scholarly journals Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database

2016 ◽  
Vol 24 (3) ◽  
pp. 488-495 ◽  
Author(s):  
Mike Wu ◽  
Marzyeh Ghassemi ◽  
Mengling Feng ◽  
Leo A Celi ◽  
Peter Szolovits ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Time Series ◽  
Intensive Care ◽  
Receiver Operating Curve ◽  
Post Discharge ◽  
Control Hypotension ◽  
Health Records ◽  
Physiological Time ◽  
Large Populations ◽  
Switching State

Background: The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. Objective: We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. Materials and Methods: We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. Results: The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Conclusion: Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.

Presepsin - New Marker of Sepsis Romanian Neonatal Intensive Care Unit Experience

2019 ◽  
Vol 70 (8) ◽  
pp. 3008-3013
Author(s):  
Silvia Maria Stoicescu ◽  
Ramona Mohora ◽  
Monica Luminos ◽  
Madalina Maria Merisescu ◽  
Gheorghita Jugulete ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Sepsis ◽  
Predictive Value ◽  
Neonatal Intensive Care ◽  
Complete Blood Count ◽  
Clinical Manifestations ◽  
Receiver Operating Curve ◽  
Predictive Values

Difficulties in establishing the onset of neonatal sepsis has directed the medical research in recent years to the possibility of identifying early biological markers of diagnosis. Overdiagnosing neonatal sepsis leads to a higher rate and duration in the usage of antibiotics in the Neonatal Intensive Care Unit (NICU), which in term leads to a rise in bacterial resistance, antibiotherapy complications, duration of hospitalization and costs.Concomitant analysis of CRP (C Reactive Protein), procalcitonin, complete blood count, presepsin in newborn babies with suspicion of early or late neonatal sepsis. Presepsin sensibility and specificity in diagnosing neonatal sepsis. The study group consists of newborns admitted to Polizu Neonatology Clinic between 15th February- 15th July 2017, with suspected neonatal sepsis. We analyzed: clinical manifestations and biochemical markers values used for diagnosis of sepsis, namely the value of CRP, presepsin and procalcitonin on the onset day of the disease and later, according to evolution. CRP values may be influenced by clinical pathology. Procalcitonin values were mainly influenced by the presence of jaundice. Presepsin is the biochemical marker with the fastest predictive values of positive infection. Presepsin can be a useful tool for early diagnosis of neonatal sepsis and can guide the antibiotic treatment. Presepsin value is significantly higher in neonatal sepsis compared to healthy newborns (939 vs 368 ng/mL, p [ 0.0001); area under receiver operating curve (AUC) for presepsine was 0.931 (95% confidence interval 0.86-1.0). PSP has a greater sensibility and specificity compared to classical sepsis markers, CRP and PCT respectively (AUC 0.931 vs 0.857 vs 0.819, p [ 0.001). The cut off value for presepsin was established at 538 ng/mLwith a sensibility of 79.5% and a specificity of 87.2 %. The positive predictive value (PPV) is 83.8 % and negative predictive value (NPV) is 83.3%.

scholarly journals Seeking out SARI: an automated search of electronic health records

2018 ◽  
Vol 146 (8) ◽  
pp. 1065-1069
Author(s):  
John C. O'Horo ◽  
Mikhail Dziadzko ◽  
Amra Sakusic ◽  
Rashid Ali ◽  
M. Rizwan Sohail ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Search Algorithm ◽  
Temporal Trends ◽  
Epidemiologic Study ◽  
Health Records ◽  
Severe Acute Respiratory Infection ◽  
Electronic Health ◽  
Automated Search

AbstractThe definition of severe acute respiratory infection (SARI) – a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission – has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

Delirium in Postoperative Patients Admitted to the Intensive Care Unit

2018 ◽  
Vol 84 (6) ◽  
pp. 875-880
Author(s):  
Timothy R. Romanauski ◽  
Erin E. Martin ◽  
Juraj Sprung ◽  
David P. Martin ◽  
Darrell R. Schroeder ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Assessment Method ◽  
Chronic Health Evaluation ◽  
Surgical Patients ◽  
Health Evaluation ◽  
Health Records ◽  
Chronic Health ◽  
Electronic Health

Postoperative delirium (POD) is common among surgical patients admitted to the intensive care unit (ICU) and is associated with increased resource utilization, morbidity, and death. Our primary aim was to compare rates of POD using administrative International Classification of Diseases, Ninth Revision, records and automated interrogation of electronic health records from Confusion Assessment Method for the ICU (CAM-ICU) screening. The secondary aim was to assess POD risk associated with patient and perioperative characteristics. Electronic health records of surgical patients admitted to the ICU during 2011 through 2014 were abstracted for POD assessment by CAM-ICU and by administrative codes, Charlson comorbidity index, surgical characteristics, and Acute Physiology, Age, Chronic Health Evaluation III scores. Of 6338 patients, CAM-ICU identified 606 (9.6%) and administrative records identified 55 (0.9%) POD cases, with agreement on 50 cases. In multivariable logistic regression based on POD identified with CAM-ICU, preexisting dementia had the strongest association with POD (odds ratio [95% confidence interval], 6.47 [3.68–11.37]; P < 0.001). Other associations found were older age, congestive heart failure, chronic pulmonary disease, increased surgical duration, emergency cases, blood transfusions, postoperative ventilation, and higher Acute Physiology, Age, Chronic Health Evaluation III scores (all P ≤ 0.01). POD cases had lengthier ICU and hospital stays and a higher mortality rate (all P < 0.001). CAM-ICU scores identified higher rates of POD than a search for POD based on administrative codes. Preoperative presence of dementia and major comorbidities were associated with POD. Delirium in surgical patients is associated with worse outcomes.

scholarly journals A Modified Sequential Organ Failure Assessment Score for Critical Care Triage

2010 ◽  
Vol 4 (4) ◽  
pp. 277-284 ◽  
Author(s):  
Colin K. Grissom ◽  
Samuel M. Brown ◽  
Kathryn G. Kuttler ◽  
Jonathan P. Boltax ◽  
Jason Jones ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Confidence Interval ◽  
Sequential Organ Failure Assessment ◽  
Organ Failure ◽  
Sofa Score ◽  
Laboratory Measurement ◽  
Receiver Operating Curve ◽  
Failure Assessment

ABSTRACTObjective: The Sequential Organ Failure Assessment (SOFA) score has been recommended for triage during a mass influx of critically ill patients, but it requires laboratory measurement of 4 parameters, which may be impractical with constrained resources. We hypothesized that a modified SOFA (MSOFA) score that requires only 1 laboratory measurement would predict patient outcome as effectively as the SOFA score.Methods: After a retrospective derivation in a prospective observational study in a 24-bed medical, surgical, and trauma intensive care unit, we determined serial SOFA and MSOFA scores on all patients admitted during the 2008 calendar year and compared the ability to predict mortality and the need for mechanical ventilation.Results: A total of 1770 patients (56% male patients) with a 30-day mortality of 10.5% were included in the study. Day 1 SOFA and MSOFA scores performed equally well at predicting mortality with an area under the receiver operating curve (AUC) of 0.83 (95% confidence interval 0.81-.85) and 0.84 (95% confidence interval 0.82-.85), respectively (P = .33 for comparison). Day 3 SOFA and MSOFA predicted mortality for the 828 patients remaining in the intensive care unit with an AUC of 0.78 and 0.79, respectively. Day 5 scores performed less well at predicting mortality. Day 1 SOFA and MSOFA predicted the need for mechanical ventilation on day 3, with an AUC of 0.83 and 0.82, respectively. Mortality for the highest category of SOFA and MSOFA score (>11 points) was 53% and 58%, respectively.Conclusions: The MSOFA predicts mortality as well as the SOFA and is easier to implement in resource-constrained settings, but using either score as a triage tool would exclude many patients who would otherwise survive.(Disaster Med Public Health Preparedness. 2010;4:277-284)

Sign in / Sign up

Export Citation Format

Share Document