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Background Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). Objectives We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. Sources References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. Content We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). Implications Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Background Maintenance haemodialysis (MHD) patients have a high risk of initial mortality from coronavirus disease 2019 (COVID-19). However, long-term consequences of this disease in the MHD population are poorly described. We report the clinical presentation, outcome and long-term follow-up of MHD patients affected by COVID-19 in a multicentric cohort from the Paris, France area. Methods We conducted a retrospective analysis of clinical presentation and long-term follow-up of MHD patients affected by COVID-19 in 19 MHD centres in the Paris, France area. Results In this cohort of 248 patients with an initial mortality rate of 18%, age, comorbidities, dyspnoea and previous immunosuppressive treatment were associated with death at Conclusions This work demonstrates the long-term consequences of COVID-19 in MHD patients, highlighting both initial and long-term severity of the disease, including severe cachexia.

Introduction Les patients dialyses presentent un risque important de forme severe de COVID-19 avec une mortalite elevee a la phase initiale. Cependant, les consequences a long terme de cette pathologie dans cette population sont peu decrites. Nous rapportons ici le suivi a long terme des patients en hemodialyse, touches par le COVID-19. Description Il s’agit d’une etude retrospective sur patients dialyses affectes par le COVID-19 dans 19 centres de la region parisienne. Methodes L’analyse concerne la description de la presentation clinique et du suivi a long terme de ces patients. Resultats Dans cette cohorte de 248 patients avec un taux de mortalite initial de 18 %, l’âge, les comorbidites, la dyspnee et un traitement immunosuppresseur anterieur etaient associes au deces dans les 30 premiers jours. Parmi les 203 patients survivants apres la phase aigue, le suivi a long terme (mediane 180 jours) etait disponible pour 189 (93 %) patients. Des complications majeurs (cardiovasculaires, infections severes ou tout autre evenement menant a une hospitalisation) sont survenues chez 30 (16 %) patients au cours du suivi, dont 12 deces (6 %) apres une mediane de 78 jours a compter de l’apparition des symptomes. Un âge plus avance, un taux d’albumine plus faible au moment du diagnostic et des comorbidites cardiovasculaires presentaient des tendances d’association avec le deces a long terme. Dans l’ensemble, les evenements cardiovasculaires, les infections et les hemorragies gastro-intestinales ont ete les principales complications. Une cachexie post-COVID-19 a ete observee chez 25/189 (13 %) patients. Une albuminemie initiale plus faible etait significativement associee a ce syndrome. Aucune reinfection par le SARS-CoV-2 n’a ete observee sur la periode de 6 mois. Conclusion Ce travail montre les consequences a long terme du COVID-19 chez les patients dialyses, mettant en evidence la severite initiale et a long terme de la maladie, associe notamment a une cachexie chez 13 % des patients.

Purpose Differentiating psychogenic non-epileptic seizures (PNES) from epileptic seizures (ES) can be difficult, even when expert clinicians have video recordings of seizures. Moreover, witnesses who are not trained observers may provide descriptions that differ from the expert clinicians’, which often raises concern about whether the patient has both ES and PNES. As such, quantitative, evidence-based tools to help differentiate ES from PNES based on patients’ and witnesses’ descriptions of seizures may assist in the early, accurate diagnosis of patients. Methods Based on patient- and observer-reported data from 1372 patients with diagnoses documented by video-elect roencephalography (vEEG), we used logistic regression (LR) to compare specific peri-ictal behaviors and seizure triggers in five mutually exclusive groups: ES, PNES, physiologic non-epileptic seizure-like events, mixed PNES plus ES, and inconclusive monitoring. To differentiate PNES-only from ES-only, we retrospectively trained multivariate LR and a forest of decision trees (DF) to predict the documented diagnoses of 246 prospective patients. Results The areas under the receiver operating characteristic curve (AUCs) of the DF and LR were 75% and 74%, respectively (empiric 95% CI of chance 37–62%). The overall accuracy was not significantly higher than the naive assumption that all patients have ES (accuracy DF 71%, LR 70%, naive 68%, p > 0.05). Conclusions Quantitative analysis of patient- and observer-reported peri-ictal behaviors objectively changed the likelihood that a patient’s seizures were psychogenic, but these reports were not reliable enough to be diagnostic in isolation. Instead, our scores may identify patients with “probable” PNES that, in the right clinical context, may warrant further diagnostic assessment.

Introduction La pandémie de COVID-19 a été à l’origine d’une crise sanitaire sans précédent. Nous décrivons la présentation clinique, l’évolution et les facteurs pronostiques au sein d’une vaste cohorte de patients hémodialysés. Description/Méthodes Il s’agit d’une étude rétrospective de l’ensemble des patients COVID-19 diagnostiqués par PCR ou imagerie compatible dans dix-neuf centres de dialyse en Île-de-France entre le 5 février et le 1er mai 2020. Nous rapportons l’évolution des patients à un mois. Les facteurs de risque de mortalité à la fin du suivi ont été déterminés par une régression logistique multivariée et l’effet des traitements a été analysé par score de propension. Résultats Une maladie COVID-19 a été diagnostiquée chez 248 patients (hommes 66 %, âge 67 ± 15 ans, ancienneté en dialyse de 5 ± 5 ans, suivi moyen de 39 ± 20 jours). La présentation clinique incluait fièvre (n = 152 ; 61 %), toux (n = 121 ; 49 %) et asthénie (n = 101 ; 41 %). Une diarrhée aiguë était présente dans 12 % des cas (n = 29). Cent quarante-deux patients (57 %) ont été hospitalisés et 46 sont décédés (mortalité globale 18 %, mortalité des hospitalisés 30 %). En analyse multivariée, un traitement immunosuppresseur (OR = 7,54 [2,39–24,96] ; p

Objective Psychogenic nonepileptic seizure (PNES) is a common diagnosis after evaluation of medication resistant or atypical seizures with video-electroencephalographic monitoring (VEM), but usually follows a long delay after the development of seizures, during which patients are treated for epilepsy. Therefore, more readily available diagnostic tools are needed for earlier identification of patients at risk for PNES. A tool based on patient-reported psychosocial history would be especially beneficial because it could be implemented in the outpatient clinic. Methods Based on the data from 1375 patients with VEM-confirmed diagnoses, we used logistic regression to compare the frequency of specific patient-reported historical events, demographic information, age of onset, and delay from first seizure until VEM in five mutually exclusive groups of patients: epileptic seizures (ES), PNES, physiologic nonepileptic seizure-like events (PSLE), mixed PNES plus ES, and inconclusive monitoring. To determine the diagnostic utility of this information to differentiate PNES only from ES only, we used multivariate piecewise-linear logistic regression trained using retrospective data from chart review and validated based on data from 246 prospective standardized interviews. Results The prospective area under the curve of our weighted multivariate piecewise-linear by-sex score was 73%, with the threshold that maximized overall retrospective accuracy resulting in a prospective sensitivity of 74% (95% CI: 70–79%) and prospective specificity of 71% (95% CI: 64–82%). The linear model and piecewise linear without an interaction term for sex had very similar performance statistics. In the multivariate piecewise-linear sex-split predictive model, the significant factors positively associated with ES were history of febrile seizures, current employment or active student status, history of traumatic brain injury (TBI), and longer delay from first seizure until VEM. The significant factors associated with PNES were female sex, older age of onset, mild TBI, and significant stressful events with sexual abuse, in particular, increasing the likelihood of PNES. Delays longer than 20 years, age of onset after 31 years for men, and age of onset after 40 years for women had no additional effect on the likelihood of PNES. Discussion Our promising results suggest that an objective score has the potential to serve as an early outpatient screening tool to identify patients with greater likelihood of PNES when considered in combination with other factors. In addition, our analysis suggests that sexual abuse, more than other psychological stressors including physical abuse, is more associated with PNES. There was a trend of increasing frequency of PNES for women during childbearing years and plateauing outside those years that was not observed in men.

SummaryObjective Low-cost evidence-based tools are needed to facilitate the early identification of patients with possible psychogenic nonepileptic seizures (PNES). Prior to accurate diagnosis, patients with PNES do not receive interventions that address the cause of their seizures and therefore incur high medical costs and disability due to an uncontrolled seizure disorder. Both seizures and comorbidities may contribute to this high cost. Methods Based on data from 1,365 adult patients with video-electroencephalography–confirmed diagnoses from a single center, we used logistic and Poisson regression to compare the total number of comorbidities, number of medications, and presence of specific comorbidities in five mutually exclusive groups of diagnoses: epileptic seizures (ES) only, PNES only, mixed PNES and ES, physiologic nonepileptic seizurelike events, and inconclusive monitoring. To determine the diagnostic utility of comorbid diagnoses and medication history to differentiate PNES only from ES only, we used multivariate logistic regression, controlling for sex and age, trained using a retrospective database and validated using a prospective database. Results Our model differentiated PNES only from ES only with a prospective accuracy of 78% (95% confidence interval =72–84%) and area under the curve of 79%. With a few exceptions, the number of comorbidities and medications was more predictive than a specific comorbidity. Comorbidities associated with PNES were asthma, chronic pain, and migraines (p < 0.01). Comorbidities associated with ES were diabetes mellitus and nonmetastatic neoplasm (p < 0.01). The population-level analysis suggested that patients with mixed PNES and ES may be a population distinct from patients with either condition alone. Significance An accurate patient-reported medical history and medication history can be useful when screening for possible PNES. Our prospectively validated and objective score may assist in the interpretation of the medication and medical history in the context of the seizure description and history.