Meta-analysis comparing placebo responses in clinical trials of painful HIV-associated sensory neuropathy and diabetic polyneuropathy

Post-acute sensory neurological sequelae in patients with SARS-CoV-2 infection: the COVID-PN observational cohort study

ABSTRACT

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection can cause neurological sequelae after the resolution of symptomatic COVID-19 illness, but the occurrence of peripheral neuropathy symptoms and cranial nerve dysfunction is unknown. This study aimed to characterize the occurrence and severity of pain and peripheral neuropathy symptoms in patients with SARS-CoV-2 infection.An observational cohort study included adults tested for a SARS-CoV-2 infection at an academic medical center (assigned as CV+ or control, based on test results). 30-90 days after the index SARS-CoV-2 test, patients completed a web-based questionnaire assessing pain, peripheral neuropathy-related sensory symptoms, and symptoms in the distribution of cranial nerves (current symptoms, symptoms at testing and two weeks thereafter). Univariate analyses compared the outcomes between the groups. Multivariable analysis was used to determine the odds for neuropathy symptoms after adjusting for key baseline variables.1556 participants were included: 542 CV+ and 1014 controls. CV+ patients reported a higher occurrence of peripheral neuropathy symptoms in the extremities anytime within 90 days post-infection (28.8% vs 12.9%, OR [95%CI] =2.72 [2.10-3.54]), as well as such symptoms persisting up to 90 days after infection (6.1% vs 1.9%, OR=3.39 [1.91-6.03]). The occurrence of pain in the extremities was higher in the CV+ group (24.2% vs 9.8%, OR=2.95 [2.21-3.91]). SARS-CoV-2 infection was also associated with higher occurrence of peripheral neuropathy symptoms, after adjusting for the history of chronic pain and neuropathy (OR=3.19 [2.37-4.29]. The results suggest that SARS-CoV-2 infection was independently associated with an increased risk of pain and peripheral neuropathy symptoms.

Cohort study protocol to characterize the incidence and severity of neuropathic pain in patients with severe acute respiratory syndrome coronavirus 2 infection

Supplemental Digital Content is Available in the Text.

In this protocol for a cohort study, an approach to characterize new-onset or worsening neuropathic pain in patients with COVID-19 is described.

Introduction and Objectives:

The coronavirus disease 2019 (COVID-19) pandemic has resulted in patients experiencing symptoms that include neurological dysfunction. As many viral infections are associated with neuropathy, the aim of the study is to characterize the incidence and severity of neuropathic pain in patients with COVID-19.

Methods:

A cohort study will be conducted in adult (≥18 years) patients who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Washington University/Barnes-Jewish Hospital. Participants who are deceased, with incomplete test results, or who cannot be contacted will be excluded. Approximately 1320 participants will be recruited in a 1:2 ratio of those with a positive-to-negative SARS-CoV-2 test result. Each participant will be invited to complete a survey to assess their symptoms related to neuropathy, 30 to 90 days after their initial SARS-CoV-2 test. Survey responses, demographics, and clinical data from the electronic health record will be used for analysis. The primary outcome is the incidence of new symptoms of neuropathic pain. The self-reported DN4 and Neuropathic Pain Symptom Inventory questionnaires (Appendix 1, http://links.lww.com/PR9/A103) will be used for neuropathic pain screening and severity assessment, respectively. Exploratory analyses will be performed to investigate other potential clinical endpoints and trends.

Results/Conclusion:

Similar to previous coronavirus infections, an increased incidence of new-onset neuropathic pain after COVID-19 disease is expected, along with an increase in the severity experienced by patients with COVID-19 with pre-existing chronic pain. Comprehensive understanding of how COVID-19 affects the nervous system can provide a better framework for managing pain in this disease.

Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study

BACKGROUND

Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems.

METHODS

For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification.

RESULTS

It was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control.

CONCLUSION

Depending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.