Differential Effect of PEEP Strategies in ARDS Patients: A Bayesian Analysis of Clinical Subphenotypes

BACKGROUND

Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored.

RESEARCH QUESTION

Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS?

STUDY DESIGN AND METHODS

We evaluated data from two prior ARDS trials (ALVEOLI and ART) that compared different PEEP strategies. We classified patients into one of two subphenotypes as previously described. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality.

RESULTS

We analyzed data from 1559 ARDS patients. Compared to lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in subphenotype A patients in ALVEOLI (OR, 1.61 [95% CrI 0.90 to 2.94]) and ART (OR 1.73 [ 95% CrI 1.01 to 2.98]), with a probability of harm from higher PEEP in this subphenotype of 94.3% and 97.7% in ALVEOLI and ART, respectively. Higher PEEP was not associated with mortality in subphenotype B patients in each trial (OR, 0.95 [95% CrI, 0.51 to 1.73]) and (OR, 1.00 [95% CrI 0.63 to 1.55]); probability of benefit of 56.4% and 50.7% in ALVEOLI and ART, respectively. These effects were not modified by PaO2/FiO2 ratio, driving pressure, or the severity of illness for the cohorts.

INTERPRETATION

We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials.

Insights into Treatment Alternatives for Neurosyphilis: Systematic Literature Review and Meta-Analysis

ABSTRACT

We conducted a systematic literature review and meta-analysis to assess the efficacy of alternative treatments for neurosyphilis. We searched MEDLINE, CINAHL, Embase, Cochrane, Scopus, and Web of Science from database inception to September, 2023, for studies in neurosyphilis that compared penicillin monotherapy to other treatments. We focused on the impact of these therapies on treatment response, but also assessed data regarding reinfection and adverse drug events. Random-effect models were used to obtain pooled mean differences. Of 3,415 screened studies, six met the inclusion criteria for the systematic literature review. Three studies provided quantitative data that allowed for inclusion in the meta-analysis. Our analysis revealed that the efficacy of intravenous ceftriaxone 2 g daily for 10 days (51 patients) did not appear statistically different compared to intravenous penicillin G 18-24 million units daily for 10 days (185 patients) for neurosyphilis (pooled OR, 2.85; 95% CI, 0.41-19.56; I2 = 49%). No statistical difference between ceftriaxone and penicillin was identified in people living with HIV (pooled OR, 4.51; 95% CI, 0.50-40.49; I2 = 34%). We concluded that alternative therapy with IV ceftriaxone appears similar to penicillin, potentially expanding treatment options for neurosyphilis. Other treatment options including doxycycline warrant further study.

Syphilis Treatment: Systematic Review and Meta-Analysis Investigating Nonpenicillin Therapeutic Strategies

Background

Penicillin's long-standing role as the reference standard in syphilis treatment has led to global reliance. However, this dependence presents challenges, prompting the need for alternative strategies. We performed a systematic literature review and meta-analysis to evaluate the efficacy of these alternative treatments against nonneurological syphilis.

Methods

We searched MEDLINE, the Cumulative Index to Nursing and Allied Health Literature, Embase, Cochrane, Scopus, and Web of Science from database inception to 28 August 2023, and we included studies that compared penicillin or amoxicillin monotherapy to other treatments for the management of nonneurological syphilis. Our primary outcome was serological cure rates. Random-effect models were used to obtain pooled mean differences, and heterogeneity was assessed using the I2 test.

Results

Of 6478 screened studies, 27 met the inclusion criteria, summing 6710 patients. The studies were considerably homogeneous, and stratified analyses considering each alternative treatment separately revealed that penicillin monotherapy did not outperform ceftriaxone (pooled odds ratio, 1.66 [95% confidence interval, .97-2.84]; I2 = 0%), azithromycin (0.92; [.73-1.18]; I2 = 0%), or doxycycline (0.82 [.61-1.10]; I2 = 1%) monotherapies with respect to serological conversion.

Conclusions

Alternative treatment strategies have serological cure rates equivalent to penicillin, potentially reducing global dependence on this antibiotic.

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.

Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock

PURPOSE

Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock.

MATERIALS AND METHODS

We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock.

RESULTS

We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed.

CONCLUSIONS

Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.

Identification of acute respiratory distress syndrome subphenotypes de novo using routine clinical data: a retrospective analysis of ARDS clinical trials

OBJECTIVES

The acute respiratory distress syndrome (ARDS) is a heterogeneous condition, and identification of subphenotypes may help in better risk stratification. Our study objective is to identify ARDS subphenotypes using new simpler methodology and readily available clinical variables.

SETTING

This is a retrospective Cohort Study of ARDS trials. Data from the US ARDSNet trials and from the international ART trial.

PARTICIPANTS

3763 patients from ARDSNet data sets and 1010 patients from the ART data set.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was 60-day or 28-day mortality, depending on what was reported in the original trial. K-means cluster analysis was performed to identify subgroups. Sets of candidate variables were tested to assess their ability to produce different probabilities for mortality in each cluster. Clusters were compared with biomarker data, allowing identification of subphenotypes.

RESULTS

Data from 4773 patients were analysed. Two subphenotypes (A and B) resulted in optimal separation in the final model, which included nine routinely collected clinical variables, namely heart rate, mean arterial pressure, respiratory rate, bilirubin, bicarbonate, creatinine, PaO₂, arterial pH and FiO₂. Participants in subphenotype B showed increased levels of proinflammatory markers, had consistently higher mortality, lower number of ventilator-free days at day 28 and longer duration of ventilation compared with patients in the subphenotype A.

CONCLUSIONS

Routinely available clinical data can successfully identify two distinct subphenotypes in adult ARDS patients. This work may facilitate implementation of precision therapy in ARDS clinical trials.

Prediction on critically ill patients: The role of "big data"

Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces.

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.

SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries

OBJECTIVE

Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC.

SETTING

Two intensive care units, one private and one public, from São Paulo, Brazil PATIENTS: An ICU for the first time.

INTERVENTIONS

None.

MEASUREMENTS AND MAINS RESULTS

The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 - 0.86) and standardized mortality ratio of 1.00 (0.91-1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM.

CONCLUSIONS

Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.

Clinical Note Creation, Binning, and Artificial Intelligence

The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.

Physician satisfaction with a multi-platform digital scheduling system

Objective

Physician shift schedules are regularly created manually, using paper or a shared online spreadsheet. Mistakes are not unusual, leading to last minute scrambles to cover a shift. We developed a web-based shift scheduling system and a mobile application tool to facilitate both the monthly scheduling and shift exchanges between physicians. The primary objective was to compare physician satisfaction before and after the mobile application implementation.

Methods

Over a 9-month period, three surveys, using the 4-point Likert type scale were performed to assess the physician satisfaction. The first survey was conducted three months prior mobile application release, a second survey three months after implementation and the last survey six months after.

Results

51 (77%) of the physicians answered the baseline survey. Of those, 32 (63%) were males with a mean age of 37.8 ± 5.5 years. Prior to the mobile application implementation, 36 (70%) of the responders were using more than one method to carry out shift exchanges and only 20 (40%) were using the official department report sheet to document shift exchanges. The second and third survey were answered by 48 (73%) physicians. Forty-eight (98%) of them found the mobile application easy or very easy to install and 47 (96%) did not want to go back to the previous method. Regarding physician satisfaction, at baseline 37% of the physicians were unsatisfied or very unsatisfied with shift scheduling. After the mobile application was implementation, only 4% reported being unsatisfied (OR = 0.11, p < 0.001). The satisfaction level improved from 63% to 96% between the first and the last survey. Satisfaction levels significantly increased between the three time points (OR = 13.33, p < 0.001).

Conclusion

Our web and mobile phone-based scheduling system resulted in better physician satisfaction.