Context:
Premature delivery is an important risk factor for child mortality and psychiatric, metabolic, and cardiovascular disease later in life. In the majority of cases, the cause of prematurity ...cannot be identified. Currently, it remains controversial whether abnormal maternal thyroid function during pregnancy increases the risk of premature delivery. Therefore, we investigated the relation between maternal serum thyroid parameters and the risk of premature delivery in a large prospective population-based study.
Design:
Serum TSH, free T4 (FT4), T4, and TPO antibodies (TPOAbs) were determined during early pregnancy in 5971 pregnant women from the Generation R study. Data were available on maternal age, parity, smoking, socioeconomic status, ethnicity, maternal anthropometrics, and urinary iodine levels.
Results:
Of all women, 5.0% had a premature delivery (<37 weeks), 4.4% had a spontaneous premature delivery, and 1.4% had a very premature delivery (<34 weeks). High TSH levels and subclinical hypothyroidism were associated with premature delivery but not with spontaneous premature delivery. Maternal hypothyroxinemia was associated with a 2.5-fold increased risk of premature delivery, a 3.4-fold increased risk of spontaneous premature delivery, and a 3.6-fold increased risk of very premature delivery (all P < .01). TPOAb positivity was associated with a 1.7-fold increased risk of premature delivery (P = .01), a 2.1-fold increased risk of spontaneous premature delivery (P = .02), and a 2.5-fold increased risk of very premature delivery (P = .04). These effects remained similar after correction for TSH and FT4 levels.
Conclusions:
Hypothyroxinemia and TPOAb positivity are associated with an increased risk of premature delivery. The increased risk in TPOAb-positive women seems to be independent of thyroid function.
Intensive care units (ICUs) are increasingly interested in assessing and improving their performance. ICU Length of Stay (LoS) could be seen as an indicator for efficiency of care. However, little ...consensus exists on which prognostic method should be used to adjust ICU LoS for case-mix factors. This study compared the performance of different regression models when predicting ICU LoS. We included data from 32,667 unplanned ICU admissions to ICUs participating in the Dutch National Intensive Care Evaluation (NICE) in the year 2011. We predicted ICU LoS using eight regression models: ordinary least squares regression on untransformed ICU LoS,LoS truncated at 30 days and log-transformed LoS; a generalized linear model with a Gaussian distribution and a logarithmic link function; Poisson regression; negative binomial regression; Gamma regression with a logarithmic link function; and the original and recalibrated APACHE IV model, for all patients together and for survivors and non-survivors separately. We assessed the predictive performance of the models using bootstrapping and the squared Pearson correlation coefficient (R2), root mean squared prediction error (RMSPE), mean absolute prediction error (MAPE) and bias. The distribution of ICU LoS was skewed to the right with a median of 1.7 days (interquartile range 0.8 to 4.0) and a mean of 4.2 days (standard deviation 7.9). The predictive performance of the models was between 0.09 and 0.20 for R2, between 7.28 and 8.74 days for RMSPE, between 3.00 and 4.42 days for MAPE and between -2.99 and 1.64 days for bias. The predictive performance was slightly better for survivors than for non-survivors. We were disappointed in the predictive performance of the regression models and conclude that it is difficult to predict LoS of unplanned ICU admissions using patient characteristics at admission time only.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in ...the number of patients per nurse they had to take care of.
The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups.
We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data.
Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care.
With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse.
Context:
Maternal hyperthyroidism during pregnancy is associated with an increased risk of low birth weight, predisposing to neonatal morbidity and mortality. However, the effects of variation in ...maternal serum thyroid parameters within the normal range on birth weight are largely unknown.
Objective:
The aim was to study the effects of early pregnancy maternal serum thyroid parameters within the normal range on birth weight, as well as the relation between umbilical cord thyroid parameters and birth weight.
Design, Setting, and Participants:
In early pregnancy, serum TSH, FT4 (free T4), and thyroid peroxidase antibody levels were determined in 4464 pregnant women. Cord serum TSH and FT4 levels were determined in 2724 newborns. Small size for gestational age at birth (SGA) was defined as a gestational age-adjusted birth weight below the 2.5th percentile. The associations between normal-range maternal and cord thyroid parameters, birth weight, and SGA were studied using regression analyses.
Results:
In mothers with normal-range FT4 and TSH levels, higher maternal FT4 levels were associated with lower birth weight β = −15.4 (3.6) g/pmol · liter, mean (se); P = 1.6 × 10−5, as well as with an increased risk of SGA newborns odds ratio (95% confidence interval) = 1.09 (1.01–1.17); P = 0.03. Birth weight was positively associated with both cord TSH β = 4.1 (1.4) g/mU · liter; P = 0.007 and FT4 levels β = 23.0 (3.2) g/pmol · liter; P = 9.2 × 10−13.
Conclusions:
We show that maternal high-normal FT4 levels in early pregnancy are associated with lower birth weight and an increased risk of SGA newborns. Additionally, birth weight is positively associated with cord TSH and FT4 levels. These data demonstrate that even mild variation in thyroid function within the normal range can have important fetal consequences.
OBJECTIVE:Critical care represents a large percentage of healthcare spending in developed countries. Yet, little is known regarding international variation in critical care services. We sought to ...understand differences in critical care delivery by comparing data on the distribution of services in eight countries.
DESIGN:Retrospective review of existing national administrative data. We identified sources of data in each country to provide information on acute care hospitals and beds, intensive care units and beds, intensive care admissions, and definitions of intensive care beds. Data were all referenced and from as close to 2005 as possible.
SETTING:United States, France, United Kingdom, Canada, Belgium, Germany, The Netherlands, and Spain.
PATIENTS:Not available.
INTERVENTIONS:None.
MEASUREMENTS AND MAIN RESULTS:No standard definition existed for acute care hospital or intensive care unit beds across countries. Hospital beds varied three-fold from 221/100,000 population in the United States to 593/100,000 in Germany. Adult intensive care unit beds also ranged seven-fold from 3.3/100,000 population in the United Kingdom to 24.0/100,000 in Germany. Volume of intensive care unit admissions per year varied ten-fold from 216/100,000 population in the United Kingdom to 2353/100,000 in Germany. The ratio of intensive care unit beds to hospital beds was highly correlated across all countries except the United States (r = .90). There was minimal correlation between the number of intensive care unit beds per capita and health care spending per capita (r = .45), but high inverse correlation between intensive care unit beds and hospital mortality for intensive care unit patients across countries (r = −.82).
CONCLUSIONS:Absolute critical care services vary dramatically between countries with wide differences in both numbers of beds and volume of admissions. The number of intensive care unit beds per capita is not strongly correlated with overall health expenditure, but does correlate strongly with mortality. These findings demonstrate the need for critical care data from all countries, as they are essential for interpretation of studies, and policy decisions regarding critical care services.
Summary
Objectives
: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications ...of AI design, development, selection, use, and ongoing surveillance.
Method
: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.
Results
: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed.
Conclusion
: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.
Abstract Objective To identify all published studies evaluating computerized physician order entry (CPOE) in the inpatient setting and uniformly classify these studies on outcome measure and study ...design. Data sources All studies that evaluated the effect of CPOE on outcomes pertaining to the medication process in inpatients were electronically searched in MEDLINE® (1966 to August 2006), EMBASE® (1980 to August 2006) and the Cochrane library. In addition, the bibliographies of retrieved articles were manually searched. Articles were selected if one of their main objectives was CPOE evaluation in an inpatient setting. Review method Identified titles and abstracts were independently screened by three reviewers to determine eligibility for further review. Results We found 67 articles, which included articles on CPOE evaluation on some outcome at the time of ordering. Most papers evaluated multiple outcome measures. The outcome measures were clustered in the following categories: adherence ( n = 22); alerts and appropriateness of alerts ( n = 7); safety ( n = 21); time ( n = 7); costs and (organizational) efficiency ( n = 23); and satisfaction, usage and usability ( n = 10). Most studies used a before–after design ( n = 35) followed by observational studies ( n = 24) and randomized controlled trials ( n = 8). Conclusion The impact of CPOE systems was especially positive in the category adherence to guidelines, but also to some extent in alerts and appropriateness of alerts; costs and organizational efficiency; and satisfaction and usability. Although on average, there seems to be a positive effect of CPOE on safety, studies tended to be non-randomized and were focused on medication error rates, not powered to detect a difference in adverse drug event rates. Some recent studies suggested that errors, adverse drug events (ADEs) and even mortality increased after CPOE implementation. Only in the category time the impact has been shown to be negative, but this only refers to the physician's time, not the net time. Except for safety, on the whole spectrum of outcomes, results of RCT studies were in line with non-RCT study results.
Obesity is a risk factor for severe coronavirus disease 2019 and might play a role in its pathophysiology. It is unknown whether body mass index is related to clinical outcome following ICU ...admission, as observed in various other categories of critically ill patients. We investigated the relationship between body mass index and inhospital mortality in critically ill coronavirus disease 2019 patients and in cohorts of ICU patients with non-severe acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma.
Multicenter observational cohort study.
Eighty-two Dutch ICUs participating in the Dutch National Intensive Care Evaluation quality registry.
Thirty-five-thousand five-hundred six critically ill patients.
None.
Patient characteristics and clinical outcomes were compared between four cohorts (coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma patients) and between body mass index categories within cohorts. Adjusted analyses of the relationship between body mass index and inhospital mortality within each cohort were performed using multivariable logistic regression. Coronavirus disease 2019 patients were more likely male, had a higher body mass index, lower Pao2/Fio2 ratio, and were more likely mechanically ventilated during the first 24 hours in the ICU compared with the other cohorts. Coronavirus disease 2019 patients had longer ICU and hospital length of stay, and higher inhospital mortality. Odds ratios for inhospital mortality for patients with body mass index greater than or equal to 35 kg/m2 compared with normal weight in the coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and trauma cohorts were 1.15 (0.79-1.67), 0.64 (0.43-0.95), 0.73 (0.61-0.87), and 0.81 (0.57-1.15), respectively.
The obesity paradox, which is the inverse association between body mass index and mortality in critically ill patients, is not present in ICU patients with coronavirus disease 2019-related respiratory failure, in contrast to nonsevere acute respiratory syndrome coronavirus 2 viral and bacterial respiratory infections.
Prognostic models-used in critical care medicine for mortality predictions, for benchmarking and for illness stratification in clinical trials-have been validated predominantly in high-income ...countries. These results may not be reproducible in low or middle-income countries (LMICs), not only because of different case-mix characteristics but also because of missing predictor variables. The study objective was to systematically review literature on the use of critical care prognostic models in LMICs and assess their ability to discriminate between survivors and non-survivors at hospital discharge of those admitted to intensive care units (ICUs), their calibration, their accuracy, and the manner in which missing values were handled.
The PubMed database was searched in March 2017 to identify research articles reporting the use and performance of prognostic models in the evaluation of mortality in ICUs in LMICs. Studies carried out in ICUs in high-income countries or paediatric ICUs and studies that evaluated disease-specific scoring systems, were limited to a specific disease or single prognostic factor, were published only as abstracts, editorials, letters and systematic and narrative reviews or were not in English were excluded.
Of the 2233 studies retrieved, 473 were searched and 50 articles reporting 119 models were included. Five articles described the development and evaluation of new models, whereas 114 articles externally validated Acute Physiology and Chronic Health Evaluation, the Simplified Acute Physiology Score and Mortality Probability Models or versions thereof. Missing values were only described in 34% of studies; exclusion and or imputation by normal values were used. Discrimination, calibration and accuracy were reported in 94.0%, 72.4% and 25% respectively. Good discrimination and calibration were reported in 88.9% and 58.3% respectively. However, only 10 evaluations that reported excellent discrimination also reported good calibration. Generalisability of the findings was limited by variability of inclusion and exclusion criteria, unavailability of post-ICU outcomes and missing value handling.
Robust interpretations regarding the applicability of prognostic models are currently hampered by poor adherence to reporting guidelines, especially when reporting missing value handling. Performance of mortality risk prediction models in LMIC ICUs is at best moderate, especially with limitations in calibration. This necessitates continued efforts to develop and validate LMIC models with readily available prognostic variables, perhaps aided by medical registries.
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