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Background: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning-based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre. Methods: In this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning-based early warning system and clinical outcomes. We used propensity score-based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death. Results: The study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55-1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63-1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46-1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60-1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50-1.24). Interpretation: Implementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes., Predicting, preventing, and rapidly responding to patient deterioration in hospital is a major goal for improving patient safety. Unrecognized clinical deterioration is the leading cause of unplanned transfers to intensive [...]

Background: The drug toxicity crisis continues to accelerate across Canada, with rapid increases in opioid-related harms following the onset of the COVID-19 pandemic. We sought to describe trends in the burden of opioid-related deaths across Canada throughout the pandemic, comparing these trends by province or territory, age, and sex. Methods: We conducted a repeated cross-sectional analysis of accidental opioid-related deaths between Jan. 1, 2019, and Dec. 31, 2021, across 9 Canadian provinces and territories using aggregated national data. Our primary measure was the burden of premature opioid-related death, measured by potential years of life lost. Our secondary measure was the proportion of all deaths attributable to opioids; we used the Cochrane-Armitage test for trend to compare proportions. Results: Between 2019 and 2021, the annual number of opioid-related deaths increased from 3007 to 6222 and years of life lost increased from 126 115 to 256 336 (from 3.5 to 7.0 yr of life lost per 1000 population). In 2021, the highest number of years of life lost was among males (181 525 yr) and people aged 30-39 years (87 045 yr). In 2019, we found that 1.7% of all deaths among those younger than 85 years were related to opioids, rising to 3.2% in 2021. Significant increases in the proportion of deaths related to opioids were observed across all age groups (p < 0.001), representing 29.3% and 29.0% of deaths among people aged 20-29 and 30-39 years in 2021, respectively. Interpretation: Across Canada, the burden of premature opioid-related deaths doubled between 2019 and 2021, representing more than one-quarter of deaths among younger adults. The disproportionate loss of life in this demographic group highlights the critical need for targeted prevention efforts., In Canada, the COVID-19 pandemic occurred in the midst of a growing drug toxicity crisis. Before the emergence of COVID-19, the number of accidental opioid-related deaths across Canada rose from [...]

Background: Oxycodone is increasingly prescribed for postpartum analgesia in lieu of codeine owing to concerns regarding the neonatal safety of codeine during lactation. We examined whether initiation of oxycodone after delivery was associated with an increased risk of persistent opioid use relative to initiation of codeine. Methods: We conducted a population-based cohort study of people who filled a prescription for either codeine or oxycodone within 7 days of discharge from hospital after delivery between Sept. 1, 2012, and June 30, 2020. The primary outcome was persistent opioid use, defined as 1 or more additional prescriptions for an opioid within 90 days of the first postpartum prescription and 1 or more additional prescriptions in the 91 to 365 days thereafter. We used inverse probability of treatment weighting to assess the risk of persistent postpartum opioid use, comparing people who initiated oxycodone with those who initiated codeine. Results: Over the 8-year study period, we identified 70 607 people who filled an opioid prescription within 7 days of discharge from hospital: 21308 (30.2%) received codeine and 49299 (69.8%) oxycodone. Compared with people who filled a prescription for codeine, receipt of oxycodone was not associated with persistent opioid use (relative risk [RR] 1.04, 95% confidence interval [CI] 0.91-1.20). We found an association between a prescription for oxycodone and persistent use after vaginal delivery (RR 1.63, 95% CI 1.31-2.03), but not after cesarean delivery (RR 0.85, 95% CI 0.73-1.00). Interpretation: Initiation of oxycodone (v. codeine) was not associated with an increased risk of persistent opioid use, except after vaginal delivery., Opioids are commonly prescribed to treat pain after childbirth, but the amount prescribed often far exceeds what is taken. (1,2) In Ontario, about 40% of people who give birth fill [...]

Related articles have outlined problems with the development of machine-learned solutions for health care and suggested a framework for their optimal development. (1,2) The spectrum of clinical settings in which [...]

In a companion article, Verma and colleagues discuss how machine-learned solutions can be developed and implemented to support medical decision-making. (1) Both decision-support systems and clinical prediction tools developed using [...]

Machine learning--the process of developing systems that learn from data to recognize patterns and make accurate predictions of future events (1)--has considerable potential to transform health care. Machine-learned tools could [...]

CONTEXTE: Les caracteristiques des patients, les soins cliniques, l'utilisation des ressources et les issues cliniques des personnes atteintes de la maladie a coronavirus 2019 (COVID-19) hospitalisees au Canada ne sont pas bien connus. METHODES: Nous avons recueilli des donnees sur tous les adultes hospitalises atteints de la COVID-19 ou de l'influenza ayant obtenu leur conge d'unites medicales ou d'unites de soins intensifs medicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons compare les issues cliniques des patients a l'aide de modeles de regression multivariee, en tenant compte des facteurs sociodemographiques et de l'intensite des comorbidites. Nous avons valide le degre d'exactitude de 7 scores de risque mis au point a l'externe pour determiner leur capacite a predire le risque de deces chez les patients atteints de la COVID-19. RESULTATS: Parmi les hospitalisations retenues, 1027 patients etaient atteints de la COVID-19 (age median de 65 ans, 59,1% d'hommes) et 783 etaient atteints de l'influenza (age median de 68 ans, 50,8% d'hommes). Les patients ages de moins de 50 ans comptaient pour 21,2% de toutes les hospitalisations dues a la COVID-19 et 24,0% des sejours aux soins intensifs. Com pa rativement aux patients atteints de l'influenza, les patients atteints de la COVID-19 presentaient un taux de mortalite perhospitaliere (mortalite non ajustee 19,9% c. 6,1%; risque relatif [RR] ajuste 3,46%, intervalle de confiance [IC] a 95% 2,56-4,68) et un taux d'utilisation des ressources des unites de soins intensifs (taux non ajuste 26,4% c. 18,0%; RR ajuste 1,50, IC a 95% 1,25-1,80) significativemen t plus eleves, ainsi qu'une duree d'hospitalisation (duree mediane non ajustee 8,7 jours c. 4,8 jours; rapport des taux d'incidence ajuste 1,45; IC a 95% 1,25-1,69) significativement plus longue. Le taux de rehospitalisation dans les 30 jours n'etait pas significativement different (taux non ajuste 9,3% c. 9,6%; RR ajuste 0,98%, IC a 95% 0,70-1,39). Trois scores de risque utilisant un pointage pour predire la mortalite perhospitaliere ont montre une bonne discrimination (aire sous la courbe [ASC] de la fonction d'efficacite du recepteur [ROC] 0,72-0,81) et une bonne calibration. INTERPRETATION: Durant la premiere vague de la pandemie, l'hospitalisation des patients atteints de la COVID-19 etait associee a des taux de mortalite et d'utilisation des ressources des unites de soins intensifs et a une duree d'hospitalisation significativement plus importants que les hospitalisations des patients atteints de l'influenza. De simples scores de risque peuvent predire avec une bonne exactitude le risque de mortalite perhospitaliere des patients atteints de la COVID-19., Des etudes internationales ont rapporte que les patients hospitalises en raison de la maladie a coronavirus 2019 (COVID-19) presentaient des taux eleves de maladie grave et de mortalite (1-5). Deux [...]