Discriminant Accuracy of the SOFA Score for Determining the Probable Mortality of Patients With COVID-19 Pneumonia Requiring Mechanical Ventilation

Raschke, Robert; Agarwal, Sumit; Rangan, Pooja; Heise, C. William; Curry, Steven C.

doi:10.1001/jama.2021.1545

Cited by 153 publications

(178 citation statements)

References 6 publications

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“…Dentro de las experiencias internacionales, a la fecha de publicación, no se encontraron resultados de unidades reconvertidas con presencia de VM con máquinas de anestesia. Son varios los artículos que hacen referencia a los desafíos de trasladar la UCI a otras dependencias; respecto a espacio, staff, equipamiento, manejo de infecciones y bioseguridad [1], [8], [10], [15], [16], desafíos que también tuvieron que ser resueltos en nuestro centro.…”

Section: Discussionunclassified

“…Dentro de los factores asociados a sobrevida encontramos que tanto la edad como el puntaje SOFA fueron estadísticamente significativos, con un HR = 1,1 (IC: 1,04-1,19) y HR = 1,4 (IC: 1,19-1,65) respectivamente. El artículo de Ratschke et al concluye que el SOFA no sería un buen predictor de mortalidad, siendo peor que la edad por sí solo como factor de riesgo, refiriendo entonces que no es un buen método para categorizar a los pacientes COVID-19 y determinar necesidad de ventilación mecánica [10]. Sin embargo, los outcomes medidos fueron distintos a los nuestros.…”

Section: Discussionunclassified

“…La FDA y el Ministerio de Salud chileno autorizaron el uso de máquinas de anestesia (MA) para ventilación mecánica (VM) prolongada dada la alta demanda de cuidados críticos derivados de la pandemia por COVID-19 [3], [4], [5], [6], [7]. En este mismo contexto resultó imperativa la conversión de unidades médicas atendidas por médicos no intensivistas para poder manejar y tratar a estos pacientes [8], [9], [10].…”

Section: Introductionunclassified

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Descripción y seguimiento de pacientes COVID-19 en ventilación mecánica en unidades convertidas dirigidas por anestesiólogos: Clínica Alemana de Santiago

Chuang¹,

V.²,

N.³

et al. 2021

Rev. chil. anest.

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Introduction: The high demand for critical care beds and the need for mechanical ventilation generated by the pandemia of COVID-19 led the Ministry of Health of Chile to authorize both the conversion of beds and the use of anesthesia machines to mechanically ventilate COVID-19 patients. At Clínica Alemana de Santiago (CAS), these converted units were managed by non-intensivist anesthesiologists with technical support from the Intensive Care Unit (ICU). The objective of this study was to compare the survival rate of patients managed at converted and traditional ICU units. The secondary objective was to analyze the factors that may affect morbidity and mortality of these patients. Method: This prospective observational study included mechanically ventilated COVID-19 patients in both traditional and converted units. Patients were followed for 90 days starting at intubation day. Survival rate was determined at 30 and 90 days. A survival analysis was then performed. Results: 41 and 42 patients were admitted to CAS trough converted and traditional ICU units, respectively, between May 24 and June 30, 2020. There was not significant difference in survival rate between converted and traditional ICU units. Age (HR 1.1 per year) and SOFA (HR 1.4 per point) were associated with survival. Conclusions: At CAS, the survival of COVID-19 patients who required mechanical ventilation in converted units was not different as in those patients treated in traditional ICU units. Both age and SOFA were variables that can inform about prognosis of these patients.

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Section: Discussionunclassified

Section: Introductionunclassified

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Descripción y seguimiento de pacientes COVID-19 en ventilación mecánica en unidades convertidas dirigidas por anestesiólogos: Clínica Alemana de Santiago

Chuang¹,

V.²,

N.³

et al. 2021

Rev. chil. anest.

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“…Different studies have been conducted to analyse the performance of pre-existing scores in predicting dismal outcomes, as NEWS, NEWS 2, MEWS, REMS, qSOFA, SOFA and CURB-65 [3,4,5,6,7,8,9,10], some of them showing good results while others enhanced that the pre-existing scoring systems underestimated the risks compared with pre-COVID-19 cohorts.…”

Section: Discussionmentioning

confidence: 99%

Performance of prognostic scoring systems in patients with COVID-19 interstitial pneumonia in Acute Medical Unit

Accordino

Cazzaniga²,

Sozzi

et al. 2021

Preprint

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Background: The early detection of COVID-19 patients with interstitial pneumonia at high risk of dismal outcome is necessary to deliver proper care and optimize management of limited resources. Objective: The aim of this study was to analyse the performance of pre-existing scores in predicting in-hospital mortality and ICU transfer at admission in an Acute Medical Unit. Methods: 106 consecutive patients with acute respiratory failure due to COVID-19 interstitial pneumoni admitted to Acute Medical Unit were enrolled. The performances of NEWS, SIRS, RAPS, REMS, qSOFA, APACHE II, CURB-65 and PSI were analysed by the Area Under the Receiver Operator Characteristic (AUROCs) and standard indices of accuracy. Results: Considering in-hospital mortality PSI and APACHE II had the higher AUROCs, 0.83 (95% CI 0.75-0.91) and 0.80 (95% CI 0.71-0.88), followed by REMS, 0.77 (95% CI 0.67-0.86), and CURB-65, 0.73 (95% CI 0.63-0.82), whereas the AUROCs of the other scores were < 0.7. PSI and APACHE II had good sensitivity (0.92 and 0.97), negative predictive value (0.96 and 0.97) and negative likelihood ratio (0.1 and 0.1), accurately identifying patients at low risk to die. However, the low specificity (0.70 and 0.47) and positive likelihood ratio (3.1 and 1.8) could limit their usefulness in predicting in-hospital mortality. Considering ICU admissions all the scores, except NEWS, SIRS and qSOFA, showed a worse performance. Conclusions: PSI and APACHE II showed good prognostic results in predicting in-hospital mortality but no pre- existing score validated for acute care settings was totally satisfactory to predict adverse outcomes in COVID-19 interstitial pneumonia after admission to Acute Medical Unit. The application setting and selected outcome criteria should always be considered to evaluate and compare scoring systems’ performance analysis.

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“…Many Ethics Committees of hospitals are using SOFA to help plan for limited resources of ICU beds and ventilators during COVID-19 surges [29]. This adds risk associated with using scores in a way that they were not designed for [30] and highlights the immediate practical importance of mortality prediction.…”

Section: A Severity Scoresmentioning

confidence: 99%

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

Theis

Galanter

Boyd

et al. 2022

IEEE J. Biomed. Health Inform.

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Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and timeconsuming task due to a large number of influencing factors. Healthcare providers are interested in the detection of ICU patients at higher risk, such that risk factors can possibly be mitigated. While such severity scoring methods exist, they are commonly based on a snapshot of the health conditions of a patient during the ICU stay and do not specifically consider a patient's prior medical history. In this paper, a process mining/deep learning architecture is proposed to improve established severity scoring methods by incorporating the medical history of diabetes patients. First, health records of past hospital encounters are converted to event logs suitable for process mining. The event logs are then used to discover a process model that describes the past hospital encounters of patients. An adaptation of Decay Replay Mining is proposed to combine medical and demographic information with established severity scores to predict the inhospital mortality of diabetes ICU patients. Significant performance improvements are demonstrated compared to established risk severity scoring methods and machine learning approaches using the Medical Information Mart for Intensive Care III dataset.

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Discriminant Accuracy of the SOFA Score for Determining the Probable Mortality of Patients With COVID-19 Pneumonia Requiring Mechanical Ventilation

Abstract: This cohort study characterizes the accuracy of the Sequential Organ Failure Assessment (SOFA) score to discriminate death from survival in patients with COVID-19 pneumonia receiving oxygen therapy for 4 hours or longer before undergoing endotracheal intubation.

Cited by 153 publications

References 6 publications

Descripción y seguimiento de pacientes COVID-19 en ventilación mecánica en unidades convertidas dirigidas por anestesiólogos: Clínica Alemana de Santiago

Descripción y seguimiento de pacientes COVID-19 en ventilación mecánica en unidades convertidas dirigidas por anestesiólogos: Clínica Alemana de Santiago

Performance of prognostic scoring systems in patients with COVID-19 interstitial pneumonia in Acute Medical Unit

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture

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