A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care

Although many artificial intelligence (AI) and machine learning (ML) based algorithms are being developed by researchers, only a small fraction has been implemented in clinical-decision support (CDS) systems for clinical care. Healthcare organizations experience significant barriers implementing AI/ML models for diagnostic, prognostic, and monitoring purposes. In this perspective, we delve into the numerous and diverse quality control measures and responsibilities that emerge when moving from AI/ML-model development in a research environment to deployment in clinical care. The Sleep-Well Baby project, a ML-based monitoring system, currently being tested at the neonatal intensive care unit of the University Medical Center Utrecht, serves as a use-case illustrating our personal learning journey in this field. We argue that, in addition to quality assurance measures taken by the manufacturer, user responsibilities should be embedded in a quality management system (QMS) that is focused on life-cycle management of AI/ML-CDS models in a medical routine care environment. Furthermore, we highlight the strong similarities between AI/ML-CDS models and in vitro diagnostic devices and propose to use ISO15189, the quality guideline for medical laboratories, as inspiration when building a QMS for AI/ML-CDS usage in the clinic. We finally envision a future in which healthcare institutions run or have access to a medical AI-lab that provides the necessary expertise and quality assurance for AI/ML-CDS implementation and applies a QMS that mimics the ISO15189 used in medical laboratories

Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes

available in PMC 2016 November 07The fungal kingdom is the source of almost all industrial enzymes in use for lignocellulose bioprocessing. We developed a systems-level approach that integrates transcriptomic sequencing, proteomics, phenotype, and biochemical studies of relatively unexplored basal fungi. Anaerobic gut fungi isolated from herbivores produce a large array of biomass-degrading enzymes that synergistically degrade crude, untreated plant biomass and are competitive with optimized commercial preparations from Aspergillus and Trichoderma. Compared to these model platforms, gut fungal enzymes are unbiased in substrate preference due to a wealth of xylan-degrading enzymes. These enzymes are universally catabolite-repressed and are further regulated by a rich landscape of noncoding regulatory RNAs. Additionally, we identified several promising sequence-divergent enzyme candidates for lignocellulosic bioprocessing.United States. Dept. of Energy. Office of Science (Biological and Environmental Research (BER) program)United States. Department of Energy (DOE Grant DE-SC0010352)United States. Department of Agriculture (Award 2011-67017-20459)Institute for Collaborative Biotechnologies (grant W911NF-09-0001

Accommodative lens refilling in rhesus monkeys

PURPOSE. Accommodation can be restored to presbyopic human eyes by refilling the capsular bag with a soft polymer. This study was conducted to test whether accommodation, measurable as changes in optical refraction, can be restored with a newly developed refilling polymer in a rhesus monkey model. A specific intra-and postoperative treatment protocol was used to minimize postoperative inflammation and to delay capsular opacification. METHODS. Nine adolescent rhesus monkeys underwent refilling of the lens capsular bag with a polymer. In the first four monkeys (group A) the surgical procedure was followed by two weekly subconjunctival injections of corticosteroids. In a second group of five monkeys (group B) a treatment intended to delay the development of capsular opacification was applied during the surgery, and, in the postoperative period, eye drops and two subconjunctival injections of corticosteroids were applied. Accommodation was stimulated with carbachol iontophoresis or pilocarpine and was measured with a Hartinger refractometer at regular times during a follow-up period of 37 weeks in five monkeys. In one monkey, lens thickness changes were measured with A-scan ultrasound. RESULTS. In group A, refraction measurement was possible in one monkey. In the three other animals in group A, postoperative inflammation and capsular opacification prevented refraction measurements. In group B, the maximum accommodative amplitude of the surgically treated eyes was 6.3 D. In three monkeys the accommodative amplitude decreased to almost 0 D after 37 weeks. In the two other monkeys, the accommodative amplitude remained stable at +/- 4 D during the follow-up period. In group B, capsular opacification developed in the postoperative period, but refraction measurements could still be performed during the whole follow-up period of 37 weeks. CONCLUSIONS. A certain level of accommodation can be restored after lens refilling in adolescent rhesus monkeys. During the follow-up period refraction measurements were possible in all five monkeys that underwent the treatment designed to prevent inflammation and capsular opacification

Health-related quality of life after prophylactic cranial irradiation for stage III non-small cell lung cancer patients:Results from the NVALT-11/DLCRG-02 phase III study

BACKGROUND AND PURPOSE: The NVALT-11/DLCRG-02 phase III trial (clinicaltrials.gov identifier: NCT01282437) showed that, after standard curative intent treatment, prophylactic cranial irradiation (PCI) decreased the incidence of symptomatic brain metastases (BM) in stage III non-small cell lung cancer (NSCLC) patients compared to observation. In this study we assessed the impact of PCI on health-related quality of life (HRQoL). In addition, an exploratory analysis was performed to assess the impact of neurocognitive symptoms and symptomatic BM on HRQoL. MATERIALS AND METHODS: Stage III NSCLC patients were randomized between PCI and observation. HRQoL was measured using the EuroQol 5D (EQ-5D-3L), EORTC QLQ-C30 and QLQ-BN20 instruments at completion of standard curative intent treatment and 4 weeks, 3, 6, 12, 24 and 36 months thereafter. Generalized linear mixed effects (GLM) models were used to assess the impact of PCI compared to observation over time on three HRQoL metrics: the EORTC QLQ-C30 global health status and the EQ-5D-3L utility and visual analogue scale (EQ VAS) scores. RESULTS: In total, 86 and 88 patients were included in the PCI and observation arm, with a median follow-up of 48.5 months (95% CI 39-54 months). Baseline mean HRQoL scores were comparable between the PCI and observation arm for the three HRQoL metrics. In the GLM models, none of the HRQoL metrics were clinically relevant or statistically significantly different between the PCI and the observation arm (p-values ranged between 0.641 and 0.914). CONCLUSION: No statistically significant nor a clinically relevant impact of PCI on HRQoL was observed

Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals

Assessment of daily creatinine production and excretion plays a crucial role in the estimation of renal function. Creatinine excretion is estimated by creatinine excretion equations and implicitly in eGFR equations like MDRD and CKD-EPI. These equations are however unreliable in patients with aberrant body composition. In this study we developed and validated equations estimating creatinine production using deep learning body-composition analysis of clinically acquired CT-scans. We retrospectively included patients in our center that received any CT-scan including the abdomen and had a 24-h urine collection within 2 weeks of the scan (n = 636). To validate the equations in healthy individuals, we included a kidney donor dataset (n = 287). We used a deep learning algorithm to segment muscle and fat at the 3rd lumbar vertebra, calculate surface areas and extract radiomics parameters. Two equations for CT-based estimate of RenAl FuncTion (CRAFT 1 including CT parameters, age, weight, and stature and CRAFT 2 excluding weight and stature) were developed and compared to the Cockcroft-Gault and the Ix equations. CRAFT1 and CRAFT 2 were both unbiased (MPE = 0.18 and 0.16 mmol/day, respectively) and accurate (RMSE = 2.68 and 2.78 mmol/day, respectively) in the patient dataset and were more accurate than the Ix (RMSE = 3.46 mmol/day) and Cockcroft-Gault equation (RMSE = 3.52 mmol/day). In healthy kidney donors, CRAFT 1 and CRAFT 2 remained unbiased (MPE = − 0.71 and − 0.73 mmol/day respectively) and accurate (RMSE = 1.86 and 1.97 mmol/day, respectively). Deep learning-based extraction of body-composition parameters from abdominal CT-scans can be used to reliably estimate creatinine production in both patients as well as healthy individuals. The presented algorithm can improve the estimation of renal function in patients who have recently had a CT scan. The proposed methods provide an improved estimation of renal function that is fully automatic and can be readily implemented in routine clinical practice

GFR estimation is complicated by a high incidence of non-steady-state serum creatinine concentrations at the emergency department

Background Acquiring a reliable estimate of glomerular filtration rate (eGFR) at the emergency department (ED) is important for clinical management and for dosing renally excreted drugs. However, renal function formulas such as CKD-EPI can give biased results when serum creatinine (SCr) is not in steady-state because the assumption that urinary creatinine excretion is constant is then invalid. We assessed the extent of this by analysing variability in SCr in patients who visited the ED of a tertiary care centre. Methods Data from ED visits at the University Medical Centre Utrecht, the Netherlands between 2012 and 2019 were extracted from the Utrecht Patient Oriented Database. Three measurement time points were defined for each visit: last SCr measurement before visit as baseline (SCr-BL), first measurement during visit (SCr-ED) and a subsequent measurement between 6 and 24 hours during admission (SCr-H1). Non-steady-state SCr was defined as exceeding the Reference Change Value (RCV), with 15% decrease or 18% increase between successive SCr measurements. Exceeding the RCV was deemed as a significant change. Results Of visits where SCr-BL and SCr-ED were measured (N = 47,540), 28.0% showed significant change in SCr. Of 17,928 visits admitted to the hospital with a SCr-H1 after SCr-ED, 27,7% showed significant change. More than half (55%) of the patients with SCr values available at all three timepoints (11,054) showed at least one significant change in SCr over time. Conclusion One third of ED visits preceded and/or followed by creatinine measurement show non-stable serum creatinine concentration. At the ED automatically calculated eGFR should therefore be interpreted with great caution when assessing kidney function

Added diagnostic value of routinely measured hematology variables in diagnosing immune checkpoint inhibitor mediated toxicity in the emergency department

BACKGROUND: Immune checkpoint inhibitors (ICI) show remarkable results in cancer treatment, but at the cost of immune-related adverse events (irAE). irAE can be difficult to differentiate from infections or tumor progression, thereby challenging treatment, especially in the emergency department (ED) where time and clinical information are limited. As infections are traceable in blood, we were interested in the added diagnostic value of routinely measured hematological blood cell characteristics in addition to standard diagnostic practice in the ED to aid irAE assessment. METHODS: Hematological variables routinely measured with our hematological analyzer (Abbott CELL-DYN Sapphire) were retrieved from Utrecht Patient Oriented Database (UPOD) for all patients treated with ICI who visited the ED between 2013 and 2020. To assess the added diagnostic value, we developed and compared two models; a base logistic regression model trained on the preliminary diagnosis at the ED, sex, and gender, and an extended model trained with lasso that also assessed the hematology variables. RESULTS: A total of 413 ED visits were used in this analysis. The extended model showed an improvement in performance (area under the receiver operator characteristic curve) over the base model, 0.79 (95% CI 0.75-0.84), and 0.67 (95% CI 0.60-0.73), respectively. Two standard blood count variables (eosinophil granulocyte count and red blood cell count) and two advanced variables (coefficient of variance of neutrophil depolarization and red blood cell distribution width) were associated with irAE. CONCLUSION: Hematological variables are a valuable and inexpensive aid for irAE diagnosis in the ED. Further exploration of the predictive hematological variables could yield new insights into the pathophysiology underlying irAE and in distinguishing irAE from other inflammatory conditions

Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data

BACKGROUND: Direct feedback on quality of care is one of the key features of a learning health care system (LHS), enabling health care professionals to improve upon the routine clinical care of their patients during practice. OBJECTIVE: This study aimed to evaluate the potential of routine care data extracted from electronic health records (EHRs) in order to obtain reliable information on low-density lipoprotein cholesterol (LDL-c) management in cardiovascular disease (CVD) patients referred to a tertiary care center. METHODS: We extracted all LDL-c measurements from the EHRs of patients with a history of CVD referred to the University Medical Center Utrecht. We assessed LDL-c target attainment at the time of referral and per year. In patients with multiple measurements, we analyzed LDL-c trajectories, truncated at 6 follow-up measurements. Lastly, we performed a logistic regression analysis to investigate factors associated with improvement of LDL-c at the next measurement. RESULTS: Between February 2003 and December 2017, 250,749 LDL-c measurements were taken from 95,795 patients, of whom 23,932 had a history of CVD. At the time of referral, 51% of patients had not reached their LDL-c target. A large proportion of patients (55%) had no follow-up LDL-c measurements. Most of the patients with repeated measurements showed no change in LDL-c levels over time: the transition probability to remain in the same category was up to 0.84. Sequence clustering analysis showed more women (odds ratio 1.18, 95% CI 1.07-1.10) in the cluster with both most measurements off target and the most LDL-c measurements furthest from the target. Timing of drug prescription was difficult to determine from our data, limiting the interpretation of results regarding medication management. CONCLUSIONS: Routine care data can be used to provide feedback on quality of care, such as LDL-c target attainment. These routine care data show high off-target prevalence and little change in LDL-c over time. Registrations of diagnosis; follow-up trajectory, including primary and secondary care; and medication use need to be improved in order to enhance usability of the EHR system for adequate feedback

Routinely measured hematological parameters and prediction of recurrent vascular events in patients with clinically manifest vascular disease

BACKGROUND AND AIMS: The predictive value of traditional risk factors for vascular events in patients with manifest vascular disease is limited, underscoring the need for novel biomarkers to improve risk stratification. Since hematological parameters are routinely assessed in clinical practice, they are readily available candidates. METHODS: We used data from 3,922 vascular patients, who participated in the Second Manifestations of ARTerial Disease (SMART) study. We first investigated associations between recurrent vascular events and 22 hematological parameters, obtained from the Utrecht Patient Oriented Database (UPOD), and then assessed whether parameters associated with outcome improved risk prediction. RESULTS: After adjustment for all SMART risk score (SRS) variables, lymphocyte %, neutrophil count, neutrophil % and red cell distribution width (RDW) were significantly associated with vascular events. When individually added to the SRS, lymphocyte % improved prediction of recurrent vascular events with a continuous net reclassification improvement (cNRI) of 17.4% [95% CI: 2.1, 32.1%] and an increase in c-statistic of 0.011 [0.000, 0.022]. The combination of lymphocyte % and neutrophil count resulted in a cNRI of 22.2% [3.2, 33.4%] and improved c-statistic by 0.011 [95% CI: 0.000, 0.022]. Lymphocyte % and RDW yielded a cNRI of 18.7% [3.3, 31.9%] and improved c-statistic by 0.016 [0.004, 0.028]. However, the addition of hematological parameters only modestly increased risk estimates for patients with an event during follow-up. CONCLUSIONS: Several hematological parameters were independently associated with recurrent vascular events. Lymphocyte % alone and in combination with other parameters enhanced discrimination and reclassification. However, the incremental value for patients with a recurrent event was limited