Development and validation of a survival prediction model in elder patients with community-acquired pneumonia: a MIMIC-population-based study

Abstract

Background To develop a prediction model predicting in-hospital mortality of elder patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU). Methods In this cohort study, data of 619 patients with CAP aged ≥ 65 years were obtained from the Medical Information Mart for Intensive Care III (MIMIC III) 2001–2012 database. To establish the robustness of predictor variables, the sample dataset was randomly partitioned into a training set group and a testing set group (ratio: 6.5:3.5). The predictive factors were evaluated using multivariable logistic regression, and then a prediction model was constructed. The prediction model was compared with the widely used assessments: Sequential Organ Failure Assessment (SOFA), Pneumonia Severity Index (PSI), systolic blood pressure, oxygenation, age and respiratory rate (SOAR), CURB-65 scores using positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), area under the curve (AUC) and 95% confidence interval (CI). The decision curve analysis (DCA) was used to assess the net benefit of the prediction model. Subgroup analysis based on the pathogen was developed. Results Among 402 patients in the training set, 90 (24.63%) elderly CAP patients suffered from 30-day in-hospital mortality, with the median follow-up being 8 days. Hemoglobin/platelets ratio, age, respiratory rate, international normalized ratio, ventilation use, vasopressor use, red cell distribution width/blood urea nitrogen ratio, and Glasgow coma scales were identified as the predictive factors that affect the 30-day in-hospital mortality. The AUC values of the prediction model, the SOFA, SOAR, PSI and CURB-65 scores, were 0.751 (95% CI 0.749–0.752), 0.672 (95% CI 0.670–0.674), 0.607 (95% CI 0.605–0.609), 0.538 (95% CI 0.536–0.540), and 0.645 (95% CI 0.643–0.646), respectively. DCA result demonstrated that the prediction model could provide greater clinical net benefits to CAP patients admitted to the ICU. Concerning the pathogen, the prediction model also reported better predictive performance. Conclusion Our prediction model could predict the 30-day hospital mortality in elder patients with CAP and guide clinicians to identify the high-risk population.

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

“…Table 8 provides information about 44 diseases and the corresponding survival prediction algorithms utilized in these diseases. A deeper analysis of Table 8 shows that Cox-PH and lasso Cox-PH models have been extensively utilized for disease specific survival prediction i.e., ASCVD 29,111 , bladder cancer 40,82 , colorectal cancer [74][75][76][77] , hepatocellular carcinoma 43,86,87 , ovarian cancer [88][89][90]103 , lung adenocarcinoma 101 , heart failure 118 , HER2-negative metastatic breast cancer 67 , pancreatic cancer 26,71 , trauma 120 , nasopharyngeal carcinoma 66 , triple-negative breast cancer 68 , lymphoma 85 , breast cancer 40,81,82 , ovarian cancer [88][89][90]103 , and lower-grade glioma 80 , cardiovascular disease 112,[114][115][116][117] , invasive ductal carcinoma 70 , liver transplantation 119 , gastric cancer 42 , lung cancer 27 , esophageal squamous cell carcinoma 79 , glioma 69 , and liver cancer …”

“…A closer look at the clinical features across diverse diseases reveals a consistent set of fundamental demographic features i.e., age and gender which are prevalent in nearly all studies 85,86,91,111,112,115 . Beyond demographic features, diseasespecific features also play critical role for disease-specific survival prediction.…”

“…Figure 3 provides an overview of multiple survival prediction studies that encompass a range of cancer subtypes, either within a pancancer context or within the context of predicting survival for different subtypes. A total of 14 studies have taken into account multiple cancer subtypes whereas the majority of the studies have only covered only a single type of cancer subtype such as colorectal cancer 109 , lymphoma 85 , colon adenocarcinoma 39 , gastric cancer 42 and so on.…”

“…i.e., age and gender which are prevalent in nearly all studies 85,86,91,111,112,115 . Beyond demographic features, diseasespecific features also play critical role for disease-specific survival prediction.…”