We investigated the role of mesothelin (Msln) and thymocyte differentiation antigen 1 (Thy1) in the activation of fibroblasts across multiple organs and demonstrated that Msln^-/- mice are protected from cholestatic fibrosis caused by Mdr2 (multidrug resistance gene 2) deficiency, bleomycin-induced lung fibrosis, and UUO (unilateral urinary obstruction)-induced kidney fibrosis. On the contrary, Thy1^-/- mice are more susceptible to fibrosis, suggesting that a Msln-Thy1 signaling complex is critical for tissue fibroblast activation. A similar mechanism was observed in human activated portal fibroblasts (aPFs). Targeting of human MSLN⁺ aPFs with two anti-MSLN immunotoxins killed fibroblasts engineered to express human mesothelin and reduced collagen deposition in livers of bile duct ligation (BDL)-injured mice. We provide evidence that antimesothelin-based therapy may be a strategy for treatment of parenchymal organ fibrosis.

Background

Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

Research question

Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance?

Study design and methods

We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio₂, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value.

Results

We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.

Interpretation

A transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.