Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia.

OBJECTIVE: To determine if potential predictors for invasive mechanical ventilation (IMV) are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome (C-ARDS). DESIGN: Single center highly detailed longitudinal observational study. SETTING: Tertiary hospital ICU: two first COVID-19 pandemic waves, Madrid, Spain. PATIENTS OR PARTICIPANTS: 280 patients with C-ARDS, not requiring IMV on admission. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Target: endotracheal intubation and IMV, mortality. PREDICTORS: demographics, hourly evolution of oxygenation, clinical data, and laboratory results. RESULTS: The time between symptom onset and ICU admission, the APACHE II score, the ROX index, and procalcitonin levels in blood were potential predictors related to both IMV and mortality. The ROX index was the most significant predictor associated with IMV, while APACHE II, LDH, and DaysSympICU were the most with mortality. CONCLUSIONS: According to the results of the analysis, there are significant predictors linked with IMV and mortality in C-ARDS patients, including the time between symptom onset and ICU admission, the severity of the COVID-19 waves, and several clinical and laboratory measures. These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management.

PDSS: A Pharmacological Decision Support System for Diabetics Patients with COVID-19.

With the advent of SARS-CoV-2, several studies have shown that there is a higher mortality rate in patients with diabetes and, in some cases, it is one of the side effects of overcoming the disease. However, there is no clinical decision support tool or specific treatment protocols for these patients. To tackle this issue, in this paper we present a Pharmacological Decision Support System (PDSS) providing intelligent decision support for COVID-19 diabetic patient treatment selection, based on an analysis of risk factors with data from electronic medical records using Cox regression. The goal of the system is to create real world evidence including the ability to continuously learn to improve clinical practice and outcomes of diabetic patients with COVID-19.

A Large-Scale Multicenter Retrospective Study on Nephrotoxicity Associated With Empiric Broad-Spectrum Antibiotics in Critically Ill Patients.

BACKGROUND: Evidence regarding acute kidney injury associated with concomitant administration of vancomycin and piperacillin-tazobactam is conflicting, particularly in patients in the ICU. RESEARCH QUESTION: Does a difference exist in the association between commonly prescribed empiric antibiotics on ICU admission (vancomycin and piperacillin-tazobactam, vancomycin and cefepime, and vancomycin and meropenem) and acute kidney injury? STUDY DESIGN AND METHODS: This was a retrospective cohort study using data from the eICU Research Institute, which contains records for ICU stays between 2010 and 2015 across 335 hospitals. Patients were enrolled if they received vancomycin and piperacillin-tazobactam, vancomycin and cefepime, or vancomycin and meropenem exclusively. Patients initially admitted to the ED were included. Patients with hospital stay duration of < 1 h, receiving dialysis, or with missing data were excluded. Acute kidney injury was defined as Kidney Disease: Improving Global Outcomes stage 2 or 3 based on serum creatinine component. Propensity score matching was used to match patients in the control (vancomycin and meropenem or vancomycin and cefepime) and treatment (vancomycin and piperacillin-tazobactam) groups, and ORs were calculated. Sensitivity analyses were performed to study the effect of longer courses of combination therapy and patients with renal insufficiency on admission. RESULTS: Thirty-five thousand six hundred fifty-four patients met inclusion criteria (vancomycin and piperacillin-tazobactam, n = 27,459; vancomycin and cefepime, n = 6,371; vancomycin and meropenem, n = 1,824). Vancomycin and piperacillin-tazobactam was associated with a higher risk of acute kidney injury and initiation of dialysis when compared with that of both vancomycin and cefepime (Acute kidney injury: OR, 1.37 [95% CI, 1.25-1.49]; dialysis: OR, 1.28 [95% CI, 1.14-1.45]) and vancomycin and meropenem (Acute kidney injury: OR, 1.27 [95%, 1.06-1.52]; dialysis: OR, 1.56 [95% CI, 1.23-2.00]). The odds of acute kidney injury developing was especially pronounced in patients without renal insufficiency receiving a longer duration of vancomycin and piperacillin-tazobactam therapy compared with vancomycin and meropenem therapy. INTERPRETATION: VPT is associated with a higher risk of acute kidney injury than both vancomycin and cefepime and vancomycin and meropenem in patients in the ICU, especially for patients with normal initial kidney function requiring longer durations of therapy. Clinicians should consider vancomycin and meropenem or vancomycin and cefepime to reduce the risk of nephrotoxicity for patients in the ICU.

A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulation.

PURPOSE: Sepsis is a leading cause of morbidity and mortality worldwide and is characterized by vascular leak. Treatment for sepsis, specifically intravenous fluids, may worsen deterioration in the context of vascular leak. We therefore sought to quantify vascular leak in sepsis patients to guide fluid resuscitation. METHODS: We performed a retrospective cohort study of sepsis patients in four ICU databases in North America, Europe, and Asia. We developed an intuitive vascular leak index (VLI) and explored the relationship between VLI and in-hospital death and fluid balance using generalized additive models (GAM). RESULTS: Using a GAM, we found that increased VLI is associated with an increased risk of in-hospital death. Patients with a VLI in the highest quartile (Q4), across the four datasets, had a 1.61-2.31 times increased odds of dying in the hospital compared to patients with a VLI in the lowest quartile (Q1). VLI Q2 and Q3 were also associated with increased odds of dying. The relationship between VLI, treated as a continuous variable, and in-hospital death and fluid balance was statistically significant in the three datasets with large sample sizes. Specifically, we observed that as VLI increased, there was increase in the risk for in-hospital death and 36-84 h fluid balance. CONCLUSIONS: Our VLI identifies groups of patients who may be at higher risk for in-hospital death or for fluid accumulation. This relationship persisted in models developed to control for severity of illness and chronic comorbidities.

High resolution data modifies intensive care unit dialysis outcome predictions as compared with low resolution administrative data set.

High resolution clinical databases from electronic health records are increasingly being used in the field of health data science. Compared to traditional administrative databases and disease registries, these newer highly granular clinical datasets offer several advantages, including availability of detailed clinical information for machine learning and the ability to adjust for potential confounders in statistical models. The purpose of this study is to compare the analysis of the same clinical research question using an administrative database and an electronic health record database. The Nationwide Inpatient Sample (NIS) was used for the low-resolution model, and the eICU Collaborative Research Database (eICU) was used for the high-resolution model. A parallel cohort of patients admitted to the intensive care unit (ICU) with sepsis and requiring mechanical ventilation was extracted from each database. The primary outcome was mortality and the exposure of interest was the use of dialysis. In the low resolution model, after controlling for the covariates that are available, dialysis use was associated with an increased mortality (eICU: OR 2.07, 95% CI 1.75-2.44, p<0.01; NIS: OR 1.40, 95% CI 1.36-1.45, p<0.01). In the high-resolution model, after the addition of the clinical covariates, the harmful effect of dialysis on mortality was no longer significant (OR 1.04, 95% 0.85-1.28, p = 0.64). The results of this experiment show that the addition of high resolution clinical variables to statistical models significantly improves the ability to control for important confounders that are not available in administrative datasets. This suggests that the results from prior studies using low resolution data may be inaccurate and may need to be repeated using detailed clinical data.

Metabolic Cost of Exercise Initiation in Patients With Heart Failure With Preserved Ejection Fraction vs Community-Dwelling Adults.

Importance: Heart failure with preserved ejection fraction (HFpEF) is a joint metabolic and cardiovascular disorder with significant noncardiac contributions. Objective: To define and quantify the metabolic cost of initiating exercise in individuals with and without HFpEF and its functional consequences. Design, Setting, and Participants: This prospective cohort study included individuals with hemodynamically confirmed HFpEF from the Massachusetts General Hospital Exercise Study (MGH-ExS) and community-dwelling participants from the Framingham Heart Study (FHS). Analysis began April 2016 and ended November 2020. Exposures: Internal work (IW), a measure of work equivalents required to initiate movement. Main Outcomes and Measures: Using breath-by-breath oxygen uptake (V̇o2) measurements and V̇o2-work rate associations, cost of initiating exercise (IW) in patients with HFpEF (MGH-ExS) and in community-dwelling individuals (FHS) was quantified. Linear regression was used to estimate associations between IW and clinical/hemodynamic measures. Results: Of 3231 patients, 184 (5.7%) had HFpEF and were from MGH-ExS, and 3047 (94.3%) were community-dwelling individuals from FHS. In the MGH-ExS cohort, 86 (47%) were women, the median (interquartile range) age was 63 (53-72) years, and the median (interquartile range) peak V̇o2 level was 13.33 (11.77-15.62) mL/kg/min. In the FHS cohort, 1620 (53%) were women, the median (interquartile range) age was 54 (48-60) years, and the median (interquartile range) peak V̇o2 level was 22.2 (17.85-27.35) mL/kg/min. IW was higher in patients with HFpEF and accounted for 27% (interquartile range, 21%-39%) of the total work (IW + measured external workload on the cycle), compared with 15% (interquartile range, 12%-20%) of that in FHS participants. Body mass index accounted for greatest explained variance in patients with HFpEF from MGH-ExS and FHS participants (22% and 18%, respectively), while resting cardiac output and biventricular filling pressures were not significantly associated with variance in IW in patients with HFpEF. A higher IW in patients with HFpEF was associated with a greater increase in left- and right-sided cardiac filing pressure during unloaded exercise, despite similar resting hemodynamic measures across IW. Conclusions and Relevance: This study found that internal work, a new body mass index-related measure reflecting the metabolic cost of initiating movement, is higher in individuals with HFpEF compared with middle-aged adults in the community and is associated with steep, early increases in cardiac filling pressures. These findings highlight the importance of quantifying heterogeneous responses to exercise initiation when evaluating functional intolerance in individuals at risk for or with HFpEF.

Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery.

OBJECTIVES: Machine learning models used to predict postoperative mortality rarely include intraoperative factors. Several intraoperative factors like hypotension (IOH), vasopressor-inotropes, and cardiopulmonary bypass (CPB) time are significantly associated with postoperative outcomes. The authors explored the ability of machine learning models incorporating intraoperative risk factors to predict mortality after cardiac surgery. DESIGN: Retrospective study. SETTING: Tertiary hospital. PARTICIPANTS: A total of 5,015 adults who underwent cardiac surgery from 2008 to 2016. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: The intraoperative phase was divided into the following: (1) CPB, (2) outside CPB, and (3) total surgery for quantifying IOH only. Phase-specific IOH parameters (area under the curve for mean arterial pressure <65 mmHg), vasopressor-inotropes (norepinephrine equivalents), duration, and cross-clamp time, along with preoperative risk factors ,were incorporated into the models. The primary outcome was mortality. The following 5 models were applied to 3 intraoperative phases separately: (1) logistic regression, (2) random forests, (3) neural networks, (4) support vector machines, and (5) extreme gradient boosting (XGB). Mortality was predicted using area under the receiver operating characteristic curve. Of 5,015 patients included, 112 (2.2%) died. XGB model from the outside-CPB phase predicted mortality better with area under the receiver operating characteristic curve, 95% confidence interval (CI): 0.88(0.83-0.94); positive predictive value, 0.10(0.06-0.15); specificity 0.85 (0.83-0.87) and sensitivity 0.75 (0.57-0.90). CONCLUSION: XGB machine learning model from IOH outside the CPB phase seemed to offer a better discrimination, sensitivity, specificity, and positive predictive value compared with other models. Machine learning models incorporating intraoperative adverse factors might offer better predictive ability for risk stratification and triaging of patients after cardiac surgery.

Use of Do-Not-Resuscitate Orders for Critically Ill Patients with ESKD.

BACKGROUND: Despite having high comorbidity rates and shortened life expectancy, patients with ESKD may harbor unrealistically optimistic expectations about their prognoses. Whether this affects resuscitation orders is unknown. METHODS: To determine whether do-not-resuscitate (DNR) orders differ among patients with ESKD compared with other critically ill patients, including those with diseases of other major organs, we investigated DNR orders on admission to intensive care units (ICUs) among 106,873 patients in the United States. RESULTS: Major organ disease uniformly associated with increased risk of hospital mortality, particularly for cirrhosis (adjusted odds ratio [aOR], 2.67; 95% confidence interval [95% CI], 2.30 to 3.08), and ESKD (aOR, 1.47; 95% CI, 1.31 to 1.65). Compared with critically ill patients without major organ disease, patients with stroke, cancer, heart failure, dementia, chronic obstructive pulmonary disease, and cirrhosis were statistically more likely to have a DNR order on ICU admission; those with ESKD were not. Findings were similar when comparing patients with a single organ disease with those without organ disease. The disconnect between prognosis and DNR use was most notable among Black patients, for whom ESKD (compared with no major organ disease) was associated with a 62% (aOR, 1.62; 95% CI, 1.27 to 2.04) higher odds of hospital mortality, but no appreciable difference in DNR utilization (aOR, 1.06; 95% CI, 0.66 to 1.62). CONCLUSIONS: Unlike patients with diseases of other major organs, critically ill patients with ESKD were not more likely to have a DNR order than patients without ESKD. Whether this reflects a greater lack of advance care planning in the nephrology community, as well as a missed opportunity to minimize potentially needless patient suffering, requires further study.

Incidence and Risk Model Development for Severe Tachypnea Following Terminal Extubation.

BACKGROUND: Palliative ventilator withdrawal (PVW) in the ICU is a common occurrence. RESEARCH QUESTION: The goal of this study was to measure the rate of severe tachypnea as a proxy for dyspnea and to identify characteristics associated with episodes of tachypnea. STUDY DESIGN AND METHODS: This study assessed a retrospective cohort of ICU patients from 2008 to 2012 mechanically ventilated at a single academic medical center who underwent PVW. The primary outcome of at least one episode of severe tachypnea (respiratory rate > 30 breaths/min) within 6 h after PVW was measured by using detailed physiologic and medical record data. Multivariable logistic regression was used to examine the association between patient and treatment characteristics with the occurrence of a severe episode of tachypnea post extubation. RESULTS: Among 822 patients undergoing PVW, 19% and 30% had an episode of severe tachypnea during the 1-h and 6-h postextubation period, respectively. Within 1 h postextubation, patients with the following characteristics were more likely to experience tachypnea: no pre-extubation opiates (adjusted OR [aOR], 2.08; 95% CI, 1.03-4.19), lung injury (aOR, 3.33; 95% CI, 2.19-5.04), Glasgow Coma Scale score > 8 (aOR, 2.21; 95% CI, 1.30-3.77), and no postextubation opiates (aOR, 1.90; 95% CI, 1.19-3.00). INTERPRETATION: Up to one-third of ICU patients undergoing PVW experience severe tachypnea. Administration of pre-extubation opiates (anticipatory dosing) represents a key modifiable factor that may reduce poor symptom control.

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.

Developing well-calibrated illness severity scores for decision support in the critically ill.

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models.

Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.