Manual Medical Record Research Articles

Surveillance of surgical site infections using artificial intelligence

Abstract Background Surgical site infection (SSI) is a major health problem associated with high morbidity and mortality, the consequences of which can be very serious for the patient and costly for the organisation. Surveillance systems provide insight into the incidence of SSI, the analysis of which enables improvement plans to be established to reduce it. Traditionally, surveillance systems have been based on manual medical record review for evidence of the presence of surgical infection. Objectives To create and validate a global SSI diagnostic algorithm and to optimise the SSI workload of Preventive Medicine services. Methods The study population included patients undergoing surgical procedures of rectal surgery, colon surgery, knee replacement surgery, hip replacement surgery, coronary bypass and valve surgery, at the Hospital Universitario Vinalopó in Elche (n = 1240). Data mining was used to collect clinical, microbiological and postoperative follow-up values from the electronic medical record. Machine Learning techniques were used to train (n = 1054) and validate the model (n = 186). Time spent on SSI surveillance of the preventive medicine service was measured. Results Model performance after validation was: sensitivity of 0.83 specificity of 0.87, accuracy of 0.87, an F1 score of 0.45, and an area under the curve of 0.87. The time spent by preventive medicine staff in identifying patients with surgical site infection decreased from 168 person-hours per procedure per quarter to 34 person-hours, a 77% reduction in the workload allocated to this task. Conclusions This project not only improves surveillance efficiency, but also optimises the workload in Preventive Medicine services very significantly. Key messages • This project improves the efficiency of surgical site infection surveillance and significantly optimises the workload in preventive medicine departments. • It makes it possible to extend surveillance to new surgical procedures and to work more intensively on preventive measures.

Acute Kidney Injury in Hospitalized Patients With Exertional Rhabdomyolysis

An association between serum creatine kinase (CK) levels and the risk of kidney failure in patients with exertional rhabdomyolysis (ERM) has been suggested. However, the actual incidence of AKI in hospitalized patients with ERM along with the contributing cofactors that may increase the risk of AKI have rarely been investigated. To examine the incidence of kidney injury in hospitalized patients with ERM and to identify additional cofactors that might contribute to the development of kidney injury in patients with ERM. This retrospective cohort study was conducted in a diverse community population of patients 18 years or older with ERM who were hospitalized across Kaiser Permanente Northern California between January 1, 2009, and December 31, 2019. Patients were initially identified through electronic screening for all-cause rhabdomyolysis admissions, followed by manual medical record reviews to verify their eligibility for the study. The diagnosis of AKI and chronic kidney disease (CKD) was determined using KDIGO (Kidney Disease Improving Global Outcomes) criteria and confirmed by medical record review. Data analysis was performed from October 1, 2023, to January 31, 2024. History of strenuous physical exercise before hospitalization for ERM. Development of AKI, CKD, and compartment syndrome and number of deaths. Among 3790 patients hospitalized for rhabdomyolysis between 2009 and 2019 in Kaiser Permanente Northern California, 200 (mean [SD] age, 30.5 [8.5] years; 145 [72.5%] male) were confirmed to have ERM via medical record review. Seventeen patients (8.5%) developed AKI, none developed CKD, 1 (0.5%) developed compartment syndrome, and there were no fatalities. There was no association between serum CK levels and the risk of AKI. However, the risk of AKI was significantly higher in patients with ERM who used nonsteroidal anti-inflammatory drugs (NSAIDs) before admission (11 of 17 with AKI [64.7%] vs 40 of 183 without AKI [21.9%], P < .001) or experienced dehydration (9 of 183 without AKI [52.9%] vs 9 of 17 with AKI [4.9%], P < .001). This analysis suggests that eliminating preadmission NSAID use and dehydration could reduce the risk of potential AKI in patients with ERM by 92.6% (95% CI, 85.7%-96.1%) in this population. The findings of this cohort study of hospitalized patients with ERM suggest that serum CK elevation alone is insufficient as an indicator of AKI in patients with ERM. Concurrent risk factors, such as NSAID use or dehydration, may be associated with AKI development in patients with ERM.

Use of Diagnostic Codes for Primary Open-Angle Glaucoma Polygenic Risk Score Construction in Electronic Health Record–Linked Biobanks

Polygenic risk scores (PRSs) likely predict risk and prognosis of glaucoma. We compared the PRS performance for primary open-angle glaucoma (POAG), defined using International Classification of Diseases (ICD) codes versus manual medical record review. Retrospective cohort study METHODS: We identified POAG cases in Mount Sinai BioMe and Mass General Brigham (MGB) biobank using ICD codes. We confirmed POAG based on optical coherence tomograms and visual fields. In a separate 5% sample, the absence of POAG was confirmed with intraocular pressure and cup-disc ratio criteria. We used genotype data and either self-reported glaucoma diagnoses or ICD-10 codes for glaucoma diagnoses from the UK Biobank and the lassosum method to compute a genome-wide POAG PRS. We compared the area under the curve (AUC) for POAG prediction based on ICD codes versus medical records. We reviewed 804 of 996 BioMe and 367 of 1,006 MGB ICD-identified cases. In BioMe and MGB, respectively: positive predictive value was 53% and 55%; negative predictive value was 96% and 97%; sensitivity was 97% and 97%; and specificity was 44% and 53%. Adjusted PRS AUCs for POAG using ICD codes vs. manual record review in BioMe were not statistically different (p≥0.21) by ancestry: 0.77 vs. 0.75 for African, 0.80 vs. 0.80 for Hispanic, and 0.81 vs. 0.81 for European. Results were similar in MGB (p≥0.18): 0.72 vs. 0.80 for African, 0.83 vs. 0.86 for Hispanic, and 0.74 vs. 0.73 for European. A POAG PRS performed similarly using either manual review or ICD codes in two EHR-linked biobanks; manual assessment of glaucoma status might be unnecessary for some PRS studies. However, caution should be exercised with using ICD codes for glaucoma diagnosis given their low specificity (44-53%) for manually confirmed cases of glaucoma.

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis

AbstractVenous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study’s best performing model that evaluated for pulmonary embolism and/or deep vein thrombosis. Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI, 0.881-0.962), specificity 0.984 (95% CI, 0.967-0.992), PPV 0.910 (95% CI, 0.865-0.941) and NPV 0.985 (95% CI, 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words and deep-learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies, and only 4 validated their model on an external data set. Further standardization of ML studies can help progress this novel technology toward real-world implementation.

SOCIALIZATION OF MANUAL AND ELECTRONIC MEDICAL RECORD SYSTEMS AT PUSKESMAS SEI. NANAM SOLOK REGENCY

The use of the Electronic Medical Record System (RME) in health service facilities is a need and demand carried out by health workers, which is related to Permenkes No. 24 of 2022, that RME has been used before December 31, 2023, in all Health Service Facilities. At the Sii Health Center. Nanam Kab. Solok, medical record activities are still carried out manually. The problem is that medical record staff do not understand electronic medical records which will be implemented in health care facilities throughout Indonesia. Once the importance of RME, RME Socialization, and the Manual Medical Record System was carried out at the Sei Health Center. Nanam Kab. Solok on September 22, 2022, there were 12 participants, namely dr, P-Care Health Center, and medical record staff at Sei Health Center. Nanam Kab. Solok and student Apikes Iris. During this socialization, it was explained to participants of the RME system, Manual Medical Record (RMM), Legal Basis for Medical Records, advantages and disadvantages of manual and electronic RME systems, Obstacles and challenges of RME, participants who attended the socialization listened to information with enthusiasm and enthusiasm, in elaborating on this material, some asked questions and discussions who did not understand Manual and Electronic Medical Records. The purpose of this activity is to make the puskesmas staff, especially the medical record department, understand and understand RME and RMM.

Analysis of Optimizing Achievement of Incompleteness Indicator of Manual Medical Record Files at the Gambiran Regional General Hospital, Kediri City

The purpose of this research is to analyze optimizing achievement of incompleteness indicator of manual medical record files at the Gambiran Regional General Hospital, Kediri City. This type of research design is qualitative. The population in the study is 37 respondents. The sample collection technique is non-probability sampling. The non-probability sampling technique used is purposive sampling. The research sample used in this study is 15 respondents from medical records officers and head medical records triangulators. The focus of discussion in this research is influencer exposure, so data analysis techniques are carried out by means of data reduction, data presentation, and drawing conclusions. The results of the research show that the human resources at the Gambiran Regional General Hospital, Kediri City are sufficient in each unit. The availability of forms in the medical records section, doctors, and nurses at the Gambiran Regional General Hospital, Kediri City said that they are available. The Gambiran Regional General Hospital, Kediri City regarding standard operating procedure methods, medical record health workers said that the existing standard operating procedures are not clear. The head of medical records said that there are no sanctions given for making mistakes in filling out medical records. Keywords: Optimizing Achievement, Incompleteness, Human Resources, Standard Operating Procedure

Electronic Health Records (EHR) Usability and User Experience Evaluation: A Case Study

Manual medical record documents have a high risk of leakage and loss. This is because the storage is only in the cupboard. In Indonesia, Electronic Health Records (EHR) have been widely adopted. Clinics, the Rumah Keluarga Sehat Clinic located in Jember Regency. The implemented EHR is a complement to the manual medical record. To obtain a successful EHR implementation, it is necessary to evaluate the usability and user experience. Usability testing is an evaluation method that involves end users in the system development process. This study aims to evaluate the usability and user experience of the EHR developed at the Rumah Keluarga Sehat Clinic. Tests were carried out using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) instruments. Instruments were given to 7 respondents consisting of doctors, medical recorders, admins, and pharmacists at Rumah Keluarga Sehat Clinic, Jember. The results of the usability evaluation with SUS showed a score of 77.14 which indicated the Good category. And the UEQ results show above-average results on a scale (perspicuity and dependability), and excellent on 4 scales (attractiveness, efficiency, stimulation, and novelty). The results of the evaluation with 2 instruments show that the developed EHR has met the user's needs.

SOCIALIZATION OF MANUAL AND ELECTRONIC MEDICAL RECORD SYSTEMS AT PUSKESMAS SEI. NANAM SOLOK REGENCY

The use of the Electronic Medical Record System (RME) in health service facilities is a need and demand carried out by health workers, which is related to Permenkes No. 24 of 2022, that RME has been used before December 31, 2023, in all Health Service Facilities. At the Sii Health Center. Nanam Kab. Solok, medical record activities are still carried out manually. The problem is that medical record staff do not understand electronic medical records which will be implemented in health care facilities throughout Indonesia. Once the importance of RME, RME Socialization, and the Manual Medical Record System was carried out at the Sei Health Center. Nanam Kab. Solok on September 22, 2022, there were 12 participants, namely dr, P-Care Health Center, and medical record staff at Sei Health Center. Nanam Kab. Solok and student Apikes Iris. During this socialization, it was explained to participants of the RME system, Manual Medical Record (RMM), Legal Basis for Medical Records, advantages and disadvantages of manual and electronic RME systems, Obstacles and challenges of RME, participants who attended the socialization listened to information with enthusiasm and enthusiasm, in elaborating on this material, some asked questions and discussions who did not understand Manual and Electronic Medical Records. The purpose of this activity is to make the puskesmas staff, especially the medical record department, understand and understand RME and RMM.&#x0D;

Bloodstream Infections in Infants and Children With Congenital Heart Disease Undergoing Cardiac Surgery

Children with congenital heart disease undergoing cardiac surgery are at risk for laboratory-confirmed bloodstream infections (LCBIs). These infections can lead to morbidity, mortality, and increased health care costs. The role of mucosal barrier injury in causing LCBIs is unknown. To describe characteristics of LCBIs in patients admitted to cardiac intensive care and step-down units and to assess frequencies of National Healthcare Safety Network infection types and associations with organism classification, patient clinical factors, and infection outcomes. A retrospective cohort analysis using manual electronic medical record data abstraction included children with congenital heart disease who developed an LCBI while receiving inpatient cardiac care between August 2011 and November 2018 at one institution. Demographic, clinical, laboratory, and outcome variables were collected and analyzed with descriptive and inferential statistics. Eighty-seven patients with congenital heart disease developed 103 LCBIs during the study time frame. The most common causative microorganisms were gram-positive bacteria, including Enterococcus faecalis and Staphylococcus epidermidis. Sixty-three percent of causative organisms were characterized as originating from mucosal barrier injury, although no infections met National Healthcare Safety Network criteria for mucosal barrier injury LCBIs. Translocation of bacteria through injured gut mucosa may cause bloodstream infections in children with congenital heart disease. Further investigation is warranted to understand microbiome changes that adversely select pathogenic gut organisms. Preventive care to maintain intact gut function and a healthy microbiome should be explored for this patient population.

Development and Validation of an Emergency Department Electronic Medical Record Gout Flare Alert.

Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation for gout research in the ED is the timely ability to identify acute gout patients. Our goal was to refine a multicriteria, electronic medical record alert for gout flares and to determine its diagnostic characteristics in the ED. The gout flare alert used electronic medical record data from ED nursing notes and was triggered by the term 'gout' preceding past medical history in the chief complaint, the term 'gout' and a musculoskeletal problem in the chief complaint, or the term 'gout' in the problem list and a musculoskeletal chief complaint. We validated its diagnostic properties to assess presence/absence of gout through manual medical record review using adjudicated expert consensus as the gold standard. In January 2020, we analyzed 202 patient records from 2 university-based EDs; from these records, 57 patients were identified by our gout flare alert, and 145 were identified by other means as potentially having an acute gout flare. The gout flare alert's positive predictive value was 47% (95% confidence interval [95% CI] 34-60%), negative predictive value was 94% (95% CI 90-98%), sensitivity was 75% (95% CI 61-89%), and specificity was 82% (95% CI 76-88%). The diagnostic properties were similar at both institutions. Our multicomponent gout flare alert had reasonable sensitivity and specificity, albeit a modest positive predictive value. An electronic gout flare alert may help enable the conduct of gout research in the ED setting.

Identifying Recurrences Among Non-Metastatic Colorectal Cancer Patients Using National Health Data Registries: Validation and Optimization of a Registry-Based Algorithm in a Modern Danish Cohort.

Colorectal cancer (CRC) recurrence is not routinely recorded in Danish health data registries. Here, we aimed to revalidate a registry-based algorithm to identify recurrences in a contemporary cohort and to investigate the accuracy of estimating the time to recurrence (TTR). We ascertained data on 1129 patients operated for UICC TNM stage I-III CRC during 2012-2017 registered in the CRC biobank at the Department of Molecular Medicine, Aarhus University Hospital, Denmark. Individual-level data were linked with data from the Danish Colorectal Cancer Group database, Danish Cancer Registry, Danish National Registry of Patients, and Danish Pathology Registry. The algorithm identified recurrence based on diagnosis codes of local recurrence or metastases, the receipt of chemotherapy, or a pathological tissue assessment code of recurrence more than 180 days after CRC surgery. A subgroup was selected for validation of the algorithm using medical record reviews as a reference standard. We found a 3-year cumulative recurrence rate of 20% (95% CI: 17-22%). Manual medical record review identified 80 recurrences in the validation cohort of 522 patients. The algorithm detected recurrence with 94% sensitivity (75/80; 95% CI: 86-98%) and 98% specificity (431/442; 95% CI: 96-99%). The positive and negative predictive values of the algorithm were 87% (95% CI: 78-93%) and 99% (95% CI: 97-100%), respectively. The median difference in TTR (TTRMedical_chart-TTRalgorithm) was -8 days (IQR: -21 to +3 days). Restricting the algorithm to chemotherapy codes from oncology departments increased the positive predictive value from 87% to 94% without changing the negative predictive value (99%). The algorithm detected recurrence and TTR with high precision in this contemporary cohort. Restriction to chemotherapy codes from oncology departments using department classifications improves the algorithm. The algorithm is suitable for use in future observational studies.

The development and validation of screening tools for semi-automated surveillance of surgical site infection following various surgeries.

Surveillance of surgical site infections (SSIs) is essential for better prevention. We developed a screening method for SSIs in adults. The training dataset included data from patients who underwent orthopedic surgeries (N = 1,090), colorectal surgeries (N = 817), and abdominal hysterectomies (N = 523) during 2015-2018. The gold standard for the validation of the screening tool was the presence of SSI as determined by a trained infection control practitioner, via manual full medical record review, using the US Center for Disease Control and Prevention criteria. Using multivariable regression models, we identified the correlates of SSI. Patients who had at least one of these correlates were classified as likely to having SSI and those who did not have any of the correlates were classified as unlikely to have SSI. We calculated the sensitivity and specificity of this tool compared to the gold standard and applied the tool to a validation dataset (N = 1,310, years 2019-2020). SSI was diagnosed by an infection control specialist in 8.2, 5.2, and 31.2% of the patients in the training dataset who underwent hysterectomies, orthopedic surgeries and colorectal surgeries, respectively, vs. 6.2, 6.6, and 25.5%, respectively, in the validation dataset. The correlates of SSI after abdominal hysterectomy were prolonged hospitalization, ordering wound or blood culture, emergency room visit and reoperation; in orthopedic surgery, emergency room visit, wound culture, reoperation, and documentation of SSI, and in colorectal surgeries prolonged hospitalization, readmission, and ordering wound or blood cultures. Area under the curve was >90%. The sensitivity and specificity (95% CI) of the screening tool were 98% (88-100) and 58% (53-62), for abdominal hysterectomy, 91% (81-96) and 82% (80-84) in orthopedic surgeries and 96% (90-98) and 62% (58-66) in colorectal surgeries. The corresponding values for the validation dataset were 89% (67-97) and 75% (69-80) in abdominal hysterectomy; 85% (72-93) and 83% (80-86) in orthopedic surgeries and 98% (93-99) and 59% (53-64) in colorectal surgeries. The number of files needed to be fully reviewed declined by 61-66. The presented semi-automated simple screening tool for SSI surveillance had good sensitivity and specificity and it has great potential of reducing workload and improving SSI surveillance.

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal.

One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ≥65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F1-score, sensitivity, specificity, and positive and negative predictive values. After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ≥65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. DERR1-10.2196/40456.

Degitalization of Medical Record Administration Services at Dian Rapha Medika Toraja Clinic

In the world of health, the management of administrative documents, especially medical records, is a very important thing to do. Management of medical records in collaboration with information technology can improve services effectively and efficiently in managing information data quickly and avoiding delays in service. Until now, the Dian Rapha Medika Toraja Clinic still uses manual medical record administration management with several existing obstacles. This causes health services that should be handled quickly cannot be carried out. Therefore, the researcher is interested and aims to design a website-based medical record administration management system at the Dian Rapha Medika Clinic Toraja. This study uses the waterfall model design method, which consists of 1) Needs Analysis, 2) System Design, 3) Implementation, and 4) Testing. The data collection techniques in this design are 1) Interview and 2) Observation. The results of this study indicate several advantages when using digitization of administrative services for medical colleagues at Dian Rapha Medika Toraja clinic, namely: 1) assisting in digitally documenting patient medical record data; 2) input of patient medical record data is easier and faster; 3) facilitate doctors in analyzing the patient's medical records; 4) searching for patient medical record data is easier and faster; 5) storage of medical record data is more concise and well-structured due to the integration of patient data and medical records so that it can accelerate administrative management at the clinic.

Comparison between Retrieval Time of Manual and Electronic Medical Records –A Case Study

Purpose: A safe and effective healthcare service provides good quality medical records whether electronic or handwritten, which is essential for the continuity of patient care. In this context, a comparative study on the retrieval time of Manual and Electronic Medical Records is made to evaluate the advantages and limitations of both systems. Methodology: The present study was conducted in two phases. In the first phase, the retrieval time taken by the Manual medical record and in the second phase the retrieval time taken for electronic medical records was collected. The study site was one of the departments in a multispecialty hospital. The study period was three months and a convenient sampling technique was used to collect the data. The sample was collected during the peak hours of the day. Hence 75 medical records which were documented manually and collected in the first phase and after the introduction of electronic method of documenting medical records, another 75 electronic medical record were collected to find the retrieval time. Finding/Result: The study result revealed that standard turnaround time (TAT) as per the policy of the hospital for the retrieval of paper records exceeds whereas in electronic medical records most of the files receives within standard time and Electronic Medical Records to be implemented in various outpatient departments in order to reduce patient waiting time for the medical records during the revisit. These changes in the procedure will definitely improve the quality of healthcare and patient satisfaction. Originality/value: The result of the study will help to know the retrieval time for manual and Electronic Medical Record, its advantages and disadvantages. Study Type: Observational case study.

Using health insurance claims data to assess long-term disease progression in a prostate cancer cohort.

Long-term population-based cohort studies of men diagnosed with prostate cancer are limited. However, adverse outcomes can occur many years after treatment. Herein, we aim to assess the utility of using claims data to identify prostate cancer progression 10-15 years after diagnosis. The study population was derived from the North Carolina-Louisiana Prostate Cancer Project (PCaP). PCaP-North Carolina (NC) included 1031 men diagnosed with prostate cancer from 2004 to 2009.An initial follow-up with a survey and manual medical record abstraction occurred from 2008 to 2011 (Follow-up 1). Herein, we extended this follow-up with linkage to healthcare claims data from North Carolina (2011-2017) and a second, supplementary 10-year follow-up survey (2018-2020) (Follow-up 2). Vital statistics data also were utilized. Long-term oncological progression was determined using these data sources in combination with expert clinical input. Among the 1031 baseline PCaP-NC participants, 652 were linked to medical claims. Forty-two percent of the men had insurance coverage for the entire 72 months of follow-up. In addition, 275 baseline participants completed the supplementary 10-year follow-up survey. Using all sources of follow-up data, we identified a progression event in 259 of 1031 (25%) men with more than 10 years of follow-up data after diagnosis. Understanding long-term clinical outcomes is essential for improving the lives of prostate cancer survivors. However, access and utility of long-term clinical outcomes with claims alone remain a challenge due to individualized agreements required with each insurer for data access, lack of detailed clinical information, and gaps in insurance coverage. We were able to utilize claims data to determine long-term progression due to several unique advantages that included the availability of detailed baseline clinical characteristics and treatments, detailed manually abstracted clinical data at 5 years of follow-up, vital statistics data, and a supplementary 10-year follow-up survey.

Commentary: Effectiveness of two-dose vaccination with mRNA COVID-19 vaccines against COVID-19-associated hospitalizations among immunocompromised adults—nine states, January–September 2021

Embi PJ, Levy ME, Naleway AL, et al. Effectiveness of 2-Dose Vaccination with mRNA COVID-19 Vaccines Against COVID-19–Associated Hospitalizations Among Immunocompromised Adults — Nine States, January–September 2021. MMWR Morb Mortal Wkly Rep 2021;70:1553–1559. doi:http://dx.doi.org/10.15585/mmwr.mm7044e3external icon. This month’s selection presents the results of a multicenter study led by the Centers for Disease Control and Prevention evaluating the effectiveness of mRNA COVID-19 vaccines in immunosuppressed adults, including solid organ transplant recipients.1Embi PJ Levy ME Naleway AL et al.Effectiveness of 2-dose vaccination with mRNA COVID-19 vaccines against COVID-19-associated hospitalizations among immunocompromised adults - Nine States, January-September 2021.MMWR Morb Mortal Wkly Rep. 2021; 70: 1553-1559Crossref PubMed Google Scholar This study was performed due to several prior reports documenting reduced antibody titers following COVID-19 vaccination in immunosuppressed persons.2Deepak P Kim W Paley MA et al.Effect of Immunosuppression on the Immunogenicity of mRNA vaccines to SARS-CoV-2: a prospective cohort study.Ann Intern Med. 2021; 174: 1572-1585Crossref PubMed Scopus (150) Google Scholar,3Boyarsky BJ Werbel WA Avery RK et al.Antibody response to 2-dose SARS-CoV-2 mRNA vaccine series in solid organ transplant recipients.JAMA. 2021; 325: 2204-2206Crossref PubMed Scopus (591) Google Scholar The linked report evaluated immunocompetent and immunocompromised adults who were hospitalized with COVID-19 infection at 187 hospitals in nine US states between January and September 2021. Of note, the study only evaluated the effectiveness of a two-dose mRNA vaccine regimen; patients who received only 1 mRNA vaccine dose, ≥3 mRNA vaccine doses, or the Janssen (Johnson & Johnson [Ad26.COV2]) vaccine were excluded. Vaccine effectiveness was determined using a test-negative design, comparing the odds of a positive test result for SARS-CoV-2 between fully vaccinated and unvaccinated patients, after adjusting for patient age, geographic region, calendar time, local virus circulation, and weighting for the inverse propensity to be vaccinated or unvaccinated. The study included 1416 recipients of solid organ or stem cell transplantation, of which 809 (57%) were fully vaccinated with a two-dose mRNA vaccine series. COVID-19-associated hospitalizations were identified among 92 (15%) of the unvaccinated transplant recipients, and 80 (10%) of the fully vaccinated transplant recipients, resulting in an estimated vaccine effectiveness of 59% (95% confidence interval, 38%–73%). The vaccine effectiveness was higher among those who received two Moderna vaccine doses (70%) compared to two Pfizer vaccine doses (45%), although their 95% confidence intervals overlapped, suggesting this difference was not statistically significant. Notably, transplant recipients had the single lowest vaccine effectiveness of all of the immunocompromised patient groups evaluated. The vaccine effectiveness was significantly lower for transplant recipients (compared to immunocompetent individuals) regardless of patient age, mRNA vaccine product, or timing relative to the Delta variant predominance. There are several important limitations of this study, including (1) solid organ transplant recipients and stem cell transplant recipients were evaluated together as a single group, while vaccine effectiveness may be different in these two patient populations, and (2) transplant recipient status was identified by discharge diagnosis codes, rather than manual medical record review or evaluation of medication data. Importantly, however, the study did include a large cohort of transplant recipients across nine different states, and the authors were able to evaluate the impact of mRNA vaccination on an important clinical outcome (namely, COVID-19-associated hospitalization), rather than solely measuring antibody or T cell responses following vaccination. In summary, this report suggests that immunocompromised persons, including solid organ transplant recipients, benefit from mRNA COVID-19 vaccination but are less protected from severe COVID-19 outcomes than immunocompetent persons. Transplant recipients, in particular, may be even less protected than other immunocompromised individuals following vaccination. This report underscores the urgent need for further study of the optimal prevention and management of COVID-19 infection focused in the solid organ transplant population.

Severity Index and Outcome of Hospitalized COVID-19 Patients in Capital Hospital, CDA, Islamabad

Objective: To assess clinical characteristics and outcome of patients with Covid-19 infection. Study design: Descriptive cross-sectional study.Place and duration of study: Department of Medicine, Capital Hospital Islamabad from February 01, 2021 to April 25, 2021.Methodology: A manual medical record of total of 221 patients was conducted, more than 18 years of age, both genders, who presented with features suggestive of Covid-19 infection were included. Patients were tested for COVID-19 PCR, blood complete picture and inflammatory markers. The data was analyzed by using SPSS version 42.Results: Among 221 patients, there were 125(56.6%) males and 96(43.4%) females. Mean age was 57.20+13.17 years. Total 155(70%) cases were Covid PCR +ve and 66(30%) –ve. 86(38.9%) cases had mild, 91(41.2%) had moderate and 44(19.9%) had severe covid-19 infection. Lymphopenia was observed in 154(69.7%) cases. C-reactive protein was positive in 178(80.5%) cases. Serum ferritin was raised in 114(51.5%) cases, D-dimers in 139(66.9%), lactic dehydrogenase in 167(75.5%) cases with significant association with disease severity. Serum interleukin, performed in moderate and severe cases only, had mean value of 54.1+105.94pg/ml. Diabetes mellitus was found significantly associated with mortality (p=0.007) as well as hypertension (p&lt;0.0001). 207 cases were managed and discharged, 14 cases expired (6.33%). Mortality was significantly associated with disease severity (p&lt;0.001). Remdesivir was given to symptomatic patients and 94.4 % patients were discharged vs 5.6 % expired. Remdesivir and tocilizumab was given in combination in severe/critical patients and 76.5% patients were discharged vs 23.5% expired.Conclusion: Covid-19 is a disease with variable presentations. Severity of Covid-19 infection is directly related to lymphopenia as well as inflammatory markers. IL-6 levels, raised in more severe cases, signify immune response to Covid-19. Disease is more severe in patients with comorbids. In moderate to severe disease remdesivir and in severe covid disease with high IL-6 levels combination of antiviral and tocilizumab may help to improve outcome.

Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.

This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition. Twenty multidisciplinary ICUs in Mayo Clinic Health System. The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019. The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard. Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao2/Fio2 ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao2/Fio2 ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao2/Fio2 ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians' documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001). Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study

ObjectivesUsing free-text clinical notes and reports from hospitalised patients, determine the performance of natural language processing (NLP) ascertainment of Framingham heart failure (HF) criteria and phenotype.Study designA retrospective observational study...