Performance features and mortality prediction of the 4C Score early in COVID-19 infection: a retrospective study in Saudi Arabia.

The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized.This retrospective cross-sectional study was performed after the first wave of COVID-19. Data of all PCR-positive COVID-19 patients who had been discharged, hospitalized, or died were retrospectively analyzed. Patients were classified into four risk groups according to the 4C Mortality Score. A total of (506) patients were classified as follows: low (57.1%), intermediate (27.9%), high (13%), and very high (2%) risk groups. Clinical, radiological, and laboratory data were significantly more severe in the high and very high-risk groups compared with other groups (p<0.001 for all). Mortality rate was correctly estimated by the model with 71% sensitivity, 88.6% specificity, and area under the curve of 0.9. The mortality rate was underestimated among the very high-risk group (66.2% vs 90%). The odds of mortality were significantly greater in the presence of hypoxia (OR 2.6, 95% CI 1.5 to 4.6, p<0.001) and high respiratory rate (OR 5.3, 95% CI 1.6 to 17.9, p<0.007), C reactive protein (CRP) (OR 3.5, 95% CI 1.8 to 6.8, p<0.001), and blood urea nitrogen (BUN) (OR 1.9, 95% CI 1.3 to 3.1, p<0.002). Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.

Peripheral Polyneuropathy and Cognitive Impairment in Type II Diabetes Mellitus.

BACKGROUND: Neuropathy is one of most common complications in diabetic patients. Diagnosis of diabetic neuropathy is essential for decreasing the rate of the disability and death. Neuron-specific enolase (NSE) is released from damaged neuronal cells and enters the blood circulation through an injured blood brain barrier. Therefore, serum NSE can reflect the damage of neurons and brain tissue. OBJECTIVE: To evaluate peripheral polyneuropathy and cognitive function in Type 2 Diabetes Mellitus (T2DM) and correlate them with NSE level as a possible biomarker of diabetic neuropathy. SUBJECTS AND METHODS: Forty five T2DM patients with polyneuropathy were randomly recruited in this study compared to 45 healthy age and sex matched subjects as a control. Patients group were divided into two subgroups, 24 diabetic patients with painful peripheral neuropathy and 21 with painless peripheral neuropathy. All were subjected to clinical assessment by diabetic neuropathy symptom score, Dyck neuropathy grading, Mini-Mental State Examination (MMSE), assessment of HbA1c, NSE biomarker and neurophysiological assessment (nerve conduction study (NCS), event related potential (P300wave) and somatosensory evoked potential (SSEP) of the right median nerve). RESULTS: There were significant decrease in cognitive functions in diabetic patients compared to controls and a significant increase in NSE in diabetic patients. There were no significant difference between patients with painless and painful diabetic neuropathy as regard MMSE, HbA1c and NSE. There were significant correlation of P300 in diabetic patients with HbA1c and NSE. CONCLUSION: Neurophysiological assessment of diabetic patients by NCS, SSEP and P300 have well evaluation of cognitive functions, painless, and painful diabetic polyneuropathy. NSE is a beneficial biomarker in diabetic patients to pick up neurological complications.