Vaccination impact: mortality and time shift to COVID-19 maximum severity in hospitalized patients. An Argentine multicenter registry

INTRODUCTION

The COVID-19 vaccine became an effective instrument to prevent severe SARS-CoV-2 infections. However, 5% of vaccinated patients will have moderate or severe disease.

OBJECTIVE

to compare mortality and days between the symptom onset to the peak disease severity, in vaccinated vs. unvaccinated COVID-19 hospitalized patients.

METHODS

Retrospective observational study in 36 hospitals in Argentina. COVID-19 adults admitted to general wards between January 1, 2021, and May 31, 2022 were included. Days between symptoms onset to peak of severity were compared between vaccinated vs. unvaccinated patients with Cox regression, adjusted by Propensity Score Matching (PSM). Results in patients with one and two doses were also compared.

RESULTS

A total of 3663 patients were included (3001 [81.9%] unvaccinated and 662 [18%] vaccinated). Time from symptom onset to peak severity was 7 days (IQR 4-12) vs. 7 days (IQR 4-11) in unvaccinated and vaccinated. In crude Cox regression analysis and matched population, no significant differences were observed. Regarding mortality, a Risk Ratio (RR) of 1.51 (IC95% 1.29-1.77) was observed in vaccinated patients, but in the PSM cohort, the RR was 0.73 (IC95% 0.60-0.88). RR in patients with one COVID-19 vaccine dose in PSM adjusted population was 0.7 (IC95% 0.45-1.03), and with two doses 0.6 (IC95% 0.46-0.79).

DISCUSSION

The time elapsed between the onset of COVID-19 symptoms to the highest severity was similar in vaccinated and unvaccinated patients. However, hospitalized vaccinated patients had a lower risk of mortality than unvaccinated patients.

Clinical Research With Large Language Models Generated Writing-Clinical Research with AI-assisted Writing (CRAW) Study

IMPORTANCE

The scientific community debates Generative Pre-trained Transformer (GPT)-3.5's article quality, authorship merit, originality, and ethical use in scientific writing.

OBJECTIVES

Assess GPT-3.5's ability to craft the background section of critical care clinical research questions compared to medical researchers with H-indices of 22 and 13.

DESIGN

Observational cross-sectional study.

SETTING

Researchers from 20 countries from six continents evaluated the backgrounds.

PARTICIPANTS

Researchers with a Scopus index greater than 1 were included.

MAIN OUTCOMES AND MEASURES

In this study, we generated a background section of a critical care clinical research question on "acute kidney injury in sepsis" using three different methods: researcher with H-index greater than 20, researcher with H-index greater than 10, and GPT-3.5. The three background sections were presented in a blinded survey to researchers with an H-index range between 1 and 96. First, the researchers evaluated the main components of the background using a 5-point Likert scale. Second, they were asked to identify which background was written by humans only or with large language model-generated tools.

RESULTS

A total of 80 researchers completed the survey. The median H-index was 3 (interquartile range, 1-7.25) and most (36%) researchers were from the Critical Care specialty. When compared with researchers with an H-index of 22 and 13, GPT-3.5 was marked high on the Likert scale ranking on main background components (median 4.5 vs. 3.82 vs. 3.6 vs. 4.5, respectively; p < 0.001). The sensitivity and specificity to detect researchers writing versus GPT-3.5 writing were poor, 22.4% and 57.6%, respectively.

CONCLUSIONS AND RELEVANCE

GPT-3.5 could create background research content indistinguishable from the writing of a medical researcher. It was marked higher compared with medical researchers with an H-index of 22 and 13 in writing the background section of a critical care clinical research question.

Risk of Volar Locking Plate Removal After Distal Radius Fractures: Time-to-Event Analysis

PURPOSE

The primary purpose of this study was to describe the rate of volar locking plate (VLP) removal after distal radius fracture and how long it takes for the risk of VLP removal to stabilize. The secondary purpose was to describe the reasons for VLP removal and analyze the relationship between it and the Soong index.

METHODS

This was a single-center retrospective cohort study. Patients aged >18 years with distal radius fracture who underwent VLP fixation were included. Hardware removal, time until VLP removal, and the primary reason for removal were recorded. The implant prominence was measured as described by Soong. We used Kaplan-Meier curves and risk tables to describe the risk of VLP removal and variation over time. Multivariable logistic regression was used to assess the relationship between Soong grade and VLP removal.

RESULTS

A total of 313 wrists were included. There were 35 cases of VLP removal, with an overall incidence of 11.2% at 15 years of follow-up. The incidence rate was 1.2 per 100 individuals per year for the entire cohort. The risk of VLP removal decreased from 6.2% in the first postoperative year to 1.7% in the second year and 1.4% in the third year. Beyond that, the rate remained <1% per year throughout the follow-up period. The median hardware removal time was 11 months. The main reasons for VLP removal were tenosynovitis, implant-associated pain, and screw protrusion. We found no association between Soong grade and VLP removal.

CONCLUSIONS

Volar locking plate removal after distal radius fracture was more common in the first year after surgery and remained notable until the third year. Regular monitoring and patient education to assess possible complications related to hardware are important during this period.

TYPE OF STUDY/LEVEL OF EVIDENCE

Therapeutic IV.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Background

The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.

Methods

We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.

Results

The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients.

Conclusions

The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.

Funding

University of Vienna.

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.