Predictors of early and long-term mortality after ICU discharge in critically ill COVID-19 patients: A prospective cohort study

BACKGROUND

To mitigate mortality among critically ill COVID-19 patients, both during their Intensive Care Unit (ICU) stay and following ICU discharge, it is crucial to measure its frequency, identify predictors and to establish an appropriate post-ICU follow-up strategy.

METHODS

In this multicentre, prospective cohort study, we included 586 critically ill COVID-19 patients.

RESULTS

We observed an overall ICU mortality of 20.1% [95%CI: 17.1% to 23.6%] (118/586) and an overall hospital mortality of 25.4% [95%CI: 22.1% to 29.1%] (149/586). For ICU survivors, 30 days (early) post-ICU mortality was 5.3% [95%CI: 3.6% to 7.8%] (25/468) and one-year (late) post-ICU mortality was 7.9% [95%CI: 5.8% to 10.8%] (37/468). Pre-existing conditions/comorbidities were identified as the main independent predictors of mortality after ICU discharge: hypertension and heart failure were independent predictors of early mortality; and hypertension, chronic kidney disease, chronic obstructive pulmonary disease and cancer were independent predictors of late mortality.

CONCLUSION

Early and late post-ICU mortality exhibited an initial surge (in the first 30 days post-ICU) followed by a subsequent decline over time. Close monitoring of critically ill COVID-19 post-ICU survivors, especially those with pre-existing conditions, is crucial to prevent adverse outcomes, reduce mortality and to establish an appropriate follow-up strategy.

Meta-analysis of STAT4 and IFIH1 polymorphisms in type 1 diabetes mellitus patients with autoimmune polyglandular syndrome type III

Type 1 diabetes mellitus (T1D) is an organ-specific autoimmune disease characterized by T-cell mediated self-destruction of insulin-producing β cells in the pancreas. T1D patients are prone to develop other glandular autoimmune disorders, such as autoimmune thyroid disease that occurs simultaneously with autoimmune polyglandular syndrome type III (APSIII). Signal transducer and activator of transcription 4 (STAT4) is a well-known regulator of proinflammatory cytokines, and interferon-induced with helicase C domain 1 (IFIH1) is activated in the interferon type I response. Both genes have been examined separately in autoimmune diseases and, in this study, we assessed their joint role in T1D and APSIII. We conducted a case-control study, enrolling 173 T1D patients and 191 healthy controls from northeastern Brazil, to assess the distribution of the rs7574865 and rs3024839 SNPs in STAT4 and the rs3747517 and rs1990760 SNPs in IFIH1 in T1D and APSIII patients. Additionally, we conducted a meta-analysis with the rs7574865 SNP in STAT4 (1392 T1D patients and 1629 controls) and the rs1990760 SNP in IFIH1 (25092 T1D patients and 28544 controls) to examine their association with T1D. Distribution of STAT4 and IFIH1 allelic frequencies did not show statistically significant differences between T1D patients and controls in our study population; however, the meta-analysis indicated that SNPs in STAT4 and IFIH1 are associated with T1D worldwide. Our findings indicate that although STAT4 and IFIH1 SNPs are not associated with T1D in a Brazilian population, they might play a role in susceptibility to T1D on a larger worldwide scale.

Class distinction between follicular adenomas and follicular carcinomas of the thyroid gland on the basis of their signature expression

BACKGROUND

Nodules of the thyroid gland are observed frequently in patients who undergo ultrasound studies. The majority of these nodules are benign, corresponding to goiters or adenomas, and only a small fraction corresponds to carcinomas. Among thyroid tumors, the diagnosis of follicular adenocarcinomas by preoperative fine-needle aspiration biopsy is a major challenge, because it requires inspection of the entire capsule to differentiate it from adenoma. Consequently, large numbers of patients undergo unnecessary thyroidectomy.

METHODS

Using data from gene expression analysis, the authors applied Fisher linear discriminant analysis and searched for expression signatures of individual samples of adenomas and follicular carcinomas that could be used as molecular classifiers for the precise classification of malignant and nonmalignant lesions.

RESULTS

Fourteen trios of genes were described that fulfilled the criteria for the correct classification of 100% of samples. The robustness of these trios was verified by using leave-1-out cross-validation and bootstrap analyses. The results demonstrated that, by combining trios, better classifiers could be generated that correctly classified >92% of samples.

CONCLUSIONS

The strategy of classifiers based on individual signatures was a useful strategy for distinguishing between samples with very similar expression profiles.