The Reviewer Academy of the Society of Critical Care Medicine: Key Principles and Strategic Plan.

The Society of Critical Care Medicine (SCCM) Reviewer Academy seeks to train and establish a community of trusted, reliable, and skilled peer reviewers with diverse backgrounds and interests to promote high-quality reviews for each of the SCCM journals. Goals of the Academy include building accessible resources to highlight qualities of excellent manuscript reviews; educating and mentoring a diverse group of healthcare professionals; and establishing and upholding standards for insightful and informative reviews. This manuscript will map the mission of the Reviewer Academy with a succinct summary of the importance of peer review, process of reviewing a manuscript, and the expected ethical standards of reviewers. We will equip readers to target concise, thoughtful feedback as peer reviewers, advance their understanding of the editorial process and inspire readers to integrate medical journalism into diverse professional careers.

Sepsis Management for the Nephrologist.

The definition of sepsis has evolved significantly over the past three decades. Today, sepsis is defined as a dysregulated host immune response to microbial invasion leading to end organ dysfunction. Septic shock is characterized by hypotension requiring vasopressors after adequate fluid resuscitation with elevated lactate. Early recognition and intervention remain hallmarks for sepsis management. We addressed the current literature and assimilated thought regarding optimum initial resuscitation of the patient with sepsis. A nuanced understanding of the physiology of lactate is provided in our review. Physiologic and practical knowledge of steroid and vasopressor therapy for sepsis is crucial and addressed. As blood purification may interest the nephrologist treating sepsis, we have also added a brief discussion of its status.

Effects of Compliance With the Early Management Bundle (SEP-1) on Mortality Changes Among Medicare Beneficiaries With Sepsis: A Propensity Score Matched Cohort Study.

BACKGROUND: US hospitals have reported compliance with the SEP-1 quality measure to Medicare since 2015. Finding an association between compliance and outcomes is essential to gauge measure effectiveness. RESEARCH QUESTION: What is the association between compliance with SEP-1 and 30-day mortality among Medicare beneficiaries? STUDY DESIGN AND METHODS: Studying patient-level data reported to Medicare by 3,241 hospitals from October 1, 2015, to March 31, 2017, we used propensity score matching and a hierarchical general linear model (HGLM) to estimate the treatment effects associated with compliance with SEP-1. Compliance was defined as completion of all qualifying SEP-1 elements including lactate measurements, blood culture collection, broad-spectrum antibiotic administration, 30 mL/kg crystalloid fluid administration, application of vasopressors, and patient reassessment. The primary outcome was a change in 30-day mortality. Secondary outcomes included changes in length of stay. RESULTS: We completed two matches to evaluate population-level treatment effects. In standard match, 122,870 patients whose care was compliant were matched with the same number whose care was noncompliant. Compliance was associated with a reduction in 30-day mortality (21.81% vs 27.48%, respectively), yielding an absolute risk reduction (ARR) of 5.67% (95% CI, 5.33-6.00; P < .001). In stringent match, 107,016 patients whose care was compliant were matched with the same number whose care was noncompliant. Compliance was associated with a reduction in 30-day mortality (22.22% vs 26.28%, respectively), yielding an ARR of 4.06% (95% CI, 3.70-4.41; P < .001). At the subject level, our HGLM found compliance associated with lower 30-day risk-adjusted mortality (adjusted conditional OR, 0.829; 95% CI, 0.812-0.846; P < .001). Multiple elements correlated with lower mortality. Median length of stay was shorter among cases whose care was compliant (5 vs 6 days; interquartile range, 3-9 vs 4-10, respectively; P < .001). INTERPRETATION: Compliance with SEP-1 was associated with lower 30-day mortality. Rendering SEP-1 compliant care may reduce the incidence of avoidable deaths.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids.

PURPOSE: Coronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time. METHODS: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment. FINDINGS: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04). IMPLICATIONS: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

Safety and Feasibility of a Novel Protocol for Percutaneous Dilatational Tracheostomy in Patients with Respiratory Failure due to COVID-19 Infection: A Single Center Experience.

INTRODUCTION: The rapidly spreading Novel Coronavirus 2019 (COVID-19) appeared to be a highly transmissible pathogen in healthcare environments and had resulted in a significant number of patients with respiratory failure requiring tracheostomy, an aerosol-generating procedure that places healthcare workers at high risk of contracting the infection. Instead of deferring or delaying the procedure, we developed and implemented a novel percutaneous dilatational tracheostomy (PDT) protocol aimed at minimizing the risk of transmission while maintaining favorable procedural outcome. Patients and Methods. All patients who underwent PDT per novel protocol were included in the study. The key element of the protocol was the use of apnea during the critical part of the insertion and upon any opening of the ventilator circuit. This was coupled with the use of enhanced personnel protection equipment (PPE) with a powered air-purifying respirator (PAPR). The operators underwent antibody serology testing and were evaluated for COVID-19 symptoms two weeks from the last procedure included in the study. RESULTS: Between March 12th and June 30th, 2020, a total of 32 patients underwent PDT per novel protocol. The majority (80%) were positive for COVID-19 at the time of the procedure. The success rate was 94%. Only one patient developed minor self-limited bleeding. None of the proceduralists developed positive serology or any symptoms compatible with COVID-19 infection. CONCLUSION: A novel protocol that uses periods of apnea during opening of the ventilator circuit along with PAPR-enhanced PPE for PDT on COVID-19 patients appears to be effective and safe for patients and healthcare providers.

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?-The IDENTIFY Trial.

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11-0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial.

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study.

BACKGROUND: Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. OBJECTIVE: The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. METHODS: Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care-III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). RESULTS: The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). CONCLUSIONS: This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

BACKGROUND: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. METHODS: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020. RESULTS: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05). CONCLUSIONS: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.

The Association of Increasing Hospice Use With Decreasing Hospital Mortality: An Analysis of the National Inpatient Sample.

EXECUTIVE SUMMARY: Usage of hospice services for patients facing life-limiting illness has steadily increased. In these services, hospitals discharge patients to various hospice settings, including the inpatient model, where a patient may remain in the discharging hospital to receive hospice services. In this discharge practice, the patient is considered a hospital survivor and subsequent hospice death. The purpose of the study was to determine if the decline of in-hospital mortality for six common high-volume admission diagnoses could be attributed in part to an increase in discharges to a hospice setting for end-of-life care. In this retrospective study using the National Inpatient Sample database from 2007 to 2011, we identified patients ≥18 years for six acute and chronic diagnoses: heart failure, chronic obstructive pulmonary disease, acute myocardial infarction, acute myocardial infarction with cardiogenic shock, septic shock, and lung neoplasm (cancer). We categorized patients according to their hospital discharge disposition as hospice or in-hospital mortality. A total of 10,458,728 patients met our criteria, of which 2.72% were discharged to hospice and 6.38% died. Compared to patients who died in the hospital, hospice patients were older, had a shorter length of stay, and experienced more comorbidities. Hospice use was more common in Medicare patients, in nonteaching hospitals, and in the South. White individuals were more likely to be discharged to hospice compared to nonwhites. Among the six selected diagnoses over the 5-year period, hospice use rose as observed mortality decreased. Our findings suggest that variability among hospitals in hospice use will affect benchmarked hospital mortality comparisons and could inappropriately reward or penalize hospitals in their public reporting.

The initial resuscitation of septic shock.

Septic shock is the most severe form of sepsis, characterized by (a) persistent hypotension despite fluid resuscitation and (b) the presence of tissue hypoperfusion. Delays in the diagnosis and initiation of treatment of septic shock is associated with increasing risk for mortality. Early and effective fluid resuscitation and vasopressor administration play a crucial role in maintaining tissue perfusion in septic shock patients. A low diastolic arterial pressure (DAP) correlates with severity of arteriolar vasodilation, compromises left ventricle oxygen supply and can be used for identifying septic shock patients that would potentially benefit from earlier vasopressor therapy. Controversy currently exists as to the balance of fluids and vasopressors to maintain target mean arterial pressure. The aim of this article is to review the rationale for fluid resuscitation and vasopressor therapy and the importance of both mean and diastolic blood pressure during the initial resuscitation of the septic shock. We relate our personal prescription of balancing fluids and vasopressors in the resuscitation of septic shock.