Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

We present a case of continuous ambulatory peritoneal dialysis peritonitis caused by Citrobacter freundii complicated by a fungal infection with abscess formation. A 34-year-old woman was admitted to our hospital with abdominal pain. Isolate cultures were confirmed as Citrobacter freundii by DNA sequencing of the 16s ribosomal ribonucleic acid (RNA). Antibiotic therapy was ineffective and Candida tropicalis was isolated in follow-up blood cultures. We administered an antifungal agent and removed the peritoneal catheter. A sudden fever developed, and abdominal computed tomography showed intra-abdominal abscesses. Percutaneous drainage was performed, but no bacteria were cultured. After draining the abscesses, the patient recovered. Citrobacter species are unusual pathogens in peritonitis, and fungal peritonitis is a serious complication of bacterial peritonitis. Indwelling catheters should be removed and appropriate antibiotic therapy provided. Suspicion of a fungal infection combined with bacterial peritonitis will improve the prognosis of patients on peritoneal dialysis.
Abdominal Abscess ; Abdominal Pain ; Abscess ; Adult ; Bacteria ; Candida tropicalis ; Catheters ; Catheters, Indwelling ; Citrobacter ; Citrobacter freundii* ; Drainage ; Female ; Fever ; Follow-Up Studies ; Humans ; Peritoneal Dialysis ; Peritoneal Dialysis, Continuous Ambulatory* ; Peritonitis* ; Prognosis ; RNA ; RNA, Ribosomal, 16S* ; Sequence Analysis, DNA

Background/Aims: Hypertension is considered a risk factor in immunoglobulin A nephropathy (IgAN). However, after IgAN diagnosis, the relationship between early blood pressure control and renal prognosis remains unclear. This study aimed to analyze the association between the prognosis of IgAN patients and a controlled status of hypertension within the first year of IgAN diagnosis. Methods: We retrospectively analyzed 2,945 patients diagnosed with IgAN by renal biopsy. The patients were divided into ‘normal,’ ‘new-onset,’ ‘well-controlled,’ and ‘poorly-controlled’ groups using blood pressure data from two consecutive measurements performed within a year. The Kaplan-Meier survival analysis and Cox proportional-hazards regression model were used to survey the independent association between recovery from hypertension and the risk of IgAN progression. The primary endpoint was IgAN progression defined as the initiation of dialysis or kidney transplantation. Results: Before IgAN diagnosis, 1,239 patients (42.1%) had been diagnosed with hypertension. In the fully adjusted Cox proportional-hazards models, the risk of IgAN progression increased by approximately 1.7-fold for the prevalence of hypertension. In the subgroup analyses, the ‘well-controlled’ group showed a statistically significant risk of IgAN progression (hazard ratio [HR], 3.19; 95% confidence interval [CI], 1.103 to 9.245; p = 0.032). Moreover, the ‘new-onset’ and ‘poorly-controlled’ groups had an increased risk of IgAN progression compared to the ‘normal’ group (HR, 2.58; 95% CI, 1.016 to 6.545; p = 0.046 and HR, 3.85;95% CI, 1.541 to 9.603; p = 0.004, respectively). Conclusions Although hypertension was well-controlled in the first year after IgAN diagnosis, it remained a risk factor for IgAN progression.

Background/Aims: The obesity paradox has been known in end-stage renal disease (ESRD). However, the effect of body mass index (BMI) or waist circumference (WC) prior to percutaneous coronary intervention (PCI) on the development of ESRD is not clear. Methods: Using nationally representative data from the Korean National Health Insurance System, we enrolled 140,164 subjects without ESRD at enrolment who underwent PCI between 2010 and 2015, and were followed-up until 2017. Patients were stratified into five levels based on their baseline BMI and six levels based on their WC with 5-cm increments. BMI and WC were measured at least 2 years prior to PCI. The primary outcome was the development of ESRD. Results: During a median follow-up of 5.4 years, 2,082 (1.49%) participants developed ESRD. The underweight group (hazard ratio [HR], 1.331; 95% confidence interval [CI], 0.955 to 1.856) and low WC (< 80/< 75) (HR, 1.589; 95% CI, 1.379 to 1.831) showed the highest ESRD risk and the BMI 25 to 30 group showed the lowest ESRD risk (HR, 0.604; 95% CI, 0542 to 0.673) in all participants after adjusting for all covariates. In the subgroup analysis for diabetes mellitus (DM) duration, WC < 85/80 cm (men/women) increased ESRD risk in only the DM group (DM < 5 years and DM ≥ 5 years) compared to the reference group (85–90/80–85 of WC), but not the normal or impaired fasting glucose group. Conclusions Low WC prior to PCI showed an increased ESRD risk in patients with DM undergoing PCI as compared to those without DM.

Background/Aims: We investigated the impact of obesity on the clinical outcomes following percutaneous coronary intervention (PCI). Methods: We included South Koreans aged > 20 years who underwent the Korean National Health Screening assessment between 2009 and 2012. Obesity was defined using the body mass index (BMI), according to the World Health Organization’s recommendations. Abdominal obesity was defined using the waist circumference (WC), as defined by the Korean Society for Obesity. The odds and hazard ratios in all-cause mortality were calculated after adjustment for multiple covariates. Patients were followed up to the end of 2017. Results: Among 130,490 subjects who underwent PCI, the mean age negatively correlated with BMI. WC, hypertension, diabetes, dyslipidemia, fasting glucose, total cholesterol, low-density lipoprotein cholesterol, and triglyceride levels correlated with the increased BMI. The mortality rates were higher in the lower BMI and WC groups than the higher BMI and WC groups. The non-obese with abdominal obesity group showed a mortality rate of 2.11 per 1,000 person-years. Obese with no abdominal obesity group had the lowest mortality rate (0.88 per 1,000 person-years). The mortality showed U-shaped curve with a cut-off value of 29 in case of BMI and 78 cm of WC. Conclusions The mortality showed U-shaped curve and the cut-off value of lowest mortality was 29 in case of BMI and 78 cm of WC. The abdominal obesity may be associated with poor prognosis in Korean patients who underwent PCI.