Hypotension During Vasopressor Infusion Occurs in Predictable Clusters: A Multicenter Analysis.

Background: Published evidence indicates that mean arterial pressure (MAP) below a goal range (hypotension) is associated with worse outcomes, though MAP management failures are common. We sought to characterize hypotension occurrences in ICUs and consider the implications for MAP management. Methods: Retrospective analysis of 3 hospitals' cohorts of adult ICU patients during continuous vasopressor infusion. Two cohorts were general, mixed ICU patients and one was exclusively acute spinal cord injury patients. "Hypotension-clusters" were defined where there were ≥10 min of cumulative hypotension over a 60-min period and "constant hypotension" was ≥10 continuous minutes. Trend analysis was performed (predicting future MAP using 14 min of preceding MAP data) to understand which hypotension-clusters could likely have been predicted by clinician awareness of MAP trends. Results: In cohorts of 155, 66, and 16 ICU stays, respectively, the majority of hypotension occurred within the hypotension-clusters. Failures to keep MAP above the hypotension threshold were notable in the bottom quartiles of each cohort, with hypotension durations of 436, 167, and 468 min, respectively, occurring within hypotension-clusters per day. Mean arterial pressure trend analysis identified most hypotension-clusters before any constant hypotension occurred (81.2%-93.6% sensitivity, range). The positive predictive value of hypotension predictions ranged from 51.4% to 72.9%. Conclusions: Across 3 cohorts, most hypotension occurred in temporal clusters of hypotension that were usually predictable from extrapolation of MAP trends.

Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review.

OBJECTIVE: Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS). METHODS: A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses. RESULTS: We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3. CONCLUSION: This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.

Is novel coronavirus 2019 reinfection possible? Interpreting dynamic SARS-CoV-2 test results.

Since December 2019, COVID-19, the clinical syndrome associated with SARS-CoV-2 infection, has infected more than 6.2 million people and brought the function of the global community to a halt. As the number of patients recovered from COVID-19 rises and the world transitions toward reopening, the question of acquired immunity versus the possibility of reinfection are critical to anticipating future viral spread. Here, we present a case of a patient previously recovered from COVID-19 who re-presents with new respiratory, radiographical, laboratory, and real-time reverse transcriptase-polymerase chain reaction (RT-PCR) findings concerning for possible re-infection. We review this case in the context of the evolving discussion and theories surrounding dynamic RT-PCR results, prolonged viral shedding, and the possibility of developed immunity. Understanding how to interpret dynamic and late-positive SARS-CoV-2 RT-PCR results after primary infection will be critical for understanding disease prevalence and spread among communities worldwide.

Antibiotic Delays and Feasibility of a 1-Hour-From-Triage Antibiotic Requirement: Analysis of an Emergency Department Sepsis Quality Improvement Database.

STUDY OBJECTIVE: We identify factors associated with delayed emergency department (ED) antibiotics and determine feasibility of a 1-hour-from-triage antibiotic requirement in sepsis. METHODS: We studied all ED adult septic patients in accordance with Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock National Quality Measures in 2 consecutive 12-month intervals. During the second interval, a quality improvement intervention was conducted: a sepsis screening protocol plus case-specific feedback to clinicians. Data were abstracted retrospectively through electronic query and chart review. Primary outcomes were antibiotic delay greater than 3 hours from documented onset of hypoperfusion (per Centers for Medicare & Medicaid Services Severe Sepsis and Septic Shock National Quality Measures) and antibiotic delay greater than 1 hour from triage (per 2018 Surviving Sepsis Campaign recommendations). RESULTS: We identified 297 and 357 septic patients before and during the quality improvement intervention, respectively. Before and during quality improvement intervention, antibiotic delay in accordance with Centers for Medicare & Medicaid Services measures occurred in 30% and 21% of cases (-9% [95% confidence interval -16% to -2%]); and in accordance with 2018 Surviving Sepsis Campaign recommendations, 85% and 71% (-14% [95% confidence interval -20% to -8%]). Four factors were independently associated with both definitions of antibiotic delay: vague (ie, nonexplicitly infectious) presenting symptoms, triage location to nonacute areas, care before the quality improvement intervention, and lower Sequential [Sepsis-related] Organ Failure Assessment scores. Most patients did not receive antibiotics within 1 hour of triage, with the exception of a small subset post-quality improvement intervention who presented with explicit infectious symptoms and triage hypotension. CONCLUSION: The quality improvement intervention significantly reduced antibiotic delays, yet most septic patients did not receive antibiotics within 1 hour of triage. Compliance with the 2018 Surviving Sepsis Campaign would require a wholesale alteration in the management of ED patients with either vague symptoms or absence of triage hypotension.

Repeat CT Performed Within One Month of CT Conducted in the Emergency Department for Abdominal Pain: A Secondary Analysis of Data From a Prospective Multicenter Study.

OBJECTIVE: The purpose of this study is to determine both the frequency of repeat CT performed within 1 month after a patient visits the emergency department (ED) and undergoes CT evaluation for abdominal pain and the frequency of worsened or new CT-based diagnoses. SUBJECTS AND METHODS: Secondary analysis was performed on data collected during a prospective multicenter study. The parent study included patients who underwent CT in the ED for abdominal pain between 2012 and 2014, and these patients constituted the study group of the present analysis. The proportion of patients who underwent (in any setting) repeat abdominal CT within 1 month of the index CT examination was calculated. For each of these patients, results of the index and repeat CT scans were compared by an independent panel and categorized as follows: no change (group 1); same process, improved (group 2); same process, worse (group 3); or different process (group 4). The proportion of patients in groups 1 and 2 versus groups 3 and 4 was calculated, and patient and ED physician characteristics were compared. RESULTS: The parent study included 544 patients (246 of whom were men [45%]; mean patient age, 49.4 years). Of those 544 patients, 53 (10%; 95% CI, 7.5-13%) underwent repeat abdominal CT. Patients' CT comparisons were categorized as follows: group 1 for 43% of patients (23/53), group 2 for 26% (14/53), group 3 for 15% (8/53), and group 4 for 15% (8/53). New or worse findings were present in 30% of patients (16/53) (95% CI, 19-44%). When patients with findings in groups 1 and 2 were compared to patients with findings in groups 3 and 4, no significant difference was noted in patient age (p = 0.25) or sex (p = 0.76), the number of days between scans (p = 0.98), and the diagnostic confidence of the ED physician after the index CT scan was obtained (p = 0.33). CONCLUSION: Short-term, repeat abdominal CT was performed for 10% of patients who underwent CT in the ED for abdominal pain, and it yielded new or worse findings for 30% of those patients.

Presenting Symptoms Independently Predict Mortality in Septic Shock: Importance of a Previously Unmeasured Confounder.

OBJECTIVES: Presenting symptoms in patients with sepsis may influence rapidity of diagnosis, time-to-antibiotics, and outcome. We tested the hypothesis that vague presenting symptoms are associated with delayed antibiotics and increased mortality. We further characterized individual presenting symptoms and their association with mortality. DESIGN: Retrospective cohort study. SETTING: Emergency department of large, urban, academic U.S. hospital. PATIENTS: All adult patients with septic shock treated in the emergency department between April 2014 and March 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 654 septic shock cases, 245 (37%) presented with vague symptoms. Time-to-antibiotics from first hypotension or elevated lactate was significantly longer for those with vague symptoms versus those with explicit symptoms of infection (1.6 vs 0.8 hr; p < 0.01), and in-hospital mortality was also substantially higher (34% vs 16%; p < 0.01). Patients with vague symptoms were older and sicker as evidenced by triage hypotension, Sequential Organ Failure Assessment score, initial serum lactate, and need for intubation. In multivariate analysis, vague symptoms were independently associated with mortality (adjusted odds ratio, 2.12; 95% CI, 1.32-3.40; p < 0.01), whereas time-to-antibiotics was not associated with mortality (adjusted odds ratio, 1.01; 95% CI, 0.94-1.08; p = 0.78). Of individual symptoms, only the absence of fever, chills, or rigors (odds ratio, 2.70; 95% CI, 1.63-4.47; p < 0.01) and presence of shortness of breath (odds ratio, 1.97; 95% CI, 1.23-3.15; p < 0.01) were independently associated with mortality. CONCLUSIONS: More than one third of patients with septic shock presented to the emergency department with vague symptoms that were not specific to infection. These patients had delayed antibiotic administration and higher risk of mortality even after controlling for demographics, illness acuity, and time-to-antibiotics in multivariate analysis. These findings suggest that the nature of presenting symptoms is an important component of sepsis clinical phenotyping and may be an important confounder in sepsis epidemiologic studies.

Role of CT in the Diagnosis of Nonspecific Abdominal Pain: A Multicenter Analysis.

OBJECTIVE: The objective of our study was to determine whether specific patient and physician factors-known before CT-are associated with a diagnosis of nonspecific abdominal pain (NSAP) after CT in the emergency department (ED). MATERIALS AND METHODS: We analyzed data originally collected in a prospective multicenter study. In the parent study, we identified ED patients referred to CT for evaluation of abdominal pain. We surveyed their physicians before and after CT to identify changes in leading diagnoses, diagnostic confidence, and admission decisions. In the current study, we conducted a multiple regression analysis to identify whether the following were associated with a post-CT diagnosis of NSAP: patient age; patient sex; physicians' years of experience; physicians' pre-CT diagnostic confidence; and physicians' pre-CT admission decision if CT had not been available. We analyzed patients with and those without a pre-CT diagnosis of NSAP separately. For the sensitivity analysis, we excluded patients with different physicians before and after CT. RESULTS: In total, 544 patients were included: 10% (52/544) with a pre-CT diagnosis of NSAP and 90% (492/544) with a pre-CT diagnosis other than NSAP. The leading diagnoses changed after CT in a large proportion of patients with a pre-CT diagnosis of NSAP (38%, 20/52). In regression analysis, we found that physicians' pre-CT diagnostic confidence was inversely associated with a post-CT diagnosis of NSAP in patients with a pre-CT diagnosis other than NSAP (p = 0.0001). No other associations were significant in both primary and sensitivity analyses. CONCLUSION: With the exception of physicians' pre-CT diagnostic confidence, the factors evaluated were not associated with a post-CT diagnosis of NSAP.

CT in the Emergency Department: A Real-Time Study of Changes in Physician Decision Making.

PURPOSE: To determine how physicians' diagnoses, diagnostic uncertainty, and management decisions are affected by the results of computed tomography (CT) in emergency department settings. MATERIALS AND METHODS: This study was approved by the institutional review board and compliant with HIPAA. Data were collected between July 12, 2012, and January 13, 2014. The requirement to obtain patient consent was waived. In this prospective, four-center study, patients presenting to the emergency department who were referred for CT with abdominal pain, chest pain and/or dyspnea, or headache were identified. Physicians were surveyed before and after CT to determine the leading diagnosis, diagnostic confidence (on a scale of 0% to 100%), alternative "rule out" diagnosis, and management decisions. Primary measures were the proportion of patients for whom the leading diagnosis or admission decision changed and median changes in diagnostic confidence. Secondary measures addressed alternative diagnoses and return-to-care visits (eg, to emergency department) at 1-month follow-up. Regression analysis was used to identify associations between primary measures and site and participant characteristics. RESULTS: Both surveys were completed for 1280 patients by 245 physicians. The leading diagnosis changed in 235 of 460 patients with abdominal pain (51%), 163 of 387 with chest pain and/or dyspnea (42%), and 103 of 433 with headache (24%). Pre-CT diagnostic confidence was inversely associated with the likelihood of a diagnostic change (P < .0001). Median changes in confidence were substantial (increases of 25%, 20%, and 13%, respectively, for patients with abdominal pain, chest pain and/or dyspnea, and headache; P < .0001); median post-CT confidence was high (95% for all three groups). CT helped confirm or exclude at least 95% of alternative diagnoses. Admission decisions changed in 116 of 457 patients with abdominal pain (25%), 72 of 387 with chest pain and/or dyspnea (19%), and 81 of 426 with headache (19%). During follow-up, 70 of 450 patients with abdominal pain (15%), 53 of 387 with chest pain and/or dyspnea (14%), and 49 of 433 with headache (11%) returned for the same indication. In general, changes in leading diagnosis, diagnostic confidence, and admission decisions were not well explained with site or participant characteristics. CONCLUSION: Physicians' diagnoses and admission decisions changed frequently after CT, and diagnostic uncertainty was alleviated.

Is heart rate variability better than routine vital signs for prehospital identification of major hemorrhage?

OBJECTIVE: During initial assessment of trauma patients, metrics of heart rate variability (HRV) have been associated with high-risk clinical conditions. Yet, despite numerous studies, the potential of HRV to improve clinical outcomes remains unclear. Our objective was to evaluate whether HRV metrics provide additional diagnostic information, beyond routine vital signs, for making a specific clinical assessment: identification of hemorrhaging patients who receive packed red blood cell (PRBC) transfusion. METHODS: Adult prehospital trauma patients were analyzed retrospectively, excluding those who lacked a complete set of reliable vital signs and a clean electrocardiogram for computation of HRV metrics. We also excluded patients who did not survive to admission. The primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that HRV metrics could improve the diagnosis of hemorrhagic injury plus PRBC transfusion vs routine vital signs alone. RESULTS: As univariate predictors, HRV metrics in a data set of 402 subjects had comparable areas under receiver operating characteristic curves compared with routine vital signs. In multivariate regression models containing routine vital signs, HRV parameters were significant (P<.05) but yielded areas under receiver operating characteristic curves with minimal, nonsignificant improvements (+0.00 to +0.05). CONCLUSIONS: A novel diagnostic test should improve diagnostic thinking and allow for better decision making in a significant fraction of cases. Our findings do not support that HRV metrics add value over routine vital signs in terms of prehospital identification of hemorrhaging patients who receive PRBC transfusion.

A computational model of hemorrhage and dehydration suggests a pathophysiological mechanism: Starling-mediated protein trapping.

We sought to understand the degree to which a single computational cardiovascular model could replicate the typical responses of healthy subjects through a breadth of blood loss patterns and whether such a model could illuminate the cause-effect relationships that underlie the observed responses. The model consisted of compartments for the upper body, lower body, viscera, and kidneys as well as a four-chambered heart and a pulmonary compartment. Transcapillary fluid flux was governed by Starling forces, whereas lymphatic flow was driven by hydrostatic tissue pressure and scaled by a lymphatic activation term. We adjusted parameters based on results from one protocol involving moderate continual blood loss in a canine model. Next, we simulated six additional protocols spanning euvolemic and dehydrated subjects and compared in silico behavior with in vivo hemodynamic responses and fluid shifts. The model was able to replicate group-averaged behavior (i.e., within 1 or 2 SEs) of the rate and quantity of vascular refill and the associated cardiac output during slow, moderate, and rapid ongoing blood losses, the restitution after the cessation of blood loss, and the absence of restitution in dehydrated subjects. The model suggested that the earlier phase of restitution, i.e., transcapillary fluid shifts, was antagonistic to the later phase of restitution, i.e., protein return via lymphatics. This phenomenon was termed "interstitial protein trapping." In conclusion, the model appears valid for a range of blood loss patterns and prehydration states. Further investigation into the in vivo relevance of interstitial protein trapping is justified.

Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings.

STUDY OBJECTIVE: As use of radiology studies increases, there is a concurrent increase in incidental findings (eg, lung nodules) for which the radiologist issues recommendations for additional imaging for follow-up. Busy emergency physicians may be challenged to carefully communicate recommendations for additional imaging not relevant to the patient's primary evaluation. The emergence of electronic health records and natural language processing algorithms may help address this quality gap. We seek to describe recommendations for additional imaging from our institution and develop and validate an automated natural language processing algorithm to reliably identify recommendations for additional imaging. METHODS: We developed a natural language processing algorithm to detect recommendations for additional imaging, using 3 iterative cycles of training and validation. The third cycle used 3,235 radiology reports (1,600 for algorithm training and 1,635 for validation) of discharged emergency department (ED) patients from which we determined the incidence of discharge-relevant recommendations for additional imaging and the frequency of appropriate discharge documentation. The test characteristics of the 3 natural language processing algorithm iterations were compared, using blinded chart review as the criterion standard. RESULTS: Discharge-relevant recommendations for additional imaging were found in 4.5% (95% confidence interval [CI] 3.5% to 5.5%) of ED radiology reports, but 51% (95% CI 43% to 59%) of discharge instructions failed to note those findings. The final natural language processing algorithm had 89% (95% CI 82% to 94%) sensitivity and 98% (95% CI 97% to 98%) specificity for detecting recommendations for additional imaging. For discharge-relevant recommendations for additional imaging, sensitivity improved to 97% (95% CI 89% to 100%). CONCLUSION: Recommendations for additional imaging are common, and failure to document relevant recommendations for additional imaging in ED discharge instructions occurs frequently. The natural language processing algorithm's performance improved with each iteration and offers a promising error-prevention tool.

APPRAISE-HRI: AN ARTIFICIAL INTELLIGENCE ALGORITHM FOR TRIAGE OF HEMORRHAGE CASUALTIES.

ABSTRACT: Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to assess the ability of an artificial intelligence triage algorithm to automatically analyze vital-sign data and stratify hemorrhage risk in trauma patients. Methods: Here, we developed the APPRAISE-Hemorrhage Risk Index (HRI) algorithm, which uses three routinely measured vital signs (heart rate and diastolic and systolic blood pressures) to identify trauma patients at greatest risk of hemorrhage. The algorithm preprocesses the vital signs to discard unreliable data, analyzes reliable data using an artificial intelligence-based linear regression model, and stratifies hemorrhage risk into low (HRI:I), average (HRI:II), and high (HRI:III). Results: To train and test the algorithm, we used 540 h of continuous vital-sign data collected from 1,659 trauma patients in prehospital and hospital (i.e., emergency department) settings. We defined hemorrhage cases (n = 198) as those patients who received ≥1 unit of packed red blood cells within 24 h of hospital admission and had documented hemorrhagic injuries. The APPRAISE-HRI stratification yielded a hemorrhage likelihood ratio (95% confidence interval) of 0.28 (0.13-0.43) for HRI:I, 1.00 (0.85-1.15) for HRI:II, and 5.75 (3.57-7.93) for HRI:III, suggesting that patients categorized in the low-risk (high-risk) category were at least 3-fold less (more) likely to have hemorrhage than those in the average trauma population. We obtained similar results in a cross-validation analysis. Conclusions: The APPRAISE-HRI algorithm provides a new capability to evaluate routine vital signs and alert medics to specific casualties who have the highest risk of hemorrhage, to optimize decision-making for triage, treatment, and evacuation.

Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.

Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate "low risk" simply because the testing data were never ordered. We considered predictive methodologies to identify sepsis at triage, before diagnostic tests are ordered, in a busy Emergency Department (ED). One algorithm used "bland clinical data" (data available at triage for nearly every patient). The second algorithm added three yes/no questions to be answered after the triage interview. Retrospectively, we studied adult patients from a single ED between 2014-16, separated into training (70%) and testing (30%) cohorts, and a final validation cohort of patients from four EDs between 2016-2018. Sepsis was defined per the Rhee criteria. Investigational predictors were demographics and triage vital signs (downloaded from the hospital EMR); past medical history; and the auxiliary queries (answered by chart reviewers who were blinded to all data except the triage note and initial HPI). We developed L2-regularized logistic regression models using a greedy forward feature selection. There were 1164, 499, and 784 patients in the training, testing, and validation cohorts, respectively. The bland clinical data model yielded ROC AUC's 0.78 (0.76-0.81) and 0.77 (0.73-0.81), for training and testing, respectively, and ranged from 0.74-0.79 in four hospital validation. The second model which included auxiliary queries yielded 0.84 (0.82-0.87) and 0.83 (0.79-0.86), and ranged from 0.78-0.83 in four hospital validation. The first algorithm did not require clinician input but yielded middling performance. The second showed a trend towards superior performance, though required additional user effort. These methods are alternatives to predictive algorithms downstream of clinical evaluation and diagnostic testing. For hospital early warning algorithms, consideration should be given to bias and usability of various methods.

Modeling of Usual Care: Vasopressor Initiation for Sepsis With Hypotension.

Usual care regarding vasopressor initiation is ill-defined. We aimed to develop a quantitative "dynamic practice" model for usual care in the emergency department (ED) regarding the timing of vasopressor initiation in sepsis. In a retrospective study of 589 septic patients with hypotension in an urban tertiary care center ED, we developed a multi-variable model that distinguishes between patients who did and did not subsequently receive sustained (>24 h) vasopressor therapy. Candidate predictors were vital signs, intravenous fluid (IVF) volumes, laboratory measurements, and elapsed time from triage computed at timepoints leading up to the final decision timepoint of either vasopressor initiation or ED hypotension resolution without vasopressors. A model with six independently significant covariates (respiratory rate, Glasgow Coma Scale score, SBP, SpO2, administered IVF, and elapsed time) achieved a C-statistic of 0.78 in a held-out test set at the final decision timepoint, demonstrating the ability to reliably model usual care for vasopressor initiation for hypotensive septic patients. The included variables measured depth of hypotension, extent of disease severity and organ dysfunction. At an operating point of 90% specificity, the model identified a minority of patients (39%) more than an hour before actual vasopressor initiation, during which time a median of 2,250 (IQR 1,200-3,300) mL of IVF was administered. This single-center analysis shows the feasibility of a quantitative, objective tool for describing usual care. Dynamic practice models may help assess when management was atypical; such tools may also be useful for designing and interpreting clinical trials.

End-user evaluation of an interface for clinical decision support using predictive algorithms.

There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology. Clinical Relevance- This end-user evaluation of a novel CDSS highlights the importance of end-user experience in the workflow integration of advanced computational algorithms for bedside decision support during critical care.

Predicting Hypertensive Events with Time-Series Analysis of Mean Arterial Pressure.

We investigated whether a statistical model used previously to predict hypotension from mean arterial pressure (MAP) time series analysis could predict hypertension. We performed a retrospective analysis of minute-by-minute MAP records from two cohorts of intensive care unit (ICU) patients. The first cohort was comprised of surgical and medical ICUs while the second cohort was comprised of acute spinal cord injury (ASCI) patients in a neurological ICU. At each time point with physiological MAP, time series analysis was used to predict the median MAP for the subsequent 20 min. This method was used to predict hypertensive episodes, i.e., intervals of 20 or more minutes where at least half of the MAP measurements were > 105 mmHg. Advance prediction of hypertensive episodes was similar in the two cohorts (69.15% vs. 82.61%, respectively), as was positive predictive value of the hypertension predictions (67.42% vs. 71.57%). The results suggest that the methodology may be useable for predicting hypertension from time-series analysis of MAP. Patients requiring continuous vasopressor infusion are at risk of hypertension and excessive vasoconstriction. We found evidence that time-series analysis previously validated for predicting hypotension may also be usable for predicting hypertension.

Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension.

OBJECTIVE: To compare invasive blood pressure measurements recorded using an automated archiving method against clinician-documented values from the same invasive monitor and determine which method of recording blood pressure is more highly associated with the subsequent onset of hypotension. DESIGN: Retrospective comparative analysis. SETTING: Intensive care patients in a university hospital. PATIENTS: Mixed medical/surgical patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Using intervals of hemodynamic stability from 2,320 patient records, we retrospectively compared paired sources of invasive blood pressure data: 1) measurements documented by the nursing staff and 2) measurements generated by an automated archiving method that intelligently excludes unreliable (e.g., noisy or excessively damped) blood pressure values. The primary outcome was the occurrence of subsequent "consensus" hypotension, i.e., hypotension documented jointly by the nursing staff and the automated archive. The automated method could be adjusted to alter its operating characteristics (sensitivity and specificity). At a matched level of specificity (96%), blood pressures from the automated archiving method were more sensitive (28%) for subsequent consensus hypotension vs. the nurse-documented values (21%). Likewise, at a matched level of sensitivity (21%), the values from the automated method were more specific (99%) vs. the nurse-documented values (96%). These significant findings (p < .001) were consistent in a set of sensitivity analyses that employed alternative criteria for patient selection and the clinical outcome definition. CONCLUSIONS: During periods of hemodynamic stability in an intensive care unit patient population, clinician-documented blood pressure values were inferior to values from an intelligent automated archiving method as early indicators of hemodynamic instability. Human oversight may not be necessary for creating a valid archive of vital sign data within an electronic medical record. Furthermore, if clinicians do have a tendency to disregard early indications of instability, then an automated archive may be a preferable source of data for so-called early warning systems that identify patients at risk of decompensation.

Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

OBJECTIVE: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. DESIGN: Data collection and retrospective analysis. SETTING: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. PATIENTS: Adult patients admitted to intensive care units between 2001 and 2007. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). CONCLUSIONS: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.