American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Aug 2021
Multicenter Study Observational StudyLong-Term Outcomes in Intensive Care Unit Patients with Delirium: A Population-Based Cohort Study.
Rationale: Delirium is common in the ICU and portends worse ICU and hospital outcomes. The effect of delirium in the ICU on post-hospital discharge mortality and health resource use is less well known. Objectives: To estimate mortality and health resource use 2.5 years after hospital discharge in critically ill patients admitted to the ICU. ⋯ There was no significant difference in mortality more than 30 days after hospital discharge (delirium: 3.9%, no delirium: 2.6%). There was a persistent increased risk of emergency department visits, hospital readmissions, or mortality after hospital discharge (hazard ratio, 1.12 [95% confidence interval, 1.07-1.17]) throughout the study period. Conclusions: ICU delirium is associated with increased mortality 0-30 days after hospital discharge.
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Am. J. Respir. Crit. Care Med. · Aug 2021
Mortality from Pulmonary Hypertension in the Pediatric Cardiac Intensive Care Unit.
Rationale: Patients with pulmonary hypertension (PH) admitted to pediatric cardiac ICUs are at high risk of mortality. Objectives: To identify factors associated with mortality in cardiac critical care admissions with PH. Methods: We evaluated medical admissions with PH to Pediatric Cardiac Critical Care Consortium institutions over 5 years. ⋯ Conclusions: Patients with PH admitted to pediatric cardiac critical care units have high mortality rates. Those receiving invasive ventilation and vasoactive infusions on Day 1 or Day 2 had an observed mortality rate that was more than fivefold greater than that of those who did not. These data highlight the illness severity of patients with PH in this setting and could help inform conversations with families regarding the prognosis.
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Am. J. Respir. Crit. Care Med. · Aug 2021
Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data.
Rationale: Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives: To develop a model to predict a future diagnosis of lung cancer on the basis of routine clinical and laboratory data by using machine learning. ⋯ The machine learning model was more accurate than standard eligibility criteria for lung cancer screening and more accurate than the mPLCOm2012 when applied to a screening-eligible population. Influential model variables included known risk factors and novel predictors such as white blood cell and platelet counts. Conclusions: A machine learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the mPLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.