A 40 años de la descripción del índice de Kirby (PaO2/FiO2)

Sandoval-Gutiérrez, José Luis

doi:10.1016/j.medin.2015.06.003

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…En febrero de 1975 Kirby y colaboradores (Kirby et al, 1975) describieron la utilidad de los niveles altos de presión positiva al final de la espiración (PEEP) en la insuficiencia respiratoria aguda, presentando 28 pacientes con diversas patologías donde se midieron los cortocircuitos intrapulmonares y la diferencia arteriovenosa, el gasto cardíaco y la PaO2/FiO2. Con este índice se puede estimar el cortocircuito si se presume un gasto cardíaco normal y una diferencia arteriovenosa de contenido de oxígeno normal (Sandoval-Gutiérrez, 2015).…”

Section: Monitorización Del Intercambio Gaseoso Y La Ventilaciónunclassified

Respiratory Mechanics

Jian-xin

Yang

et al. 2021

Respiratory Monitoring in Mechanical Ventilation

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SIGLAS ABREVIATURAS C. vital Capacidad vital C.S. respiratorio Compliance del sistema respiratorio CaO2 Concentración arterial de oxígeno CcO2 Contenido capilar de oxígeno CO2 Dióxido de carbono COVID-19 Enfermedad causada por el coronavirus 2019 (por sus siglas en inglés) Cpulmón "Compliance" o distensibilidad del pulmón CRF Capacidad residual funcional CvO2 Contenido venoso de oxígeno Cw Caja torácica por sus silgas en inglés

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Section: Monitorización Del Intercambio Gaseoso Y La Ventilaciónunclassified

Respiratory Mechanics

Jian-xin

Yang

et al. 2021

Respiratory Monitoring in Mechanical Ventilation

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“…/ FiO2> 300 mmHg [17]. Subsequent studies have shown a higher mortality the lower the index, for example, PaO2 / FiO2 <100 mmHg [18][19][20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Clinical characteristics of COVID-19 patients admitted to Intensive Care Unit in Panama during the first pandemic wave admissions in 2020

Blizniak

Ballanis

Cisneros

et al. 2022

Preprint

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The severe acute respiratory syndrome Coronavirus 2 (SARS COV-2) caused a global pandemic of COVID-19. Most of people affected are admitted to hospital with various grades of ADRS. A small proportion of these patients requires intensive care unit management and treatment. However not all of them survive. This study aims to describe the epidemiological and clinical characteristics of patients admitted to the intensive care units in Panama main hospital in the first six months of pandemic with available information. Special focus has been oriented to blood and respiratory biomarkers to correlate with survivors and non-survivors. Our results show that patients between 56-75 years old, with hypertension, obesity, and diabetes comorbid conditions are more likely to die in intensive care units. Regarding the PaFi ratio, we observed a greater proportion of non-survivor with values less than 200. The triglycerides, urea nitrogen, creatinine and procalcitonin levels resulted significantly higher in those non survivors. During clinical management, half of patient that were administered Tocilizumab did not survived. These results support the notion that age, comorbidities as well as therapeutic management of patient in intensive care units contribute to the final outcome. We recommend reinforcing patient care strategy, especially in those patients with clinical conditions that favor fatal outcomes.

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“…Other scales were specifically created for COVID-19, such as Viral Pneumonia Mortality Score (MuLBSTA: multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) and COVID-Guangzhou Institute of Respiratory Health Calculator at Admission (GRAM) (16,17). The Kirby Index (PaO 2 /FiO 2 , arterial oxygen pressure/fraction of inspired oxygen) is a tool used to measure lung capacity and functionality, particularly for diagnosing and prognosticating the severity of acute respiratory distress syndrome (18,19). The liver fibrosis index (FIB-4) is another scale that is worth studying, because previous studies showed that it has promising predictive power for mortality rate in patients with COVID-19, without underlying liver disease and in all age groups (20,21).…”

Section: Time-dependent Roc Curve Analysis To Determine the Predictiv...mentioning

confidence: 99%

“…Additionally, they have been previously validated in similar patient populations and have shown promising results in predicting outcomes in patients with COVID-19. Furthermore, the effectiveness of these predictive tools relies on the availability of relevant data types, including clinical observations, laboratory results, and patient demographics (15,16,18,20,(27)(28)(29). The present study aimed to identify severity indices maintaining consistent predictive capacity in patients with fluctuating health status, such as those hospitalized with COVID-19, within a cohort exhibiting one of the highest mortality rates globally.…”

Section: Time-dependent Roc Curve Analysis To Determine the Predictiv...mentioning

confidence: 99%

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

Mendoza‑Hernandez,

Hernandez‑Fuentes,

Sanchez‑Ramirez

et al. 2024

Biomed Rep

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Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO 2 /FiO 2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P= 0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

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A 40 años de la descripción del índice de Kirby (PaO2/FiO2)

Cited by 5 publications

References 4 publications

Respiratory Mechanics

Respiratory Mechanics

Clinical characteristics of COVID-19 patients admitted to Intensive Care Unit in Panama during the first pandemic wave admissions in 2020

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

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