Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.

BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients without obvious risk factors for postpartum hemorrhage with severe maternal morbidity. Current tools for hemorrhage risk assessment use lists of risk factors rather than predictive models. OBJECTIVE: To develop, validate (internally and externally), and compare a machine learning model for predicting PPH associated with SMM against a standard hemorrhage risk assessment tool in a lower risk laboring obstetric population. STUDY DESIGN: This retrospective cross-sectional study included clinical data from singleton, term births (>=37 weeks' gestation) at 19 US hospitals (2016-2021) using data from 58,023 births at 11 hospitals to train a generalized additive model (GAM) and 27,743 births at 8 held-out hospitals to externally validate the model. The outcome of interest was PPH with severe maternal morbidity (blood transfusion, hysterectomy, vascular embolization, intrauterine balloon tamponade, uterine artery ligation suture, uterine compression suture, or admission to intensive care). Cesarean birth without a trial of vaginal birth and patients with a history of cesarean were excluded. We compared the model performance to that of the California Maternal Quality Care Collaborative (CMQCC) Obstetric Hemorrhage Risk Factor Assessment Screen. RESULTS: The GAM predicted PPH with an area under the receiver-operating characteristic curve (AUROC) of 0.67 (95% CI 0.64-0.68) on external validation, significantly outperforming the CMQCC risk screen AUROC of 0.52 (95% CI 0.50-0.53). Additionally, the GAM had better sensitivity of 36.9% (95% CI 33.01-41.02) than the CMQCC screen sensitivity of 20.30% (95% CI 17.40-22.52) at the CMQCC screen positive rate of 16.8%. The GAM identified in-vitro fertilization as a risk factor (adjusted OR 1.5; 95% CI 1.2-1.8) and nulliparous births as the highest PPH risk factor (adjusted OR 1.5; 95% CI 1.4-1.6). CONCLUSION: Our model identified almost twice as many cases of PPH as the CMQCC rules-based approach for the same screen positive rate and identified in-vitro fertilization and first-time births as risk factors for PPH. Adopting predictive models over traditional screens can enhance PPH prediction.

Estimating the Burden and Distribution of Post-COVID-19 Condition in Washington State, March 2020-October 2023.

Introduction: After SARS-CoV-2 infection, some people will experience long-term sequelae known as post-COVID-19 condition (PCC). Although PCC is recognized as a public health problem, estimates of the prevalence of PCC are sparse. We described a framework for estimating the incidence and prevalence of PCC by population subgroups and geography over time in Washington State. Methods: We collected data on reported COVID-19 cases and hospitalizations and estimated SARS-CoV-2 infections in Washington State from March 2020 through October 2023. The reported case data were incorporated with parameter estimates from published articles and prevalence estimates from the Household Pulse Survey into a mathematical compartmental model of PCC progression. The model used differential equations to describe how the population of people with PCC moved through the model's various stages. This framework allowed us to integrate data on age group, sex, race and ethnicity, vaccination status, and county to estimate incidence and prevalence of PCC for each subgroup. Results: Our model indicated that 6.4% (95% CI, 5.9%-6.8%) of all adults in Washington State were experiencing PCC as of October 2023. In addition to temporal differences in PCC prevalence and incidence, we found substantial differences across age groups, race and ethnicity, and sex. Geographic heterogeneity was pronounced, with the highest rates of PCC in central and eastern Washington. Conclusion: Estimation of PCC prevalence is essential for addressing PCC as a public health problem. Responding to PCC will require continued surveillance, research, and dedicated financial and public health action. This analysis, accounting for heterogeneities, highlights disparities in the prevalence, incidence, and distribution of PCC in Washington State and can better guide awareness and response efforts.

Quantifying abnormal emotion processing: A novel computational assessment method and application in schizophrenia.

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or "emotional alignment" (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.

Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.

BACKGROUND AND AIMS: Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. METHODS: A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. RESULTS: Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. CONCLUSIONS: Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.

Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes.

Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.

Association of Reproductive Autonomy and Rates of State-Level Racialized Disparities in Preterm Birth and Low Birthweight.

Introduction: Reproductive policies' impact on disparities in neonatal outcomes is understudied. Thus, we aimed to assess whether an index of reproductive autonomy is associated with black-white disparities in preterm birth (PTB) and low birthweight (LBW). Methods: We used publicly available state-level PTB and LBW data for all live-births among persons aged 15-44 from January 1, 2016, to December 31, 2018. The independent measure was an index of state laws characterizing each state's reproductive autonomy, ranging from 5 (most restrictive) to 43 (most enabling), used continuously and as quartiles. Linear regression was performed to evaluate the association between both the index score (continuous, primary analysis; quartiles, secondary analysis) and state-level aggregated black-white disparity rates in PTB and LBW per 100 live births. Results: Among 10,297,437 black (n=1,829,051 [17.8%]) and white (n=8,468,386 [82.2%]) births, rates of PTB and LBW were 6.46 and 8.24 per 100, respectively. Regression models found that every 1-U increase in the index was associated with a -0.06 (confidence interval [CI]: -0.10 to -0.01) and -0.05 (CI: -0.08, to -0.01) per 100 lower black-white disparity in PTB and LBW rates (p<0.05, p<0.01), respectively. The most enabling quartiles were associated with -1.21 (CI: -2.38 to -0.05) and -1.62 (CI: -2.89 to -0.35) per 100 lower rates of the black-white disparity in LBW, compared with the most restrictive quartile (both p<0.05). Conclusion: Greater reproductive autonomy is associated with lower rates of state-level disparities in PTB and LBW. More research is needed to better understand the importance of state laws in shaping racialized disparities, reproductive autonomy, and birth outcomes.

Development of a Composite Risk Index of Reproductive Autonomy Using State Laws: Association With Maternal and Neonatal Outcomes.

OBJECTIVE: We developed a composite index to quantify state legislation related to reproductive autonomy and examined its association with maternal and neonatal outcomes. We hypothesized that greater reproductive autonomy would be associated with lower rates of severe maternal morbidity (SMM), pregnancy-related mortality (PRM), preterm birth (PTB), and low birthweight. DESIGN: A Delphi panel was used to inform development of the index. Restrictive policies were assigned values of -1 and enabling policies +1. Publicly available data were used to conduct a cross-sectional study among all live births in the 50 U.S. states to people aged 15 to 44 between January 1, 2016, and December 31, 2018, to examine the association between the risk index and PRM, SMM, PTB, and low birthweight. We used linear regression with state scores and quartiles, adjusted for state-level proportions of White, Black, and Hispanic live births; percent living in rural areas; percent of population foreign born; Health Resources and Services Administration spending on maternal and child health; and the Opportunity Index, a composite measure of indicators of the economy, education, and community. RESULTS: From 2016 to 2018, there were 11,530,785 births, 2,846 pregnancy-related deaths, and 154,384 cases of SMM. The Delphi panel resulted in a summed state measure of 106 laws in 8 categories that could affect reproductive autonomy. In adjusted analyses, states in the most enabling (most reproductive autonomy) quartile had a 44.7 per 10,000 higher rate of SMM compared with the most restrictive quartile. However, the most enabling quartile was associated with a 9.87 per 100,000 lower rate of PRM and 0.67 per 100 lower rate of PTB compared with the most restrictive quartile (least reproductive autonomy). CONCLUSIONS: A composite policy index of reproductive autonomy was found to be associated with higher rates of SMM but lower rates of PRM and PTB. Further research is needed to understand how reproductive autonomy in the cumulative index may influence these and other maternal and birth outcomes.

Pregnancy loss and risk of incident CVD within 5 years: Findings from the Women's Health Initiative.

Background: Previous studies have demonstrated an increased risk of cardiovascular disease (CVD) in women with a history of pregnancy loss. Less is known about whether pregnancy loss is associated with age at the onset of CVD, but this is a question of interest, as a demonstrated association of pregnancy loss with early-onset CVD may provide clues to the biological basis of the association, as well as having implications for clinical care. We conducted an age-stratified analysis of pregnancy loss history and incident CVD in a large cohort of postmenopausal women aged 50-79 years old. Methods: Associations between a history of pregnancy loss and incident CVD were examined among participants in the Women's Health Initiative Observational Study. Exposures were any history of pregnancy loss (miscarriage and/or stillbirth), recurrent (2+) loss, and a history of stillbirth. Logistic regression analyses were used to examine associations between pregnancy loss and incident CVD within 5 years of study entry in three age strata (50-59, 69-69, and 70-79). Outcomes of interest were total CVD, coronary heart disease (CHD), congestive heart failure, and stroke. To assess the risk of early onset CVD, Cox proportional hazard regression was used to examine incident CVD before the age of 60 in a subset of subjects aged 50-59 at study entry. Results: After adjustment for cardiovascular risk factors, a history of stillbirth was associated with an elevated risk of all cardiovascular outcomes in the study cohort within 5 years of study entry. Interactions between age and pregnancy loss exposures were not significant for any cardiovascular outcome; however, age-stratified analyses demonstrated an association between a history of stillbirth and risk of incident CVD within 5 years in all age groups, with the highest point estimate seen in women aged 50-59 (OR 1.99; 95% CI, 1.16-3.43). Additionally, stillbirth was associated with incident CHD among women aged 50-59 (OR 3.12; 95% CI, 1.33-7.29) and 60-69 (OR 2.06; 95% CI, 1.24-3.43) and with incident heart failure and stroke among women aged 70-79. Among women aged 50-59 with a history of stillbirth, a non-significantly elevated hazard ratio was observed for heart failure before the age of 60 (HR 2.93, 95% CI, 0.96-6.64). Conclusions: History of stillbirth was strongly associated with a risk of cardiovascular outcomes within 5 years of baseline in a cohort of postmenopausal women aged 50-79. History of pregnancy loss, and of stillbirth in particular, might be a clinically useful marker of cardiovascular disease risk in women.

Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.

Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility-affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with 7 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. METHODS: Our study includes individuals with positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) in the Washington Disease Reporting System with available viral genome data, from 1 December 2020 to 14 January 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. RESULTS: In total, 58 848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95% confidence interval [CI] 2.40-4.26), Beta (HR 2.85, 95% CI 1.56-5.23), Delta (HR 2.28 95% CI 1.56-3.34), or Alpha (HR 1.64, 95% CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95% CI .56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSIONS: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Exclusive Breastfeeding in the Northwest: Disparities Related to Race/Ethnicity and Substance Use.

BACKGROUND AND OBJECTIVES: Mothers who are Black, Indigenous, and people of color (BIPOC) are disproportionately impacted by substance use in pregnancy and less likely to breastfeed. Our objectives were to assess relationships between substance use in pregnancy and exclusive breastfeeding at discharge (EBF) and race/ethnicity and EBF, and determine the extent to which substance use influences the relationship between race/ethnicity and EBF. METHODS: This is a retrospective cohort study of term mother-infant dyads using 2016 to 2019 data from a Northwest quality improvement collaborative, Obstetrical Care Outcomes Assessment Program. Stepwise and stratified multivariable logistic regression analyses were conducted to determine associations between independent variables consisting of characteristics, including maternal race/ethnicity and substance use, and the dependent variable, EBF. RESULTS: Our sample consisted of 84,742 dyads, 69.5% of whom had EBF. The adjusted odds of EBF for non-Hispanic Black and Hispanic mothers were half, and for American Indian/Alaska Native mothers two-thirds, that of White mothers (aOR [95% CI]: 0.52 [0.48, 0.57], 0.51 [0.48, 0.54], 0.64 [0.55, 0.76], respectively). Substance use did not mediate the association between race/ethnicity and EBF, but it modified the association. Among those reporting nicotine or marijuana use, Hispanic mothers were half as likely as White mothers were to exclusively breastfeed. Other factors associated with a lower likelihood of EBF included public or no insurance, rural setting, C-section, NICU admission, and LBW. CONCLUSIONS: Disparities in EBF related to race/ethnicity and substance use were pronounced in this study, particularly among Hispanic mothers with nicotine or marijuana use.

A retrospective cohort study of race/ethnicity, pre-pregnancy weight, and pregnancy complications.

OBJECTIVE: To examine the relationship between race/ethnicity, pre-pregnancy overweight/obesity status, and pregnancy complications. METHODS: We conducted a retrospective cohort study among mothers with singleton live births using data from hospitals contributing to the Obstetrical Care Outcomes Assessment Program database (N = 72,697). Race was categorized as Non-Hispanic (NH) White, NH African-American, Hispanic, NH Asian, NH American Indian/Alaskan Native, and NH Native-Hawaiian/Other Pacific Islander. Pre-pregnancy overweight/obesity status was defined as body mass index (BMI)≥25 kg/m2. Pregnancy complications evaluated were gestational diabetes, pre-eclampsia, and cesarean delivery. We fitted adjusted and unadjusted stratified Poisson regression models with robust standard errors. Interaction terms were used to assess statistical significance of interactions between race/ethnicity and pre-pregnancy overweight/obesity status. RESULTS: Most women were NH White (52.1%) and more than half had overweight/obesity (54.3%). Among women with overweight/obesity, Hispanics had a lower risk of cesarean delivery as compared to NH White (adjusted relative risk, aRR:0.89; 95%CI:0.84-0.93). Similarly, among women with overweight/obesity, Hispanic and NH Native-Hawaiian/Other Pacific Islander had a lower risk of preeclampsia (aRR:0.74; 95%CI:0.66-0.82 and aRR:0.64; 95%CI:0.44-0.92, respectively) and NH African-American had a greater risk of gestational diabetes (aRR:1.23; 95%CI:1.07-1.42) when compared with NH White women. These associations were not present among normal-weight women. Women with overweight/obesity, when compared with women of normal-weight, had an increased risk of gestational diabetes and cesarean delivery among all race/ethnicities except NH American Indian/Alaskan Native and NH Native-Hawaiian/Other Pacific Islander, respectively (p-values < .05). The multiplicative interaction terms between race/ethnicity and overweight/obesity status were significant for all three complications (interaction p-values < .05). CONCLUSION: Pre-pregnancy overweight/obesity status modifies associations of race/ethnicity with pregnancy complications. Conversely, race/ethnicity modifies associations of pre-pregnancy overweight/obesity status with pregnancy complications. Our findings have implications for public health and clinical practice, supporting the focus on healthy preconception weight and risk stratification across racial/ethnic groups.

Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study.

BACKGROUND: The COVID-19 pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with seven SARS-CoV-2 variants. METHODS: Our study includes individuals with positive SARS-CoV-2 RT-PCR in the Washington Disease Reporting System with available viral genome data, from December 1, 2020 to January 14, 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. FINDINGS: 58,848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95%CI 2.40-4.26), Beta (HR 2.85, 95%CI 1.56-5.23), Delta (HR 2.28 95%CI 1.56-3.34) or Alpha (HR 1.64, 95%CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95%CI 0.56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSION: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance. SUMMARY: Hospitalization risk following infection with SARS-CoV-2 variant remains unclear. We find a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta, but not Omicron, with vaccination lowering risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Personal health information management among healthy older adults: Varying needs and approaches.

OBJECTIVE: With age, older adults experience a greater number of chronic diseases and medical visits, and an increased need to manage their health information. Technological advances in consumer health information technologies (HITs) help patients gather, track, and organize their health information within and outside of clinical settings. However, HITs have not focused on the needs of older adults and their caregivers. The goal of the SOARING (Studying Older Adults and Researching their Information Needs and Goals) Project was to understand older adult personal health information management (PHIM) needs and practices to inform the design of HITs that support older adults. MATERIALS AND METHODS: Drawing on the Work System Model, we took an ecological approach to investigate PHIM needs and practices of older adults in different residential settings. We conducted in-depth interviews and surveys with adults 60 years of age and older. RESULTS: We performed on-site in-person interview sessions with 88 generally healthy older adults in various settings including independent housing, retirement communities, assisted living, and homelessness. Our analysis revealed 5 key PHIM activities that older adults engage in: seeking, tracking, organizing, sharing health information, and emergency planning. We identified 3 major themes influencing older adults' practice of PHIM: (1) older adults are most concerned with maintaining health and preventing illness, (2) older adults frequently involve others in PHIM activities, and (3) older adults' approach to PHIM is situational and context-dependent. DISCUSSION: Older adults' approaches to PHIM are dynamic and sensitive to changes in health, social networks, personal habits, motivations, and goals. CONCLUSIONS: PHIM tools that meet the needs of older adults should accommodate the dynamic nature of aging and variations in individual, organizational, and social contexts.

Properties of Thermal Analgesia in a Human Chronic Low Back Pain Model.

PURPOSE: For years, heat has been used for comfort and analgesia is recommended as a first-line therapy in many clinical guidelines. Yet, there are questions that remain about the actual effectiveness of heat for a condition as common as chronic low back pain, and factors such as time of onset, optimal temperature, and duration of effect. MATERIALS AND METHODS: A randomized double-blinded controlled trial was designed to compare the analgesic response to heat delivered via pulses at 45°C (experimental group, N=49) to steady heat at 37°C (control group, N=51) in subjects with longstanding low back pain. Treatment lasted 30 minutes with follow-up out to four hours. The hypothesis was that the experimental group would experience a higher degree of analgesia compared to the control group. Time of onset and duration of effect were also measured. RESULTS: Both groups were similar in average duration of pain (10.3 years). The primary outcome measure was pain reduction at 30 minutes after the end of treatment, using a 10-points numeric pain scale. Reduction in pain was greater for the experimental group than the control group (difference in mean reduction = 0.72, 95% CI 0.15-1.29, p = 0.014). Statistically significant differences in pain levels were observed from the first measure at 5 minutes of treatment through 120 minutes after completion of treatment. Reduction in pain associated movement was greater in the active heat group than the placebo group (p = 0.04). CONCLUSION: High-level pulsed heat (45°C) produced significantly more analgesia as compared to steady heat at 37°C at the primary end point and for an additional 2 hours after treatment. The onset of analgesia was rapid, <5 minutes of treatment. The results of this trial provide insight into the mechanisms and properties of thermal analgesia that are not well understood in a chronic low back pain model.

Association Between 90-Minute Door-to-Balloon Time, Selective Exclusion of Myocardial Infarction Cases, and Access Site Choice: Insights From the Cardiac Care Outcomes Assessment Program (COAP) in Washington State.

BACKGROUND: For patients presenting with ST-segment-elevation myocardial infarction, national quality initiatives monitor hospitals' proportion of cases with door-to-balloon (D2B) time under 90 minutes. Hospitals are allowed to exclude patients from reporting and may modify behavior to improve performance. We sought to identify whether there is a discontinuity in the number of cases included in the D2B time metric at 90 minutes and whether operators were increasingly likely to pursue femoral access in patients with less time to meet the 90-minute quality metric. METHODS: Adult patients with ST-segment-elevation myocardial infarction undergoing primary percutaneous coronary intervention from 2011 to 2018 were identified from the Cardiac Care Outcomes Assessment Program, a quality improvement registry in Washington state. We used the regression discontinuity framework to test for discontinuity at 90 minutes among the included cases. We defined a novel variable, remaining D2B as 90 minutes minus the time between hospital arrival and catheterization laboratory arrival. We estimated multivariable logistic regression models to assess the relationship between remaining D2B time and access site. RESULTS: A total of 19 348 patients underwent primary percutaneous coronary intervention and were included in the analysis. Overall, 7436 (38.4%) were excluded from the metric. There appeared to be a visual discontinuity in included cases around 90 minutes; however, local quadratic regression around the 90-minute cutoff did not reveal evidence of a significant discontinuity (P=0.66). Multivariable analysis showed no significant relationship between remaining D2B time and the odds of undergoing femoral access (P=0.73). CONCLUSIONS: Among patients undergoing percutaneous coronary intervention for ST-segment-elevation myocardial infarction, we did not find evidence of a statistically significant discontinuity in the frequency of included cases around 90 minutes or an increased preference for femoral access correlated with decreasing time to meet the 90-minute D2B time quality metric. Together, these findings indicate no evidence of widespread inappropriate methods to improve performance on D2B time metrics.

Older Adult Health-Related Support Networks: Implications for the Design of Digital Communication Tools.

BACKGROUND AND OBJECTIVES: Digital communication tools facilitate the provision of health-related social support to older adults. However, little is known about what design features make such tools most useful and feasible. The purpose of this study was to describe health-related social support networks of older adults and outline recommendations for the design of tools that facilitate the giving and receiving of support. RESEARCH DESIGN AND METHODS: We collected data through validated instruments and semistructured qualitative interviews with 88 older adults. We calculated descriptive statistics for the quantitative data and analyzed qualitative data using directed content analysis. RESULTS: Health-related support networks described by our sample of generally healthy older adults varied in size from 0 to 10 members. Some networks did not include any family members, and others did not include any friends. Seventy-four percent of network members lived in the same state as the older adult participant, but only 15% of them lived with the participant. Emotional support was the main type of health-related support provided, followed by instrumental, informational, and appraisal support. DISCUSSION AND IMPLICATIONS: Health-related support networks of older adults are varied and complex. Our results suggest that digital communication tools to promote and leverage support from network members should facilitate the involvement of friends and family regardless of their physical location, allow for the transparent allocation of concrete tasks to prevent overburdening any one network member, and facilitate sharing of personal health information with family and friends while ensuring privacy and autonomy.

Delays in recognition of the need for telephone-assisted CPR due to caller descriptions of chief complaint.

BACKGROUND: The objective of this study was to test if caller descriptions of chief complaint delays emergency medical dispatchers' (EMDs) recognition of the need for telephone-assisted CPR (T-CPR). METHODS: We conducted an analysis of N = 433 cardiac arrest calls from six large call centers in the United States. Calls were abstracted for initial chief complaint description: caller reports (1) correct medical condition (CMC); (2) incorrect medical condition (IMC), or (3) signs/symptoms only (SS), as well the time interval between call pickup and recognition of the need for T-CPR. In addition, we abstracted if EMDs asked questions related to the caller's chief complaint (rather than, or before), asking about patients' consciousness and breathing status. RESULTS: The majority of cardiac arrest calls (60%) were reported as SS. Median time to recognition of the need for T-CPR was 64 s for SS chief complaints, 47 s for CMC chief complaints, and 100 s for IMC chief complaints. EMDs pursued chief complaint descriptions for 9% of the calls with SS chief complaints, 41% of the calls with IMC chief complaints, and 19% of the calls with CMC chief complaints. Median time to recognition of the need for CPR for calls in which the chief complaint description was pursued was 166 s compared to 62 s for calls in which the chief complaint description was not pursued. CONCLUSION: Caller chief complaint description affects the time to recognition of the need for T-CPR. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov Trial # NCT01972087.

Enhanced Perioperative Care for Major Spine Surgery.

STUDY DESIGN: The enhanced perioperative care (EPOC) program is an institutional quality improvement initiative. We used a historically controlled study design to evaluate patients who underwent major spine surgery before and after the implementation of the EPOC program. OBJECTIVE: To determine whether multidisciplinary EPOC program was associated with an improvement in clinical and financial outcomes for elective adult major spine surgery patients. SUMMARY OF BACKGROUND DATA: The enhanced recovery after surgery (ERAS) programs successfully implemented in hip and knee replacement surgeries, and improved clinical outcomes and patient satisfaction. METHODS: We compared 183 subjects in traditional care (TRDC) group to 267 intervention period (EPOC) in a single academic quaternary spine surgery referral center. One hundred eight subjects in no pathway (NOPW) care group was also examined to exclude if the observed changes between the EPOC and TRDC groups might be due to concurrent changes in practice or population over the same time period. Our primary outcome variables were hospital and intensive care unit lengths of stay and the secondary outcomes were postoperative complications, 30-day hospital readmission and cost. RESULTS: In this highly complex patient population, we observed a reduction in mean hospital length of stay (HLOS) between TRDC versus EPOC groups (8.2 vs. 6.1 d, standard deviation [SD] = 6.3 vs. 3.6, P < 0.001) and intensive care unit length of stay (ILOS) (3.1 vs. 1.9 d, SD = 4.7 vs. 1.4, P = 0.01). The number (rate) of postoperative intensive care unit (ICU) admissions was higher for the TRDC n = 109 (60%) than the EPOC n = 129 (48%) (P = 0.02). There was no difference in postoperative complications and 30-day hospital readmissions. The EPOC spine program was associated with significant average cost reduction-$62,429 to $53,355 (P < 0.00). CONCLUSION: The EPOC program has made a clinically relevant contribution to institutional efforts to improve patient outcomes and value. We observed a reduction in HLOS, ILOS, costs, and variability. LEVEL OF EVIDENCE: 3.