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IMPORTANCE: The opioid crisis is impacting people across the country and deserves attention to be able to curb the rise in opioid-related deaths. OBJECTIVES: To evaluate practice patterns in opioid infusion administration and dosing for patients with acute respiratory failure receiving invasive mechanical ventilation. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: Patients from 21 hospitals in Kaiser Permanente Northern California and 96 hospitals in Philips electronic ICU Research Institute. MAIN OUTCOMES AND MEASURES: We assessed whether patients received opioid infusion and the dose of said opioid infusion. RESULTS: We identified patients with a diagnosis of acute respiratory failure who were initiated on invasive mechanical ventilation. From each patient, we determined if opioid infusions were administered and, among those who received an opioid infusion, the median daily dose of fentanyl infusion. We used hierarchical regression models to quantify variation in opioid infusion use and the median daily dose of fentanyl equivalents across hospitals. We included 13,140 patients in the KPNC cohort and 52,033 patients in the eRI cohort. A total of 7,023 (53.4%) and 16,311 (31.1%) patients received an opioid infusion in the first 21 days of mechanical ventilation in the KPNC and eRI cohorts, respectively. After accounting for patient- and hospital-level fixed effects, the hospital that a patient was admitted to explained 7% (95% CI, 3-11%) and 39% (95% CI, 28-49%) of the variation in opioid infusion use in the KPNC and eRI cohorts, respectively. Among patients who received an opioid infusion, the median daily fentanyl equivalent dose was 692 µg (interquartile range [IQR], 129-1341 µg) in the KPNC cohort and 200 µg (IQR, 0-1050 µg) in the eRI cohort. Hospital explained 4% (95% CI, 1-7%) and 20% (95% CI, 15-26%) of the variation in median daily fentanyl equivalent dose in the KPNC and eRI cohorts, respectively. CONCLUSIONS AND RELEVANCE: In the context of efforts to limit healthcare-associated opioid exposure, our findings highlight the considerable opioid exposure that accompanies mechanical ventilation and suggest potential under and over-treatment with analgesia. Our results facilitate benchmarking of hospitals' analgesia practices against risk-adjusted averages and can be used to inform usual care control arms of analgesia and sedation clinical trials.
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Analgésicos Opioides , Fentanilo , Pautas de la Práctica en Medicina , Respiración Artificial , Insuficiencia Respiratoria , Humanos , Analgésicos Opioides/administración & dosificación , Analgésicos Opioides/uso terapéutico , Analgésicos Opioides/efectos adversos , Masculino , Estudios Retrospectivos , Femenino , Persona de Mediana Edad , Pautas de la Práctica en Medicina/estadística & datos numéricos , Anciano , Fentanilo/administración & dosificación , Fentanilo/uso terapéutico , Insuficiencia Respiratoria/terapia , Insuficiencia Respiratoria/tratamiento farmacológico , Insuficiencia Respiratoria/epidemiología , Estudios de Cohortes , California , Adulto , Unidades de Cuidados IntensivosRESUMEN
Importance: Guidelines recommend an analgesia-first strategy for sedation during mechanical ventilation, but associations between opioids provided during mechanical ventilation and posthospitalization opioid-related outcomes are unclear. Objective: To evaluate associations between an intravenous opioid dose received during mechanical ventilation and postdischarge opioid-related outcomes in medical (nonsurgical) patients. Design, Setting, and Participants: This retrospective cohort study evaluated adults receiving mechanical ventilation lasting 24 hours or more for acute respiratory failure and surviving hospitalization. Participants from 21 Kaiser Permanente Northern California hospitals from January 1, 2012, to December 31, 2019, were included. Data were analyzed from October 1, 2020, to October 31, 2023. Exposures: Terciles of median daily intravenous fentanyl equivalents during mechanical ventilation. Main Outcomes and Measures: The primary outcome was the first filled opioid prescription in 1 year after discharge. Secondary outcomes included persistent opioid use and opioid-associated complications. Secondary analyses tested for interaction between opioid doses during mechanical ventilation, prior opioid use, and posthospitalization opioid use. Estimates were based on multivariable-adjusted time-to-event analyses, with death as a competing risk, and censored for hospice or palliative care referral, rehospitalization with receipt of opioid, or loss of Kaiser Permanente plan membership. Results: The study included 6746 patients across 21 hospitals (median age, 67 years [IQR, 57-76 years]; 53.0% male). Of the participants, 3114 (46.2%) filled an opioid prescription in the year prior to admission. The median daily fentanyl equivalent during mechanical ventilation was 200 µg (IQR, 40-1000 µg), with terciles of 0 to 67 µg, more than 67 to 700 µg, and more than 700 µg. Compared with patients who did not receive opioids during mechanical ventilation (n = 1013), a higher daily opioid dose was associated with opioid prescriptions in the year after discharge (n = 2942 outcomes; tercile 1: adjusted hazard ratio [AHR], 1.00 [95% CI, 0.85-1.17], tercile 2: AHR, 1.20 [95% CI, 1.03-1.40], and tercile 3: AHR, 1.25 [95% CI, 1.07-1.47]). Higher doses of opioids during mechanical ventilation were also associated with persistent opioid use after hospitalization (n = 1410 outcomes; tercile 3 vs no opioids: odds ratio, 1.44 [95% CI, 1.14-1.83]). No interaction was observed between opioid dose during mechanical ventilation, prior opioid use, and posthospitalization opioid use. Conclusions and Relevance: In this retrospective cohort study of patients receiving mechanical ventilation, opioids administered during mechanical ventilation were associated with opioid prescriptions following hospital discharge. Additional studies to evaluate risks and benefits of strategies using lower opioid doses are warranted.
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Analgésicos Opioides , Alta del Paciente , Respiración Artificial , Humanos , Masculino , Femenino , Analgésicos Opioides/uso terapéutico , Analgésicos Opioides/administración & dosificación , Respiración Artificial/estadística & datos numéricos , Estudios Retrospectivos , Persona de Mediana Edad , Alta del Paciente/estadística & datos numéricos , Anciano , California , Insuficiencia Respiratoria/terapia , Administración IntravenosaRESUMEN
BACKGROUND: Racial disparities exist in outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. OBJECTIVE: To evaluate the contribution of race/ethnicity in SARS-CoV-2 testing, infection, and outcomes. DESIGN: Retrospective cohort study (1 February 2020 to 31 May 2020). SETTING: Integrated health care delivery system in Northern California. PARTICIPANTS: Adult health plan members. MEASUREMENTS: Age, sex, neighborhood deprivation index, comorbid conditions, acute physiology indices, and race/ethnicity; SARS-CoV-2 testing and incidence of positive test results; and hospitalization, illness severity, and mortality. RESULTS: Among 3 481 716 eligible members, 42.0% were White, 6.4% African American, 19.9% Hispanic, and 18.6% Asian; 13.0% were of other or unknown race. Of eligible members, 91 212 (2.6%) were tested for SARS-CoV-2 infection and 3686 had positive results (overall incidence, 105.9 per 100 000 persons; by racial group, White, 55.1; African American, 123.1; Hispanic, 219.6; Asian, 111.7; other/unknown, 79.3). African American persons had the highest unadjusted testing and mortality rates, White persons had the lowest testing rates, and those with other or unknown race had the lowest mortality rates. Compared with White persons, adjusted testing rates among non-White persons were marginally higher, but infection rates were significantly higher; adjusted odds ratios [aORs] for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 2.01 (95% CI, 1.75 to 2.31), 3.93 (CI, 3.59 to 4.30), 2.19 (CI, 1.98 to 2.42), and 1.57 (CI, 1.38 to 1.78), respectively. Geographic analyses showed that infections clustered in areas with higher proportions of non-White persons. Compared with White persons, adjusted hospitalization rates for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 1.47 (CI, 1.03 to 2.09), 1.42 (CI, 1.11 to 1.82), 1.47 (CI, 1.13 to 1.92), and 1.03 (CI, 0.72 to 1.46), respectively. Adjusted analyses showed no racial differences in inpatient mortality or total mortality during the study period. For testing, comorbid conditions made the greatest relative contribution to model explanatory power (77.9%); race only accounted for 8.1%. Likelihood of infection was largely due to race (80.3%). For other outcomes, age was most important; race only contributed 4.5% for hospitalization, 12.8% for admission illness severity, 2.3% for in-hospital death, and 0.4% for any death. LIMITATION: The study involved an insured population in a highly integrated health system. CONCLUSION: Race was the most important predictor of SARS-CoV-2 infection. After infection, race was associated with increased hospitalization risk but not mortality. PRIMARY FUNDING SOURCE: The Permanente Medical Group, Inc.
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Prueba de COVID-19 , COVID-19/diagnóstico , COVID-19/etnología , Neumonía Viral/diagnóstico , Neumonía Viral/etnología , APACHE , Adulto , Anciano , COVID-19/mortalidad , California/epidemiología , Comorbilidad , Prestación Integrada de Atención de Salud , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Incidencia , Masculino , Persona de Mediana Edad , Neumonía Viral/mortalidad , Neumonía Viral/virología , Características de la Residencia , Estudios Retrospectivos , Factores de Riesgo , SARS-CoV-2 , Índice de Severidad de la EnfermedadRESUMEN
Importance: Identifying the most efficient COVID-19 vaccine allocation strategy may substantially reduce hospitalizations and save lives while ensuring an equitable vaccine distribution. Objective: To simulate the association of different vaccine allocation strategies with COVID-19-associated morbidity and mortality and their distribution across racial and ethnic groups. Design Setting and Participants: We developed and internally validated the risk of COVID-19 infection and risk of hospitalization models on randomly split training and validation data sets. These were used in a computer simulation study of vaccine prioritization among adult health plan members who were drawn from an integrated health care delivery system. The study was conducted from January 3, 2021, to June 1, 2021, in Oakland, California, and the data were analyzed during the same period. Main Outcomes and Measures: We simulated the association of different vaccine allocation strategies, including (1) random, (2) a US Centers for Disease Control and Prevention (CDC) proxy, (3) age based, and (4) combinations of models for the risk of adverse outcomes (CRS) and COVID-19 infection (PROVID), with COVID-19-related hospitalizations between May 1, 2020, and December 31, 2020, that were randomly permuted by month across 250 simulations and assessed vaccine allocation by race and ethnicity and the neighborhood deprivation index across time. Results: The study included 3 202 679 adult patients (mean [SD] age, 48.2 [18.0] years; 1 677 637 women [52.4%]; 1 525 042 men [47.6%]; 611 154 Asian [19.1%], 206 363 Black [6.4%], 642 344 Hispanic [20.1%], and 1 390 638 White individuals [43.4%]), of whom 36 137 (1.1%) were positive for SARS-CoV-2. A risk-based strategy (CRS/PROVID) showed the largest avoidable hospitalization estimates (4954; 95% CI, 3452-5878) followed by age-based (4362; 95% CI, 2866-5175) and CDC proxy (4085; 95% CI, 2805-5109) strategies. Random vaccination showed substantially lower reductions in adverse outcomes. Risk-based strategies also showed the largest number of avoidable COVID-19 deaths (joint CRS/PROVID) and household transmissions. Risk-based (PROVID) and CDC proxy strategies were estimated to vaccinate the highest percentage of Hispanic and Black patients in 8 months (joint CRS/PROVID: 642 570 [100%] Hispanic, 185 530 [90%] Black; PROVID: 642 570 [100%] Hispanic, 198 480 [96%] Black; CDC proxy: 605 770 [95%] Hispanic and 151 772 [74%] Black) compared with an age-based approach (438 423 [68%] Hispanic, 154 714 [75%] Black). Overall, the PROVID and joint CRS/PROVID risk-based strategies were estimated to be followed by the most patients from areas with high neighborhood deprivation index being vaccinated early. Conclusions and Relevance: In this simulation modeling study of adults from a large integrated health care delivery system, risk-based strategies were associated with the largest estimated reductions in COVID-19 hospitalizations, deaths, and household transmissions compared with the CDC proxy and age-based strategies, with a higher proportion of Hispanic and Black patients were estimated to be vaccinated early in the process compared with the CDC strategy.
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COVID-19 , Etnicidad , Adulto , COVID-19/prevención & control , Vacunas contra la COVID-19/uso terapéutico , Simulación por Computador , Femenino , Humanos , Masculino , Persona de Mediana Edad , SARS-CoV-2 , VacunaciónRESUMEN
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to develop a predictive model suitable for real-time use with an electronic medical record. We used a retrospective cohort study design with split validation. The denominator consisted of women admitted to a delivery service. The numerator consisted of women who experienced a composite outcome that included both maternal (eg, uterine rupture, postpartum hemorrhage), fetal (eg, stillbirth), and neonatal (eg, hypoxic ischemic encephalopathy) adverse events. We employed machine learning methods, assessing model performance using the area under the receiver operator characteristic curve and number needed to evaluate. A total of 303,678 deliveries took place at 15 study hospitals between January 1, 2010, and March 31, 2018, and 4130 (1.36%) had ≥1 obstetrical complication. We employed data from 209,611 randomly selected deliveries (January 1, 2010, to March 31, 2017) as a derivation dataset and validated our findings on data from 52,398 randomly selected deliveries during the same time period (validation 1 dataset). We then applied our model to data from 41,669 deliveries from the last year of the study (April 1, 2017, to March 31, 2018 [validation 2 dataset]). Our model included 35 variables (eg, demographics, vital signs, laboratory tests, progress of labor indicators). In the validation 2 dataset, a gradient boosted model (area under the receiver operating characteristic curve or c statistic, 0.786) was slightly superior to a logistic regression model (c statistic, 0.778). Using an alert threshold of 4.1%, our final model would flag 16.7% of women and detect 52% of adverse outcomes, with a number needed to evaluate of 20.9 and 0.455 first alerts per day per 1000 annual deliveries. In conclusion, electronic medical record data can be used to predict obstetrical complications. The clinical utility of these automated models has not yet been demonstrated. To conduct interventions to assess whether using these models results in patient benefit, future work will need to focus on the development of clinical protocols suitable for use in interventions.
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Reglas de Decisión Clínica , Registros Electrónicos de Salud , Hipoxia-Isquemia Encefálica/epidemiología , Aprendizaje Automático , Complicaciones del Trabajo de Parto/epidemiología , Preeclampsia/epidemiología , Mortinato/epidemiología , Adulto , Presión Sanguínea , Femenino , Humanos , Edad Materna , Obesidad Materna/epidemiología , Paridad , Hemorragia Posparto/epidemiología , Embarazo , Nacimiento Prematuro/epidemiología , Reproducibilidad de los Resultados , Estudios Retrospectivos , Datos de Salud Recolectados Rutinariamente , Factores de Tiempo , Rotura Uterina/epidemiologíaRESUMEN
OBJECTIVE: This study aimed to evaluate the performance of the California Maternal Quality Care Collaborative (CMQCC) admission risk criteria for stratifying postpartum hemorrhage risk in a large obstetrics population. STUDY DESIGN: Using detailed electronic health record data, we classified 261,964 delivery hospitalizations from Kaiser Permanente Northern California hospitals between 2010 and 2017 into high-, medium-, and low-risk groups based on CMQCC criteria. We used logistic regression to assess associations between CMQCC risk groups and postpartum hemorrhage using two different postpartum hemorrhage definitions, standard postpartum hemorrhage (blood loss ≥1,000 mL) and severe postpartum hemorrhage (based on transfusion, laboratory, and blood loss data). Among the low-risk group, we also evaluated associations between additional present-on-admission factors and severe postpartum hemorrhage. RESULTS: Using the standard definition, postpartum hemorrhage occurred in approximately 5% of hospitalizations (n = 13,479), with a rate of 3.2, 10.5, and 10.2% in the low-, medium-, and high-risk groups. Severe postpartum hemorrhage occurred in 824 hospitalizations (0.3%), with a rate of 0.2, 0.5, and 1.3% in the low-, medium-, and high-risk groups. For either definition, the odds of postpartum hemorrhage were significantly higher in medium- and high-risk groups compared with the low-risk group. Over 40% of postpartum hemorrhages occurred in hospitalizations that were classified as low risk. Among the low-risk group, risk factors including hypertension and diabetes were associated with higher odds of severe postpartum hemorrhage. CONCLUSION: We found that the CMQCC admission risk assessment criteria stratified women by increasing rates of severe postpartum hemorrhage in our sample, which enables early preparation for many postpartum hemorrhages. However, the CMQCC risk factors missed a substantial proportion of postpartum hemorrhages. Efforts to improve postpartum hemorrhage risk assessment using present-on-admission risk factors should consider inclusion of other nonobstetrical factors.
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Hemorragia Posparto/epidemiología , Medición de Riesgo , Adolescente , Adulto , California/epidemiología , Parto Obstétrico/métodos , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Modelos Logísticos , Embarazo , Factores de Riesgo , Índice de Severidad de la Enfermedad , Adulto JovenRESUMEN
Compared with adults who are admitted to general medical-surgical wards, women who are admitted to labor and delivery services are at much lower risk of experiencing unexpected critical illness. Nonetheless, critical illness and other complications that put either the mother or fetus at risk do occur. One potential approach to prevention is to use automated early warning systems, such as those used for nonpregnant adults. Predictive models that use data extracted in real time from electronic records constitute the cornerstone of such systems. This article addresses several issues that are involved in the development of such predictive models: specification of temporal characteristics, choice of denominator, selection of outcomes for model calibration, potential uses of existing adult severity of illness scores, approaches to data processing, statistical considerations, validation, and options for instantiation. These have not been addressed explicitly in the obstetrics literature, which has focused on the use of manually assigned scores. In addition, this article provides some results from work in progress to develop 2 obstetric predictive models with the use of data from 262,071 women who were admitted to a labor and delivery service at 15 Kaiser Permanente Northern California hospitals between 2010 and 2017.