Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

OBJECTIVE: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. METHODS: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. RESULTS: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. CONCLUSION: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

Post-Implementation Outcomes of a Remote Patient Monitoring Program After Emergency Department Discharge.

To better communicate and improve post-visit outcomes, a remote patient monitoring (RPM) program was implemented for patients discharged from emergency departments (ED) across 10 hospitals. The solution was offered to patients at the time of ED discharge and staffed by a group of care coordinators to respond to questions/urgent needs. Of 107,477 consecutive patients offered RPM, 28,425 patients (26.4%) engaged with the program. Activated patients with RPM were less likely to return to the ED within 90 days of their index visit [19.8% compared to 23.6%, p<.001]. While activation rates were modest, we observed fewer return visits to the ED in patients using RPM, with a 16.2% lower hazard of returning in the next year. Future research is needed to understand methods to improve RPM activation, any causal effects of RPM activation on return ED visits, and external validation of these findings.

Deep transcranial magnetic stimulation for adolescents with treatment-resistant depression: A preliminary dose-finding study exploring safety and clinical effectiveness.

BACKGROUND: Transcranial magnetic stimulation (TMS) is an intervention for treatment-resistant depression (TRD) that modulates neural activity. Deep TMS (dTMS) can target not only cortical but also deeper limbic structures implicated in depression. Although TMS has demonstrated safety in adolescents, dTMS has yet to be applied to adolescent TRD. OBJECTIVE/HYPOTHESIS: This pilot study evaluated the safety, tolerability, and clinical effects of dTMS in adolescents with TRD. We hypothesized dTMS would be safe, tolerable, and efficacious for adolescent TRD. METHODS: 15 adolescents with TRD (Age, years: M = 16.4, SD = 1.42) completed a six-week daily dTMS protocol targeting the left dorsolateral prefrontal cortex (BrainsWay H1 coil, 30 sessions, 10 Hz, 3.6 s train duration, 20s inter-train interval, 55 trains; 1980 total pulses per session, 80 % to 120 % of motor threshold). Participants completed clinical, safety, and neurocognitive assessments before and after treatment. The primary outcome was depression symptom severity measured by the Children's Depression Rating Scale-Revised (CDRS-R). RESULTS: 14 out of 15 participants completed the dTMS treatments. One participant experienced a convulsive syncope; the other participants only experienced mild side effects (e.g., headaches). There were no serious adverse events and minimal to no change in cognitive performance. Depression symptom severity significantly improved pre- to post-treatment and decreased to a clinically significant degree after 10 treatment sessions. Six participants met criteria for treatment response. LIMITATIONS: Main limitations include a small sample size and open-label design. CONCLUSIONS: These findings provide preliminary evidence that dTMS may be tolerable and associated with clinical improvement in adolescent TRD.

Hypoxia within subcutaneously implanted macroencapsulation devices limits the viability and functionality of densely loaded islets.

Introduction: Subcutaneous macroencapsulation devices circumvent disadvantages of intraportal islet therapy. However, a curative dose of islets within reasonably sized devices requires dense cell packing. We measured internal PO2 of implanted devices, mathematically modeled oxygen availability within devices and tested the predictions with implanted devices containing densely packed human islets. Methods: Partial pressure of oxygen (PO2) within implanted empty devices was measured by noninvasive 19F-MRS. A mathematical model was constructed, predicting internal PO2, viability and functionality of densely packed islets as a function of external PO2. Finally, viability was measured by oxygen consumption rate (OCR) in day 7 explants loaded at various islet densities. Results: In empty devices, PO2 was 12 mmHg or lower, despite successful external vascularization. Devices loaded with human islets implanted for 7 days, then explanted and assessed by OCR confirmed trends proffered by the model but viability was substantially lower than predicted. Co-localization of insulin and caspase-3 immunostaining suggested that apoptosis contributed to loss of beta cells. Discussion: Measured PO2 within empty devices declined during the first few days post-transplant then modestly increased with neovascularization around the device. Viability of islets is inversely related to islet density within devices.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning / Predição de áreas de aglomeração de tuberculose nos municípios ribeirinhos da Amazônia brasileira com aprendizagem de máquina

ABSTRACT Objective: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. We also aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Methods: Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Results: Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Conclusion: Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

RESUMO Objetivo: A tuberculose (TB) é a segunda doença infecciosa que mais mata no mundo, representando um problema de saúde pública no Brasil, especialmente na região amazônica. Este estudo analisa a TB nos municípios ribeirinhos" com o objetivo de identificar aglomerados de alta incidência, também conhecidos como "hot spots". Posteriormente, utilizando aprendizagem de máquina, visamos prever estes aglomerados por meio de variáveis de vigilância epidemiológica. Assim buscamos auxiliar o ente público no combate à TB nesta região. Métodos: Dados da incidência de TB nos "municípios ribeirinhos" foram coletados entre os anos de 2019 e 2022 do Departamento de Informática do Ministério da Saúde. O índice de Moran foi utilizado para a determinação de autocorrelação espacial global, enquanto o método Getis-Ord GI* foi empregado para a autocorrelação espacial local. Variáveis referentes ao diagnóstico, tratamento e características socioeconômicas associadas aos casos foram utilizadas para a predição de aglomerados de alta incidência por meio de um modelo Random Forest. Resultados: Foram identificados aglomerados com alta incidência de TB a oeste e baixa incidência a leste. O total de seis variáveis de vigilância epidemiológica foi identificado como relevante para a predição. Nosso modelo Random Forest alcança uma área sob a curva da característica operacional do receptor (AUC-ROC) de 0,81. Conclusão: Municípios com altas porcentagens de casos recorrentes, mortes por TB, mudança do esquema de tratamento, casos novos e casos com história de tabagismo estão associados a aglomerados de alta incidência. Esperamos que este método de identificação de possíveis aglomerados de TB seja útil para o ente público no combate à doença na região.