Leeaphorn, Napat

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

AbstractBackgroundIn the United States, increasing ethnic diversity has been apparent. However, the epidemiology and trends of BKV genotypes remain unclear. This meta‐analysis was conducted with the aim to assess the prevalence of BKV genotypes among kidney transplant (KTx) recipients in the United States.MethodsA comprehensive literature review was conducted through October 2018 utilizing MEDLINE, Embase, and Cochrane Database to identify studies that reported the prevalence of BKV subtypes and/or subgroups in KTx recipients in the United States. Pooled prevalence rates were combined using random effects, generic inverse variance method. The protocol for this study is registered with PROSPERO (no. CRD42019134582).ResultsA total of eight observational studies with a total of 193 samples (urine, blood, and kidney tissues) from 188 BKV‐infected KTX recipients were enrolled. Overall, the pooled estimated prevalence rates of BKV subtypes were 72.2% (95% confidence of interval [CI]: 62.7‐80.0%) for subtype I, 6.8% (95% CI: 2.5‐16.9%) for subtype II, 8.3% (95% CI: 4.4‐15.1%) for subtype III, and 16.1% (95% CI: 10.4‐24.2%) for subtype IV, respectively. While metaregression analysis demonstrated a significant positive correlation between year of study and the prevalence of BKV subtype I (slopes = +0.1023, P = .01), there were no significant correlations between year of study and percentages of BKV subtype II‐IV (P > .05). Among KTx recipients with BKV subtype I, the pooled estimated percentages of BKV subgroups were 22.4% (95% CI: 13.7‐34.5%) for subgroup Ia, 30.6% (95% CI: 17.7‐47.5%) for subgroup Ib1, 47.7% (95% CI: 35.8‐59.9%) for subgroup Ib2, and 4.1% (95% CI:1.2‐13.3%) for subgroup Ic, respectively.ConclusionBKV subtype I is the most prevalent subtype among KTx recipients in the United States and its prevalence seems to increasing overtime. Subgroup Ib2 is the most common subgroup among BKV subtype I.

Background: Kidney transplantation in patients with atypical hemolytic uremic syndrome (aHUS) is frequently complicated by recurrence, resulting in thrombotic microangiopathy in the renal allograft and graft loss. We aimed to assess the use of eculizumab in the prevention and treatment of aHUS recurrence after kidney transplantation. Methods: Databases (MEDLINE, EMBASE and Cochrane Database) were searched through February 2019. Studies that reported outcomes of adult kidney transplant recipients with aHUS treated with eculizumab were included. Estimated incidence rates from the individual studies were extracted and combined using random-effects, generic inverse variance method of DerSimonian and Laird. Protocol for this systematic review has been registered with PROSPERO (International Prospective Register of Systematic Reviews; no. CRD42018089438). Results: Eighteen studies (13 cohort studies and five case series) consisting of 380 adult kidney transplant patients with aHUS who received eculizumab for prevention and treatment of post-transplant aHUS recurrence were included in the analysis. Among patients who received prophylactic eculizumab, the pooled estimated incidence rates of recurrent thrombotic microangiopathy (TMA) after transplantation and allograft loss due to TMA were 6.3% (95%CI: 2.8–13.4%, I2 = 0%) and 5.5% (95%CI: 2.9–10.0%, I2 = 0%), respectively. Among those who received eculizumab for treatment of post-transplant aHUS recurrence, the pooled estimated rates of allograft loss due to TMA was 22.5% (95%CI: 13.6–34.8%, I2 = 6%). When the meta-analysis was restricted to only cohort studies with data on genetic mutations associated with aHUS, the pooled estimated incidence of allograft loss due to TMA was 22.6% (95%CI: 13.2–36.0%, I2 = 10%). We found no significant publication bias assessed by the funnel plots and Egger’s regression asymmetry test (p > 0.05 for all analyses). Conclusions: This study summarizes the outcomes observed with use of eculizumab for prevention and treatment of aHUS recurrence in kidney transplantation. Our results suggest a possible role for anti-C5 antibody therapy in the prevention and management of recurrent aHUS.

Background: This study aimed to evaluate the hospitalization rates for subarachnoid hemorrhage (SAH) among renal transplant patients with adult polycystic kidney disease (ADPKD) and its outcomes, when compared to non-ADPKD renal transplant patients. Methods: The 2005–2014 National Inpatient Sample databases were used to identify all hospitalized renal transplant patients. The inpatient prevalence of SAH as a discharge diagnosis between ADPKD and non-ADPKD renal transplant patients was compared. Among SAH patients, the in-hospital mortality, use of aneurysm clipping, hospital length of stay, total hospitalization cost and charges between ADPKD and non-ADPKD patients were compared, adjusting for potential confounders. Results: The inpatient prevalence of SAH in ADPKD was 3.8/1000 admissions, compared to 0.9/1000 admissions in non-ADPKD patients (p < 0.01). Of 833 renal transplant patients with a diagnosis of SAH, 30 had ADPKD. Five (17%) ADPKD renal patients with SAH died in hospitals compared to 188 (23.4%) non-ADPKD renal patients (p = 0.70). In adjusted analysis, there was no statistically significant difference in mortality, use of aneurysm clipping, hospital length of stay, or total hospitalization costs and charges between ADPKD and non-ADPKD patients with SAH. Conclusion: Renal transplant patients with ADPKD had a 4-fold higher inpatient prevalence of SAH than those without ADPKD. Further studies are needed to compare the incidence of overall admissions in ADPKD and non-ADPKD patients. When renal transplant patients developed SAH, inpatient mortality rates were high regardless of ADPKD status. The outcomes, as well as resource utilization, were comparable between the two groups.