intensive care and critical care medicine papers

Intensive and critical care medicine focuses on the most vital moments in healthcare, where specialized teams monitor and support patients with life-threatening conditions. This field bridges the gap between emergency intervention and recovery, utilizing advanced technology to manage organ failure, severe infections, and complex trauma. It is a dynamic landscape where split-second decisions can determine survival, constantly evolving through rigorous research and real-world clinical experience.

Gist.Science tracks the latest developments in this critical arena by processing every new preprint from medRxiv as soon as it is published. We transform these raw scientific reports into both plain-language explanations for general readers and detailed technical summaries for medical professionals, ensuring that groundbreaking findings are understood and accessible immediately. Below are the latest papers in intensive care and critical care medicine, curated to keep you informed of the newest insights shaping patient survival and recovery.

📄 intensive care and critical care medicine

Development of an Intelligent Predictive Indicator System for Weaning and Extubation Timing in Mechanically Ventilated Patients Based on the Delphi Method

This study utilized the Delphi method to develop a comprehensive, expert-validated intelligent predictive indicator system comprising six primary and 58 secondary indicators to guide the timing of weaning and extubation for mechanically ventilated patients, thereby establishing a robust framework for clinical decision support and intelligent assessment in critical care.

Li, P., Wang, Y., Zhang, Y., Meng, X., Zhang, H.2026-08-04

📄 intensive care and critical care medicine

First-24-hour machine learning for 30-day mortality prediction in ICU trauma patients: development in MIMIC-III and cross-database evaluation in MIMIC-IV

This study developed and validated an XGBoost model using first-24-hour clinical data from MIMIC-III to predict 30-day mortality in ICU trauma patients, demonstrating robust cross-database performance in MIMIC-IV while highlighting limitations in generalizability due to internal selection biases and feature mapping inconsistencies.

Kudrot, N., Si, Y., Sanjaya, J., Pathak, S., Haghi, M., Alaei, K., Placencia, G., Pishgar, M.2026-07-30

📄 intensive care and critical care medicine

Comparative Evaluation of Central Venous Oxygen Saturation, Carbon Dioxide Venous Arterial Gradient, and Lactate Levels as Markers of Tissue Perfusion After Cardiac Surgery: A Prospective Exploratory Observational Study

In a prospective exploratory study of 100 cardiac surgery patients, central venous oxygen saturation (ScvO2) measured 24 hours post-admission demonstrated superior discriminatory performance and a significant association with early ICU discharge compared to lactate levels and venous-arterial carbon dioxide gradient, though these hypothesis-generating findings require further validation.

Neves, J. K., Venturini, V., Zeferino, S., Galas, F. R. B. G., Auler Junior, J.2026-07-10

📄 intensive care and critical care medicine

Is simple better? Comparing Computational Cost and Carbon Impact of Machine Learning Models for Traumatic Brain Injury Prediction; A Case Study for Sustainable Digital Health Implementation

This study demonstrates that for traumatic brain injury prediction, simpler, resource-efficient machine learning models often achieve comparable clinical utility to complex, data-intensive alternatives while significantly reducing computational costs, carbon emissions, and deployment barriers, thereby supporting more sustainable and accessible digital health implementations.

Gauss, T., Delude, T. F., Kalimouttou, A., Seddiki, O., Sanchez, C., Greze, J., Brossard, C., Moyer, J.-D., Brelurut, G. (…)2026-07-08

📄 intensive care and critical care medicine

Temporal Feature Engineering and Ensemble Learning for Predicting 28-Day Mortality in ICU Patients with Alcoholic Cirrhosis

This study develops and validates a high-performing, interpretable ensemble learning model that leverages temporal feature engineering on MIMIC-IV data to accurately predict 28-day mortality in ICU patients with alcoholic cirrhosis, demonstrating superior generalizability across external datasets and highlighting the critical importance of dynamic clinical trajectories.

Sanjaya, J., Haghi, M., Kudrot, N., Pathak, S., Chandramouli, S. V., Alaei, K., Pishgar, M.2026-07-02

📄 intensive care and critical care medicine

Antifungal use with and without fungal diagnoses in septic shock across U.S. hospitals, 2022-2024

This study of nearly 555,000 septic shock admissions in U.S. hospitals reveals a significant disconnect in antifungal management, characterized by the widespread empiric use of antifungals in patients without confirmed fungal infections and the frequent delay in treating those with culture-confirmed candidemia.

Flick, R. J., Yan, L., Law, A. C., Hochberg, C., Levy, J., Iwashyna, T. J., Bosch, N. A.2026-06-30

📄 intensive care and critical care medicine

Accounting for uncertainty in the expected treatment effect substantially increases the sample size required for randomised trials: implications for the feasibility of clinical trials in anaesthesia and critical care

This study demonstrates that incorporating uncertainty regarding the expected treatment effect into sample size calculations for anaesthesia and critical care trials substantially increases the required participant numbers compared to conventional methods, thereby challenging the feasibility of many proposed randomized trials.

Sidebotham, D., Barlow, J.2026-06-22

📄 intensive care and critical care medicine

Development and validation of a dynamic risk stratification tool for predicting multidrug-resistant bacterial infections in ICU patients: A clinical prediction model and web-based calculator

This study developed and validated a dynamic, interpretable web-based calculator using five routinely collected clinical indicators to accurately predict multidrug-resistant bacterial infections in ICU patients, thereby facilitating real-time risk stratification and targeted antimicrobial stewardship.

Ye, L., Lyu, B., Yang, Q., Mou, X., Nawawonganun, R., Laohasiriwong, W.2026-05-26

📄 intensive care and critical care medicine

Pre-admission polypharmacy burden and intensive care unit outcomes in patients with sepsis: A retrospective cohort study using the MIMIC-IV-ED linked database

This retrospective cohort study utilizing the MIMIC-IV-ED database demonstrates that high pre-admission polypharmacy (≥10 medications) serves as an independent predictor of increased ICU and 28-day mortality in sepsis patients, offering immediate prognostic value and highlighting specific high-risk drug interactions for targeted clinical intervention.

Haque, F., Hasan, M.2026-05-15

📄 intensive care and critical care medicine

Multicohort development and validation of a machine learning model to predict six-month functional traumatic brain injury outcomes in a large national registry

This study developed and validated a random forest machine learning model using data from two clinical trials to accurately predict six-month functional outcomes for moderate-to-severe traumatic brain injury patients, subsequently applying it to a large national registry to estimate population-level recovery patterns despite the registry's lack of systematic follow-up.

Vattipally, V. N., Jillala, R. R., Kramer, P., Elshareif, M., Singh, S., Jo, J., Suarez, J. I., Sakran, J. V., Haut, E. (…)2026-04-27