Factors predicting long-term survival of patients with sepsis on arrival at the emergency department

Roh, Jong Seong; Jo, Eun-Jung; Eom, Jung Seop; Mok, Jeongha; Kim, Mi‐Hyun; Kim, Ki Uk; Park, Hye Kyung; Lee, Min Ki; Yeom, Seok-Ran; Lee, Kwangha

doi:10.1097/md.0000000000016871

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…Stepwise logistic regression analysis was used to identify factors that are independent predictors of 90-day mortality. In addition, ß-coefficients derived from multiple logistic regression were simplified and presented as natural numbers >0; these were calculated as the sum of the simplified ß-coefficients, as previously described [19][20][21][22]26]. The prognostic model (PAMV prognosis score) was developed based on the sum of the ß-coefficients.…”

Section: Discussionmentioning

confidence: 99%

“…The analysis identified older age, being underweight, hemato-oncologic diseases as comorbidities, and clinical factors (requirement of vasopressors and NMBAs) on day 4 of MV as useful predictors of 90-day mortality. These indicators are readily applicable to these patients because they have been noted as prognostic factors in various reports in the field of critical care medicine [ 6 , 7 , 9 , 20 - 22 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…Studies have reported that some clinical variables are prognostic factors in patients under ventilator care [6,[19][20][21][22]. Therefore, the following laboratory and clinical data obtained on day 4 of MV were recorded: complete blood count, the lowest PaO2/FiO2 ratio, the need of vasopressors and hemodialysis, and unavoidable requirement of neuromuscular blocking agents (NMBAs) due to ventilator dyssynchrony (intermittent or continuous).…”

Section: Data Collectionmentioning

confidence: 99%

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Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

Kang

Lee

2021

Acute Crit Care

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Background: The aim of the present study was to develop a prognostic model using demographic characteristics, comorbidities, and clinical variables measured on day 4 of mechanical ventilation (MV) for patients with prolonged acute mechanical ventilation (PAMV; MV for >96 hours). Methods: Data from 437 patients (70.9% male; median age, 68 years) were obtained over a period of 9 years. All patients were diagnosed with pneumonia. Binary logistic regression identified factors predicting mortality at 90 days after the start of MV. A PAMV prognosis score was calculating ß-coefficient values and assigning points to variables. Results: The overall 90-day mortality rate was 47.1%. Five factors (age ≥65 years, body mass index <18.5 kg/m 2 , hemato-oncologic diseases as comorbidities, requirement for vasopressors on day 4 of MV and requirement for neuromuscular blocking agents on day 4 of MV) were identified as prognostic indicators. Each factor was valued as +1 point, and used to develop a PAMV prognosis score. This score showed acceptable discrimination (area under the receiver operating characteristic curve of 0.695 for mortality, 95% confidence interval 0.650-0.738, p<0.001), and calibration (Hosmer-Lemeshow chi-square=6.331, with df 7 and p=0.502). The cutoff value for predicting mortality based on the maximum Youden index was ≤2 (sensitivity, 87.5%; specificity, 41.3%). For patients with PAMV scores ≤1, 2, 3 and ≥4, the 90-day mortality rates were 29.2%, 45.7%, 67.9%, and 90.9%, respectively (P<0.001). Conclusions: Our study developed a PAMV prognosis score for predicting 90-day mortality. Further research is needed to validate the utility of this score.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

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Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

Kang

Lee

2021

Acute Crit Care

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“…Septic shock is the most feared progression of sepsis due to its higher case mortality rate. Mortality is also dependent on age, organism, therapy, and organ failure [ 66 ]. Higher mortalities correlate to SIRS.…”

Section: Reviewmentioning

confidence: 99%

Septic Shock: Management and Outcomes

Basodan¹,

Mehmadi²,

Mehmadi³

et al. 2022

Cureus

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The incidence rates of sepsis and septic shock as a complication have become more common over the past several decades. With this increase, sepsis remains the most common cause of intensive care unit (ICU) admissions and one of the most mortality factors, with a huge burden on healthcare facilities. Septic shock has devastating consequences on patients' lives, including organ failures and other long-term complications. Due to its dynamic clinical presentations, guidelines and tools have been established to improve the diagnosis and management effectively. However, there is still a need for evidence-based standardized procedures for the diagnosis, treatment, and follow-up of sepsis and septic shock patients due to the inconsistency of current guidelines and studies contrasting with each other. The standardization would help physicians better manage sepsis, minimize complications and reduce mortality. Septic shock is usually challenging to manage due to its variety of clinical characteristics and physiologic dynamics, affecting the outcomes. Therefore, this review presented the available data in the literature on septic shock diagnosis, management, and prognosis to have an overview of the updated best practice approach to septic shock.

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“…However, although early warning scores have been widely adopted, there is only limited evidence of their effectiveness in the improvement of patient outcome 16 . Among all statistical methods, algorithms based on multivariate (Cox) regression on clinical variables have played a key role 22 , 23 , since back in the early years 24 to nowadays 25 . Notably, the features involved in these methods are not limited to clinical variables: in the last few years a number of teams tried alternative elements from modern omics technologies, such as metabolomics 26 , SNPs genomics 27 , circulating microRNA 28 , blood metabolites 29 or lymphocytes apoptosis 30 , often coupled with more classical biomarkers and compared with the different scores.…”

Section: Introductionmentioning

confidence: 99%

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Chicco

Jurman

2020

Sci Rep

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Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.

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Factors predicting long-term survival of patients with sepsis on arrival at the emergency department

Cited by 9 publications

References 34 publications

Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

Septic Shock: Management and Outcomes

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

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