Outcomes of adults with invasive meningococcal disease with reduced penicillin susceptibility in Auckland 2004-2017.

PURPOSE: The purpose of this study was to assess the clinical outcomes of adults with invasive meningococcal disease (IMD) and to compare the outcomes of patients with IMD caused by a penicillin susceptible isolate (minimum inhibitory concentration (MIC) ≤ 0.06 mg/L) with patients with IMD caused by an isolate with reduced penicillin susceptibility (MIC > 0.06 mg/L). We also assessed the outcomes of patients with IMD caused by an isolate with reduced penicillin susceptibility who were treated exclusively with intravenous (IV) benzylpenicillin. METHODS: Retrospective study of all culture positive IMD in adult patients (age ≥ 15 years) in the Auckland region from 2004 to 2017. RESULTS: One hundred and thirty-nine patients were included; 94 had penicillin susceptible isolates (88 cured, 6 died), and 45 had an isolate with reduced penicillin susceptibility (41 cured, 1 possible relapse, 3 died). The median benzylpenicillin/ceftriaxone treatment duration was 3 days for both groups. There was no difference in the patient outcomes of both groups. Eighteen patients with IMD caused by an isolate with reduced penicillin susceptibility received benzylpenicillin alone and were cured. CONCLUSIONS: This study provides further support to existing data that has shown that short duration IV beta-lactam treatment is effective for IMD in adults. Only a small number of patients with meningitis caused by an isolate with reduced penicillin susceptibility received benzylpenicillin alone, limiting its evaluation. For Neisseria meningitidis meningitis, we recommend ceftriaxone as empiric treatment and as definitive treatment when this is caused by an isolate with reduced penicillin susceptibility.

A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.