Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis

BACKGROUND: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care.DESIGN: Systematic review and meta-analyses.PARTICIPANTS: Older adults (≥ 65 years) in any setting.INTERVENTION: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.</p

Drug Burden Index is a Modifiable Predictor of 30-Day-Hospitalization in Community-Dwelling Older Adults with Complex Care Needs:Machine Learning Analysis of InterRAI Data

BACKGROUND: Older adults (≥ 65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimise ML models further. Accurately predicting hospitalization may tremendously impact the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.METHODS: In this retrospective cohort study, a dataset of 14,198 community-dwelling older adults (≥ 65 years) with complex care needs from the Inter-Resident Assessment Instrument database was used to develop and optimise three ML models to predict 30-day-hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all three models to identify key predictors of 30-day-hospitalization.RESULTS: The area under the receiver operating characteristics curve for the RF, XGB and LR models were 0.97, 0.90 and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day-hospitalization.CONCLUSIONS: Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day-hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.</p

Efficient Dynamic Importance Sampling of Rare Events in One Dimension

Exploiting stochastic path integral theory, we obtain \emph{by simulation} substantial gains in efficiency for the computation of reaction rates in one-dimensional, bistable, overdamped stochastic systems. Using a well-defined measure of efficiency, we compare implementations of ``Dynamic Importance Sampling'' (DIMS) methods to unbiased simulation. The best DIMS algorithms are shown to increase efficiency by factors of approximately 20 for a $5 k_B T$ barrier height and 300 for $9 k_B T$, compared to unbiased simulation. The gains result from close emulation of natural (unbiased), instanton-like crossing events with artificially decreased waiting times between events that are corrected for in rate calculations. The artificial crossing events are generated using the closed-form solution to the most probable crossing event described by the Onsager-Machlup action. While the best biasing methods require the second derivative of the potential (resulting from the ``Jacobian'' term in the action, which is discussed at length), algorithms employing solely the first derivative do nearly as well. We discuss the importance of one-dimensional models to larger systems, and suggest extensions to higher-dimensional systems.Comment: version to be published in Phys. Rev.

Investigating Rare Events by Transition Interface Sampling

We briefly review simulation schemes for the investigation of rare transitions and we resume the recently introduced Transition Interface Sampling, a method in which the computation of rate constants is recast into the computation of fluxes through interfaces dividing the reactant and product state.Comment: 12 pages, 1 figure, contributed paper to the proceedings of NEXT 2003, Second Sardinian International Conference on News and Expectations in Thermostatistics, 21-28 Sep 2003, Cagliari (Italy

Global analysis of contact-dependent human-to-mouse intercellular mRNA and lncRNA transfer in cell culture

Full-length mRNAs transfer between adjacent mammalian cells via direct cell-to-cell connections called tunneling nanotubes (TNTs). However, the extent of mRNA transfer at the transcriptome-wide level (the 'transferome') is unknown. Here, we analyzed the transferome in an human-mouse cell co-culture model using RNA-sequencing. We found that mRNA transfer is non-selective, prevalent across the human transcriptome, and that the amount of transfer to mouse embryonic fibroblasts (MEFs) strongly correlates with the endogenous level of gene expression in donor human breast cancer cells. Typically, <1% of endogenous mRNAs undergo transfer. Non-selective, expression-dependent RNA transfer was further validated using synthetic reporters. RNA transfer appears contact-dependent via TNTs, as exemplified for several mRNAs. Notably, significant differential changes in the native MEF transcriptome were observed in response to co-culture, including the upregulation of multiple cancer and cancer-associated fibroblast-related genes and pathways. Together, these results lead us to suggest that TNT-mediated RNA transfer could be a phenomenon of physiological importance under both normal and pathogenic conditions

USH3A transcripts encode clarin-1, a four-transmembrane-domain protein with a possible role in sensory synapses

[EN] Usher syndrome type 3 (USH3) is an autosomal recessive disorder characterised by the association of post-lingual progressive hearing loss, progressive visual loss due to retinitis pigmentosa and variable presence of vestibular dysfunction. Because the previously defined transcripts do not account for all USH3 cases, we performed further analysis and revealed the presence of additional exons embedded in longer human and mouse USH3A transcripts and three novel USH3A mutations. Expression of Ush3a transcripts was localised by whole mount in situ hybridisation to cochlear hair cells and spiral ganglion cells. The full length USH3A transcript encodes clarin-1, a four-transmembrane-domain protein, which defines a novel vertebrate-specific family of three paralogues. Limited sequence homology to stargazin, a cerebellar synapse four-transmembrane-domain protein, suggests a role for clarin-1 in hair cell and photoreceptor cell synapses, as well as a common pathophysiological pathway for different Usher syndromes.We are grateful to all patients and their family members who participated in this study. We would also like to thank Ronna Hertzano for the preparation of the mouse inner ear cDNA. This work was funded by an Infrastructure grant of the Israeli Ministry of Science Culture and Sports, the Crown Human Genome Center at The Weizmann Institute of Science, the Alfried Krupp Foundation and by the Finnish Eye and Tissue Bank Foundation, the Finnish Eye Foundation, the Maud Kuistila Memorial Foundation, the Oskar Oflund Foundation, Finnish State grant TYH9235, the European Commission (QLG2-CT-1999-00988) (KB Araham) and by the Foundation Fighting Blindness. JS Beckman holds the, Hermann Mayer professorial chair and D Lancet holds the Ralf and Lois Silver professorial chair.Adato, A.; Vreugde, S.; Joensuu, T.; Avidan, N.; Hamalainen, R.; Belenkiy, O.; Olender, T.... (2002). USH3A transcripts encode clarin-1, a four-transmembrane-domain protein with a possible role in sensory synapses. European Journal of Human Genetics. 10(6):339-350. https://doi.org/10.1038/sj.ejhg.520083133935010

Genome Analysis of the Domestic Dog (Korean Jindo) by Massively Parallel Sequencing

Although pioneering sequencing projects have shed light on the boxer and poodle genomes, a number of challenges need to be met before the sequencing and annotation of the dog genome can be considered complete. Here, we present the DNA sequence of the Jindo dog genome, sequenced to 45-fold average coverage using Illumina massively parallel sequencing technology. A comparison of the sequence to the reference boxer genome led to the identification of 4 675 437 single nucleotide polymorphisms (SNPs, including 3 346 058 novel SNPs), 71 642 indels and 8131 structural variations. Of these, 339 non-synonymous SNPs and 3 indels are located within coding sequences (CDS). In particular, 3 non-synonymous SNPs and a 26-bp deletion occur in the TCOF1 locus, implying that the difference observed in cranial facial morphology between Jindo and boxer dogs might be influenced by those variations. Through the annotation of the Jindo olfactory receptor gene family, we found 2 unique olfactory receptor genes and 236 olfactory receptor genes harbouring non-synonymous homozygous SNPs that are likely to affect smelling capability. In addition, we determined the DNA sequence of the Jindo dog mitochondrial genome and identified Jindo dog-specific mtDNA genotypes. This Jindo genome data upgrade our understanding of dog genomic architecture and will be a very valuable resource for investigating not only dog genetics and genomics but also human and dog disease genetics and comparative genomics

A Framework for Exploring Functional Variability in Olfactory Receptor Genes

BACKGROUND: Olfactory receptors (ORs) are the largest gene family in mammalian genomes. Since nearly all OR genes are orphan receptors, inference of functional similarity or differences between odorant receptors typically relies on sequence comparisons. Based on the alignment of entire coding region sequence, OR genes are classified into families and subfamilies, a classification that is believed to be a proxy for OR gene functional variability. However, the assumption that overall protein sequence diversity is a good proxy for functional properties is untested. METHODOLOGY: Here, we propose an alternative sequence-based approach to infer the similarities and differences in OR binding capacity. Our approach is based on similarities and differences in the predicted binding pockets of OR genes, rather than on the entire OR coding region. CONCLUSIONS: Interestingly, our approach yields markedly different results compared to the analysis based on the entire OR coding-regions. While neither approach can be tested at this time, the discrepancy between the two calls into question the assumption that the current classification reliably reflects OR gene functional variability