Improving ICU Risk Predictive Models Through Automation Designed for Resiliency Against Documentation Bias.

OBJECTIVES: Electronic health records enable automated data capture for risk models but may introduce bias. We present the Philips Critical Care Outcome Prediction Model (CCOPM) focused on addressing model features sensitive to data drift to improve benchmarking ICUs on mortality performance. DESIGN: Retrospective, multicenter study of ICU patients randomized in 3:2 fashion into development and validation cohorts. Generalized additive models (GAM) with features designed to mitigate biases introduced from documentation of admission diagnosis, Glasgow Coma Scale (GCS), and extreme vital signs were developed using clinical features representing the first 24 hours of ICU admission. SETTING: eICU Research Institute database derived from ICUs participating in the Philips eICU telecritical care program. PATIENTS: A total of 572,985 adult ICU stays discharged from the hospital between January 1, 2017, and December 31, 2018, were included, yielding 509,586 stays in the final cohort; 305,590 and 203,996 in development and validation cohorts, respectively. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Model discrimination was compared against Acute Physiology and Chronic Health Evaluation (APACHE) IVa/IVb models on the validation cohort using the area under the receiver operating characteristic (AUROC) curve. Calibration assessed by actual/predicted ratios, calibration-in-the-large statistics, and visual analysis. Performance metrics were further stratified by subgroups of admission diagnosis and ICU characteristics. Historic data from two health systems with abrupt changes in Glasgow Coma Scale (GCS) documentation were assessed in the year prior to and after data shift. CCOPM outperformed APACHE IVa/IVb for ICU mortality (AUROC, 0.925 vs 0.88) and hospital mortality (AUROC, 0.90 vs 0.86). Better calibration performance was also attained among subgroups of different admission diagnoses, ICU types, and over unique ICU-years. The CCOPM provided more stable predictions compared with APACHE IVa within an external cohort of greater than 120,000 patients from two health systems with known changes in GCS documentation. CONCLUSIONS: These mortality risk models demonstrated excellent performance compared with APACHE while appearing to mitigate bias introduced through major shifts in GCS documentation at two large health systems. This provides evidence to support using automated capture rather than trained personnel for capture of GCS data used in benchmarking ICUs on mortality performance.

Descriptive Comparison of Two Models of Tele-Critical Care Delivery in a Large Multi-Hospital Health Care System.

Introduction: The Society of Critical Care Medicine Tele-Critical Care (TCC) Committee has identified the need for rigorous comparative research of different TCC delivery models to support the development of best practices for staffing, application, and approaches to workflow. Our objective was to describe and compare outcomes between two TCC delivery models, TCC with 24/7 Bedside Intensivist (BI) compared with TCC with Private Daytime Attending Intensivist (PI) in relation to intensive care unit (ICU) and hospital mortality, ICU and hospital length of stay (LOS), cost, and complications across the spectrum of routine ICU standards of care. Methods: Observational cohort study at large health care system in 12 ICUs and included patients, ≥18, with Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores and predictions (October 2016-June 2019). Results: Of the 19,519 ICU patients, 71.7% (n = 13,993) received TCC with 24/7 BI while 28.3% (n = 5,526) received TCC with PI. ICU and Hospital mortality (4.8% vs. 3.1%, p < 0.0001; 12.6% vs. 8.1%, p < 0.001); and ICU and Hospital LOS (3.2 vs. 2.4 days, p < 0.001; 9.8 vs. 7.2 days, p < 0.001) were significantly higher among 24/7 BI compared with PI. The APACHE observed/expected ratios (odds ratio [OR]; 95% confidence interval [CI]) for ICU mortality (0.62; 0.58-0.67) vs. (0.53; 0.46-0.61) and Hospital mortality (0.95; 0.57-1.48) vs. (0.77; 0.70-0.84) were significantly different for 24/7 BI compared with PI. Multivariate mixed models that adjusted for confounders demonstrated significantly greater odds of (OR; 95% CI) ICU mortality (1.58; 1.28-1.93), Hospital mortality (1.52; 1.33-1.73), complications (1.55; 1.18-2.04), ICU LOS [3.14 vs. 2.59 (1.25; 1.19-1.51)], and Hospital LOS [9.05 vs. 7.31 (1.23; 1.21-1.25)] among 24/7 BI when compared with PI. Sensitivity analyses adjusting for ICU admission within 24 h of hospital admission, receiving active ICU treatments, nighttime admission, sepsis, and highest third acute physiology score indicated significantly higher odds for 24/7 BI compared with PI. Conclusion: Our comparison demonstrated that TCC delivery model with PI provided high-quality care with significant positive effects on outcomes. This suggests that TCC delivery models have broad-ranging applicability and benefits in routine critical care, thus necessitating progressive research in this direction.

The Global Open Source Severity of Illness Score (GOSSIS).

OBJECTIVES: To develop and demonstrate the feasibility of a Global Open Source Severity of Illness Score (GOSSIS)-1 for critical care patients, which generalizes across healthcare systems and countries. DESIGN: A merger of several critical care multicenter cohorts derived from registry and electronic health record data. Data were split into training (70%) and test (30%) sets, using each set exclusively for development and evaluation, respectively. Missing data were imputed when not available. SETTING/PATIENTS: Two large multicenter datasets from Australia and New Zealand (Australian and New Zealand Intensive Care Society Adult Patient Database [ANZICS-APD]) and the United States (eICU Collaborative Research Database [eICU-CRD]) representing 249,229 and 131,051 patients, respectively. ANZICS-APD and eICU-CRD contributed data from 162 and 204 hospitals, respectively. The cohort included all ICU admissions discharged in 2014-2015, excluding patients less than 16 years old, admissions less than 6 hours, and those with a previous ICU stay. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: GOSSIS-1 uses data collected during the ICU stay's first 24 hours, including extrema values for vital signs and laboratory results, admission diagnosis, the Glasgow Coma Scale, chronic comorbidities, and admission/demographic variables. The datasets showed significant variation in admission-related variables, case-mix, and average physiologic state. Despite this heterogeneity, test set discrimination of GOSSIS-1 was high (area under the receiver operator characteristic curve [AUROC], 0.918; 95% CI, 0.915-0.921) and calibration was excellent (standardized mortality ratio [SMR], 0.986; 95% CI, 0.966-1.005; Brier score, 0.050). Performance was held within ANZICS-APD (AUROC, 0.925; SMR, 0.982; Brier score, 0.047) and eICU-CRD (AUROC, 0.904; SMR, 0.992; Brier score, 0.055). Compared with GOSSIS-1, Acute Physiology and Chronic Health Evaluation (APACHE)-IIIj (ANZICS-APD) and APACHE-IVa (eICU-CRD), had worse discrimination with AUROCs of 0.904 and 0.869, and poorer calibration with SMRs of 0.594 and 0.770, and Brier scores of 0.059 and 0.063, respectively. CONCLUSIONS: GOSSIS-1 is a modern, free, open-source inhospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries.

Master's Degrees Among Academic Plastic Surgeons and Plastic Surgery Residents: What Are the Trends?

BACKGROUND: As more plastic surgery clinicians pursue advanced degrees and strive to become stronger physician-scientists, an objective understanding of how such degrees influence careers becomes important. We hypothesized that having a master's degree is associated with higher scholarly activity, research funding, academic progression, and leadership appointments. METHODS: Accreditation Council for Graduate Medical Education-accredited integrated plastic surgery residency program Web sites were queried to create a data set of current academic plastic surgeons (APSs) and plastic surgery residents (PSRs). Scholarly metrics such as publications, citations, and H-indices were extracted from the Scopus database. National Institutes of Health and Plastic Surgery Foundation funding information was collected through their respective Web sites. RESULTS: Our cohort comprised 799 APSs and 922 PSRs, of whom 8% and 7.4%, respectively, had at least one master's degree. Academic plastic surgeons with master's of public health degrees had a significantly higher median number of publications and citations than APSs without a master's of public health. There was no association between any master's degree and academic rank or being a department chairman or program director. Academic plastic surgeons with master of science degrees were more likely to receive National Institutes of Health grants. Among PSRs, master's of science graduates had a higher median number of publications. Other master's degrees did not significantly influence scholarly productivity or funding. CONCLUSIONS: Certain master's degrees had an impact on scholarly productivity, with no significant effect on academic rank or leadership positions. The value of master's degrees in programs focusing on healthcare management, leadership skills, and business acumen likely extends beyond the scope of this study.

Temporal Trends in Critical Care Outcomes in U.S. Minority-Serving Hospitals.

Rationale: Whether critical care improvements over the last 10 years extend to all hospitals has not been described.Objectives: To examine the temporal trends of critical care outcomes in minority and non-minority-serving hospitals using an inception cohort of critically ill patients.Measurements and Main Results: Using the Philips Health Care electronic ICU Research Institute Database, we identified minority-serving hospitals as those with an African American or Hispanic ICU census more than twice its regional mean. We examined almost 1.1 million critical illness admissions among 208 ICUs from across the United States admitted between 2006 and 2016. Adjusted hospital mortality (primary) and length of hospitalization (secondary) were the main outcomes. Large pluralities of African American (25%, n = 27,242) and Hispanic individuals (48%, n = 26,743) were cared for in minority-serving hospitals, compared with only 5.2% (n = 42,941) of white individuals. Over the last 10 years, although the risk of critical illness mortality steadily decreased by 2% per year (95% confidence interval [CI], 0.97-0.98) in non-minority-serving hospitals, outcomes within minority-serving hospitals did not improve comparably. This disparity in temporal trends was particularly noticeable among African American individuals, where each additional calendar year was associated with a 3% (95% CI, 0.96-0.97) lower adjusted critical illness mortality within a non-minority-serving hospital, but no change within minority-serving hospitals (hazard ratio, 0.99; 95% CI, 0.97-1.01). Similarly, although ICU and hospital lengths of stay decreased by 0.08 (95% CI, -0.08 to -0.07) and 0.16 (95% CI, -0.16 to -0.15) days per additional calendar year, respectively, in non-minority-serving hospitals, there was little temporal change for African American individuals in minority-serving hospitals.Conclusions: Critically ill African American individuals are disproportionately cared for in minority-serving hospitals, which have shown significantly less improvement than non-minority-serving hospitals over the last 10 years.

A comparison of characteristics and outcomes of patients admitted to the ICU with asthma in Australia and New Zealand and United states.

Objective: To compare the characteristics, use of invasive ventilation and outcomes of patients admitted with critical asthma syndrome (CAS) to ICUs in Australia and New Zealand (ANZ), and a large cohort of ICUs in the United States (US). Methods: We examined two large databases of ICU for patients admitted with CAS in 2014 and 2015. We obtained, analyzed, and compared information on demographic and physiological characteristics, use of invasive mechanical ventilation, and clinical outcome and derived predictive models. Results: Overall, 2202 and 762 patients were admitted with a primary diagnosis of CAS in the ANZ and US databases respectively (0.73% vs. 0.46% of all ICU admissions, P < 0.001). A similar percentage of patients received invasive mechanical ventilation in the first 24 h (24.7% vs. 24.4%, P = 0.87) but ANZ patients had lower respiratory rates and higher PaCO2 levels. Overall mortality was low (1.23 for ANZ and 1.71 for USA; P = 0.36) and even among invasively ventilated patients (2.4% for ANZ vs. 1.1% for USA; P = 0.38). However, ANZ patients also had longer length of stay in ICU (43 vs. 37 h, P = 0.001) and hospital (105 vs. 78 h, P = 0.003). Conclusions: Patients admitted to ANZ and USA ICU with CAS are broadly similar and have a low and similar rate of invasive ventilation and mortality. However, ANZ patients made up a greater proportion of ICU patients and had longer ICU and hospital stays. These findings provide a modern invasive ventilation and mortality rates benchmark for future studies of CAS.

Red Blood Cell Product Transfusion Thresholds and Clinical Outcomes.

OBJECTIVE: To determine whether patients transfused red blood cell (RBC) products according to guideline-specified pretransfusion hemoglobin (Hb) concentrations or for other reasons were more likely to survive their intensive care unit (ICU) stay. DESIGN: An observational study of 375 478 episodes of ICU care, over 5 years, was performed with ICU survival as the primary outcome. Outcomes were analyzed as a function of pretransfusion Hb concentration for groups with distinct transfusion indications while adjusting for potential confounders. SETTING AND PATIENTS: This study included all adult patients discharged from 1 of 203 adult ICUs from 32 US health-care systems. The patients were from community hospitals, tertiary, and academic medical centers. INTERVENTION: Transfusion of allogenic packed RBCs or whole blood was prescribed at the discretion of the treating clinicians. MEASUREMENTS AND MAIN RESULTS: We found that 15% of adult ICU patients are transfused RBC products, and most transfusions for hemodynamically stable patients are administered above the guideline-specified pretransfusion Hb threshold of 7 g/dL. Hemodynamically stable patients transfused below this threshold were significantly more likely to survive their ICU stay than those not transfused (odds ratio [OR] 0.59, 95% confidence interval [CI], 0.43-0.81; P = .001), and patients transfused at thresholds above 9 g/dL were less likely to survive their ICU stay than those not transfused. Patients of the acute blood loss group who were transfused appeared to benefit or were not harmed by transfusion. CONCLUSION: Conservative RBC product transfusion practices for groups that are targeted by guidelines are justified by outcomes observed in clinical practice. This study provides evidence for the liberal administration of RBC products to critically ill adults with acute blood loss based on association with lower risk of mortality.

Evaluation of ICU Risk Models Adapted for Use as Continuous Markers of Severity of Illness Throughout the ICU Stay.

OBJECTIVES: Evaluate the accuracy of different ICU risk models repurposed as continuous markers of severity of illness. DESIGN: Nonintervention cohort study. SETTING: eICU Research Institute ICUs using tele-ICU software calculating continuous ICU Discharge Readiness Scores between January 2013 and March 2016. PATIENTS: Five hundred sixty-one thousand four hundred seventy-eight adult ICU patients with an ICU length of stay between 4 hours and 30 days. INTERVENTIONS: Not available. MEASUREMENTS AND MAIN RESULTS: Hourly Acute Physiology and Chronic Health Evaluation IV, Sequential Organ Failure Assessment, and Discharge Readiness Scores were calculated beginning hour 4 of the ICU stay. Primary outcome was the area under the receiver operating characteristic curve for the mean score with ICU mortality. Secondary outcomes included area under the receiver operating characteristic curves for ICU mortality with admission, median, maximum and last scores, and for death within 24 hours. The trajectories of each score were visualized by plotting the hourly averages against time in the ICU, stratified by mortality and length of stay. The area under the receiver operating characteristic curves for mean Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores were 0.90 (0.89-0.90), 0.86 (0.86-0.86), and 0.94 (0.94-0.94), respectively. The area under the receiver operating characteristic curves for hourly Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores predicting 24-hour mortality were 0.81 (0.81-0.81), 0.76 (0.76-0.76), and 0.86 (0.86-0.86). Discharge Readiness Scores had a higher area under the receiver operating characteristic curve than both Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment for each metric. Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment scores increased throughout the first 24 hours in both survivors and nonsurvivors; Discharge Readiness Scores continuously decreased in survivors and temporarily decreased before increasing by hour 36 in nonsurvivors with longer length of stays. CONCLUSIONS: Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores all have relatively high discrimination for ICU mortality when used continuously; Discharge Readiness Scores tended to have slightly higher area under the receiver operating characteristic curves for each endpoint. These findings validate the use of these models on a population level for continuous risk adjustment in the ICU, although Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment appear slower to respond to improvements in patient status than Discharge Readiness Scores, and Discharge Readiness Scores may reflect physiologic improvement from interventions, potentially underestimating risk.

The Association of ICU Acuity With Outcomes of Patients at Low Risk of Dying.

OBJECTIVE: Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients. DESIGN: Retrospective cohort study. SETTING: Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015. PATIENTS: Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less. EXPOSURE: ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles. MEASUREMENTS AND MAIN RESULTS: We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p < 0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs). CONCLUSIONS: Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.

The Obesity Paradox Is Not Observed in Critically Ill Patients on Early Enteral Nutrition.

OBJECTIVES: To investigate the association between body mass index and mortality in a large, ICU population and determine if the relationship is observed among a subgroup of patients ordered early enteral nutrition. DESIGN: Retrospective cohort study within a national clinical mixed ICU database of patients admitted between January 1, 2008, and June 30, 2015. SETTING: Initial ICU admissions among patients monitored by tele-ICU programs and recorded in the Philips eICU Research Institute database. PATIENTS: A total of 1,042,710 adult patient stays with ICU length of stay more than 24 hours, of which 74,771 were ordered enteral nutrition within the first 48 hours. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Patient stays from 409 ICUs were included. The average age, Acute Physiology and Chronic Health Evaluation IV score, and hospital mortality were 63.6 years, 56.7, and 9.0%, respectively. Hospital mortality among body mass index categories was estimated by multivariable modified Poisson regression models. Compared with the body mass index category 25.0-29.9 kg/m, hospital mortality was higher among underweight (body mass index, < 18.5; relative risk, 1.35; 95% CI, 1.32-1.39), normal weight (body mass index, 18.5-24.9; relative risk, 1.10; 95% CI, 1.09-1.12), and the extremely obese (body mass index, ≥ 50.0; relative risk, 1.10; 95% CI, 1.05-1.15). However, the risk was not statistically different from patients with body mass index 30.0-49.9 kg/m. Among patients ordered early enteral nutrition, the risk of mortality in the body mass index category 25.0-29.9 kg/m was not statistically different from those in the normal weight or extremely obese groups. CONCLUSIONS: A survival advantage for overweight and obese patients was observed in this large cohort of critically ill patients. However, among those ordered early enteral nutrition, the survival disadvantage for body mass index categories less than 25.0 kg/m was minimal or unobservable when compared with higher body mass index categories.

Sildenafil Use and Cigarette Smoking Associated with Intracerebral Hemorrhage: A Rare Case Report.

Background: Intracerebral hemorrhage (ICH) is a serious condition characterized by bleeding within the brain tissue. Although the use of sildenafil, a vasodilator agent for erectile dysfunction, has been associated with rare cases of ICH, the combination of sildenafil usage and smoking as risk factors for ICH has not yet been reported. This case report describes the occurrence of ICH in a patient with a history of both sildenafil usage and heavy smoking. Case Presentation: A 53-year-old male, with a history of smoking and regular sildenafil use, was brought to the emergency department due to loss of consciousness with right-side weakness, he initially experienced with nausea, vomiting and dizziness after taking sildenafil 100mg tablet once. The Glasgow Coma Score (GCS) was 10 with side hemiparesis. Non-contrast CT revealed left thalamic acute hemorrhage with ventricular extension. Furthermore, a head CT angiography ruled out any vascular anomalies after that the patient was admitted to the intensive care unit (ICU) for conservative management. After three days on clinical and neurological improvement, the patient was transferred to the inpatient ward for further management, monitoring and physiotherapy. On day 6, the patient was discharged and planned for flow up. Conclusion: This rare case highlights the need for further research and awareness regarding the potential risks associated with the combination of sildenafil and heavy smoking. Healthcare professionals should carefully evaluate the individual risk factors of patients, educate them about potential complications, and consider alternative treatments if necessary. Additionally, patients should be encouraged to quit smoking and adopt a healthy lifestyle to minimize the risk of cerebrovascular events.

Single-dose etomidate is not associated with increased mortality in ICU patients with sepsis: analysis of a large electronic ICU database.

OBJECTIVE: Retrospective analyses of several trials suggest etomidate may be unsafe for intubation in patients with sepsis. We evaluated the association of etomidate and mortality in a large cohort of septic patients to determine if single-dose etomidate was associated with increased in-hospital mortality. DESIGN AND SETTING: Retrospective cohort study at the Philips eICU Research Institute ICU clinical database. INTERVENTIONS: None. PATIENTS: Among 741,036 patients monitored from 2008 through 2010, we identified 2,014 adults intubated in the ICU 4-96 hrs after admission, having clinical criteria consistent with sepsis, severe sepsis, or septic shock. In all, 1,102 patients received etomidate and 912 received other induction agents for intubation. MEASUREMENTS AND MAIN RESULTS: The primary endpoint was in-hospital mortality, but we also evaluated demographic and clinical factors, severity of illness, ICU mortality, ICU length of stay, hospital length of stay, ventilator days, and vasopressor days. Competing risk Cox proportional hazard regression models were used for primary outcomes. Demographics and illness severity were similar between the groups. Hospital mortality was similar between the groups (37.2% vs. 37.8%, p = 0.77), as were ICU mortality (30.1% vs. 30.2%, p = 0.99), ICU length of stay (8.7 days vs. 8.9 days, p = 0.66), and hospital length of stay (15.2 vs. 14.6 days, p = 0.31). More patients in the etomidate group received steroids before and after intubation (52.9% vs. 44.5%, p < 0.001), but vasopressor use and duration of mechanical ventilation were similar. No regression model showed an independent association of etomidate with mortality, shock, duration of mechanical ventilation, ICU or hospital length of stay, or vasopressor use. A hospital mortality model limited to only patients with septic shock (n = 650) also showed no association of etomidate and hospital mortality. CONCLUSION: In a mixed-diagnosis group of critically ill patients with sepsis, severe sepsis, and septic shock, single-dose etomidate administration for intubation in the ICU was not associated with higher mortality or other adverse clinical outcomes.

Evaluation of evolving sepsis screening criteria in discriminating suspected sepsis and mortality among adult patients admitted to the intensive care unit.

BACKGROUND: Institutions struggle with successful use of sepsis alerts within electronic health records. OBJECTIVE: Test the association of sepsis screening measurement criteria in discrimination of mortality and detection of sepsis in a large dataset. DESIGN: Retrospective, cohort study using a large United States (U.S.) intensive care database. The Institutional Review Board exempt status was obtained from Kansas University Medical Center Human Research Protection Program (10-1-2015). SETTING: 334 U.S. hospitals participating in the eICU Research Institute. PARTICIPANTS: Nine hundred twelve thousand five hundred and nine adult intensive care admissions from 183 hospitals. METHODS: Exposures included: systemic inflammatory response syndrome criteria ≥ 2 (Sepsis-1); systemic inflammatory response syndrome criteria with organ failure criteria ≥ 3.5 points (Sepsis-2); and sepsis-related organ failure assessment score ≥ 2 and quick score ≥ 2 (Sepsis-3). Discrimination of outcomes was determined with/without (adjusted/unadjusted) baseline risk exposure to a model. The receiver operating characteristic curve (AUROC) and odds ratios (ORs) for each decile of baseline risk of sepsis or death were assessed. RESULTS: Within the eligible cohort of 912,509, a total of 86,219 (9.4 %) patients did not survive their hospital stay and 186,870 (20.5 %) met the definition of suspected sepsis. For suspected sepsis discrimination, Sepsis-2 (unadjusted AUROC 0.67, 99 % CI: 0.66-0.67 and adjusted AUROC 0.77, 99 % CI: 0.77-0.77) outperformed Sepsis-3 (SOFA unadjusted AUROC 0.61, 99 % CI: 0.61-0.61 and adjusted AUROC 0.74, 99 % CI: 0.74-0.74) (qSOFA unadjusted AUROC 0.59, 99 % CI: 0.59-0.60 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). Sepsis-2 also outperformed Sepsis-1 (unadjusted AUROC 0.58, 99 % CI: 0.58-0.58 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). In between differences of AUROCs were statistically significantly different. Sepsis-2 ORs were higher for the outcome of suspected sepsis when considering deciles of risk than the other measurement systems. CONCLUSIONS AND RELEVANCE: Sepsis-2 outperformed other systems in suspected sepsis detection and was comparable to SOFA in prognostic accuracy of mortality in adult intensive care patients.

Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay.

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features. These models are trained on 2.6 million patient stays across 350 US hospitals between 2010 to 2019. The mean absolute error (MAE) for the prediction of duration was 2.08 days for invasive ventilation and 0.36 days for non-invasive ventilation. The total ventilation duration predicted by our model had MAE of 2.38 days, which outperformed the gold standard (APACHE) with MAE of 3.02 days. The feature importance analysis of the trained models showed that, for invasive ventilation, high average heart rate, diagnosis of respiratory infection and admissions from locations other than the operating room were associated with longer ventilation durations. For non-invasive ventilation, higher respiratory rates and having any GCS measurement were associated with longer durations.

Association between intensive care unit-acquired dysglycemia and in-hospital mortality.

OBJECTIVE: Our objective was to quantify the association between intensive care unit-acquired dysglycemia (hyperglycemia, hypoglycemia, and high variability) and in-hospital mortality. DESIGN: Retrospective, observational study. SETTING: eICU Research Institute participating hospitals with an active tele-ICU program between January 1, 2008, and September 30, 2010, representing 784,392 adult intensive care unit patients. PATIENTS: A total of 194,772 patients met inclusion criteria with an intensive care unit length of stay >48 hrs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Acute Physiology and Chronic Health Evaluation IV standardized mortality ratios were calculated for dysglycemia present at admission and acquired in the intensive care unit. Intensive care unit-acquired dysglycemia was modeled using multivariable modified Poisson regression to account for confounding not incorporated in Acute Physiology and Chronic Health Evaluation. Dysglycemia severity was assessed by the relative risk of in-hospital mortality associated with the maximum, time-weighted average daily glucose; lowest glucose value throughout the intensive care unit stay; and quintiles of variability (coefficient of variation). The association of duration beyond thresholds of dysglycemia on mortality was also modeled. The adjusted relative risk (95% confidence interval) of mortality for the maximum intensive care unit average daily glucose was 1.13 (1.04-1.58), 1.43 (1.30-1.58), 1.63 (1.47-1.81), 1.76 (1.55-1.99), and 1.89 (1.62-2.19) for 110-150 mg/dL, 151-180 mg/dL, 180-240 mg/dL, 240-300 mg/dL, and >300 mg/dL, respectively, compared to patients whose highest average daily glucose was 80-110 mg/dL. The relative risk of mortality for the lowest glucose value was 1.67 (1.37-2.03), 1.53 (1.37-1.70), 1.12 (1.04-1.21), and 1.06 (1.01-1.11) for <20 mg/dL, 20-40 mg/dL, 40-60 mg/dL, and 60-80 mg/dL, respectively, compared to patients whose lowest value was 80-110 mg/dL. The relative risk of mortality increased with greater duration of hyperglycemia and with increased variability. The relative risk for the highest compared to lowest quintile of variability was 1.61 (1.47-1.78). The association of duration of hyperglycemia on mortality was more pronounced with more severe hyperglycemia. CONCLUSIONS: The risk of mortality progressively increased with severity and duration of deviation from euglycemia and with increased variability. These data suggest that severe intensive care unit-acquired hyperglycemia, hypoglycemia, and variability are associated with similar risks of mortality.

Factors associated with acute renal failure in falciparum malaria infected patients.

To identify factors associated with acute renal failure among patients with severe falciparum malaria (MARF), we studied 189 severe malaria patients admitted to the Hospital for Tropical Diseases, Faculty of Tropical Medicine, Mahidol University, in Bangkok, Thailand. Among these, 63 had MARF, and 126 did not. Baseline clinical demographics and laboratory variables were evaluated with univariate analysis. Logistic regression was used to ascertain adjusted odds ratios. By univariate analysis, factors associated with MARF included male gender, fever duration > 4 days, patients who lived in a non-endemic area prior to malaria infection, body mass index > 18.5 kg/m(2), oliguria, abdominal pain, impaired consciousness, jaundice, anemia, liver enlargement, total white blood cell count > 10x10(9)/1, total bilirubin > 3 mg/dl, aspartate aminotransferase > 120 U/l, alanine aminotransferase > 120 U/l, albumin < 3 g/dl, fever clearance time >72 hours, and parasite clearance time > 72 hours. A hemoglobin > 10 g/dl, patients living in a malaria endemic area, non-oliguria on the day of admission, and splenomegaly were negatively associated with MARF. After multivariate logistic regression, oliguria during the first 24 hours of admission and a history of living in a nonendemic area prior to malarial infection were factors associated with MARF. We conclude the most significant factors associated with MARF were oliguria on the day of admission and living in a non-endemic area prior to malaria infection.

Using technology to prevent adverse drug events in the intensive care unit.

Critically ill patients are particularly susceptible to adverse drug events (ADEs) due to their rapidly changing and unstable physiology, complex therapeutic regimens, and large percentage of medications administered intravenously. There are a wide variety of technologies that can help prevent the points of failure commonly associated with ADEs (i.e., the five "Rights": right patient; right drug; right route; right dose; right frequency). These technologies are often categorized by their degree of complexity to design and engineer and the type of error they are designed to prevent. Focusing solely on the software and hardware design of technology may over- or underestimate the degree of difficulty to avoid ADEs at the bedside. Alternatively, we propose categorizing technological solutions by identifying the factors essential for success. The two major critical success factors are: 1) the degree of clinical assessment required by the clinician to appropriately evaluate and disposition the issue identified by a technology; and 2) the complexity associated with effective implementation. This classification provides a way of determining how ADE-preventing technologies in the intensive care unit can be successfully integrated into clinical practice. Although there are limited data on the effectiveness of many technologies in reducing ADEs, we will review the technologies currently available in the intensive care unit environment. We will also discuss critical success factors for implementation, common errors made during implementation, and the potential errors using these systems.