Digital economy and the medical and health service supply in China.

The impact of the digital economy on the healthcare sector is becoming increasingly profound. This article focuses on the relationship between the development of China's digital economy and medical and health services supply. Based on panel data from 30 provinces in China from 2012 to 2021, the CRITIC weight method was applied to measure the supply capacity of medical and health services and the level of digital economy development, and the kernel density estimation method and Dagum Gini coefficient method was used to characterize the evolutionary trends and regional differences. Additionally, a two-way fixed-effects model is adopted to investigate the impact of digital economy development on medical and health services supply. The results show that both the supply capacity of healthcare services and the level of digital economy development have been increasing continuously in terms of evolutionary trends. From the perspective of regional differences, compared to the supply level of healthcare services, the regional differences in digital economy development are more significant. The intra-regional differences in medical and health services supply are greater than the inter-regional differences, while the development of the digital economy exhibits the opposite trend. The findings of this paper provide supports for China to enhance the development level of digital economy and improve supply of medical and health service.

Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

Identifying biomarkers related to motor function in chronic stroke: A fNIRS and TMS study.

BACKGROUND: Upper limb motor impairment commonly occurs after stroke, impairing quality of life. Brain network reorganization likely differs between subgroups with differing impairment severity. This study explored differences in functional connectivity (FC) and corticospinal tract (CST) integrity between patients with mild/moderate versus severe hemiplegia poststroke to clarify the neural correlates underlying motor deficits. METHOD: Sixty chronic stroke patients with upper limb motor impairment were categorized into mild/moderate and severe groups based on Fugl-Meyer scores. Resting-state FC was assessed using functional near-infrared spectroscopy (fNIRS) to compare connectivity patterns between groups across motor regions. CST integrity was evaluated by inducing motor evoked potentials (MEP) via transcranial magnetic stimulation. RESULTS: Compared to the mild/moderate group, the severe group exhibited heightened premotor cortex-primary motor cortex (PMC-M1) connectivity (t = 4.56, p < 0.01). Absence of MEP was also more frequent in the severe group (χ2 = 12.31, p = 0.01). Bayesian models effectively distinguished subgroups and identified the PMC-M1 connection as highly contributory (accuracy = 91.30%, area under the receiver operating characteristic curve [AUC] = 0.86). CONCLUSION: Distinct patterns of connectivity and corticospinal integrity exist between stroke subgroups with differing impairments. Strengthened connectivity potentially indicates recruitment of additional motor resources to compensate for damage. These findings elucidate the neural correlates underlying motor deficits poststroke and could guide personalized, network-based therapies targeting predictive biomarkers to improve rehabilitation outcomes.

Modularized supramolecular assemblies for hypoxia-activatable fluorescent visualization and image-guided theranostics.

Rationale: Molecular imaging of microenvironment by hypoxia-activatable fluorescence probes has emerged as an attractive approach to tumor diagnosis and image-guided treatment. Difficulties remain in its translational applications due to hypoxia heterogeneity in tumor microenvironments, making it challenging to image hypoxia as a reliable proxy of tumor distribution. Methods: We report a modularized theranostics platform to fluorescently visualize hypoxia via light-modulated signal compensation to overcome tumor heterogeneity, thereby serving as a diagnostic tool for image-guided surgical resection and photodynamic therapy. Specifically, the platform integrating dual modules of fluorescence indicator and photodynamic moderator using supramolecular host-guest self-assembly, which operates cooperatively as a cascaded "AND" logic gate. First, tumor enrichment and specific fluorescence turn-on in hypoxic regions were accessible via tumor receptors and cascaded microenvironment signals as simultaneous inputs of the "AND" gate. Second, image guidance by a lighted fluorescence module and light-mediated endogenous oxygen consumption of a photodynamic module as dual inputs of "AND" gate collaboratively enabled light-modulated signal compensation in situ, indicating homogeneity of enhanced hypoxia-related fluorescence signals throughout a tumor. Results: In in vitro and in vivo analyses, the biocompatible platform demonstrated several strengths including a capacity for dual tumor targeting to progressively facilitate specific fluorescence turn-on, selective signal compensation, imaging-time window extension conducive to precise normalized image-guided treatment, and the functionality of tumor glutathione depletion to improve photodynamic efficacy. Conclusion: The hypoxia-activatable, image-guided theranostic platform demonstrated excellent potential for overcoming hypoxia heterogeneity in tumors.

Investigating gut microbiota-blood and urine metabolite correlations in early sepsis-induced acute kidney injury: insights from targeted KEGG analyses.

Background: The interplay between gut microbiota and metabolites in the early stages of sepsis-induced acute kidney injury (SA-AKI) is not yet clearly understood. This study explores the characteristics and interactions of gut microbiota, and blood and urinary metabolites in patients with SA-AKI. Methods: Utilizing a prospective observational approach, we conducted comparative analyses of gut microbiota and metabolites via metabolomics and metagenomics in individuals diagnosed with SA-AKI compared to those without AKI (NCT06197828). Pearson correlations were used to identify associations between microbiota, metabolites, and clinical indicators. The Comprehensive Antibiotic Resistance Database was employed to detect antibiotic resistance genes (ARGs), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways informed on metabolic processes and microbial resistance patterns. Results: Our study included analysis of four patients with SA-AKI and five without AKI. Significant disparities in bacterial composition were observed, illustrated by diversity indices (Shannon index: 2.0 ± 0.4 vs. 1.4 ± 0.6, P = 0.230; Simpson index: 0.8 ± 0.1 vs. 0.6 ± 0.2, P = 0.494) between the SA-AKI group and the non-AKI group. N6, N6, N6-Trimethyl-L-lysine was detected in both blood and urine metabolites, and also showed significant correlations with specific gut microbiota (Campylobacter hominis and Bacteroides caccae, R > 0, P < 0.05). Both blood and urine metabolites were enriched in the lysine degradation pathway. We also identified the citrate cycle (TCA cycle) as a KEGG pathway enriched in sets of differentially expressed ARGs in the gut microbiota, which exhibits an association with lysine degradation. Conclusions: Significant differences in gut microbiota and metabolites were observed between the SA-AKI and non-AKI groups, uncovering potential biomarkers and metabolic changes linked to SA-AKI. The lysine degradation pathway may serve as a crucial link connecting gut microbiota and metabolites.

Cynanchum paniculatum (Bunge) Kitag. ex H.Hara inhibits pancreatic cancer progression by inducing caspase-dependent apoptosis and suppressing TGF-ß-mediated epithelial-mesenchymal transition.

Background: Cynanchum paniculatum (Bunge) Kitag. ex H.Hara, a member of the Asclepiadaceae family, has a rich history as a traditional Chinese medicinal plant used to treat digestive disorders. However, its potential anti-cancer effects in pancreatic cancer remain largely unexplored. Aim: This study delves into the intricate anti-pancreatic cancer mechanisms of C. paniculatum (Bunge) Kitag. ex H.Hara aqueous extract (CPAE) by elucidating its role in apoptosis induction and the inhibition of invasion and migration. Methods: A comprehensive set of methodologies was employed to assess CPAE's impact, including cell viability analyses using MTT and colony formation assays, flow cytometry for cell cycle distribution and apoptosis assessment, scratch-wound and Matrigel invasion assays for migration and invasion capabilities, and immunoblotting to measure the expression levels of key proteins involved in apoptosis and metastasis. Additionally, a murine xenograft model was established to investigate CPAE's in vivo anti-cancer potential. Results: CPAE exhibited time- and dose-dependent suppression of proliferation and colony formation in pancreatic cancer cells. Notably, CPAE induced apoptosis and G2/M phase arrest, effectively activating the caspase-dependent PARP pathway. At non-cytotoxic doses, CPAE significantly curtailed the metastatic abilities of pancreatic cells, effectively suppressing epithelial-mesenchymal transition (EMT) and downregulating the TGF-ß1/Smad2/3 pathway. In vivo experiments underscored CPAE's ability to inhibit tumor proliferation. Conclusion: This study illuminates the multifaceted anti-proliferative, pro-apoptotic, anti-invasive, and anti-migratory effects of CPAE, both in vitro and in vivo. CPAE emerges as a promising herbal medicine for pancreatic cancer treatment, with its potential mediated through apoptosis induction via the caspase-dependent PARP pathway and MET suppression via the TGF-ß1/Smad2/3 signaling pathway at non-cytotoxic doses. These findings advocate for further exploration of CPAE's therapeutic potential in pancreatic cancer.

Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units.

Objective: To identify subclasses of acute pancreatitis (AP) patients in the intensive care unit (ICU) by analyzing blood urea nitrogen (BUN) trajectories. Methods: AP patients in West China Hospital System (development cohort) and three public databases in the United States (validation cohort) were included. Latent class trajectory modelling was used to identify subclasses based on BUN trajectories within the first 21 days after ICU admission. Clinical characteristics and outcomes were compared, and results were externally validated. Results: The study comprised 2971 and 930 patients in the development and validation cohorts, respectively, with five subclasses: Class 1 ("Moderate-azotemia, slow decreasing"), Class 2 ("Non-azotemia"), Class 3 ("Severe-azotemia, slow decreasing"), Class 4 ("Moderate-azotemia, rapid increasing"), and Class 5 ('Moderate-azotemia, slow increasing) identified. Azotemia patients showed significantly higher 30-day mortality risk in development and validation cohorts. Specifically, Class 4 patients exhibited notably highest mortality risk in both the development cohort (HR 5.32, 95% CI 2.62-10.82) and validation cohort (HR 6.23, 95% CI 2.93-13.22). Regarding clinical characteristics, AP patients in Class 4 showed lower mean arterial pressure and a higher proportion of renal disease. We also created an online early classification model to further identify Class 4 patients among all patients with moderate azotemia at baseline. Conclusion: This multinational study uncovers heterogeneity in BUN trajectories among AP patients. Patients with "Moderate-azotemia, rapid increasing" trajectory, had a higher mortality risk than patients with severe azotemia at baseline. This finding complements studies that solely rely on baseline BUN for risk stratification and enhanced our understanding of longitudinal progression of AP.

A step-scheme-based Cs3Bi2Br9 perovskite quantum dots@mesoporous Nb2O5 photocatalyst with boosted charge separation and CO2 reduction.

Solar-energy-powered CO2 reduction into valuable chemical fuels represents a highly promising strategy to address the currently energy and environmental issues. Owing to the nontoxicity and robust reduction capability, lead-free Cs3Bi2Br9 perovskite quantum dots (PQDs) are regarded as an attractive material for CO2 photoreduction. Nevertheless, the potential of their applications in this field has been restricted by the severe charge recombination, resulting in unsatisfactory photocatalytic performance. Herein, a step-scheme-based Cs3Bi2Br9@Nb2O5 (CBB@Nb2O5) nanocomposite was fabricated by embedding the CBB PQDs into mesoporous Nb2O5. Experimental studies, along with theoretical calculations, revealed that the charge migration route in the CBB@Nb2O5 nanocomposite conformed to the step-scheme (S-scheme) mode, enabling effective charge separation and strong redox ability preservation. Profiting from the promoted charge separation, as well as the improved CO2 adsorption contributed by mesoporous Nb2O5, the CBB@Nb2O5 nanocomposite unveiled superior CO2 photoreduction performance, with CO evolution rate reaching 143.63 µmol g-1h-1. The present study provides a potential strategy to manufacture highly-efficient perovskite-based photocatalysts for achieving carbon neutrality.

A review on fermented vegetables: Microbial community and potential upgrading strategy via inoculated fermentation.

Fermentation is a traditional method utilized for vegetable preservation, with microorganisms playing a crucial role in the process. Nowadays, traditional spontaneous fermentation methods are widely employed, which excessively depend on the microorganisms attached to the surface of raw materials, resulting in great difficulties in ideal control over the fermentation process. To achieve standardized production and improve product quality, it is essential to promote inoculated fermentation. In this way, starter cultures can dominate the fermentation processes successfully. Unfortunately, inoculated fermentation has not been thoroughly studied and applied. Therefore, this paper provides a systematic review of the potential upgrading strategy of vegetable fermentation technology. First, we disclose the microbial community structures and succession rules in some typical spontaneously fermented vegetables to comprehend the microbial fermentation processes well. Then, internal and external factors affecting microorganisms are explored to provide references for the selection of fermented materials and conditions. Besides, we widely summarize the potential starter candidates with various characteristics isolated from spontaneously fermented products. Subsequently, we exhibited the inoculated fermentation strategies with those isolations. To optimize the product quality, not only lactic acid bacteria that lead the fermentation, but also yeasts that contribute to aroma formation should be combined for inoculation. The inoculation order of the starter cultures also affects the microbial fermentation. It is equally important to choose a proper processing method to guarantee the activity and convenience of starter cultures. Only in this way can we achieve the transition from traditional spontaneous fermentation to modern inoculated fermentation.

The Use of Real-World Evidence for Regulatory Decisions in China.

There is a growing demand for the use of high-quality real-world evidence (RWE) to support regulatory decision-making worldwide and in China, which highlights the need for conducting literature reviews to evaluate the available data and evidence. This study aims to review the use of RWE in Chinese regulatory decisions and to summarize relevant regulatory and methodological considerations to inform the future use of RWE in China. We identified policy documents, technical guidance documents, and cases on official Chinese government websites and extracted their contents separately. We consulted experts from the National Medical Products Administration (NMPA) and academic institutes and searched case-related articles for enrichment. We also searched and included articles related to the use of RWE/Real-world data in Chinese regulatory decisions. Six trial versions of technical guidance documents, 7 case studies, and 40 articles related to the Chinese regulatory decisions were included in this study. Based on the technical guidance, data quality, and appropriate study design and statistical analysis are the main concerns for RWE generation. The cases and articles related to regulatory decisions revealed 9 main concerns, including data sources and applicability, data quality, strength of existing evidence, appropriate study design and statistical analysis, regulated and transparent process for analysis and evidence generation, product safety and efficacy, product characteristics and clinical needs, ethical considerations and data security, and communicate adequately with regulatory authorities. Among these concerns, data issues are central. Preliminary attempts have been made by the NMPA to promote the use of RWE, but substantial challenges still remain.

Na3K6(CO3)3(NO3)2X·6H2O (X = NO3, Cl, Br): Exploring High-Performance UV Birefringent Crystals Induced by Coplanar π-Conjugated CO3 and NO3 Groups.

Planar π-conjugated groups, like CO3, NO3, and BO3 triangles, are ideal functional units for designing birefringent materials due to their large optical anisotropy and wide band gap. The key point for designing birefringent crystals is to select appropriate functional building blocks (FBBs) and the proper arrangement mode. It is well known that the substitution strategy has proven to be a promising and accessible approach. In this work, alkali metals were chosen to regulate and control two different π-conjugated groups, CO3 and NO3, to build new compounds with large birefringence. Subsequently, three new compounds, Na3K6(CO3)3(NO3)2X·6H2O (X = NO3, Cl, Br), were successfully synthesized using the hydrothermal method. The aliovalent substitution between the [NO3]- anionic group and halogen anions [Cl]-/[Br]- has been achieved in these compounds. Na3K6(CO3)3(NO3)2X·6H2O feature the well-coplanar CO3 and NO3 groups in their crystal structure. This coplanar arrangement mode may effectively enhance the anisotropic polarizability of Na3K6(CO3)3(NO3)2X·6H2O. And their experimental birefringence can reach 0.094-0.131 at 546 nm. Diffuse reflectance spectra demonstrate that these compounds exhibit short ultraviolet (UV) absorption edges of ∼235 nm. Meanwhile, Na3K6(CO3)3(NO3)2X·6H2O also have an easily grown capacity under facile conditions. This work not only reports three new potential UV birefringent crystals but also provides a strategy to make the π-conjugated MO3 group coplanar.

A Three-in-One Integrated Cs3Bi2Br9@Co3O4 Heterostructure with Photoinduced Self-Heating Effect for Synergistically Enhancing the Photothermal CO2 Reduction.

Photothermal catalysis, which applies solar energy to produce photogenerated e-/h+ pairs as well as provide heat input, is recognized as a promising technology for high conversion efficiency of CO2 to value-added solar fuels. In this work, a "shooting three birds with one stone" approach is demonstrated to significantly enhance the photothermal CO2 reduction over the Cs3Bi2Br9@Co3O4 (CBB@Co3O4) heterostructure. Initially, Co3O4 with photoinduced self-heating effect serves as a photothermal material to elevate the temperature of the photocatalyst, which kinetically accelerates the catalytic reaction. Meanwhile, a p-n heterojunction is constructed between the p-type Co3O4 and n-type Cs3Bi2Br9 semiconductors, which has an intrinsic built-in electric field (BEF) to facilitate the separation of photogenerated e-/h+ pairs. Furthermore, the mesoporous Co3O4 matrix can afford abundant active sites for promoting adsorption/activation of CO2 molecules. Benefiting from these synergistic effects, the as-developed CBB@Co3O4 heterostructure achieves an impressive CO2-to-CO conversion rate of 168.56 µmol g-1 h-1 with no extra heat input. This work provides an insightful guidance for the construction of effective photothermal catalysts for CO2 reduction with high solar-to-fuel conversion efficiency.

Handling time-varying treatments in observational studies: A scoping review and recommendations.

OBJECTIVE: Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations. METHODS: We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts. RESULTS: Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings. CONCLUSIONS: There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

Paeoniflorin ameliorates oxaliplatin-induced peripheral neuropathy via inhibiting neuroinflammation through influence on gut microbiota.

Oxaliplatin (OXA)-induced peripheral neuropathy (OIPN) is a severe side effect that greatly limits OXA clinical use and threatens patients' life and health. Paeoniflorin exhibits extensive anti-inflammatory and neuroprotective effects, but whether it can protect against OIPN and the underlying mechanisms remain unclear. This study aimed to investigate the effects of paeoniflorin on OIPN and probe into the underlying mechanisms. The OIPN model was established through oxaliplatin injection in rats. The ameliorative effects of paeoniflorin on OIPN was assessed by nociceptive hypersensitivities through pain behavioral methods. Neuroinflammation were examined by measuring the levels of inflammatory cytokines and immune cells infiltration. The signaling pathway of TLR4/MyD88/NF-κB was evaluated by Western blotting. Gut microbial changes were detected by 16S rDNA sequencing technology. In addition, antibiotics-induced microbiota eradication and fecal microbial transplantation (FMT) were applied for exploring the function of gut microbiota in the protective effects of paeoniflorin. The results revealed that paeoniflorin significantly alleviated mechanical and cold hypersensitivity, mitigated neuroinflammation and influenced gut microbial composition in OIPN rats. Fecal microbiota transplantation further verified that gut microbiota was required for paeoniflorin ameliorating OIPN and that the underlying mechanism involved downregulation of TLR4/MyD88/NF-κB signaling. Specifically, Akkermansia, Dubosiella and Corynebacterium might serve as crucial genera regulated by paeoniflorin in the treatment of OIPN. In summary, our investigations delineate paeoniflorin's ameliorative effects on OIPN by alleviating neuroinflammation through regulations of gut microbiota. This suggests that paeoniflorin may serve as a new potential strategy for treatment of OIPN in clinical practice.

Electroencephalography oscillations can predict the cortical response following theta burst stimulation.

BACKGROUND: Continuous theta burst stimulation and intermittent theta burst stimulation are clinically popular models of repetitive transcranial magnetic stimulation. However, they are limited by high variability between individuals in cortical excitability changes following stimulation. Although electroencephalography oscillations have been reported to modulate the cortical response to transcranial magnetic stimulation, their association remains unclear. This study aims to explore whether machine learning models based on EEG oscillation features can predict the cortical response to transcranial magnetic stimulation. METHOD: Twenty-three young, healthy adults attended two randomly assigned sessions for continuous and intermittent theta burst stimulation. In each session, ten minutes of resting-state electroencephalography were recorded before delivering brain stimulation. Participants were classified as responders or non-responders based on changes in resting motor thresholds. Support vector machines and multi-layer perceptrons were used to establish predictive models of individual responses to transcranial magnetic stimulation. RESULT: Among the evaluated algorithms, support vector machines achieved the best performance in discriminating responders from non-responders for intermittent theta burst stimulation (accuracy: 91.30%) and continuous theta burst stimulation (accuracy: 95.66%). The global clustering coefficient and global characteristic path length in the beta band had the greatest impact on model output. CONCLUSION: These findings suggest that EEG features can serve as markers of cortical response to transcranial magnetic stimulation. They offer insights into the association between neural oscillations and variability in individuals' responses to transcranial magnetic stimulation, aiding in the optimization of individualized protocols.

Regulating Oxygen Vacancies and Fermi Level of Mesoporous CeO2-x for Intensified Built-In Electric Field and Boosted Charge Separation of Cs3 Bi2 Br9 /CeO2-x S-Scheme Heterojunction.

Regulating the built-in electric field (BEF) in the heterojunction is is a great challenge in developing high-efficiency photocatalysts. Herein, by tailoring the content of oxygen vacancies in the constituent reduction semiconductor (mesoporous CeO2-x ), a precise Fermi level (EF ) regulation of CeO2-x is realized, yielding an amplified EF gap and intensified BEF in the Cs3 Bi2 Br9 perovskite quantum dots/CeO2-x S-scheme heterojunction. Such an enhanced BEF offers a strong driving force for directional electron transfer, boosting charge separation in the S-scheme heterojunction. As a result, the optimized Cs3 Bi2 Br9 /CeO2-x heterojunction delivers a remarkable CO2 conversion efficiency, with an impressive CO production rate of 80.26 µmol g-1  h-1 and a high selectivity of 97.6%. The S-scheme charge transfer mode is corroborated comprehensively by density functional theory (DFT) calculations, in situ X-ray photoelectron spectroscopy (XPS), and photo-irradiated Kelvin probe force microscopy (KPFM). Moreover, diffuse reflectance infrared Fourier transform spectra (DRIFTS) and theoretical calculations are conducted cooperatively to reveal the CO2 photoreduction pathway.

Validation and impact of algorithms for identifying variables in observational studies of routinely collected data.

BACKGROUND AND OBJECTIVES: Among observational studies of routinely collected health data (RCD) for exploring treatment effects, algorithms are used to identify study variables. However, the extent to which algorithms are reliable and impact the credibility of effect estimates is far from clear. This study aimed to investigate the validation of algorithms for identifying study variables from RCD, and examine the impact of alternative algorithms on treatment effects. METHODS: We searched PubMed for observational studies published in 2018 that used RCD to explore drug treatment effects. Information regarding the reporting, validation, and interpretation of algorithms was extracted. We summarized the reporting and methodological characteristics of algorithms and validation. We also assessed the divergence in effect estimates given alternative algorithms by calculating the ratio of estimates of the primary vs. alternative analyses. RESULTS: A total of 222 studies were included, of which 93 (41.9%) provided a complete list of algorithms for identifying participants, 36 (16.2%) for exposure, and 132 (59.5%) for outcomes, and 15 (6.8%) for all study variables including population, exposure, and outcomes. Fifty-nine (26.6%) studies stated that the algorithms were validated, and 54 (24.3%) studies reported methodological characteristics of 66 validations, among which 61 validations in 49 studies were from the cross-referenced validation studies. Of those 66 validations, 22 (33.3%) reported sensitivity and 16 (24.2%) reported specificity. A total of 63.6% of studies reporting sensitivity and 56.3% reporting specificity used test-result-based sampling, an approach that potentially biases effect estimates. Twenty-eight (12.6%) studies used alternative algorithms to identify study variables, and 24 reported the effects estimated by primary analyses and sensitivity analyses. Of these, 20% had differential effect estimates when using alternative algorithms for identifying population, 18.2% for identifying exposure, and 45.5% for classifying outcomes. Only 32 (14.4%) studies discussed how the algorithms may affect treatment estimates. CONCLUSION: In observational studies of RCD, the algorithms for variable identification were not regularly validated, and-even if validated-the methodological approach and performance of the validation were often poor. More seriously, different algorithms may yield differential treatment effects, but their impact is often ignored by researchers. Strong efforts, including recommendations, are warranted to improve good practice.