Establishment of a novel scoring model for mortality risk prediction in HIV-infected patients with cryptococcal meningitis

Abstract

Background Cryptococcal meningitis (CM) remains a leading cause of death in HIV-infected patients, despite advances in CM diagnostic and therapeutic strategies. This study was performed with the aim to develop and validate a novel scoring model to predict mortality risk in HIV-infected patients with CM (HIV/CM). Methods Data on HIV/CM inpatients were obtained from a Multicenter Cohort study in China. Independent risk factors associated with mortality were identified based on data from 2013 to 2017, and a novel scoring model for mortality risk prediction was established. The bootstrapping statistical method was used for internal validation. External validation was performed using data from 2018 to 2020. Results We found that six predictors, including age, stiff neck, impaired consciousness, intracranial pressure, CD4+ T-cell count, and urea levels, were associated with poor prognosis in HIV/CM patients. The novel scoring model could effectively identify HIV/CM patients at high risk of death on admission (area under curve 0.876; p<0.001). When the cut-off value of 5.5 points or more was applied, the sensitivity and specificity was 74.1 and 83.8%, respectively. Our scoring model showed a good discriminatory ability, with an area under the curve of 0.879 for internal validation via bootstrapping, and an area under the curve of 0.886 for external validation. Conclusions Our developed scoring model of six variables is simple, convenient, and accurate for screening high-risk patients with HIV/CM, which may be a useful tool for physicians to assess prognosis in HIV/CM inpatients.

Clinical Characteristics and Risk Factors for Mortality in Cryptococcal Meningitis: Evidence From a Cohort Study

“…A superior approach is to combine these two diagnostic methods. We found that seizure and altered consciousness are associated with an increased risk of 1-year death in CM, which is consistent with previous findings (19,20). Moreover, we observed that the number of symptoms shows positive correlation with the risk of death.…”

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

“…On the other hand, in eight cases of input studies from ML algorithms regarding the ability to predict meningitis disease (including cases related to the discovery of risk factors and identification of high‐risk patients, 46,47,57–59 the risk of death in patients, 50 consequences of disease in childhood, 53 and etiology, 51 in this context, Zhao and colleagues reported that the use of a predictive model can assist clinicians in determining whether lumbar puncture and antibiotic use are appropriate in preterm infants with high‐risk factors. This, in turn, lowers the incidence of unwarranted treatments and sequelae and decreases the chance of missed meningitis diagnoses 50 . In a different study, scientists presented and simulated a model that predicts meningococcal meningitis and its varieties using an AI technique known as the Bayesian Belief Network.…”

“…The ML algorithms used in the studies include logistic regression (LR) (25%), 46,48,51,54 multiple logistic regression (MLR) (25%), 50,52,53,61 support vector machine (SVM) (19%), 48,54,56 artificial neural network (ANN) (19%), 47,54,60 random forest (RF) (19%), 52,54,56 decision tree (DT) (12%), 57,58 nave-Bayes (NB) (12%), 52,54 fast-and-frugal trees (FFTs) algorithm (6%), 56 and unsupervised ML approach (6%). 59 The target meningitis diseases in the studies included bacterial meningitis (BM) (37%) [51][52][53]56,57,61 or enteroviral meningitis (EVM) (6%), 56 tuberculous meningitis (TBM) (25%), 48,54,55,61 viral meningitis (VM) (19%), 52,54,55 Neisseria meningitides (6%), 59 lumbar drainagerelated meningitis (LDRM) (6%), 47 healthcare-associated ventriculitis and meningitis (HAVM) (6%), 58 cryptococcal meningitis (CM) (6%), 50 PM (6%), 46 and pediatric purulent meningitis (PPM) (6%). 49…”

“…67 The majority of the included studies (56%) were carried out in low-and middle-income countries (LMICs), according to the results of our systematic review. [46][47][48]50,56,57,60,61 This can become a strong point for improving primary care services and thus improving their economies, especially due to the unequal distribution of resources and health care provision in LMICs. It is also critical to identify the agents of disease spread to implement control and prevention measures, as the identification of these factors can yield forecasts that help policymakers make informed decisions about vaccination purchases, public awareness campaigns, and health professional training programs.…”

High socioeconomic status is associated with stroke severity among stroke patients in the National Brain Centre Hospital, Jakarta, Indonesia

“…Therefore, the development of standard metrics for SES to enable interstudy comparisons is a major challenge. However, achieving this goal will certainly contribute to improving our understanding of how SES operates and creating successful strategies and interventions for reducing health inequalities ( Zhang et al, 2019 , Zhao et al, 2021 ). Combining data consisting of employment status, education level, national health insurance class, and hospitalization class into a single metric might create a single acceptable global SES measurement.…”