Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare

Home and Online Management and Evaluation of Blood Pressure (HOME BP) using a digital intervention in poorly controlled hypertension: randomised controlled trial

Objective: The HOME BP (Home and Online Management and Evaluation of Blood Pressure) trial aimed to test a digital intervention for hypertension management in primary care by combining self-monitoring of blood pressure with guided self-management. Design: Unmasked randomised controlled trial with automated ascertainment of primary endpoint. Setting: 76 general practices in the United Kingdom. Participants: 622 people with treated but poorly controlled hypertension (>140/90 mm Hg) and access to the internet. Interventions: Participants were randomised by using a minimisation algorithm to self-monitoring of blood pressure with a digital intervention (305 participants) or usual care (routine hypertension care, with appointments and drug changes made at the discretion of the general practitioner; 317 participants). The digital intervention provided feedback of blood pressure results to patients and professionals with optional lifestyle advice and motivational support. Target blood pressure for hypertension, diabetes, and people aged 80 or older followed UK national guidelines. Main outcome measures: The primary outcome was the difference in systolic blood pressure (mean of second and third readings) after one year, adjusted for baseline blood pressure, blood pressure target, age, and practice, with multiple imputation for missing values. Results: After one year, data were available from 552 participants (88.6%) with imputation for the remaining 70 participants (11.4%). Mean blood pressure dropped from 151.7/86.4 to 138.4/80.2 mm Hg in the intervention group and from 151.6/85.3 to 141.8/79.8 mm Hg in the usual care group, giving a mean difference in systolic blood pressure of −3.4 mm Hg (95% confidence interval −6.1 to −0.8 mm Hg) and a mean difference in diastolic blood pressure of −0.5 mm Hg (−1.9 to 0.9 mm Hg). Results were comparable in the complete case analysis and adverse effects were similar between groups. Within trial costs showed an incremental cost effectiveness ratio of £11 ($15, €12; 95% confidence interval £6 to £29) per mm Hg reduction. Conclusions: The HOME BP digital intervention for the management of hypertension by using self-monitored blood pressure led to better control of systolic blood pressure after one year than usual care, with low incremental costs. Implementation in primary care will require integration into clinical workflows and consideration of people who are digitally excluded. Trial registration: ISRCTN13790648

Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare

Angular analysis of D0→π+π−μ+μ−D^0 \to \pi^+\pi^-\mu^+\mu^- and D0→K+K−μ+μ−D^0 \to K^+K^-\mu^+\mu^- decays and search for CPCP violation

The first full angular analysis and an updated measurement of the decay-rate $CP$ asymmetry of the $D^0 \to \pi^+\pi^-\mu^+\mu^-$ and $D^0 \to K^+K^-\mu^+\mu^-$ decays are reported. The analysis uses proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7, 8 and 13 TeV. The data set corresponds to an integrated luminosity of 9 fb$^{-1}$. The full set of $CP$-averaged angular observables and their $CP$ asymmetries are measured as a function of the dimuon invariant mass. The results are consistent with expectations from the Standard Model and with $CP$ symmetry

Study of Bc+ {\mathrm{B}}_{\mathrm{c}}^{+} decays to charmonia and three light hadrons

International audienceUsing proton-proton collision data, corresponding to an integrated luminosity of 9 fb$^{−1}$ collected with the LHCb detector, seven decay modes of the ${\mathrm{B}}_{\mathrm{c}}^{+}$ meson into a J/ψ or ψ(2S) meson and three charged hadrons, kaons or pions, are studied. The decays ${\mathrm{B}}_{\mathrm{c}}^{+}$ → (ψ(2S) → J/ψπ$^{+}$π$^{−}$)π$^{+}$, ${\mathrm{B}}_{\mathrm{c}}^{+}$ → ψ(2S)π$^{+}$π$^{−}$π$^{+}$, ${\mathrm{B}}_{\mathrm{c}}^{+}$ → J/ψK$^{+}$π$^{−}$π$^{+}$ and ${\mathrm{B}}_{\mathrm{c}}^{+}$ → J/ψK$^{+}$K$^{−}$K$^{+}$ are observed for the first time, and evidence for the ${\mathrm{B}}_{\mathrm{c}}^{+}$ → ψ(2S)K$^{+}$K$^{−}$π$^{+}$, decay is found, where J/ψ and ψ(2S) mesons are reconstructed in their dimuon decay modes. The ratios of branching fractions between the different ${\mathrm{B}}_{\mathrm{c}}^{+}$ decays are reported as well as the fractions of the decays proceeding via intermediate resonances. The results largely support the factorisation approach used for a theoretical description of the studied decays.[graphic not available: see fulltext

Constraints on the CKM angle γ\gamma from B±→Dh±B^\pm\rightarrow Dh^\pm decays using D→h±h′∓π0D\rightarrow h^\pm h^{\prime\mp}\pi^0 final states

A data sample collected with the LHCb detector, corresponding to an integrated luminosity of $9~{\rm fb}^{-1}$, is used to measure $CP$ observables in $B^\pm \to D h^\pm$ decays, where $h^{(\prime)}$ is either a kaon or a pion, and the neutral $D$ meson decay is reconstructed in the three-body final states $K^\pm \pi^\mp \pi^0$, $\pi^\pm \pi^\mp \pi^0$, and $K^\pm K^\mp \pi^0$. The most suppressed of these modes, $B^\pm \to [\pi^\pm K^\mp \pi^0]_D K^\pm$, is observed with a significance greater than seven standard deviations and constraints on the CKM angle $\gamma$ are calculated from the combination of the measurements

Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma.

Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10(-8) to P = 10(-190)). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function