Nationwide, County-Level Analysis of the Patterns, Trends, and System-Level Predictors of Opioid Prescribing in Surgery in the US: Social Determinants and Access to Mental Health Services Matter.

BACKGROUND: The diversion of unused opioid prescription pills to the community at large contributes to the opioid epidemic in the US. In this county-level population-based study, we aimed to examine the US surgeons' opioid prescription patterns, trends, and system-level predictors in the peak years of the opioid epidemic. STUDY DESIGN: Using the Medicare Part D database (2013 to 2017), the mean number of opioid prescriptions per beneficiary (OPBs) was determined for each US county. Opioid-prescribing patterns were compared across counties. Multivariable linear regression was performed to determine relationships between county-level social determinants of health (demographic, eg median age and education level; socioeconomic, eg median income; population health status, eg percentage of current smokers; healthcare quality, eg rate of preventable hospital stays; and healthcare access, eg healthcare costs) and OPBs. RESULTS: Opioid prescription data were available for 1,969 of 3,006 (65.5%) US counties, and opioid-related deaths were recorded in 1,384 of 3,006 counties (46%). Nationwide, the mean OPBs decreased from 1.08 ± 0.61 in 2013 to 0.87 ± 0.55 in 2017; 81.6% of the counties showed the decreasing trend. County-level multivariable analyses showed that lower median population age, higher percentages of bachelor's degree holders, higher percentages of adults reporting insufficient sleep, higher healthcare costs, fewer mental health providers, and higher percentages of uninsured adults are associated with higher OPBs. CONCLUSIONS: Opioid prescribing by surgeons decreased between 2013 and 2017. A county's suboptimal access to healthcare in general and mental health services in specific may be associated with more opioid prescribing after surgery.

The use of POTTER (Predictive Optimal Trees in Emergency Surgery Risk) calculator to predict mortality and complications in patients submitted to Emergency Surgery / O uso do aplicativo POTTER (Predictive Optimal Trees in Emergency Surgery Risk) para prever mortalidade e complicações em pacientes submetidos à cirurgia de emergência

ABSTRACT Introduction: the ability of the care team to reliably predict postoperative risk is essential for improvements in surgical decision-making, patient and family counseling, and resource allocation in hospitals. The Artificial Intelligence (AI)-powered POTTER (Predictive Optimal Trees in Emergency Surgery Risk) calculator represents a user-friendly interface and has since been downloaded in its iPhone and Android format by thousands of surgeons worldwide. It was originally developed to be used in non-traumatic emergency surgery patients. However, Potter has not been validated outside the US yet. In this study, we aimed to validate the POTTER calculator in a Brazilian academic hospital. Methods: mortality and morbidity were analyzed using the POTTER calculator in both trauma and non-trauma emergency surgery patients submitted to surgical treatment between November 2020 and July 2021. A total of 194 patients were prospectively included in this analysis. Results: regarding the presence of comorbidities, about 20% of the population were diabetics and 30% were smokers. A total of 47.4% of the patients had hypertensive prednisone. After the analysis of the results, we identified an adequate capability to predict 30-day mortality and morbidity for this group of patients. Conclusion: the POTTER calculator presented excellent performance in predicting both morbidity and mortality in the studied population, representing an important tool for surgical teams to define risks, benefits, and outcomes for the emergency surgery population.

RESUMO Introdução: a capacidade da equipe de atendimento de prever de forma confiável o risco pós-operatório é essencial para melhorar a tomada de decisões cirúrgicas, o aconselhamento ao paciente e à família e a alocação de recursos nos hospitais. A calculadora POTTER (Predictive Optimal Trees in Emergency Surgery Risk), alimentada por inteligência artificial (IA) e com uma interface amigável, foi baixada em seu formato para iPhone e Android por milhares de cirurgiões em todo o mundo e foi originalmente desenvolvida para ser usada em pacientes de cirurgia de emergência não traumática. No entanto, a POTTER ainda não foi validada fora dos EUA. Neste estudo, nosso objetivo foi validar a calculadora POTTER em um hospital acadêmico brasileiro. Métodos: a mortalidade e a morbidade foram analisadas usando a calculadora POTTER em pacientes de cirurgia de emergência com e sem trauma submetidos a tratamento cirúrgico entre novembro de 2020 e julho de 2021. Um total de 194 pacientes foi incluído prospectivamente nessa análise. Resultados: Em relação à presença de comorbidades, cerca de 20% da população era diabética e 30%, fumante. Um total de 47,4% dos pacientes eram hipertensos antes da admissão. Após a análise dos resultados, identificamos uma capacidade adequada de prever a mortalidade e a morbidade em 30 dias para esse grupo de pacientes. Conclusão: a calculadora POTTER apresentou um excelente desempenho para prever a morbidade e a mortalidade na população estudada, representando uma ferramenta importante para as equipes cirúrgicas definirem riscos, benefícios e resultados para a população de cirurgia de emergência.