Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients with comorbid cardiovascular disease: a retrospective cohort study

Abstract

Background Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. Patients and methods The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. Results Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758–0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. Conclusion An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.

Remote photoplethysmography‐based human vital sign prediction using cyclical algorithm

“…For several classification algorithms, their accuracy is detected by comparing the graphs as shown in Figure 18. In contrast to the predictive value of current vital signs, the four different models namely the C5 decision tree [14], Random Forest [23], Gradient Tree boosting algorithm [24], and FFT sensor‐based vital sign prediction [25] are examined. To evaluate the accuracy of several modelling vital sign trends the proposed noninvasive temperature and cycle algorithm was developed for diagnosing clinical deterioration, and it achieves a precision of about 90.37% in ambient light video applications.…”

“…Thus, the presented algorithm senses slight head swings following the heart cycle. Observing the heart and RRs can help predict cardiovascular and pulmonary disorders, which cause more than 31% of deaths around the world [14]. The observation of the health of an individual is influenced by respiratory concentrations.…”

Clinical profile analysis and nomogram for predicting in-hospital mortality among elderly severe community-acquired pneumonia patients: a retrospective cohort study

“…In some studies that only enrolled elderly patients, the conclusion still held [ 24 ]. Age was also an independent risk factor in SCAP patients with heart disease [ 28 ] and type 2 diabetes [ 29 ]. Immunoreaction influences the prognosis.…”

Ambulant erworbene Pneumonie: Entscheidung zu stationärer Aufnahme sollte geriatrische Probleme berücksichtigen

“…Ein neuer Score mit höherer Vorhersagewahrscheinlichkeit bezüglich der Prognose wurde auf der Basis multipler Gesundheits- und anthropometrischer Daten im Rahmen einer retrospektive Studie ermittelt [8]. Aus acht unabhängigen Faktoren wurde der sogenannte expanded CURB-65-Score entwickelt, der auch die Laborwerte LDH, Albumin und Thrombozytenzahl integriert.…”

“…Die hier diskutierte Studie von Linjing Gong und Kollegen [8] liefert einfach zu erhebende Parameter, die zur besseren Abschätzung der Prognose einer schweren ambulant erworbenen Pneumonie verwendet werden können. Allerdings müssen prospektive Studien noch zeigen, dass die Berücksichtigung dieser Parameter und deren Integration in ein umfassendes Behandlungskonzept auch zu einer Senkung der Mortalität führt.…”