Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults.

INTRODUCTION: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. METHODS: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. RESULTS: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). CONCLUSIONS: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

A binary trait model reveals the fitness effects of HIV-1 escape from T cell responses.

Natural selection often acts on multiple traits simultaneously. For example, the virus HIV-1 faces pressure to evade host immunity while also preserving replicative fitness. While past work has studied selection during HIV-1 evolution, it is challenging to quantitatively separate different contributions to fitness. This task is made more difficult because a single mutation can affect both immune escape and replication. Here, we develop an evolutionary model that disentangles the effects of escaping CD8+ T cell-mediated immunity, which we model as a binary trait, from other contributions to fitness. After validation in simulations, we applied this model to study within-host HIV-1 evolution in a clinical data set. We observed strong selection for immune escape, sometimes greatly exceeding past estimates, especially early in infection. Conservative estimates suggest that roughly half of HIV-1 fitness gains during the first months to years of infection can be attributed to T cell escape. Our approach is not limited to HIV-1 or viruses, and could be adapted to study the evolution of quantitative traits in other contexts.

Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data.

In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimate linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

popDMS infers mutation effects from deep mutational scanning data.

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

Characterizing Spatial Information Loss for Wastewater Surveillance Using crAssphage: Effect of Decay, Temperature, and Population Mobility.

Populations contribute information about their health status to wastewater. Characterizing how that information degrades in transit to wastewater sampling locations (e.g., wastewater treatment plants and pumping stations) is critical to interpret wastewater responses. In this work, we statistically estimate the loss of information about fecal contributions to wastewater from spatially distributed populations at the census block group resolution. This was accomplished with a hydrologically and hydraulically influenced spatial statistical approach applied to crAssphage (Carjivirus communis) load measured from the influent of four wastewater treatment plants in Hamilton County, Ohio. We find that we would expect to observe a 90% loss of information about fecal contributions from a given census block group over a travel time of 10.3 h. This work demonstrates that a challenge to interpreting wastewater responses (e.g., during wastewater surveillance) is distinguishing between a distal but large cluster of contributions and a near but small contribution. This work demonstrates new modeling approaches to improve measurement interpretation depending on sewer network and wastewater characteristics (e.g., geospatial layout, temperature variability, population distribution, and mobility). This modeling can be integrated into standard wastewater surveillance methods and help to optimize sewer sampling locations to ensure that different populations (e.g., vulnerable and susceptible) are appropriately represented.

An inference model gives insights into innate immune adaptation and repertoire diversity.

The innate immune system is the body's first line of defense against infection. Natural killer (NK) cells, a vital part of the innate immune system, help to control infection and eliminate cancer. Studies have identified a vast array of receptors that NK cells use to discriminate between healthy and unhealthy cells. However, at present, it is difficult to explain how NK cells will respond to novel stimuli in different environments. In addition, the expression of different receptors on individual NK cells is highly stochastic, but the reason for these variegated expression patterns is unclear. Here, we studied the recognition of unhealthy target cells as an inference problem, where NK cells must distinguish between healthy targets with normal variability in ligand expression and ones that are clear "outliers." Our mathematical model fits well with experimental data, including NK cells' adaptation to changing environments and responses to different target cells. Furthermore, we find that stochastic, "sparse" receptor expression profiles are best able to detect a variety of possible threats, in agreement with experimental studies of the NK cell repertoire. While our study was specifically motivated by NK cells, our model is general and could also apply more broadly to explain principles of target recognition for other immune cell types.

Cattle-FRETS71, a novel fluorogenic substrate with broad applicability for characterizing ADAMTS13 properties and function.

BACKGROUND: Current ADAMTS13 activity assays are important for diagnosing thrombotic thrombocytopenic purpura (TTP) but are unreliable to assay ADAMTS13 activity in animal models. The Cattle-FRETS71 assay is capable of detecting ADAMTS13 activity in plasma from multiple animal species, making it a potentially useful reagent at all stages of clinical research. The performance of Cattle-FRETS71 in TTP diagnosis is not yet known. OBJECTIVES: We evaluated the performance of the Cattle-FRETS71 substrate against the human FRETS-rVWF71 and the FRETS-VWF73 commercial substrates in human plasma and serum samples to validate its utility in diagnosing TTP in patients. METHODS: Internal validation was performed using heparinized plasma samples (n = 81). External validation was a blinded study using serum samples from the Oklahoma TTP Registry (n = 118, collected 2004-2014) that had been initially assayed by FRETS-VWF73 within 1 year of collection. Additional validation was performed with citrated plasma samples with variable ADAMTS13 activities (n = 32) that were analyzed by FRETS-VWF73. RESULTS: There was an excellent correlation (r = 0.94) between Cattle-FRETS71 and FRETS-rVWF71 for assayed heparinized plasma samples (n = 81). Assay results between Cattle-FRETS71 and FRETS-VWF73 of Oklahoma TTP Registry serum samples (n = 118) and citrated plasma samples (n = 32) were comparably good (r = 0.81 and r = 0.85, respectively). CONCLUSION: The Cattle-FRETS71 assay is comparable with other assays in quantifying ADAMTS13 activity in human plasma collected from patients with documented or suspected TTP. The versatility of Cattle-FRETS71, combined with its specificity and sensitivity, makes it a useful tool for the standardization of ADAMTS13 activity across basic and clinical research paradigms.

Small RNA sequencing of field Culex mosquitoes identifies patterns of viral infection and the mosquito immune response.

Mosquito-borne disease remains a significant burden on global health. In the United States, the major threat posed by mosquitoes is transmission of arboviruses, including West Nile virus by mosquitoes of the Culex genus. Virus metagenomic analysis of mosquito small RNA using deep sequencing and advanced bioinformatic tools enables the rapid detection of viruses and other infecting organisms, both pathogenic and non-pathogenic to humans, without any precedent knowledge. In this study, we sequenced small RNA samples from over 60 pools of Culex mosquitoes from two major areas of Southern California from 2017 to 2019 to elucidate the virome and immune responses of Culex. Our results demonstrated that small RNAs not only allowed the detection of viruses but also revealed distinct patterns of viral infection based on location, Culex species, and time. We also identified miRNAs that are most likely involved in Culex immune responses to viruses and Wolbachia bacteria, and show the utility of using small RNA to detect antiviral immune pathways including piRNAs against some pathogens. Collectively, these findings show that deep sequencing of small RNA can be used for virus discovery and surveillance. One could also conceive that such work could be accomplished in various locations across the world and over time to better understand patterns of mosquito infection and immune response to many vector-borne diseases in field samples.

Optical and physical properties of annealed amorphous niobium oxide thin films.

The physical and optical properties of coatings deposited using physical vapor deposition techniques exhibit changes during post deposition annealing. Optical properties including the index of refraction and spectral transmission vary when coatings are annealed. Physical and mechanical properties such as thickness, density, and stress are also impacted by annealing. In this paper, we study the source of these changes by examining the impact of 150-500°C annealing on N b 2 O 5 films deposited using thermal evaporation and reactive magnetron sputtering. Models based on the Lorentz-Lorenz equation and potential energy considerations explain the data, and rationalize conflicts between previously reported results.

Care plan for individuals at risk for preeclampsia: shared approach to education, strategies for prevention, surveillance, and follow-up.

Preeclampsia is a multisystemic disorder of pregnancy that affects 250,000 pregnant individuals in the United States and approximately 10 million worldwide per annum. Preeclampsia is associated with substantial immediate morbidity and mortality but also long-term morbidity for both mother and offspring. It is now clearly established that a low dose of aspirin given daily, beginning early in pregnancy modestly reduces the occurrence of preeclampsia. Low-dose aspirin seems safe, but because there is a paucity of information about long-term effects on the infant, it is not recommended for all pregnant individuals. Thus, several expert groups have identified clinical factors that indicate sufficient risk to recommend low-dose aspirin preventive therapy. These risk factors may be complemented by biochemical and/or biophysical tests that either indicate increased probability of preeclampsia in individuals with clinical risk factors, or more importantly, identify increased likelihood in those without other evident risk. In addition, the opportunity exists to provide this population with additional care that may prevent or mitigate the short- and long-term effects of preeclampsia. Patient and provider education, increased surveillance, behavioral modification, and other approaches to improve outcomes in these individuals can improve the chance of a healthy outcome. We assembled a group with diverse, relevant expertise (clinicians, investigators, advocates, and public and private stakeholders) to develop a care plan in which providers and pregnant individuals at risk can work together to reduce the risk of preeclampsia and associated morbidities. The plan is for care of individuals at moderate to high risk for developing preeclampsia, sufficient to receive low-dose aspirin therapy, as identified by clinical and/or laboratory findings. The recommendations are presented using the GRADE methodology with the quality of evidence upon which each is based. In addition, printable appendices with concise summaries of the care plan's recommendations for patients and healthcare providers are provided. We believe that this shared approach to care will facilitate prevention of preeclampsia and its attendant short- and long-term morbidity in patients identified as at risk for development of this disorder.

Bézier interpolation improves the inference of dynamical models from data.

Many dynamical systems, from quantum many-body systems to evolving populations to financial markets, are described by stochastic processes. Parameters characterizing such processes can often be inferred using information integrated over stochastic paths. However, estimating time-integrated quantities from real data with limited time resolution is challenging. Here, we propose a framework for accurately estimating time-integrated quantities using Bézier interpolation. We applied our approach to two dynamical inference problems: Determining fitness parameters for evolving populations and inferring forces driving Ornstein-Uhlenbeck processes. We found that Bézier interpolation reduces the estimation bias for both dynamical inference problems. This improvement was especially noticeable for data sets with limited time resolution. Our method could be broadly applied to improve accuracy for other dynamical inference problems using finitely sampled data.

Estimating linkage disequilibrium and selection from allele frequency trajectories.

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study.

BACKGROUND: The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. OBJECTIVE: This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. METHODS: Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants' intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. RESULTS: The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants' age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. CONCLUSIONS: This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Maternal Sepsis: A Review of National and International Guidelines.

Sepsis is a life-threatening syndrome caused by the body's response to infection. The Global Maternal Sepsis Study (GLOSS) suggests sepsis plays a larger role in maternal morbidity and mortality than previously thought. We therefore sought to compare national and international guidelines for maternal sepsis to determine their consistency with each other and the Third International Consensus for Sepsis and Septic Shock (SEPSIS-3). Using Cochrane Database of Systematic Reviews, PubMed, Google Scholar, and organization Web sites, we identified seven guidelines on maternal sepsis in the English language-The American College of Obstetricians and Gynecologists, Society for Maternal-Fetal Medicine, Royal Australian and New Zealand College of Obstetricians and Gynaecologists, Society of Obstetric Medicine of Australia and New Zealand, Royal College of Obstetricians and Gynaecologists, Royal College of Physicians of Ireland Institute of Obstetricians and Gynaecologists, and World Health Organization. Guidelines were reviewed to ascertain the commonality and variation, if any, in definitions of maternal sepsis, tools and criteria utilized for diagnosis, obstetric warning systems used, as well as evaluation and management of maternal sepsis. These variables were also compared with SEPSIS-3. All guidelines provided definitions consistent with a version of the SEPSIS, although the specific version utilized were varied. Clinical variables and tools employed for diagnosis of maternal sepsis were also varied. Evaluation and management of maternal sepsis and septic shock were similar. In conclusion, national and international maternal sepsis guidelines were incongruent with each other and SEPSIS-3 in diagnostic criteria and tools but similar in evaluation and management recommendations. KEY POINTS: · Definitions for maternal sepsis and septic shock are varied.. · Maternal sepsis guidelines differ in proposed criteria and tools.. · Maternal sepsis guidelines have similar management recommendations..

Gestational hypertension and "severe" disease: time for a change.

Our understanding and management of gestational hypertension and its variants are substantially hindered by a reliance on antiquated terminology and on practice recommendations based largely on tradition rather than outcomes-based evidence. Unsurprisingly, gestational hypertension remains a major contributor to maternal and neonatal morbidity and mortality rates, with little improvement seen over the past half century except as it relates to better newborn care. Reliance on a binary classification of vastly disparate types and degrees of organ dysfunction (severe or not severe) and the use of nonphysiological and largely arbitrary gestational age cutoffs are particularly problematic. If this situation is to improve, it will be necessary to abandon current misleading terminology and non-evidence-based traditional practice patterns and start again, building on management approaches validated by outcomes-based data.

Development of the Sepsis-Associated Adverse Outcomes in Pregnancy Score.

OBJECTIVE: This study aimed to develop and evaluate a scoring system-called the Sepsis-Associated Adverse Outcomes in Pregnancy (SAAP) Score-to identify individuals with maternal infection that have composite maternal adverse outcomes (CMAO). STUDY DESIGN: Using the International Classification of Disease codes, we identified pregnant and postpartum (up to 6 weeks after birth) individuals admitted at our center with a primary diagnosis of infection. The primary outcome was CMAO which included any of the following: maternal intensive care unit admission, surgical intervention, vasopressor use, acute respiratory distress syndrome, pulmonary edema, mechanical ventilation, high-flow nasal cannula, disseminated intravascular coagulation, dialysis, organ failure, venous thromboembolism, or maternal death. Regularized logistic regression was used to identify variables that best discriminate CMAO status. Variables were chosen for inclusion following evaluation of statistical and clinical significance. Model performance was evaluated using area under the curve (AUC) with 95% confidence intervals (CIs), sensitivity, specificity, and predictive values. RESULTS: Of the 23,235 deliveries during the study period, 227 (0.9%) individuals met inclusion criteria and among them CMAO occurred in 39.2% (95% CI: 33.1-45.7%). The SAAP score consisted of six variables (white blood cell count, systolic blood pressure, respiratory rate, heart rate, lactic acid, and abnormal diagnostic imaging) with scores ranging from 0 to 11 and a score of ≥7 being abnormal. An abnormal SAAP score had an AUC of 0.80 (95% CI: 0.74-0.86) for CMAO. The sensitivity and specificity of the SAAP score for CMAO was 0.71 (95% CI: 0.60-0.80) and 0.73 (95% CI: 0.64-0.80), respectively. The positive predictive value was 0.62 (95% CI: 0.52-0.72) and negative predictive value was 0.79 (95% CI: 0.71-0.86). CONCLUSION: Pending external validation, the sixth variable SAAP score may permit early recognition of pregnant and postpartum individuals with infection who are likely to develop adverse maternal outcomes. KEY POINTS: · Sepsis is a leading cause of maternal morbidity and mortality.. · Early recognition improves maternal sepsis outcomes.. · The SAAP score may permit early recognition of maternal adverse outcomes due to infection..

Inferring Epistasis from Genetic Time-series Data.

Epistasis refers to fitness or functional effects of mutations that depend on the sequence background in which these mutations arise. Epistasis is prevalent in nature, including populations of viruses, bacteria, and cancers, and can contribute to the evolution of drug resistance and immune escape. However, it is difficult to directly estimate epistatic effects from sampled observations of a population. At present, there are very few methods that can disentangle the effects of selection (including epistasis), mutation, recombination, genetic drift, and genetic linkage in evolving populations. Here we develop a method to infer epistasis, along with the fitness effects of individual mutations, from observed evolutionary histories. Simulations show that we can accurately infer pairwise epistatic interactions provided that there is sufficient genetic diversity in the data. Our method also allows us to identify which fitness parameters can be reliably inferred from a particular data set and which ones are unidentifiable. Our approach therefore allows for the inference of more complex models of selection from time-series genetic data, while also quantifying uncertainty in the inferred parameters.

State-of-the-Art Sensors Research in Ireland.

This Special Issue captures a significant portion of the current sensors research excellence in Ireland [...].

The Views and Needs of People With Parkinson Disease Regarding Wearable Devices for Disease Monitoring: Mixed Methods Exploration.

BACKGROUND: Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. OBJECTIVE: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. METHODS: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. RESULTS: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. CONCLUSIONS: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease-related health barriers and the users' confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

Clinical findings predictive of maternal adverse outcomes with pyelonephritis.

BACKGROUND: Pyelonephritis, a leading cause of infection during pregnancy, is associated with increased maternal adverse outcomes. Therefore, early recognition and intervention of pyelonephritis are important. Early recognition of pyelonephritis has historically been dependent on fever and costovertebral angle tenderness. However, the reliability of these findings to distinguish women who will develop maternal adverse outcomes is limited. OBJECTIVE: This study aimed to identify clinical variables on presentation that are predictive of maternal adverse outcomes associated with pyelonephritis during pregnancy. STUDY DESIGN: A retrospective cohort study of pregnant women admitted with pyelonephritis at a single hospital from October 1, 2015, to July 31, 2021, was conducted. The primary outcome was a composite maternal adverse outcome consisting of any of the following: maternal intensive care unit admission, surgical intervention, hypotension requiring vasopressors, acute respiratory distress syndrome, pulmonary edema, mechanical ventilation, high-flow nasal cannula, disseminated intravascular coagulation, altered mental status, dialysis, organ failure, venous thromboembolism, or maternal death. Differences in maternal characteristics and clinical signs and symptoms on admission were stratified by the presence or absence of a maternal adverse outcome. Categorical variables were analyzed using the Fisher exact test. Continuous data were analyzed with Wilcoxon rank-sum test. Sensitivity and specificity and likelihood ratios with 95% confidence intervals were calculated for the detection of maternal adverse outcomes by specified admission physical examination and laboratory findings. RESULTS: Of 97 women that met the inclusion criteria, 28 (28.9%) had maternal adverse outcomes. Moreover, 50 of 97 women (51.5%) had fever on presentation, with no difference between cohorts recognized. Admission vital signs were not significantly different between cohorts. Costovertebral angle tenderness was present in 78 of 97 women (80.4%) on presentation, with no difference between cohorts. Compared with women without a maternal adverse outcome, women with maternal adverse outcomes were more likely to have leukocytosis (18/28 [64.3%] vs 20/69 [29.0%]), thrombocytosis (3/28 [10.7%] vs 5/69 [7.2%]), bandemia (8/28 [28.6%] vs 7/69 [10.1%]), and an abnormal serum potassium (20/28 [71.4%] vs 32/63 [50.8%]). CONCLUSION: Admission vital signs may not reliably identify women with pyelonephritis at risk of maternal adverse outcomes. Laboratory studies, particularly a complete blood count and electrolytes, may provide a means of distinguishing those at risk of maternal adverse outcomes.