Mixture and Non-Mixture Cure Models for the survival analysis of SARS-CoV-2 patients in Khyber Pakhtunkhwa, Pakistan.

Objective: To examine the potential difference in survival and risk of death between asymptomatic and symptomatic SARS-CoV-2 patients, controlled by age and gender for all the attendance in hospitals of Khyber Pakhtunkhwa (KP), Pakistan. Methods: In this retrospective study, the medical records of 6273 SARS-CoV-2 patients admitted to almost all hospitals in Khyber Pakhtunkhwa during the first wave of the coronavirus outbreak from March to June 2020 were analysed. The effects of gender, age, and being symptomatic on the survival of SARS-CoV-2 patients were assessed using cure-survival models as opposed to the conventional Cox proportional hazards model. Results: The prevalence of initially symptomatic patients was 55.8%, and the overall mortality rate was 11.8%. The fitted cure-survival models suggest that age affects the probability of death (incidence) but not the short-term survival time of patients (latency); symptomatic patients have a higher risk of death than their asymptomatic counterparts, but the survival time of symptomatic patients is longer on average; gender has no significant effect on the probability of death and survival time. Conclusion: The available data and statistical results suggest that asymptomatic and young patients are generally less susceptible to initial infection with SARS-CoV-2 and therefore have a lower risk of death. Our regression models show that uncured asymptomatic patients generally have poorer short-term survival than their uncured symptomatic counterparts. The association between gender and survival outcome was not significant.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

Long-term outcomes after aortic root replacement for bicuspid aortic valve-associated aneurysm.

OBJECTIVE: Patients with congenital bicuspid aortic valve often require root replacement. This study aims to describe their long-term rates of mortality and reoperation. METHODS: This is a multicenter retrospective study of 747 patients with bicuspid aortic valve who underwent aortic root replacement for aortic aneurysm between 2004 and 2020. Cumulative incidence curves for aortic valve and aortic reoperations were graphed. A Kaplan-Meier survival curve for the patient cohort was created alongside an age- and sex-matched curve for the US population. Multivariable Cox regression was used to determine characteristics associated with long-term mortality. RESULTS: The median age of our cohort was 54 [43-64] years old, and 101 (13.5%) patients were female. In patients with bicuspid aortic valve dysfunction, 274 (36.7%) had aortic insufficiency, 187 (25.0%) had aortic stenosis, and 142 (19.0%) had both. In-hospital mortality occurred in 10 (1.3%) patients. There were 56 aortic valve reoperations and 19 aortic reoperations, with a combined cumulative incidence of 35% (95% confidence interval [CI], 23%-46%) at 15 years. In addition, there was comparable survival between the patient cohort and the age- and sex-matched US population. Age (hazard ratio [HR], 1.04; 95% CI, 1.01-1.06), concomitant CABG (HR, 2.28; 95% CI, 1.29-4.04), and bypass time (HR, 1.01; 95% CI, 1.00-1.01) were associated with increased mortality. CONCLUSIONS: Patients who undergo aortic root replacement with bicuspid aortic valve have an increased rate of aortic reoperation (35%; 95% CI, 23%-46%) while their survival appears to be comparable to the general US population (79%; 95% CI, 73%-87%) at 15 years.

Arterial Thromboembolism in Japanese Patients With Cancer: Incidence, Predictors, and Survival Impact.

Background: Thromboembolism is a significant complication for patients with cancer, leading to treatment interruptions and poor outcomes. Objectives: The aim of this study was to investigate the incidence of arterial thromboembolism (ATE) within cancer populations, identify the predictors of ATE, and determine its survival impact. Methods: A retrospective multicenter study was performed using data from the Osaka Cancer Registry linked with administrative data from 2010 to 2015. Patients were monitored for 5 years after cancer diagnosis, and ATE incidence was calculated with death as a competing risk. Fine and Gray competing risk regression models and Cox proportional hazards models were used to evaluate the predictors of ATE and the survival impact. Restricted mean survival time (RMST) was used to assess whether antithrombotic therapy after ATE contributed to improved survival. Results: The cohort comprised 97,448 patients with cancer (42.3% women, median age 70 years). ATE incidence displayed an annual increase, peaking 1 year after cancer diagnosis (1-, 2-, 3-, 4-, and 5-year cumulative incidences were 1.29%, 1.77%, 2.05%, 2.22%, and 2.32%, respectively). Male sex, advanced age, advanced cancer stage, and hematologic malignancies correlated with a high risk for ATE. Patients with ATE had a 2-fold increased risk for mortality compared with those without ATE. The 90-day and 1-year RMST differences for those on antithrombotic therapy were 13.3 days (95% CI: 10.4-16.2 days; P < 0.001) and 57.8 days (95% CI: 43.1-72.5 days; P < 0.001), favoring the antithrombotic therapy group. The RMST differences varied by cancer stage. Conclusions: The risk for ATE varies according to sex, age, and cancer progression and type. Antithrombotic therapy after ATE is associated with improved survival among patients with cancer.

The effect of first-lactation calving season, milk production, and morphology on the survival of Simmental cows.

Longevity in dairy and dual-purpose cattle is a complex trait which depends on many individual and managerial factors. The purpose of the present study was to investigate the survival (SURV) rate of Italian Simmental dual-purpose cows across different parities. Data of this study referred to 2 173 primiparous cows under official milk recording that calved between 2002 and 2020. Only cows linearly classified for type traits, including muscularity (MU) and body condition score (BCS) were kept. Survival analysis was carried out, through the Cox regression model, for different pairwise combinations of classes of milk productivity MU, BCS, and calving season. Herd-year of first calving was also considered in the model. SURV (0 = culled; 1 = survived) at each lactation up to the 6th were the dependent variables, so that, for example, SURV2 equal to 1 was attributed to cows that entered the 2nd lactation. Survival rates were 98, 71, 63, 56, and 53% for 2nd, 3rd, 4th, 5th, and 6th lactation, respectively. Results revealed that SURV2 was not dependent on milk yield, while in subsequent parities, low-producing cows were characterized by higher SURV compared to high-producing ones. Additionally, cows starting the lactation in autumn survived less (47.38%) than those starting in spring (53.49%), suggesting that facing the late gestation phase in summer could increase the culling risk. The present study indicates that SURV in Italian Simmental cows is influenced by various factors in addition to milk productivity. However, it is important to consider that in this study all first-calving cows culled before the linear evaluation - carried out between mid- and late lactation in this breed - were not accounted for. Finding can be transferred to other dual-purpose breeds, where the cows' body conformation and muscle development - i.e. meat-related features - are often considered as important as milk performance by farmers undertaking culling decisions.

Clinical characteristics and outcomes among hospitalised COVID-19 patients across epidemic waves in Spain: An unCoVer analysis.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic had a significant impact in population health worldwide, and particularly in people with pre-existing chronic diseases. Early risk identification and stratification is essential to reduce the impact of future outbreaks of pandemic potential. This study aimed to comprehensively examine factors associated with COVID-19 mortality across the pandemic waves in Spain. METHODS: A retrospective study analyzed the characteristics of 13,974 patients admitted to Spanish hospitals due to SARS-CoV-2 infection from 2020-01-28 to 2022-12-31. The demographic and clinical features of patients during hospitalization on each pandemic waves were analyzed. MAIN FINDINGS: The findings highlight the heterogeneity of patient characteristics, comorbidities and outcomes, across the waves. The high prevalence of cardiometabolic diseases (53.9%) among COVID-19 patients emphasizes the importance of controlling these risk factors to prevent severe COVID-19 outcomes. CONCLUSIONS: In summary, the study associate hospital mortality with factors such as advanced age and comorbidities. The decline in mortality after the 4th wave indicates potential influences like vaccination, viral adaptation, or improved treatments. Notably, dementia and cancer metastases emerge as critical factors linked to higher mortality, highlighting the importance of addressing these conditions in COVID-19 management and preparing for future challenges.

From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images.

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

RKHS-based covariate balancing for survival causal effect estimation.

Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan-Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.

Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 down-regulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, Kaplan-Meier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment.

Combination Therapy of Choline Alfoscerate With Ginkgo biloba Monotherapy in Age-Related Hearing Loss: Effects and Outcomes.

BACKGROUND AND OBJECTIVES: Ginkgo biloba and choline alfoscerate are used as adjuvant treatment for presbycusis, but studies on how the monotherapy differs from the combination therapy are lacking. Therefore, this study aimed to compare the audiologic outcomes between Ginkgo biloba monotherapy and Ginkgo biloba and choline alfoscerate combination therapy. Subjects and. METHODS: The study groups are divided into three: negative control, monotherapy, and combination therapy groups. All groups' pure tone audiometry was measured by dividing the study period into Initial, 3-6, 6-9, 9-12, 12-15 months, and checked whether differences between groups were present. RESULTS: The combination therapy showed less gradient gap than the monotherapy, indicating less hearing loss rate than the monotherapy. Based on the Kaplan-Meier curve, the combination therapy showed better results in terms of survival time of hearing. CONCLUSIONS: As a pharmacological treatment for presbycusis, combination therapy shows better results than monotherapy.

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application.

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

A Flexible Regression Modeling Approach Applied to Observational Laboratory Virological Data Suggests That SARS-CoV-2 Load in Upper Respiratory Tract Samples Changes with COVID-19 Epidemiology.

(1) Background. Exploring the evolution of SARS-CoV-2 load and clearance from the upper respiratory tract samples is important to improving COVID-19 control. Data were collected retrospectively from a laboratory dataset on SARS-CoV-2 load quantified in leftover nasal pharyngeal swabs (NPSs) collected from symptomatic/asymptomatic individuals who tested positive to SARS-CoV-2 RNA detection in the framework of testing activities for diagnostic/screening purpose during the 2020 and 2021 winter epidemic waves. (2) Methods. A Statistical approach (quantile regression and survival models for interval-censored data), novel for this kind of data, was applied. We included in the analysis SARS-CoV-2-positive adults >18 years old for whom at least two serial NPSs were collected. A total of 262 SARS-CoV-2-positive individuals and 784 NPSs were included: 193 (593 NPSs) during the 2020 winter wave (before COVID-19 vaccine introduction) and 69 (191 NPSs) during the 2021 winter wave (all COVID-19 vaccinated). We estimated the trend of the median value, as well as the 25th and 75th centiles of the viral load, from the index episode (i.e., first SARS-CoV-2-positive test) until the sixth week (2020 wave) and the third week (2021 wave). Interval censoring methods were used to evaluate the time to SARS-CoV-2 clearance (defined as Ct < 35). (3) Results. At the index episode, the median value of viral load in the 2021 winter wave was 6.25 log copies/mL (95% CI: 5.50-6.70), and the median value in the 2020 winter wave was 5.42 log copies/mL (95% CI: 4.95-5.90). In contrast, 14 days after the index episode, the median value of viral load was 3.40 log copies/mL (95% CI: 3.26-3.54) for individuals during the 2020 winter wave and 2.93 Log copies/mL (95% CI: 2.80-3.19) for those of the 2021 winter wave. A significant difference in viral load shapes was observed among age classes (p = 0.0302) and between symptomatic and asymptomatic participants (p = 0.0187) for the first wave only; the median viral load value is higher at the day of episode index for the youngest (18-39 years) as compared to the older (40-64 years and >64 years) individuals. In the 2021 epidemic, the estimated proportion of individuals who can be considered infectious (Ct < 35) was approximately half that of the 2020 wave. (4) Conclusions. In case of the emergence of new SARS-CoV-2 variants, the application of these statistical methods to the analysis of virological laboratory data may provide evidence with which to inform and promptly support public health decision-makers in the modification of COVID-19 control measures.

Primary tumour category, site of metastasis, and baseline serum S100B and LDH are independent prognostic factors for survival in metastatic melanoma patients treated with anti-PD-1.

Background: Prognostic classification of metastatic melanoma patients treated with anti-PD-1 is of great interest to clinicians. Objective: We aimed to determine the anti-PD-1 treatment related prognostic performance of demographics, clinical and histological prognostic markers and baseline serum S100B and LDH levels in advanced melanoma. Methods: A total of 200 patients with unresectable metastatic melanoma were included in this retrospective study. 34.5% had stage M1c disease and 11.5% had stage M1d disease at the start of therapy. 30% had pT4b primary melanoma. 55.5% had elevated baseline serum S100B levels and 62.5% had elevated baseline serum LDH levels. We analysed the risk of death using univariate and multivariate Cox proportional-hazards models and the median overall (OS) and progression-free (PFS) survival using the Kaplan-Meier estimator. Results: The median follow-up time from the start of anti-PD-1 treatment in patients who were alive at the end of the study (N=81) was 37 months (range: 6.1-95.9). The multivariate Cox regression analysis showed that M1c stage (vs. M1a, p=0.005) or M1d stage at the start of therapy (vs. M1a, p=0.001), pT4b category (vs. pT1a, p=0.036), elevated baseline serum S100B levels (vs. normal S100B, p=0.008) and elevated LDH levels (vs. normal LDH, p=0.049) were independently associated with poor survival. The combination of M1d stage, elevated baseline serum S100B and LDH levels and pT4b category was associated with a very high risk of death (HR 4.72 [1.81; 12.33]). In the subgroup of patients with pT4b primary melanoma, the median OS of patients with normal serum S100B levels was 37.25 months [95% CI 11.04; 63.46]), while the median OS of patients with elevated serum S100B levels was 8.00 months [95% CI 3.49; 12.51]) (p<0.001); the median OS of patients with normal serum LDH levels was 41.82 months [95% CI 11.33; 72.32]), while the median OS of patients with elevated serum LDH levels was 12.29 months [95% CI 4.35; 20.23]) (p=0.002). Conclusion: Our real-world study indicates that the prognostic role of primary melanoma parameters is preserved in anti-PD-1 treated stage IV patients. Furthermore, there seems to be perspective in combining clinical, histological and serum prognostic markers in a prognostic model.

Survival Analysis, Kaplan-Meier Curves, and Cox Regression: Basic Concepts.

Survival analysis is used to analyze data from patients who are followed for different periods of time and in whom the outcome of interest, a dichotomous event, may or may not have occurred at the time the study is halted; data from all patients are used in the analysis, including data from patients who dropped out, regardless of the duration of follow-up. This article discusses basic concepts in survival analysis, explains technical terms such as censoring, and provides reasons why ordinary methods of analysis cannot be applied to such data. The Kaplan-Meier survival curve is described, as is the Cox proportional hazards regression and the hazard ratio. Supplementary information includes a data file, graphs with explanations, and additional discussions; these are provided to enhance the reader's experience and understanding.

Does Active Aging Reduce Mortality Risk Among Older Adults? A Survival Analysis in Malaysia.

Active aging is important for promoting the health, wellbeing, and participation of older adults. This study investigated the association between active aging and mortality risk among 2 230 respondents aged 60 and older. Principal component analysis extracted a five-factor structure from 15 indicators of active aging. The mean active aging score was 55.57 and the median was 53.33. The Kaplan-Meier curve showed that individuals with active aging scores of 53.33 and above had significantly longer survival than those below the median. Cox regression analysis indicated the significance of active aging in reducing mortality risk by 2.5% after adjusting for sex, marital status, age, ethnicity, chronic diseases, and risk factors. The active aging approach comprising health, economic, and social factors is crucial in improving survival among older adults. Hence, policies and programs that promote active aging should be encouraged to enhance the health and wellbeing of older adults and their engagement in society.

Low LncRNA LUCAT1 Expression Assists in the Diagnosis of Chronic Heart Failure and Predicts Poor Prognosis.

Chronic heart failure (CHF) is a complicated syndrome caused by structural and functional abnormalities. Long noncoding RNA (LncRNA) lung cancer-associated transcript 1 (LUCAT1) downregulation inhibits cardiomyocyte apoptosis. This study aimed to measure LUCAT1 expression in patients with CHF and to explore its clinical value on CHF diagnosis and prognosis. A total of 94 patients with CHF and 90 participants without CHF were registered, followed by recording of their clinical characteristics and grading of their cardiac function. LUCAT1 expression in sera of patients with CHF and participants without CHF was detected. The correlation of LUCAT1 with brain natriuretic peptide (BNP) and left ventricular ejection fraction (LVEF) in patients with CHF and the diagnostic efficiency of LUCAT1, BNP, and LUCAT1 combined with BNP on patients with CHF were analyzed. Patients with CHF were treated with conventional drugs and followed up. The LUCAT1 expression in patients with CHF was lower than that in participants without CHF and was downregulated with the increase of New York Heart Association stage. LUCAT1 expression was negatively associated with BNP but positively associated with LVEF in the sera of patients with CHF. The receiver operating characteristic curve of LUCAT1 combined with BNP had better result than that of LUCAT1 and BNP alone. Low LUCAT1 expression indicated poor prognosis of patients with CHF and was an independent prognostic factor for the survival of patients with CHF. To summarize, low lncRNA LUCAT1 expression might help diagnose and predict the poor prognosis of CHF.

Valgus Arthritic Knee Responds Better to Conservative Treatment than the Varus Arthritic Knee.

Background and Objectives: Clinically, it is beneficial to determine the knee osteoarthritis (OA) subtype that responds well to conservative treatments. Therefore, this study aimed to determine the differences between varus and valgus arthritic knees in the response to conservative treatment. We hypothesized that valgus arthritic knees would respond better to conservative treatment than varus arthritic knees. Materials and Methods: Medical records of 834 patients who received knee OA treatment were retrospectively reviewed. Patients with Kellgren-Lawrence grades III and IV were divided into two groups according to knee alignment (varus arthritic knee, hip-knee-ankle angle [HKA] > 0° or valgus arthritic knee, HKA < 0°). The Kaplan-Meier curve with total knee arthroplasty (TKA) as an endpoint was used to compare the survival probability between varus and valgus arthritic knees at one, two, three, four, and five years after the first visit. A receiver operating characteristic (ROC) curve was used to compare the HKA thresholds for TKA between varus and valgus arthritic knees. Results: Valgus arthritic knees responded better to conservative treatment than varus arthritic knees. With TKA as an endpoint, the survival probabilities for varus and valgus arthritic knees were 24.2% and 61.4%, respectively, at the 5-year follow-up (p < 0.001). The thresholds of HKA for varus and valgus arthritic knees for TKA were 4.9° and -8.1°, respectively (varus: area under the ROC curve [AUC] = 0.704, 95% confidence interval [CI] 0.666-0.741, p < 0.001, sensitivity = 0.870, specificity = 0.524; valgus: AUC = 0.753, 95% CI 0.693-0.807, p < 0.001, sensitivity = 0.753, specificity = 0.786). Conclusions: Conservative treatment is more effective for valgus than for varus arthritic knees. This should be considered when explaining the prognosis of conservative treatment for knees with varus and valgus arthritis.

Expression pattern of miR-16-2-3p and its prognostic values on pediatric acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a debilitating illness that easily occurs in adolescents. microRNAs (miRNAs) are potential biomarkers for multiple diseases. This paper was to elaborate on the expression of miR-16-2-3p in childhood ALL and its clinical values on ALL diagnosis and prognosis. First, serum miR-16-2-3p expression in ALL children and healthy volunteers was measured using RT-qPCR. Next, diagnostic potential and prognostic values of miR-16-2-3p on ALL were analyzed through receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and multivariate Cox regression analysis, respectively. No significant difference was observed in the clinical baseline data between ALL patients and healthy children. ALL patients showed downregulated serum miR-16-2-3p (0.65 ± 0.27) (p < .01), whose area under the ROC curve was 0.837 with a cut-off value of 0.745 (67.92% sensitivity, 96.94% specificity). ALL patients with higher miR-16-2-3p expression had higher survival rates than those with lower miR-16-2-3p expression. Low miR-16-2-3p expression predicted poor prognosis of ALL patients. After adjusting LDH and lymphomyelocyte proportion (p = 0.003, HR = 0.003, 95%CI = 0.000-0.145), miR-16-2-3p was recognized as an independent prognostic factor for ALL patient survival. Briefly, low serum miR-16-2-3p expression in ALL children could aid ALL diagnosis and predict poor prognosis.

Competing risks modeling of length of hospital stay enhances risk-stratification of patient care: application to under-five children hospitalized in Malawi.

Introduction: Length of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conventional methods for estimating LOS such as the Kaplan-Meier survival curve and the Cox proportional hazards regression for time to discharge cannot account for competing risks such as death, referral, and abscondment. This study applied competing risk methods to investigate factors important for risk-stratifying patients based on LOS in order to enhance patient care. Methods: This study analyzed data from ongoing safety surveillance of the malaria vaccine implementation program in Malawi's four district hospitals of Balaka, Machinga, Mchinji, and Ntchisi. Children aged 1-59 months who were hospitalized (spending at least one night in hospital) with a medical illness were consecutively enrolled between 1 November 2019 and 31 July 2021. Sub-distribution-hazard (SDH) ratios for the cumulative incidence of discharge were estimated using the Fine-Gray competing risk model. Results: Among the 15,463 children hospitalized, 8,607 (55.7%) were male and 6,856 (44.3%) were female. The median age was 22 months [interquartile range (IQR): 12-33 months]. The cumulative incidence of discharge was 40% lower among HIV-positive children compared to HIV-negative (sub-distribution-hazard ratio [SDHR]: 0.60; [95% CI: 0.46-0.76]; P < 0.001); lower among children with severe and cerebral malaria [SDHR: 0.94; (95% CI: 0.86-0.97); P = 0.04], sepsis or septicemia [SDHR: 0.90; (95% CI: 0.82-0.98); P = 0.027], severe anemia related to malaria [SDHR: 0.54; (95% CI: 0.48-0.61); P < 0.001], and meningitis [SDHR: 0.18; (95% CI: 0.09-0.37); P < 0.001] when compared to non-severe malaria; and also 39% lower among malnourished children compared to those that were well-nourished [SDHR: 0.61; (95% CI: 0.55-0.68); P < 0.001]. Conclusions: This study applied the Fine-Gray competing risk approach to more accurately model LOS as the time to discharge when there were significant rates of in-hospital mortality, referrals, and abscondment. Patient care can be enhanced by risk-stratifying by LOS based on children's age, HIV status, diagnosis, and nutritional status.