsive state requiring IV anesthetics or induction of pharmacological coma. To prevent seizures and further complications in critically ill patients with acute neurological disease or injury, short-term seizure prophylaxis should be considered in certain patients.

scholarly journals Colonization with Extensively Drug-Resistant Acinetobacter Baumannii and Prognosis in Critically Ill Patients: An Observational Cohort Study

2021 ◽  
Author(s):  
Yue Zheng ◽  
Nana Xu ◽  
Jiaojiao Pang ◽  
Hui Han ◽  
Hongna Yang ◽  
...  
Keyword(s):  
Regression Model ◽  
Acinetobacter Baumannii ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Drug Resistant ◽  
Short Term ◽  
Term Survival ◽  
Extensively Drug Resistant ◽  
Kaplan Meier

Abstract Background: Acinetobacter baumannii is one of the most often isolated opportunistic pathogens in intensive care units (ICUs). Extensively drug-resistant A. baumannii (XDR-AB) strains lack susceptibility to almost all antibiotics and pose a heavy burden on healthcare institutions. In this study, we evaluated the impact of XDR-AB colonization on both the short-term and long-term survival of critically ill patients.Methods: We prospectively enrolled patients from two adult ICUs in Qilu Hospital of Shandong University from April 2018 through December 2018. Using nasopharyngeal and perirectal swabs, we evaluated the presence of XDR-AB colonization. Participants were followed up for six months. Primary endpoints were 28-day and six-month mortality after ICU admission. For survival analysis, we used the Kaplan-Meier curve. We identified risk factors associated with 28-day and six-month mortality using the logistic regression model and Cox proportional-hazards survival regression model, respectively. Results: Out of 431 patients, 77 were colonized with XDR-AB. Based on the Kaplan-Meier curve results, the survival before 28 days did not differ by colonization status; however, a significant lower survival rate was obtained at six months in colonized patients. Univariate and multivariate results confirmed that XDR-AB colonization was not associated with 28-day mortality, but was an independent risk factor of lower survival days at six months, resulting in a 1.97 times higher risk of death at six months.Conclusions: XDR-AB colonization has no effect on short-term mortality but is associated with lower long-term survival in critically ill patients.

scholarly journals Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shubhayu Bhattacharyay ◽  
John Rattray ◽  
Matthew Wang ◽  
Peter H. Dziedzic ◽  
Eusebia Calvillo ◽  
...  
Keyword(s):  
Brain Injury ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
High Frequency ◽  
Prediction Models ◽  
Severe Brain Injury ◽  
Short Term ◽  
Time Frequency ◽  
Yield Information ◽  
Functional States

AbstractOur goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

scholarly journals Short-term outcome of critically ill patients with severe acute respiratory syndrome

2004 ◽  
Vol 30 (3) ◽  
pp. 381-387 ◽  
Author(s):  
Charles D. Gomersall ◽  
Gavin M. Joynt ◽  
Philip Lam ◽  
Thomas Li ◽  
Florence Yap ◽  
...  
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Short Term ◽  
Term Outcome ◽  
Short Term Outcome

Association Effects Of Comorbid Versus Uncomorbid Diabetes And Blood Glucose Concentrations On Short-term Outcomes In Patients With Critical Illness: A Real-World Study Based On Propensity Score Analyses

2020 ◽  
Author(s):  
Shan Lin ◽  
Shanhui Ge ◽  
Wanmei He ◽  
Mian Zeng
Keyword(s):  
Critical Illness ◽  
Glycemic Control ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Cox Model ◽  
Inverse Probability Weighting ◽  
Probability Weighting ◽  
Optimal Range ◽  
Short Term ◽  
Inverse Probability

Abstract Background: The effects of combined diabetes and glycemic control strategies on the short-term prognosis in patients with a critical illness are currently ambiguous. The objectives of our study were to determine whether comorbid diabetes affects short-term prognosis and the optimal range of glycemic control in critically ill patients.Methods: We performed this study with the critical care database. The primary outcomes were 28-day mortality in critically ill patients with comorbid diabetes and the optimal range of glycemic control. Association of comorbid diabetes with 28-day mortality was assessed by multivariable Cox regression model with inverse probability weighting. Smooth curves were applied to fit the association for glucose and 28-day mortality.Results: Of the 33,680 patients enrolled in the study, 8,701 (25.83%) had diabetic comorbidity. Cox model with inverse probability weighting showed that the 28-day mortality rate was reduced by 29% (HR=0.71, 95% CI 0.67-0.76) in the group with diabetes in comparison to the group without diabetes. The E value of 2.17 indicated robustness to unmeasured confounders. The effect of the association between comorbid diabetes and 28-day mortality was generally in line for all subgroup variables, significant interactions were observed for glucose on first day, admission type, and use of insulin or not (Interaction P <0.05). A V-shaped relationship was observed between glucose concentrations and 28-day mortality in patients without diabetes, with the lowest 28-day mortality corresponding to the glucose level was 101.75 mg/dl (95% CI 94.64-105.80 mg/dl); whereas in patients with comorbid diabetes, the effect of glucose concentration on 28-day mortality was structurally softer than in those with uncomorbid diabetes. Lastly, of all patients, hyperglycemia had the greatest deleterious effect on patients admitted to CSRU.Conclusions: Our study further confirmed the protective effect of comorbid diabetes on the short-term prognosis of critically ill patients, resulting in an approximately 29% reduction in 28-day mortality. Besides, we also demonstrated the personalized glycemic control strategy for critically ill patients. Lastly, clinicians should be aware of the occurrence and the prompt management of hyperglycemia in critically ill patients admitted to the CSRU.

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