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It is a life-threatening condition where survival is determined by those decisions made in the first hour of healthcare. The risk prediction models are tools used to estimate the risk of an outcome in the context of specified variables and uncertainties. Until now, the tools to predict mortality in patients with suspected sepsis have focused mainly on predicting short-term prognosis, but sepsis and septic shock are also associated with a deterioration in the quality of life and higher long-term mortality after being discharged from hospital. Mortality prediction models (MPMs) estimate the risk of death in a patient population, and they can help stratify the risk and prognosis of an ill patient and thus help shared decision-making. Numerous scoring systems have been developed that predict sepsis-related mortality and its severity (PIRO, MEDS, MISSED, SOFA, qSOFA) and their usefulness is still under debate<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a>. All of them can only be used as tools to guide decision-making, in an individual context, according to patient preferences, and not only as a strict criterion for admission to intensive care or other units.</p><p id="par0010" class="elsevierStylePara elsevierViewall">Recently, modifications to some models have been put forward, such as the addition of the determination of troponin (Tnt<span class="elsevierStyleHsp" style=""></span>I) to the SOFA-T model. Since cardiovascular abnormalities are common in sepsis, and the troponins are elevated in these patients even in the absence of coronary artery disease, the addition of Tnt<span class="elsevierStyleHsp" style=""></span>I has been shown to improve the prognostic performance of the SOFA model in elderly patients with sepsis admitted to intermediate care units<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a>. Likewise, the combination of qSOFA and frailty makes it possible to identify the elderly with the worst prognosis<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a>.</p><p id="par0015" class="elsevierStylePara elsevierViewall">A recent article in this magazine<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> analyses the performance of the qSOFA scale versus the classic inflammatory response criteria (SIRS), and it leads the authors to question the validity of the qSOFA index for decision-making in the A&E. The authors refer in their article to new classification tools in the A&E, such as the <span class="elsevierStyleItalic">National Early Warning Score</span> (NEWS)<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> scale, although this has also shown weaknesses<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a>.</p><p id="par0020" class="elsevierStylePara elsevierViewall">Currently we do not have a specific MPM for adults with sepsis that meets adequate quality criteria. Physicians in emergency situations are unlikely to use complex MPMs that require many checkpoints. Recent data suggest that risk stratification tools based on machine learning may enable clinical decision support tools to stratify patient risk and prognosis and facilitate A&E decision-making<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a>.</p></span>" "pdfFichero" => "main.pdf" "tienePdf" => true "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:5 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Pronostic tools for elderly patientes with sepsis: in search of new predictive models" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "F.M. 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Letter to the Editor
Morbidity and mortality risk prediction models in sepsis
Modelos de predicción de riesgo de morbimortalidad en sepsis
Fernando Miguel Gamboa Antiñolo
Medicina Interna, Hospital Universitario de Valme, Universidad de Sevilla, Sevilla, Spain